Miracle-dz/UniFAR-FTUs huggingface dataset:
How Effectively Is Defective Code Actually Tested?: An Analysis of JUnit Tests in Seven Open Source Systems

Background: Newspaper headlines still regularly report latent software defects. Such defects have often evaded testing for many years. It remains difficult to identify how well a system has been tested. It also remains difficult to assess how successful at finding defects particular tests are. Coverage and mutation testing are frequently used to asses test effectiveness. We look more deeply at the performance of commonly used JUnit testing by assessing how much JUnit testing was done and how effective that testing was at detecting defects in seven open source systems. Aim: We aim to identify whether defective code has been effectively tested by JUnit tests as non-defective code. We also aim to identify the characteristics of JUnit tests that are related to identifying defects. Methodology: We first extract the defects from seven open source projects using the SZZ algorithm. We match those defects with JUnit tests to identify the proportion of defects that were covered by JUnit tests. We also do the same for non-defective code. We then use Principal Component Analysis and machine learning to investigate the characteristics of JUnit tests that were successful in identifying defects. Results: Our findings suggest that most of the open source systems we investigated are under-tested. On average over 66% of defective methods were not linked to any JUnit tests. We show that the number of methods touched by a JUnit test is strongly related to that test uncovering a defect. Conclusion: More JUnit tests need to be produced for the seven open source systems that we investigate. JUnit tests need to be relatively sophisticated, in particular they should touch more than just one method during the test.
Grape polyphenols reduce gut-localized reactive oxygen species associated with the development of metabolic syndrome in mice

High-fat diet (HFD)-induced leaky gut syndrome combined with low-grade inflammation increase reactive oxygen species (ROS) in the intestine and may contribute to dysbiosis and metabolic syndrome (MetS). Poorly bioavailable and only partially metabolizable dietary polyphenols, such as proanthocyanidins (PACs), may exert their beneficial effects on metabolic health by scavenging intestinal ROS. To test this hypothesis, we developed and validated a novel, noninvasive, in situ method for visualizing intestinal ROS using orally administered ROS-sensitive indocyanine green (ICG) dye. C57BL/6J mice fed HFD for 10 weeks accumulated high levels of intestinal ROS compared to mice fed low-fat diet (LFD). Oral administration of poorly bioavailable grape polyphenol extract (GPE) and β-carotene decreased HFD-induced ROS in the gut to levels comparable to LFD-fed mice, while administration of more bioavailable dietary antioxidants (α- lipoic acid, vitamin C, vitamin E) did not. Forty percent of administered GPE antioxidant activity was measured in feces collected over 24 h, confirming poor bioavailability and persistence in the gut. The bloom of beneficial anaerobic gut bacteria, such as Akkermansia muciniphila, associated with improved metabolic status in rodents and humans may be directly linked to protective antioxidant activity of some dietary components. These findings suggest a possible mechanistic explanation for the beneficial effects of poorly bioavailable polyphenols on metabolic health.
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)

At the dawn of the fourth industrial revolution, we are witnessing a fast and widespread adoption of artificial intelligence (AI) in our daily life, which contributes to accelerating the shift towards a more algorithmic society. However, even with such unprecedented advancements, a key impediment to the use of AI-based systems is that they often lack transparency. Indeed, the black-box nature of these systems allows powerful predictions, but it cannot be directly explained. This issue has triggered a new debate on explainable AI (XAI). A research field holds substantial promise for improving trust and transparency of AI-based systems. It is recognized as the sine qua non for AI to continue making steady progress without disruption. This survey provides an entry point for interested researchers and practitioners to learn key aspects of the young and rapidly growing body of research related to XAI. Through the lens of the literature, we review the existing approaches regarding the topic, discuss trends surrounding its sphere, and present major research trajectories.
Current approaches to immunotherapy in noncolorectal gastrointestinal malignancies

Noncolorectal gastrointestinal (GI) malignancies are among the most frequently diagnosed cancers. Despite the undeniable progress in systemic treatments in recent decades, further improvements using cytotoxic chemotherapy seem unlikely. In this setting, recent discoveries regarding the mechanism underlying immune evasion have prompted the study of molecules capable of inducing strong antitumor responses. Thus, according to early data, immunotherapy is a very promising tool for the treatment of patients with GI malignancies. Noncolorectal GI cancers are a major public health problem worldwide. Traditional treatment options, such as chemotherapy, surgery, radiation therapy, monoclonal antibodies and antiangiogenic agents, have been the backbone of treatment for various stages of GI cancers, but overall mortality remains a major problem. Thus, there is a substantial unmet need for new drugs and therapies to further improve the outcomes of treatment for noncolorectal GI malignancies. “Next-generation” immunotherapy is emerging as an effective and promising treatment option in several types of cancers. Therefore, encouraged by this recent success, many clinical trials evaluating the efficacy of immune checkpoint inhibitors and other strategies in treating noncolorectal GI malignancies are ongoing. This review will summarize the current clinical progress of modern immunotherapy in the field of noncolorectal GI tumors.
Evaluation of the hybrid-dynamic conformal arc therapy technique for radiotherapy of lung cancer

Purpose A hybrid-dynamic conformal arc therapy (HDCAT) technique consisting of a single half-rotated dynamic conformal arc beam and static field-in-field beams in two directions was designed and evaluated in terms of dosimetric benefits for radiotherapy of lung cancer. Materials and Methods This planning study was performed in 20 lung cancer cases treated with the VERO system (BrainLAB AG, Feldkirchen, Germany). Dosimetric parameters of HDCAT plans were compared with those of three-dimensional conformal radiotherapy (3D-CRT) plans in terms of target volume coverage, dose conformity, and sparing of organs at risk. Results HDCAT showed better dose conformity compared with 3D-CRT (conformity index: 0.74 ± 0.06 vs. 0.62 ± 0.06, p < 0.001). HDCAT significantly reduced the lung volume receiving more than 20 Gy (V20: 21.4% ± 8.2% vs. 24.5% ± 8.8%, p < 0.001; V30: 14.2% ± 6.1% vs. 15.1% ± 6.4%, p = 0.02; V40: 8.8% ± 3.9% vs. 10.3% ± 4.5%, p < 0.001; and V50: 5.7% ± 2.7% vs. 7.1% ± 3.2%, p < 0.001), V40 and V50 of the heart (V40: 5.2 ± 3.9 Gy vs. 7.6 ± 5.5 Gy, p < 0.001; V50: 1.8 ± 1.6 Gy vs. 3.1 ± 2.8 Gy, p = 0.001), and the maximum spinal cord dose (34.8 ± 9.4 Gy vs. 42.5 ± 7.8 Gy, p < 0.001) compared with 3D-CRT. conclusions HDCAT could achieve highly conformal target coverage and reduce the doses to critical organs such as the lung, heart, and spinal cord compared to 3D-CRT for the treatment of lung cancer patients.
An Enhanced Approach to Cloud-based Privacy-preserving Benchmarking (Long Version)

Benchmarking is an important measure for companies to investigate their performance and to increase efficiency. As companies usually are reluctant to provide their key performance indicators (KPIs) for public benchmarks, privacy-preserving benchmarking systems are required. In this paper, we present an enhanced privacy-preserving benchmarking protocol that is based on homomorphic encryption. It enables cloud-based KPI comparison including the statistical measures mean, variance, median, maximum, best-in-class, bottom quartile, and top quartile. The theoretical and empirical evaluation of our benchmarking system underlines its practicability. Even under worst-case assumptions regarding connection quality and asymmetric encryption key-security, it fulfils the performance requirements of typical KPI benchmarking systems.
Evidence from India

We use the exogenous policy shock of the extension of provision of school meals to upper primary grades in public schools in Delhi to study the effects of school meal intake on the cognitive effort of students within the classroom. Using individual level data on the performance of students in effort games both before and after the extension of the program, we find that the provision of meals significantly improved the classroom effort of students in grade seven. We conclude that school meals have the potential to increase educational attainment in the classroom. JEL Classification: I21, I25, H52
The triple‐filter bubble: Using agent‐based modelling to test a meta‐theoretical framework for the emergence of filter bubbles and echo chambers

Filter bubbles and echo chambers have both been linked recently by commentators to rapid societal changes such as Brexit and the polarization of the US American society in the course of Donald Trump's election campaign. We hypothesize that information filtering processes take place on the individual, the social, and the technological levels (triple‐filter‐bubble framework). We constructed an agent‐based modelling (ABM) and analysed twelve different information filtering scenarios to answer the question under which circumstances social media and recommender algorithms contribute to fragmentation of modern society into distinct echo chambers. Simulations show that, even without any social or technological filters, echo chambers emerge as a consequence of cognitive mechanisms, such as confirmation bias, under conditions of central information propagation through channels reaching a large part of the population. When social and technological filtering mechanisms are added to the model, polarization of society into even more distinct and less interconnected echo chambers is observed. Merits and limits of the theoretical framework, and more generally of studying complex social phenomena using ABM, are discussed. Directions for future research such as ways of comparing our simulations with actual empirical data and possible measures against societal fragmentation on the three different levels are suggested.
Directory-Based Dependency Processing for Software Architecture Recovery

3Directory structure contains a wealth of software design information; it is used to transfer thoughts of architects to developers. Information extracted from directory paths should play an important role in architecture recovery techniques, but it has been proved that modules or components directly represented by directories are not accurate due to the inconsistency between stages of development and design. To make better use of information extracted from directories, we propose a directory-based dependency processing technique to utilize the information of directories in the process of structure-based architecture recovery. The technique groups the selected inter-coupling files and intra-coupling files in the same directory into a submodule and generates submodule-level dependency graph based on file-level dependency graph. We apply both manual and automatic architecture recovery techniques on submodule-level dependency graph, and the results show that our technique can greatly improve the efficiency and effectiveness of manual and automatic architecture recovery techniques; the technique can also make other structure-based recovery techniques easily scalable to large-sized projects.
Genetic risk for schizophrenia is associated with substance use in emerging adulthood: an event-level polygenic prediction model

Abstract Background Emerging adulthood is a peak period of risk for alcohol and illicit drug use. Recent advances in psychiatric genetics suggest that the co-occurrence of substance use and psychopathology arises, in part, from a shared genetic etiology. We sought to extend this research by investigating the influence of genetic risk for schizophrenia on trajectories of four substance use behaviors as they occurred across emerging adulthood. Method Young adult participants of non-Hispanic European descent provided DNA samples and completed daily reports of substance use for 1 month per year across 4 years (N = 30 085 observations of N = 342 participants). A schizophrenia polygenic score was included in two-level hierarchical linear models designed to test associations between genetic risk for schizophrenia, participant age, and four substance use phenotypes. Results Participants with a greater schizophrenia polygenic score experienced greater age-related increases in the likelihood of using substances across emerging adulthood (p < 0.005). Additionally, our results suggest that the polygenic score was positively associated with participants’ overall likelihood to engage in illicit drug use but not alcohol-related substance use. Conclusions This study used a novel combination of polygenic prediction and intensive longitudinal methods to characterize the influence of genetic risk for schizophrenia on patterns of age-related change in substance use across emerging adulthood. Results suggest that genetic risk for schizophrenia has developmentally specific effects on substance use behaviors in a non-clinical population of young adults.
Inactivation of Pseudomonas aeruginosa biofilms formed under high shear stress on various hydrophilic and hydrophobic surfaces by a continuous flow of ozonated water

Abstract The inactivation of Pseudomonas aeruginosa biofilms grown on glass under high shear stress and exposed to a range of dissolved ozone concentrations (2, 5 and 7 ppm) at 10 and 20 min was investigated. The regression equation, log reduction (biofilm) = 0.64 + 0.59×(C – 2) + 0.33×(T – 10), described the dependence of biofilm inactivation on the dissolved ozone concentration (C, ppm) and contact time (T, min). The predicted D-values were 11.1, 5.7 and 2.2 min at 2, 5 and 7 ppm, respectively. Inactivation of biofilms grown on various surfaces was tested at a single dissolved ozone concentration of 5 ppm and a single exposure time of 20 min. Biofilms grown on plastic materials showed inactivation results similar to that of biofilms on glass, while biofilms grown on ceramics were statistically significantly more difficult to inactivate, suggesting the importance of utilizing non-porous materials in industrial and clinical settings.
More Realistic Forecasting of Future Life Events After Psilocybin for Treatment-Resistant Depression

Background: Evidence suggests that classical psychedelics can promote enduring changes in personality, attitudes and optimism, as well as improvements in mental health outcomes. Aim: To investigate the effects of a composite intervention, involving psilocybin, on pessimism biases in patients with treatment-resistant depression (TRD). Methods: Patients with TRD (n = 15) and matched, untreated non-depressed controls (n = 15) performed the Prediction Of Future Life Events (POFLE) task. The POFLE task requires participants to predict the likelihood of certain life events occurring within a 30-day period, after which the actual rate of event occurrence is reported; this gives an index of potential pessimism versus optimism bias. Psilocybin was administered in two oral dosing sessions (10 and 25 mg) one week apart. Main outcome measures were collected at baseline and one week after the second dosing session. Results: Patients showed a significant pessimism bias at baseline [t(14) = -3.260, p = 0.006; 95% CI (-0.16, -0.03), g = 1.1] which was related to the severity of their depressive symptoms (rs = -0.55, p = 0.017). One week after psilocybin treatment, this bias was significantly decreased [t(14) = -2.714, p = 0.017; 95% CI (-0.21, -0.02), g = 0.7] and depressive symptoms were greatly improved [t(14) = 7.900, p < 0.001; 95% CI (16.17, 28.23), g = 1.9]; moreover, the magnitude of change in both variables was significantly correlated (r = -0.57, p = 0.014). Importantly, post treatment, patients became significantly more accurate at predicting the occurrence of future life events [t(14) = 1.857, p = 0.042; 95% CI (-0.01, 0.12), g = 0.6] whereas no such change was observed in the control subjects. Conclusion: These findings suggest that psilocybin with psychological support might correct pessimism biases in TRD, enabling a more positive and accurate outlook.
Robust Sliding Mode Control for Three Time Scale System with LMI Approach

In this paper, robust sliding mode control (SMC) is designed for three time scale system using linear matrix inequality (LMI) approach. First of all, the original three time scale system is transformed into lower block triangular form and then by assuming fast 1 and fast 2 subsystems as stable, robust SMC design is carried out using slow subsystem alone. Controller, thus, obtained is expressed in terms of original states by similarity transformation and applied to the full-order system. Performance of the designed control is evaluated by simulating a nonlinear system. The simulation results, obtained with the proposed controller, show the acceptable performance when compared with other controllers.
Twitches, Blinks, and Fidgets: Important Generators of Ongoing Neural Activity

Animals and humans continuously engage in small, spontaneous motor actions, such as blinking, whisking, and postural adjustments (“fidgeting”). These movements are accompanied by changes in neural activity in sensory and motor regions of the brain. The frequency of these motions varies in time, is affected by sensory stimuli, arousal levels, and pathology. These fidgeting behaviors can be entrained by sensory stimuli. Fidgeting behaviors will cause distributed, bilateral functional activation in the 0.01 to 0.1 Hz frequency range that will show up in functional magnetic resonance imaging and wide-field calcium neuroimaging studies, and will contribute to the observed functional connectivity among brain regions. However, despite the large potential of these behaviors to drive brain-wide activity, these fidget-like behaviors are rarely monitored. We argue that studies of spontaneous and evoked brain dynamics in awake animals and humans should closely monitor these fidgeting behaviors. Differences in these fidgeting behaviors due to arousal or pathology will “contaminate” ongoing neural activity, and lead to apparent differences in functional connectivity. Monitoring and accounting for the brain-wide activations by these behaviors is essential during experiments to differentiate fidget-driven activity from internally driven neural dynamics.
Prediction of Arteriovenous Fistula Clinical Maturation from Postoperative Ultrasound Measurements: Findings from the Hemodialysis Fistula Maturation Study.

BACKGROUND The utility of early postoperative ultrasound measurements in predicting arteriovenous fistula (AVF) clinical maturation is uncertain. METHODS We investigated the relationships of ultrasound parameters with AVF clinical maturation in newly created AVF, measured at 1 day and 2 and 6 weeks, in 602 participants of a multicenter, observational cohort study. A backward elimination algorithm identified ultrasound measurements that independently predicted unassisted and overall AVF maturation. Candidate variables included AVF blood flow, diameter, and depth, upper arm arterial diameter, presence of stenosis, presence of accessory veins, seven case-mix factors (age, sex, black race, AVF location, diabetes, dialysis status, and body mass index), and clinical center. We evaluated the accuracy of the resulting models for clinical prediction. RESULTS At each ultrasound measurement time, AVF blood flow, diameter, and depth each predicted in a statistically significant manner both unassisted and overall clinical maturation. Moreover, neither the remaining ultrasound parameters nor case-mix factors were associated with clinical AVF maturation after accounting for blood flow, diameter, and depth, although maturation probabilities differed among clinical centers before and after accounting for these parameters. The crossvalidated area under the receiver operating characteristic curve for models constructed using these three ultrasound parameters was 0.69, 0.74, and 0.79 at 1 day and 2 and 6 weeks, respectively, for unassisted AVF clinical maturation and 0.69, 0.71, and 0.76, respectively, for overall AVF maturation. CONCLUSIONS AVF blood flow, diameter, and depth moderately predicted unassisted and overall AVF clinical maturation. The other factors considered did not further improve AVF maturation prediction.
Hybrid Noise for LIC-Based Pencil Hatching Simulation

Line Integral Convolution (LIC) has been widely adopted in pencil hatching generation, where an image degraded by random binary white noise (RBWN) is filtered by LIC along a priori vector field. Nonetheless, an RBWN degraded image through LIC produces hatching graduation only in terms of stroke intensity, and can neither produce hatching graduation explicitly in stroke density nor create visually clear hatching strokes while input pixel values are fairly low. In this paper we address these issues from a noise point of view by assessing several noise models and subsequently constructing a new noise model, called hybrid noise. The new noise model has been experimentally demonstrated in simulating hatching graduation in terms of both stroke intensity and stroke density with quantified graduality measurements. To oversee the effectiveness of hybrid noise we implement the whole pipeline of pencil drawing simulation and compare our results with those state of the art algorithms.
Online Filter Weakening and Pruning for Efficient Convnets

Pruning is an effective method to address the limitation of deploying deep neural networks (DNNs) on embedded systems. Most existing methods prune weights on a given pre-trained DNN followed by a costly fine-tuning process. In this paper, we propose a new and efficient pruning algorithm which can prune the structures of filters and filter shapes effectively. This is achieved by defining filter-wise and shape-wise scaling factors to indicate those to be weakened. Then train the network from scratch and multiply weights with corresponding scaling factors. With iterative update, the weights of the selected filters and shapes are gradually weakened to zero and then pruned with little loss to the model capability. We demonstrate the effectiveness of our approach on several CNN models and datasets. For VGG-16 on CIFAR10, we achieve more than 2 x FLOPs reduction and compression with higher accuracy. And for WRN-16-4 on CIFAR100, our method exhibits more than 2 x speedup and compression with less than 1% accuracy drop.