Reviewerly/exHarmony huggingface dataset:
Towards Differential Privacy-Based Online Double Auction for Smart Grid

In this paper, to address the issue of demand response in the smart grid with island MicroGrids (MGs), we introduce an effective and secure auction market that allows electric vehicles (EVs) having surplus energy to act as sellers, and the EVs having insufficient energy in the island MGs to act as buyers. There are two primary challenges in designing an effective auction market in the smart grid. First, the auction market scheme shall be online, allowing buyers and sellers to enter the market at any time, and satisfy several critical economic properties (individual rationality, incentive compatibility, and so on.). Second, the sensitive information of participants shall be protected in the auction process. To address these challenges, we present a novel privacy-preserving online double auction scheme based on differential privacy. In our auction market, the MicroGrid Center Controller (MGCC) acts as the auctioneer, aiming at solving the social welfare maximization problem to match buyers and sellers. The principle of differential privacy is leveraged to protect the privacy of EVs' sensitive bidding information. Via theoretical analysis, we demonstrate that our designed auction scheme satisfies both economic and privacy-preserving properties, including individual rationality, incentive compatibility, weak budget balance, and ε-differential privacy. We conduct an extensive performance evaluation to measure the effectiveness of our proposed scheme. Our experimental results show that the proposed auction scheme can not only ensure the privacy of participants but also effectively facilitates demand response in the smart grid, with respect to social welfare, satisfaction ratio, social efficiency, and computational overhead.
Understanding from Machine Learning Models

Simple idealized models seem to provide more understanding than opaque, complex, and hyper-realistic models. However, an increasing number of scientists are going in the opposite direction by utilizing opaque machine learning models to make predictions and draw inferences, suggesting that scientists are opting for models that have less potential for understanding. Are scientists trading understanding for some other epistemic or pragmatic good when they choose a machine learning model? Or are the assumptions behind why minimal models provide understanding misguided? In this article, using the case of deep neural networks, I argue that it is not the complexity or black box nature of a model that limits how much understanding the model provides. Instead, it is a lack of scientific and empirical evidence supporting the link that connects a model to the target phenomenon that primarily prohibits understanding.
HG-DAgger: Interactive Imitation Learning with Human Experts

Imitation learning has proven to be useful for many real-world problems, but approaches such as behavioral cloning suffer from data mismatch and compounding error issues. One attempt to address these limitations is the DAgger algorithm, which uses the state distribution induced by the novice to sample corrective actions from the expert. Such sampling schemes, however, require the expert to provide action labels without being fully in control of the system. This can decrease safety and, when using humans as experts, is likely to degrade the quality of the collected labels due to perceived actuator lag. In this work, we propose HG-DAgger, a variant of DAgger that is more suitable for interactive imitation learning from human experts in real-world systems. In addition to training a novice policy, HG-DAgger also learns a safety threshold for a model-uncertainty-based risk metric that can be used to predict the performance of the fully trained novice in different regions of the state space. We evaluate our method on both a simulated and real-world autonomous driving task, and demonstrate improved performance over both DAgger and behavioral cloning.
I Can See Clearly Now: Image Restoration via De-Raining

We present a method for improving segmentation tasks on images affected by adherent rain drops and streaks. We introduce a novel stereo dataset recorded using a system that allows one lens to be affected by real water droplets while keeping the other lens clear. We train a denoising generator using this dataset and show that it is effective at removing the effect of real water droplets, in the context of image reconstruction and road marking segmentation. To further test our de-noising approach, we describe a method of adding computer-generated adherent water droplets and streaks to any images, and use this technique as a proxy to demonstrate the effectiveness of our model in the context of general semantic segmentation. We benchmark our results using the CamVid road marking segmentation dataset, Cityscapes semantic segmentation datasets and our own realrain dataset, and show significant improvement on all tasks.
Image encryption scheme by combining the hyper-chaotic system with quantum coding

The application of quantum mechanisms in image processing has attracted wide attention. In this paper, an image encryption scheme that is based on hyper-chaos and quantum coding is proposed. First, based on a novel enhanced quantum representation (NEQR), the bit-level adjacent-exchange operation is implemented to pre-process the plain-image, which has lower computational complexity compared to conventional bit-level switching operations. Then, the processed image is diffused by the quantified sequences where the self-adapting parameters are applied to quantify the sequences that are generated by Chen's hyper-chaotic system. In addition, inspired by the quantum coding structure, a novel scrambling method that is based on quantum location coding is proposed. This method is used to further scramble the diffused image to obtain the final cipher-image. The experimental results and performance analysis demonstrate that the proposed image encryption scheme has high security performance.
Towards Blockchain Interoperability

In recent years, distributed ledger technologies like blockchains have gained much popularity both within industry and research. Today, blockchains do not only act as the underlying technology for cryptocurrencies like Bitcoin, but have also been identified as a potentially disruptive technology in many different fields, e.g., supply chain tracking and healthcare. The widespread attention for blockchains has led to manifold research and development activities. As a result, today’s blockchain landscape is heavily fragmented, with different, incompatible technologies being available to potential users. Since interoperability between different blockchains is usually not foreseen in existing protocols and standards, functionalities like sending tokens from one participant to another, or invoking and executing smart contracts can only be carried out within a single blockchain. In this paper, we discuss the need for blockchain interoperability and how it could help to stimulate a paradigm shift from today’s closed blockchains to an open system where devices and users can interact with each other across the boundaries of blockchains. For this, we consider the areas of cross-blockchain token transfers, as well as cross-blockchain smart contract invocation and interaction.
Toward Blockchain-Based Fair and Anonymous Ad Dissemination in Vehicular Networks

Commercial advertisement (ad) dissemination has been proliferating on connected vehicles, allowing users to promote their products via vehicle-to-vehicle/-infrastructure communications. Despite the prospect of ad dissemination in vehicular networks, it faces challenges upon deployment especially on security and privacy. Particularly, vehicles may collude to defraud the advertiser to obtain rewards without disseminating ads, which may cause unfair “free-riding” issue in these activities. Furthermore, concerns on possible privacy leakage may discourage vehicles to participate in the process of ad dissemination. In addition, external DDoS attacks and internal single point of failure may also affect service availability. To address these issues, we explore the potential of blockchain technology to construct a fair and anonymous scheme for advertising in vehicular networks. We first present the overview of the blockchain-based ad dissemination framework. Then, under the framework, we design a concrete, fair and anonymous scheme. To ensure fairness, we utilize the Merkle hash tree together with smart contracts to achieve the “proof-of-ad-receiving” property (i.e., check whether a vehicle indeed receives an ad without deception or introducing significant storage cost) to mitigate the “free-riding” attack. On the other hand, any ad receiver who acquires a dissemination reward per ad more than once can be effectively detected and will be punished which is achieved by using smart contracts. Additionally, the proposed scheme can protect vehicles' privacy in terms of anonymity and conditional linkability based on zero-knowledge proof techniques. Lastly, extensive security analysis and implementations demonstrate the feasibility and efficiency of the scheme.
A Blockchain-Based Byzantine Consensus Algorithm for Information Authentication of the Internet of Vehicles

This paper attempts to solve the security problems in communication, consensus-making and authentication of nodes in the Internet of vehicles (IoV) for intelligent transport.Considering the defects of the central node and service complexity in the IoV, the blockchain was integrated with the IoV to create a decentralized mechanism for communication and consensus-making.In the architecture of the blockchain-based IoV, the Byzantine consensus algorithm based on time sequence and gossip protocol is used to complete information communication and consensus authentication, which not only ensures communication security, improves the consensus efficiency of nodes, but also improves the fault tolerance of the algorithm.The experimental results show that our algorithm outshined the traditional authentication method in information security and consensus efficiency of the IoV.The research findings provide a reference solution to the authentication problems in the IoV for intelligent transport.
How Much Does Your Data Exploration Overfit? Controlling Bias via Information Usage

Modern data is messy and high-dimensional, and it is often not clear a priori what are the right questions to ask. Instead, the analyst typically needs to use the data to search for interesting analyses to perform and hypotheses to test. This is an adaptive process, where the choice of analysis to be performed next depends on the results of the previous analyses on the same data. Ultimately, which results are reported can be heavily influenced by the data. It is widely recognized that this process, even if well-intentioned, can lead to biases and false discoveries, contributing to the crisis of reproducibility in science. But while any data-exploration renders standard statistical theory invalid, experience suggests that different types of exploratory analysis can lead to disparate levels of bias, and the degree of bias also depends on the particulars of the data set. In this paper, we propose a general information usage framework to quantify and provably bound the bias and other error metrics of an arbitrary exploratory analysis. We prove that our mutual information based bound is tight in natural settings, and then use it to give rigorous insights into when commonly used procedures do or do not lead to substantially biased estimation. Through the lens of information usage, we analyze the bias of specific exploration procedures such as filtering, rank selection and clustering. Our general framework also naturally motivates randomization techniques that provably reduce exploration bias while preserving the utility of the data analysis. We discuss the connections between our approach and related ideas from differential privacy and blinded data analysis, and supplement our results with illustrative simulations.
Structural Test Coverage Criteria for Deep Neural Networks

Deep neural networks (DNNs) have a wide range of applications, and software employing them must be thoroughly tested, especially in safety-critical domains. However, traditional software test coverage metrics cannot be applied directly to DNNs. In this paper, inspired by the MC/DC coverage criterion, we propose a family of four novel test coverage criteria that are tailored to structural features of DNNs and their semantics. We validate the criteria by demonstrating that test inputs that are generated with guidance by our proposed coverage criteria are able to capture undesired behaviours in a DNN. Test cases are generated using a symbolic approach and a gradient-based heuristic search. By comparing them with existing methods, we show that our criteria achieve a balance between their ability to find bugs (proxied using adversarial examples and correlation with functional coverage) and the computational cost of test input generation. Our experiments are conducted on state-of-the-art DNNs obtained using popular open source datasets, including MNIST, CIFAR-10 and ImageNet.
Automating E-Government Services With Artificial Intelligence

Artificial Intelligence (AI) has recently advanced the state-of-art results in an ever-growing number of domains. However, it still faces several challenges that hinder its deployment in the e-government applications-both for improving the e-government systems and the e-government-citizens interactions. In this paper, we address the challenges of e-government systems and propose a framework that utilizes AI technologies to automate and facilitate e-government services. Specifically, we first outline a framework for the management of e-government information resources. Second, we develop a set of deep learning models that aim to automate several e-government services. Third, we propose a smart e-government platform architecture that supports the development and implementation of AI applications of e-government. Our overarching goal is to utilize trustworthy AI techniques in advancing the current state of e-government services in order to minimize processing times, reduce costs, and improve citizens' satisfaction.
A New Binary Particle Swarm Optimization Approach: Momentum and Dynamic Balance Between Exploration and Exploitation

Particle swarm optimization (PSO) is a heuristic optimization algorithm generally applied to continuous domains. Binary PSO is a form of PSO applied to binary domains but uses the concepts of velocity and momentum from continuous PSO, which leads to its limited performance. In our previous work, we reformulated momentum as a stickiness property and velocity as a flipping probability to develop sticky binary PSO. The initial design provides a good base, but many key factors need to be investigated. In this article, we propose a new algorithm called dynamic sticky binary PSO by developing a dynamic parameter control strategy based on an investigation of exploration and exploitation in the binary search spaces. The proposed algorithm is compared with four state-of-the-art dynamic binary algorithms on two types of binary problems: 1) knapsack and 2) feature selection. The experimental results on the knapsack datasets show that the new velocity and momentum assist sticky binary PSO in evolving better solutions than the benchmark algorithms. On feature selection, the dynamic strategy takes the advantages of these two newly defined movement concepts to help the proposed algorithm to produce smaller feature subsets with higher classification performance. This is the first time in the binary PSO, the four important concepts, that is, velocity, momentum, exploration, and exploitation, are investigated systematically to capture the properties of the binary search spaces to evolve better solutions for binary problems.
Autonomous deep learning: A genetic DCNN designer for image classification

Recent years have witnessed the breakthrough success of deep convolutional neural networks (DCNNs) in image classification and other vision applications. DCNNs have distinct advantages over traditional solutions in providing a uniform feature extraction-classification framework to free users from troublesome handcrafted feature extraction. However, DCNNs are far from autonomous, since their performance relies heavily on the handcrafted architectures, which also requires a lot expertise and experience to design, and cannot be continuously improved once the tuning of hyper-parameters converges. In this paper, we propose an autonomous and continuous learning (ACL) algorithm to generate automatically a DCNN architecture for each given vision task. We first partition a DCNN into multiple stacked meta convolutional blocks and fully connected blocks, each of which may contain the operations of convolution, pooling, fully connection, batch normalization, activation and drop out, and thus convert the architecture into an integer code. Then, we use genetic evolutionary operations, including selection, mutation and crossover to evolve a population of DCNN architectures. We have evaluated this algorithm on six image classification tasks, i.e., MNIST, Fashion-MNIST, EMNIST-Letters, EMNIST-Digits, CIFAR10 and CIFAR100. Our results indicate that the proposed ACL algorithm is able to evolve the DCNN architecture continuously if more time cost is allowed and can find a suboptimal DCNN architecture, whose performance is comparable to the state of the art.
Joint Ranking SVM and Binary Relevance with robust Low-rank learning for multi-label classification

Multi-label classification studies the task where each example belongs to multiple labels simultaneously. As a representative method, Ranking Support Vector Machine (Rank-SVM) aims to minimize the Ranking Loss and can also mitigate the negative influence of the class-imbalance issue. However, due to its stacking-style way for thresholding, it may suffer error accumulation and thus reduces the final classification performance. Binary Relevance (BR) is another typical method, which aims to minimize the Hamming Loss and only needs one-step learning. Nevertheless, it might have the class-imbalance issue and does not take into account label correlations. To address the above issues, we propose a novel multi-label classification model, which joints Ranking support vector machine and Binary Relevance with robust Low-rank learning (RBRL). RBRL inherits the ranking loss minimization advantages of Rank-SVM, and thus overcomes the disadvantages of BR suffering the class-imbalance issue and ignoring the label correlations. Meanwhile, it utilizes the hamming loss minimization and one-step learning advantages of BR, and thus tackles the disadvantages of Rank-SVM including another thresholding learning step. Besides, a low-rank constraint is utilized to further exploit high-order label correlations under the assumption of low dimensional label space. Furthermore, to achieve nonlinear multi-label classifiers, we derive the kernelization RBRL. Two accelerated proximal gradient methods (APG) are used to solve the optimization problems efficiently. Extensive comparative experiments with several state-of-the-art methods illustrate a highly competitive or superior performance of our method RBRL.
Improving the Learning of Multi-column Convolutional Neural Network for Crowd Counting

Tremendous variation in the scale of people/head size is a critical problem for crowd counting. To improve the scale invariance of feature representation, recent works extensively employ Convolutional Neural Networks with multi-column structures to handle different scales and resolutions. However, due to the substantial redundant parameters in columns, existing multi-column networks invariably exhibit almost the same scale features in different columns, which severely affects counting accuracy and leads to overfitting. In this paper, we attack this problem by proposing a novel Multicolumn Mutual Learning (McML) strategy. It has two main innovations: 1) A statistical network is incorporated into the multi-column framework to estimate the mutual information between columns, which can approximately indicate the scale correlation between features from different columns. By minimizing the mutual information, each column is guided to learn features with different image scales. 2) We devise a mutual learning scheme that can alternately optimize each column while keeping the other columns fixed on each mini-batch training data. With such asynchronous parameter update process, each column is inclined to learn different feature representation from others, which can efficiently reduce the parameter redundancy and improve generalization ability. More remarkably, McML can be applied to all existing multi-column networks and is end-to-end trainable. Extensive experiments on four challenging benchmarks show that McML can significantly improve the original multi-column networks and outperform the other state-of-the-art approaches.
Web Stereo Video Supervision for Depth Prediction from Dynamic Scenes

We present a fully data-driven method to compute depth from diverse monocular video sequences that contain large amounts of non-rigid objects, e.g., people. In order to learn reconstruction cues for non-rigid scenes, we introduce a new dataset consisting of stereo videos scraped in-the-wild. This dataset has a wide variety of scene types, and features large amounts of nonrigid objects, especially people. From this, we compute disparity maps to be used as supervision to train our approach. We propose a loss function that allows us to generate a depth prediction even with unknown camera intrinsics and stereo baselines in the dataset. We validate the use of large amounts of Internet video by evaluating our method on existing video datasets with depth supervision, including SINTEL, and KITTI, and show that our approach generalizes better to natural scenes.
A systematic literature review of machine learning techniques for software maintainability prediction

Software maintainability is one of the fundamental quality attributes of software engineering. The accurate prediction of software maintainability is a significant challenge for the effective management of the software maintenance process. The major aim of this paper is to present a systematic review of studies related to the prediction of maintainability of object-oriented software systems using machine learning techniques. This review identifies and investigates a number of research questions to comprehensively summarize, analyse and discuss various viewpoints concerning software maintainability measurements, metrics, datasets, evaluation measures, individual models and ensemble models. The review uses the standard systematic literature review method applied to the most common computer science digital database libraries from January 1991 to July 2018. We survey 56 relevant studies in 35 journals and 21 conference proceedings. The results indicate that there is relatively little activity in the area of software maintainability prediction compared with other software quality attributes. CHANGE maintenance effort and the maintainability index were the most commonly used software measurements (dependent variables) employed in the selected primary studies, and most made use of class-level product metrics as the independent variables. Several private datasets were used in the selected studies, and there is a growing demand to publish datasets publicly. Most studies focused on regression problems and performed k-fold cross-validation. Individual prediction models were employed in the majority of studies, while ensemble models relatively rarely. Based on the findings obtained in this systematic literature review, ensemble models demonstrated increased accuracy prediction over individual models, and have been shown to be useful models in predicting software maintainability. However, their application is relatively rare and there is a need to apply these, and other models to an extensive variety of datasets with the aim of improving the accuracy and consistency of results.