Miracle-dz/UniFAR-FTUs huggingface dataset:
An Assessment of the Usability of Cybercrime Datasets

Cybersecurity datasets play a vital role in cybersecurity research. Following the identification of potential cybersecurity datasets, researchers access and manipulate data in the selected datasets. This work aims to identify potential usability issues associated with dataset access and data manipulation through a case study of the data sharing process at the Cambridge Cybercrime Centre (CCC). We collect survey response from current users of the datasets offered by the CCC, and apply a thematic analysis approach to identify obstacles in the uptake of these datasets, and areas of improvement in the data sharing process. The identified themes suggest that users’ level of technological competence, including previous experiences with other datasets, facilitate the uptake of CCC’s datasets. Additionally, users’ experiences with different stages of the data sharing process, such as dataset usage and its various aspects, including downloading and setting up the dataset, highlight areas of improvement. We conclude that addressing the identified issues would facilitate cybersecurity dataset adoption in the wider research community.
A Self-Report Measure of End-User Security Attitudes (SA-6)

We present SA-6, a six-item scale for assessing people’s security attitudes that we developed by following standardized processes for scale development. We identify six scale items based on theoretical and empirical research with sufficient response variance, reliability, and validity in a combined sample ( N = 478) from Amazon Mechanical Turk and a university-based study pool. We validate the resulting measure with a U.S. Census-tailored Qualtrics panel ( N = 209). SA-6 significantly associates with self-report measures of behavior intention and recent secure behaviors. Our work contributes a lightweight method for (1) quantifying and comparing people’s attitudes toward using recommended security tools and practices, and (2) improving predictive modeling of who will adopt security behaviors.
How Sharp is SHARP ?

Cross-core last-level cache based side-channel attacks are becoming practical, affecting all forms of computing devices like mobiles, desktops, servers, and cloud based systems. Mitigating last-level cache based side channel attacks has become an active area of research and many proposals target to mitigate cross-core based conflict attacks. Secure Cache Hierarchy Aware Replacement Policy (SHARP) is one of the recent proposals that mitigate the conflict attacks by changing the underlying last-level cache replacement policy. Though SHARP is an elegant proposal; there are many subtle points, which were not part of the original SHARP proposal that appeared in the ISCA ’17. Through this paper, we discuss and debate the subtle issues that are left unanswered in the original SHARP paper.
Learnable Gated Convolutional Neural Network for Semantic Segmentation in Remote-Sensing Images

Semantic segmentation in high-resolution remote-sensing (RS) images is a fundamental task for RS-based urban understanding and planning. However, various types of artificial objects in urban areas make this task quite challenging. Recently, the use of Deep Convolutional Neural Networks (DCNNs) with multiscale information fusion has demonstrated great potential in enhancing performance. Technically, however, existing fusions are usually implemented by summing or concatenating feature maps in a straightforward way. Seldom do works consider the spatial importance for global-to-local context-information aggregation. This paper proposes a Learnable-Gated CNN (L-GCNN) to address this issue. Methodologically, the Taylor expression of the information-entropy function is first parameterized to design the gate function, which is employed to generate pixelwise weights for coarse-to-fine refinement in the L-GCNN. Accordingly, a Parameterized Gate Module (PGM) was designed to achieve this goal. Then, the single PGM and its densely connected extension were embedded into different levels of the encoder in the L-GCNN to help identify the discriminative feature maps at different scales. With the above designs, the L-GCNN is finally organized as a self-cascaded end-to-end architecture that is able to sequentially aggregate context information for fine segmentation. The proposed model was evaluated on two public challenging benchmarks, the ISPRS 2Dsemantic segmentation challenge Potsdam dataset and the Massachusetts building dataset. The experiment results demonstrate that the proposed method exhibited significant improvement compared with several related segmentation networks, including the FCN, SegNet, RefineNet, PSPNet, DeepLab and GSN.For example, on the Potsdam dataset, our method achieved a 93.65% F 1 score and 88.06% I o U score for the segmentation of tiny cars in high-resolution RS images. As a conclusion, the proposed model showed potential for object segmentation from the RS images of buildings, impervious surfaces, low vegetation, trees and cars in urban settings, which largely varies in size and have confusing appearances.
Context-Enabled Extraction of Large-Scale Urban Functional Zones from Very-High-Resolution Images: A Multiscale Segmentation Approach

Urban functional-zone (UFZ) analysis has been widely used in many applications, including urban environment evaluation, and urban planning and management. How to extract UFZs’ spatial units which delineates UFZs’ boundaries is fundamental to urban applications, but it is still unresolved. In this study, an automatic, context-enabled multiscale image segmentation method is proposed for extracting spatial units of UFZs from very-high-resolution satellite images. First, a window independent context feature is calculated to measure context information in the form of geographic nearest-neighbor distance from a pixel to different image classes. Second, a scale-adaptive approach is proposed to determine appropriate scales for each UFZ in terms of its context information and generate the initial UFZs. Finally, the graph cuts algorithm is improved to optimize the initial UFZs. Two datasets including WorldView-2 image in Beijing and GaoFen-2 image in Nanchang are used to evaluate the proposed method. The results indicate that the proposed method can generate better results from very-high-resolution satellite images than widely used approaches like image tiles and road blocks in representing UFZs. In addition, the proposed method outperforms existing methods in both segmentation quality and running time. Therefore, the proposed method appears to be promising and practical for segmenting large-scale UFZs.
IDAPro for IoT Malware analysis?

Defending against the threat of IoT malware will require new techniques and tools. An important security capability, that precedes a number of security analyses, is the ability to reverse engineer IoT malware binaries effectively. A key question is whether PC-oriented disassemblers can be effective on IoT malware, given the difference in the malware programs and the processors that support them. In this paper, we develop a systematic approach and a tool for evaluating the effectiveness of disassemblers on IoT malware binaries. The key components of the approach are: (a) we ﬁnd the source code for 20 real-world malware programs, (b) we compile them to form a test set of 240 binaries using various compiler optimization options, device architectures, and consid-ering both stripped and unstripped versions of the binaries, and (c) we establish the ground-truth for all these binaries for six disassembly accuracy metrics, such as the percentage of correctly disassembled instructions, and the accuracy of the control ﬂow graph. Overall, we ﬁnd that IDA Pro performs well for unstripped binaries with a precision and recall accuracy of over 85% for all the metrics. However, IDA Pro’s performance deteriorates signiﬁcantly with stripped binaries, mainly because the recall accuracy of identifying the start of functions drops to around 60% for both platforms. The results for the stripped ARM and MIPS binaries are similar to stripped x86 binaries in [2]. Interestingly, we ﬁnd that most compiler optimization options, except the -O3 option for the MIPS architecture, do not cause any noticeable effect in the accuracy. We view our approach as an important capability
Editorial for Special Issue: "Remotely Sensed Albedo"

Land surface (bare soil, vegetation, and snow) albedo is an essential climate variable that affects the Earth’s radiation budget, and therefore, is of vital interest for a broad number of applications: Thematic (urban, cryosphere, land cover, and bare soil), climate (Long Term Data Record), processing technics (gap filling, data merging), and products validation (cal/val) [. ]
On the Segmentation of the Cephalonia-Lefkada Transform Fault Zone (Greece) from an InSAR Multi-Mode Dataset of the Lefkada 2015 Sequence

We use Interferometric Synthetic Aperture Radar (InSAR) to study the Cephalonia–Lefkada Transform Fault Zone (CTF) in the Ionian Sea. The CTF separates continental subduction to the north from oceanic subduction to the south, along the Hellenic Subduction Zone. We exploit a rich multi-modal radar dataset of the most recent major earthquake in the region, the 17 November 2015 Mw 6.4 event, and present new surface displacement results that offer additional constraints on the fault segmentation of the area. Based on this dataset, and by exploiting available information of earthquake relocation, we propose a new rupture process for the 2015 sequence, complementary to those published already. Our modelling includes an additional southern fault segment, oblique to the segment related with the mainshock, which indicates that the CTF structure is more complex than previously believed.
ORIGINAL PAPER

Aim: The aim of the study was to examine the differences in engagement and burnout syndrome in students of nursing/midwifery and psychology in Slovakia. Design: A cross-sectional design was used. Methods: 171 university students on a baccalaureate program participated in the research (90.9% females; age 20.6 ± 1.3; 80 psychology students, 91 nursing/midwifery students). The School Burnout Inventory (SBI) and Utrecht Work Engagement Scale (UWES) were employed as measurement methods. Results: A significant negative association between levels of burnout and engagement (R = 0.42; p < 0.01) was found. A linear regression model showed a significant effect of engagement on burnout (β = -0.34; 95% CI: -0.50; -0.19). However, the total explained variance was only 19.4%. Students of psychology scored higher in engagement compared to nursing and midwifery students (t = 6.89; p < 0.001). Conversely, midwifery and nursing students had higher levels of burnout compared to the group of psychology students (t = -4.55; p < 0,001). Conclusion: Nursing is considered to be a high risk profession in terms of development of burnout, which was demonstrated in this study by the higher burnout, and lower engagement levels in nursing and midwifery students. Higher attention to coping mechanisms for stress and burnout symptoms among students of healthcare professions is required in the school curriculum, especially in nursing programs.
Association of Use of the Neonatal Early-Onset Sepsis Calculator With Reduction in Antibiotic Therapy and Safety: A Systematic Review and Meta-analysis.

Importance The neonatal early-onset sepsis (EOS) calculator is a clinical risk stratification tool increasingly used to guide the use of empirical antibiotics for newborns. Evidence on the effectiveness and safety of the EOS calculator is essential to inform clinicians considering implementation. Objective To assess the association between management of neonatal EOS guided by the neonatal EOS calculator (compared with conventional management strategies) and reduction in antibiotic therapy for newborns. Data Sources Electronic searches in MEDLINE, Embase, Web of Science, and Google Scholar were conducted from 2011 (introduction of the EOS calculator model) through January 31, 2019. Study Selection All studies with original data that compared management guided by the EOS calculator with conventional management strategies for allocating antibiotic therapy to newborns suspected to have EOS were included. Data Extraction and Synthesis Following PRISMA-P guidelines, relevant data were extracted from full-text articles and supplements. CHARMS (Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies) and GRADE (Grades of Recommendation, Assessment, Development and Evaluation) tools were used to assess the risk of bias and quality of evidence. Meta-analysis using a random-effects model was conducted for studies with separate cohorts for EOS calculator and conventional management strategies. Main Outcomes and Measures The difference in percentage of newborns treated with empirical antibiotics for suspected or proven EOS between management guided by the EOS calculator and conventional management strategies. Safety-related outcomes involved missed cases of EOS, readmissions, treatment delay, morbidity, and mortality. Results Thirteen relevant studies analyzing a total of 175 752 newborns were included. All studies found a substantially lower relative risk (range, 3%-60%) for empirical antibiotic therapy, favoring the EOS calculator. Meta-analysis revealed a relative risk of antibiotic use of 56% (95% CI, 53%-59%) in before-after studies including newborns regardless of exposure to chorioamnionitis. Evidence on safety was limited, but proportions of missed cases of EOS were comparable between management guided by the EOS calculator (5 of 18 [28%]) and conventional management strategies (8 of 28 [29%]) (pooled odds ratio, 0.96; 95% CI, 0.26-3.52; P = .95). Conclusions and Relevance Use of the neonatal EOS calculator is associated with a substantial reduction in the use of empirical antibiotics for suspected EOS. Available evidence regarding safety of the use of the EOS calculator is limited, but shows no indication of inferiority compared with conventional management strategies.
Conformational change of a G-quadruplex under molecular crowding conditions

Abstract This study examined the influence of the molecular crowding condition induced by polyethylene glycol (PEG) on the G-quadruplex structure of the thrombin-binding aptamer sequence, 5′-GGGTTGGGTGTGGGTTGGG (G3), in a solution containing a sufficient concentration of mono cations (K+ and Na+). Although the G3 sequence preferably formed the antiparallel type G-quadruplex structure in a Na+ solution, conversion to the parallel type occurred when PEG was added. The antiparallel type was maintained at low PEG concentrations. When the PEG concentration reached 30%, the antiparallel type and parallel type coexist. At PEG concentrations above 40%, the G-quadruplex structure adopted the parallel type completely. In the presence of K+ ions, G3 showed a parallel conformation and remained as a parallel conformation with increasing PEG concentration. The dissociation temperature increased with increasing PEG concentration in all cases, suggesting that the G-quadruplex conformation is more stable under molecular crowding conditions. Communicated by Ramaswamy H. Sarma
The effects of ethnic identity and acculturation in mental health of immigrants: A literature review

Objectives: The purpose of this literature review was to highlight the studies on mental health in relation to acculturation and ethnic identity and to identify research gaps. The results can be used to help inform future immigrant mental health workers and policy makers as well as provide information about the implications of immigrant mental health. Methods: The literature search was conducted electronically, directed by a variety of key words. The search focused on five areas of immigrant mental health importance: (1) acculturation; (2) acculturation and mental health; (3) ethnic identity; (4) ethnic identity and mental health; and (5) acculturation, ethnic identity and mental health. A total of 171 articles were examined and 110 were included in the review. Results: The information is presented in the following categories: acculturation; acculturation and mental health; ethnic identity; ethnic identity and mental health; and acculturation, ethnic identity and mental health. Research gaps are also presented. Conclusion: Overall, literature shows that acculturation, ethnic identity and mental health are closely related to one another affecting both, individuals and communities. This literature review will help to better inform practitioners who may encounter immigrants experiencing mental health issues due to acculturation and ethnic identity pressure. This review may also help policy makers identify specific obstacles that are not addressed in immigration policies which can affect the implementation of these policies. In addition, the information presented can be used as an instrument for researchers in the field. Despite the limited amount of information about immigrant mental health challenges, much work remains to fully support the affected individuals and their families.
Solid phase microextraction as a powerful alternative for screening of secondary metabolites in actinomycetes.

Actinobacteria are one of the most promising producers of medically and industrially relevant secondary metabolites. However, screening of such compounds in actinobacteria growth demands simple, fast, and efficient extraction procedures that enable detection and precise quantification of biologically active compounds. In this regard, solid phase microextraction (SPME) emerges as an ideal extraction technique for screening of secondary metabolites in bacteria culture due to its non-exhaustive, minimally invasive, and non-destructive nature; its integrated sample preparation workflow; balanced coverage feature; metabolism quenching capabilities; and superior cleanup, as well as its versatility in configuration, which enables automation and high throughput applications. The current work provides a comparison of micro-scale and direct immersion SPME (DI-SPME) for screening of secondary metabolites, describes the optimization of the developed DI-SPME method, and introduces the developed technique for mapping of target secondary metabolites as well as its direct coupling to mass spectrometry for such applications. The optimized DI-SPME method provided higher amounts of extracted ions and intensity signals, yielding superior extraction and desorption efficiency as compared to micro-scale extraction. Studied compounds presented stability on the coating for 24 h at room temperature. The DI-SPME mapping approach revealed that Lysolipin I and the Lienomycin analog are distributed along the center and edges of the colony, respectively. Direct coupling of SPME to MS provided a similar ions profile as SPME-LC-MS while enabling a significant decrease in analysis time, demonstrating its suitability for such applications. DI-SPME is herein presented as an alternative to micro-scale extraction for screening of secondary metabolites in actinobacteria solid medium, as well as a feasible alternative to DESI-IMS for mapping of biologic radial distribution of secondary metabolites and cell life cycle studies. Lastly, the direct coupling of DI-SPME to MS is presented as a fast, powerful technique for high throughput analysis of secondary metabolites in this medium.
Realtime and Accurate 3D Eye Gaze Capture with DCNN-Based Iris and Pupil Segmentation

This paper presents a realtime and accurate method for 3D eye gaze tracking with a monocular RGB camera. Our key idea is to train a deep convolutional neural network(DCNN) that automatically extracts the iris and pupil pixels of each eye from input images. To achieve this goal, we combine the power of Unet [1] and Squeezenet [2] to train an efficient convolutional neural network for pixel classification. In addition, we track the 3D eye gaze state in the Maximum A Posteriori (MAP) framework, which sequentially searches for the most likely state of the 3D eye gaze at each frame. When eye blinking occurs, the eye gaze tracker can obtain an inaccurate result. We further extend the convolutional neural network for eye close detection in order to improve the robustness and accuracy of the eye gaze tracker. Our system runs in realtime on desktop PCs and smart phones. We have evaluated our system on live videos and Internet videos, and our results demonstrate that the system is robust and accurate for various genders, races, lighting conditions, poses, shapes and facial expressions. A comparison against Wang et al. [3] shows that our method advances the state of the art in 3D eye tracking using a single RGB camera.
Clinical Usefulness of Dual Red Imaging in Gastric Endoscopic Submucosal Dissection: A Pilot Study

Background/Aims Dual red imaging (DRI) is a new, image-enhanced endoscopy technique. There are few reports about the usefulness of DRI during gastric endoscopic submucosal dissection (ESD). We aimed to examine the usefulness of DRI in endoscopic hemostasis during gastric ESD. Methods We enrolled a total of 20 consecutive patients who underwent gastric ESD. Five endoscopists compared DRI with white light imaging (WLI) for the visibility of blood vessels and bleeding points while performing endoscopic hemostasis. Results The visibility of blood vessels was increased in 56% (19/34) of the cases, and the visibility of bleeding points was improved in 55% (11/20) of the cases with the use of DRI compared with the use of WLI. Conclusions DRI improved the visibility of blood vessels and bleeding points in cases with oozing bleeding, blood pooling around the bleeding points, and multiple bleeding points.
A Review of Signal Processing Techniques for Non-Invasive Fetal Electrocardiography

Fetal electrocardiography (fECG) is a promising alternative to cardiotocography continuous fetal monitoring. Robust extraction of the fetal signal from the abdominal mixture of maternal and fetal electrocardiograms presents the greatest challenge to effective fECG monitoring. This is mainly due to the low amplitude of the fetal versus maternal electrocardiogram and to the non-stationarity of the recorded signals. In this review, we highlight key developments in advanced signal processing algorithms for non-invasive fECG extraction and the available open access resources (databases and source code). In particular, we highlight the advantages and limitations of these algorithms as well as key parameters that must be set to ensure their optimal performance. Improving or combining the current or developing new advanced signal processing methods may enable morphological analysis of the fetal electrocardiogram, which today is only possible using the invasive scalp electrocardiography method.
The dark side of the force: Multiplicity issues in network meta‐analysis and how to address them

Standard models for network meta‐analysis simultaneously estimate multiple relative treatment effects. In practice, after estimation, these multiple estimates usually pass through a formal or informal selection procedure, eg, when researchers draw conclusions about the effects of the best performing treatment in the network. In this paper, we present theoretical arguments as well as results from simulations to illustrate how such practices might lead to exaggerated and overconfident statements regarding relative treatment effects. We discuss how the issue can be addressed via multilevel Bayesian modelling, where treatment effects are modelled exchangeably, and hence estimates are shrunk away from large values. We present a set of alternative models for network meta‐analysis, and we show in simulations that in several scenarios, such models perform better than the usual network meta‐analysis model.