Reviewerly/exHarmony huggingface dataset:
Local discriminative based sparse subspace learning for feature selection

Subspace learning is a matrix decomposition method. Some algorithms apply subspace learning to feature selection, but they ignore the local discriminative information contained in data. In this paper, we propose a new unsupervised feature selection algorithm to address this issue, which is called local discriminative based sparse subspace learning for feature selection (LDSSL). We first introduce a local discriminant model in our feature selection framework of subspace learning. This model preserves both the local discriminant structure and local geometric structure of the data, simultaneously. It can not only improve the discriminate ability of the algorithm, but also utilize the local geometric structure information contained in data. Local discriminant model is a linear model, which cannot deal with nonlinear data effectively. Therefore, we need to kernelize the local discriminant model to get a nonlinear version. We next introduce the L1-norm to constrain the feature selection matrix, and this can ensure the sparsity of the feature selection matrix and improve the algorithm's discriminate ability. Then we give the objective function, convergence proof and iterative update rules of the algorithm. We compare LDSSL with eight state-of-the-art algorithms on six datasets. The experimental results show that LDSSL is more effective than eight other feature selection algorithms.
Design Choices and Trade-Offs in Health Care Blockchain Implementations: Systematic Review

A blockchain is a list of records that uses cryptography to make stored data immutable; their use has recently been proposed for electronic medical record (EMR) systems. This paper details a systematic review of trade-offs in blockchain technologies that are relevant to EMRs. Trade-offs are defined as "a compromise between two desirable but incompatible features."This review's primary research question was: "What are the trade-offs involved in different blockchain designs that are relevant to the creation of blockchain-based electronic medical records systems?"Seven databases were systematically searched for relevant articles using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Papers published from January 1, 2017 to June 15, 2018 were selected. Quality assessments of papers were performed using the Risk Of Bias In Non-randomized Studies-of Interventions (ROBINS-I) tool and the Critical Assessment Skills Programme (CASP) tool. Database searches identified 2885 articles, of which 15 were ultimately included for analysis.A total of 17 trade-offs were identified impacting the design, development, and implementation of blockchain systems; these trade-offs are organized into themes, including business, application, data, and technology architecture.The key findings concluded the following: (1) multiple trade-offs can be managed adaptively to improve EMR utility; (2) multiple trade-offs involve improving the security of blockchain systems at the cost of other features, meaning EMR efficacy highly depends on data protection standards; and (3) multiple trade-offs result in improved blockchain scalability. Consideration of these trade-offs will be important to the specific environment in which electronic medical records are being developed. This review also uses its findings to suggest useful design choices for a hypothetical National Health Service blockchain.RR2-10.2196/10994.
A Comparative Study of Summarization Algorithms Applied to Legal Case Judgments

Summarization of legal case judgments is an important problem because the huge length and complexity of such documents make them difficult to read as a whole. Many summarization algorithms have been proposed till date, both for general text documents and a few specifically targeted to summarizing legal documents of various countries. However, to our knowledge, there has not been any systematic comparison of the performances of different algorithms in summarizing legal case documents. In this paper, we perform the first such systematic comparison of summarization algorithms applied to legal judgments. We experiment on a large set of Indian Supreme Court judgments, and a large variety of summarization algorithms including both unsupervised and supervised ones. We assess how well domain-independent summarization approaches perform on legal case judgments, and how approaches specifically designed for legal case documents of other countries (e.g., Canada, Australia) generalize to Indian Supreme Court documents. Apart from quantitatively evaluating summaries by comparing with gold standard summaries, we also give important qualitative insights on the performance of different algorithms from the perspective of a law expert.
Cognitive and human factors in digital forensics: Problems, challenges, and the way forward

Digital forensics is an important and growing forensic domain. Research on miscarriages of justice and misleading evidence, as well as various inquires in the UK and the US, have highlighted human error as an issue within forensic science. This has led to increased attention to the sources of cognitive bias and potential countermeasures within many forensic disciplines. However, the area of digital forensics has yet to pay sufficient attention to this issue. The main goal of this article is to contribute to a more scientifically sound digital forensics domain by addressing the issues of cognitive bias as a source of error. In this paper we present an analysis of seven sources of cognitive and human error specifically within the digital forensics process, and discuss relevant countermeasures. We conclude that although some cognitive and bias issues are very similar across forensic domains, others are different and dependent on the specific characteristic of the domain in question, such as digital forensics. There is a need for new directions in research with regard to cognitive and human factors in digital forensics.
Detection of Real-world Fights in Surveillance Videos

CCTVs have since long been used to enforce security, e.g. to detect fights arising from many different situations. But their effectiveness is questionable, because they rely on continuous and specialized human supervision, demanding automated solutions. Previous work are either too superficial (classification of short-clips) or unrealistic (movies, sports, fake fights). None performed detection of actual fights on long duration CCTV recordings. In this work, we tackle this problem by firstly proposing CCTV-Fights <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> , a novel and challenging dataset containing 1,000 videos of real fights, with more than 8 hours of annotated CCTV footage. Then we propose a pipeline, on which we assess the impact of different feature extractors, through Two-stream CNN, 3D CNN and a local interest point descriptor, as well as different classifiers, such as end-to-end CNN, LSTM and SVM. Results confirm how challenging the problem is, and highlight the importance of explicit motion information to improve performance.
Contrast Pattern-Based Classification for Bot Detection on Twitter

Detecting non-human activity in social networks has become an area of great interest for both industry and academia.In this context, obtaining a high detection accuracy is not the only desired quality; experts in the application domain would also like having an understandable model, with which one may explain a decision.An explanatory decision model may help experts to consider, for example, taking legal action against an account that has displayed offensive behavior, or forewarning an account holder about suspicious activity.In this paper, we shall use a pattern-based classification mechanism to social bot detection, specifically for Twitter.Furthermore, we shall introduce a new feature model for social bot detection, which extends (part of) an existing model with features out of Twitter account usage and tweet content sentiment analysis.From our experimental results, we shall see that our mechanism outperforms other, state-of-the-art classifiers, not based on patterns; and that our feature model yields better classification results than others reported on in the literature.
Cross-lingual Voice Conversion with Bilingual Phonetic Posteriorgram and Average Modeling

This paper presents a cross-lingual voice conversion approach using bilingual Phonetic PosteriorGram (PPG) and average modeling. The proposed approach makes use of bilingual PPGs to represent speaker-independent features of speech signals from different languages in the same feature space. In particular, a bilingual PPG is formed by stacking two monolingual PPG vectors, which are extracted from two monolingual speech recognition systems. The conversion model is trained to learn the relationship between bilingual PPGs and the corresponding acoustic features. To leverage the linguistic and acoustic information from other speakers in different languages, an average model is trained with multiple speakers in both source and target languages. I-vector is utilized as an additional input feature of the average model for network adaptation. Experiments are performed for intralingual and cross-lingual voice conversion between English and Mandarin speakers. Both objective and subjective evaluations demonstrate the effectiveness of our proposed approach.
A Local Mean Representation-based <i>K</i> -Nearest Neighbor Classifier

K -nearest neighbor classification method (KNN), as one of the top 10 algorithms in data mining, is a very simple and yet effective nonparametric technique for pattern recognition. However, due to the selective sensitiveness of the neighborhood size k , the simple majority vote, and the conventional metric measure, the KNN-based classification performance can be easily degraded, especially in the small training sample size cases. In this article, to further improve the classification performance and overcome the main issues in the KNN-based classification, we propose a local mean representation-based k -nearest neighbor classifier (LMRKNN). In the LMRKNN, the categorical k -nearest neighbors of a query sample are first chosen to calculate the corresponding categorical k -local mean vectors, and then the query sample is represented by the linear combination of the categorical k -local mean vectors; finally, the class-specific representation-based distances between the query sample and the categorical k -local mean vectors are adopted to determine the class of the query sample. Extensive experiments on many UCI and KEEL datasets and three popular face databases are carried out by comparing LMRKNN to the state-of-art KNN-based methods. The experimental results demonstrate that the proposed LMRKNN outperforms the related competitive KNN-based methods with more robustness and effectiveness.
What Players of Virtual Reality Exercise Games Want: Thematic Analysis of Web-Based Reviews

Background Physical activity (PA) is associated with a variety of physical and psychosocial health benefits, but levels of moderate-to-vigorous intensity PA remain low worldwide. Virtual reality (VR) gaming systems involving movement (VR exergames) could be used to engage people in more PA. Objective This study aimed to synthesize public reviews of popular VR exergames to identify common features that players liked or disliked to inform future VR exergame design. Methods We conducted a thematic analysis of 498 reviews of the 29 most popular exergames sold in the top 3 VR marketplaces: Steam (Valve Corporation), Viveport (Valve Corporation), and Oculus (Oculus VR). We categorized reviews as positive and negative as they appeared in the marketplaces and identified the most common themes using an inductive thematic analysis. Results The reviews were often mixed, reporting a wide variety of expectations, preferences, and gaming experiences. Players preferred highly realistic games (eg, closely simulated real-world sport), games that were intuitive (in terms of body movement and controls), and games that provided gradual increases in skill acquisition. Players reported feeling that they reached a high level of exertion when playing and that the immersion distracted them from the intensity of the exercise. Some preferred features included music and social aspects of the games, with multiplayer options to include friends or receive help from experienced players. There were 3 main themes in negative reviews. The first concerned bugs that rendered games frustrating. Second, the quality of graphics had a particularly strong impact on perceived enjoyment. Finally, reviewers disliked when games had overly complex controls and display functions that evoked motion sickness. Conclusions Exergames prove to be a stimulating avenue for players to engage in PA and distract themselves from the negative perceptions of performing exercise. The common negative aspects of VR exergames should be addressed for increased uptake and continued engagement.
The Effect of Field-of-View Restriction on Sex Bias in VR Sickness and Spatial Navigation Performance

Recent studies show that women are more susceptible to visually-induced VR sickness, which might explain the low adoption rate of VR technology among women. Reducing field-of-view (FOV) during locomotion is already a widely used strategy to reduce VR sickness as it blocks peripheral optical flow perception and mitigates visual/vestibular conflict. Prior studies show that men are more adept at 3D spatial navigation than women, though this sex bias can be minimized by providing women with a larger FOV. Our study provides insight into the relationship between sex and FOV restriction with respect to VR sickness and spatial navigation performance which seem to conflict. We find the use of an FOV restrictor to be effective in mitigating VR sickness in both sexes while we did not find a negative effect of FOV restriction on spatial navigation performance.
When to consult precision-recall curves

Receiver operating characteristic (ROC) curves are commonly used to evaluate predictions of binary outcomes. When there is a small percentage of items of interest (as would be the case with fraud detection, for example), ROC curves can provide an inflated view of performance. This can cause challenges in determining which set of predictions is better. In this article, we discuss the conditions under which precision-recall curves may be preferable to ROC curves. As an illustrative example, we compare two commonly used fraud predictors (Beneish’s [1999, Financial Analysts Journal 55: 24–36] M score and Dechow et al.’s [2011, Contemporary Accounting Research 28: 17–82] F score) using both ROC and precision-recall curves. To aid the reader with using precision-recall curves, we also introduce the command prcurve to plot them.
Human-Computer Insurrection

The HCI community has worked to expand and improve our consideration of the societal implications of our work and our corresponding responsibilities. Despite this increased engagement, HCI continues to lack an explicitly articulated politic, which we argue re-inscribes and amplifies systemic oppression. In this paper, we set out an explicit political vision of an HCI grounded in emancipatory autonomy-an anarchist HCI, aimed at dismantling all oppressive systems by mandating suspicion of and a reckoning with imbalanced distributions of power. We outline some of the principles and accountability mechanisms that constitute an anarchist HCI. We offer a potential framework for radically reorienting the field towards creating prefigurative counterpower-systems and spaces that exemplify the world we wish to see, as we go about building the revolution in increment.
Hey Google, Can I Ask You Something in Private?

MModern day voice-activated virtual assistants allow users to share and ask for information that could be considered as personal through different input modalities and devices. Using Google Assistant, this study examined if the differences in modality (i.e., voice vs. text) and device (i.e., smartphone vs. smart home device) affect user perceptions when users attempt to retrieve sensitive health information from voice assistants. Major findings from this study suggest that voice (vs. text) interaction significantly enhanced perceived social presence of the voice assistant, but only when the users solicited less sensitive health-related information. Furthermore, when individuals reported less privacy concerns, voice (vs. text) interaction elicited positive attitudes toward the voice assistant via increased social presence, but only in the low (vs. high) information sensitivity condition. Contrary to modality, the device difference did not exert any significant impact on the attitudes toward the voice assistant regardless of the sensitivity level of the health information being asked or the level of individuals' privacy concerns.
Online, VR, AR, Lab, and In-Situ

Empirical studies are a cornerstone of HCI research. Technical progress constantly enables new study methods. Online surveys, for example, make it possible to collect feedback from remote users. Progress in augmented and virtual reality enables to collect feedback with early designs. In-situ studies enable researchers to gather feedback in natural environments. While these methods have unique advantages and disadvantages, it is unclear if and how using a specific method affects the results. Therefore, we conducted a study with 60 participants comparing five different methods (online, virtual reality, augmented reality, lab setup, and in-situ) to evaluate early prototypes of smart artifacts. We asked participants to assess four different smart artifacts using standardized questionnaires. We show that the method significantly affects the study result and discuss implications for HCI research. Finally, we highlight further directions to overcome the effect of the used methods.
Symbiotic Encounters

Recent sustainable HCI research has advocated "working with nature" as a potentially efficacious alternative to human efforts to control it: yet it is less clear how to do so. We contribute to the theoretical aspect of this research by presenting an ethnographic study on alternative farming practices, in which the farm is not so much a system but an assemblage characterized by multiple systems or rationalities always evolving and changing. In them, relationships among species alternate between mutually beneficial in one moment (or season), and harmful in the next. If HCI is to participate in and to support working with nature, we believe that it will have to situate itself within such assemblages and temporalities. In this work, we look into nontraditional users (e.g., nonhumans) and emerging forms of uses (e.g., interactions between human and other species) to help open a design space for technological interventions. We offer three ethnographic accounts in which farmers-and ourselves as researchers-learn to notice, respond, and engage in symbiotic encounters with companion species and the living soil itself.
Self-Sovereign Digital Identity: A Paradigm Shift for Identity

Self-sovereignty is a paradigm shift for digital identity that promises important benefits but lacks a definitional consensus. Herein, we validate nine properties of self-sovereignty proposed by credible sources, propose five new properties, and apply the features of our architecture for digital identity to reason about and validate these properties.
Image encryption using complex hyper chaotic system by injecting impulse into parameters

To resist the finite precision of computer that leads chaotic sequence into periodic orbits, and enhance the complexity of chaotic trajectory to mitigate the dynamical degradation, we investigated the complex hyper chaotic Lü system, and analyzed its nonlinear dynamical behavior by chaotic attractors, Lyapunov exponents, and bifurcation diagrams. The novelty is to mitigate the dynamical degradation and enhance the randomness of the chaotic system through injecting impulse into control parameter. To investigate its application in cryptography, we used the perturbed system by impulse to encrypt color image, three time-saving bitwise operations are adopted to speed up the encryption process. One-time-keys with larger key space sampled from noise made the algorithm resistant to brute-force, known-plaintext, chosen-plaintext, chosen-cipher and differential attacks. Numerical simulation and statistical analysis demonstrated excellent performance of the perturbed complex hyper chaotic system and application in image encryption.
Dynamic window based approaches for avoiding obstacles in moving

In recent years, Unmanned Ground Vehicles (UGVs) have been widely used as service robots. Unlike industrial robots, which are situated in fixed and controlled positions, UGVs work in dynamic environments, sharing the environment with other vehicles and humans. These robots should be able to move without colliding with any obstacle, assuring its integrity and the environment safety. In this paper, we propose two adaptations of the classical Dynamic Window algorithm for dealing with dynamic obstacles like Dynamic Window for Dynamic Obstacles (DW4DO) and Dynamic Window for Dynamic Obstacles Tree (DW4DOT). These new algorithms are compared with our previous algorithms based on Curvature Velocity Methods: Predicted Curvature Velocity Method (PCVM) and Dynamic Curvature Velocity Method (DCVM). Proposals have been validated in both simulated and real environment using several robotic platforms.
Bluesky's Ahead: A Multi-Facility Collaboration for an <i>a la Carte</i> Software Project for Data Acquisition and Management

To address the growing scale of data at user facilities, a multi-facility collaboration is developing a collection of Python libraries, which are co-developed but may be used a la carte, to leverag...
A Novel Methodology for HYIP Operators’ Bitcoin Addresses Identification

Bitcoin is one of the most popular decentralized cryptocurrencies to date. However, it has been widely reported that it can be used for investment scams, which are referred to as high yield investment programs (HYIP). Although from the security forensic point of view it is very important to identify the HYIP operators' Bitcoin addresses, so far in the open technical literature no systematic method which reliably collects and identifies such Bitcoin addresses has been proposed. In this paper, a novel methodology is introduced, which efficiently collects a large number of the HYIP operators' Bitcoin addresses and identifies them based upon a novel analysis of their transactions history. In particular, a scraping-based method is first proposed which is able to collect more than 2,000 HYIP operators' Bitcoin addresses from the Internet thus providing a large number of the HYIPs' samples. Second, a supervised machine learning technique, which classifies, whether or not, specific Bitcoin addresses belong to the HYIP operators, is introduced and its performance is evaluated. The proposed classification method is based upon two novel approaches, namely the rate conversion technique that mitigates the effect of Bitcoin price volatility and the sampling technique that reduces the computational amount without sacrificing the classification performance. By employing close to 30,000 real Bitcoin addresses, extensive performance evaluation results obtained by means of computer simulation experiments have shown that the proposed methodology achieves excellent performance, i.e., 95% of the HYIP addresses can be correctly classified, while maintaining a false positive rate less than 4.9%. In order to further validate the proposed classifier's ability to detect the HYIP operators' Bitcoin addresses, our designed classifier has been tested against a recently published list of the HYIP addresses maintaining its excellent detection accuracy by achieving a 93.75% success rate.