Miracle-dz/UniFAR-FTUs huggingface dataset:
A Usability Study of Five Two-Factor Authentication Methods

Two-factor authentication (2FA) defends against account compromise. An account secured with 2FA typically requires an individual to authenticate using something they know— typically a password—as well as something they have, such as a cell phone or hardware token. Many 2FA methods in widespread use today have not been subjected to adequate usability testing. Furthermore, previous 2FA usability research is difﬁcult to compare due to widely-varying contexts across different studies. We conducted a two-week, between-subjects usability study of ﬁve common 2FA methods with 72 participants, collecting both quantitative and qualitative data. Participants logged into a simulated banking website nearly every day using 2FA and completed an assigned task. Participants generally gave high marks to the methods studied, and many expressed an interest in using 2FA to provide more security for their sensitive online accounts. We also conducted a within-subjects laboratory study with 30 participants to assess the general usability of the setup procedure for the ﬁve methods. While a few participants experienced difﬁculty setting up a hardware token and a one-time password, in general, users found the methods easy to set up.
Evaluating Users' Perceptions about a System's Privacy: Differentiating Social and Institutional Aspects

System design has a crucial effect on users’ privacy, but privacy-by-design processes in organizations rarely involve end-users. To bridge this gap, we investigate how UserCentered Design (UCD) concepts can be used to test how users perceive their privacy in system designs. We describe a series of three online experiments, with 1,313 participants overall, in which we attempt to develop and validate the reliability of a scale for Users’ Perceived Systems’ Privacy (UPSP). We found that users’ privacy perceptions of information systems consist of three distinctive aspects: institutional, social and risk. We combined our scale with A/B testing methodology to compare different privacy design variants for given background scenarios. Our results show that the methodology and the scale are mostly applicable for evaluating the social aspects of privacy designs.
Direct, ECOC, ND and END Frameworks - Which One Is the Best? An Empirical Study of Sentinel-2A MSIL1C Image Classification for Arid-Land Vegetation Mapping in the Ili River Delta, Kazakhstan

To facilitate the advances in Sentinel-2A products for land cover from Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat imagery, Sentinel-2A MultiSpectral Instrument Level-1C (MSIL1C) images are investigated for large-scale vegetation mapping in an arid land environment that is located in the Ili River delta, Kazakhstan. For accurate classification purposes, multi-resolution segmentation (MRS) based extended object-guided morphological profiles (EOMPs) are proposed and then compared with conventional morphological profiles (MPs), MPs with partial reconstruction (MPPR), object-guided MPs (OMPs), OMPs with mean values (OMPsM), and object-oriented (OO)-based image classification techniques. Popular classifiers, such as C4.5, an extremely randomized decision tree (ERDT), random forest (RaF), rotation forest (RoF), classification via random forest regression (CVRFR), ExtraTrees, and radial basis function (RBF) kernel-based support vector machines (SVMs) are adopted to answer the question of whether nested dichotomies (ND) and ensembles of ND (END) are truly superior to direct and error-correcting output code (ECOC) multiclass classification frameworks. Finally, based on the results, the following conclusions are drawn: 1) the superior performance of OO-based techniques over MPs, MPPR, OMPs, and OMPsM is clear for Sentinel-2A MSIL1C image classification, while the best results are achieved by the proposed EOMPs; 2) the superior performance of ND, ND with class balancing (NDCB), ND with data balancing (NDDB), ND with random-pair selection (NDRPS), and ND with further centroid (NDFC) over direct and ECOC frameworks is not confirmed, especially in the cases of using weak classifiers for low-dimensional datasets; 3) from computationally efficient, high accuracy, redundant to data dimensionality and easy of implementations points of view, END, ENDCB, ENDDB, and ENDRPS are alternative choices to direct and ECOC frameworks; 4) surprisingly, because in the ensemble learning (EL) theorem, “weaker” classifiers (ERDT here) always have a better chance of reaching the trade-off between diversity and accuracy than “stronger” classifies (RaF, ExtraTrees, and SVM here), END with ERDT (END-ERDT) achieves the best performance with less than a 0.5% difference in the overall accuracy (OA) values, but is 100 to 10000 times faster than END with RaF and ExtraTrees, and ECOC with SVM while using different datasets with various dimensions; and, 5) Sentinel-2A MSIL1C is better choice than the land cover products from MODIS and Landsat imagery for vegetation species mapping in an arid land environment, where the vegetation species are critically important, but sparsely distributed.
Evaluation of the Uncertainty in Satellite-Based Crop State Variable Retrievals Due to Site and Growth Stage Specific Factors and Their Potential in Coupling with Crop Growth Models

Coupling crop growth models and remote sensing provides the potential to improve our understanding of the genotype x environment x management (G × E × M) variability of crop growth on a global scale. Unfortunately, the uncertainty in the relationship between the satellite measurements and the crop state variables across different sites and growth stages makes it difficult to perform the coupling. In this study, we evaluate the effects of this uncertainty with MODIS data at the Mead, Nebraska Ameriflux sites (US-Ne1, US-Ne2, and US-Ne3) and accurate, collocated Hybrid-Maize (HM) simulations of leaf area index (LAI) and canopy light use efficiency (LUECanopy). The simulations are used to both explore the sensitivity of the satellite-estimated genotype × management (G × M) parameters to the satellite retrieval regression coefficients and to quantify the amount of uncertainty attributable to site and growth stage specific factors. Additional ground-truth datasets of LAI and LUECanopy are used to validate the analysis. The results show that uncertainty in the LAI/satellite measurement regression coefficients lead to large uncertainty in the G × M parameters retrievable from satellites. In addition to traditional leave-one-site-out regression analysis, the regression coefficient uncertainty is assessed by evaluating the retrieval performance of the temporal change in LAI and LUECanopy. The weekly change in LAI is shown to be retrievable with a correlation coefficient absolute value (|r|) of 0.70 and root-mean square error (RMSE) value of 0.4, which is significantly better than the performance expected if the uncertainty was caused by random error rather than secondary effects caused by site and growth stage specific factors (an expected |r| value of 0.36 and RMSE value of 1.46 assuming random error). As a result, this study highlights the importance of accounting for site and growth stage specific factors in remote sensing retrievals for future work developing methods coupling remote sensing with crop growth models.
The Impact of Secure Transport Protocols on Phishing Efficacy

Secure transport protocols have become widespread in recent years, primarily due to growing adoption of HTTPS and SMTP over TLS. Worryingly, prior user studies have shown that users often do not understand the security that is provided by these protocols and may assume protections that do not exist. This study investigates how the security protocol knowledge gap impacts user behavior by performing a phishing experiment on 266 users that A/B tests the effects of HTTP/HTTPS and SMTP/SMTP+TLS on phishing susceptibility. Secure email transport had minimal effect, while HTTPS increased the click-through rate of email phishing links (72.0% HTTPS, 60.0% HTTP) and the credential-entry rate of phishing sites (58.0% HTTPS, 55.6% HTTP). However, our results are merely suggestive and do not rise to the level of statistical signiﬁcance ( p = 0 . 17 click-through, p = 0 . 31 credential-entry). To better understand the factors that affect credential-entry, we categorized differences in browser presentation of HTTP/HTTPS and correlated participant susceptibility with browser URL display features. We administered a follow-up survey for phishing victims, which was designed to provide qualitative insights for observed outcomes, but it did not yield meaningful results. Overall, this study is a suggestive look at the behavioral impact of secure transport protocols and can serve as a basis for future larger-scale studies.
Distributed Password Hash Computation on Commodity Heterogeneous Programmable Platforms

In this paper, we present the Cool Cracker Cluster CCC: a heterogeneous distributed system for parallel, energy–efficient, and high–speed bcrypt password hash computation. The cluster consists of up to 32 heterogeneous nodes with Zynq-7000– based SoCs featuring a dual–core, general–purpose ARM processor coupled with FPGA programmable logic. Each node uses our custom bcrypt accelerator which executes the most costly parts of the hash computation in programmable logic. We integrated our bcrypt implementation into John the Ripper, an open source password cracking software. Message Passing interface (MPI) support in John the Ripper is used to form a distributed cluster. We tested the cluster, trying different configurations of boards (Zedboards and Pynq boards), salt randomness, and cost parameters finding out that password cracking scales linearly with the number of nodes. In terms of performance (number of computed hashes per second) and energy efficiency (performance per Watt), CCC outperforms current systems based on high–end GPU cards, namely Nvidia Tesla V100, by a factor of 2.72 and 5 respectively.
Why people (don't) use password managers effectively

Security experts often recommend using passwordmanagement tools that both store passwords and generate random passwords. However, research indicates that only a small fraction of users use password managers with password generators. Past studies have explored factors in the adoption of password managers using surveys and online store reviews. Here we describe a semi-structured interview study with 30 participants that allows us to provide a more comprehensive picture of the mindsets underlying adoption and effective use of password managers and password-generation features. Our participants include users who use no password-specific tools at all, those who use password managers built into browsers or operating systems, and those who use separately installed password managers. Furthermore, past field data has indicated that users of built-in, browser-based password managers more often use weak and reused passwords than users of separate password managers that have password generation available by default. Our interviews suggest that users of built-in password managers may be driven more by convenience, while users of separately installed tools appear more driven by security. We advocate tailored designs for these two mentalities and provide actionable suggestions to induce effective password manager usage.
Who Spent My EOS? On the (In)Security of Resource Management of EOS.IO

EOS is a popular cryptocurrency, whose market cap is over seven billion USD. Its ecosystem operates in the EOS.IO system, which is devised to speed up the slow transaction rate of previous blockchain technologies. Whereas many previous studies have investigated the security issues of Bitcoin and Ethereum, the security of EOS.IO has thus far drawn little attention despite its popularity. Even the studies that have addressed the security of EOS and its underlying blockchain system mostly focused on implementational bugs in the core of the EOS.IO system or in smart contracts, rather than addressing the fundamental problems stemming from the EOS.IO design. To address this void in the previous literature, we investigate the design architecture of EOS.IO. Based on this investigation, we introduce four attacks whose root causes stem from the unique characteristics of EOS.IO, including intentionally slowing down the block creation time—which can disrupt the essential functions of its blockchain and incapacitate the entire EOS.IO system. In addition, we find that an adversary can partially freeze the execution of a target smart contract or maliciously consume all the resources of a target user with crafted requests. We report all the identified threats to the EOS.IO foundation, one of which is confirmed to be fatal. Finally, we discuss possible mitigations against the proposed attacks.
TRACTION: Fast Non-Parametric Improvement of Estimated Gene Trees

Gene tree correction aims to improve the accuracy of a gene tree by using computational techniques along with a reference tree (and in some cases available sequence data). It is an active area of research when dealing with gene tree heterogeneity due to duplication and loss (GDL). Here, we study the problem of gene tree correction where gene tree heterogeneity is instead due to incomplete lineage sorting (ILS, a common problem in eukaryotic phylogenetics) and horizontal gene transfer (HGT, a common problem in bacterial phylogenetics). We introduce TRACTION, a simple polynomial time method that provably finds an optimal solution to the RF-Optimal Tree Refinement and Completion Problem, which seeks a refinement and completion of an input tree t with respect to a given binary tree T so as to minimize the Robinson-Foulds (RF) distance. We present the results of an extensive simulation study evaluating TRACTION within gene tree correction pipelines on 68,000 estimated gene trees, using estimated species trees as reference trees. We explore accuracy under conditions with varying levels of gene tree heterogeneity due to ILS and HGT. We show that TRACTION matches or improves the accuracy of well-established methods from the GDL literature under conditions with HGT and ILS, and ties for best under the ILS-only conditions. Furthermore, TRACTION ties for fastest on these datasets. TRACTION is available at https://github.com/pranjalv123/TRACTION-RF and the study datasets are available at https://doi.org/10.13012/B2IDB-1747658_V1.
Hydrological Response of Dry Afromontane Forest to Changes in Land Use and Land Cover in Northern Ethiopia

This study analyzes the impact of land use/land cover (LULC) changes on the hydrology of the dry Afromontane forest landscape in northern Ethiopia. Landsat satellite images of thematic mapper (TM) (1986), TM (2001), and Operational Land Imager (OLI) (2018) were employed to assess LULC. All of the images were classified while using the maximum likelihood image classification technique, and the changes were assessed by post-classification comparison. Seven LULC classes were defined with an overall accuracy 83–90% and a Kappa coefficient of 0.82–0.92. The classification result for 1986 revealed dominance of shrublands (48.5%), followed by cultivated land (42%). Between 1986 and 2018, cultivated land became the dominant (39.6%) LULC type, accompanied by a decrease in shrubland to 32.2%, as well as increases in forestland (from 4.8% to 21.4%) and bare land (from 0% to 0.96%). The soil conservation systems curve number model (SCS-CN) was consequently employed to simulate forest hydrological response to climatic variations and land-cover changes during three selected years. The observed changes in direct surface runoff, the runoff coefficient, and storage capacity of the soil were partially linked to the changes in LULC that were associated with expanding bare land and built-up areas. This change in land use aggravates the runoff potential of the study area by 31.6 mm per year on average. Runoff coefficients ranged from 25.3% to 47.2% with varied storm rainfall intensities of 26.1–45.4 mm/ha. The temporal variability of climate change and potential evapotranspiration increased by 1% during 1981–2018. The observed rainfall and modelled runoff showed a strong positive correlation (R2 = 0.78; p < 0.001). Regression analysis between runoff and rainfall intensity indicates their high and significant correlation (R2 = 0.89; p < 0.0001). Changes were also common along the slope gradient and agro-ecological zones at varying proportions. The observed changes in land degradation and surface runoff are highly linked to the change in LULC. Further study is suggested on climate scenario-based modeling of hydrological processes that are related to land use changes to understand the hydrological variability of the dry Afromontane forest ecosystems.
Assessing Spatial Information in Physical Environments (Short Paper)

Many approaches have dealt with the hypothesis that the environment contain information, mostly focusing on how humans decode information from the environment in visual perception, navigation, and spatial decision-making. A question yet to be fully explored is how the built environment could encode forms of information in its own physical structures. This paper explores a new measure of spatial information, and applies it to twenty cities from different spatial cultures and regions of the world. Findings suggest that this methodology is able to identify similarities between cities, generating a classification scheme that opens up new questions about what we call "cultural hypothesis": the idea that spatial configurations find consistent differences between cultures and regions.
Semiautomated Detection and Mapping of Vegetation Distribution in the Antarctic Environment Using Spatial-Spectral Characteristics of WorldView-2 Imagery

Effective monitoring of changes in the geographic distribution of cryospheric vegetation requires high-resolution and accurate baseline maps. The rationale of the present study is to compare multiple feature extraction approaches to remotely mapping vegetation in Antarctica, assessing which give the greatest accuracy and reproducibility relative to those currently available. This study provides precise, high-resolution, and refined baseline information on vegetation distribution as is required to enable future spatiotemporal change analyses of the vegetation in Antarctica. We designed and implemented a semiautomated customized normalized difference vegetation index (NDVI) approach for extracting cryospheric vegetation by incorporating very high resolution (VHR) 8-band WorldView-2 (WV-2) satellite data. The viability of state-of-the-art target detection, spectral processing/matching, and pixel-wise supervised classification feature extraction techniques are compared with the customized NDVI approach devised in this study. An extensive quantitative and comparative assessment was made by evaluating four semiautomatic feature extraction approaches consisting of 16 feature extraction standalone methods (four customized NDVI plus 12 existing methods) for mapping vegetation on Fisher Island and Stornes Peninsula in the Larsemann Hills, situated on continental east Antarctica. The results indicated that the customized NDVI approach achieved superior performance (average bias error ranged from ~6.44 ± 1.34% to ~11.55 ± 1.34%) and highest statistical stability in terms of performance when compared with existing feature extraction approaches. Overall, the accuracy analysis of the vegetation mapping relative to manually digitized reference data (supplemented by validation with ground truthing) indicated that the 16 semi-automatic mapping methods representing four general feature extraction approaches extracted vegetated area from Fisher Island and Stornes Peninsula totalling between 2.38 and 3.72 km2 (2.85 ± 0.10 km2 on average) with bias values ranging from 3.49 to 31.39% (average 12.81 ± 1.88%) and average root mean square error (RMSE) of 0.41 km2 (14.73 ± 1.88%). Further, the robustness of the analyses and results were endorsed by a cross-validation experiment conducted to map vegetation from the Schirmacher Oasis, East Antarctica. Based on the robust comparative analysis of these 16 methods, vegetation maps of the Larsemann Hills and Schirmacher Oasis were derived by ensemble merging of the five top-performing methods (Mixture Tuned Matched Filtering, Matched Filtering, Matched Filtering/Spectral Angle Mapper Ratio, NDVI-2, and NDVI-4). This study is the first of its kind to detect and map sparse and isolated vegetated patches (with smallest area of 0.25 m2) in East Antarctica using VHR data and to use ensemble merging of feature extraction methods, and provides access to an important indicator for environmental change.
Acute effects of continuous positive airway pressure in patients with obstructive sleep apnea

Abstract Objectives. To enhance the initial compliance of continuous positive airway pressure (CPAP) in the patient with obstructive sleep apnea (OSA), we investigate the acute effects of one-night CPAP on morning blood pressure (BP) and, if any exist, to determine which factors predict BP-lowering effects of one-night CPAP in OSA subgroups stratified by the presence or absence of uncontrolled hypertension. Design. Newly diagnosed OSA patients (a respiratory distress index (RDI) ≥ 15) without a history of hypertension were stratified by the presence or absence of morning hypertension. Comparisons were made of morning BP, overnight change in mean BP (MBP), and circadian BP pattern (RM/E, the ratio of morning to evening MBP) between two time points of diagnostic and CPAP titrations in each subgroup. In the subgroup with a significantly reduced morning BP, predictors of changes in morning MBP were determined by multiple linear regression analyses. Results. The prevalence of morning hypertension was 48.8%. One-night CPAP reduced BP immediately only in patients with morning hypertension. By contrast, patients without morning hypertension did not experience any BP-lowering effects. In the subgroup with morning hypertension, baseline morning MBP, female sex, and RM/E were independently associated with reduced morning MBP, and RM/E was the most important predictor. Conclusions. These results confirm the acute effects of one-night CPAP on lowering BP in OSA patients with morning hypertension. An unfavorable circadian BP pattern is the most important predictor.