Reviewerly/exHarmony huggingface dataset:
An accurate classifier based on adaptive neuro-fuzzy and features selection techniques for fault classification in analog circuits

This paper presents a new approach for faults classification in analog integrated circuits using a multiclass adaptive neuro fuzzy inference system classifier. This is carried out to assist analog circuit's faults diagnosis suffering from inaccurate faults classification on one hand, and to lessen computational burden on the other hand. This has been achieved from features number reduction. These features serving as input feature vector are extracted from the selected circuits (CUT) frequency and transient responses under both fault free and faulty conditions. The considered faults are resistors and capacitors values variations of about 50% low and high from their nominal ones. The method accuracy has been validated with three experiment circuits, the Sallen Key band-pass, the four opamp biquad high-pass and the leapfrog filters. The obtained results reveal a high level of efficiency with an accuracy average reach to 99.76%. Hence, the proposed method has shown a good performance in term of fault classification accuracy when compared with those of both the Artificial Neural Networks (ANN) approach and the fractional Fourier transform (FRFT) method based on a statistical property.
Temporal Action Localization in Untrimmed Videos Using Action Pattern Trees

In this paper, we present a novel framework of automatically localizing action instances based on action pattern trees (AP-Trees) in a long untrimmed video. For localizing action instances in videos with varied temporal lengths, we first split videos into sequential segments and then use the AP-Trees to produce precise temporal boundaries of action instances. The AP-Trees can exploit the temporal information between segments of videos based on the label vectors of segments, by learning the occurrence frequency and order of segments. In AP-Trees, nodes stand for action class labels of segments and edges represent the temporal relationships between two consecutive segments. Thus, we can discover the occurrence frequencies of segments by searching paths of AP-Trees. In order to obtain accurate labels of video segments, we introduce deep neural networks to annotate the segments by simultaneously leveraging the spatio-temporal information and the high-level semantic feature of segments. In the networks, informative action maps are generated by a global average pooling layer to retain the spatio-temporal information of segments. An overlap loss function is employed to further improve the precision of label vectors of segments by considering the temporal overlap between segments and the ground truth. The experiments on THUMOS2014, MSR ActionII, and MPII Cooking datasets demonstrate the effectiveness of the method.
MotionRugs: Visualizing Collective Trends in Space and Time

vs Fig. 1.Example of a MotionRug applied to swarm data comprising 151 golden shiner fish swimming through a water tank.Each frame of the data is represented as one vertical slice, and all slices are aligned sequentially on a time axis.The color encodes the speed of the fish, leading to this visual representation.We selected six frames that indicate the overall states and movement of the fish, as shown above the MotionRug.Below the MotionRug, we highlight key areas of the visualization: Overall, there is a strong spatial dynamic at the beginning.First, the fish are slow as they approach the right wall of the tank.Then, the fish speed up slowly and slow down when hitting the next wall.The fish repeat this behavior when approaching the wall on the left.Arrived, the fish show a spatial stagnation, meaning as a whole, the group remains in one place.Trends are typically indicated by tapered feature values.Abstract-Understanding the movement patterns of collectives, such as flocks of birds or fish swarms, is an interesting open research question.The collectives are driven by mutual objectives or react to individual direction changes and external influence factors and stimuli.The challenge in visualizing collective movement data is to show space and time of hundreds of movements at the same time to enable the detection of spatiotemporal patterns.In this paper, we propose MotionRugs, a novel space efficient technique for visualizing moving groups of entities.Building upon established space-partitioning strategies, our approach reduces the spatial dimensions in each time step to a one-dimensional ordered representation of the individual entities.By design, MotionRugs provides an overlap-free, compact overview of the development of group movements over time and thus, enables analysts to visually identify and explore group-specific temporal patterns.We demonstrate the usefulness of our approach in the field of fish swarm analysis and report on initial feedback of domain experts from the field of collective behavior.
BIGOWL: Knowledge centered Big Data analytics

Knowledge extraction and incorporation is currently considered to be beneficial for efficient Big Data analytics. Knowledge can take part in workflow design, constraint definition, parameter selection and configuration, human interactive and decision-making strategies. This paper proposes BIGOWL, an ontology to support knowledge management in Big Data analytics. BIGOWL is designed to cover a wide vocabulary of terms concerning Big Data analytics workflows, including their components and how they are connected, from data sources to the analytics visualization. It also takes into consideration aspects such as parameters, restrictions and formats. This ontology defines not only the taxonomic relationships between the different concepts, but also instances representing specific individuals to guide the users in the design of Big Data analytics workflows. For testing purposes, two case studies are developed, which consists in: first, real-world streaming processing with Spark of traffic Open Data, for route optimization in urban environment of New York city; and second, data mining classification of an academic dataset on local/cloud platforms. The analytics workflows resulting from the BIGOWL semantic model are validated and successfully evaluated.
Practical Design and Implementation of Cloud Computing for Power System Planning Studies

The increased penetration of renewable resources and retirement of traditional generation units are posing new challenges to power system operations and planning to meet the ever-growing demand for more computing resources to run large-scale, computationally intensive simulations. As the traditional on-premises infrastructure loses pace to the constantly increasing computing needs, cloud computing has become an attractive addition for increasing average computing capacity, and alternative for meeting peak demands. ISO New England (ISO-NE) pioneered the use of cloud computing technology for large-scale power system simulations, believing it to be the only viable solution that is capable of keeping pace with the growing challenge in the foreseeable future. This paper shares the practical design and implementation of an elastic, on-demand, application-independent cloud platform that ISO-NE deployed in production at the end of 2017. As a secure, cost-effective and scalable platform for addressing the intensive computing demand in planning studies, this platform made using Amazon Web service as easy as using on-premises computers by completely automating the access to cloud based resources. This frees ISO engineers to focus on power system analysis, and thus significantly increased their productivity. To accommodate simulation jobs with varying urgency requirements, this platform allows the users to make the tradeoff between cost and completion time. The platform also further strengthened its support for cyber security and data privacy.
Canonical Correlation Analysis Regularization: An Effective Deep Multiview Learning Baseline for RGB-D Object Recognition

Object recognition methods based on multimodal data, color plus depth (RGB-D), usually treat each modality separately in feature extraction, which neglects implicit relations between two views and preserves noise from any view to the final representation. To address these limitations, we propose a novel canonical correlation analysis (CCA)-based multiview convolutional neural network (CNNs) framework for RGB-D object representation. The RGB and depth streams process corresponding images, respectively, then are connected by CCA module leading to a common-correlated feature space. In addition, to embed CCA into deep CNNs in a supervised manner, two different schemes are explored. One considers CCA as a regularization (CCAR) term adding to the loss function. However, solving CCA optimization directly is neither computationally efficient nor compatible with the mini-batch-based stochastic optimization. Thus, we further propose an approximation method of CCAR, using the obtained CCA projection matrices to replace the weights of feature concatenation layer at regular intervals. Such a scheme enjoys benefits of full CCAR and is efficient by amortizing its cost over many training iterations. Experiments on benchmark RGB-D object recognition datasets have shown that the proposed methods outperform most existing methods using the very same of their network architectures.
Intracluster Structured Low-Rank Matrix Analysis Method for Hyperspectral Denoising

Hyperspectral images (HSIs) denoising aims at eliminating the noise generated during the acquisition and transmission of HSIs. Since denoising is an ill-posed problem, utilizing proper knowledge of HSIs as regularization is essential for a good denoiser. Many HSI denoising methods have been proposed to leverage various prior knowledge, e.g., total variation, sparsity, and so on. Among those knowledge, a low-rank property has been shown to be effective for HSI denoising since it has the ability to deal with the missing values. However, most existing low-rank methods seldom consider mining the useful structures inside the low-rank matrix for a better denoising result. In addition, the rank number needs to be assigned manually. To address these problems, we propose an intracluster structured low-rank matrix analysis method for HSI denoising. First, we divide the original HSI into some clusters by taking advantages of both local similarity and nonlocal similarity structures, with which the resulted clusters are simpler and show more obvious low-rank property. Second, with singular value decomposition on the low-rank matrix in each cluster, the structured sparsity is modeled among the singular values to capture the structure of the low-rank matrix. Finally, an efficient optimization method is proposed to learn the structured sparsity adaptively from the data, as well as to inversely estimate the latent clean HSI from the noisy counterpart. The proposed method can not only obtain better denoising results compared with the-state-of-the-art methods but also automatically determine the rank number. Extensive experimental results demonstrate the effectiveness of the proposed method.
Robust high dynamic range color image watermarking method based on feature map extraction

In order to protect the copyright of high dynamic range (HDR) images, a novel watermarking method based on feature map extraction is proposed. Since the HDR image provides wide dynamic ranges of visible luminance, the correlations of the three RGB channels are much stronger than those of neighbor pixels. In order to preserve strong relationships of the three color channels, the Tucker decomposition is used to divide the HDR color image along the three color channels so that three feature maps are extracted. The first feature map of the three feature maps is employed to embed watermark for robustness, because it contains most of the energies in the HDR image. The first feature map is divided into non-overlapping blocks, and the Schur decomposition is operated on each block to generate a unitary matrix. Since the texture or edge remains after the tone mapping operation (TMO) of the HDR image, the stable size relation between the pair-wise coefficients in the unitary matrix is established to embed watermark. The experimental results show that the proposed method can efficiently resist different TMOs and common image attacks, outperforming other existing HDR image watermarking methods.
RePair and All Irreducible Grammars are Upper Bounded by High-Order Empirical Entropy

Irreducible grammars are a class of context-free grammars with well-known representatives, such as Repair (with a few tweaks), Longest Match, Greedy, and Sequential. We show that a grammar-based compression method described by Kieffer and Yang (2000) is upper bounded by the high-order empirical entropy of the string when the underlying grammar is irreducible. Specifically, given a string S over an alphabet of size σ, we prove that if the underlying grammar is irreducible, then the length of the binary code output by this grammar-based compression method is bounded by |S|H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sub> (S) + o(|S|log σ) for any k ∈ 0(log <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">σ</sub> |S|), where H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sub> (S) is the k-order empirical entropy of S. This is the first bound encompassing the whole class of irreducible grammars in terms of the high-order empirical entropy, with coefficient 1.
Assessing variables of regional reanalysis data sets relevant for modelling small-scale renewable energy systems

Data that allow the characterization of short-term and inter-annual variability of renewable energy resources availability are becoming highly valuable for energy system modellers. Global reanalysis data provide long time series of records without gaps and full spatial coverage at no cost for the final user. However, these exhibit coarse spatial resolution. The COSMO-REA6 and COSMOS-REA2 regional reanalysis for Europe overcome this limitation by increasing the resolution of the reanalysis to six and two kilometres respectively. This work presents an assessment of solar radiation and wind speed variables of these data sets that were available to the public in January 2018. This assessment is performed using data of the Bavarian agro-meteorological network and the Czech Hydrometeorological Institute. Eight accuracy indicators for hourly data in the period 1995–2015 are calculated. Widely used indicators such as the Pearson's correlation coefficient in some cases reach values above 0.82 for wind speeds and above 0.92 for global horizontal irradiance. The mean bias error is consistently better than ± 9.3 W/m2 for the full set of irradiance data, ±25.1 W/m2 for only day-time irradiance data and is, with a few exceptions, lower than ± 1 m/s for the wind speed data.
Generative Adversarial Network-Based Image Super-Resolution Using Perceptual Content Losses

In this paper, we propose a deep generative adversarial network for super-resolution considering the trade-off between perception and distortion. Based on good performance of a recently developed model for super-resolution, i.e., deep residual network using enhanced upscale modules (EUSR) [9], the proposed model is trained to improve perceptual performance with only slight increase of distortion. For this purpose, together with the conventional content loss, i.e., reconstruction loss such as L1 or L2, we consider additional losses in the training phase, which are the discrete cosine transform coefficients loss and differential content loss. These consider perceptual part in the content loss, i.e., consideration of proper high frequency components is helpful for the trade-off problem in super-resolution. The experimental results show that our proposed model has good performance for both perception and distortion, and is effective in perceptual super-resolution applications.
A lossless DNA data hiding approach for data authenticity in mobile cloud based healthcare systems

We present a lossless Deoxyribonucleic Acid (DNA) sequence hiding method that can be used for ensuring authenticity of DNA sequence in the context of Mobile Cloud based healthcare systems. Hiding data within DNA sequence results in permanent information loss in DNA sequence. Therefore, providing DNA sequence authenticity using data hiding is challenging. Moreover, existing works on DNA data hiding require a reference DNA sequence data to retrieve hidden data. Hence, current methods are not blind approaches and inappropriate for ensuring authenticity of DNA sequence in the Mobile Cloud. The proposed method hides authentication data within DNA sequence, extracts authentication data, and reconstructs the DNA sequence without any loss of information. From there, our proposed approach guarantees DNA sequence authenticity and integrity in Mobile Cloud based healthcare systems. We present a security analysis of our method to show that the method is secured. We conduct several experiments to demonstrate the performance of our proposed method.
Operational interpretation of coherence in quantum key distribution

Quantum coherence was recently formalized as a physical resource to measure the strength of superposition. Based on the resource theory, we present a systematic framework that connects a coherence measure to the security of quantum key distribution. By considering a generic entanglement-based key distribution protocol under the collective attack scenario, we show that the key rate can be quantified by the coherence of the shared bipartite states. This framework allows us to derive the key rate of the BB84 and six-state protocols. By utilizing fine-grained parameters, we also derive the improved key rates of both protocols within the coherence-based framework. Furthermore, we apply it to a practical issue, detection efficiency mismatch, and obtain an improved result. In conclusion, this framework demonstrates the interplay among coherence, entanglement, and quantum key distribution at a fundamental level.
Early Expression Detection via Online Multi-Instance Learning With Nonlinear Extension

Video-based facial expression recognition has received substantial attention over the past decade, while early expression detection (EED) is still a relatively new and challenging problem. The goal of EED is to identify an expression as quickly as possible after the expression starts and before it ends. This timely ability has many potential applications, ranging from human-computer interaction to security. The max-margin early event detector (MMED) is a well-known ranking model for early event detection. It can achieve competitive EED performance but suffers from several critical limitations: 1) MMED lacks flexibility in extracting useful information for segment comparison, which leads to poor performance in exploring the ranking relation between segment pairs; 2) the training process is slow due to the large number of constraints, and the memory requirement is also usually hard to satisfy; and 3) MMED is linear in nature, and hence may not be appropriate for data in a nonlinear feature space. To overcome these limitations, we propose an online multi-instance learning (MIL) framework for EED. In particular, the MIL technique is first introduced to generalize MMED, resulting in the proposed MIL-based EED (MIED), which is more general and flexible than MMED, since various instance construction and combination strategies can be adopted. To accelerate the training process, we reformulate MIED in the online setting and develop online multi-instance learning framework for EED (OMIED). To further exploit the nonlinear structure of the data distribution, we incorporate the kernel methods in OMIED, which results in the proposed online kernel multi-instance learning for early expression detection. Experiments on two popular and one challenging video-based expression data sets demonstrate both the efficiency and effectiveness of the proposed methods.
Laser-Based Intersection-Aware Human Following With a Mobile Robot in Indoor Environments

Human following in structured indoor environments has to face the challenge of full occlusion caused by the walls when the target person makes a turn at the corridor intersections. This may result in short-term, and even permanent loss of the target from the field of view of the robot. In this paper, human following with a mobile robot in presence of potential occlusions occurring at corridor intersections is addressed. The robot detects four different types of corridor intersections using the on-board laser scanner. Then, a potential-field-based human tracker is designed by integrating the intersection information into the potential function, in order to increase the visibility of the target, while maintaining the relative distance and orientation between the target and the robot. Simultaneously, the robot builds a multihypothesis topological map of the environment based on an improved generalized Voronoi graph, where the edges represent the corridors and the vertices are the virtual meet points extracted from the intersections. In such a way, simultaneous human following and topological mapping are achieved. Simulation and experimental results show that the proposed method can largely avoid occlusion of the target and obtain good performance of human following and topological mapping.
GP-PDA Filter for Extended Target Tracking With Measurement Origin Uncertainty

Extended target tracking (ETT) is an issue in high-resolution radar surveillance, ship tracking, and video tracking. Most of the previous works focus on tracking an ellipsoidal extended target without measurement origin uncertainty (missed detections and clutter). In this paper, a new estimator called the Gaussian process probabilistic data association (GP-PDA) filter is proposed to track an irregularly shaped extended target with measurement origin uncertainty. First, a generalized measurement model for ETT using the Gaussian process (GP) is presented. Both the interior scattering points and the external clutter are considered in this model. Second, a GP-based gating technique is constructed to select validated measurements to feed the filter. Third, the GP-PDA filter is proposed to simultaneously estimate the kinematic state and the contour state of the extended target with measurement origin uncertainty. It is proven that the GP-PDA is a generalized version of the classic PDA, and the latter is a special case of the former in the point target tracking applications. Finally, the GP-based posterior Cramér-Rao lower bound (PCRLB) is derived to evaluate the performance of the ETT with measurement origin uncertainty. Two cases of the PCRLB are discussed, with the number of scattering points being known and unknown. Simulation results verify the effectiveness of the proposed method.
Weighted nearest neighbors feature selection

Huge amounts of data are pervasive in many domains and applications. Unfortunately, high-dimensional data are tightly associated with the curse of dimensionality, a phenomenon that adversely affects many data mining algorithms. Therefore, it is desirable to reduce the dimensionality of the data through preprocessing techniques such as feature selection (FS). Although FS is frequently perceived as a preprocessing technique, in some domains, such as bioinformatics, it is of paramount importance for identifying relevant attributes, and therefore, provides answers to the investigated research question. In this paper, we propose a novel supervised FS method based on k-nearest neighbors algorithm. In particular, we use distance and attribute weighted k-nearest neighbors with gradient descent as an iterative optimization algorithm for finding the function minima. The new method is compared with the state-of-the-art FS algorithms using eight artificial and twelve high-dimensional real-world datasets. The experimental results indicate that the proposed algorithm is able to identify the relevant features and shows the highest prediction performance for all four considered prediction algorithms.