Reviewerly/exHarmony huggingface dataset:
Interpretable Anomaly Detection with DIFFI: Depth-based feature importance of Isolation Forest

Anomaly Detection is an unsupervised learning task aimed at detecting anomalous behaviors with respect to historical data. In particular, multivariate Anomaly Detection has an important role in many applications thanks to the capability of summarizing the status of a complex system or observed phenomenon with a single indicator (typically called ‘anomaly score’) and thanks to the unsupervised nature of the task that does not require human tagging. The Isolation Forest is one of the most commonly adopted algorithms in the field of Anomaly Detection due to its proven effectiveness and low computational complexity. A major problem affecting Isolation Forest is represented by the lack of interpretability, an effect of the inherent randomness governing the splits performed by the Isolation Trees, the building blocks of the Isolation Forest. In this paper, we propose effective yet computationally inexpensive methods to define feature importance scores at both global and local levels for the Isolation Forest. Moreover, we define a procedure to perform unsupervised feature selection for Anomaly Detection problems based on our interpretability method. Such a procedure also serves the purpose of tackling the challenging task of feature importance evaluation in unsupervised anomaly detection. We assess the performance on several synthetic and real-world datasets, including comparisons against state-of-the-art interpretability techniques, and make the code publicly available to enhance reproducibility and foster research in the field.
KaRuNa: A Blockchain-Based Sentiment Analysis Framework for Fraud Cryptocurrency Schemes

The current open cryptocurrency markets pose varied challenges on a prospective investor (PI), such as pseudoanonymity of cryptocurrency transactions, selection criteria for investments in crowdfunding schemes (CF), modus-operandi for these schemes, non-transparency of money generation and distribution among peers, and untraceable scams. PIs are susceptible to monetary losses in the open market due to the aforementioned issues. The fraudsters could be both internal (operator of the scheme) and external (financial institutions (FI), such as banks, money-lenders, and insurance companies). The centrality of trust among stakeholders like PI, CF, and FI is a prime concern. Motivated from these facts, this paper proposes a decentralized framework, KaRuNa, A Blockchain-based Sentiment analysis framework for Fraud Cryptocurrency schemes. KaRuNa operates on public blockchain three phases of trust modeling among stakeholders. In the first phase, transactions are performed on the blockchain that offers trust, auditability, and transparency among stakeholders. In the second phase, sentiment analysis (SA) of cryptocurrencies is proposed based on a novel algorithm of hash addresses to generate classification scores (CS). Parameters like social trends, rise/fall in cryptocurrency price, measured standard deviation, peak and low are selected to fed to proposed novel Long-short term memory (LSTM) classifier to generate recommendations based on CS. An accuracy of 98.99% is achieved using LSTM over generated CS to evaluate risks in the investment. Results demonstrate that KaRuNa achieves more scalability compared to conventional approaches.
No-Reference Image Quality Assessment Based on the Fusion of Statistical and Perceptual Features

The goal of no-reference image quality assessment (NR-IQA) is to predict the quality of an image as perceived by human observers without using any pristine, reference images. In this study, an NR-IQA algorithm is proposed which is driven by a novel feature vector containing statistical and perceptual features. Different from other methods, normalized local fractal dimension distribution and normalized first digit distributions in the wavelet and spatial domains are incorporated into the statistical features. Moreover, powerful perceptual features, such as colorfulness, dark channel feature, entropy, and mean of phase congruency image, are also incorporated to the proposed model. Experimental results on five large publicly available databases (KADID-10k, ESPL-LIVE HDR, CSIQ, TID2013, and TID2008) show that the proposed method is able to outperform other state-of-the-art methods.
Robustifying Reinforcement Learning Agents via Action Space Adversarial Training

Adoption of machine learning (ML)-enabled cyber-physical systems (CPS) are becoming prevalent in various sectors of modern society such as transportation, industrial, and power grids. Recent studies in deep reinforcement learning (DRL) have demonstrated its benefits in a large variety of data-driven decisions and control applications. As reliance on ML-enabled systems grows, it is imperative to study the performance of these systems under malicious state and actuator attacks. Traditional control systems employ resilient/fault-tolerant controllers that counter these attacks by correcting the system via error observations. However, in some applications, a resilient controller may not be sufficient to avoid a catastrophic failure. Ideally, a robust approach is more useful in these scenarios where a system is inherently robust (by design) to adversarial attacks. While robust control has a long history of development, robust ML is an emerging research area that has already demonstrated its relevance and urgency. However, the majority of robust ML research has focused on perception tasks and not on decision and control tasks, although the ML (specifically RL) models used for control applications are equally vulnerable to adversarial attacks. In this paper, we show that a well-performing DRL agent that is initially susceptible to action space perturbations (e.g. actuator attacks) can be robustified against similar perturbations through adversarial training.
Exactly sparse Gaussian variational inference with application to derivative-free batch nonlinear state estimation

We present a Gaussian variational inference (GVI) technique that can be applied to large-scale nonlinear batch state estimation problems. The main contribution is to show how to fit both the mean and (inverse) covariance of a Gaussian to the posterior efficiently, by exploiting factorization of the joint likelihood of the state and data, as is common in practical problems. This is different than maximum a posteriori (MAP) estimation, which seeks the point estimate for the state that maximizes the posterior (i.e., the mode). The proposed exactly sparse Gaussian variational inference (ESGVI) technique stores the inverse covariance matrix, which is typically very sparse (e.g., block-tridiagonal for classic state estimation). We show that the only blocks of the (dense) covariance matrix that are required during the calculations correspond to the non-zero blocks of the inverse covariance matrix, and further show how to calculate these blocks efficiently in the general GVI problem. ESGVI operates iteratively, and while we can use analytical derivatives at each iteration, Gaussian cubature can be substituted, thereby producing an efficient derivative-free batch formulation. ESGVI simplifies to precisely the Rauch–Tung–Striebel (RTS) smoother in the batch linear estimation case, but goes beyond the ‘extended’ RTS smoother in the nonlinear case because it finds the best-fit Gaussian (mean and covariance), not the MAP point estimate. We demonstrate the technique on controlled simulation problems and a batch nonlinear simultaneous localization and mapping problem with an experimental dataset.
Facial Expression Recognition via a CBAM Embedded Network

Facial expression recognition plays an important role in face recognition. Face recognition helps computer to figure out people faces from a simple scenery and to recognize who they are. But facial expression recognition assists computer to analyze the emotion state of one single person in order to improve the human-computer interaction experience. For facial expressions, there are certainly lots of conspicuous features for observers to look on, like shapes of eyes, mouth. When people smile, their lips turn up and their eyebrows bend downward. In this way, we get features of smile. And in same way, we can also do the same to anger, sadness, and surprise etc. Single neural network can make it. However, the accuracy is always low, nearly 70% up and down, which always companied with fluctuation, like electrocardiogram, though not that severe, according to experiments. Therefore, we choose to include Convolutional Block Attention Module (CBAM) into some layers of VGG network we used to improve the accuracy and also the stability. CBAM is an effective attention module for neural networks to concentrate on features. With CBAM, we simplify the scenery that networks need to analyze, and then we can finally make an improvement. This way, we can build better human-computer interfaces.
FPGA-Based Low-Visibility Enhancement Accelerator for Video Sequence by Adaptive Histogram Equalization With Dynamic Clip-Threshold

In the natural and practical scenario, the captured video sequence under bad weather situations or low light conditions often suffers from poor visibility and low-contrast problems. This hurts the performance of the high-level processing, e.g. object tracking or recognition. In this paper, we develop an FPGA-based low-visibility enhancement accelerator for video sequence by adaptive histogram equalization with dynamic clip-threshold (AHEwDC) which is determined by the visibility assessment. The main goal is to improve the low visibility with high image quality for both hazy and low-light video sequences in real-time. Firstly, a concept to quantify the visual perception based on supervised learning is to estimate the visibility score. Then, to avoid the problem of noise amplification in the conventional method, we propose a visibility assessment model to find an optimal clip-threshold. The contrast energy of gray channel, yellow-blue channel and red-green channel, average saturation, and gradients are statistical features in the model to describe the visibility of an image. Finally, to meet the speed requirement for video sequence processing, a specified hardware architecture for both visibility assessment and AHEwDC is implemented on FPGA. Besides, a mean spatial filter for cumulative distribution functions (CDFs) of the AHE is developed for suppressing the noise caused by a single-color local region. The demonstration system on the DE1-SoC platform with the Intel Cyclone V FPGA device with the max working frequency of 75.84 MHz is capable of processing 30 fps FHD ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1920\times 1080$ </tex-math></inline-formula> ) video.
Feature Selection for High Dimensional Data Using Weighted K-Nearest Neighbors and Genetic Algorithm

Too many input features in applications may lead to over-fitting and reduce the performance of the learning algorithm.Moreover, in most cases, each feature containing different information content has different effects on the prediction target.Therefore, a feature selection method for calculating the importance of each feature, called WKNNGAFS, is proposed in this paper.In this method, the genetic algorithm (GA) is adopted to search the optimal weight vector, the value of the ith component of which corresponds to the contribution degree of the ith feature to the classification from a global perspective.Besides, weighted K-nearest neighbors algorithm (WKNN), which takes both the different contributions of nearest neighbors and the different classification ability of each feature into account, is used to determine the target label.To evaluate the effectiveness of the proposed method, nine existing feature selection methods are compared with it on 13 real datasets, including 6 high dimensional microarray datasets.Experimental results demonstrate the method is more effective and can improve classification performance.INDEX TERMS Feature selection, weighted K-nearest neighbors, genetic algorithm, real coding.• WKNNGAFS is performed on 13 publicly available real datasets, including 6 high dimensional datasets.Compared with the existing 5 filter feature selection methods and 4 wrapper feature selection methods, the results demonstrate the effectiveness and superiority of the proposed method.
A global annual optimum tilt angle model for photovoltaic generation to use in the absence of local meteorological data

This manuscript proposes a series of global models to estimate optimum annual tilt angle (βopt) as a function of local variables (latitude, diffuse fraction and albedo) based on the hourly irradiance data of 14,468 sites spread across the globe from the One Building database. As a result, these models can be used for any location in the absence of local meteorological data. First, a polynomial regression model, applicable worldwide, is proposed to estimate βopt as a function of latitude. This model fits the global data considered with a 2% RMSE error. Average energy losses are estimated to be 1% for a 10° variation from βopt. A variation of 40° with respect to βopt, implies a 12–18% energy loss depending on latitude. In addition, if only latitude is considered to estimate βopt, different expressions should be used for latitudes >50° depending on the hemisphere. These variations are a result of the influence of diffuse irradiance on βopt, due to the fact that sites with higher amounts of diffuse irradiance have a lower βopt. Secondly, a polynomial surface regression model to estimate βopt as a function of latitude and the annual diffuse fraction is proposed improving the results, reaching a 0.7% RMSE error. Thirdly, a simplified polynomial surface regression model to estimate βopt as a function of latitude and albedo (without the influence of the diffuse fraction) is proposed, and finally a model gathering all three variables under study (latitude, annual diffuse fraction and albedo) to calculate the optimum tilt angle is presented.
Asymmetric multiple image elliptic curve cryptography

A novel asymmetric multiple-image encryption method based on quick response code and elliptic curve cryptography is proposed through our research. In this new scheme, four quick response code images converted from four images and a random generated intensity key are used as the input for the elliptic curve cryptography image encryption system, which can yield two real number ciphertexts. The ciphertext image is further decrypted through a digital decryption scheme. The proposed system uses both public and private keys thus it is an asymmetric encryption system. The method can encrypt up to sixteen images simultaneously. Comprehensive numerical simulations have demonstrated the robustness and effectiveness of the encryption scheme.
A single-shot phase retrieval method for phase measuring deflectometry based on deep learning

Phase Measuring Deflectometry (PMD) is an effective metrology technique for specular surfaces, but it is hard to achieve rapid measurement precisely with prevenient fringe analysis methods. Inspired by recent successes of deep learning techniques for fringe analysis, we demonstrate for the first time that deep learning can be used to achieve high-precision three-dimensional (3D) measurement of specular surfaces with a single frame. In this research, it is proposed to use depthwise separable convolution to optimize measured results. Experimental results proved that this proposed method has better performance than the existing fringe analysis method employing deep learning.
Provably Secure Isolation for Interruptible Enclaved Execution on Small Microprocessors

Computer systems often provide hardware support for isolation mechanisms like privilege levels, virtual memory, or enclaved execution. Over the past years, several successful software-based side-channel attacks have been developed that break, or at least significantly weaken the isolation that these mechanisms offer. Extending a processor with new architectural or micro-architectural features, brings a risk of introducing new such side-channel attacks. This paper studies the problem of extending a processor with new features without weakening the security of the isolation mechanisms that the processor offers. We propose to use full abstraction as a formal criterion for the security of a processor extension, and we instantiate that criterion to the concrete case of extending a microprocessor that supports enclaved execution with secure interruptibility of these enclaves. This is a very relevant instantiation as several recent papers have shown that interruptibility of enclaves leads to a variety of software-based side-channel attacks. We propose a design for interruptible enclaves, and prove that it satisfies our security criterion. We also implement the design on an open-source enclave-enabled microprocessor, and evaluate the cost of our design in terms of performance and hardware size.
Geometric rectification of document images using adversarial gated unwarping network

Document images captured in natural scenes with a hand-held camera often suffer from geometric distortions and cluttered backgrounds. In this paper, we propose a simple yet efficient deep model named Adversarial Gated Unwarping Network (AGUN) to rectify these images. In this model, the rectification task is recast as a dense grid prediction problem. We thereby develop a pyramid encoder-decoder architecture to predict the unwarping grid at multiple resolutions in a coarse-to-fine fashion. Based on the observation that the structural visual cues, e.g., text-lines, text blocks, lines in tables, which are critical for the estimation of unwarping mapping, are non-uniformly distributed in the images, three gated modules are introduced to guide the network focusing on these informative cues rather than other interferences such as blank areas and complex backgrounds. To generate more visually pleasing rectification results, we further adopt adversarial training mechanism to implicitly constrain the unwarping grid estimation. Our model can rectify arbitrarily distorted document images with complicated page layouts and cluttered backgrounds. Experiments on the public benchmark dataset and the synthetic dataset demonstrate that our approach outperforms the state-of-the-art methods in terms of OCR accuracy and several widely used quantitative evaluation metrics.
Automatic clustering using a local search-based human mental search algorithm for image segmentation

Clustering is a commonly employed approach to image segmentation. To overcome the problems of conventional algorithms such as getting trapped in local optima, in this paper, we propose an improved automatic clustering algorithm for image segmentation based on the human mental search (HMS) algorithm, a recently proposed method to solve complex optimisation problems. In contrast to most existing methods for image clustering, our approach does not require any prior knowledge about the number of clusters but rather determines the optimal number of clusters automatically. In addition, for further improved efficacy, we incorporate local search operators which are designed to make changes to the current cluster configuration. To evaluate the performance of our proposed algorithm, we perform an extensive comparison with several state-of-the-art algorithms on a benchmark set of images and using a variety of metrics including cost function, correctness of the obtained numbers of clusters, stability, as well as supervised and unsupervised segmentation criteria. The obtained results clearly indicate excellent performance compared to existing methods with our approach yielding the best result in 16 of 17 cases based on cost function evaluation, 9 of 11 cases based on number of identified clusters, 13 of 17 cases based on the unsupervised Borsotti image segmentation criterion, and 7 of 11 cases based on the supervised PRI image segmentation metric.
Deterrence with Imperfect Attribution

Motivated by recent developments in cyberwarfare, we study deterrence in a world where attacks cannot be perfectly attributed to attackers. In the model, each of $$ n $$ attackers may attack the defender. The defender observes a noisy signal that probabilistically attributes the attack. The defender may retaliate against one or more attackers and wants to retaliate against the guilty attacker only. We note an endogenous strategic complementarity among the attackers: if one attacker becomes more aggressive, that attacker becomes more “suspect” and the other attackers become less suspect, which leads the other attackers to become more aggressive as well. Despite this complementarity, there is a unique equilibrium. We identify types of improvements in attribution that strengthen deterrence—namely, improving attack detection independently of any effect on the identifiability of the attacker, reducing false alarms, or replacing misidentification with non-detection. However, we show that other improvements in attribution can backfire, weakening deterrence—these include detecting more attacks where the attacker is difficult to identify or pursuing too much certainty in attribution. Deterrence is improved if the defender can commit to a retaliatory strategy in advance, but the defender should not always commit to retaliate more after every signal.
Accuracy and fairness trade-offs in machine learning: a stochastic multi-objective approach

In the application of machine learning to real-life decision-making systems, e.g., credit scoring and criminal justice, the prediction outcomes might discriminate against people with sensitive attributes, leading to unfairness. The commonly used strategy in fair machine learning is to include fairness as a constraint or a penalization term in the minimization of the prediction loss, which ultimately limits the information given to decision-makers. In this paper, we introduce a new approach to handle fairness by formulating a stochastic multi-objective optimization problem for which the corresponding Pareto fronts uniquely and comprehensively define the accuracy-fairness trade-offs. We have then applied a stochastic approximation-type method to efficiently obtain well-spread and accurate Pareto fronts, and by doing so we can handle training data arriving in a streaming way.