Reviewerly/exHarmony huggingface dataset:
Data Collaboration Analysis Framework Using Centralization of Individual Intermediate Representations for Distributed Data Sets

This paper proposes a data collaboration analysis framework for distributed data sets. The proposed framework involves centralized machine learning while the original data sets and models remain distributed over a number of institutions. Recently, data has become larger and more distributed with decreasing costs of data collection. Centralizing distributed data sets and analyzing them as one data set can allow for novel insights and attainment of higher prediction performance than that of analyzing distributed data sets individually. However, it is generally difficult to centralize the original data sets because of a large data size or privacy concerns. This paper proposes a data collaboration analysis framework that does not involve sharing the original data sets to circumvent these difficulties. The proposed framework only centralizes intermediate representations constructed individually rather than the original data set. The proposed framework does not use privacy-preserving computations or model centralization. In addition, this paper proposes a practical algorithm within the framework. Numerical experiments reveal that the proposed method achieves higher recognition performance for artificial and real-world problems than individual analysis.
New Applications of Self-Control Theory to Computer-Focused Cyber Deviance and Victimization: A Comparison of Cognitive and Behavioral Measures of Self-Control and Test of Peer Cyber Deviance and Gender as Moderators

This study tests the effects of behavioral and cognitive measures of self-control on computer-focused cyber deviance and cyber victimization with survey data from 1,036 adult employees. We examine moderating effects of cyber deviant peers and gender in the relationship between self-control, and cyber deviance and victimization. Cognitive and behavioral measures of self-control are negativity associated with cyber deviance, whereas only behavioral self-control predicted reduced cyber victimization. Moderation analyses show that cyber deviant peer associations condition the relationship between self-control, and both cyber deviance and victimization. Gender moderation models reveal no consistent significant effects. The results have implications for the understanding of cognitive predictors of computer-focused cybercrime and victimization, as well as institutional cybercrime prevention policies. Our findings can inform the future integration of self-control and social learning theories in cyberspace.
Temporal Parallelization of Bayesian Smoothers

This article presents algorithms for temporal parallelization of Bayesian smoothers. We define the elements and the operators to pose these problems as the solutions to all-prefix-sums operations for which efficient parallel scan-algorithms are available. We present the temporal parallelization of the general Bayesian filtering and smoothing equations, and specialize them to linear/Gaussian models. The advantage of the proposed algorithms is that they reduce the linear complexity of standard smoothing algorithms with respect to time to logarithmic.
A Comparison of Machine Learning Algorithms for Fall Detection using Wearable Sensors

The proportion of people 60 years old and above is expected to double globally to reach 22 % by 2050. This creates societal challenges such as the increase of age-related illnesses and the need for caregivers. Falls are a major threat for the elderly, often causing serious injuries especially when the fallen person stays on the ground for a long time without assistance. This paper presents the development of a Fall Detection System (FDS) using an accelerometer combined with a gyroscope worn at the waist. Data come from SisFall, a publicly available dataset containing records of Activities of Daily Living and falls. We compared five Machine Learning algorithms. We first applied preprocessing and a feature extraction stage before using five Machine Learning algorithms, allowing us to compare them. Ensemble learning algorithms such as Random Forest and Gradient Boosting have the best performance, with a Sensitivity and Specificity both close to 99%.
A Multi Purpose and Large Scale Speech Corpus in Persian and English for Speaker and Speech Recognition: The Deepmine Database

DeepMine is a speech database in Persian and English designed to build and evaluate text-dependent, text-prompted, and text-independent speaker verification, as well as Persian speech recognition systems. It contains more than 1850 speakers and 540 thousand recordings overall, more than 480 hours of speech are transcribed. It is the first public large-scale speaker verification database in Persian, the largest public text-dependent and text-prompted speaker verification database in English, and the largest public evaluation dataset for text-independent speaker verification. It has a good coverage of age, gender, and accents. We provide several evaluation protocols for each part of the database to allow for research on different aspects of speaker verification. We also provide the results of several experiments that can be considered as baselines: HMM-based i-vectors for text-dependent speaker verification, and HMM-based as well as state-of-the-art deep neural network based ASR. We demonstrate that the database can serve for training robust ASR models.
Developing a measure of information seeking about phishing

Abstract Phishing e-mails are fraudulent e-mails used to gain access to sensitive information or secure computer systems. They persuade users to click on malicious links, download attachments or provide sensitive information, such as usernames or passwords. One approach that aims to reduce people’s susceptibility to phishing is the provision of information to users regarding the phishing threat and the techniques used within phishing e-mails. In line with this, awareness campaigns are often used within organizations and wider society to raise awareness of phishing and encourage people to engage with protective information. However, the potential effectiveness of such approaches in reducing susceptibility remains uncertain. In particular, there is a lack of research investigating (i) whether the propensity to access such information may in itself influence susceptibility to phishing and (ii) the different factors that motivate people to engage with information in the first place. In order to understand how current and future interventions regarding phishing may be consumed by users, as well as their potential impact on phishing susceptibility, it is important to conduct theoretically based research that provides a foundation to investigate these issues. This study provides a first step in addressing this by developing and validating a theoretically based survey measure across two studies centred upon the constructs of protection motivation theory (perceived vulnerability, severity, self-efficacy and response efficacy) to assess the factors that influence whether people choose to keep up to date with protective information about phishing. This survey measure is then used within Study 2 to provide an initial investigation of the role of these constructs in (i) self-reported user intentions to keep up to date with phishing techniques in the future and (ii) phishing discrimination ability, assessed using a phishing quiz. Overall, higher perceived threat severity, self-efficacy and response efficacy were associated with greater intentions, while greater perceived vulnerability was associated with lower intentions. No relationship was found with phishing discrimination ability. By understanding the factors that influence user intention to maintain knowledge and seek information about phishing threats, it will be possible to ensure that, as effective interventions are developed, their potential impact can be maximized.
Residual-Sparse Fuzzy <i>C</i>-Means Clustering Incorporating Morphological Reconstruction and Wavelet Frame

Instead of directly utilizing an observed image including some outliers, noise or intensity inhomogeneity, the use of its ideal value (e.g. noise-free image) has a favorable impact on clustering. Hence, the accurate estimation of the residual (e.g. unknown noise) between the observed image and its ideal value is an important task. To do so, we propose an $\ell_0$ regularization-based Fuzzy $C$-Means (FCM) algorithm incorporating a morphological reconstruction operation and a tight wavelet frame transform. To achieve a sound trade-off between detail preservation and noise suppression, morphological reconstruction is used to filter an observed image. By combining the observed and filtered images, a weighted sum image is generated. Since a tight wavelet frame system has sparse representations of an image, it is employed to decompose the weighted sum image, thus forming its corresponding feature set. Taking it as data for clustering, we present an improved FCM algorithm by imposing an $\ell_0$ regularization term on the residual between the feature set and its ideal value, which implies that the favorable estimation of the residual is obtained and the ideal value participates in clustering. Spatial information is also introduced into clustering since it is naturally encountered in image segmentation. Furthermore, it makes the estimation of the residual more reliable. To further enhance the segmentation effects of the improved FCM algorithm, we also employ the morphological reconstruction to smoothen the labels generated by clustering. Finally, based on the prototypes and smoothed labels, the segmented image is reconstructed by using a tight wavelet frame reconstruction operation. Experimental results reported for synthetic, medical, and color images show that the proposed algorithm is effective and efficient, and outperforms other algorithms.
High Sensitivity Fringe Projection Profilometry Combining Optimal Fringe Frequency and Optimal Fringe Direction

Phase sensitivity plays a vital role in accurately retrieving three-dimensional (3D) shape of diffused objects in fringe projection profilometry (FPP). With regard to traditional means that simply project horizontal or vertical fringe patterns, the fringe frequency and direction may not be the best choice to acquire optimal sensitivity to depth variations. To overcome this problem, this paper proposes a novel method to obtain 3D shape with high accuracy by combining the optimal fringe frequency and the optimal fringe direction (OFF + OFD). Firstly, in order to reduce phase error caused by periodic inconsistency of the traditional fringe projection methods, period of the projected fringe is corrected by using the high-accuracy planar homography. Secondly, the optimal fringe direction is calculated by projecting sets of corrected horizontal and vertical fringe patterns onto a depth-known standard block with the optimal fringe frequency. Finally, the validation is conducted by projecting the optimized fringe patterns onto the measured objects to reconstruct 3D shape. The simulations and actual experiments demonstrate that the proposed optimization method is feasible and effective in increasing phase sensitivity and measurement accuracy.
Robust Multivehicle Tracking With Wasserstein Association Metric in Surveillance Videos

Vehicle tracking based on surveillance videos is of great significance in the highway traffic monitoring field. In real-world vehicle-tracking applications, partial occlusion and objects with similarly appearing distractors pose significant challenges. For addressing the above issues, we propose a robust multivehicle tracking with Wasserstein association metric (MTWAM) method. In MTWAM, we analyze the advantage of the 1-Wasserstein distance (WD-1) on partial occlusion and employ the WD-1 as the similarity criterion to measure the similarity between tracklets and detections. Moreover, for distinguishing different objects with a similar appearance, we improve the feature presentation of vehicles by developing target-specific feature sparse coding (TSSC). To demonstrate the validity of this method, we present a quantitative evaluation of both the UA-DETRAC dataset and our vehicle highway surveillance videos dataset (VecHSV). In both cases, our method achieves state-of-the-art performances.
Spatio-Temporal Pyramid Graph Convolutions for Human Action Recognition and Postural Assessment

Recognition of human actions and associated interactions with objects and the environment is an important problem in computer vision due to its potential applications in a variety of domains. Recently, graph convolutional networks that extract features from the skeleton have demonstrated promising performance. In this paper, we propose a novel Spatio-Temporal Pyramid Graph Convolutional Network (ST-PGN) for online action recognition for ergonomics risk assessment that enables the use of features from all levels of the skeleton feature hierarchy. The proposed algorithm outperforms state-of-art action recognition algorithms tested on two public benchmark datasets typically used for postural assessment (TUM and UW-IOM). We also introduce a pipeline to enhance postural assessment methods with online action recognition techniques. Finally, the proposed algorithm is integrated with a traditional ergonomics risk index (REBA) to demonstrate the potential value for assessment of musculoskeletal disorders in occupational safety.
On Hallucinating Context and Background Pixels from a Face Mask using Multi-scale GANs

We propose a multi-scale GAN model to hallucinate realistic context (forehead, hair, neck, clothes) and background pixels automatically from a single input face mask, without any user supervision. Instead of swapping a face on to an existing picture, our model directly generates realistic context and background pixels based on the features of the provided face mask. Unlike facial inpainting algorithms, it can generate realistic hallucinations even for a large number of missing pixels. Our model is composed of a cascaded network of GAN blocks, each tasked with hallucination of missing pixels at a particular resolution while guiding the synthesis process of the next GAN block. The hallucinated full face image is made photo-realistic by using a combination of reconstruction, perceptual, adversarial and identity preserving losses at each block of the network. With a set of extensive experiments, we demonstrate the effectiveness of our model in hallucinating context and background pixels from face masks varying in facial pose, expression and lighting, collected from multiple datasets subject disjoint with our training data. We also compare our method with popular face inpainting and face swapping models in terms of visual quality, realism and identity preservation. Additionally, we analyze our cascaded pipeline and compare it with the progressive growing of GANs, and explore its usage as a data augmentation module for training CNNs.
Supervised and Unsupervised Learning of Parameterized Color Enhancement

We treat the problem of color enhancement as an image translation task, which we tackle using both supervised and unsupervised learning. Unlike traditional image to image generators, our translation is performed using a global parameterized color transformation instead of learning to directly map image information. In the supervised case, every training image is paired with a desired target image and a convolutional neural network (CNN) learns from the expert retouched images the parameters of the transformation. In the unpaired case, we employ two-way generative adversarial networks (GANs) to learn these parameters and apply a circularity constraint. We achieve state-of-the-art results compared to both supervised (paired data) and unsupervised (unpaired data) image enhancement methods on the MIT-Adobe FiveK benchmark. Moreover, we show the generalization capability of our method, by applying it on photos from the early 20th century and to dark video frames.
A novel multi-label classification algorithm based on <i>K</i>-nearest neighbor and random walk

The multi-label classification problem occurs in many real-world tasks where an object is naturally associated with multiple labels, that is, concepts. The integration of the random walk approach in the multi-label classification methods attracts many researchers’ sight. One challenge of using the random walk-based multi-label classification algorithms is to construct a random walk graph for the multi-label classification algorithms, which may lead to poor classification quality and high algorithm complexity. In this article, we propose a novel multi-label classification algorithm based on the random walk graph and the K-nearest neighbor algorithm (named MLRWKNN). This method constructs the vertices set of a random walk graph for the K-nearest neighbor training samples of certain test data and the edge set of correlations among labels of the training samples, thus considerably reducing the overhead of time and space. The proposed method improves the similarity measurement by differentiating and integrating the discrete and continuous features, which reflect the relationships between instances more accurately. A label predicted method is devised to reduce the subjectivity of the traditional threshold method. The experimental results with four metrics demonstrate that the proposed method outperforms the seven state-of-the-art multi-label classification algorithms in contrast and makes a significant improvement for multi-label classification.
Big Data and its Effect on the Music Industry

This research discusses the effect of big data and Internet technologies on the music industry. Specifically, this paper addresses two research questions; (1) how do modern businesses in the music industry implement the use of Internet technologies and big data to ensure their success in the market, and (2) what are the advantages and drawbacks of implementing digital business models in the music industry? To answer the research questions, two real-life cases (i.e. Shazam and Spotify) were analyzed to show how modern businesses in the music industry implement big data and Internet technologies to ensure their success in the market. Furthermore, previous literature and secondary resources were used to explain the development of traditional business models into digital business models in the music industry. In addition to discussing the benefits and drawbacks of implementing the modern digital business model.
Graph-Based Non-Convex Low-Rank Regularization for Image Compression Artifact Reduction

Block transform coded images usually suffer from annoying artifacts at low bit-rates, because of the independent quantization of DCT coefficients. Image prior models play an important role in compressed image reconstruction. Natural image patches in a small neighborhood of the high-dimensional image space usually exhibit an underlying sub-manifold structure. To model the distribution of signal, we extract sub-manifold structure as prior knowledge. We utilize graph Laplacian regularization to characterize the sub-manifold structure at patch level. And similar patches are exploited as samples to estimate distribution of a particular patch. Instead of using Euclidean distance as similarity metric, we propose to use graph-domain distance to measure the patch similarity. Then we perform low-rank regularization on the similar-patch group, and incorporate a non-convex lp penalty to surrogate matrix rank. Finally, an alternatively minimizing strategy is employed to solve the non-convex problem. Experimental results show that our proposed method is capable of achieving more accurate reconstruction than the state-of-the-art methods in both objective and perceptual qualities.
Discovering web services in social web service repositories using deep variational autoencoders

Web Service registries have progressively evolved to social networks-like software repositories. Users cooperate to produce an ever-growing, rich source of Web APIs upon which new value-added Web applications can be built. Such users often interact in order to follow, comment on, consume and compose services published by other users. In this context, Web Service discovery is a core functionality of modern registries as needed Web Services must be discovered before being consumed or composed. Many efforts to provide effective keyword-based service discovery mechanisms are based on Information Retrieval techniques as services are described using structured or unstructured textdocuments that specify the provided functionality. However, traditional techniques suffer from term-mismatch, which means that only the terms that are contained in both user queries and descriptions are exploited to perform service retrieval. Early feature learning techniques such as LSA or LDA tried to solve this problem by finding hidden or latent features in text documents. Recently, alternative feature learning based techniques such as Word Embeddings achieved state of the art results for Web Service discovery. In this paper, we propose to learn features from service descriptions by using Variational Autoencoders, a special kind of autoencoder which restricts the encoded representation to model latent variables. Autoencoders in turn are deep neural networks used for unsupervised learning of efficient codings. We train our autoencoder using a real 17 113-service dataset extracted from the ProgrammableWeb.com API social repository. We measure discovery efficacy by using both Recall and Precision metrics, achieving significant gains compared to both Word Embeddings and classic latent features modelling techniques. Also, performance-oriented experiments show that the proposed approach can be readily exploited in practice.
Autonomous Model Update Scheme for Deep Learning Based Network Traffic Classifiers

Network traffic classification is essential in access network for end-to-end network management and measurement such as network intrusion detection, network resource allocation. State-of- the-art Deep Learning based classifiers have high accuracy even when processing encrypted data packets. Such classifiers would need to be updated when a new application is in the network traffic. However, it is challenging to build and label a dataset of the unknown application from active network traffic. In this paper, we propose an autonomous model update scheme to (i) filter the data packets of a new application from active network traffic and build a corresponding training dataset; and (ii) update the current network traffic classifier with transfer learning. In particular, the core of the proposed scheme is a discriminator that consists of a statistical filter and a convolutional neural network based binary classifier to filter and build a dataset of new application packets from active network traffic. Evaluation is conducted based on an open dataset (i.e., ISCX VPN-nonVPN dataset). The results demonstrated that our proposed autonomous classifier update scheme can successfully filter packets of a new application from network traffic and build a corresponding training dataset. Moreover, the packet classifier can be effectively updated through transfer learning. The proposed update scheme can contribute significantly in the access network for further end-to-end network measurement and management.