Reviewerly/exHarmony huggingface dataset:
Content-Aware Eye Tracking for Autostereoscopic 3D Display

This study develops an eye tracking method for autostereoscopic three-dimensional (3D) display systems for use in various environments. The eye tracking-based autostereoscopic 3D display provides low crosstalk and high-resolution 3D image experience seamlessly without 3D eyeglasses by overcoming the viewing position restriction. However, accurate and fast eye position detection and tracking are still challenging, owing to the various light conditions, camera control, thick eyeglasses, eyeglass sunlight reflection, and limited system resources. This study presents a robust, automated algorithm and relevant systems for accurate and fast detection and tracking of eye pupil centers in 3D with a single visual camera and near-infrared (NIR) light emitting diodes (LEDs). Our proposed eye tracker consists of eye–nose detection, eye–nose shape keypoint alignment, a tracker checker, and tracking with NIR LED on/off control. Eye–nose detection generates facial subregion boxes, including the eyes and nose, which utilize an Error-Based Learning (EBL) method for the selection of the best learnt database (DB). After detection, the eye–nose shape alignment is processed by the Supervised Descent Method (SDM) with Scale-invariant Feature Transform (SIFT). The aligner is content-aware in the sense that corresponding designated aligners are applied based on image content classification, such as the various light conditions and wearing eyeglasses. The conducted experiments on real image DBs yield promising eye detection and tracking outcomes, even in the presence of challenging conditions.
Analyzing Genetic Testing Discourse on the Web Through the Lens of Twitter, Reddit, and 4chan

Recent progress in genomics has enabled the emergence of a flourishing market for direct-to-consumer (DTC) genetic testing. Companies like 23andMe and AncestryDNA provide affordable health, genealogy, and ancestry reports, and have already tested tens of millions of customers. Consequently, news, experiences, and views on genetic testing are increasingly shared and discussed on social media. At the same time, far-right groups have also taken an interest in genetic testing, using them to attack minorities and prove their genetic “purity.” In this article, we set to study the genetic testing discourse on a number of mainstream and fringe Web communities. We do so in two steps. First, we conduct an exploratory, large-scale analysis of the genetic testing discourse on a mainstream social network such as Twitter. We find that the genetic testing discourse is fueled by accounts that appear to be interested in digital health and technology. However, we also identify tweets with highly racist connotations. This motivates us to explore the connection between genetic testing and racism on platforms with a reputation for toxicity, namely, Reddit and 4chan, where we find that discussions around genetic testing often include highly toxic language expressed through hateful and racist comments. In particular, on 4chan’s politically incorrect board (/pol/), content from genetic testing conversations involves several alt-right personalities and openly anti-semitic rhetoric, often conveyed through memes.
A Secure Middlebox Framework for Enabling Visibility Over Multiple Encryption Protocols

Network middleboxes provide the first line of defense for enterprise networks. Many of them typically inspect packet payload to filter malicious attack patterns. However, the widespread use of end-to-end cryptographic protocols designed to promote security and privacy, either inhibits deep packet inspection in the network or forces enterprises to use solutions that are not secure. This article introduces a complete framework for building secure and practical network middleboxes, called EVE, which enables visibility over encrypted traffic. EVE securely processes encrypted traffic using a combination of hardware-based trusted execution and software security technology. For enhanced programmability and security, EVE provides a high-level programming interface based on the Rust language. The high-level APIs of EVE provide security and significantly ease the development effort by hiding the details of cryptographic operations, enclave processing, TCP reassembly, and out-of-band key sharing. Our evaluation shows EVE supports diverse use cases with multiple encryption protocols in a secure fashion while delivering high performance.
Transductive Zero-Shot Action Recognition via Visually Connected Graph Convolutional Networks

With the explosive growth of action categories, zero-shot action recognition aims to extend a well-trained model to novel/unseen classes. To bridge the large knowledge gap between seen and unseen classes, in this brief, we visually associate unseen actions with seen categories in a visually connected graph, and the knowledge is then transferred from the visual features space to semantic space via the grouped attention graph convolutional networks (GAGCNs). In particular, we extract visual features for all the actions, and a visually connected graph is built to attach seen actions to visually similar unseen categories. Moreover, the proposed grouped attention mechanism exploits the hierarchical knowledge in the graph so that the GAGCN enables propagating the visual-semantic connections from seen actions to unseen ones. We extensively evaluate the proposed method on three data sets: HMDB51, UCF101, and NTU RGB + D. Experimental results show that the GAGCN outperforms state-of-the-art methods.
A Novel Scalable Kernelized Fuzzy Clustering Algorithms Based on In-Memory Computation for Handling Big Data

Traditional scalable clustering algorithms mainly deal with the clustering of linearly separable data, but it is challenging to cluster the non-linear separable data efficiently in the feature space. In this article, we propose a novel Kernelized Scalable Random Sampling with Iterative Optimization Fuzzy c-Means (KSRSIO-FCM) clustering algorithm using Big Data framework. To propose the KSRSIO-FCM, we also propose the Kernelized version of Scalable Literal Fuzzy c-Means (KSLFCM) clustering algorithm, which is an integral part of the proposed KSRSIO-FCM algorithm. These kernelized clustering algorithms are evolved to deal with the non-linear separable problems by applying a kernel Radial Basis Functions (RBF) which maps the input data space non-linearly into a high dimensional feature space. We aim to design and implement the kernelized fuzzy clustering algorithms on Apache Spark, which performs the efficient clustering of Big Data due to its in-memory cluster computing technique. Exhaustive experiments are performed on various big datasets to show the effectiveness of proposed KSRSIO-FCM in comparison with other scalable clustering algorithms, i.e., KSLFCM, SRSIO-FCM, and SLFCM. The reported experimental results show that the KSRSIO-FCM algorithm in comparison with KSLFCM, SRSIO-FCM, and SLFCM achieves significant improvement in terms of time and space complexity, Normalized Mutual Information (NMI), Adjusted Rand Index (ARI), and F-score, respectively. Furthermore, we have carried out a performance analysis of KSRSIO-FCM versus KSLFCM. Thus, the reported results show that the KSRSIO-FCM implemented on Apache Spark has better potential for Big Data clustering as compared to traditional scalable fuzzy clustering methods.
Parasite Detection in Thick Blood Smears Based on Customized Faster-RCNN on Smartphones

Malaria is a worldwide life-threatening disease. The gold standard for malaria diagnosis is microscopy examination, which includes thick blood smears to detect the presence of parasites and thin blood smears to differentiate the development stages of parasites. Microscopy examination is of low cost but is time consuming and error-prone. Therefore, the development of an automated parasite detection system for malaria diagnosis in thick blood smears is an important research goal, especially in resource-limited areas. In this paper, based on a customized Faster-RCNN model, we develop a machine-learning system that can automatically detect parasites in thick blood smear images on smartphones. To make Faster-RCNN more efficient for small object detection, we split an input image of 4032×3024×3 pixels into small blocks of 252×189×3 pixels, and then train the FasterRCNN model with the small blocks and corresponding parasite annotations. Moreover, we customize the convolutional layers of Faster-RCNN with four convolutional layers and two maxpooling layers to extract features according to the input image size and characteristics. We perform experiments on 2967 thick blood smear images from 200 patients, including 1819 images from 150 patients who are infected with parasites. The customized FasterRCNN model is first trained on small image blocks from 120 patients, including 90 infected patients and 30 normal patients, and then tested on the remaining 80 patients. For testing, we also split each input image into small blocks of 252×189×3 pixels that are screened by our trained Faster-RCNN model to detect parasite coordinates, which are then re-projected into the original image space. Detection rates of our system on image level and patient level are 96.84% and 96.81%, respectively.
Major Factors Influencing the Adoption of Cloud Computing in Jordan

Cloud computing is emerging as a major advancement in organizational infrastructure, which aims to bring with it great benefits to technology users. Jordan is one of the developing countries that seeks to adopt such technology. Based on that, this study focused on how Jordanians perceive and respond to cloud computing, and the main factors that govern its adoption and use. Using a quantitative approach, responses from cloud computing users were gathered to investigate their perceptions on the factors affecting their adoption of such technology. A sample of 134 online users was utilized and an instrument of 21 items was used in this study. Results indicated that service quality, cost reduction, IT background, and perceived control are major predictors of online users' intention to adopt cloud computing. Perceived security was not significant in this relation. The researchers conclude this work with their discussion, implications, limitations and future work.
Splitting the Web Analytics Atom

Web analytics Key Performance Indicators (KPIs) are important metrics used to evaluate websites and web pages against objectives. The power of KPIs is in their simplicity. Every web page can be assessed by numeric KPI values, which can be easily calculated, compared, and tracked over time. KPIs highlight the strengths and weaknesses of individual web pages and significantly help in maintaining, improving, and optimizing websites. Current web analytics metrics and KPIs, in academic studies as well as in commercial tools, relate to entire websites and web pages. This paper advocates extending KPIs use to sub-page elements, such as paragraphs, as an effective way to refine knowledge and leverage web analytics capabilities. We discuss the potential and challenges of sub-page web analytics and define a framework for calculating sub-page metrics from accumulated in-page user activity data, such as mouse and keyboard events. Then we propose potential KPIs that may be effective in highlighting the strengths and weaknesses of individual page parts, such as paragraphs. We use web usage data from a sample website to demonstrate these ideas. This study is the first step towards sub-page web analytics metrics and KPIs. Further work is required in order to gain more knowledge about potential KPIs that are introduced in this work, as well as to explore new methods, metrics, and KPIs.
HONEM: Learning Embedding for Higher Order Networks

Representation learning on networks offers a powerful alternative to the oft painstaking process of manual feature engineering, and, as a result, has enjoyed considerable success in recent years. However, all the existing representation learning methods are based on the first-order network, that is, the network that only captures the pairwise interactions between the nodes. As a result, these methods may fail to incorporate non-Markovian higher order dependencies in the network. Thus, the embeddings that are generated may not accurately represent the underlying phenomena in a network, resulting in inferior performance in different inductive or transductive learning tasks. To address this challenge, this study presents higher order network embedding (HONEM), a higher order network (HON) embedding method that captures the non-Markovian higher order dependencies in a network. HONEM is specifically designed for the HON structure and outperforms other state-of-the-art methods in node classification, network reconstruction, link prediction, and visualization for networks that contain non-Markovian higher order dependencies.
Automated Student Engagement Monitoring and Evaluation during Learning in the Wild

With the explosive growth of edge computing and massive open online courses (MOOCs), there is an urgent need to enable pervasive learning so that students could study with high efficiency at any comfortable places at their own pace. Although there have been a number of studies and applications for student engagement monitoring and evaluation in the pervasive learning, most of the existing works are either supported by commercial eye tracking devices/software or designed for off-line studies on the basis of questionnaires, self-reports, checklists, quizzes, teacher introspective evaluations, and assignments. In this work, we investigate the feasibility of real-time student engagement monitoring and evaluation with low-cost off-the-shelf web-cameras in realistic learning scenarios. To recognizing and evaluating student engagement, a new model is developed and trained by a deep learning Convolutional Neural Network (CNN) with an open source dataset. The quantitative experimental results demonstrate that the deep learning CNN and our model work well and efficiently when monitoring student learning and detecting student engagement in real time.
TAVeer

A central problem that has been researched for many years in the field of digital text forensics is the question whether two documents were written by the same author. Authorship verification (AV) is a research branch in this field that deals with this question. Over the years, research activities in the context of AV have steadily increased, which has led to a variety of approaches trying to solve this problem. Many of these approaches, however, make use of features that are related to or influenced by the topic of the documents. Therefore, it may accidentally happen that their verification results are based not on the writing style (the actual focus of AV), but on the topic of the documents. To address this problem, we propose an alternative AV approach that considers only topic-agnostic features in its classification decision. In addition, we present a post-hoc interpretation method that allows to understand which particular features have contributed to the prediction of the proposed AV method. To evaluate the performance of our AV method, we compared it with eight competing baselines (including the current state of the art) on four challenging data sets. The results show that our approach outperforms all baselines in two cases (with a maximum accuracy of 84%), while in the other two cases it performs as well as the strongest baseline.
Z-Miner: An Efficient Method for Mining Frequent Arrangements of Event Intervals

Mining frequent patterns of event intervals from a large collection of interval sequences is a problem that appears in several application domains. In this paper, we propose Z-Miner, a novel algorithm for solving this problem that addresses the deficiencies of existing competitors by employing two novel data structures: Z-Table, a hierarchical hash-based data structure for time-efficient candidate generation and support count, and Z-Arrangement, a data structure for efficient memory consumption. The proposed algorithm is able to handle patterns with repetitions of the same event label, allowing for gap and error tolerance constraints, as well as keeping track of the exact occurrences of the extracted frequent patterns. Our experimental evaluation on eight real-world and six synthetic datasets demonstrates the superiority of Z-Miner against four state-of-the-art competitors in terms of runtime efficiency and memory footprint.
Comparative Media Regulation in the United States and Europe

In current debates over the Internet's impact on global democracy, the prospect of state regulation of social media has been proffered as a solution to problems like fake news, hate speech, conspiracy-mongering, and similar ills. For example, Mark Warner, a senator in the US Congress, has proposed a bill that would enhance privacy protections required of internet platforms, create rules for labeling bot accounts, and change the legal terms of the platforms' legal relationship with their users. In Europe, regulation has already been enacted in the form of the European Union's General Data Protection Regulation and new laws like the Network Enforcement Law (NetzDG). This chapter will survey this rapidly developing field, putting current efforts of liberal democracies to regulate internet content in the broader perspective of legacy media regulation. As we will see, there are very different national approaches to this issue among contemporary liberal democracies, and in many respects the new internet regulations, actual and proposed, are extensions of existing practices.
Knowledge Transfer for Out-of-Knowledge-Base Entities: Improving Graph-Neural-Network-Based Embedding Using Convolutional Layers

Knowledge base completion (KBC) aims to predict missing information in a knowledge base. Most existing embedding-based KBC models assume that all test entities are available at training time. Thus, a question arises-that is, how to answer queries concerning test entities not observed at training time, which is called the out-of-knowledge-base (OOKB) entity problem. In this article, we propose a parameter-efficient embedding model that combines the benefits of a graph neural network (GNN) and a convolutional neural network (CNN) to solve the KBC task with OOKB entities. First, we apply the GNN architecture to learn the information between nodes in the graph. Second, convolution layers are used as a transition matrix in GNN to learn more expressive embeddings with fewer parameters. Finally, we use a transition-based knowledge graph embedding model to solve the KBC task. The model has learnable weights that adapt based on information from neighbors and can exploit auxiliary knowledge for OOKB entities to compute their embedding while remaining parameter efficient. We demonstrate the effectiveness of the proposed model on OOKB datasets, and the code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Tianchen627/Knowledge-Transfer-for-Out-of-Knowledge-Base-Entities</uri> .
Uncertainty-Gated Stochastic Sequential Model for EHR Mortality Prediction

Electronic health records (EHRs) are characterized as nonstationary, heterogeneous, noisy, and sparse data; therefore, it is challenging to learn the regularities or patterns inherent within them. In particular, sparseness caused mostly by many missing values has attracted the attention of researchers who have attempted to find a better use of all available samples for determining the solution of a primary target task through defining a secondary imputation problem. Methodologically, existing methods, either deterministic or stochastic, have applied different assumptions to impute missing values. However, once the missing values are imputed, most existing methods do not consider the fidelity or confidence of the imputed values in the modeling of downstream tasks. Undoubtedly, an erroneous or improper imputation of missing variables can cause difficulties in the modeling as well as a degraded performance. In this study, we present a novel variational recurrent network that: 1) estimates the distribution of missing variables (e.g., the mean and variance) allowing to represent uncertainty in the imputed values; 2) updates hidden states by explicitly applying fidelity based on a variance of the imputed values during a recurrence (i.e., uncertainty propagation over time); and 3) predicts the possibility of in-hospital mortality. It is noteworthy that our model can conduct these procedures in a single stream and learn all network parameters jointly in an end-to-end manner. We validated the effectiveness of our method using the public data sets of MIMIC-III and PhysioNet challenge 2012 by comparing with and outperforming other state-of-the-art methods for mortality prediction considered in our experiments. In addition, we identified the behavior of the model that well represented the uncertainties for the imputed estimates, which showed a high correlation between the uncertainties and mean absolute error (MAE) scores for imputation.
Distributed MHT and ML-PMHT Approaches to Multi-Sensor Passive Sonar Tracking

This paper introduces a distributed multiple-hypothesis tracking (MHT) approach to passive sonar tracking with multiple sensors. Specific advances include the ability to contend with multiple emitted frequencies per target and the design of a statistically-consistent and stable sequential extended Kalman filter. We consider as well a maximum likelihood probabilistic multi-hypothesis tracker (ML-PMHT) and provide a qualitative comparison of the two methods with simulated sonar data. Directions for future work including a hybrid scheme that exploits the strengths of both tracking paradigms are provided.
The Role of Sense of Voice Presence and Anxiety Reduction in AVATAR Therapy

AVATAR therapy offers a unique therapeutic context that uses virtual reality technology to create a virtual embodiment of the voice-hearing experience, enabling the person to visualize their persecutory voice and engage in real-time "face-to-face" dialogue. The present study explores, for the first time, the contribution of sense of voice presence, together with session-by-session reduction of anxiety and paranoid attributions about the avatar, to changes in primary outcomes following AVATAR therapy. Data from 39 participants, who completed AVATAR therapy and attended a 12-week follow-up assessment, were analysed. Mid- to high-levels of sense of voice presence were reported across the therapy sessions, along with significant reductions of anxiety levels and paranoid attributions about the avatar. The interaction of sense of voice presence and reduction of anxiety was associated with two of the significant therapy outcomes: PSYRATS total and frequency of voices. The findings suggest that improvements in voice severity and frequency at post AVATAR therapy may be influenced by the combination of feeling less anxious in the context of a realistic simulation of the voice, while voice-related distress may involve additional cognitive and relational processes.