Miracle-dz/UniFAR-FTUs huggingface dataset:
Scholars Walk: A Markov Chain Framework for Course Recommendation

Course selection is a crucial and challenging problem that students have to face while navigating through an undergraduate degree program. The decisions they make shape their future in ways that they cannot conceive in advance. Available departmental sample degree plans are not personalized for each student, and personal discussion time with an academic advisor is usually limited. Data-driven meth-ods supporting decision making have gained importance to empower student choices and scale advice to large cohorts. We propose Scholars Walk , a random-walk-based approach that captures the sequential relationships between the different courses. Based on the “wisdom of the crowd” and the students’ prior courses, we recommend a short list of courses for next semester. Our experimental evaluation illustrates that Scholars Walk outperforms other collaborative ﬁltering and popularity-based approaches. At the same time, our framework is very eﬃcient, easily interpretable, while also being able to take into consideration important aspects of the educational domain.
Improving random GUI testing with image-based widget detection

Graphical User Interfaces (GUIs) are amongst the most common user interfaces, enabling interactions with applications through mouse movements and key presses. Tools for automated testing of programs through their GUI exist, however they usually rely on operating system or framework specific knowledge to interact with an application. Due to frequent operating system updates, which can remove required information, and a large variety of different GUI frameworks using unique underlying data structures, such tools rapidly become obsolete, Consequently, for an automated GUI test generation tool, supporting many frameworks and operating systems is impractical. We propose a technique for improving GUI testing by automatically identifying GUI widgets in screen shots using machine learning techniques. As training data, we generate randomized GUIs to automatically extract widget information. The resulting model provides guidance to GUI testing tools in environments not currently supported by deriving GUI widget information from screen shots only. In our experiments, we found that identifying GUI widgets in screen shots and using this information to guide random testing achieved a significantly higher branch coverage in 18 of 20 applications, with an average increase of 42.5% when compared to conventional random testing.
Modeling User Behavior Patterns in LBSNs: A Graph Embedding Approach

Author(s): Xu, Weiqi | Advisor(s): McAuley, Julian; Nguyen, Truong Quang | Abstract: With the emerging of various location-based social networks (LBSNs), the study on user mobility patterns and many related tasks have become heated research topics, such as personalized location recommendation and friend recommendation. Many factors affect users’ behavior patterns, such as geographical influence, temporal effect and semantic effect. However, most of the previous work on modeling user trajectories lacks consideration on treating these factors from a graph perspective, therefore fails to capture the potential correlations among the rich context. In this thesis, we demonstrate that using a heterogeneous graph-based model to jointly embed user and POI attribute networks with a unified framework can well preserve the network properties. Multiple factors are embedded into a shared low-dimensional latent space where their joint effect and potential correlations can be well captured. We conduct extensive experiments on large real-world datasets to evaluate our model performance on several major tasks in LBSNs. The experimental results highlight the versatility of our method which shows higher recommendation effectiveness compared with the state-of-the-art baselines. In addition, an online updating strategy is proposed to incorporate new visiting records and dynamically track users’ latest preference in linear time. We also show that this framework has the inherent ability to handle cold-start recommendations, which is a non-trivial task considering the network sparsity of LBSNs. The scalability and flexibility of our framework indicate that this method is promising to be put into practical use.
Rank-Based Tensor Factorization for Student Performance Prediction

One of the essential problems, in educational data mining, is to predict students’ performance on future learning materials, such as problems, assignments, and quizzes. Pio-neer algorithms for predicting student performance mostly rely on two sources of information: students’ past performance, and learning materials’ domain knowledge model. The domain knowledge model, traditionally curated by domain experts, maps learning materials to concepts, topics, or knowledge components that are presented in them. However, creating a domain model by manually labeling the learning material can be a diﬃcult and time-consuming task. In this paper, we propose a tensor factorization model for student performance prediction that does not rely on a pre-deﬁned domain model. Our proposed algorithm models student knowledge as a soft membership of latent concepts. It also represents the knowledge acquisition process with an added rank-based constraint in the tensor factorization ob-jective function. Our experiments show that the proposed model outperforms state-of-the-art algorithms in predicting student performance in two real-world datasets, and is robust to hyper-parameters.
Application hardware-software co-design for reconfigurable computing systems

Reconfigurable computing (RC) systems are composed of nodes with tightly coupled microprocessor(s) and field programmable gate arrays (FPGA). These systems provide unique application acceleration opportunities through the reconfigurability of the FPGA co-processor, which enables the customization of the hardware to the underlying applications and the processing synergy between the microprocessor and the FPGA. A formal methodology for hardware-software co-design, however, has not yet been established for the mapping of applications onto such reconfigurable systems. To the best of our knowledge, this is the first effort to formalize application co-design on reconfigurable computing systems. This research addresses the task of formalizing hardware-software co-scheduling for RC systems. Scheduling algorithms from related domains such as embedded computing (EC), heterogeneous computing (HC), and reconfigurable hardware (RH) were evaluated for their potential benefits to scheduling for RC. Selected algorithms from these domains were extended to provide scheduling capabilities for RC systems. The enhanced algorithms are analytically and experimentally shown to provide over one order of magnitude improvement in application throughput. The execution models of EC, HC, and RH, however, are quite different from that of RC systems and thus scheduling solutions were often not optimal. To address this, a formal co-design methodology and an efficient co-scheduling algorithm, Reconfigurable-Computing Co-Scheduling (ReCoS), has been proposed. The co-design methodology proposes a framework to identify an optimal co-schedule based on the available resources, constraints, and performance targets. The ReCoS algorithm is shown to provide up to one order of magnitude improvement over the enhanced scheduling algorithms from related domains for mapping applications onto an RC system. The proposed algorithms can be used by Hardware-Software compilers to deliver optimized solutions. They can also be used as the foundation for productivity tools that can guide users in Hardware-Software co-design of RC applications. To increase the usability and decrease the time necessary to co-design an application for an RC system, The Center for High-Performance Reconfigurable Computing (CHREC) Performance Analysis and Co-Design Tool (CHREC-PACT) was created. PACT provides application performance analysis, resource utilization, constraint analysis, and co-design assistance tools to quickly identify an optimal co-schedule between the microprocessor and its reconfigurable processor.
RAPID-CPU: a prospective study on implementation of the ESC 0/1-hour algorithm and safety of discharge after rule-out of myocardial infarction

Background: Although the value of fast diagnostic protocols in suspected acute coronary syndrome has been validated, there is insufficient real world evidence including patients with lower pre-test probability, atypical symptoms and confounding comorbidities. The feasibility, efficacy and safety of European Society of Cardiology (ESC) 0/1 and 0/3-hour algorithms using high-sensitivity troponin T were evaluated in a consecutive cohort with suspected acute coronary syndrome. Methods: During 12 months, 2525 eligible patients were enrolled. In a pre-implementation period of 6 months, the prevalence of protocols, disposition, lengths of emergency department stay and treatments were registered. Implementation of the 0/1-hour protocol was monitored for another 6 months. Primary endpoints comprised the change of diagnostic protocols and 30-day mortality after direct discharge from the emergency department. Results: Use of the ESC 0/1-hour algorithm increased by 270% at the cost of the standard 0/3-hour protocol. After rule-out (1588 patients), 1309 patients (76.1%) were discharged directly from the emergency department, with an all-cause mortality of 0.08% at 30 days (one death due to lung cancer). Median lengths of stay were 2.9 (1.9–3.8) and 3.2 (2.7–4.4) hours using a single high-sensitivity troponin T below the limit of detection (5 ng/L) at presentation and the ESC 0/1-hour algorithm, respectively, as compared to 5.3 (4.7–6.5) hours using the ESC 0/3-hour rule-out protocol (P<0.001). Discharge rates increased from 53.9% to 62.8% (P<0.001), without excessive use of diagnostic resources within 30 days. Conclusion: Implementation of the ESC 0/1-hour algorithm is feasible and safe, is associated with shorter emergency department stay than the ESC 0/3-hour protocol, and an increase in discharge rates. Trial registration: ClinicalTrials.gov, Unique identifier: NCT03111862.
8-Day Inpatient Directly Observed Therapy for ART Failure: A Tool For Preventing Unnecessary ART Changes and Optimizing Adherence Support.

Among 20 highly treatment experienced people with HIV with extensive antiretroviral drug resistance and high levels of self-reported adherence, an inpatient 8-day directly observed therapy confirmed nonadherence as the major cause of virologic failure for 9/20 (45%) participants, preventing unnecessary regimen changes that could jeopardize future treatment options.
De Novo Mutation Rate Estimation in Wolves of Known Pedigree

Abstract Knowledge of mutation rates is crucial for calibrating population genetics models of demographic history in units of years. However, mutation rates remain challenging to estimate because of the need to identify extremely rare events. We estimated the nuclear mutation rate in wolves by identifying de novo mutations in a pedigree of seven wolves. Putative de novo mutations were discovered by whole-genome sequencing and were verified by Sanger sequencing of parents and offspring. Using stringent filters and an estimate of the false negative rate in the remaining observable genome, we obtain an estimate of ∼4.5 × 10−9 per base pair per generation and provide conservative bounds between 2.6 × 10−9 and 7.1 × 10−9. Although our estimate is consistent with recent mutation rate estimates from ancient DNA (4.0 × 10−9 and 3.0–4.5 × 10−9), it suggests a wider possible range. We also examined the consequences of our rate and the accompanying interval for dating several critical events in canid demographic history. For example, applying our full range of rates to coalescent models of dog and wolf demographic history implies a wide set of possible divergence times between the ancestral populations of dogs and extant Eurasian wolves (16,000–64,000 years ago) although our point estimate indicates a date between 25,000 and 33,000 years ago. Aside from one study in mice, ours provides the only direct mammalian mutation rate outside of primates and is likely to be vital to future investigations of mutation rate evolution.
Cellular Responses to Proteasome Inhibition: Molecular Mechanisms and Beyond

Proteasome inhibitors have been actively tested as potential anticancer drugs and in the treatment of inflammatory and autoimmune diseases. Unfortunately, cells adapt to survive in the presence of proteasome inhibitors activating a variety of cell responses that explain why these therapies have not fulfilled their expected results. In addition, all proteasome inhibitors tested and approved by the FDA have caused a variety of side effects in humans. Here, we describe the different types of proteasome complexes found within cells and the variety of regulators proteins that can modulate their activities, including those that are upregulated in the context of inflammatory processes. We also summarize the adaptive cellular responses activated during proteasome inhibition with special emphasis on the activation of the Autophagic-Lysosomal Pathway (ALP), proteaphagy, p62/SQSTM1 enriched-inclusion bodies, and proteasome biogenesis dependent on Nrf1 and Nrf2 transcription factors. Moreover, we discuss the role of IRE1 and PERK sensors in ALP activation during ER stress and the involvement of two deubiquitinases, Rpn11 and USP14, in these processes. Finally, we discuss the aspects that should be currently considered in the development of novel strategies that use proteasome activity as a therapeutic target for the treatment of human diseases.
Flexible Prediction of CT Images From MRI Data Through Improved Neighborhood Anchored Regression for PET Attenuation Correction

Given the complicated relationship between the magnetic resonance imaging (MRI) signals and the attenuation values, the attenuation correction in hybrid positron emission tomography (PET)/MRI systems remains a challenging task. Currently, existing methods are either time-consuming or require sufficient samples to train the models. In this paper, an efficient approach for predicting pseudo computed tomography (CT) images from T1- and T2-weighted MRI data with limited data is proposed. The proposed approach uses improved neighborhood anchored regression (INAR) as a baseline method to pre-calculate projected matrices to flexibly predict the pseudo CT patches. Techniques, including the augmentation of the MR/CT dataset, learning of the nonlinear descriptors of MR images, hierarchical search for nearest neighbors, data-driven optimization, and multi-regressor ensemble, are adopted to improve the effectiveness of the proposed approach. In total, 22 healthy subjects were enrolled in the study. The pseudo CT images obtained using INAR with multi-regressor ensemble yielded mean absolute error (MAE) of 92.73 <inline-formula><tex-math notation="LaTeX">$\pm$</tex-math></inline-formula> 14.86 HU, peak signal-to-noise ratio of 29.77 <inline-formula><tex-math notation="LaTeX">$\pm$</tex-math></inline-formula> 1.63 dB, Pearson linear correlation coefficient of 0.82 <inline-formula><tex-math notation="LaTeX">$\pm$</tex-math></inline-formula> 0.05, dice similarity coefficient of 0.81 <inline-formula><tex-math notation="LaTeX">$\pm$</tex-math></inline-formula> 0.03, and the relative mean absolute error (rMAE) in PET attenuation correction of 1.30 <inline-formula><tex-math notation="LaTeX">$\pm$</tex-math></inline-formula> 0.20% compared with true CT images. Moreover, our proposed INAR method, without any refinement strategies, can achieve considerable results with only seven subjects (MAE 106.89 <inline-formula><tex-math notation="LaTeX">$\pm$</tex-math></inline-formula> 14.43 HU, rMAE 1.51 <inline-formula><tex-math notation="LaTeX">$\pm$</tex-math></inline-formula> 0.21%). The experiments prove the superior performance of the proposed method over the six innovative methods. Moreover, the proposed method can rapidly generate the pseudo CT images that are suitable for PET attenuation correction.
An Approximation of Heart Failure Using Cardiovascular Simulation Toolbox

In this paper, we present the simulation of 5 different heart failures with the help of the Cardiovascular Simulation Toolbox (CVST) proposed by O. Barnea et al. at Tel-Aviv University. This is a modified version of the CVST, proposed by G.Ortiz; here, we show that the pathological failures can be covered by this tool. We varied the value of the tool blocks, included the results of the hemodynamic parameters and the P-V loop curves for each disease and compared them to the medical data to prove the effectiveness of the simulation. Based on these changes, we achieved an effective simulation of the following heart failures in the CVST: Diastolic Heart Failure (DHF), Systolic Heart Failure (SHF), Right Ventricle Heart Failure (RVHF), Low Output Heart Failure (LOHF) and High Output Heart Failure (HOHF).
Flavonoid Composition of Salacia senegalensis (Lam.) DC. Leaves, Evaluation of Antidermatophytic Effects, and Potential Amelioration of the Associated Inflammatory Response

Predominantly spread in West Tropical Africa, the shrub Salacia senegalensis (Lam.) DC. is known because of its medicinal properties, the leaves being used in the treatment of skin diseases. Prompted by the ethnomedicinal use, a hydroethanolic extract obtained from the leaves of the plant was screened against a panel of microbial strains, the majority of which involved in superficial infections. The extract was found to be active against the dermatophytes Trichophyton rubrum and Epidermophyton floccosum. Notable results were also recorded regarding the attenuation of the inflammatory response, namely the inhibitory effects observed against soybean 5-lipoxygenase (IC50 = 71.14 μg mL−1), no interference being recorded in the cellular viability of RAW 264.7 macrophages and NO levels. Relevantly, the extract did not lead to detrimental effects against the keratinocyte cell line HaCaT, at concentrations displaying antidermatophytic and anti-inflammatory effects. Flavonoid profiling of S. senegalensis leaves was achieved for the first time, allowing the identification and quantitation of myricitrin, three 3-O-substituted quercetin derivatives, and three other flavonoid derivatives, which may contribute, at least partially, to the observed antidermatophytic and anti-inflammatory effects. In the current study, the plant S. senegalensis is assessed concerning its antidermatophytic and anti-inflammatory properties.
Outcomes of immunocompromised adults hospitalized with laboratory-confirmed influenza in the United States, 2011-2015.

BACKGROUND Hospitalized immunocompromised (IC) adults with influenza may have worse outcomes than hospitalized non-immunocompromised adults. METHODS We identified adults (≥18 years) hospitalized with laboratory-confirmed influenza during 2011-2015 seasons through CDC's Influenza Hospitalization Surveillance Network. IC patients had ≥1: HIV/AIDS, cancer, stem cell or organ transplantation, non-steroid immunosuppressive therapy, immunoglobulin deficiency, asplenia, or other rare conditions. We compared demographic and clinical characteristics of IC and non-IC adults using descriptive statistics and used multivariable logistic regression and Cox proportional hazards models to control for confounding by patient demographic characteristics, pre-existing medical conditions, influenza vaccination, and other factors. RESULTS Among 35,348 adults, 3633 (10%) were IC; cancer (44%), non-steroid immunosuppressive therapy (44%), and HIV (17%) were most common. IC patients were more likely than non-IC patients to have received influenza vaccination (53% vs. 46%; p<0.001), and ~85% of both groups received antivirals. In multivariable analysis, IC adults had higher mortality (adjusted odds ratio (aOR) [95% confidence interval (CI)]: 1.46 [1.20-1.76]). Intensive care was more likely among IC patients 65-79 years (aOR [95% CI]: 1.25 [1.06-1.48]) and >80 years (aOR [95% CI]: 1.35 [1.06-1.73]) compared with non-IC patients in those age groups. IC patients were hospitalized longer (adjusted hazard ratio of discharge [95% CI]: 0.86 [0.83-0.88]) and were more likely to require mechanical ventilation (aOR [95% CI] 1.19 [1.05-1.36]). CONCLUSIONS Substantial morbidity and mortality occurred among IC adults hospitalized with influenza. Influenza vaccination and antiviral administration could be increased in both IC and non-IC adults.
Exploring Effects of Auditory Feedback on Menu Selection in Hand-Gesture Interfaces

With the increasing number of new motion-sensing hardware, improving the usability of gesture interfaces is becoming more important. However, people do not accept gesture interfaces as comfortably as traditional ones because they lack in tactile or haptic feedback that traditional input methods such as mice or keyboards provide. However, having tactile feedback in gesture interfaces is not possible unless users are wearing a device with actuators. Therefore, auditory feedback is an appropriate and unique alternative for assisting visual feedback in gesture interfaces. In this article, we propose various types of novel auditory feedback methods and explore their effects as secondary feedback on complementing visual feedback. We performed a user study for a menu selection task and experimental results show that the proposed auditory feedback is significantly more efficient and more effective than visual-only feedback or conventional auditory feedback in terms of time and accuracy.
Aminophylline and progesterone prevent inflammation-induced preterm parturition in the mouse†

Abstract Although progesterone (P4) supplementation is the most widely used therapy for the prevention of preterm labor (PTL), reports of its clinical efficacy have been conflicting. We have previously shown that the anti-inflammatory effects of P4 can be enhanced by increasing intracellular cyclic adenosine monophosphate (cAMP) levels in primary human myometrial cells. Here, we have examined whether adding aminophylline (Am), a non-specific phosphodiesterase inhibitor that increases intracellular cAMP levels, to P4 might improve its efficacy using in vivo and in vitro models of PTL. In a mouse model of lipopolysaccharide (LPS)-induced PTL, we found that the combination of P4 and Am delayed the onset of LPS-induced PTL, while the same dose of P4 and Am alone had no effect. Pup survival was not improved by either agent alone or in combination. Myometrial prolabor and inflammatory cytokine gene expression was reduced, but the reduction was similar in P4 and P4/Am treated mice. There was no effect of the combination of P4 and Am on an ex vivo assessment of myometrial contractility. In human myometrial cells and myometrial tissue explants, we found that the combination had marked anti-inflammatory effects, reducing cytokine and COX-2 mRNA and protein levels to a greater extent than either agent alone. These data suggest that the combination of P4 and Am has a more potent anti-inflammatory effect than either agent alone and may be an effective combination in women at high-risk of PTL. Summary Sentence The combination of aminophylline and progesterone delays LPS-induced parturition and represses inflammation induced gene expression in myometrial explants and cell cultures