Reviewerly/exHarmony huggingface dataset:
FaDec: A Fast Decision-based Attack for Adversarial Machine Learning

Due to the excessive use of cloud-based machine learning (ML) services, the smart cyber-physical systems (CPS) are increasingly becoming vulnerable to black-box attacks on their ML modules. Traditionally, the black-box attacks are either transfer attacks requiring model stealing, or score/decision-based gradient estimation attacks requiring a large number of queries. In practical scenarios, especially for cloud-based ML services and timing-constrained CPS use-cases, every query incurs a huge cost, thereby rendering state-of-the-art decision-based attacks ineffective in such settings. Towards this, we propose a novel methodology for automatically generating an extremely fast and imperceptible decision-based attack called FaDec. It follows two main steps: (1) fast estimation of the classification boundary by combining the half-interval search-based algorithm with gradient sign estimation to reduce the number of queries; and (2) adversarial noise optimization to ensure the imperceptibility. For illustration, we evaluate FaDec on the image recognition and traffic sign detection using multiple state-of-the-art DNNs trained on CIFAR-10 and the German Traffic Sign Recognition Benchmarks (GTSRB) datasets. The experimental analysis shows that the proposed FaDec attack is 16x faster compared to the state-of-the-art decision-based attacks, and generates an attack image with better imperceptibility for a much lesser number of iterations, thereby making our attack more powerful in practical scenarios. We open-sourced the complete code and results of our methodology at https://github.com/fklodhi/FaDec.
Multi-Scale Feature Enhanced Domain Adaptive Object Detection For Power Transmission Line Inspection

Domain adaptive object detection aims to build an object detector for the unlabeled target domain by transferring knowledge from a well-labeled source domain, which can alleviate the problem of cumbersome labeling of object detection in cross-scene power transmission line inspection.Remarkable advances are made recently by mitigating distributional shifts via hierarchical domain feature alignment training of detection networks.However, domain adaptive object detection is still limited in learning the invariance representation of multi-scale features.Specifically, the scale of objects varies in the scenes of aerial inspection, which hinders the knowledge transfer from the labeled source domain.In this paper, we propose a multi-scale feature enhanced domain adaptation method for cross-domain object detection of power transmission lines inspection.The proposed method consists of two components: 1) Multi-Scale Fusion Feature Alignment module (MSFA) to strengthen similar representation characteristics of different scales object in domain adaptive by utilizing context information conveyed from other levels; 2) Multi-Scale Consistency Regularization module (MSCR) to jointly optimize the multi-scale feature learning of each level, which promotes domain invariant feature learning at each level.Experimental results demonstrate that our method significantly increases the performance of the object detector in several cross-scene transmission line inspection tasks.
Time–frequency analysis of keystroke dynamics for user authentication

With the increasing need for information security, one of the key options ahead is to provide security based on biometrics. Authentication based on keystroke dynamics is a low cost and convenient biometric authentication technique. In this paper, a method for user authentication based on keystroke dynamics with a novel similarity measure is introduced. Using time–frequency analysis, a similarity measure between an input sample and user reference samples is directly obtained. The input sample is initially converted to a keystroke dynamics signal. Dynamic time warping method is applied to equalize the length of signals. Then, using Wigner distribution, the time–frequency representation of the samples is obtained. Finally, exploiting the correlation coefficient, the similarity between two signals in the time–frequency domain is measured. We also added an update procedure to the proposed method to enhance its performance. The performance of the proposed method is investigated and compared with the state-of-the-art methods. Experimental results show the superiority of the proposed method.
Adversarial Defense Via Local Flatness Regularization

Adversarial defense is a popular and important research area. Due to its intrinsic mechanism, one of the most straightforward and effective ways of defending attacks is to analyze the property of loss surface in the input space. In this paper, we define the local flatness of the loss surface as the maximum value of the chosen norm of the gradient regarding to the input within a neighborhood centered on the benign sample, and discuss the relationship between the local flatness and adversarial vulnerability. Based on the analysis, we propose a novel defense approach via regularizing the local flatness, dubbed local flatness regularization (LFR). We also demonstrate the effectiveness of the proposed method from other perspectives, such as human visual mechanism, and analyze the relationship between LFR and other related methods theoretically. Experiments are conducted to verify our theory and demonstrate the superiority of the proposed method.
An Improved Online Learning Algorithm for General Fuzzy Min-Max Neural Network

This paper proposes an improved version of the current online learning algorithm for a general fuzzy min-max (GFMM) neural network to tackle existing issues concerning expansion and contraction steps as well as the way of dealing with unseen data located on decision boundaries. These drawbacks lower its classification performance, so an improved algorithm is proposed in this study to address the above limitations. The proposed approach does not use the contraction process for overlapping hyperboxes, which is more likely to increase the error rate as shown in the literature. The empirical results indicated the improvement in the classification accuracy and stability of the proposed method compared to the original version and other fuzzy min-max classifiers. In order to reduce the sensitivity to the training samples presentation order of this new on-line learning algorithm, a simple ensemble method is also proposed.
Reliability Validation of Learning Enabled Vehicle Tracking

This paper studies the reliability of a real-world learning-enabled system, which conducts dynamic vehicle tracking based on a high-resolution wide-area motion imagery input. The system consists of multiple neural network components - to process the imagery inputs - and multiple symbolic (Kalman filter) components - to analyse the processed information for vehicle tracking. It is known that neural networks suffer from adversarial examples, which make them lack robustness. However, it is unclear if and how the adversarial examples over learning components can affect the overall system-level reliability. By integrating a coverage-guided neural network testing tool, DeepConcolic, with the vehicle tracking system, we found that (1) the overall system can be resilient to some adversarial examples thanks to the existence of other components, and (2) the overall system presents an extra level of uncertainty which cannot be determined by analysing the deep learning components only. This research suggests the need for novel verification and validation methods for learning-enabled systems.
Gap between theory and practice

Ethereum, one of the most popular blockchain platforms, provides financial transactions like payments and auctions through smart contracts. Due to the immense interest in smart contracts in academia, the research community of smart contract security has made a significant improvement recently. Researchers have reported various security vulnerabilities in smart contracts, and developed static analysis tools and verification frameworks to detect them. However, it is unclear whether such great efforts from academia has indeed enhanced the security of smart contracts in reality.
Regularized robust fuzzy least squares twin support vector machine for class imbalance learning

Twin support vector machines (TWSVM) have been successfully applied to the classification problems. TWSVM is computationally efficient model of support vector machines (SVM). However, in real world classification problems issues of class imbalance and noise provide great challenges. Due to this, models lead to the inaccurate classification either due to higher tendency towards the majority class or due to the presence of noise. We provide an improved version of robust fuzzy least squares twin support vector machine (RFLSTSVM) known as regularized robust fuzzy least squares twin support vector machine (RRFLSTSVM) to handle the imbalance problem. The advantage of RRFLSTSVM over RFLSTSVM is that the proposed RRFLSTSVM implements the structural risk minimization principle by the introduction of regularization term in the primal formulation of the objective functions. This modification leads to the improved classification as it embodies the marrow of statistical learning theory. The proposed RRFLSTSVM doesn't require any extra assumption as the matrices resulting in the dual are positive definite. However, RFLSTSVM is based on the assumption that the inverse of the matrices resulting in the dual always exist as the matrices are positive semi-definite. To subsidize the effects of class imbalance and noise, the data samples are assigned weights via fuzzy membership function. The fuzzy membership function incorporates the imbalance ratio knowledge and assigns appropriate weights to the data samples. Unlike TWSVM which solves a pair of quadratic programming problem (QPP), the proposed RRFLSTSVM method solves a pair of system of linear equations and hence is computationally efficient. Experimental and statistical analysis show the efficacy of the proposed RRFLSTSVM method.
Optimal Routing Schedules for Robots Operating in Aisle-Structures

In this paper, we consider the Constant-cost Orienteering Problem (COP) where a robot, constrained by a limited travel budget, aims at selecting a path with the largest reward in an aisle-graph. The aisle-graph consists of a set of loosely connected rows where the robot can change lane only at either end, but not in the middle. Even when considering this special type of graphs, the orienteering problem is known to be intractable. We optimally solve in polynomial time two special cases, COP-FR where the robot can only traverse full rows, and COP-SC where the robot can access the rows only from one side. To solve the general COP, we then apply our special case algorithms as well as a new heuristic that suitably combines them. Despite its light computational complexity and being confined into a very limited class of paths, the optimal solutions for COP-FR turn out to be competitive in terms of achieved rewards even for COP. This is shown by means of extended simulations performed on both real and synthetic scenarios. Furthermore, our new heuristic for the general case outperforms state-of-art algorithms, especially for input with highly unbalanced rewards.
Hyperparameter Optimization for Deep Residual Learning in Image Classification

Convolutional Neural Network (CNN) is one of the most commonly used deep learning models to train a large number of datasets and getting valuable results in image recognition. Deep Residual Learning (ResNet) is one of the most famous CNNs for the computer vision tasks that won the ILSVR-2015 classification competition. ResNet is also one of the deepest models to train the neural networks with the idea of identity mapping for short connections. In this paper, we built a hyperparameter based approach for ResNet architecture and show that our optimization leads to significant performance improvements. We have built a manual search approach by altering the traditional data augmentation proposed by the previous approaches. Our manual approach significantly reduces the computing time compared with the previous automatic approaches. Experimental results show that our approach significantly improves the classification error rate of the ResNet for the CIFAR-10 dataset. We achieved a 5.20% error rate for ResNet-164 with the significantly reduced computing time.
Learning Cross-Modal Representations for Language-Based Image Manipulation

In this paper, we propose a generative architecture for manipulating images/scenes with natural language descriptions. This is a challenging task as the generative network is expected to perform the given text instruction without changing the non-affiliating contents of the input image. Two main drawbacks of the existing methods are their limitation of performing changes that would affect only a limited region and the inability of handling complex instructions. The proposed approach, designed to address these limitations initially uses two sets of networks to extract the image and text features respectively. Rather than a simple combination of these two modalities during the image manipulation process, we use an improved technique to compose image and text features. Additionally, the generative network utilizes similarity learning to improve text manipulation which also enforces only the text-relevant changes on the input image. Our experiments on CSS and Fashion Synthesis datasets show that the proposed approach performs remarkably well and outperforms the baseline frameworks in terms of R-precision and FID.
Explaining pair programming session dynamics from knowledge gaps

Background: Despite a lot of research on the effectiveness of Pair Programming (PP), the question when it is useful or less useful remains unsettled.
Survey on Automated End-to-End Data Science?

Data science is labor-intensive and human experts are scarce but heavily involved in every aspect of it. This makes data science time consuming and restricted to experts with the resulting quality heavily dependent on their experience and skills. To make data science more accessible and scalable, we need its democratization. Automated Data Science (AutoDS) is aimed towards that goal and is emerging as an important research and business topic. We introduce and define the AutoDS challenge, followed by a proposal of a general AutoDS framework that covers existing approaches but also provides guidance for the development of new methods. We categorize and review the existing literature from multiple aspects of the problem setup and employed techniques. Then we provide several views on how AI could succeed in automating end-to-end AutoDS. We hope this survey can serve as insightful guideline for the AutoDS field and provide inspiration for future research.
A Data-driven Approach for Forecasting State Level Aggregated Solar Photovoltaic Power Production

Reliable forecasting of power output from solar Photovoltaic (PV) systems is indispensable for successful penetration of increasingly high solar PV capacity into the structure of power systems. Forecasting is as equally important for efficient demand management as solar power output is variable in nature. Farm level forecasting is the primary focus of the current literature and various methods are available. However, the grid operators need to have the forecasts at aggregated regional level (e.g. state level) for decision making. Aggregated system level forecasting has received very limited attention in literature with only a very few methods so far. In this paper, we consider the task of forecasting PV power production at aggregated regional level (at state level in Australia) and present a data-driven approach. To develop the prediction models, we investigate three widely used machine learning algorithms for farm level solar power forecasting and two state-of-the-art deep learning algorithms. As the inputs to the prediction algorithms, we study two different feature sets based on the lagged power data and their descriptive statistics. An evaluation of the presented approach using PV power generation data from Australian Energy Market Operator (AEMO) demonstrates up to 6.30% improvement in forecast skill over the baseline models.
Uncertainty Estimates as Data Selection Criteria to Boost Omni-Supervised Learning

For many medical applications, large quantities of imaging data are routinely obtained but it can be difficult and time-consuming to obtain high-quality labels for that data. We propose a novel uncertainty-based method to improve the performance of segmentation networks when limited manual labels are available in a large dataset. We estimate segmentation uncertainty on unlabeled data using test-time augmentation and test-time dropout. We then use uncertainty metrics to select unlabeled samples for further training in a semi-supervised learning framework. Compared to random data selection, our method gives a significant boost in Dice coefficient for semi-supervised volume segmentation on the EADC-ADNI/HARP MRI dataset and the large-scale INTERGROWTH-21st ultrasound dataset. Our results show a greater performance boost on the ultrasound dataset, suggesting that our method is most useful with data of lower or more variable quality.
Object Tracking Method for Videomonitoring in Intelligent Transport Systems

Traffic management is one of the fundamental tasks of intelligent transport systems (ITS). It includes real-time data collection from the roads, its processing, and transmission of useful information to various decision-making systems. Loop detectors and different kinds of integrated into road infrastructure sensors can describe traffic flow for an adaptive traffic control system, city security system, etc. Vision-based technology (CCTV camera) can provide more detailed information about the traffic flow. It can identify anomalies and traffic incidents, which is very important for traffic police. This will allow us to instantly respond to situations and prevent traffic jams. An urban video monitoring system has to be supported by smart computer vision algorithms. There are many object detection and tracking algorithms that could be used in video monitoring systems. However, we faced the problem of inaccurate work of the algorithms in a real environment with occlusions, video stream interruptions. It significantly affects on object tracking, which is important for the task of vehicle counting, speed detection, and trajectory formation. In this paper, we developed and evaluated object tracking method on video streams from real road environment. We compared the results with other tracking algorithms.
Data-Driven Randomized Learning of Feedforward Neural Networks

Randomized methods of neural network learning suffer from a problem with the generation of random parameters as they are difficult to set optimally to obtain a good projection space. The standard method draws the parameters from a fixed interval which is independent of the data scope and activation function type. This does not lead to good results in the approximation of the strongly nonlinear functions. In this work, a method which adjusts the random parameters, representing the slopes and positions of the sigmoids, to the target function features is proposed. The method randomly selects the input space regions, places the sigmoids in these regions and then adjusts the sigmoid slopes to the local fluctuations of the target function. A bias-variance tradeoff is controlled by the region size. This brings very good results in the approximation of the complex target functions when compared to the standard fixed interval method and other methods recently proposed in the literature.
Image Interpolation Using Multi-Scale Attention-Aware Inception Network

A new multi-scale deep learning (MDL) framework is proposed and exploited for conducting image interpolation in this paper. The core of the framework is a seeding network that needs to be designed for the targeted task. For image interpolation, a novel attention-aware inception network (AIN) is developed as the seeding network; it has two key stages: 1) feature extraction based on the low-resolution input image; and 2) feature-to-image mapping to enlarge image's size or resolution. Note that the designed seeding network, AIN, needs to be trained with a matched training dataset at each scale. For that, multi-scale image patches are generated using our proposed pyramid cut, which outperforms the conventional image pyramid method by completely avoiding aliasing issue. After training, the trained AINs are then combined for processing the input image in the testing stage. Extensive experimental simulation results obtained from seven image datasets (comprising 359 images in total) have clearly shown that the proposed MAIN consistently delivers highly accurate interpolated images.
Mutual Information Maximization in Graph Neural Networks

A variety of graph neural networks (GNNs) frameworks for representation learning on graphs have been recently developed. These frameworks rely on aggregation and ITERATION scheme to learn the representation of nodes. However, information between nodes is inevitably lost in the scheme during learning. In order to reduce the loss, we extend the GNNs frameworks by exploring the aggregation and iteration scheme in the methodology of mutual information. We propose a new approach of enlarging the normal neighborhood in the aggregation of GNNs, which aims at maximizing mutual information. Based on a series of experiments conducted on several benchmark datasets, we show that the proposed approach improves the state-of-the-art performance for four types of graph tasks, including supervised and semi-supervised graph classification, graph link prediction and graph edge generation and classification.
High-Performance Lossless Compression of Hyperspectral Remote Sensing Scenes Based on Spectral Decorrelation

The capacity of the downlink channel is a major bottleneck for applications based on remote sensing hyperspectral imagery (HSI). Data compression is an essential tool to maximize the amount of HSI scenes that can be retrieved on the ground. At the same time, energy and hardware constraints of spaceborne devices impose limitations on the complexity of practical compression algorithms. To avoid any distortion in the analysis of the HSI data, only lossless compression is considered in this study. This work aims at finding the most advantageous compression–complexity trade-off within the state of the art in HSI compression. To do so, a novel comparison of the most competitive spectral decorrelation approaches combined with the best performing low-complexity compressors of the state is presented. Compression performance and execution time results are obtained for a set of 47 HSI scenes produced by 14 different sensors in real remote sensing missions. Assuming only a limited amount of energy is available, obtained data suggest that the FAPEC algorithm yields the best trade-off. When compared to the CCSDS 123.0-B-2 standard, FAPEC is 5.0 times faster and its compressed data rates are on average within 16% of the CCSDS standard. In scenarios where energy constraints can be relaxed, CCSDS 123.0-B-2 yields the best average compression results of all evaluated methods.