Miracle-dz/UniFAR-FTUs huggingface dataset:
Shepherd: Enabling Automatic and Large-Scale Login Security Studies

More and more parts of the internet are hidden behind a login field. This poses a barrier to any study predicated on scanning the internet. Moreover, the authentication process itself may be a weak point. To study authentication weaknesses at scale, automated login capabilities are needed. In this work we introduce Shepherd, a scanning framework to automatically log in on websites. The Shepherd framework enables us to perform large-scale scans of post-login aspects of websites. Shepherd scans a website for login fields, attempts to submit credentials and evaluates whether login was successful. We illustrate Shepherd's capabilities by means of a scan for session hijacking susceptibility. In this study, we use a set of unverified website credentials, some of which will be invalid. Using this set, Shepherd is able to fully automatically log in and verify that it is indeed logged in on 6,273 unknown sites, or 12.4% of the test set. We found that from our (biased) test set, 2,579 sites, i.e., 41.4%, are vulnerable to simple session hijacking attacks.
Amplification of Wild-type KRAS Imparts Resistance to Crizotinib in MET Exon 14 Mutant Non–Small Cell Lung Cancer

Purpose: MET inhibitors can be effective therapies in patients with MET exon 14 (METex14) mutant non–small cell lung cancer (NSCLC). However, long-term efficacy is limited by the development of drug resistance. In this study, we characterize acquired amplification of wild-type (WT) KRAS as a molecular mechanism behind crizotinib resistance in three cases of METex14-mutant NSCLC and propose a combination therapy to target it. Experimental Design: The patient-derived cell line and xenograft (PDX) DFCI358 were established from a crizotinib-resistant METex14-mutant patient tumor with massive focal amplification of WT KRAS. To characterize the mechanism of KRAS-mediated resistance, molecular signaling was analyzed in the parental cell line and its KRAS siRNA-transfected derivative. Sensitivity of the cell line to ligand stimulation was assessed and KRAS-dependent expression of EGFR ligands was quantified. Drug combinations were screened for efficacy in vivo and in vitro using viability and apoptotic assays. Results: KRAS amplification is a recurrent genetic event in crizotinib-resistant METex14-mutant NSCLC. The key characteristics of this genetic signature include uncoupling MET from downstream effectors, relative insensitivity to dual MET/MEK inhibition due to compensatory induction of PI3K signaling, KRAS-induced expression of EGFR ligands and hypersensitivity to ligand-dependent and independent activation, and reliance on PI3K signaling upon MET inhibition. Conclusions: Using patient-derived cell line and xenografts, we characterize the mechanism of crizotinib resistance mediated by KRAS amplification in METex14-mutant NSCLC and demonstrate the superior efficacy of the dual MET/PI3K inhibition as a therapeutic strategy addressing this resistance mechanism.
Adaptive Temporal Encoding Network for Video Instance-level Human Parsing

Beyond the existing single-person and multiple-person human parsing tasks in static images, this paper makes the first attempt to investigate a more realistic video instance-level human parsing that simultaneously segments out each person instance and parses each instance into more fine-grained parts (\eg, head, leg, dress). We introduce a novel Adaptive Temporal Encoding Network (ATEN) that alternatively performs temporal encoding among key frames and flow-guided feature propagation from other consecutive frames between two key frames. Specifically, ATEN first incorporates a Parsing-RCNN to produce the instance-level parsing result for each key frame, which integrates both the global human parsing and instance-level human segmentation into a unified model. To balance between accuracy and efficiency, the flow-guided feature propagation is used to directly parse consecutive frames according to their identified temporal consistency with key frames. On the other hand, ATEN leverages the convolution gated recurrent units (convGRU) to exploit temporal changes over a series of key frames, which are further used to facilitate the frame-level instance-level parsing. By alternatively performing direct feature propagation between consistent frames and temporal encoding network among key frames, our ATEN achieves a good balance between frame-level accuracy and time efficiency, which is a common crucial problem in video object segmentation research. To demonstrate the superiority of our ATEN, extensive experiments are conducted on the most popular video segmentation benchmark (DAVIS) and a newly collected Video Instance-level Parsing (VIP) dataset, which is the first video instance-level human parsing dataset comprised of 404 sequences and over 20k frames with instance-level and pixel-wise annotations.
Self-compassion and health-promoting lifestyle behaviors in college students

ABSTRACT Lifestyle choices play a major role in health outcomes. Hence, examination of factors associated with a healthy lifestyle is important for health promotion. Self-compassion is related to health behaviors but research on the role of self-compassion facets (self-kindness vs. self-judgment, common humanity vs. isolation, mindfulness vs. over-identification) on health behaviors is scarce. A total of 423 Turkish undergraduates completed Self-Compassion Scale and Health-Promoting Lifestyle Profile II. Health behaviors positively correlated with self-kindness, common humanity, mindfulness, and total self-compassion but not with self-judgment, isolation, and over-identification. Hierarchical multiple regression analysis showed that mindfulness, self-kindness, and self-judgment were significant predictors of health behaviors. Results may guide clinicians about which components to focus on in self-compassion interventions designed for health promotion.
Effectiveness of Virtual Reality for Children and Adolescents with Autism Spectrum Disorder: An Evidence-Based Systematic Review

Autism Spectrum Disorder (ASD) is a neurodevelopmental disease that is specially characterized by impairments in social communication and social skills. ASD has a high prevalence in children, affecting 1 in 160 subjects. Virtual reality (VR) has emerged as an effective tool for intervention in the health field. Different recent papers have reviewed the VR-based treatments in ASD, but they have an important limitation because they only use clinical databases and do not include important technical indexes such as the Web of Science index or the Scimago Journal & Country Rank. To our knowledge, this is the first contribution that has carried out an evidence-based systematic review including both clinical and technical databases about the effectiveness of VR-based intervention in ASD. The initial search identified a total of 450 records. After the exclusion of the papers that are not studies, duplicated articles, and the screening of the abstract and full text, 31 articles met the PICO (Population, Intervention, Comparison and Outcomes) criteria and were selected for analysis. The studies examined suggest moderate evidence about the effectiveness of VR-based treatments in ASD. VR can add many advantages to the treatment of ASD symptomatology, but it is necessary to develop consistent validations in future studies to state that VR can effectively complement the traditional treatments.
Exploring Undergraduate Students’ Computational Modeling Abilities and Conceptual Understanding of Electric Circuits

Contribution: This paper adds to existing literature on teaching basic concepts of electricity using computer-based instruction; findings suggest that students can develop an accurate understanding of electric circuits when they generate multiple and complementary representations that build toward computational models. Background: Several studies have explored the efficacy of computer-based, multi-representational teaching of electric circuits for novice learners. Existing research has found that instructional use of computational models that move from abstract to concrete representations can foster students’ comprehension of electric circuit concepts, but other features of effective instruction using computational models need further investigation. Research Questions: 1) Is there a correlation between students’ representational fluency and their ability to reason qualitatively on electric circuits? and 2) Is the quality of student-generated computational representations correlated to their conceptual understanding of electric circuits? Methodology: The study comprised two cases in which 51 sophomore-engineering students completed a voluntary assignment designed to assess their representational fluency and conceptual understanding of electric circuits. Qualitative insights from the first case informed the design of a scoring rubric that served as both the assessment and the data collection instrument. Findings: The results suggest that a multi-representational approach aimed at the construction of computational models can foster conceptual understanding of electric circuits. The number and quality of students’ representations showed a positive correlation with their conceptual understanding. In particular, the quality of the computational representations was found to be highly, and significantly, correlated with the correctness of students’ answers to qualitative reasoning questions.
A Mutual Information-Based Two-Phase Memetic Algorithm for Large-Scale Fuzzy Cognitive Map Learning

Various automatic learning algorithms have been proposed to learn fuzzy cognitive maps (FCMs), but most of them were only applied to learn small-scale FCMs and the learned maps obtained by such methods are usually much denser than the real maps. Learning FCMs requires the learning methods to not only determine the existence of links between concepts but also optimize the edge weights, which is the difficulty for FCM learning methods. Therefore, we propose a mutual information (MI)-based two-phase memetic algorithm (MA) for learning large-scale FCMs, termed as MIMA-FCM. In MIMA-FCM, the first phase is oriented to determine the existence of links between concepts by MI, which can reduce the search space significantly for MA, and then MA is used to optimize the edge weights according to the multiple observed response sequences in the second phase. Experiments on both synthetic and real-life data and the application for the gene regulatory network reconstruction problem demonstrate that the proposed method can not only find the plausible existence of links between concepts, but also optimize the edge weights rapidly. The comparison with existing algorithms shows that MIMA-FCM can learn large-scale FCMs with higher accuracy without expert knowledge.
Effect of Web-based diabetes training program on diabetes-related knowledge, attitudes, and skills of health professionals: A randomized controlled trial.

AIM To determine the effect of a Web-Based Diabetes Training Program (WB-DTP) on the diabetes-related knowledge, attitudes, and skills of health professionals. METHODS This was an experimental pretest-post-test with a 1 month follow-up study design that included a control group. The results of the power analysis suggested that 50 individuals with diabetes should be in both the intervention and the control groups. The WB-DTP was developed in accordance with the Effective Teaching Instruction Model. Measures included the information form, Achievement Test (AT), Diabetes Attitudes Scale (DAS), and skill observation forms. The Wilcoxon's Signed Rank test, Mann-Whitney U-test, and Friedman test were used to analyze group differences on these measures. RESULTS There was a significant difference between the AT post-test and the follow-up test scores of the intervention and control groups. No significant difference was present for the DAS post-test and follow-up scores between the intervention and control groups. A significant difference emerged on the Insulin Injection Skill Observation Form score between the intervention and control groups. In addition, there was a significant difference in the measurement level of blood glucose by the Glucometer Skill Observation Form between the intervention and control groups. CONCLUSIONS As a result, it was determined that the WB-DTP is effective in increasing the diabetes-related knowledge and skills of healthcare professionals. However, the program was not adequate at increasing the diabetes-related attitudes of health professionals.
Tetraspanins, Another Piece in the HIV-1 Replication Puzzle

Despite the great research effort placed during the last decades in HIV-1 study, still some aspects of its replication cycle remain unknown. All this powerful research has succeeded in developing different drugs for AIDS treatment, but none of them can completely remove the virus from infected patients, who require life-long medication. The classical approach was focused on the study of virus particles as the main target, but increasing evidence highlights the importance of host cell proteins in HIV-1 cycle. In this context, tetraspanins have emerged as critical players in different steps of the viral infection cycle. Through their association with other molecules, including membrane receptors, cytoskeletal proteins, and signaling molecules, tetraspanins organize specialized membrane microdomains called tetraspanin-enriched microdomains (TEMs). Within these microdomains, several tetraspanins have been described to regulate HIV-1 entry, assembly, and transfer between cells. Interestingly, the importance of tetraspanins CD81 and CD63 in the early steps of viral replication has been recently pointed out. Indeed, CD81 can control the turnover of the HIV-1 restriction factor SAMHD1. This deoxynucleoside triphosphate triphosphohydrolase counteracts HIV-1 reverse transcription (RT) in resting cells via its dual function as dNTPase, catalyzing deoxynucleotide triphosphates into deoxynucleosides and inorganic triphosphate, and as exonuclease able to degrade single-stranded RNAs. SAMHD1 has also been related with the detection of viral nucleic acids, regulating the innate immune response and would promote viral latency. New evidences demonstrating the ability of CD81 to control SAMHD1 expression, and as a consequence, HIV-1 RT activity, highlight the importance of TEMs for viral replication. Here, we will briefly review how tetraspanins modulate HIV-1 infection, focusing on the latest findings that link TEMs to viral replication.
Executive Function in Deaf Children: Auditory Access and Language Access.

Purpose Deaf children are frequently reported to be at risk for difficulties in executive function (EF); however, the literature is divided over whether these difficulties are the result of deafness itself or of delays/deficits in language that often co-occur with deafness. The purpose of this study is to discriminate these hypotheses by assessing EF in populations where the 2 accounts make contrasting predictions. Method We use a between-groups design involving 116 children, ages 5-12 years, across 3 groups: (a) participants with normal hearing (n = 45), (b) deaf native signers who had access to American Sign Language from birth (n = 45), and (c) oral cochlear implant users who did not have full access to language prior to cochlear implantation (n = 26). Measures include both parent report and performance-based assessments of EF. Results Parent report results suggest that early access to language has a stronger impact on EF than early access to sound. Performance-based results trended in a similar direction, but no between-group differences were significant. Conclusions These results indicate that healthy EF skills do not require audition and therefore that difficulties in this domain do not result primarily from a lack of auditory experience. Instead, results are consistent with the hypothesis that language proficiency, whether in sign or speech, is crucial for the development of healthy EF. Further research is needed to test whether sign language proficiency also confers benefits to deaf children from hearing families.
Chemerin and PEDF Are Metaflammation-Related Biomarkers of Disease Activity and Obesity in Rheumatoid Arthritis

Objective: Obesity is a risk factor for Rheumatoid Arthritis (RA) being associated to low grade inflammation. This study aimed to determine whether PEDF and Chemerin are biomarkers of inflammation related to fat accumulation in RA and to investigate whether weight loss associates with clinical disease improvement through the modification of fat-related biomarkers in overweight/obese RA with low-moderate disease. Participants and Methods: Two-hundred and thirty RA patients were enrolled, of whom 176 at disease onset treated according to a treat-to-target strategy (T2T) and 54 overweight/obese RA in stable therapy and low-moderate disease activity. Gene expression of adipokines, interleukin-6 and their receptors were examined in adipose tissue from obese RA. Obese RA with low-moderate disease activity underwent low-calories diet aiming to Body Mass Index (BMI) reduction >5%, maintaining RA therapy unchanged. Chemerin, PEDF and Interleukin-6 plasma values were assessed by ELISA and disease activity was evaluated. Results: At RA onset, PEDF and Chemerin plasma values correlated with BMI (p < 0.001) but only Chemerin plasma values correlated with disease activity (p < 0.001). After adopting a T2T strategy, Chemerin arose as an independent factor associated with remission in early RA [OR(95%CIs):0.49(0.25–0.97)]. Moreover, after low-calories diet, RA with low-moderate disease activity reaching BMI reduction ≥5% (62.6%) at 6 months had significant decrease of PEDF (p < 0.05) and Chemerin (p < 0.05) plasma values, in parallel with the improvement in disease activity. Conclusions: PEDF and Chemerin arose as biomarkers of obesity and metaflammation respectively, providing a link between chronic inflammation and excess of body weight in RA. Therefore, BMI reduction of at least 5% in obese RA allowed better disease control without modifying RA treatment.
Caregiver depression: The contributing role of depression in patients, stigma, social support and religiosity

Background: Depression has been well studied as part of caregiver burden among patients with severe mental illnesses. Curiously, though, there has been little data in terms of caregiver burden with specific focus on depression among caregivers of patients with major depressive disorder (MDD). Aim: This study aims to determine the rate of depression among caregivers of person with depression and its psychosocial correlates, which include stigma, perceived social support, religious commitment and the severity of the patient’s symptoms. Methods: A cross-sectional study was conducted among 165 patients diagnosed with MDD using the Mini-International Neuropsychiatric Interview (M.I.N.I.) together with their caregivers. Apart from gathering social demographic data, patients were administered the 16-item Quick Inventory of Depressive Symptomatology–Self-Rated Version (QIDS-SR 16), whereas the caregivers were required to answer Patient Health Questionnaire–9 (PHQ-9), Multidimensional Scale of Perceived Social Support (MSPSS), Duke University Religion Index (DUREL) and Depression Stigma Scale (DSS). Those who scored ⩾5 on PHQ-9 were further assessed with interviewer-rated M.I. N.I. to diagnose the presence of depression. Results: A total of 47 (28.5%) caregivers were found to have depressive symptoms. Out of that total, 13 (7.9%) were diagnosed to have MDD using M.I. N.I. From univariate analysis, factors associated with depression in caregivers were the severity of symptoms in patients (p < .001), personal stigma in caregivers (p = .037), the patients’ current depressive episode (p = .026) and lower perceived social support from friends (p = .048). From multivariate analysis, only the patients’ severity of depressive symptoms (p < .001) and personal stigma in caregivers (p = .048) were significantly associated with the caregivers’ depressive symptoms. Conclusion: Our findings suggested that the severity of patient depression and personal stigma of the caregivers were significant factors correlated with caregiver depression. Therefore, beyond optimizing the treatment of depression in patients, the issue of stigma among caregivers also needs to be addressed as a potential target of intervention.
Hierarchical Extended Bilateral Motion Estimation-Based Frame Rate Upconversion Using Learning-Based Linear Mapping

We present a novel and effective learning-based frame rate upconversion (FRUC) scheme, using linear mapping. The proposed learning-based FRUC scheme consists of: 1) a new hierarchical extended bilateral motion estimation (HEBME) method; 2) a light-weight motion deblur (LWMD) method; and 3) a synthesis-based motion-compensated frame interpolation (S-MCFI) method. First, the HEBME method considerably enhances the accuracy of the motion estimation (ME), which can lead to a significant improvement of the FRUC performance. The proposed HEBME method consists of two ME pyramids with a three-layered hierarchy, where the motion vectors (MVs) are searched in a coarse-to-fine manner via each pyramid. The found MVs are further refined in an enhanced resolution of four times by jointly combining the MVs from the two pyramids. The HEBME method employs a new elaborate matching criterion for precise ME which effectively combines a bilateral absolute difference, an edge variance, pixel variances, and an MV difference among two consecutive blocks and its neighboring blocks. Second, the LWMD method uses the MVs found by the HEBME method and removes the small motion blurs in original frames via transformations by linear mapping. Third, the S-MCFI method finally generates interpolated frames by applying linear mapping kernels for the deblurred original frames. In consequence, our FRUC scheme is capable of precisely generating interpolated frames based on the HEBME for accurate ME, the S-MCFI for elaborate frame interpolation, and the LWMD for contrast enhancement. The experimental results show that our FRUC significantly outperforms the state-of-the-art non-deep learning-based schemes with an average of 1.42 dB higher in the peak signal-to-noise-ratio and shows comparable performance with the state-of-the-art deep learning-based scheme.
Robust Sliding Mode Control for T-S Fuzzy Systems via Quantized State Feedback

This paper is concerned with the robust sliding mode control (SMC) problem for a class of T-S fuzzy systems subject to both matched and mismatched uncertainties. Different from the conventional T-S fuzzy SMC design approach, the quantized states rather than states themselves, are utilized for the control design. By the combination of the proposed zooming-out/zooming-in adjustment policy of the quantizer sensitivity, the quantized state feedback fuzzy sliding mode control scheme is developed to ensure the stabilization of the T-S fuzzy systems. Finally, some simulation results are presented to illustrate the effectiveness of the proposed approach.