Reviewerly/exHarmony huggingface dataset:
ShARe: Enabling Co-Located Asymmetric Multi-User Interaction for Augmented Reality Head-Mounted Displays

Head-Mounted Displays (HMDs) are the dominant form of enabling Virtual Reality (VR) and Augmented Reality (AR) for personal use. One of the biggest challenges of HMDs is the exclusion of people in the vicinity, such as friends or family. While recent research on asymmetric interaction for VR HMDs has contributed to solving this problem in the VR domain, AR HMDs come with similar but also different problems, such as conflicting information in visualization through the HMD and projection. In this work, we propose ShARe, a modified AR HMD combined with a projector that can display augmented content onto planar surfaces to include the outside users (non-HMD users). To combat the challenge of conflicting visualization between augmented and projected content, ShARe visually aligns the content presented through the AR HMD with the projected content using an internal calibration procedure and a servo motor. Using marker tracking, non-HMD users are able to interact with the projected content using touch and gestures. To further explore the arising design space, we implemented three types of applications (collaborative game, competitive game, and external visualization). ShARe is a proof-of-concept system that showcases how AR HMDs can facilitate interaction with outside users to combat exclusion and instead foster rich, enjoyable social interactions.
MonoEye

We present MonoEye, a multimodal human motion capture system using a single RGB camera with an ultra-wide fisheye lens, mounted on the user's chest. Existing optical motion capture systems use multiple cameras, which are synchronized and require camera calibration. These systems also have usability constraints that limit the user's movement and operating space. Since the MonoEye system is based on a wearable single RGB camera, the wearer's 3D body pose can be captured without space and environment limitations. The body pose, captured with our system, is aware of the camera orientation and therefore it is possible to recognize various motions that existing egocentric motion capture systems cannot recognize. Furthermore, the proposed system captures not only the wearer's body motion but also their viewport using the head pose estimation and an ultra-wide image. To implement robust multimodal motion capture, we design three deep neural networks: BodyPoseNet, HeadPoseNet, and CameraPoseNet, that estimate 3D body pose, head pose, and camera pose in real-time, respectively. We train these networks with our new extensive synthetic dataset providing 680K frames of renderings of people with a wide range of body shapes, clothing, actions, backgrounds, and lighting conditions. To demonstrate the interactive potential of the MonoEye system, we present several application examples from common body gestural to context-aware interactions.
Continuous Security Testing: A Case Study on Integrating Dynamic Security Testing Tools in CI/CD Pipelines

Continuous Integration (CI) and Continuous Delivery (CD) have become a well-known practice in DevOps to ensure fast delivery of new features. This is achieved by automatically testing and releasing new software versions, e.g. multiple times per day. However, classical security management techniques cannot keep up with this quick Software Development Life Cycle (SDLC). Nonetheless, guaranteeing high security quality of software systems has become increasingly important. The new trend of DevSecOps aims to integrate security techniques into existing DevOps practices. Especially, the automation of security testing is an important area of research in this trend. Although plenty of literature discusses security testing and CI/CD practices, only a few deal with both topics together. Additionally, most of the existing works cover only static code analysis and neglect dynamic testing methods. In this paper, we present an approach to integrate three automated dynamic testing techniques into a CI/CD pipeline and provide an empirical analysis of the introduced overhead. We then go on to identify unique research/technology challenges the DevSecOps communities will face and propose preliminary solutions to these challenges. Our findings will enable informed decisions when employing DevSecOps practices in agile enterprise applications engineering processes and enterprise security.
Intrinsic value in crypto currencies

Over the last years, cryptocurrencies have acquired a growing social and economic interest. The most representative features of this new phenomenon are strong price increases and high volatility. The investigations developed in this field are not excessively numerous nor unanimous. Some researches consider this phenomenon to be a bubble, since they don't agree that the price pattern of the cryptocurrencies is justified or explained by their intrinsic value. The present article aims to provide a theoretical framework for the assessment of these new realities embodied by cryptocurrencies. We have created a model which differentiates two types of cryptocurrencies: the cryptocurrencies meant to fund a specific project, and the cryptocurrencies designed for general or non-specific uses. We will also make a distinction between block chain networks designed to work only with cryptocurrencies from those which admit other tasks. Our model establishes the difference between the value of the currency and that of the network. The result of this research is a theoretical valuation model based on three fundamental categories of cryptocurrencies by purpose or finality.
"At the End of the Day Facebook Does What ItWants"

Interest has grown in designing algorithmic decision making systems for contestability. In this work, we study how users experience contesting unfavorable social media content moderation decisions. A large-scale online experiment tests whether different forms of appeals can improve users' experiences of automated decision making. We study the impact on users' perceptions of the Fairness, Accountability, and Trustworthiness of algorithmic decisions, as well as their feelings of Control (FACT). Surprisingly, we find that none of the appeal designs improve FACT perceptions compared to a no appeal baseline. We qualitatively analyze how users write appeals, and find that they contest the decision itself, but also more fundamental issues like the goal of moderating content, the idea of automation, and the inconsistency of the system as a whole. We conclude with suggestions for -- as well as a discussion of the challenges of -- designing for contestability.
Typosquatting and Combosquatting Attacks on the Python Ecosystem

Limited automated controls integrated into the Python Package Index (PyPI) package uploading process make PyPI an attractive target for attackers to trick developers into using malicious packages.Several times this goal has been achieved via the combosquatting and typosquatting attacks when attackers give malicious packages similar names to already existing legitimate ones.In this paper, we study the attacks, identify potential attack targets, and propose an approach to identify combosquatting and typosquatting package names automatically.The approach might serve as a basis for an automated system that ensures the security of the packages uploaded and distributed via PyPI.
Deep Time-Aware Item Evolution Network for Click-Through Rate Prediction

For better user satisfaction and business effectiveness, Click-Through Rate (CTR) prediction is one of the most important tasks in E-commerce. It is often the case that users' interests different from their past routines may emerge or impressions such as promotional items may burst in a very short period. In essence, such changes relate to item evolution problem, which has not been investigated by previous studies. The state-of-the-art methods in the sequential recommendation, which use simple user behaviors, are incapable of modeling these changes sufficiently. It is because, in the user behaviors, outdated interests may exist and the popularity of an item over time is not well represented. To address these limitations, we introduce time-aware item behaviors for addressing the recommendation of emerging preference. The time-aware item behavior for an item is a set of users who interact with this item with timestamps. The rich interaction information of users for an item may help to model its evolution. In this work, we propose a CTR prediction model TIEN based on the time-aware item behavior. In TIEN, by leveraging the interaction time intervals, information of similar users in a short time interval helps identify the emerging user interest of the target user. By using the sequential time intervals, the item's popularity over time can be captured in evolutionary item dynamics. Noisy users who interact with items accidentally are further eliminated thus learning robust personalized item dynamics. To the best of our knowledge, this is the first study to the item evolution problem for E-commerce CTR prediction. We conduct extensive experiments on five real-world CTR prediction datasets. The results show that the TIEN model consistently achieves remarkable improvements to the state-of-the-art methods.
A Comparative Study on Various Binary Classification Algorithms and their Improved Variant for Optimal Performance

Machine Learning (ML) has become a vast umbrella of various algorithms. Certainly, even for classification models, there are numerous algorithms such as Logistic Regression, Naïve Bayes Classifier, K-Nearest Neighbors, Decision tree and Random Forest Classifiers. The proposed works present a comparative study of various binary classifier and have implemented various boosting algorithms and finally have summarized the related arguments for optimal performance of the presented classification models.
Federated Generative Adversarial Learning

This work studies training generative adversarial networks under the federated learning setting. Generative adversarial networks (GANs) have achieved advancement in various real-world applications, such as image editing, style transfer, scene generations, etc. However, like other deep learning models, GANs are also suffering from data limitation problems in real cases. To boost the performance of GANs in target tasks, collecting images as many as possible from different sources becomes not only important but also essential. For example, to build a robust and accurate bio-metric verification system, huge amounts of images might be collected from surveillance cameras, and/or uploaded from cellphones by users accepting agreements. In an ideal case, utilize all those data uploaded from public and private devices for model training is straightforward. Unfortunately, in the real scenarios, this is hard due to a few reasons. At first, some data face the serious concern of leakage, and therefore it is prohibitive to upload them to a third-party server for model training; at second, the images collected by different kinds of devices, probably have distinctive biases due to various factors, e.g., collector preferences, geo-location differences, which is also known as “domain shift”. To handle those problems, we propose a novel generative learning scheme utilizing a federated learning framework. Following the configuration of federated learning, we conduct model training and aggregation on one center and a group of clients. Specifically, our method learns the distributed generative models in clients, while the models trained in each client are fused into one unified and versatile model in the center. To the best of our knowledge, this is the first work on touching GAN training under a federated learning setting. We perform extensive experiments to compare different federation strategies, and empirically examine the effectiveness of federation under different levels of parallelism and data skewness.
KBGN: Knowledge-Bridge Graph Network for Adaptive Vision-Text Reasoning in Visual Dialogue

Visual dialogue is a challenging task that needs to extract implicit information from both visual (image) and textual (dialogue history) contexts. Classical approaches pay more attention to the integration of the current question, vision knowledge and text knowledge, despising the heterogeneous semantic gaps between the cross-modal information. In the meantime, the concatenation operation has become de-facto standard to the cross-modal information fusion, which has a limited ability in information retrieval. In this paper, we propose a novel Knowledge-Bridge Graph Network (KBGN) model by using graph to bridge the cross-modal semantic relations between vision and text knowledge in fine granularity, as well as retrieving required knowledge via an adaptive information selection mode. Moreover, the reasoning clues for visual dialogue can be clearly drawn from intra-modal entities and inter-modal bridges. Experimental results on VisDial v1.0 and VisDial-Q datasets demonstrate that our model outperforms existing models with state-of-the-art results.
Reverse nearest neighbors Bhattacharyya bound linear discriminant analysis for multimodal classification

Recently, an effective improvement of linear discriminant analysis (LDA) called L2-norm linear discriminant analysis via the Bhattacharyya error bound estimation (L2BLDA) was proposed in its adaptability and nonsingularity. However, L2BLDA assumes all samples from the same class are independently identically distributed (i.i.d.). In real world, this assumption sometimes fails. To solve this problem, in this paper, reverse nearest neighbor (RNN) technique is imbedded into L2BLDA and a novel linear discriminant analysis named RNNL2BLDA is proposed. Rather than using classes to construct within-class and between-class scatters, RNNL2BLDA divides each class into subclasses by using RNN technique, and then defines the scatter matrices on these classes that may contain several subclasses. This makes RNNL2BLDA get rid of the i.i.d.assumption in L2BLDA and applicable to multimodal data, which have mixture of Gaussian distributions. In addition, by setting a threshold in RNN, RNNL2BLDA achieves robustness. RNNL2BLDA can be solved through a simple standard generalized eigenvalue problem. Experimental results on an artificial data set, some benchmark data sets as well as two human face databases demonstrate the effectiveness of the proposed method.
Black Widow: Blackbox Data-driven Web Scanning

Modern web applications are an integral part of our digital lives. As we put more trust in web applications, the need for security increases. At the same time, detecting vulnerabilities in web applications has become increasingly hard, due to the complexity, dynamism, and reliance on third-party components. Blackbox vulnerability scanning is especially challenging because (i) for deep penetration of web applications scanners need to exercise such browsing behavior as user interaction and asynchrony, and (ii) for detection of nontrivial injection attacks, such as stored cross-site scripting (XSS), scanners need to discover inter-page data dependencies.This paper illuminates key challenges for crawling and scanning the modern web. Based on these challenges we identify three core pillars for deep crawling and scanning: navigation modeling, traversing, and tracking inter-state dependencies. While prior efforts are largely limited to the separate pillars, we suggest an approach that leverages all three. We develop Black Widow, a blackbox data-driven approach to web crawling and scanning. We demonstrate the effectiveness of the crawling by code coverage improvements ranging from 63% to 280% compared to other crawlers across all applications. Further, we demonstrate the effectiveness of the web vulnerability scanning by featuring no false positives and finding more cross-site scripting vulnerabilities than previous methods. In older applications, used in previous research, we find vulnerabilities that the other methods miss. We also find new vulnerabili-ties in production software, including HotCRP, osCommerce, PrestaShop and WordPress.
A Design Space for Social Presence in VR

After the outbreak of COVID-19, creating believable social virtual environments to substitute face-to-face interactions became vital. Social presence is essential for creating realistic and engaging social virtual reality (VR) experiences. However, mimicking realistic social presence within virtual environments is an ongoing research endeavor. In this paper, we fill this gap by reviewing (N = 347) and analyzing (N = 68) the existing literature about social VR across four venues during the past 9 years to elicit a novel design space for techniques and design parameters that enhance social presence and identify open research opportunities and common design trends across various social VR domain areas. We conclude that asymmetric interactions are under-explored due to their novelty. Additionally, self-embodiment and non-verbal cues are key features for social presence in social VR applications. We envision that our work will aid in shaping users' virtual experience through building believable social VR experiences.
Convex Density Constraints for Computing Plausible Counterfactual Explanations

The increasing deployment of machine learning as well as legal regulations such as EU's GDPR cause a need for user-friendly explanations of decisions proposed by machine learning models. Counterfactual explanations are considered as one of the most popular techniques to explain a specific decision of a model. While the computation of "arbitrary" counterfactual explanations is well studied, it is still an open research problem how to efficiently compute plausible and feasible counterfactual explanations. We build upon recent work and propose and study a formal definition of plausible counterfactual explanations. In particular, we investigate how to use density estimators for enforcing plausibility and feasibility of counterfactual explanations. For the purpose of efficient computations, we propose convex density constraints that ensure that the resulting counterfactual is located in a region of the data space of high density.
Statistical Significance Testing at CHI PLAY: Challenges and Opportunities for More Transparency

Statistical Significance Testing -- or Null Hypothesis Significance Testing (NHST) -- is common to quantitative CHI PLAY research. Drawing from recent work in HCI and psychology promoting transparent statistics and the reduction of questionable research practices, we systematically review the reporting quality of 119 CHI PLAY papers using NHST (data and analysis plan at OSF.io). We find that over half of these papers employ NHST without specific statistical hypotheses or research questions, which may risk the proliferation of false positive findings. Moreover, we observe inconsistencies in the reporting of sample sizes and statistical tests. These issues reflect fundamental incompatibilities between NHST and the frequently exploratory work common to CHI PLAY. We discuss the complementary roles of exploratory and confirmatory research, and provide a template for more transparent research and reporting practices.
SentiDraw: Using star ratings of reviews to develop domain specific sentiment lexicon for polarity determination

Lexicon based methods use Sentiment Orientation (SO) scores of words contained in the text for polarity determination of documents. These SO scores are obtained from sentiment lexicons like MPQA and SentiWordNet which are built using methods such as Pointwise Mutual Information (PMI) to calculate the SO scores of the words. . The more popular PMI based methods for creating lexicons do not make use of the rich information that can be obtained from star ratings available for text documents such as reviews available for various product categories on online platforms like amazon, yelp.com or IMDB. Star ratings have only recently been used in some studies to calculate SO value of words in reviews for developing domain specific sentiment lexicons. This paper also makes use of star ratings but proposes a novel approach ‘SentiDraw’ where the probability distribution of words across reviews with different star ratings is used to calculate their SO scores. A comprehensive assessment of SentiDraw performance across multiple domains and datasets is also presented by comparing it with other methods that make use of star ratings of reviews for building lexicons. The results show that lexicons built with SentiDraw method delivers superior performance versus other lexicons in six out of nine cases. The accuracy score of sentiment classification using SentiDraw method ranges from 78.0% to 81.6% across domains and SentiDraw lexicon built using Hollywood dataset outperforms any other purely lexicon based method known to authors on the most experimented datasets like Cornell Movie Reviews Dataset (CMRD) and Large Movie Review Dataset (LMRD). Finally, a hybrid approach is also proposed that uses SentiDraw along with supervised methods to deliver state-of-art performance for polarity determination of reviews.
Blockchain Data Privacy Access Control Based on Searchable Attribute Encryption

Data privacy is important to the security of our society, and enabling authorized users to query this data efficiently is facing more challenge.Recently, blockchain has gained extensive attention with its prominent characteristics as public, distributed, decentration and chronological characteristics.However, the transaction information on the blockchain is open to all nodes, the transaction information update operation is even more transparent.And the leakage of transaction information will cause huge losses to the transaction party.In response to these problems, this paper combines hierarchical attribute encryption with linear secret sharing, and proposes a blockchain data privacy protection control scheme based on searchable attribute encryption, which solves the privacy exposure problem in traditional blockchain transactions.The user's access control is implemented by the verification nodes, which avoids the security risks of submitting private keys and access structures to the blockchain network.Associating the private key component with the random identity of the user node in the blockchain can solve the collusion problem.In addition, authorized users can quickly search and supervise transaction information through searchable encryption.The improved algorithm ensures the security of keywords.Finally, based on the DBDH hypothesis, the security of the scheme is proved in the random prediction model.
initKmix-A novel initial partition generation algorithm for clustering mixed data using k-means-based clustering

Mixed datasets consist of both numeric and categorical attributes. Various k-means-based clustering algorithms have been developed for these datasets. Generally, these algorithms use random partition as a starting point, which tends to produce different clustering results for different runs. In this paper, we propose, initKmix, a novel algorithm for finding an initial partition for k-means-based clustering algorithms for mixed datasets. In the initKmix algorithm, a k-means-based clustering algorithm is run many times, and in each run, one of the attributes is used to create initial clusters for that run. The clustering results of various runs are combined to produce the initial partition. This initial partition is then used as a seed to a k-means-based clustering algorithm to cluster mixed data. Experiments with various categorical and mixed datasets showed that initKmix produced accurate and consistent results, and outperformed the random initial partition method and other state-of-the-art initialization methods. Experiments also showed that k-means-based clustering for mixed datasets with initKmix performed similar to or better than many state-of-the-art clustering algorithms for categorical and mixed datasets.