Reviewerly/exHarmony huggingface dataset:
Information-Theoretic Bounds for the Forensic Detection of Downscaled Signals

The detection of rescaling operations represents an important task in multimedia forensics. While many effective heuristics have been proposed, there is no theory on the forensic detectability revealing the conditions of more or less reliable detection. We study the problem of discriminating 1D and 2D genuine signals from signals that have been downscaled with the goal of quantifying the statistical distinguishability between these two hypotheses. This is done by assuming known signal models and deriving the expressions of statistical distances that are linked to the hypothesis testing theory, namely, the symmetrized form of Kullback-Leibler divergence known as the Jeffreys divergence, and the Bhattacharyya divergence. The analysis is performed for varying parameters of both the genuine signal model (variance and one-step correlation) and the rescaling process (rescaling factor, interpolation kernel, grid shift, and anti-alias filter), thus allowing us to reveal the insights on their influence and interplay. In addition to the signal itself, we consider the signal transformations (prefilter and covariance matrix estimators) that are often involved in practical rescaling detectors, showing that they yield similar results in terms of distinguishability. Numerical tests on synthetic and real signals confirm the main observations from the theoretical analysis.
A blockchain based access control framework for the security and privacy of IoT with strong anonymity unlinkability and intractability guarantees

Motivated by the recent explosion of interest around the blockchain, we examine whether they make a good fit to build a lightweight and robust access control framework to address security and privacy issues in the Internet of Things (IoT) sector. In this direction, this chapter discusses the limitations of the centralized model to secure IoT and proposes the blockchain approach as example of successful distributed system to bring security and privacy to IoT devices. In this direction, we introduce FairAccess and PPDAC, as a lightweight and privacy-preserving access control framework based on the emergent blockchain technology, mainly the permissionless and public type, to ensure fine-grained access control functions for IoT devices with strong anonymity guarantee for IoT end-users. The proposed framework retains the benefits of the blockchain to meet IoT security and privacy arising needs while overcoming the challenges in integrating the blockchain to IoT.
FPCANet: Fisher discrimination for Principal Component Analysis Network

With the development of Deep Learning (DL) in recent years, integrating traditional machine learning methods with DL has received a lot of attention. One of such representative work is the Principal Component Analysis Network (PCANet), which adopts Principal Component Analysis (PCA) to learn convolutional kernels (or filters) for image classification. Nevertheless, PCANet does not use the discriminative information during learning filters. In this paper, based on PCA in the PCANet, we propose a new model called Fisher PCA (FPCA) which combines Fisher Linear Discriminant Analysis (LDA) with PCA. To facilitate the practical calculation, a approximate model of FPCA is given by introducing a intermediate variable. Theoretically, we analyze the relationship between the original FPCA model and its approximate model, and give a convergence analysis of the approximate model. Additionally, stacking the approximate model of FPCA, we also construct a deep network named FPCA Network (FPCANet). Extensive experiments are conducted to compare FPCANet with other state-of-the-art models for classification problems. The results show that the proposed FPCANet can learn features with more discriminative information, and thus demonstrating its competitive performances on classification tasks.
An Asymptotic Ensemble Learning Framework for Big Data Analysis

In order to enable big data analysis when data volume goes beyond the available computing resources, we propose a new method for big data analysis.This method uses only a few random sample data blocks of a big data set to obtain approximate results for the entire data set.The random sample partition (RSP) distributed data model is used to represent a big data set as a set of non-overlapping random sample data blocks.Each block is saved as an RSP data block file that can be used directly to estimate the statistical properties of the entire data set.A subset of RSP data blocks is randomly selected and analyzed with existing sequential algorithms in parallel.Then, the results from these blocks are combined to obtain ensemble estimates and models which can be improved gradually by appending new results from the newly analyzed RSP data blocks.To this end, we propose a distributed data-parallel framework (Alpha framework) and develop a prototype of this framework using Microsoft R Server packages and Hadoop distributed file system.The experimental results of three real data sets show that a subset of RSP data blocks of a data set is sufficient to obtain estimates and models which are equivalent to those computed from the entire data set.
Probing with Pokémon: Feeling of presence and sense of community belonging

Once frequently associated with a social disconnection and a sedentary life style, digital games are inviting reconsideration of their values for health benefits and community building. Pokémon GO, a location-based mobile game application, has the capacity to motivate their users to get out and explore the community. Based on a survey with 325 Pokémon GO users, this study tested if a feeling of presence during Pokémon GO gameplay is related to building a sense of community belonging. From a serial multiple mediator test, the primary findings demonstrated that a feeling of presence increases enjoyment, and increased enjoyment promotes exploring the community. In return, this ultimately leads to building a stronger sense of community belonging.
An omnidirectional DIC system for dynamic strain measurement on soft biological tissues and organs

A limitation to the potential use of inverse methods in identifying the spatially varying properties of biological tissues consists in obtaining the required set of experimental data. In fact, whereas on the numerical side there are almost no limits to complexity in terms of geometry and material behavior of the model, on the experimental side, the collection of full-field displacement and strain data over the full surface of complex 3D geometries still represents a challenge. To address this issue, this work proposes an omnidirectional-DIC method capable of obtaining full-field information over the entire surface of native intact biological parts undergoing large deformation. This is made possible by the use of a concave conical mirror that enlarges the field of view of a conventional binocular stereo-DIC system up to 360° × 320° The pair of highly distorted panoramic scenes reflected by the conical mirror are then matched by using a novel iterative image deformation scheme. In particular, first, a robust feature-based algorithm is used to calculate the gross disparity between images, then, an iterative NURBS-based intensity interpolation scheme refines the correlation through a hybrid local-global DIC approach. The accuracy of the developed image matching scheme was evaluated on synthetically generated images simulating a large and heterogeneous deformation field. Then, the potential of the proposed omnidirectional-DIC method was tested with rubber analogs reproducing the most representative geometries and loading states involved in biological materials characterization. Finally, as an illustrative example of application, full-field DIC shape and deformation measurements was performed over a whole porcine eye tested in-vitro under reproduced physiological load.
Simulation and applications of cumulative anisotropic sky radiance patterns

The prediction of the incident solar energy on a surface under a partially obstructed sky is an important task for most solar energy-related architectural and engineering applications. In previous contributions, Ivanova described an approach that solves this problem using cumulative sky radiance patterns. The method considers the numerical integration of fisheye orthographic projections of these patterns on a considered surface, taking into account the shading caused by all existing obstructing objects in the environment. Further developments of the approach are proposed here to improve the method's accuracy. The resolution system called SOLARES includes two separate units: a pre-processor and a main program. For each new site, the sky radiance patterns have to be computed once with the pre-processor. The latter prepares daily cumulative sky radiance patterns based on the PVGIS TMY solar database. The direct and diffuse components of the sky radiance are modeled separately. The values of diffuse radiance are generated using an anisotropic model for the luminance and radiance sky distributions. The results are saved in binary files for ulterior use in the main program. The fisheye orthographic projections of the cumulative radiance patterns of the sky hemisphere onto a receiving surface are discretized into matrices of pixels and corresponding direct and diffuse radiance values. The shading objects are projected onto that surface, and the results are saved in a separate shading matrix generated for a specific viewpoint or averaged over the whole surface with the help of the finite-element method. The present numerical method can be used to generate accurate long-term time series of irradiance on any tilted exterior or interior surface in complex urban environments with many neighboring shading surfaces of any tilt and orientation. Additionally, the method allows the visualization of the different system components to enhance architectural or solar applications.
Multi-video summarization with query-dependent weighted archetypal analysis

Given the tremendous growth of web videos, video summarization is becoming increasingly important to improve user’s browsing experience. Since most existing methods focus on generating an informative summarization from a single video and often fail to generate satisfying results for multiple videos, we propose an unsupervised framework for summarizing a set of topic-related videos. We develop a Multi-Video Summarization via Multi-modal Weighted Archetypal Analysis (MVS-MWAA) method to extract a concise summarization that is both representative and informative. To ensure the summarization query-dependent, we design a multi-modal graph to guide the generation of the weight in WAA, which we call query-dependent weight. Specifically, the multi-modal graph fuses information of video frames, tags, and query-dependent web images. Furthermore, we present a Ranking from Bottom to Top (RBT) approach to make it understandable. Extensive experimental results demonstrate that our approach clearly outperforms the state-of-the-art methods.
An echo state network based approach to room classification of office buildings

Office buildings commonly contain such room types as office rooms, server rooms, storage rooms, meeting rooms, etc., while the power consumption inside the rooms generally comes from appliances, lights and air-conditioners. Based on the features of power consumption in different rooms, the aim of this study is to classify the rooms into different types by proposing an echo state network (ESN) based approach. Given the data on power consumption, the proposed approach is divided into two steps, where the first step is to establish three ESNs to model the three categories of power consumption, and the second step is to establish a fourth ESN to determine the type of a room by using the outputs of the first three ESNs. The practical performance of the proposed approach is displayed by a detailed experimental study, where the proposed approach achieves high classification accuracies and shows great superiority to several classical algorithms.
Recall cues interfere with retrieval from visuospatial working memory

Visuospatial working memory allows us to hold multiple visual objects over short delays. It is typically tested by presenting an array of objects, then after a delay showing a 'probe' indicating which memory item to recall or reproduce by adjusting a target feature. However, recent studies demonstrate that information at the time of probe can disrupt recall. Here, in three experiments we test whether traditional memory probes, which contain features that compete with the feature to be recalled, may themselves interfere with performance. We asked participants to report the direction of one of the several coloured arrows in memory, based on its colour. First, we demonstrate that recall is better when the probe is initially just a coloured dot, rather than a coloured arrow which has to be adjusted to match orientation memory, consistent with interference from features of the probe itself. Second, this interference is present even when a mask follows the memory array, suggesting that the interference does not work by degrading immediate or iconic memory. Finally, when items are shown sequentially, the first and last items are invulnerable to probe interference. Our findings support recent theories of associative recall, in which probes reactivate features in WM, retrieving information by pattern completion.
A K-anonymous clustering algorithm based on the analytic hierarchy process

Abstract To protect the privacy of users, tables generally must be anonymized before publication. All existing anonymous methods have deficiencies. They do not consider the differences in attributes, or the optimization of information loss and time efficiency. his paper proposes a new method called KACM to realize k-anonymity. This method is mainly used for hybrid tables. The calculation of the distance between records considers the connection between quasi-identifier attributes and sensitive attributes, their effect on the sensitive privacy, and the information loss during the anonymity process. In the clustering process, the records with the minimum distance are always selected to add, and the clustering is individually controlled according to k to realize the equalization division of the equivalence class and reduce the total amount of distance calculation. Finally, the validity and practicability of the method are proved using theory and experiment.
A sharing multi-view feature selection method via Alternating Direction Method of Multipliers

The matrix-based multi-view feature selection, which can integrate the information of multiple views for selecting representative features, has attracted wide attention in recent years. In this paper, we propose a novel supervised sharing multi-view feature selection method. The proposed method makes all views share a common penalty that regresses samples to their labels. Meanwhile, it adopts the structured sparsity-inducing norm to implement sparsity for each view. The proposed method considers not only the complementary of different views, but also the specificity of each view instead of concatenating all views into high-dimensional vectors. In addition, the proposed model can be decomposed into N small scale subproblems (where N is the number of views) and solved efficiently via Alternating Direction Method of Multipliers (ADMM), especially for high-dimensional large scale data sets. The comparison experiments with several state-of-the-art feature selection methods show the effectiveness of the proposed method.
Automatic Text-Independent Kannada Dialect Identification System

This paper proposes a dialect identification system for the Kannada language. A system that can automatically identify the dialects of the language being spoken has a wide variety of applications. However, not many Automatic Speech Recognition (ASR) and dialect identification tasks are carried out in majority of the Indian languages. Further, there are only a few good quality annotated audio datasets available. In this paper, a new dataset for 5 spoken dialects of the Kannada language is introduced. Spectral and prosodic features have captured the most prominent features for recognition of Kannada dialects. Support Vector Machine (SVM) and neural networks algorithms are used for modeling text-independent recognition system. A neural network model that attempts for identification dialects based on sentence level cues has also been built. Hyper-parameters for SVM and neural network models are chosen using grid search. Neural network models have outperformed SVMs when complete utterances are considered.
Jointly learning perceptually heterogeneous features for blind 3D video quality assessment

3D videos quality assessment (3D-VQA) is essential to various 3D video processing applications. However, it has not been well investigated on how to make use of perceptual multi-channel video information to improve 3D-VQA under different distortion categories and degrees, especially under asymmetrical distortions. In the paper, we propose a new blind 3D-VQA metric by jointly learning perceptually heterogeneous features. Firstly, a binocular spatio-temporal internal generative mechanism (BST-IGM) is proposed to decompose the views of 3D video into multi-channel videos. Then, we extract perceptually heterogeneous features by proposed multi-channel natural video statistics (MNVS) model, which are characterized 3D video information. Furthermore, a robust AdaBoosting Radial Basis Function (RBF) neural network is utilized to map the features to the overall quality of 3D video. On two benchmark databases, the extensive evaluations demonstrate that the proposed algorithm significantly outperforms several state-of-the-art quality metrics in term of prediction accuracy and robustness.
MalReG

Given a retweeter network in Twitter for any event, how can we detect the group of users that collude to retweet together maliciously? A large number of retweets of a post often indicates the virality of the post. It also helps increase the visibility and volume of hashtags, topics or URLs, to promote the event associated with it. Our primary hunch is that there is synchronization or indicative pattern in the behavior of such users. In this paper, we propose (i) MalReG, a novel algorithm to detect retweeter groups, and (ii) a set of 23 group-based features (entropy-based and temporal-based) to train a supervised model to identify malicious retweeter groups (MRG). We present experiments on three real-world datasets with more than 10 million retweets crawled from Twitter. MalReG identifies 1, 017 retweeter groups present in our dataset. We train a supervised learning model to detect MRG which achieves 0.921 ROC AUC using Random Forest, outperforming the baseline by 7.97% higher AUC. Additionally, we perform geographical location-based and temporal analysis of these groups. Interestingly, we find the presence of the same group, retweeting different political events that took place in different continents at different times. We also discover masquerading techniques used by MRG to evade detection.
Solving the Hamiltonian Cycle Problem using a Quantum Computer

We review existing quantum computational methods for solving the Hamiltonian cycle problem in different computational frameworks such as quantum circuits, quantum walks and adiabatic quantum computation. Then we present a QUBO (quadratic unconstrained binary optimization) formulation, which is suitable for the adiabatic quantum computation for a D-Wave architecture. Further, we derive a physical Hamiltonian from the QUBO formulation and discuss its adequateness in the adiabatic framework. Finally, we discuss the complexity of running the Hamiltonian cycle QUBO on a D-Wave quantum computer, and compare it with existing quantum computational methods.
Sparse2Dense: From Direct Sparse Odometry to Dense 3-D Reconstruction

In this letter, we proposed a new deep learning based dense monocular simultaneous localization and mapping (SLAM) method. Compared to existing methods, the proposed framework constructs a dense three-dimensional (3-D) model via a sparse to dense mapping using learned surface normals. With single view learned depth estimation as prior for monocular visual odometry, we obtain both accurate positioning and high-quality depth reconstruction. The depth and normal are predicted by a single network trained in a tightly coupled manner. Experimental results show that our method significantly improves the performance of visual tracking and depth prediction in comparison to the state-of-the-art in deep monocular dense SLAM.
Effects of Extended Stochastic Gradient Descent Algorithms on Improving Latent Factor-Based Recommender Systems

High-dimensional and sparse (HiDS) matrices from recommender systems contain various useful patterns. A latent factor (LF) analysis is highly efficient in grasping these patterns. Stochastic gradient descent (SGD) is a widely adopted algorithm to train an LF model. Can its extensions be capable of further improving an LF models' convergence rate and prediction accuracy for missing data? To answer this question, this work selects two of representative extended SGD algorithms to propose two novel LF models. Experimental results from two HiDS matrices generated by real recommender systems show that compared standard SGD, extended SGD algorithms enable an LF model to achieve a higher prediction accuracy for missing data of an HiDS matrix, a faster convergence rate, and a larger model diversity.
Evolutionary deep learning based on deep convolutional neural network for anime storyboard recognition

Recently, image recognition based on deep learning has gained considerable research attention. In this study, we focus on anime storyboards and apply deep convolutional neural networks (DCNNs) to those data. However, it is difficult to tune the DCNN hyperparameters by the gird search method. Therefore, we propose a novel method called evolutionary deep learning (evoDL) by adopting a genetic algorithm (GA) to solve this problem. The effectiveness of evoDL is validated by computer simulations by taking a real anime storyboard recognition problem as an example.
FastLane: Test Minimization for Rapidly Deployed Large-Scale Online Services

Today, we depend on numerous large-scale services for basic operations such as email. These services, built on the basis of Continuous Integration/Continuous Deployment (CI/CD) processes, are extremely dynamic: developers continuously commit code and introduce new features, functionality and fixes. Hundreds of commits may enter the code-base in a single day. Therefore one of the most time-critical, yet resource-intensive tasks towards ensuring code-quality is effectively testing such large code-bases. This paper presents FastLane, a system that performs data-driven test minimization. FastLane uses light-weight machine-learning models built upon a rich history of test and commit logs to predict test outcomes. Tests for which we predict outcomes need not be explicitly run, thereby saving us precious test-time and resources. Our evaluation on a large-scale email and collaboration platform service shows that our techniques can save 18.04%, i.e., almost a fifth of test-time while obtaining a test outcome accuracy of 99.99%.