Reviewerly/exHarmony huggingface dataset:
Cost-sensitive ensemble of stacked denoising autoencoders for class imbalance problems in business domain

Standard classification algorithms assume the class distribution of data to be roughly balanced. Class imbalance problem usually occurs in real-life applications, such as direct marketing, fraud detection and churn prediction. Class imbalance problem is referred to the issue that the number of examples belonging to a class is significantly higher than those of the others. When training a standard classifier with class imbalance data, the classifier is usually biased toward the majority class. In this work, we propose two novel cost-sensitive methods to address class imbalance problem, namely Cost-Sensitive Deep Neural Network (CSDNN) and Cost-Sensitive Deep Neural Network Ensemble (CSDE). CSDNN is a cost-sensitive version of Stacked Denoising Autoencoders. CSDE is an ensemble learning version of CSDNN. Random undersampling and layer-wise feature extraction from the hidden layers of the deep neural network are applied in CSDE to improve the generalization performance over CSDNN. In some literatures, various methods handling class imbalance problem were proposed. However, the experiments discussed in those studies were usually conducted on relatively small data sets and also on artificial data. The performance of those methods on modern real-life data sets, which are more complicated, is unclear. In our experiment, we examine the performance of our proposed methods and the other methods using six large real-life data sets in different business domains ranging from direct marketing, churn prediction, default payment to firm fraud detection. The results show that the proposed methods obtain promising results in handling class imbalance problem and also outperform all the other compared methods.
Spatial temporal graph convolutional networks for skeleton-based dynamic hand gesture recognition

Abstract Hand gesture recognition methods play an important role in human-computer interaction. Among these methods are skeleton-based recognition techniques that seem to be promising. In literature, several methods have been proposed to recognize hand gestures with skeletons. One problem with these methods is that they consider little the connectivity between the joints of a skeleton, constructing simple graphs for skeleton connectivity. Observing this, we built a new model of hand skeletons by adding three types of edges in the graph to finely describe the linkage action of joints. Then, an end-to-end deep neural network, hand gesture graph convolutional network, is presented in which the convolution is conducted only on linked skeleton joints. Since the training dataset is relatively small, this work proposes expanding the coordinate dimensionality so as to let models learn more semantic features. Furthermore, relative coordinates are employed to help hand gesture graph convolutional network learn the feature representation independent of the random starting positions of actions. The proposed method is validated on two challenging datasets, and the experimental results show that it outperforms the state-of-the-art methods. Furthermore, it is relatively lightweight in practice for hand skeleton-based gesture recognition.
HitGraph: High-throughput Graph Processing Framework on FPGA

This paper presents, HitGraph, an FPGA framework to accelerate graph processing based on the edge-centric paradigm. HitGraph takes in an edge-centric graph algorithm and hardware resource constraints, determines design parameters and then generates a Register Transfer Level (RTL) FPGA design. This makes accelerator design for various graph analytics transparent and user-friendly by masking internal details of the accelerator design process. HitGraph enables increased data reuse and parallelism through novel algorithmic optimizations, including (1) an optimized data layout that reduces non-sequential external memory accesses, (2) an efficient update merging and filtering scheme to reduce the data communication between the FPGA and external memory, and (3) a partition skipping scheme to reduce redundant edge traversals for non-stationary graph algorithms. Based on our design methodology, we accelerate Sparse Matrix Vector Multiplication (SpMV), PageRank (PR), Single Source Shortest Path (SSSP), and Weakly Connected Component (WCC). Experimental results show that HitGraph sustains a high throughput of 2076 Million Traversed Edges Per Second (MTEPS) for SpMV, 2225 MTEPS for PR, 2916 MTEPS for SSSP, and 3493 MTEPS for WCC, respectively. Compared with highly-optimized multi-core implementations, HitGraph achieves up to 37.9× speedup. Compared with state-of-the-art FPGA frameworks, HitGraph achieves up to 50.7× throughput improvement.
Research Design: Qualitative, Quantitative, and Mixed Methods Approaches, by John Creswell and J. David Creswell. Thousand Oaks, CA: Sage Publication, Inc. 275 pages, $67.00 (Paperback).

New Horizons in Adult Education and Human Resource DevelopmentVolume 31, Issue 3 p. 75-77 Book Review Research Design: Qualitative, Quantitative, and Mixed Methods Approaches, by John Creswell and J. David Creswell. Thousand Oaks, CA: Sage Publication, Inc. 275 pages, $67.00 (Paperback). Debaro Huyler, Corresponding Author Debaro Huyler dhuyl001@fiu.edu Florida International University Corresponding Author: Debaro Huyler, Florida International University 11200 S.W. 8th Street, Miami, Florida 33199 Email: dhuyl001@fiu.eduSearch for more papers by this authorCraig M. McGill, Craig M. McGill University of Nebraska-LincolnSearch for more papers by this author Debaro Huyler, Corresponding Author Debaro Huyler dhuyl001@fiu.edu Florida International University Corresponding Author: Debaro Huyler, Florida International University 11200 S.W. 8th Street, Miami, Florida 33199 Email: dhuyl001@fiu.eduSearch for more papers by this authorCraig M. McGill, Craig M. McGill University of Nebraska-LincolnSearch for more papers by this author First published: 02 October 2019 https://doi.org/10.1002/nha3.20258Citations: 3Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article.Citing Literature Volume31, Issue3Summer 2019Pages 75-77 RelatedInformation
Relation-Aware Graph Convolutional Networks for Agent-Initiated Social E-Commerce Recommendation

Recent years have witnessed a phenomenal success of agent-initiated social e-commerce models, which encourage users to become selling agents to promote items through their social connections. The complex interactions in this type of social e-commerce can be formulated as Heterogeneous Information Networks (HIN), where there are numerous types of relations between three types of nodes, i.e., users, selling agents and items. Learning high quality node embeddings is of key interest, and Graph Convolutional Networks (GCNs) have recently been established as the latest state-of-the-art methods in representation learning. However, prior GCN models have fundamental limitations in both modeling heterogeneous relations and efficiently sampling relevant receptive field from vast neighborhood. To address these problems, we propose RecoGCN, which stands for a RElation-aware CO-attentive GCN model, to effectively aggregate heterogeneous features in a HIN. It makes up current GCN's limitation in modelling heterogeneous relations with a relation-aware aggregator, and leverages the semantic-aware meta-paths to carve out concise and relevant receptive fields for each node. To effectively fuse the embeddings learned from different meta-paths, we further develop a co-attentive mechanism to dynamically assign importance weights to different meta-paths by attending the three-way interactions among users, selling agents and items. Extensive experiments on a real-world dataset demonstrate RecoGCN is able to learn meaningful node embeddings in HIN, and consistently outperforms baseline methods in recommendation tasks.
Instructor presence in video lectures: Eye gaze matters, but not body orientation

The instructor's on-screen presence, rather than just voice, has rapidly become a popular feature in video lectures. Eye gaze and body orientation are core indicators of an instructor's attentional focus in classroom settings, but it is not well known how these factors influence learners' attention allocation and learning performance in video lectures with the on-screen presence of instructors presenting slides. This study tested the effects of the instructor's eye gaze and body orientation on attention and learning from a video lecture in a sample of 174 undergraduates. Specifically, the instructor's presence was manipulated in terms of eye gaze (direct, guided, or averted) and body orientation (frontal or lateral). Eye tracking data revealed that regardless of an instructor's body orientation, learners who viewed the video lectures with the instructor's guided gaze paid greater attention to the slides, and those who viewed the video lectures with the instructor's direct gaze paid greater attention to her face; paper-and-pencil assessments showed that learners who viewed the video lectures with guided gaze showed better retention and transfer. These results held regardless of body orientation, suggesting that an instructor's eye gaze has a stronger influence than body orientation on attention and learning from video lectures. The findings suggest that an instructor should not look directly at the camera continuously throughout the lecture, and should instead use guided gaze to draw learners' attention to the learning materials.
Semantically Aligned Bias Reducing Zero Shot Learning

Zero shot learning (ZSL) aims to recognize unseen classes by exploiting semantic relationships between seen and unseen classes. Two major problems faced by ZSL algorithms are the hubness problem and the bias towards the seen classes. Existing ZSL methods focus on only one of these problems in the conventional and generalized ZSL setting. In this work, we propose a novel approach, Semantically Aligned Bias Reducing (SABR) ZSL, which focuses on solving both the problems. It overcomes the hubness problem by learning a latent space that preserves the semantic relationship between the labels while encoding the discriminating information about the classes. Further, we also propose ways to reduce bias of the seen classes through a simple cross-validation process in the inductive setting and a novel weak transfer constraint in the transductive setting. Extensive experiments on three benchmark datasets suggest that the proposed model significantly outperforms existing state-of-the-art algorithms by ~1.5-9% in the conventional ZSL setting and by ~2-14% in the generalized ZSL for both the inductive and transductive settings.
Bangla Handwritten Character Recognition using Convolutional Neural Network with Data Augmentation

This paper proposes a process of Handwritten Character Recognition to recognize and convert images of individual Bangla handwritten characters into electronically editable format, which will create opportunities for further research and can also have various practical applications. The dataset used in this experiment is the BanglaLekha-Isolated dataset [1]. Using Convolutional Neural Network, this model achieves 91.81% accuracy on the alphabets (50 character classes) on the base dataset, and after expanding the number of images to 200,000 using data augmentation, the accuracy achieved on the test set is 95.25%. The model was hosted on a web server for the ease of testing and interaction with the model. Furthermore, a comparison with other machine learning approaches is presented.
Exploiting Deep Features for Remote Sensing Image Retrieval: A Systematic Investigation

Remote sensing (RS) image retrieval is of great significant for geological information mining. Over the past two decades, a large amount of research on this task has been carried out, which mainly focuses on the following three core issues: feature extraction, similarity metric, and relevance feedback. Due to the complexity and multiformity of ground objects in high-resolution remote sensing (HRRS) images, there is still room for improvement in the current retrieval approaches. In this article, we analyze the three core issues of RS image retrieval and provide a comprehensive review on existing methods. Furthermore, for the goal to advance the state-of-the-art in HRRS image retrieval, we focus on the feature extraction issue and delve how to use powerful deep representations to address this task. We conduct systematic investigation on evaluating correlative factors that may affect the performance of deep features. By optimizing each factor, we acquire remarkable retrieval results on publicly available HRRS datasets. Finally, we explain the experimental phenomenon in detail and draw conclusions according to our analysis. Our work can serve as a guiding role for the research of content-based RS image retrieval.
A Tale of Two Worlds

This paper analyzes the vulnerability space arising in Trusted Execution Environments (TEEs) when interfacing a trusted enclave application with untrusted, potentially malicious code. Considerable research and industry effort has gone into developing TEE runtime libraries with the purpose of transparently shielding enclave application code from an adversarial environment. However, our analysis reveals that shielding requirements are generally not well-understood in real-world TEE runtime implementations. We expose several sanitization vulnerabilities at the level of the Application Binary Interface (ABI) and the Application Programming Interface (API) that can lead to exploitable memory safety and side-channel vulnerabilities in the compiled enclave. Mitigation of these vulnerabilities is not as simple as ensuring that pointers are outside enclave memory. In fact, we demonstrate that state-of-the-art mitigation techniques such as Intel's edger8r, Microsoft's "deep copy marshalling", or even memory-safe languages like Rust fail to fully eliminate this attack surface. Our analysis reveals 35 enclave interface sanitization vulnerabilities in 8 major open-source shielding frameworks for Intel SGX, RISC-V, and Sancus TEEs. We practically exploit these vulnerabilities in several attack scenarios to leak secret keys from the enclave or enable remote code reuse. We have responsibly disclosed our findings, leading to 5 designated CVE records and numerous security patches in the vulnerable open-source projects, including the Intel SGX-SDK, Microsoft Open Enclave, Google Asylo, and the Rust compiler.
LIP: Local Importance-Based Pooling

Spatial downsampling layers are favored in convolutional neural networks (CNNs) to downscale feature maps for larger receptive fields and less memory consumption. However, for discriminative tasks, there is a possibility that these layers lose the discriminative details due to improper pooling strategies, which could hinder the learning process and eventually result in suboptimal models. In this paper, we present a unified framework over the existing downsampling layers (e.g., average pooling, max pooling, and strided convolution) from a local importance view. In this framework, we analyze the issues of these widely-used pooling layers and figure out the criteria for designing an effective downsampling layer. According to this analysis, we propose a conceptually simple, general, and effective pooling layer based on local importance modeling, termed as Local Importance-based Pooling (LIP). LIP can automatically enhance discriminative features during the downsampling procedure by learning adaptive importance weights based on inputs. Experiment results show that LIP consistently yields notable gains with different depths and different architectures on ImageNet classification. In the challenging MS COCO dataset, detectors with our LIP-ResNets as backbones obtain a consistent improvement (≥1.4%) over the vanilla ResNets, and especially achieve the current state-of-the-art performance in detecting small objects under the single-scale testing scheme <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Dredas: Decentralized, reliable and efficient remote outsourced data auditing scheme with blockchain smart contract for industrial IoT

The development of cloud computing and the Internet of things (IOT) attracts more and more enterprises to outsource the data from their Industrial Internet of things (IIOT) to cloud servers in order to save operating costs and improve efficiency. However, in this environment, protecting the security and privacy of data storage is an important challenge for IIOT and cloud server provider (CSP). Data auditing could allow data owner discover malicious behaviors of CSP which destroy their outsourced data. The public auditing authorizes the trusted third part auditor (TPA) to audit the owner’s outsourced data and frees owner from regular tasks. However, the public auditing using TPA is considered a centralized auditing, and the TPA is assumed totally honest, but it is difficult to find a reliable auditing organization. In this paper, a novel decentralized auditing smart contract in Ethereum is proposed. By replacing the TPA with a designed smart contract, a decentralized auditing scheme (Dredas) is proposed, where anyone can obtain the auditing result from Ethereum without worrying about semi-honest TPA. Compared with traditional auditing, apart from being able to perform traditional auditing functions, Dredas has three important benefits over previous work. First, the random values of challenge are more secure. Dredas chooses the current blockchain nonce as a random seed to prevent any party forging random values. Secondly, in order to achieve a safe, regular, proactive auditing, the protocol writes the auditing rules into the blockchain, and uses the number blocks on the Ethereum as the security timestamp. Finally, data owner, user and CSP must pay some ether for smart contract as deposit. This way not only inhibits the malicious behavior of these three parties, but also makes it more reasonable in real life. We implement Dredas to show that the computation costs are reasonable and efficient.
A matrix factorization based dynamic granularity recommendation with three-way decisions

Recommender systems (RSs) are effective technologies and tools used to deal with the problems of information overload, and have been developed rapidly in nearly two decades. In this paper, with the consideration of uncertain and multi-level characteristic of recommendation information (RI), we combine three-way decisions with granular computing, and build a novel dynamic three-way recommendation model to address the limitations of static two-way recommendation. First, we propose a dynamic three-way granularity recommendation (DTWGR), which adopts a top-down dynamic recommendation strategy based on the granular structure of RI. Second, we introduce a matrix factorization framework and further utilize three methods: SVD, SVD++ and NMF, to construct the granular structure of RI. In addition, three extended recommendation algorithms, namely, DTWGR-SVD, DTWGRSVD ++ and DTWGR-NMF, are proposed by combining DTWGR with matrix factorization. The recommendation cost caused by misclassification cost and teaching cost, is carefully investigated in our work. Finally, experimental results of three well-known Movielens data sets validate the effectiveness and reliability of our proposed model.
Predicting the Future: A Jointly Learnt Model for Action Anticipation

Inspired by human neurological structures for action anticipation, we present an action anticipation model that enables the prediction of plausible future actions by forecasting both the visual and temporal future. In contrast to current state-of-the-art methods which first learn a model to predict future video features and then perform action anticipation using these features, the proposed framework jointly learns to perform the two tasks, future visual and temporal representation synthesis, and early action anticipation. The joint learning framework ensures that the predicted future embeddings are informative to the action anticipation task. Furthermore, through extensive experimental evaluations we demonstrate the utility of using both visual and temporal semantics of the scene, and illustrate how this representation synthesis could be achieved through a recurrent Generative Adversarial Network (GAN) framework. Our model outperforms the current state-of-the-art methods on multiple datasets: UCF101, UCF101-24, UT-Interaction and TV Human Interaction.
Fog IoT for Health: A new Architecture for Patients and Elderly Monitoring.

The important increase of the elderly population and their desire to conduct an independent life, even when having medical diseases related to their age, requires the development of new technologies to ensure optimal living comfort for this population. In addition, another category of people, those who are patients with life-threatening problems, may benefit from preventive medical monitoring. In this paper, we present a Fog IoT Cloud-Based Health Monitoring System by using physiological and environmental signals allowing to provide contextual information in terms of Daily Living Activities. Our system enables healthcare providers to follow up health state and behavioral changes of elderly or alone people. Moreover, our system provides a monitoring rehabilitation and recovery processes of patients. Our Fog-IoT architecture consists of a wireless sensor network, a local gateway for data stored locally and quickly, and a Lambda cloud architecture for data processing and storage. The originality of our work resides in the graphical monitoring of new and recent patient data at local smart gateway level. This checkup gives the opportunity to the medical staff quick access to the data, and allows them to validate automatically the observed anomalies. Finally, if a telematic break occurs, the gateway continues to accumulate the data while conducting their analysis. Anonymized data are sent periodically from Smart Gateways to the cloud for archiving and for checkup by medical staff who follow up with patients.
Delving Deep Into Hybrid Annotations for 3D Human Recovery in the Wild

Though much progress has been achieved in single-image 3D human recovery, estimating 3D model for in-the-wild images remains a formidable challenge. The reason lies in the fact that obtaining high-quality 3D annotations for in-the-wild images is an extremely hard task that consumes enormous amount of resources and manpower. To tackle this problem, previous methods adopt a hybrid training strategy that exploits multiple heterogeneous types of annotations including 3D and 2D while leaving the efficacy of each annotation not thoroughly investigated. In this work, we aim to perform a comprehensive study on cost and effectiveness trade-off between different annotations. Specifically, we focus on the challenging task of in-the-wild 3D human recovery from single images when paired 3D annotations are not fully available. Through extensive experiments, we obtain several observations: 1) 3D annotations are efficient, whereas traditional 2D annotations such as 2D keypoints and body part segmentation are less competent in guiding 3D human recovery. 2) Dense Correspondence such as DensePose is effective. When there are no paired in-the-wild 3D annotations available, the model exploiting dense correspondence can achieve 92% of the performance compared to a model trained with paired 3D data. We show that incorporating dense correspondence into in-the-wild 3D human recovery is promising and competitive due to its high efficiency and relatively low annotating cost. Our model trained with dense correspondence can serve as a strong reference for future research.