Reviewerly/exHarmony huggingface dataset:
Performance analysis of JPEG Pleno light field coding

Light fields can nowadays be acquired by several methods and devices in the form of light field images, which are at the core of new forms of media technologies. Many research challenges are still open in light field imaging, such as data representation formats, data compression tools, communication protocols, subjective and objective quality of experience measurement metrics and methods. This paper presents a brief overview of the current architecture of the JPEG Pleno light field coding standard under development within the JPEG committee (ISO/IEC JTC1/SC29/WG1). Thereafter, a comparative analysis between the performance of the JPEG Pleno Light Field codec under various modes and configurations and the performance of the considered anchor codecs is reported and discussed.
Confidence Evaluation of BPSK Signal Analysis Based on EVT

Confidence evaluation algorithms for the analysis of binary phase-shift keying (BPSK) signals are discussed. The phase of the product of the observed signal and constructed reference signal is extracted, and its spectrum is utilized to verify confidence. Two extreme statistics of the phase spectrum are developed for the confidence evaluation, which depend on goodness-of-fit tests based on the generalized extreme value distribution and generalized Pareto distribution respectively. Via extensive simulations, we verify the effectiveness of the proposed algorithms.
Sparse autoencoder for social image understanding

The rapid increase of social media images has made organizing these resources effectively a huge problem. Labeling unlabeled images becomes the crucial division of social image understanding. However, the enhancement of social image sharpness leads to the increase of surface feature dimension. These multidimensional complex features leads to the curse of dimensionality and the difficulty of feature extraction. In this paper, sparse autoencoder is studied to solve the problem of social image understanding, because sparse autoencoder can make these features represent the original data in a refined way, thus avoiding curse of dimensionality as much as possible and significantly improve the understanding effect. First, we explore the dimensional reduction capability of sparse autoencoder, and use sparse autoencoder to get low-dimensional features. Second, for low-dimensional features, an enhanced multi-label classifier is utilized to assign labels with the help of cosine similarity about tags correlation. The ability of dimensionality reduction of sparse autoencoder is proved by mapping matrix of image-label. Finally, we test our approach on several publicly available social media datasets. The results demonstrate that our proposed method is superior to lots of non-deep learning method among three evaluation indexes of social image understanding.
An early characterisation of wearing variability on motion signals for wearables

We explore a new variability observed in motion signals acquired from modern wearables. Wearing variability refers to the variations of the device orientation and placement across wearing events. We collect the accelerometer data on a smartwatch and an earbud and analyse how motion signals change due to the wearing variability. Our analysis shows that the wearing variability can bring an unexpected change to motion signals, not only from different users but also from different wearing sessions of the same user. We also provide empirical ranges of changes in device orientations.
Multi-layered optimal navigation system for quadrotor UAV

Purpose This paper aims to propose a new multi-layered optimal navigation system that jointly optimizes the energy consumption, improves the robustness and raises the performance of a quadrotor unmanned aerial vehicle (UAV). Design/methodology/approach The proposed system is designed as a multi-layered system. First, the control architecture layer links the input and the output spaces via quaternion-based differential flatness equations. Then, the trajectory generation layer determines the optimal reference path and avoids obstacles to secure the UAV from collisions. Finally, the control layer allows the quadrotor to track the generated path and guarantees the stability using a double loop non-linear optimal backstepping controller (OBS). Findings All the obtained results are confirmed using several scenarios in different situations to prove the accuracy, energy optimization and the robustness of the designed system. Practical implications The proposed controllers are easily implementable on-board and are computationally efficient. Originality/value The originality of this research is the design of a multi-layered optimal navigation system for quadrotor UAV. The proposed control architecture presents a direct relation between the states and their derivatives, which then simplifies the trajectory generation problem. Furthermore, the derived differentially flat equations allow optimization to occur within the output space as opposed to the control space. This is beneficial because constraints such as obstacle avoidance occur in the output space; hence, the computation time for constraint handling is reduced. For the OBS, the novelty is that all controller parameters are derived using the multi-objective genetic algorithm (MO-GA) that optimizes all the quadrotor state’s cost functions jointly.
Leakage-resilient group signature: Definitions and constructions

Group signature scheme provides a way to sign messages without revealing identities of the authentic signers. To achieve such functionality and to avoid the abuse of its power, anonymity and traceability are two essential properties for group signature scheme. In traditional group signature schemes, however, these two security properties are based on the perfectly-secure storage of secret information. Unfortunately, defective implementation of a cryptosystem always exists, and therefore unexpected information leakage is inevitable. In reality, side-channel attacks allow an adversary breaks the security of the whole system by eavesdropping a portion of secret information. To tackle this issue, in our work we present the security models of leakage-resilient group signature in bounded leakage setting and furthermore, propose three new black-box constructions of leakage-resilient group signature based on the proposed security models.
An Anonymous Delegatable Attribute-based Credential Scheme for a Collaborative E-health Environment

We propose an efficient anonymous, attribute-based credential scheme capable of provisioning multi-level credential delegations. It is integrated with a mechanism to revoke the anonymity of credentials for resolving access disputes and making users accountable for their actions. The proposed scheme has a lower end-user computational complexity in comparison to existing credential schemes with delegatability and has a comparable level of performance with the credential standards of U-Prove and Idemix. Furthermore, we demonstrate how the proposed scheme can be applied to a collaborative e-health environment to provide its users with the necessary anonymous access with delegation capabilities.
Agile Management in Product Development

Agile emerged in software development in the 1990s and became popular mainly after the issue of the Agile Manifesto by Beck and coauthors in 2001. Influenced by the culture of value maximization and waste reduction that found its first expression in lean manufacturing, Agile’s methods contrast with traditional methods of software engineering, which tend to be bureaucratic and hierarchical. Agile values include collaboration, team empowerment, iterative and incremental development, increased customer engagement, and adaptability to change.
Exploring Duality in Visual Question-Driven Top-Down Saliency

Top-down, goal-driven visual saliency exerts a huge influence on the human visual system for performing visual tasks. Text generations, like visual question answering (VQA) and visual question generation (VQG), have intrinsic connections with top-down saliency, which is usually involved in both VQA and VQG processes in an unsupervised manner. However, it is shown that the regions that humans choose to look at to answer questions are very different from the unsupervised attention models. In this brief, we aim to explore the intrinsic relationship between top-down saliency and text generations, and to figure out whether an accurate saliency response benefits text generation. To this end, we propose a dual supervised network with dynamic parameter prediction. Dual-supervision explicitly exploits the probabilistic correlation between the primal task top-down saliency detection and the dual task text generation, while dynamic parameter prediction encodes the given text (i.e., question or answer) into the fully convolutional network. Extensive experiments show the proposed top-down saliency method achieves the best correlation with human attention among various baselines. In addition, the proposed model can be guided by either questions or answers, and output the counterpart. Furthermore, we show that combining human-like visual question-saliency improves the performance of both answer and question generations.
Security Analysis for CBTC Systems under Attack–Defense Confrontation

Communication-based train controls (CBTC) systems play a major role in urban rail transportation. As CBTC systems are no longer isolated from the outside world but use other networks to increase efficiency and improve productivity, they are exposed to huge cyber threats. This paper proposes a generalized stochastic Petri net (GSPN) model to capture dynamic interaction between the attacker and the defender to evaluate the security of CBTC systems. Depending on the characteristics of the system and attack–defense methods, we divided our model into two phases: penetration and disruption. In each phase, we provided effective means of attack and corresponding defensive measures, and the system state was determined correspondingly. Additionally, a semiphysical simulation platform and game model were proposed to assist the GSPN model parameterization. With the steady-state probability of the system output from the model, we propose several indicators for assessing system security. Finally, we compared the security of the system with single defensive measures and multiple defensive measures. Our evaluations indicated the significance of the defensive measures and the seriousness of the system security situation.
Novel Chroma Sampling Methods for CFA Video Compression in AVC, HEVC and VVC

The images obtained by the camera sensors are stored in the format of CFA, Color Filter Array, which only contains limited information about the R, G, and B signals of images. The images in CFA are upsampled into RGB 444 format by demosaicking algorithms, converted into YCbCr 444, then chroma-downsampled into YCrCb 420 format for image/video compression. In this paper, a novel chroma-downsampling method is developed to improve a state-of-the-art method. The optimization problem for the proposed method considers more factors and provides a better closed-form solution for the optimally sampled chroma values. Reversely in the decoder, a chroma-upsampling method is required for the image reconstruction. In the proposed chroma-upsampling method, the neighboring chroma pixels and their distances to the recovered pixel are considered; the procedure is to produce weightings to redistribute the total pixel values in the original estimates. Combining the proposed chroma-downsampling and the proposed chroma-upsampling works, the proposed method outperforms the state-of-the-art method by BDPSNR (Bjøntegaard Delta peak signal-to-noise ratio) 0.3161 for H.264/AVC, 0.3845 for HEVC (High Efficiency Video Compression) and 0.3739 for VVC (Versatile Video Coding), averaged over all tested videos and all 7 different considered CFA formats.
Discrete Stochastic Search and Its Application to Feature-Selection for Deep Relational Machines

We use a model for discrete stochastic search in which one or more objects (“targets”) are to be found by a search over n locations (“boxes”), where n is infinitely large. Each box has some probability that it contains a target, resulting in a distribution H over boxes. We model the search for the targets as a stochastic procedure that draws boxes using some distribution S. We derive first a general expression on the expected number of misses $$\text {E}[Z]$$ made by the search procedure in terms of H and S. We then obtain an expression for an optimal distribution $$S^{*}$$ to minimise $$\text {E}[Z]$$ . This results in a relation between: the entropy of H and the KL-divergence between H and $$S^{*}$$ . This result induces a 2-partitions over the boxes consisting of those boxes with H probability greater than $$\frac{1}{n}$$ and the rest. We use this result to devise a stochastic search procedure for the practical situation when H is unknown. We present results from simulations that agree with theoretical predictions; and demonstrate that the expected misses by the optimal seeker decreases as the entropy of H decreases, with a maximum obtained for uniform H. Finally, we demonstrate applications of this stochastic search procedure with a coarse assumption about H. The theoretical results and the procedure are applicable to stochastic search over any aspect of machine learning that involves a discrete search-space: for example, choice over features, structures or discretized parameter-selection. In this work, the procedure is used to select features for Deep Relational Machines (DRMs) which are Deep Neural Networks (DNNs) defined in terms of domain-specific knowledge and built with features selected from large, potentially infinite-attribute space. Empirical results obtained across over 70 real-world datasets show that using the stochastic search procedure results in significantly better performances than the state-of-the-art.
Neuromorphic dynamics with optically injected quantum dot lasers

Optically injected quantum dot lasers display many unique nonlinear phenomena and are in particular, excellent testbeds for different forms of excitability. We analyse the recent discovery of Type II excitability in such devices. An optothermal instability leads to the phenomenon and while an underlying Hopf bifurcation is ultimately responsible for the observation, intriguingly there are two potential routes: One via a subcritical bifurcation and an associated bistable region and the other via a supercritical bifurcation and an associated canard explosion.
Deep Metric Learning With Density Adaptivity

The problem of distance metric learning is mostly considered from the perspective of learning an embedding space, where the distances between pairs of examples are in correspondence with a similarity metric. With the rise and success of Convolutional Neural Networks (CNN), deep metric learning (DML) involves training a network to learn a nonlinear transformation to the embedding space. Existing DML approaches often express the supervision through maximizing inter-class distance and minimizing intra-class variation. However, the results can suffer from overfitting problem, especially when the training examples of each class are embedded together tightly and the density of each class is very high. In this paper, we integrate density, i.e., the measure of data concentration in the representation, into the optimization of DML frameworks to adaptively balance inter-class similarity and intra-class variation by training the architecture in an end-to-end manner. Technically, the knowledge of density is employed as a regularizer, which is pluggable to any DML architecture with different objective functions such as contrastive loss, N-pair loss and triplet loss. Extensive experiments on three public datasets consistently demonstrate clear improvements by amending three types of embedding with the density adaptivity. More remarkably, our proposal increases Recall@1 from 67.95% to 77.62%, from 52.01% to 55.64% and from 68.20% to 70.56% on Cars196, CUB-200-2011 and Stanford Online Products dataset, respectively.
Edge-Sensitive Human Cutout With Hierarchical Granularity and Loopy Matting Guidance

Human parsing and matting play important roles in various applications, such as dress collocation, clothing recommendation, and image editing. In this paper, we propose a lightweight hybrid model that unifies the fully-supervised hierarchical-granularity parsing task and the unsupervised matting one. Our model comprises two parts, the extensible hierarchical semantic segmentation block using CNN and the matting module composed of guided filters. Given a human image, the segmentation block stage-1 first obtains a primitive segmentation map to separate the human and the background. The primitive segmentation is then fed into stage-2 together with the original image to give a rough segmentation of human body. This procedure is repeated in the stage-3 to acquire a refined segmentation. The matting module takes as input the above estimated segmentation maps and produces the matting map, in a fully unsupervised manner. The obtained matting map is then in turn fed back to the CNN in the first block for refining the semantic segmentation results.
Keyword Generation for Sponsored Search Advertising: Balancing Coverage and Relevance

How to automatically generate a pool of keywords used by potential consumers is a challenging issue for advertisers in sponsored search advertising (SSA). Such a keyword pool serves as the base for market research and determines the feasible space of consequent keyword related decisions. This article presents a novel method for keyword generation with Wikipedia as a corpus of the source text (WIKG). Starting with a few seed keywords, the WIKG supports flexible keywords generation by taking advantage of Wikipedia's rich link structure to construct a graph of entry articles in an iterative way. The termination condition is determined by a threshold reflecting the tradeoff between coverage of the generated keyword set and its relevance to seed keywords. Experimental results show that the WIKG outperforms three baselines derived from the extant literature, in terms of both coverage and relevance.
Motion detect application with frame difference method on a surveillance camera

Abstract Security becomes one of the major necessities in our lives nowadays however criminal activities are still at large with criminals unable to be persecuted without eligible proofs of their misdeeds. Surveillance Camera is one of the better solutions to these problems in which they can be positioned at every corner of a building even streets and alleys. Their functions can be enhanced by adding algorithms that can identify objects. Frame Differences method is an algorithm to identify an object’s motion. Using this algorithm, we could differentiate an object moving in the environment. Background subtraction is one of the methods suitable to further improve frame differences thus increasing its effectiveness and precision. After implementing the method on a camera, the luminosity was founded to influence the threshold value significantly, the threshold value of 35 is the optimal value.
Prospects of Machine and Deep Learning in Analysis of Vital Signs for the Improvement of Healthcare Services

The advent of eHealth and the need for real-time patient monitoring and assessment has prompted interest in understanding people behavior for improving care services. In this paper, the application of machine learning algorithms in clustering and predicting vital signs was pursued. In the context of big data and the debate surrounding vital signs data is fast becoming more relevant and applicable in predictive medicine. This paper assesses the applicability of k-Means and x-Means in clustering signals and used deep learning, Naïve Bayes, Random Forests, Decision Trees, and Generalized Linear Models to predict human dynamic motion-based vital signal patterns.
Interactive Voice Assisted Home Healthcare Systems

Home healthcare is the new industry where voice assistants are making their way, by increasing efficiency and offering a new experience filled with a wide range of tools and capabilities. Newly developed voice enabled applications made it more convenient for patients to access medical information, monitor and analyze health issues and communicate with the caretaker, which is especially beneficial for the elderly patients. This paper showcases the patient-focused voice and web services developed for Amazon's Alexa and Google Assistant and pinpoint the benefits of this technology in healthcare in the comfort of the patient's home.
Simulating the dynamics of single photons in boson sampling devices with matrix product states

Boson sampling is a well-defined scheme for demonstrating quantum supremacy with photons in the near term. However, relying only on multiphoton interference in nonadaptive linear-optical networks, the scheme is difficult to simulate classically. Here we study boson sampling using matrix product states, a powerful method from quantum many-body physics. This method stores the instantaneous quantum state during the evolution of photons in the optical quantum circuit, which allows us to reveal the dynamical features of single photons in boson sampling devices, such as entanglement entropy growth. We show the flexibility of this method by also applying it to dissipative optical quantum circuits, as well as circuits with fermionic particles. Our paper shows that matrix product states provide a powerful platform to simulate optical quantum circuits, which is readily extended to study quantum dynamics in multiparticle quantum walks beyond boson sampling.
Trust-Based Scheduling Framework for Big Data Processing with MapReduce

Security and privacy have become a great concern in cloud computing platforms in which users risk the leakage of their private data. The leakage can happen while the data is at rest (in storage), in processing, or on moving within a cloud or between different cloud infrastructures, e.g., from private to public clouds. This paper focuses on protecting data “in processing”. For big data applications, the MapReduce framework has been proven as an efficient solution and has been widely deployed, e.g., in healthcare and business data analysis. In this article, we propose a trust-based framework for MapReduce in big data processing tasks. Specifically, we first quantify and propose to assign the sensitive values for data and trust values for map and reduce slots. We then compute the trust value of each resource employed in the big data processing tasks. Depending on the data's sensitivity level of a task, the task requires a given level of trust (i.e., higher sensitive data requires servers/slots with higher trust level). The MapReduce scheduling problem is then formulated as the maximum weighted matching problem of a bipartite graph that aims to maximize the total trust value over all possible assignments subject to various trust requirement of different tasks. The problem is known to be NP-hard. To tackle it, we observe that within a computing node (VM), slots share the same trust value granted from the secured transformation phase. This helps reduce the number of slot nodes of a weight bipartite graph. Leveraging this fact, we propose an efficient heuristic algorithm that achieves 94.7 percent of the optimal solution obtained via exhaustive search. Extensive simulations show that the trust-based scheduling scheme provides much higher protection for data sensitivity while ensuring good performance for big data applications.