Reviewerly/exHarmony huggingface dataset:
Transmission Tower Detection Algorithm Based on Feature-Enhanced Convolutional Network in Remote Sensing Image

Transmission tower object detection from aerial image is an important task of power transmission fundamental infrastructure monitoring. However, the geometric variations, large-scale variation, complex background features and truss structure of transmission tower in aerial remote sensing images bring huge challenges to object detection. In this work, we propose a feature enhanced convolution network method (DSA-YOLOv3) to improve the performance of transmission tower detection in aerial images by modifying YOLOv3. The four layer feature extraction and fusion in darknet53 deep residual network is designed to enhance the feature expression ability of the network. Secondly, the DSA enhanced feature extraction module (the module consists of deformable convolution, SPP module, and attention module, which is named DSA module) is proposed. We use deformable convolution to improve the network's generalization ability due to the geometric variations of transmission tower in aerial image. Considering the large-scale variation of the transmission towers, we use the SPP module to increase the effective receptive field of the network. Then an attention module is used to eliminate the interference for the mixed characteristics of target and background environment, which is caused by truss structure of the transmission tower. Finally, we use CIoU as the loss function and DIoU as the non-maximum suppression judgment condition to improve the discrimination of highly overlapping targets. The proposed method was used in the transmission tower data sets to experiment. Experimental results demonstrate that the proposed method's AP, recall rate, and precision are 93.52%, 87.55%, and 96.16%, respectively. Compared with the original YOLOv3, the indexes of our method improved by 3.72%, 2.32%, and 5.91%, respectively.
Bitcoin Transaction Confirmation Time Prediction: A Classification View

With Bitcoin being universally recognised as the most popular cryptocurrency, more Bitcoin transactions are expected to be populated to the Bitcoin blockchain system. As a result, many transactions can encounter different confirmation delays. One of the most demanding requirements for users is to estimate the confirmation time of a newly submitted transaction. In this paper, we argue that it is more practical to predict the confirmation time as falling into a time interval rather than falling onto a specific timestamp. After dividing the future into a set of time intervals (i.e. classes), the prediction of a transaction’s confirmation can be considered as a classification problem. Consequently, a number of mainstream classification methods, including neural networks and ensemble learning models, are evaluated. For comparison, we also design a baseline classifier that considers only the transaction feerate. Experiments on real-world blockchain data demonstrate that ensemble learning models can obtain higher accuracy, while neural network models perform better on the f1-score, especially when more classes are used.
INIT: Inpainting Network for Incomplete Text

In recent years, scene text recognition algorithms have achieved great progress, but they still face some challenges in practical environment, such as the incomplete scene text, which includes structurally broken characters and occluded characters, as shown in Fig. 1. Existing scene text recognition algorithms can not accurately recognize such incomplete scene text. In this paper, we design an end-to-end Inpainting Network for Incomplete Text (INIT), which can reconstruct each incomplete character into complete character. INIT can separate the text from the background and just reconstruct the text regions, which reduces the influence caused by the background. And INIT is supervised by reconstruction loss and semantic loss to reach the most likely text. Furthermore, to compensate for the absence of incomplete scene text dataset, we propose an incomplete text synthesis method and build an incomplete text dataset (SITD), which is more suitable for practical scenarios. On SITD, the recognition accuracy can achieve 82.99% by the existing text recognition method with the help of INIT, while the accuracy is only 61.99% by the same recognition method with the conventional image inpainting method. Experimental results show that the proposed method has better reconstruction ability for incomplete text compared with the existing image inpainting algorithms.
Black Swan in Blockchain: Micro Analysis of Natural Forking

Natural forking is tantamount to the “black swan” event in blockchain since it emerges unexpectedly with a small probability, and may incur low resource utilization and costly economic loss. The ongoing literature analyzes natural forking mainly from the macroscopic perspective, which is insufficient to further understand this phenomenon since it roots in the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">instantaneous difference</i> between block creation and propagation microscopically. Hence, in this article, we fill this gap by leveraging the large deviation theory to conduct the first micro study of natural forking, aiming to reveal its inherent mechanism substantially. Our work is featured by 1) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">conceptual innovation</i> . We creatively abstract the blockchain overlay network as a “service system”. This allows us to investigate natural forking from the perspective of “supply and demand”. Based on this, we can identify the competitive dynamics of blockchain and construct a queuing model to characterize natural forking; 2) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">progressiveness</i> . We scrutinize the natural forking probability as well as its decay rate via a three-step scheme from simple to complex, which are the single-source i.i.d. scheme, the single-source non-i.i.d. scheme, and the many-source non-i.i.d. scheme. By doing so, we can answer <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">when</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">how fast</i> should we take actions and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">what</i> actions should we take against natural forking. Our valuable findings can not only put forward decisive guidelines theoretically from the top level, but also engineer optimal countermeasures operationally on a practical level to thwart natural forking.
Human Factors at the Speed of Relevance for Agile Engineering

Traditional Human System Integration (HSI) processes are at risk of being eliminated in the development process given the high velocity and continuous delivery methods of Agile and DevOps engineering. New processes and tools are needed to enable human factors practitioners to promote usable and effective products for users. This paper describes tools and approaches developed to synchronize HF and UX product design with quick-turn feature development in the DevOps environment. The process involved rapid user research survey and design validation usability testing tools for gathering feedback from users quickly enough to support DevOps and Agile timelines. The tools and processes afforded objective data to validate design decisions prior to agile development, which had become informal and based upon subjective judgement rather than empirical evidence to keep pace with the development velocity. The tools developed worked remotely to increase the ability to access users and decrease the logistics effort associated with gathering participants. This paper addresses a case study that successfully addressed the challenges for HF and UX practioners in our role of developing designs using agile and DevOps environment to improve the quality of the design proving relevancy.
FLoadNet: Load Balancing in Fog Networks With Cooperative Multiagent Using Actor–Critic Method

The growing demands of the Internet of Things (IoT) require a platform that supports real-time interactions and high availability of services to devices. In this context, the fog computing paradigm has emerged as an attractive solution for processing the data of IoT applications. Owing to the unpredictable traffic demands and resource heterogeneity in the fog environment, a smart workload distribution is essential to achieve high resource utilization and computing efficiency. To this end, this paper considers a joint link and server load balancing problem with multiple cooperative access points, (APs), in a combined edge-fog-cloud environment. The joint optimization problem is formulated as a stochastic game, and an actor-critic reinforcement learning framework, called FLoadNet, is proposed to optimize the joint policy of the multi-agents. FLoadNet consists of a centralized critic network, with parameter sharing and distributed individual actor networks in all the APs. Due to the learning dynamics and partially observable environment, we propose an extended critic network model, where cooperative APs learn to communicate among themselves while evaluating the value function. Unlike previous studies, the proposed critic network is designed to train both value and message functions, which is shown to significantly reduce the computational cost. The main goal of this work is to advance the development of efficient edge learning and the application of distributed learning algorithms specifically to fog network load balancing. The experimental results show that FLoadNet outperforms baseline load balancing methods.
Malicious URL Recognition Based on Multi-feature Fusion and Machine Learning

With the rapid development of the Internet, malicious web pages including phishing, spamming and malware are gradually increasing, resulting in endangering network security. To address this challenging issue, a novel malicious URL recognition method based on multi-feature fusion and machine learning is proposed. More specifically, three classic features (URL, HTML and JavaScript code features) and two presented effective features (HTTP request and Text features) are first extracted from URL dataset. Then, several machine learning models are adopted to train binary classification model with the selected discriminant features. Finally, we use ten-fold cross-validation accuracy to evaluate the performance of different classification models. Experimental results verify that the selected features are conductive for malicious URLs detection and the random forest model trained using the presented hybrid feature set performs better than other classification techniques.
Flexible Nonlinear Error Correction Method Based on Support Vector Regression in Fringe Projection Profilometry

The gamma effect of a projector is the most critical factor affecting three-dimensional shape measurements. A flexible and efficient error correction algorithm based on machine learning is proposed that facilitates correcting the system nonlinear error without any system nonlinear parameter calibration or knowledge of prior system information. The proposed method is a two-stage framework. The first stage is the projector gamma estimation module. First, the probability density function (PDF) of the wrapped phase with different gamma values is generated by simulation. The corresponding PDF sequence and gamma values are combined into a dataset to train a support vector regression (SVR) model. The trained SVR model can estimate the system gamma value according to the PDF sequence of the actual measured wrapped phases. The second stage is the phase error compensation module. In this stage, we establish the objective function according to the mathematical model and the gamma value estimated in the first part, and then use an iterative algorithm to solve the precise phase. Experimental and simulation results confirm the effectiveness of this method in directly identifying the system gamma value from the wrapped phase without complicated system calibration or any prior system information. The proposed algorithm is suitable for time-varying nonlinear systems.
Against the Cynicism of Common Sense: Guattari and the Micropolitics of Expression

In this chapter I want to highlight how the work of Félix Guattari offers a generative means for thinking speculatively insofar as it challenges us to think outside of the comfortable and predictable style of thinking that we might here term ‘common sense’. For Guattari, the task that thinking must confront is to break with the prevailing modes of “consensus”, that is, “the infantile ‘reassurance’ distilled by dominant subjectivity” (Guattari, 1995: 117). As such, what Guattari’s work suggests is that this challenge to think differently is an eminently ethico-political one. It must necessarily involve a break from the regime of commonly held beliefs that serve to homogenise and standardise, to shore up extant social formations and practices. In fact, as Gerlach and Jellis (2015) have argued, the drama of his work is accentuated in an era in which thought itself is increasingly captured by capitalist semiotisation. What critical thinking must face up to today is a contemporary spirit of cynicism in which thought itself becomes reduced to its market value, overdetermined according to logics of applicability and practicality aligned with the status quo. What I refer to here, in other words, is what Marcus Doel (2009: 1054) calls the ascendency of a form of “miserly thinking” that “enjoins us to conserve, constrain, and sustain”, or what Rosi Braidotti (2019: 471) calls a “retreat into the … protocol of commonsensical reasoning” that is nothing more than “the unfolding of established rules of thought”—damning summations of a zeitgeist characterised by a disempowering cynicism, by sad passions, affective disinvestments, that leave thought tethered to ‘what is’. In this context the very sense of the term ‘speculation’ is a site of contestation: Does speculation mean experimenting in thought towards the production of different possible futures (Savransky et al., 2017)? Or does it mean a more cynical exercise in calculation, prediction, even ‘risk-management’?
Affine Correspondences Between Multi-camera Systems for 6DOF Relative Pose Estimation

We present a novel method to compute the 6DOF relative pose of multi-camera systems using two affine correspondences (ACs). Existing solutions to the multi-camera relative pose estimation are either restricted to special cases of motion, have too high computational complexity, or require too many point correspondences (PCs). Thus, these solvers impede an efficient or accurate relative pose estimation when applying RANSAC as a robust estimator. This paper shows that the relative pose estimation problem using ACs permits a feasible minimal solution, when exploiting the geometric constraints between ACs and multi-camera systems using a special parameterization. We present a problem formulation based on two ACs that encompass two common types of ACs across two views, i.e., inter-camera and intra-camera. Experiments on both virtual and real multi-camera systems prove that the proposed solvers are more efficient than the state-of-the-art algorithms, while resulting in a better relative pose accuracy. Source code is available at https://github.com/jizhaox/relpose-mcs-depth .
Towards Efficient Reasoning of Quantum Programs

Qubit is the basic unit of information in quantum computing. It takes exponential time in the number of qubits to simulate the evolution of general quantum programs. This fact makes simulation infeasible beyond 50 qubits on current supercomputers. This observation motivates us to use static techniques to understand larger programs. We will briefly overview our recent efforts to develop static methods for quantum programming.
Advanced Quantum Inspired Evolutionary Optimization Algorithm

In this paper, an advanced quantum-inspired evolutionary optimization algorithm (A-QEOA) is proposed. This algorithm is based on the principle of quantum computing such as quantum bit representation and superposition of states. Like other evolutionary algorithms, the quantum-inspired evolutionary algorithm has characteristics of exploration and exploitation. The quantum-inspired evolutionary algorithm achieves exploration using observation process and exploitation using quantum rotation gate. Exploration helps to find the search space for a parameter that needs to be optimized. However, the observation process needs a random number which may sometimes increase the number of iterations to converge the algorithm or may lead to premature convergence. In this paper, we propose an algorithm with an updated observation process that helps to converge the algorithm optimally and helps to get results faster in comparison to the existing quantum inspired evolutionary algorithm (QEA).
Risk-Aware Contextual Learning for Edge-Assisted Crowdsourced Live Streaming

This paper proposes an edge-assisted crowdsourced live video transcoding approach where the transcoding capabilities of the edge transcoders are unknown and dynamic. The resilience and trustworthiness of highly unstable transcoders in decision making are characterized with mean-variance-based measures to avoid making highly risky decisions. The risk level of each device’s situation is assessed and two upper confidence bounds of the variance of transcoding performance are presented. Based on the derived bounds and by leveraging the contextual information of devices, two risk-aware contextual learning schemes are developed to efficiently estimate the transcoding capabilities of the edge devices. Combining context awareness and risk sensitivity, a novel transcoding task assignment and viewer association algorithm is proposed. Simulation results demonstrate that the proposed algorithm achieves robust task offloading with superior network utility performance as compared to the linear upper confidence bound and the risk-aware mean-variance upper confidence bound-based algorithms. In particular, an epoch-based task assignment strategy is designed to reduce the task switching costs incurred in assigning the same transcoding task to different transcoders over time. This strategy also reduces the computational time needed. Numerical results confirm that this strategy achieves up to 86.8% switching costs reduction and 92.3% computational time reduction.
Through the Looking-Glass: Benchmarking Secure Multi-party Computation Comparisons for ReLU ’s

Comparisons or Inequality Tests are an essential building block of Rectified Linear Unit functions (ReLU ’s), ever more present in Machine Learning, specifically in Neural Networks. Motivated by the increasing interest in privacy-preserving Artificial Intelligence, we explore the current state of the art of privacy preserving comparisons over Multi-Party Computation (MPC). We then introduce constant round variations and combinations, which are compatible with customary fixed point arithmetic over MPC. Our main focus is implementation and benchmarking; hence, we showcase our contributions via an open source library, compatible with current MPC software tools. Furthermore, we include a comprehensive comparative analysis on various adversarial settings. Our results improve running times in practical scenarios. Finally, we offer conclusions about the viability of these protocols when adopted for privacy-preserving Machine Learning.
Development Hybrid Model Deep Learning Neural Network (DL-NN) For Probabilistic Forecasting Solar Irradiance on Solar Cells To Improve Economics Value Added

A new hybrid deep convolutional neural network (CNN) method for efficient probabilistic forecasting solar irradiance approach is proposed for research to accurately quantify of solar irradiance from solar cell systems. Distinguished from combination models, a Deep Convolutional Learning Neural Network optimization model with a multilayer-based prediction model for solar irradiance in PV generation system is constructed based on a learning machine and features high reliability and computational efficiency. The proposed using deep convolutional neural network approach is validated through the proposed approach is validated through the studies on PV data from Indonesia. From the computation, the proposed hybrid Deep Convolutional Neural Network model using solar irradiance performs RMSE maximal of 12.1 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of solar irradiance.
FOA-based QR-factorized CKF Algorithm for AUV Navigation

Navigation is one of the key technologies for autonomous underwater vehicles (AUVs). To improve the accuracy of the navigation system, it is necessary to select an appropriate algorithm to fuse the navigation parameters. This paper selects the Cubature Kalman Filter (CKF) as the fusion algorithm. Different from the basic CKF, this paper uses the QR decomposition method to update the error covariance matrix of the CKF, which improves the numerical stability and accuracy of the algorithm. Since the process noise covariance matrix and the measurement noise covariance matrix are very important to the accuracy and stability of the navigation system, this paper uses FOA to adjust its parameters. Based on the data collected by AUV, the navigation algorithm proposed in this paper is verified, and the accuracy and reliability of the navigation system are proved.
Enhancing Sparsity of Support Vector Machines by Ridge Regression

Reducing the number of support vectors which construct the Support Vector Machine (SVM) has been a research topic of major significance. The paper introduces a framework consisting of three consecutive procedures to enhance sparsity of the SVM post its training. First, upon the observation that outliers always end up support vectors and are likely to compromise the generalization ability of the SVM, outliers are identified and removed from the training set after the SVM is trained. Next, assuming the normal vector can be spanned by a fixed number of basis functions, the paper propose to optimize the expansion coefficients by ridge regression. Meanwhile, a strategy is introduced to ensure linear independence among the basis functions. The resultant classifier is referred to as “Ridge Regression SVM” (RR-SVM). In the end, the support vectors are pruned iteratively from the RR-SVM. At each iteration, the LOOCV of the RR-SVM due to the absence of a support vector from the training set is computed efficiently. The support vector corresponding to the maximal LOOCV accuracy is eventually removed. Evaluation metrics alternative to the LOOCV accuracy are also developed and used for optimal pruning of the support vectors. Experiments on the checkerboard dataset and four datasets from the UCI repository confirm that, compared to the standard SVM, the RR-SVM maintains excellent generalization performance and features enhanced sparsity.