Reviewerly/exHarmony huggingface dataset:
Predicting individuals’ vulnerability to social engineering in social networks

Abstract The popularity of social networking sites has attracted billions of users to engage and share their information on these networks. The vast amount of circulating data and information expose these networks to several security risks. Social engineering is one of the most common types of threat that may face social network users. Training and increasing users’ awareness of such threats is essential for maintaining continuous and safe use of social networking services. Identifying the most vulnerable users in order to target them for these training programs is desirable for increasing the effectiveness of such programs. Few studies have investigated the effect of individuals’ characteristics on predicting their vulnerability to social engineering in the context of social networks. To address this gap, the present study developed a novel model to predict user vulnerability based on several perspectives of user characteristics. The proposed model includes interactions between different social network-oriented factors such as level of involvement in the network, motivation to use the network, and competence in dealing with threats on the network. The results of this research indicate that most of the considered user characteristics are factors that influence user vulnerability either directly or indirectly. Furthermore, the present study provides evidence that individuals’ characteristics can identify vulnerable users so that these risks can be considered when designing training and awareness programs.
Generalized Canonical Correlation Analysis: A Subspace Intersection Approach

Generalized Canonical Correlation Analysis (GCCA) is an important tool that finds numerous applications in data mining, machine learning, and artificial intelligence. It aims at finding `common' random variables that are strongly correlated across multiple feature representations (views) of the same set of entities. CCA and to a lesser extent GCCA have been studied from the statistical and algorithmic points of view, but not as much from the standpoint of linear algebra. This paper offers a fresh algebraic perspective of GCCA based on a (bi-)linear generative model that naturally captures its essence. It is shown that from a linear algebra point of view, GCCA is tantamount to subspace intersection; and conditions under which the common subspace of the different views is identifiable are provided. A novel GCCA algorithm is proposed based on subspace intersection, which scales up to handle large GCCA tasks. Synthetic as well as real data experiments are provided to showcase the effectiveness of the proposed approach.
Reducing sentiment polarity for demographic attributes in word embeddings using adversarial learning

The use of word embedding models in sentiment analysis has gained a lot of traction in the Natural Language Processing (NLP) community. However, many inherently neutral word vectors describing demographic identity have unintended implicit correlations with negative or positive sentiment, resulting in unfair downstream machine learning algorithms. We leverage adversarial learning to decorrelate demographic identity term word vectors with positive or negative sentiment, and re-embed them into the word embeddings. We show that our method effectively minimizes unfair positive/negative sentiment polarity while retaining the semantic accuracy of the word embeddings. Furthermore, we show that our method effectively reduces unfairness in downstream sentiment regression and can be extended to reduce unfairness in toxicity classification tasks.
G <scp>raph</scp> O <scp>ne</scp>

There is a growing need to perform a diverse set of real-time analytics (batch and stream analytics) on evolving graphs to deliver the values of big data to users. The key requirement from such applications is to have a data store to support their diverse data access efficiently, while concurrently ingesting fine-grained updates at a high velocity. Unfortunately, current graph systems, either graph databases or analytics engines, are not designed to achieve high performance for both operations; rather, they excel in one area that keeps a private data store in a specialized way to favor their operations only. To address this challenge, we have designed and developed G raph O ne , a graph data store that abstracts the graph data store away from the specialized systems to solve the fundamental research problems associated with the data store design. It combines two complementary graph storage formats (edge list and adjacency list) and uses dual versioning to decouple graph computations from updates. Importantly, it presents a new data abstraction, GraphView , to enable data access at two different granularities of data ingestions (called data visibility ) for concurrent execution of diverse classes of real-time graph analytics with only a small data duplication. Experimental results show that G raph O ne is able to deliver 11.40× and 5.36× average speedup in ingestion rate against LLAMA and Stinger, the two state-of-the-art dynamic graph systems, respectively. Further, they achieve an average speedup of 8.75× and 4.14× against LLAMA and 12.80× and 3.18× against Stinger for BFS and PageRank analytics (batch version), respectively. G raph O ne also gains over 2,000× speedup against Kickstarter, a state-of-the-art stream analytics engine in ingesting the streaming edges and performing streaming BFS when treating first half as a base snapshot and rest as streaming edge in a synthetic graph. G raph O ne also achieves an ingestion rate of two to three orders of magnitude higher than graph databases. Finally, we demonstrate that it is possible to run concurrent stream analytics from the same data store.
Journal data policies: Exploring how the understanding of editors and authors corresponds to the policies themselves

Despite the increase in the number of journals issuing data policies requiring authors to make data underlying reporting findings publicly available, authors do not always do so, and when they do, the data do not always meet standards of quality that allow others to verify or extend published results. This phenomenon suggests the need to consider the effectiveness of journal data policies to present and articulate transparency requirements, and how well they facilitate (or hinder) authors' ability to produce and provide access to data, code, and associated materials that meet quality standards for computational reproducibility. This article describes the results of a research study that examined the ability of journal-based data policies to: 1) effectively communicate transparency requirements to authors, and 2) enable authors to successfully meet policy requirements. To do this, we conducted a mixed-methods study that examined individual data policies alongside editors' and authors' interpretation of policy requirements to answer the following research questions. Survey responses from authors and editors along with results from a content analysis of data policies found discrepancies among editors' assertion of data policy requirements, authors' understanding of policy requirements, and the requirements stated in the policy language as written. We offer explanations for these discrepancies and offer recommendations for improving authors' understanding of policies and increasing the likelihood of policy compliance.
Performance and Usability of Smartglasses for Augmented Reality in Precision Livestock Farming Operations

In recent years, smartglasses for augmented reality are becoming increasingly popular in professional contexts. However, no commercial solutions are available for the agricultural field, despite the potential of this technology to help farmers. Many head-wearable devices in development possess a variety of features that may affect the smartglasses wearing experience. Over the last decades, dairy farms have adopted new technologies to improve their productivity and profit. However, there remains a gap in the literature as regards the application of augmented reality in livestock farms. Head-wearable devices may offer invaluable benefits to farmers, allowing real-time information monitoring of each animal during on-farm activities. The aim of this study was to expand the knowledge base on how augmented reality devices (smartglasses) interact with farming environments, focusing primarily on human perception and usability. Research has been conducted examining the GlassUp F4 smartglasses during animal selection process. Sixteen participants performed the identification and grouping trials in the milking parlor, reading different types of contents on the augmented reality device optical display. Two questionnaires were used to evaluate the perceived workload and usability of the device. Results showed that the information type could influence the perceived workload and the animal identification process. Smart glasses for augmented reality were a useful tool in the animal genetic improvement program offering promising opportunities for adoption in livestock operations in terms of assessing data consultation and information about animals.
A Real-Time Action Representation With Temporal Encoding and Deep Compression

Deep neural networks have achieved remarkable success for video-based action recognition. However, most of existing approaches cannot be deployed in practice due to the high computational cost. To address this challenge, we propose a new real-time convolutional architecture, called Temporal Convolutional 3D Network (T-C3D), for action representation. T-C3D learns video action representations in a hierarchical multi-granularity manner while obtaining a high process speed. Specifically, we propose a residual 3D Convolutional Neural Network (CNN) to capture complementary information on the appearance of a single frame and the motion between consecutive frames. Based on this CNN, we develop a new temporal encoding method to explore the temporal dynamics of the whole video. Furthermore, we integrate deep compression techniques with T-C3D to further accelerate the deployment of models via reducing the size of the model. By these means, heavy calculations can be avoided when doing the inference, which enables the method to deal with videos beyond real-time speed while keeping promising performance. We validate our approach by studying its action representation performance on four benchmarks over three different tasks. Our method achieves clear improvements on UCF101 action recognition benchmark against the state-of-the-art real-time methods by 5.4% in terms of accuracy and 2 times faster in terms of inference speed with a less than 5MB storage model. The source code and the pre-trained models are publicly available at https://github.com/tc3d.
Using Natural Language Processing Techniques to Provide Personalized Educational Materials for Chronic Disease Patients in China: Development and Assessment of a Knowledge-Based Health Recommender System

Health education emerged as an important intervention for improving the awareness and self-management abilities of chronic disease patients. The development of information technologies has changed the form of patient educational materials from traditional paper materials to electronic materials. To date, the amount of patient educational materials on the internet is tremendous, with variable quality, which makes it hard to identify the most valuable materials by individuals lacking medical backgrounds.The aim of this study was to develop a health recommender system to provide appropriate educational materials for chronic disease patients in China and evaluate the effect of this system.A knowledge-based recommender system was implemented using ontology and several natural language processing (NLP) techniques. The development process was divided into 3 stages. In stage 1, an ontology was constructed to describe patient characteristics contained in the data. In stage 2, an algorithm was designed and implemented to generate recommendations based on the ontology. Patient data and educational materials were mapped to the ontology and converted into vectors of the same length, and then recommendations were generated according to similarity between these vectors. In stage 3, the ontology and algorithm were incorporated into an mHealth system for practical use. Keyword extraction algorithms and pretrained word embeddings were used to preprocess educational materials. Three strategies were proposed to improve the performance of keyword extraction. System evaluation was based on a manually assembled test collection for 50 patients and 100 educational documents. Recommendation performance was assessed using the macro precision of top-ranked documents and the overall mean average precision (MAP).The constructed ontology contained 40 classes, 31 object properties, 67 data properties, and 32 individuals. A total of 80 SWRL rules were defined to implement the semantic logic of mapping patient original data to the ontology vector space. The recommender system was implemented as a separate Web service connected with patients' smartphones. According to the evaluation results, our system can achieve a macro precision up to 0.970 for the top 1 recommendation and an overall MAP score up to 0.628.This study demonstrated that a knowledge-based health recommender system has the potential to accurately recommend educational materials to chronic disease patients. Traditional NLP techniques combined with improvement strategies for specific language and domain proved to be effective for improving system performance. One direction for future work is to explore the effect of such systems from the perspective of patients in a practical setting.
Automated classification of social network messages into Smart Cities dimensions

A Smart City can be defined as a high-tech city with several public and private services capable to strategically solve (or mitigate) problems normally generated by rapid urbanization. Different models of indicators have been developed to follow cities’ evolution to become a Smart City. An example of such model is the standard 37120 from the International Organization for Standardization (ISO) that proposes a set of dimensions and indicators (e.g. Transportation, Recreation, Solid Waste) for services and quality of life for sustainable cities and communities. It has been common to find official social network profiles of organizations and governmental entities related to the services they provide or are responsible for (water, waste, transportation, cultural events, etc.) and that are used by citizens as a gateway to directly interact and communicate their complains and problems about those services. The present paper proposes to apply machine learning algorithms over the urban data generated by social networks in order to create classifiers to automatically categorize citizens messages according to the different cities services dimensions. For that, two distinct text datasets in Portuguese were collected from two social networks: Twitter (1,950 tweets) and Colab.re (65,066 posts). The texts were mapped according to the different ISO 37120 categories, preprocessed and mined through the use of 8 algorithms implemented in Scikit-Learn. Initial results pointed out the feasibility of the proposal with models achieving average F1-measures around 55% for F1-macro and 78% for F1-micro when using Linear Vector Classification, Logistic Regression, Decision Tree and Complement Naive Bayes. However, as the datasets were highly unbalanced, the performances of the models vary significantly for each ISO category, with the best results occurring for Wastewater, Water & Sanitation, Energy and Transportation. The classifiers generated here can be integrated on a number of different city services and systems such as: governmental support decision systems, customer complain systems, communities dashboards, police offices, transportation’s companies, cultural producers, environmental agencies, and recyclers’ companies.
Stain Standardization Capsule for Application-Driven Histopathological Image Normalization

Color consistency is crucial to developing robust deep learning methods for histopathological image analysis. With the increasing application of digital histopathological slides, the deep learning methods are probably developed based on the data from multiple medical centers. This requirement makes it a challenging task to normalize the color variance of histopathological images from different medical centers. In this paper, we propose a novel color standardization module named stain standardization capsule based on the capsule network and the corresponding dynamic routing algorithm. The proposed module can learn and generate uniform stain separation outputs for histopathological images in various color appearance without the reference to manually selected template images. The proposed module is light and can be jointly trained with the application-driven CNN model. The proposed method was validated on three histopathology datasets and a cytology dataset, and was compared with state-of-the-art methods. The experimental results have demonstrated that the SSC module is effective in improving the performance of histopathological image analysis and has achieved the best performance in the compared methods.
Similarity Learning Networks for Animal Individual Re-Identification - Beyond the Capabilities of a Human Observer

Deep learning has become the standard methodology to approach computer vision tasks when large amounts of labeled data are available. One area where traditional deep learning approaches fail to perform is one-shot learning tasks where a model must correctly classify a new category after seeing only one example. One such domain is animal re-identification, an application of computer vision which can be used globally as a method to automate species population estimates from camera trap images. Our work demonstrates both the application of similarity comparison networks to animal re-identification, as well as the capabilities of deep convolutional neural networks to generalize across domains. Few studies have considered animal re-identification methods across species. Here, we compare two similarity comparison methodologies: Siamese and Triplet-Loss, based on the AlexNet, VGG-19, DenseNet201, MobileNetV2, and InceptionV3 architectures considering mean average precision (mAP)@1 and mAP@5. We consider five data sets corresponding to five different species: humans, chimpanzees, humpback whales, fruit flies, and Siberian tigers, each with their own unique set of challenges. We demonstrate that Triplet Loss outperformed its Siamese counterpart for all species. Without any species-specific modifications, our results demonstrate that similarity comparison networks can reach a performance level beyond that of humans for the task of animal re-identification. The ability for researchers to re-identify an animal individual upon re-encounter is fundamental for addressing a broad range of questions in the study of population dynamics and community/behavioural ecology. Our expectation is that similarity comparison networks are the beginning of a major trend that could stand to revolutionize animal re-identification from camera trap data.
An Improved Harris’s Hawks Optimization for SAR Target Recognition and Stock Market Index Prediction

Harris's hawks optimization (HHO) algorithm proposed in 2019 is a novel population-based, nature-inspired optimization paradigm that imitates the cooperative behavior and chasing style of Harris's hawks in nature called surprise pounce.Inspired by particle swarm optimization algorithm, velocity is added into the HHO algorithm in the exploration phase.The soft besiege with progressive rapid dives and the hard besiege with progressive rapid dives in the attacking stages of the HHO algorithm are improved by use of the crossover operator of the artificial tree algorithm.Thus the improved HHO algorithm is obtained, written as IHHO.The effectiveness of the IHHO algorithm is tested on 23 benchmark problems by comparison with the other 11 state-of-art meta-heuristic algorithms.The IHHO algorithm is used to optimize the parameters of support vector machine for synthetic aperture radar (SAR) target recognition and of the extreme learning machine for stock market index prediction by considering Google Trends.The comparable results show that the IHHO algorithm is very promising and has some competitive potential.
Can Voters Detect Malicious Manipulation of Ballot Marking Devices?

Ballot marking devices (BMDs) allow voters to select candidates on a computer kiosk, which prints a paper ballot that the voter can review before inserting it into a scanner to be tabulated. Unlike paperless voting machines, BMDs provide voters an opportunity to verify an auditable physical record of their choices, and a growing number of U.S. jurisdictions are adopting them for all voters. However, the security of BMDs depends on how reliably voters notice and correct any adversarially induced errors on their printed ballots. In order to measure voters' error detection abilities, we conducted a large study (N = 241) in a realistic polling place setting using real voting machines that we modified to introduce an error into each printout. Without intervention, only 40% of participants reviewed their printed ballots at all, and only 6.6% told a poll worker something was wrong. We also find that carefully designed interventions can improve verification performance. Verbally instructing voters to review the printouts and providing a written slate of candidates for whom to vote both significantly increased review and reporting rates-although the improvements may not be large enough to provide strong security in close elections, especially when BMDs are used by all voters. Based on these findings, we make several evidence-based recommendations to help better defend BMD-based elections.
Efficient Image Super Resolution Via Channel Discriminative Deep Neural Network Pruning

Deep convolutional neural networks (CNN) have demonstrated superior performance in image super-resolution (SR) problem. However, CNNs are known to be heavily over-parameterized, and suffer from abundant redundancy. The growing size of CNNs may be incompatible with their deployment on mobile or embedded devices. Network pruning has benefited classification tasks by removing redundant parameters and associated computation. However, it has rarely been studied for SR, because existing methods assume the channel-wise features are of equal importance to the final reconstruction. On the contrary, we show the existence of uninformative feature-maps with no contribution to the task. In order to identify and remove such uninformative channels, we propose a new pruning criterion, Discriminant Information, by characterizing the dependency of the output w.r.t to the hidden-layer feature-maps. Empirically, our DI-based channel pruning algorithm is able to trim the state-of-the-art SR networks significantly (e.g. 8.7x model size compression and 3.6x CPU acceleration on SRResNet), with no quantitative or visual performance loss.