Reviewerly/exHarmony huggingface dataset:
KOC+: Kernel ridge regression based one-class classification using privileged information

A kernel-based one-class classifier is mainly used for outlier or novelty detection. Kernel ridge regression (KRR) based methods have received quite a lot of attention in recent years due to its non-iterative approach of learning. In this paper, KRR-based one-class classifier (KOC) has been extended for learning using privileged information (LUPI) framework. LUPI-based KOC method is referred to as KOC+ in this paper. This privileged information is available as feature/features of the dataset, but only during training (not during testing). KOC+ utilizes privileged features information differently compared to other features information. It uses this information in KOC+ by the help of so-called correction function. This information helps KOC+ in achieving better generalization performance. Existing and proposed classifiers are evaluated on the datasets taken from UCI machine learning repository and MNIST dataset. Moreover, experimental results exhibit that KOC+ outperforms KOC and other LUPI-based state-of-the-art one-class classifiers. Source code of this paper is provided on the corresponding author's GitHub homepage:https://github.com/Chandan-IITI/KOCPlus_or_OCKELMPlus_or_OCLSSVMPlus
Asymmetric multiple-image encryption system based on a chirp z-transform

An asymmetric multi-image encryption system based on the chirp z-transform (CZT) is demonstrated. The setup is a hybrid architecture that combines a double-random-phase encryption scheme in 4f configuration and a multiplexing procedure based on the CZT. The setup allows encodement of multiple images and their transmittal in a single multiplexed element. The decryption stage has a compact design that allows retrieval of several data without cross-talk noise. Since the system is asymmetric, the users' decryption keys are different from those used in the encryption process, resulting in an encoding scheme resistant to cryptanalysis attacks. It is demonstrated that multiplexing based on the CZT allows functionality expansion of the static encoding setups (optical, digital or hybrid) to a dynamic range, a fact that provides a versatile solution to handle large volumes of encrypted data efficiently and safely. The viability of this proposal is verified using virtual optical systems.
Robust Hybrid Lightweight Cryptosystem for Protecting IoT Smart Devices

There are limited numbers of reliable hybrid cryptosystems that can be used to protect IoT smart devices, specifically in smart cities, smart hospitals, smart homes, and industrial fields. Therefore, much related work has to be performed. The aim is to study the trade-off between performance and security in these constrained environments and to achieve more secure hybrid cryptosystem with high demanded performance. Several types of recommended lightweight encryption algorithms will be investigated. These hybrid cryptosystems combine symmetric encryption algorithms such as TEA, XTEA, XXTEA, and asymmetric encryption algorithms such as RSA and ECC. They have the capability to protect IoT smart devices from internet attacks. They can efficiently achieve confidentiality, authenticity, integrity, and non-repudiation. Comparative analysis and evaluation are achieved; hence a robust hybrid cryptosystem was proposed. It uses chaotic theory to generate random keys. The analysis included the most important factors that have to be tackled in case of using lightweight ciphers to suit limited resources of IoT smart devices. Among these factors are security level, memory size, power consumption, encryption time, decryption time, and throughput. Results show that the proposed hybrid cryptosystem that combined ECC and XXTEA gives better security and higher performance than RSA and XXTEA with 40%.
Tag-accessed memory for genetic programming

Here, we demonstrate the use of tags (evolvable labels that can be specified with imperfect matching) to identify memory positions in genetic programming (GP). Specifically, we conducted a series of experiments using simple linear-GP representations on five problems from the general program-synthesis benchmark suite [2]. We show that tag-indexed memory does not substantively affect problem solving success relative to more traditional, direct-indexed memory.
Detecting memory errors at runtime with source-level instrumentation

The unsafe language features of C, such as low-level control of memory, often lead to memory errors, which can result in silent data corruption, security vulnerabilities, and program crashes. Dynamic analysis tools, which have been widely used for detecting memory errors at runtime, usually perform instrumentation at the IR-level or binary-level. However, their underlying non-source-level instrumentation techniques have three inherent limitations: optimization sensitivity, platform dependence and DO-178C non-compliance. Due to optimization sensitivity, these tools are used to trade either performance for effectiveness by compiling the program at -O0 or effectiveness for performance by compiling the program at a higher optimization level, say, -O3.
FD‐based detector for medical image watermarking

In this study, the authors propose a novel technique for medical image watermark detection using the concept of the fractional differentiator (FD). The feature of FD as a non-linear high-pass filter helps in watermark detection. In the region of non-interest, the watermark image has been added in a mid-band frequency range of the discrete cosine transform coefficients of different blocks by generating direct spread spectrum sequence. Their scheme produces noise-free watermarked medical images. Furthermore, they derive the test statistics of the proposed detector, which is characterised by the fractional order q. The average errors in pixels (PEs), peak-signal-to-noise ratio (PSNR), structural similarity index (SSIM) and cross-correlation coefficient (CC) have been used to quantify the capability of the proposed technique over some state-of-the-art techniques. The proposed technique shows that at a particular value of fractional order q, there is a significant reduction in average PEs. It causes an increment in PSNR, SSIM and CC. The proposed technique is tested on a large number of medical images and it is found that their proposed technique works better or comparable with other state-of-the-art techniques.
A novel approach to word sense disambiguation in Bengali language using supervised methodology

An attempt is made in this paper to report how a supervised methodology has been adopted for the task of Word Sense Disambiguation (WSD) in Bengali with necessary modifications. At the initial stage, four commonly used supervised methods, Decision Tree (DT), Support Vector Machine (SVM), Artificial Neural Network (ANN) and Naïve Bayes (NB), are developed at the baseline. These algorithms are applied individually on a data set of 13 most frequently used Bengali ambiguous words. On experimental basis, the baseline strategy is modified with two extensions: (a) inclusion of lemmatization process into the system and (b) bootstrapping of the operational process. As a result, the levels of accuracy of the baseline methods are slightly improved, which is a positive signal for the whole process of disambiguation as it opens scope for further modification of the existing method for better result. In this experiment, the data sets are prepared from the Bengali corpus, developed in the Technology Development for Indian Languages (TDIL) project of the Government of India and from the Bengali WordNet, which is developed at the Indian Statistical Institute, Kolkata. The paper reports the challenges and pitfalls of the work that have been closely observed during the experiment.
E2BoWs: An end-to-end Bag-of-Words model via deep convolutional neural network for image retrieval

Traditional Bag-of-Words (BoWs) model is commonly generated with many steps, including local feature extraction, codebook generation and feature quantization, etc. Those steps are relatively independent with each other and are hard to be jointly optimized. Moreover, the dependency on hand-crafted local feature makes BoWs model not effective in conveying high-level semantics. These issues largely hinder the performance of BoWs model in large-scale image applications. To conquer these issues, we propose an End-to-End BoWs (E2BoWs) model based on Deep Convolutional Neural Network (DCNN). Our model takes an image as input, then identifies and separates semantic objects in it, and finally outputs visual words with high semantic discriminative power. Specifically, our model firstly generates Semantic Feature Maps (SFMs) corresponding to different object categories through convolutional layers, then introduces Bag-of-Words Layers (BoWL) to generate visual words from each individual feature map. We also introduce a novel learning algorithm to reinforce the sparsity of the generated E2BoWs model, which further ensures the time and memory efficiency. We evaluate the proposed E2BoWs model on several image search datasets including MNIST, SVHN, CIFAR-10, CIFAR-100, MIRFLICKR-25K and NUS-WIDE. Experimental results show that our method achieves promising accuracy and efficiency compared with recent deep learning based retrieval works.
Spatiotemporal Pattern Extraction by Spectral Analysis of Vector-Valued Observables

We present a data-driven framework for extracting complex spatiotemporal patterns generated by ergodic dynamical systems. Our approach, called vector-valued spectral analysis (VSA), is based on an eigendecomposition of a kernel integral operator acting on a Hilbert space of vector-valued observables of the system, taking values in a space of functions (scalar fields) on a spatial domain. This operator is constructed by combining aspects of the theory of operator-valued kernels for multitask machine learning with delay-coordinate maps of dynamical systems. In contrast to conventional eigendecomposition techniques, which decompose the input data into pairs of temporal and spatial modes with a separable, tensor product structure, the patterns recovered by VSA can be manifestly non-separable, requiring only a modest number of modes to represent signals with intermittency in both space and time. Moreover, the kernel construction naturally quotients out dynamical symmetries in the data and exhibits an asymptotic commutativity property with the Koopman evolution operator of the system, enabling decomposition of multiscale signals into dynamically intrinsic patterns. Application of VSA to the Kuramoto–Sivashinsky model demonstrates significant performance gains in efficient and meaningful decomposition over eigendecomposition techniques utilizing scalar-valued kernels.
Loss Reserving Models: Granular and Machine Learning Forms

The purpose of this paper is to survey recent developments in granular models and machine learning models for loss reserving, and to compare the two families with a view to assessment of their potential for future development. This is best understood against the context of the evolution of these models from their predecessors, and the early sections recount relevant archaeological vignettes from the history of loss reserving. However, the larger part of the paper is concerned with the granular models and machine learning models. Their relative merits are discussed, as are the factors governing the choice between them and the older, more primitive models. Concluding sections briefly consider the possible further development of these models in the future.
A Unified Framework for Domain Adaptation Using Metric Learning on Manifolds

We present a novel framework for domain adaptation, whereby both geometric and statistical differences between a labeled source domain and unlabeled target domain can be reconciled using a unified mathematical framework that exploits the curved Riemannian geometry of statistical manifolds. We exploit a simple but important observation that as the space of covariance matrices is both a Riemannian space as well as a homogeneous space, the shortest path geodesic between two covariances on the manifold can be computed analytically. Statistics on the SPD matrix manifold, such as the geometric mean of two SPD matries can be reduced to solving the well-known Riccati equation. We show how the Ricatti-based solution can be constrained to not only reduce the statistical differences between the source and target domains, such as aligning second order covariances and minimizing the maximum mean discrepancy, but also the underlying geometry of the source and target domains using diffusions on the underlying source and target manifolds. Our solution also emerges as a consequence of optimal transport theory, which shows that the optimal transport mapping between source and target distributions that are multivariate Gaussians is a function of the geometric mean of the source and target covariances, a quantity that also minimizes the Wasserstein distance. A key strength of our proposed approach is that it enables integrating multiple sources of variation between source and target in a unified way, by reducing the combined objective function to a nested set of Ricatti equations where the solution can be represented by a cascaded series of geometric mean computations. In addition to showing the theoretical optimality of our solution, we present detailed experiments using standard transfer learning testbeds from computer vision comparing our proposed algorithms to past work in domain adaptation, showing improved results over a large variety of previous methods. Code related to this paper is available at: https://github.com/sridharmahadevan/Geodesic-Covariance-Alignment .
Path Planning for Minimizing the Expected Cost Until Success

Consider a general path planning problem of a robot on a graph with edge costs, where each node has a Boolean value of success or failure (with respect to some task) with a given probability. The objective is to plan a path for the robot on the graph that minimizes the expected cost until success. In this paper, it is our goal to bring a foundational understanding to this problem. We start by showing how this problem can be optimally solved by formulating it as an infinite-horizon Markov decision process, but with an exponential space complexity. We then formally prove its NP-hardness. To address the space complexity, we then propose a path planner, using a game-theoretic framework, that asymptotically gets arbitrarily close to the optimal solution. Moreover, we also propose two fast and nonmyopic path planners. To show the performance of our framework, we do extensive simulations for two scenarios: a rover on Mars searching for an object for scientific studies, and a robot looking for a connected spot to a remote station (with real data from downtown San Francisco). Our numerical results show a considerable performance improvement over existing state-of-the-art approaches.
Actor-Critic Instance Segmentation

Most approaches to visual scene analysis have emphasised parallel processing of the image elements. However, one area in which the sequential nature of vision is apparent, is that of segmenting multiple, potentially similar and partially occluded objects in a scene. In this work, we revisit the recurrent formulation of this challenging problem in the context of reinforcement learning. Motivated by the limitations of the global max-matching assignment of the ground-truth segments to the recurrent states, we develop an actor-critic approach in which the actor recurrently predicts one instance mask at a time and utilises the gradient from a concurrently trained critic network. We formulate the state, action, and the reward such as to let the critic model long-term effects of the current prediction and in- corporate this information into the gradient signal. Furthermore, to enable effective exploration in the inherently high-dimensional action space of instance masks, we learn a compact representation using a conditional variational auto-encoder. We show that our actor-critic model consistently provides accuracy benefits over the recurrent baseline on standard instance segmentation benchmarks.
WMF self-tuning Kalman estimators for multisensor singular system

For the multisensor linear stochastic singular system with unknown noise variances, the weighted measurement fusion (WMF) self-tuning Kalman estimation problem is solved in this paper. The consistent estimates of these unknown noise variances are obtained based on the correlation method. Applying the WMF method and the singular value decomposition (SVD) method yields the WMF reduced-order subsystems. Based on these consistent estimates of unknown noise variances and the new non-singular systems, the WMF self-tuning Kalman estimators of the state components and white noise deconvolution estimators are presented. Then the WMF self-tuning Kalman estimators of the original state are presented, and their convergence has been proved by dynamic error system analysis (DESA) method and dynamic variance error system analysis (DVESA) method. A simulation example of 3-sensors circuits systems verifies the effectiveness, the accuracy relationship and the convergence.
Dominant Sets for “Constrained” Image Segmentation

Image segmentation has come a long way since the early days of computer vision, and still remains a challenging task. Modern variations of the classical (purely bottom-up) approach, involve, e.g., some form of user assistance (interactive segmentation) or ask for the simultaneous segmentation of two or more images (co-segmentation). At an abstract level, all these variants can be thought of as "constrained" versions of the original formulation, whereby the segmentation process is guided by some external source of information. In this paper, we propose a new approach to tackle this kind of problems in a unified way. Our work is based on some properties of a family of quadratic optimization problems related to dominant sets, a graph-theoretic notion of a cluster which generalizes the concept of a maximal clique to edge-weighted graphs. In particular, we show that by properly controlling a regularization parameter which determines the structure and the scale of the underlying problem, we are in a position to extract groups of dominant-set clusters that are constrained to contain predefined elements. In particular, we shall focus on interactive segmentation and co-segmentation (in both the unsupervised and the interactive versions). The proposed algorithm can deal naturally with several types of constraints and input modalities, including scribbles, sloppy contours and bounding boxes, and is able to robustly handle noisy annotations on the part of the user. Experiments on standard benchmark datasets show the effectiveness of our approach as compared to state-of-the-art algorithms on a variety of natural images under several input conditions and constraints.
ExNET: Deep Neural Network for Exercise Pose Detection

Pose detection estimate human activity in images or video frames using computer vision technique. Pose detection has many applications, such as body to augmented reality, fitness, animation etc. ExNET represents a way to detect human pose from 2D human exercises image using Convolutional Neural Network. In recent time Deep Learning based systems are making it possible to detect human exercise poses from images. We refer to the model we have built for this task as ExNET: Deep Neural Network for Exercise Pose Detection. We have evaluated our proposed model on our own dataset that contains a total of 2000 images. And those images are distributed into 5 classes as well as images are divided into training and test dataset, and obtained improved performance. We have conducted various experiments with our model on the test dataset, and finally got the best accuracy of 82.68%.
Guigle: A GUI Search Engine for Android Apps

The process of developing a mobile application typically starts with the ideation and conceptualization of its user interface. This concept is then translated into a set of mock-ups to help determine how well the user interface embodies the intended features of the app. After the creation of mock-ups developers then translate it into an app that runs in a mobile device. In this paper we propose an approach, called GUIGLE, that aims to facilitate the process of conceptualizing the user interface of an app through GUI search. GUIGLE indexes GUI images and metadata extracted using automated dynamic analysis on a large corpora of apps extracted from Google Play. To perform a search, our approach uses information from text displayed on a screen, user interface components, the app name, and screen color palettes to retrieve relevant screens given a query. Furthermore, we provide a lightweight query language that allows for intuitive search of screens. We evaluate GUIGLE with real users and found that, on average, 68.8% of returned screens were relevant to the specified query. Additionally, users found the various different features of GUIGLE useful, indicating that our search engine provides an intuitive user experience. Finally, users agree that the information presented by GUIGLE is useful in conceptualizing the design of new screens for applications. Video URL: https://youtu.be/hqUuuMMj2BU.
Single image super-resolution with enhanced Laplacian pyramid network via conditional generative adversarial learning

Despite much progress has been made by applying generative adversarial network (GAN) to single image super-resolution (SISR), obvious difference remains between the details of reconstructed high-frequency and ground-truth because GAN is unstable that has a very high degree of freedom. To address this issue, we exploit conditional GAN (CGAN) for SISR, which leverages the ground-truth high-resolution (HR) image as its conditional variable to guide to learn a more stable model. To better reconstruct image with a large-scale factor, we further design an enhanced Laplacian pyramid network (ELapN) as the generator model of CGAN, which progressively reconstructs HR images at multiple pyramid levels. The proposed ELapN fuses low-and high-level features for the residual image learning achieves better generalization than those only based on high-level information. Finally, we train the proposed network via deep supervision using a combination of multi-level CGAN, VGG and robust Charbonnier loss functions to obtain high-quality SR results. Extensive evaluations on three benchmark datasets including Set5, Set14, B100 demonstrate superiority of the proposed method over state-of-the-art methods in terms of PSNR, SSIM and visual effect.
Subspace clustering via structure-enforced dictionary learning

Many real world data, which we deal with today, often have very high dimensions. These high-dimensional data can be seen as collections of data points from a union of low-dimensional subspaces. Subspace clustering, one solution to the high-dimensional data problem, refers to a method by which a set of data points is divided into multiple clusters by finding multiple subspaces that fit each cluster. Most existing subspace clustering approaches construct an affinity matrix using the self-representation model, which can propagate disturbing noisy information because even noisy data points are used to represent the data; then, they perform spectral clustering on the obtained affinity matrix, which contains the irrelevant information about noise or outliers. This paper proposes a novel subspace clustering method based on the structured sparse PCA-based dictionary learning. Our proposed method learns the reduced dimensional dictionary and coefficient matrices using the structural information as well as sparsity of data. Then, the affinity matrix is constructed from inner product of the learned dictionary coefficient vectors, which shows the correlation among data points. The experimental results on three benchmark datasets verify that the proposed method outperforms state-of-the-art subspace clustering methods.