Reviewerly/exHarmony huggingface dataset:
Bayesian Posterior Approximation via Greedy Particle Optimization

In Bayesian inference, the posterior distributions are difficult to obtain analytically for complex models such as neural networks. Variational inference usually uses a parametric distribution for approximation, from which we can easily draw samples. Recently discrete approximation by particles has attracted attention because of its high expression ability. An example is Stein variational gradient descent (SVGD), which iteratively optimizes particles. Although SVGD has been shown to be computationally efficient empirically, its theoretical properties have not been clarified yet and no finite sample bound of the convergence rate is known. Another example is the Stein points (SP) method, which minimizes kernelized Stein discrepancy directly. Althoughafinitesampleboundisassuredtheoretically, SP is computationally inefficient empirically, especially in high-dimensional problems. In this paper, we propose a novel method named maximum mean discrepancy minimization by the Frank-Wolfe algorithm (MMD-FW), which minimizes MMD in a greedy way by the FW algorithm. Our method is computationally efficient empirically and we show that its finite sample convergence bound is in a linear order in finite dimensions.
Hierarchical Clustering for Euclidean Data

Recent works on Hierarchical Clustering (HC), a well-studied problem in exploratory data analysis, have focused on optimizing various objective functions for this problem under arbitrary similarity measures. In this paper we take the first step and give novel scalable algorithms for this problem tailored to Euclidean data in R^d and under vector-based similarity measures, a prevalent model in several typical machine learning applications. We focus primarily on the popular Gaussian kernel and present our results through the lens of the objective introduced recently by [MW'17]. We show the approximation factor in [MW'17] can be improved for Euclidean data. We further demonstrate both theoretically and experimentally that our algorithms scale to very high dimension d, while outperforming average-linkage and showing competitive results against other less scalable approaches.
Developing a Virtual Reality Environment for Mining Research

Recent advances in computing, rendering, and display technologies have generated increased accessibility for virtual reality (VR). VR allows the creation of dynamic, high-fidelity environments to simulate dangerous situations, test conditions, and visualize concepts. Consequently, numerous products have been developed, but many of these are limited in scope. Therefore, the National Institute for Occupational Safety and Health researchers developed a VR framework, called VR Mine, to rapidly create an underground mine for human data collection, simulation, visualization, and training. This paper describes the features of VR Mine using self-escape and proximity detection as case studies. Features include mine generation, simulated networks, proximity detection systems, and the integration and visualization of real-time ventilation models.
A tree-structured random walking swarm optimizer for multimodal optimization

This paper develops a novel tree structured random walking swarm optimizer for seeking multiple optima in multimodal landscapes. First, we show that the artificial bee colony algorithm has some distinct advantages over the other swarm intelligence algorithms for accomplishing the multimodal optimization task, from analytical and experimental perspectives. Then, a tree-structured niching strategy is developed to assist the algorithm in exploring multiple optima simultaneously. The strategy constructs a weighted complete graph based on the positions of the food sources (candidate solutions). A minimum spanning tree that encodes the distribution of the food sources is built upon the complete graph to guide the search of the bee swarm. Each artificial bee sets out from a food source and flies along the edges of the tree to gather information about the search space. The dance trajectories of bees are simulated by a random walk model considering both distance and fitness information. Then, mutant vectors are selected from the trajectories to update the food source. This graph-based search method is introduced to simultaneously promote the progress of exploitation and exploration in multimodal environments. Extensive experiments indicate that our proposed algorithm outperforms several state-of-the-art algorithms.
Cryptanalysis of Permutation–Diffusion-Based Lightweight Chaotic Image Encryption Scheme Using CPA

In order to meet the requirement of secure image communication in a resource-constrained network environment, a novel lightweight chaotic image encryption scheme based on permutation and diffusion has been proposed. It was claimed that this scheme can resist differential attacks, statistical attacks, etc. However, the original encryption scheme is found to be vulnerable and insecure to chosen-plaintext attack (CPA). In this paper, the original encryption scheme is analyzed comprehensively and attacked successfully. Only by choosing a full zero image as the chosen-plaintext of the diffusion phase, the encrypted image can be restored into permutation-only phase, and by applying the other chosen images as the chosen-plaintexts of the permutation phase, the map matrix which is equivalent to the secret key of the permutation phase can be further revealed. Experiments and analysis verify the feasibility of our proposed attack strategy.
$O(2)$ -Valued Hopfield Neural Networks

In complex-valued Hopfield neural networks (CHNNs), the neuron states are complex numbers whose amplitudes are: 1) they can also be described in special orthogonal matrices of order and 2) here, we propose a new Hopfield model, the O(2)-valued Hopfield neural network [O(2)-HNN], whose neuron states are extended to orthogonal matrices. Its neuron states are embedded in 4-D space, while those of CHNNs are embedded in 2-D space. Computer simulations were conducted to compare the noise tolerance (NT) and storage capacity (SC) of CHNNs, O(2)-HNNs, and rotor Hopfield neural networks. In terms of SC, O(2)-HNNs outperformed the others, while in NT, they outdid CHNNs.
A Hybrid SCA Inspired BBO for Feature Selection Problems

Recent trend of research is to hybridize two and more metaheuristics algorithms to obtain superior solution in the field of optimization problems. This paper proposes a newly developed wrapper-based feature selection method based on the hybridization of Biogeography Based Optimization (BBO) and Sine Cosine Algorithm (SCA) for handling feature selection problems. The position update mechanism of SCA algorithm is introduced into the BBO algorithm to enhance the diversity among the habitats. In BBO, the mutation operator is got rid of and instead of it, a position update mechanism of SCA algorithm is applied after the migration operator, to enhance the global search ability of Basic BBO. This mechanism tends to produce the highly fit solutions in the upcoming iterations, which results in the improved diversity of habitats. The performance of this Improved BBO (IBBO) algorithm is investigated using fourteen benchmark datasets. Experimental results of IBBO are compared with eight other search algorithms. The results show that IBBO is able to outperform the other algorithms in majority of the datasets. Furthermore, the strength of IBBO is proved through various numerical experiments like statistical analysis, convergence curves, ranking methods, and test functions. The results of the simulation have revealed that IBBO has produced very competitive and promising results, compared to the other search algorithms.
Unsupervised User Behavior Representation for Fraud Review Detection with Cold-Start Problem

Detecting fraud review is becoming extremely important in order to provide reliable information in cyberspace, in which, however, handling cold-start problem is a critical and urgent challenge since the case of cold-start fraud review rarely provides sufficient information for further assessing its authenticity. Existing work on detecting cold-start cases relies on the limited contents of the review posted by the user and a traditional classifier to make the decision. However, simply modeling review is not reliable since reviews can be easily manipulated. Also, it is hard to obtain high-quality labeled data for training the classifier. In this paper, we tackle cold-start problems by (1) using a user's behavior representation rather than review contents to measure authenticity, which further (2) consider user social relations with other existing users when posting reviews. The method is completely (3) unsupervised. Comprehensive experiments on Yelp data sets demonstrate our method significantly outperforms the state-of-the-art methods.
Multi-level Fuzzy system for usable-security assessment

Indubitably, security is an integral aspect of the development of quality software. More importantly, usability is also an elemental and pivotal factor for developing quality software. In fact, it has been noticed that most of the practitioners are trying to develop a highly secure design while maintaining high usability. Unfortunately, the highly secure design of software becomes worthless because the usability of software is very low. Further, usable security is in more demand due to the increasing usage of computers with enhanced usability and need of security in it too. When improving the usability with security of software, underlying security and usability attributes play an important role. For this reason, usable security assessment employs security and usability attributes to achieve the desired security solutions with usability. Different consecutive versions of two software have been taken in this work to assess usable security. Authors are using Fuzzy-AHP methodology to assess the priorities and overall usable-security. In addition, the impact of the security on usability and impact of the usability on security have been evaluated quantitatively. The results obtained and conclusions are useful for practitioners to improve usable-security of software.
Stochastic Blockmodels meet Graph Neural Networks

Stochastic blockmodels (SBM) and their variants, $e.g.$, mixed-membership and overlapping stochastic blockmodels, are latent variable based generative models for graphs. They have proven to be successful for various tasks, such as discovering the community structure and link prediction on graph-structured data. Recently, graph neural networks, $e.g.$, graph convolutional networks, have also emerged as a promising approach to learn powerful representations (embeddings) for the nodes in the graph, by exploiting graph properties such as locality and invariance. In this work, we unify these two directions by developing a \emph{sparse} variational autoencoder for graphs, that retains the interpretability of SBMs, while also enjoying the excellent predictive performance of graph neural nets. Moreover, our framework is accompanied by a fast recognition model that enables fast inference of the node embeddings (which are of independent interest for inference in SBM and its variants). Although we develop this framework for a particular type of SBM, namely the \emph{overlapping} stochastic blockmodel, the proposed framework can be adapted readily for other types of SBMs. Experimental results on several benchmarks demonstrate encouraging results on link prediction while learning an interpretable latent structure that can be used for community discovery.
Fidelity Preserved Data Hiding in Encrypted Highly Autocorrelated Data Based on Homomorphism and Compressive Sensing

Images and videos (consisting of successive images) are highly autocorrelated due to the inherent spatial correlations. However, when the data are encrypted for privacy protection, the spatial correlations would be eliminated. Thus, it is difficult for an untrusted third party in the cloud to embed annotation information or auxiliary information in the encrypted media. In this paper, we proposed a novel method to maintain the spatial correlation in the encrypted domain to some extent by using a homomorphic cryptosystem in order to achieve high-quality data hiding. The textured and smooth blocks of the image can be identified in the encrypted domain. With a compressive sensing technology, the LSB layers of smooth blocks can be well-handled to make room for accommodating additional data. In the data hiding process, data hider embeds the additional data into LSBs of the smooth area to preserve high fidelity of the stego-image. On the receiver side, the embedded data can be extracted without any distortion from the encrypted image only by data-embedding key, and also, we can reap the directly decrypted image with a high visual quality only with the encryption key. In the case of the user possesses both the encryption key and the data-embedding key, the additional data can be extracted accurately, and the image recovery with overwhelming probability can be achieved. The vast experimental results manifest that the proposed method not merely has excellent security performance but also maintains the fidelity of the host image while providing considerable embedding capacity, which is superior to other state-of-the-art schemes.
Task-Oriented Feature-Fused Network With Multivariate Dataset for Joint Face Analysis

Deep multitask learning for face analysis has received increasing attentions. From literature, most existing methods focus on optimizing a main task by jointly learning several auxiliary tasks. It is challenging to consider the performance of each task in a multitask framework due to the following reasons: 1) different face tasks usually rely on different levels of semantic features; 2) each task has different learning convergence rate, which could affect the whole performance when joint training; and 3) multitask model needs rich label information for efficient training, but existing facial datasets provide limited annotations. To address these issues, we propose a task-oriented feature-fused network (TFN) for simultaneously solving face detection, landmark localization, and attribute analysis. In this network, a task-oriented feature-fused block is designed to learn task-specific feature combinations; then, an alternative multitask training scheme is presented to optimize each task with considering of their different learning capacities. We also present a large-scale face dataset called JFA in support of proposed method, which provides multivariate labels, including face bounding box, 68 facial landmarks, and 3 attribute labels (i.e., apparent age, gender, and ethnicity). The experimental results suggest that the TFN outperforms several multitask models on the JFA dataset. Furthermore, our approach achieves competitive performances on WIDER FACE and 300W dataset, and obtains state-of-the-art results for gender recognition on the MORPH II dataset.
Defining Schemas for Property Graphs by using the GraphQL Schema Definition Language

GraphQL is a highly popular new approach to build Web APIs. An important component of this approach is the GraphQL schema definition language (SDL). The original purpose of this language is to define a so-called GraphQL schema that specifies the types of objects that can be queried when accessing a specific GraphQL Web API. This paper focuses on the question: Can we repurpose this language to define schemas for graph databases that are based on the Property Graph model? This question is relevant because there does not exist a commonly adopted approach to define schemas for Property Graphs, and because the form in which GraphQL APIs represent their underlying data sources is very similar to the Property Graph model. To answer the question we propose an approach to adopt the GraphQL SDL for Property Graph schemas. We define this approach formally and show its fundamental properties.
EOS.IO Wallets and Smart Contracts

In Chapter 2 , I introduced EOS.IO when I covered bitcoin, altcoins, and different consensus mechanisms. Specifically, I covered how EOS.IO is an example of altcoins that turn into tokens; you created an EOS block producer and were able to create a full node capable of mining EOS tokens. Ethereum was the beginning of your blockchain smart contract development, and you learned to use the Solidity language to write smart contracts and dapps. EOS.IO has created a more robust architecture than Ethereum for smart contract and dapp development.
Managing Popularity Bias in Recommender Systems with Personalized Re-Ranking.

Many recommender systems suffer from popularity bias: popular items are recommended frequently while less popular, niche products, are recommended rarely or not at all. However, recommending the ignored products in the ``long tail'' is critical for businesses as they are less likely to be discovered. In this paper, we introduce a personalized diversification re-ranking approach to increase the representation of less popular items in recommendations while maintaining acceptable recommendation accuracy. Our approach is a post-processing step that can be applied to the output of any recommender system. We show that our approach is capable of managing popularity bias more effectively, compared with an existing method based on regularization. We also examine both new and existing metrics to measure the coverage of long-tail items in the recommendation.
Pigmento: Pigment-Based Image Analysis and Editing

The colorful appearance of a physical painting is determined by the distribution of paint pigments across the canvas, which we model as a per-pixel mixture of a small number of pigments with multispectral absorption and scattering coefficients. We present an algorithm to efficiently recover this structure from an RGB image, yielding a plausible set of pigments and a low RGB reconstruction error. We show that under certain circumstances we are able to recover pigments that are close to ground truth, while in all cases our results are always plausible. Using our decomposition, we repose standard digital image editing operations as operations in pigment space rather than RGB, with interestingly novel results. We demonstrate tonal adjustments, selection masking, cut-copy-paste, recoloring, palette summarization, and edge enhancement.
A Bayesian Approach to Robust Reinforcement Learning

Robust Markov Decision Processes (RMDPs) intend to ensure robustness with respect to changing or adversarial system behavior. In this framework, transitions are modeled as arbitrary elements of a known and properly structured uncertainty set and a robust optimal policy can be derived under the worst-case scenario. In this study, we address the issue of learning in RMDPs using a Bayesian approach. We introduce the Uncertainty Robust Bellman Equation (URBE) which encourages safe exploration for adapting the uncertainty set to new observations while preserving robustness. We propose a URBE-based algorithm, DQN-URBE, that scales this method to higher dimensional domains. Our experiments show that the derived URBE-based strategy leads to a better trade-off between less conservative solutions and robustness in the presence of model misspecification. In addition, we show that the DQN-URBE algorithm can adapt significantly faster to changing dynamics online compared to existing robust techniques with fixed uncertainty sets.
A Limitation of Gradient Descent Learning

Over decades, gradient descent has been applied to develop learning algorithm to train a neural network (NN). In this brief, a limitation of applying such algorithm to train an NN with persistent weight noise is revealed. Let V(w) be the performance measure of an ideal NN. V(w) is applied to develop the gradient descent learning (GDL). With weight noise, the desired performance measure (denoted as J(w) ) is E[V(~w)|w] , where ~w is the noisy weight vector. Applying GDL to train an NN with weight noise, the actual learning objective is clearly not V(w) but another scalar function L(w) . For decades, there is a misconception that L(w) = J(w) , and hence, the actual model attained by the GDL is the desired model. However, we show that it might not: 1) with persistent additive weight noise, the actual model attained is the desired model as L(w) = J(w) ; and 2) with persistent multiplicative weight noise, the actual model attained is unlikely the desired model as L(w) ≠ J(w) . Accordingly, the properties of the models attained as compared with the desired models are analyzed and the learning curves are sketched. Simulation results on 1) a simple regression problem and 2) the MNIST handwritten digit recognition are presented to support our claims.
A Technique for Network Design and Requirements for Medical Sensors

In recent days, a large component of people relies on readily available material in social networks in their choices (e.g. testimonials and also comments on a subject or item). The opportunity that anyone can leave a testimonial supply a gold possibility for spammers to create spam testimonials concerning product or services for a variable rate of interests. Determining these spammers and also the spam web content is a warm subject of research study and also although a substantial variety of research studies have actually been done just recently towards this end, however up until now the approaches presented still hardly find spam evaluations, and also none reveal the relevance of each drawn out function kind. In this research, we suggest a unique structure, called NetSpam, which uses spam attributes for modeling testimonial datasets as heterogeneous information networks to map spam detection treatment right into a classification trouble in such networks. Utilizing the significance of spam attributes aid us to acquire much better cause regards to various metrics explored on real-world testimonial datasets from Yelp as well as Amazon.com internet sites. The outcomes reveal that NetSpam outshines the existing approaches and also amongst 4 groups of functions; consisting of review-behavioral, user-behavioral, evaluation etymological, individual-etymological, the remainder kind of attributes does much better than the various other classifications. It additionally concentrates on providing the current improvements in both wearable and also implantable innovations. In addition, this paper deals with the challenges that exist in the different Open Solutions Affiliation (OSI) layers and also shows future research study locations worrying about the usage of cordless sensors in healthcare applications.
Cybercrime, money mules and situational crime prevention

This is a first exploratory study into possibilities to counter the recruitment of money mules, based on 112 statements of money mules from 14 Dutch criminal investigations into phishing and malware networks. The study focused on motives and techniques of neutralization as well an on possibilities for situational crime prevention.
Failure Prediction, Lead Time Estimation and Health Degree Assessment for Hard Disk Drives Using Voting based Decision Trees

Hard Disk drives (HDDs) are an essential component of cloud computing and big data, responsible for storing humongous volumes of collected data. However, HDD failures pose a huge challenge to big data servers and cloud servic... | Find, read and cite all the research you need on Tech Science Press