Reviewerly/exHarmony huggingface dataset:
Echo state network with a global reversible autoencoder for time series classification

• In order to reestablish the random initialization input weights of ESN, a single layer ESN with a global reversible AE (GRAE) algorithm is proposed to ensure that the outputs of GRAE are remarkably correlated with the input data. • The reservoir layer with a reversible function is calculated by pulling the ESN output back and injecting it into the reservoir layer. Thus, the feature learning is enriched by additional information, which results in a better performance. • The GRAE which is based on ridge regression to solve a least squares problem is entirely different from existing back-propagation (BP) based AEs. So, the training speed of GRAE is many times faster than that of the BP based AEs. • In existing ESN autoencoder methods, all weights in the reservoir layer are generated randomly. The simple cycle connection is utilized in the reservoir layer of ESN to make sure the reservoir weights can be generated deterministically. An echo state network (z) can provide an efficient dynamic solution for predicting time series problems. However, in most cases, ESN models are applied for predictions rather than classifications. The applications of ESN in time series classification (TSC) problems have yet to be fully studied. Moreover, the conventional randomly generated ESN is unlikely to be optimal because of the randomly generated input and reservoir weights, which are not always guaranteed to be optimal. Randomly generating all layer weights is improper, because a purely random layer might destroy the useful features. To overcome this disadvantage, this study provides a new input weight establishment framework of ESN based on autoencoder (AE) theory for TSC tasks. A global reversible AE (GRAE) algorithm is proposed to reestablish the random initialization input weights of the ESN. In existing ESN-AEs, the output weights obtained in the encoding process are directly reused as the initial input weights. By contrast, in GRAE, the reservoir layer with a reversible activation function is calculated by pulling the decoding layer output back and injecting it into the reservoir layer. Thus, feature learning is enriched by additional information, which results in improved performance. The current weights of the encoding layer are iteratively replaced by the decoding layer to ensure that the outputs of the GRAE are remarkably correlated with the input data. Visualization analyses and experiments of the input weights on a massive set of UCR time series datasets indicate that the proposed GRAE method can considerably improve the original two-layer ESN-based classifiers and the proposed GRAE-ESN classifier yields better performance compared with traditional state-of-the-art TSC classifiers. Furthermore, the proposed method can provide comparable performance and considerably faster training speed compared with three deep learning classifiers.
Representations and symbolic computation of generalized inverses over fields

This paper investigates representations of outer matrix inverses with prescribed range and/or null space in terms of inner inverses. Further, required inner inverses are computed as solutions of appropriate linear matrix equations (LME). In this way, algorithms for computing outer inverses are derived using solutions of appropriately defined LME. Using symbolic solutions to these matrix equations it is possible to derive corresponding algorithms in appropriate computer algebra systems. In addition, we give sufficient conditions to ensure the proper specialization of the presented representations. As a consequence, we derive algorithms to deal with outer inverses with prescribed range and/or null space and with meromorphic functional entries.
A Postphenomenological Framework for Studying User Experience of Immersive Virtual Reality

Virtual Reality (VR) is a remarkably flexible technology for interventions as it allows the construction of virtual worlds with ontologies radically different from the real world. By embodying users in avatars situated in these virtual environments, researchers can effectively intervene and instill positive change in the form of therapy or education, as well as affect a variety of cognitive changes. Due to the capabilities of VR to mediate both the environments in which we are immersed, as well as our embodied, situated relation toward those environments, VR has become a powerful technology for “changing the self.” As the virtually mediated experience is what renders these interventions effective, frameworks are needed for describing and analyzing the mediations brought by various virtual world designs. As a step toward a broader understanding of how VR mediates experience, we propose a post-phenomenological framework for describing VR mediation. Postphenomenology is a philosophy of technology concerned with empirical data that understand technologies as mediators of human-world relationships. By addressing how mediations occur within VR as a user-environment relation and outside VR as a human-world relation, the framework addresses the various constituents of the virtually mediated experience. We demonstrate the framework's capability for describing VR mediations by presenting the results of an analysis of a selected variety of studies that use various user-environment relations to mediate various human-world relations.
Salient Object Detection Based on Visual Perceptual Saturation and Two-Stream Hybrid Networks

Inspired by the perceived saturation of human visual system, this paper proposes a two-stream hybrid networks to simulate binocular vision for salient object detection (SOD). Each stream in our system consists of unsupervised and supervised methods to form a two-branch module, so as to model the interaction between human intuition and memory. The two-branch module parallel processes visual information with bottom-up and top-down SODs, and output two initial saliency maps. Then a polyharmonic neural network with random-weight (PNNRW) is utilized to fuse two-branch's perception and refine the salient objects by learning online via multi-source cues. Depend on visual perceptual saturation, we can select optimal parameter of superpixel for unsupervised branch, locate sampling regions for PNNRW, and construct a positive feedback loop to facilitate perception saturated after the perception fusion. By comparing the binary outputs of the two-stream, the pixel annotation of predicted object with high saturation degree could be taken as new training samples. The presented method constitutes a semi-supervised learning framework actually. Supervised branches only need to be pre-trained initial, the system can collect the training samples with high confidence level and then train new models by itself. Extensive experiments show that the new framework can improve performance of the existing SOD methods, that exceeds the state-of-the-art methods in six popular benchmarks.
Learners Learn More and Instructors Track Better with Real-time Gaze Sharing

Shared gaze visualizations, where the real-time gaze location of group members is shared with one another, have become increasingly studied over the last decade by HCI researchers. Shared gaze studies so far have found improved outcomes and better collaboration for peer collaborators. Less is known, however, about how gaze sharing may aid learners and instructors. In our study, an instructor teaches a learner how to assemble and program a simple microcontroller, communicating either through a webcam feed (webcam condition), a field-of-view video feed (HMC condition), or a field-of-view video feed with a gaze location pointer (gaze condition). We find that learning gain is highest in the gaze condition, especially for low achievers. Moreover, instructors predict learner post-test scores more accurately with gaze visualizations, suggesting gaze sharing can help instructors track the cognitive state of the learner. This effect was also most salient for low achievers. We find that in the HMC condition that only lacked this single dot, many of the benefits for both learning and teaching were lost. The paper concludes with discussions on how gaze visualization may support learning and teaching, and on the tool's limitations and conditions for usefulness.
Statistical image watermarking using local RHFMs magnitudes and beta exponential distribution

The imperceptibility, robustness and data payload are widely considered as the three main properties vital for any image watermarking systems. They are complimentary to each other and hence challenging to attain the right balance between them. The statistical model-based multiplicative watermarking is an effective way to achieve the tradeoff among imperceptibility, robustness and data payload. Radial harmonic Fourier moments (RHFMs) is a strong tool in image processing, which has many advantages, such as lower noise sensitivity, powerful image description ability and geometric invariance feature. In this paper, we propose a new statistical image watermarking scheme using local RHFMs magnitudes and Beta exponential distribution model. Our image watermarking scheme consists of two parts, namely, embedding and extraction. In the embedding process, we divide the host image into no-overlapping blocks and compute the local RHFMs of image blocks, then insert the watermark signal into the robust local RHFMs magnitudes through multiplicative approach. In the extraction phase, robust local RHFMs magnitudes are firstly modeled by employing the Beta exponential distribution, where the statistical properties of local RHFMs magnitudes are captured accurately. Then the modified maximum likelihood parameter estimation (MMLE) approach is introduced to estimate the statistical parameters of Beta exponential distribution model. And finally an image watermark decoder for multiplicative watermarking is developed using Beta exponential distribution and maximum likelihood decision criterion. Experimental results on some test images and comparison with well-known existing methods demonstrate the efficacy and superiority of the proposed statistical image watermarking.
Verifiable and Secure SVM Classification for Cloud-Based Health Monitoring Services

In cloud-based health monitoring services, support vector machine (SVM) classification techniques are often utilized by medical institutes to build medical decision models, which can be outsourced to a cloud server for producing medical decisions based on medical features from remote clients. In this article, we propose a verifiable and secure SVM classification scheme ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathsf {VSSVMC}$ </tex-math></inline-formula> ) for cloud-based health monitoring services in a malicious setting, where the cloud server may return invalid decisions. By constructing verifiable indices, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathsf {VSSVMC}$ </tex-math></inline-formula> ensures the verifiability of medical decisions, which enables clients to detect whether the cloud server returns incorrect or incomplete medical decisions. Symmetric key encryption is leveraged to ensure the confidentiality of the medical decision model and medical data with computational efficiency. We give security and verifiability definitions and provide formal security and verifiability proofs for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathsf {VSSVMC}$ </tex-math></inline-formula> . Performance analyses show that <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathsf {VSSVMC}$ </tex-math></inline-formula> is extremely efficient in terms of computation, communication, and storage. Experimental evaluations demonstrate that <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathsf {VSSVMC}$ </tex-math></inline-formula> achieves microsecond-level execution time with kilobyte-level communication and storage overheads on the tested data set.
Label Contrastive Coding Based Graph Neural Network for Graph Classification

Graph classification is a critical research problem in many applications from different domains. In order to learn a graph classification model, the most widely used supervision component is an output layer together with classification loss (e.g., cross-entropy loss together with softmax or margin loss). In fact, the discriminative information among instances are more fine-grained, which can benefit graph classification tasks. In this paper, we propose the novel Label Contrastive Coding based Graph Neural Network (LCGNN) to utilize label information more effectively and comprehensively. LCGNN still uses the classification loss to ensure the discriminability of classes. Meanwhile, LCGNN leverages the proposed Label Contrastive Loss derived from self-supervised learning to encourage instance-level intra-class compactness and inter-class separability. To power the contrastive learning, LCGNN introduces a dynamic label memory bank and a momentum updated encoder. Our extensive evaluations with eight benchmark graph datasets demonstrate that LCGNN can outperform state-of-the-art graph classification models. Experimental results also verify that LCGNN can achieve competitive performance with less training data because LCGNN exploits label information comprehensively.
Data Augmentation Using Generative Adversarial Network

Now a days, Deep Learning has made appreciable development which introduces intelligence in machines to work like human brain. For this learning, the presence of large and balanced dataset is essential so that we can train the machines more efficiently. However finding such data in real world is rare, and creating these data sets is a complex task. So Generative Ad- versarial Networks (GANs) are used to create dataset to enhance the unsupervised learning. In this proposed work, Convolutional GAN is used for data augmentation which produces more realistic datasets and then we analyse the performance of this GAN by doing hyper-parameter tuning of opitmizers and activation functions.
A comprehensive survey on 2D multi-person pose estimation methods

Human pose estimation is a fundamental yet challenging computer vision task and studied by many researchers around the world in recent years. As a basic task in computer vision, multi-person pose estimation is the core component for many practical applications. This paper extensively reviews recent works on multi-person pose estimation. Specifically, we illustrate and analyze popular methods in detail and compare their pros and cons to fill in the gaps existing in other surveys. In addition, the commonly used datasets, evaluation metrics, and open-source systems are also introduced respectively. Finally, we summarize the development of multi-person pose estimation frameworks and discuss the research trends.
A Federated Learning Approach for Privacy Protection in Context-Aware Recommender Systems

Abstract Privacy protection is one of the key concerns of users in recommender system-based consumer markets. Popular recommendation frameworks such as collaborative filtering (CF) suffer from several privacy issues. Federated learning has emerged as an optimistic approach for collaborative and privacy-preserved learning. Users in a federated learning environment train a local model on a self-maintained item log and collaboratively train a global model by exchanging model parameters instead of personalized preferences. In this research, we proposed a federated learning-based privacy-preserving CF model for context-aware recommender systems that work with a user-defined collaboration protocol to ensure users’ privacy. Instead of crawling users’ personal information into a central server, the whole data are divided into two disjoint parts, i.e. user data and sharable item information. The inbuilt power of federated architecture ensures the users’ privacy concerns while providing considerably accurate recommendations. We evaluated the performance of the proposed algorithm with two publicly available datasets through both the prediction and ranking perspectives. Despite the federated cost and lack of open collaboration, the overall performance achieved through the proposed technique is comparable with popular recommendation models and satisfactory while providing significant privacy guarantees.
Articulations toward a crip HCI

research-article Articulations toward a crip HCI Share on Authors: Rua M. Williams Purdue University Purdue UniversityView Profile , Kathryn Ringland University of California, Santa Cruz University of California, Santa CruzView Profile , Amelia Gibson University of North Carolina at Chapel Hill University of North Carolina at Chapel HillView Profile , Mahender Mandala Georgia Tech Georgia TechView Profile , Arne Maibaum Technical University of Berlin Technical University of BerlinView Profile , Tiago Guerreiro Faculdade de Ciências da Universidade de Lisboa Faculdade de Ciências da Universidade de LisboaView Profile Authors Info & Claims InteractionsVolume 28Issue 3May - June 2021 pp 28–37https://doi.org/10.1145/3458453Online:27 April 2021Publication History 0citation1,302DownloadsMetricsTotal Citations0Total Downloads1,302Last 12 Months1,302Last 6 weeks44 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
An intelligent content-based image retrieval methodology using transfer learning for digital IP protection

Trademarks are used by companies to help customers identify products or services using images or logos in addition to slogans, words, names, sounds, smells, color, and motions. Trademark logos are widely distributed through advertising and published through online media websites and social networks such as Facebook, Pinterest, and Flicker. The intellectual property (IP) rights of the trademark owners have strong legal protection when registered with international intellectual property platforms such as the US Patent and Trademark Office and the World Intellectual Property Office. Using a registered trademark without prior consent of the owner may result in intellectual property infringement with severe legal consequences under civil or criminal law. Companies invest large capital resources in protecting their trademark from being copied or misused in ways that confuse the customers or steal market share. This research focuses on trademark (TM) logo image retrieval systems used in the cyber marketplaces to identify similar TM logo images online automatically and intelligently. The methodology developed for TM logo similarity measurement is based on content-based image retrieval. Content retrieval reduces the gap between high-level semantic interpretation of human vision and the low-level features processed by the machine. The proposed transfer learning methodology uses embedded learning with triplet loss to fine-tune a pre-trained convolutional neural network model. The Logo-2K+ large-scale logo dataset is re-organized and divided into the top 70% as the training set and the remaining 30% as the testing set. The results show that the novel transfer learning approach is developed and demonstrated in this research for the intelligent automatic detection of similar TM logo images with high accuracy. The verification experiments (trained with 7625 logos and tested with 3221 logos) demonstrates that the [email protected] of the test set can reach 95% using the advanced convolutional neural network model (VGG19) adjusted with the novel transfer learning methodology.
An examination of data reuse practices within highly cited articles of faculty at a research university

Data sharing and reuse are regarded as important components of the research workflow and key elements in open science. While reuse is well-documented in some circumstances, the utility of data sharing for all domains is less clear, and limited evidence of wide-spread demand can make it challenging to justify effort and funds required to format, document, share, and preserve data. This paper describes a project that: (1) surveyed authors of highly cited papers published in 2015 at the University of Illinois at Urbana-Champaign in nine STEM disciplines to determine if data were generated for their article and their knowledge of reuse by other researchers, and (2) surveyed authors who cited these 2015 articles to ascertain whether they reused data from the original article and how that data was obtained. The project goal was to better understand data reuse in practice and to explore if research data from an initial publication was reused in subsequent publications. While the results revealed reuse in many situations (and deemed important in these cases), the survey results and researcher supplied comments also indicated that data does not play the same role in all studies or even in studies that build on previous ones.
Different Transfer Functions for Binary Particle Swarm Optimization with a New Encoding Scheme for Discounted {0-1} Knapsack Problem

The discounted {0-1} knapsack problem (DKP01) is a kind of knapsack problem with group structure and discount relationships among items. It is more challenging than the classical 0-1 knapsack problem. In this paper, we study binary particle swarm optimization (PSO) algorithms with different transfer functions and a new encoding scheme for DKP01. An effective binary vector with shorter length is used to represent a solution for new binary PSO algorithms. Eight transfer functions are used to design binary PSO algorithms for DKP01. A new repair operator is developed to handle isolation solution while improving its quality. Finally, we conducted extensive experiments on four groups of 40 instances using our proposed approaches. The experience results show that the proposed algorithms outperform the previous algorithms named FirEGA and SecEGA . Overall, the proposed algorithms with a new encoding scheme represent a potential approach for solving the DKP01.
Online Subclass Knowledge Distillation

Knowledge Distillation has been established as a highly promising approach for training compact and faster models by transferring knowledge from more heavyweight and powerful models, so as to satisfy the computation and storage requirements of deploying state-of-the-art deep neural models on embedded systems. However, conventional knowledge distillation requires multiple stages of training rendering it a computationally and memory demanding procedure. In this paper, a novel single-stage self knowledge distillation method is proposed, namely Online Subclass Knowledge Distillation (OSKD), that aims at revealing the similarities inside classes, improving the performance of any deep neural model in an online manner. Hence, as opposed to existing online distillation methods, we are able to acquire further knowledge from the model itself, without building multiple identical models or using multiple models to teach each other, rendering the OSKD approach more effective. The experimental evaluation on five datasets indicates that the proposed method enhances the classification performance, while comparison results against existing online distillation methods validate the superiority of the proposed method.