Reviewerly/exHarmony huggingface dataset:
Multiple model extended continuous ant colony filter applied to real-time wind estimation in a fixed-wing UAV

In this study, a new heuristic multiple model filter, called Multiple Model Extended Continuous Ant Colony Filter, is proposed to solve a nonlinear multiple model state estimation problem. In this filter, a bank of extended continuous ant colony filters are run in parallel to solve the multiple model estimation problem. The probability of each model is continually updated and consequently both the true model and the states of the nonlinear system are updated based on the weighted sum of the filters. The new multiple model filter is tested on an engineering problem. The problem is to estimate simultaneously the states of a fixed-wing unmanned aerial vehicle as well as the wind model, applied to the system. Four different wind models are considered and the proposed filter is unaware of the wind type. Then, observability of the states and the wind components are analyzed. Four new propositions are introduced and proved for unknown input observability, state and unknown input observability, the effect of time-varying unknown input matrix on the unknown input observability, and the effect of linearization errors on the state observability. Moreover, observability of the wind parameters is analyzed based on the nonlinear systems observability theory. Performance of the proposed filter is also evaluated in maneuvering flight and compared to a single extended continuous ant colony filter and a multiple model extended Kalman filter. A hardware-in-the-loop experiment is also performed to verify the real-time implementation capability of the suggested architecture.
Randomized learning and generalization of fair and private classifiers: From PAC-Bayes to stability and differential privacy

We address the problem of randomized learning and generalization of fair and private classifiers. From one side we want to ensure that sensitive information does not unfairly influence the outcome of a classifier. From the other side we have to learn from data while preserving the privacy of individual observations. We initially face this issue in the PAC-Bayes framework presenting an approach which trades off and bounds the risk and the fairness of the randomized (Gibbs) classifier. Our new approach is able to handle several different state-of-the-art fairness measures. For this purpose, we further develop the idea that the PAC-Bayes prior can be defined based on the data-generating distribution without actually knowing it. In particular, we define a prior and a posterior which give more weight to functions with good generalization and fairness properties. Furthermore, we will show that this randomized classifier possesses interesting stability properties using the algorithmic distribution stability theory. Finally, we will show that the new posterior can be exploited to define a randomized accurate and fair algorithm. Differential privacy theory will allow us to derive that the latter algorithm has interesting privacy preserving properties ensuring our threefold goal of good generalization, fairness, and privacy of the final model.
21.1 A Fully Integrated Genetic Variant Discovery SoC for Next-Generation Sequencing

Next-generation sequencing (NGS) is now indispensable for genetics research and biomedical applications, such as disease analysis and evolution tracking [1]. However, it still takes up to a couple of days to analyze all genetic mutations (variants) of a human genome, which consists of 3 billion nucleotides, through GPU acceleration. Fig. 21.1.1 shows an overview of NGS and the data analysis workflow. The NGS technology enables sequencing hundreds of millions of DNA segments, anchored and amplified on a microarray, in parallel. In each sequencing cycle, the nucleotides (A, T, C, G) are individually detected by their unique fluorescence labels and DNA segments can then be constructed as short reads. The NGS data analysis workflow consists of Preprocessing, Short-Read Mapping (including Exact Matching and Inexact Matching), Haplotype Calling, and Variant Calling [2]. Short reads are first mapped to a reference DNA and further used to assemble the genome of the DNA sample. Preprocessing is essential for constructing the data structure for indexing the reference DNA. In Short-Read Mapping, a seeding-and-extension scheme is applied to perform both Exact and Inexact Matching. The equal-length sub-sequences (seeds) of the short reads are used to find the exact locations on the reference DNA. Then, the seeds are extended to identify the most-likely locations through global alignment, allowing mismatches and insertions/deletions [2]. Next, in Haplotype Calling, the reads mapped to a specific region are assembled to reconstruct the paternal and maternal genomes (i.e. haplotypes) of the DNA sample. Finally, in Variant Calling, the assembled haplotypes are used to determine the variants between the reference DNA and the sample DNA. The outputs of Variant Calling indicate the location and likelihood of each variant. Dedicated VLSI solutions have been developed for acceleration, but only Suffix-Array (SA) Sorting for Preprocessing and Exact Matching for Short-Read Mapping were realized on silicon [3]. This work presents a fully integrated SoC for the entire NGS data analysis process.
Broad Learning System for Nonparametric Modeling of Clay Parameters

Due to the complex and uncertain nature of geomaterial properties, establishing representative parametric models between clay parameters is a challenging task. Nonparametric machine learning offers an accessible approach to develop empirical transformation models of clay parameters based on the available measurement. In this study, nonparametric modeling of clay parameter relationships via broad learning system (BLS) is introduced. Broad learning architecture provides an effective tool for nonparametric modeling based on noise-corrupted data. The architecture of deep learning is configured with stacks of hierarchical layers, which consume expensive computational cost for network training. In contrast, the network of BLS is established in a flat architecture and it can be modified incrementally. As a result, the broad learning flat network can be reconfigured efficiently to accommodate additional training data. To demonstrate the performance of the learning algorithm for clay parameters, the comprehensive global database CLAY/10/7490 with 7,490 data points from over 250 studies worldwide is utilized and analyzed.
Lookahead Converges to Stationary Points of Smooth Non-convex Functions

The Lookahead optimizer [Zhang et al., 2019] was recently proposed and demonstrated to improve performance of stochastic first-order methods for training deep neural networks. Lookahead can be viewed as a two time-scale algorithm, where the fast dynamics (inner optimizer) determine a search direction and the slow dynamics (outer optimizer) perform updates by moving along this direction. We prove that, with appropriate choice of step-sizes, Lookahead converges to a stationary point of smooth non-convex functions. Although Lookahead is described and implemented as a serial algorithm, our analysis is based on viewing Lookahead as a multi-agent optimization method with two agents communicating periodically.
Infrared Image Deblurring via High-Order Total Variation and Lp-Pseudonorm Shrinkage

The quality of infrared images is affected by various degradation factors, such as image blurring and noise pollution. Anisotropic total variation (ATV) has been shown to be a good regularization approach for image deblurring. However, there are two main drawbacks in ATV. First, the conventional ATV regularization just considers the sparsity of the first-order image gradients, thus leading to staircase artifacts. Second, it employs the L1-norm to describe the sparsity of image gradients, while the L1-norm has a limited capacity of depicting the sparsity of sparse variables. To address these limitations of ATV, a high-order total variation is introduced in the ATV deblurring model and the Lp-pseudonorm is adopted to depict the sparsity of low- and high-order total variation. In this way, the recovered image can fit the image priors with clear edges and eliminate the staircase artifacts of the ATV model. The alternating direction method of multipliers is used to solve the proposed model. The experimental results demonstrate that the proposed method does not only remove blurs effectively but is also highly competitive against the state-of-the-art methods, both qualitatively and quantitatively.
WiPOS: A POS Terminal Password Inference System Based on Wireless Signals

WiFi access points are sources of considerable security risks as the wireless signals have the potential to leak important private information such as passwords. This article examines the security issues posed by point-of-sale (POS) terminals which are widely used in WiFi-covered environments, such as restaurants, banks, and libraries. In particular, we envisage an attack model on passwords entered on POS terminals. We put forward the WiPOS, a password inference system based on wireless signals. Specifically, the WiPOS is a device-free system that uses two commercial off-the-shelf (COTS) devices to collect WiFi signals. Implementing a new keystroke segmentation algorithm and adopting support vector machine (SVM) classifiers with global alignment kernel (GAK), the WiPOS achieves improvement on both keystroke recognition and password prediction. The experimental results show that the WiPOS can achieve more than 73% accuracy for 6-digit password with the top 100 candidates. This article calls the community to take a closer look at the risks posed by the current ubiquitous WiFi devices.
Graph Metric Learning via Gershgorin Disc Alignment

We propose a general projection-free metric learning framework, where the minimization objective ${\min _{{\mathbf{M}} \in \mathcal{S}}}Q({\mathbf{M}})$ is a convex differentiable function of the metric matrix M, and M resides in the set S of generalized graph Laplacian matrices for connected graphs with positive edge weights and node degrees. Unlike low-rank metric matrices common in the literature, S includes the important positivediagonal-only matrices as a special case in the limit. The key idea for fast optimization is to rewrite the positive definite cone constraint in S as signal-adaptive linear constraints via Gershgorin disc alignment, so that the alternating optimization of the diagonal and offdiagonal terms in M can be solved efficiently as linear programs via Frank-Wolfe iterations. We prove that left-ends of the Gershgorin discs can be aligned perfectly using the first eigenvector v of M, which we update iteratively using Locally Optimal Block Preconditioned Conjugate Gradient (LOBPCG) with warm start as diagonal / off-diagonal terms are optimized. Experiments show that our efficiently computed graph metric matrices outperform metrics learned using competing methods in terms of classification tasks.
Shed+: Optimal Dynamic Speculation to Meet Application Deadlines in Cloud

With the growing deadline-sensitivity of cloud applications, adherence to specific deadlines is becoming increasingly crucial, particularly in shared clusters. A few slow tasks called stragglers can potentially adversely affect job execution times. Equally, inadequate slotting of data analytics applications could result in inappropriate resource deployment, ultimately damaging system performance. Against this backdrop, one effective way of tackling stragglers is by making extra attempts (or clones)1 for every single straggler after the submission of a job. This paper proposes Shed+, which is an optimization framework utilizing dynamic speculation that aims to maximize the jobs' PoCD (Probability of Completion before Deadline) by making full use of available resources. Notably, our work encompasses a new online scheduler that dynamically recomputes and reallocates resources during the course of a job's execution. According to our findings, Shed+ successfully leverages cloud resources and maximizes the percentage of jobs meeting their deadlines. In our experiments, we have seen this percentage for heavy load going up to 98% for Shed+ as opposed to nearly 68%, 40%, 35% and 37% for Shed, Dolly, Hopper and Hadoop with speculation enabled, respectively.
Real-time Outlier Detection Over Streaming Data

Designing outlier detection algorithms over streaming data involves several issues such as concept drift, temporal context, transience, uncertainty, etc. Moreover, to produce results in real-time with limited memory resources, the processing of such data must occur in an online fashion. Therefore, real time detection of outliers on streaming data faces more challenges than performing the same task on batches of data. Several methods have been proposed to detect outliers over streaming data, among which a sliding window technique is frequently used. In this technique, only a chunk of data is kept in memory at each point in time and used to build predictive models. The size of the data in memory simultaneously is referred to as the size of a sliding window. The correctness of the outlier detection results depends largely on the choice of window size. Other similar techniques exist but most of them fail to address the properties of streaming data, and thus produce results exhibiting poor accuracy. In this paper, we present an online outlier detection algorithm, that addresses the aforementioned challenges. The proposed algorithm adopts the sliding window technique, however efficiently mines in memory a statistical summary of previous observed data, which contributes to the prediction of incoming data. It further addresses the concept drift problem that exists in streaming data. We evaluated the accuracy of our algorithm on both synthetic and real-world datasets. Results show that the proposed method detects outliers over streaming data with higher accuracy than SOD_GPU algorithm proposed in 7516110, even when concept drifts occur. The algorithm does not require a secondary memory for processing and is further accelerated using CUDA GPU.
Shadow Removal of Text Document Images by Estimating Local and Global Background Colors

This paper proposes a simple yet effective method for removing shadows from text document images. Assuming that the document mainly contains texts, our method estimates the global and local background colors using statistical analysis of the whole image and local neighborhoods. By estimating the global and local background colors, we obtain the shadow map indicating the shadow ratio for each pixel. With the shadow map, a shadow-free image can be recovered by intrinsic decomposition. Experiments confirm that our method effectively removes the shadow of text document images regardless of the intensity, scope, and the number of shadows.
Based on Automatic Correlation Keyword Grouping and Combination Based Deep Information Search Corresponding to Specific Language Big Data — Case of Leisure Recreation

Today, huge amount of data can be searched through the Internet. To divide the big data into several and different information groups is difficult. In this paper, the proposed Automatic Correlation Keyword Grouping and Combination Based Deep Information Search could find the further grouping information according to the specific language. By the proposed procedure, the deep searching via using simple keywords can be automatically done through the data source grouping step, Correlation Keywords Combination method, and convergence step. The implementation and verification shows that the proposed system could find the deep grouped information corresponding to different language.
Mahalanobis Distance Based Adversarial Network for Anomaly Detection

Anomaly detection techniques are very crucial in multiple business applications, such as cyber security, manufacturing and finance. However, developing anomaly detection methods for high-dimensional data with high speed and good performance is still a challenge. Generative Adversarial Networks (GANs) are able to model the complex high-dimensional data, but they still require large computation in inference stage. This paper proposes an efficient method, known as Mahalanobis Distance-based Adversarial Network (MDAN), for anomaly detection. The proposed MDAN models the data using generative adversarial network (GAN) and detects anomalies by using the Mahalanobis distance. The proposed MDAN outperforms conventional GAN-based methods considerably and has a higher inference speed, when applied to several tabular and image datasets.
Studies on Big Data Mining Techniques in Wildfire Prevention for Power System

According to the propagation characteristics of big data for wildfire hazard prevention for power grid, the framework of big data platform is proposed in this paper. Internal relation between wildfire occurrence probability and the key meteorological factors like relative humidity, average daily precipitation, daily average temperature and daily extreme wind speed are unveiled through correlation analysis method. Wildfire hazard prevention system for power grid is established based on big data mining techniques. The above studies have already being successfully used in wildfire hazard prevention for provinces suffering from serious wildfire hazards of state grid corporation of China (SGCC) and is playing an important role in overcoming the wildfire for power grid.
A Literature Review and Taxonomy on Workload Prediction in Cloud Data Center

Resource management is one of the most challenging task in the cloud data center. These challenges have raised from the dynamic nature and high uncertainty in the cloud environment. Moreover, allocating resources over time may lead the sub-optimal execution environment due to significant up and drop in the workload that have some time dependent patterns. Therefore, it requires some time-sensitive techniques for optimising the resources utilization in cloud data center. In this paper, we discuss the workload prediction techniques that forecast the workload in the cloud environment and the value of predicted workload guides for optimising the resources. Furthermore, we present the workload taxonomy which is classified into (i) workload predictor and (ii) model fitting. In addition, we provide an extensive discussion on the workload predictors and further classified into temporal and non-temporal.
Action Recognition by Composite Deep Learning Architecture I3D-DenseLSTM

Two-Stream Convolutional Neural Networks have shown remarkable results for action recognition in videos. In this paper, we adopt the two-stream principle to exploit appearance and motion features from a video. We extend two-stream to three-stream to achieve diversity. Two streams are employed to capture spatial and temporal aspects, while another stream is used to capture motion modality. In a pre-processing step, we prepare the data, from where we extract features and optical flow frames. We conducted experiments on Moment-In-Time dataset available publicly. Our novel network architecture is composed of three main parts: DenseLSTM component (DenseNet-like skip connections plus LSTM), a single LSTM component, and the Inflated 3D. Altogether we call our model I3D-DenseLSTM. Through experiments, we demonstrate that our proposed model outperforms several baseline models.
Multi-Scale Octave Convolutions for Robust Speech Recognition

We propose a multi-scale octave convolution layer to learn robust speech representations efficiently. Octave convolutions were introduced by Chen et al [1] in the computer vision field to reduce the spatial redundancy of the feature maps by decomposing the output of a convolutional layer into feature maps at two different spatial resolutions, one octave apart. This approach improved the efficiency as well as the accuracy of the CNN models. The accuracy gain was attributed to the enlargement of the receptive field in the original input space. We argue that octave convolutions likewise improve the robustness of learned representations due to the use of average pooling in the lower resolution group, acting as a low-pass filter. We test this hypothesis by evaluating on two noisy speech corpora - Aurora-4 and AMI. We extend the octave convolution concept to multiple resolution groups and multiple octaves. To evaluate the robustness of the inferred representations, we report the similarity between clean and noisy encodings using an affine projection loss as a proxy robustness measure. The results show that proposed method reduces the WER by up to 6.6% relative for Aurora-4 and 3.6% for AMI, while improving the computational efficiency of the CNN acoustic models.
Multi-Scale Deep Feature Fusion for Vehicle Re-Identification

Vehicle re-identification (re-id) is challenging due to the small inter-class distance. The differences between similar vehicles can be extremely subtle and only captured at particular scales and semantic levels. In this paper, we propose a novel Multi-Scale Deep Feature Fusion Network (MSDeep) to conduct both multi-scale and multi-level features for precise vehicle re-id. Based on the backbone deep CNN, MS-Deep mainly consists of two modules: 1) Multi-Scale Fusion (MSF) Block which encapsulates combination of multi-scale streams as MSF feature; 2) Multi-Level Fusion (MLF) Block which fuses MSF features of multiple levels to build the final descriptor. Importantly, in MSF, Multi-Scale Attention (MSA) is introduced to dynamically emphasize important channels of each scale, and Level-Wise Attention(LWA) is utilized in MLF to determine the different weightings for each MSF feature of different levels. As a result, experiments show that our MSDeep outperforms state-of-the-art algorithms on challenging VeRi and VehicleID benchmarks in terms of abundant and hierarchical hyper-descriptors.
Extending the weightless WiSARD classifier for regression

This paper explores two new weightless neural network models, Regression WiSARD and ClusRegression WiSARD, in the challenging task of predicting the total palm oil production of a set of 28 (twenty eight) differently located sites under different climate and soil profiles. Both models were derived from Kolcz and Allinson’s n-Tuple Regression weightless neural model and obtained mean absolute error (MAE) rates of 0.09097 and 0.09173, respectively. Such results are very competitive with the state-of-the-art (0.07983), whilst being four orders of magnitude faster during the training phase. Additionally the models have been tested on three classic regression datasets, also presenting competitive performance with respect to other models often used in this type of task.
Crowd Detection And Tracking In Surveillance Video Sequences

The importance for video-based monitoring systems is on the rise leading to the growth of interest in the field of computer vision. With the increase of human population, crowd needs to be monitored, be it in a public place or in a building. Human monitoring can be quite tiresome and expensive, making way for the upcoming of automated crowd monitoring systems. Crowd analysis comprises of detection, tracking, behavioral analysis, etc. In this paper a framework for the detection of crowd, tracking and counting is proposed. The goal is to create a robust system with utmost accuracy in its results. Contrasted with sensor-based arrangements and human-based, the video-based ones take into account more adaptable functionalities, enhanced execution with lower costs. In this work, the dataset PETS2009 was used. The results showed that the proposed system has the capability to count all the people passing through the field of view of the surveillance camera. The system was tested for different types of crowd and the average efficiency of various scenarios is 83.14%.