Miracle-dz/UniFAR-FTUs huggingface dataset:
A New Impedance Control Method Using Backstepping Approach for Flexible Joint Robot Manipulators

In this paper, we propose a new impedance control method for flexible joint robot manipulators. An ideal nonlinear impedance dynamic model is formulated in the workspace. Three control strategies that meet the requirement of desired impedance dynamics and stability of the whole system are derived by using backstepping control approach. The control system has a cascade structure with the designed three control strategies serially connecting to each other. Stability of the closed-loop system is analyzed using Lyapunov stability theory. Impedance control experiments are carried out on a 2-link flexible joint robot manipulator with a force sensor equipped at the endeffector. The results demonstrate the effectiveness of the proposed impedance control method. 
ENHANCEMENT OF THE CYTOTOXIC ACTIVITY OF SOME α,β-UNSATURATED KETONES THROUGH AUXILIARY BINDING

ii ACKNOWLEDGEMENTS . iv DEDICATION . v TABLE OF CONTENTS . vi LIST OF TABLES . xi LIST OF FIGURES . xii LIST OF SCHEMES . xvi LIST OF ABBREVIATIONS . xvii CHAPTER 1 . 1 1. Literature Review.1 1.
Primary school staff reflections on school closures due to COVID-19 and recommendations for the future: a national qualitative survey

School closures due to the COVID-19 global pandemic are likely to have a range of negative consequences spanning the domains of child development, education and health, in addition to the widening of inequalities and inequities. Research is required to improve understanding of the impact of school closures on the education, health and wellbeing of pupils and school staff, the challenges posed during reopening and importantly to identify how countries can ensure a safe return to education and to inform policy. This qualitative study aimed to reflect on the experience of primary school staff (pupils aged 3-11) in Wales regarding school closures and the initial reopening of schools and to identify recommendations for the future. A total of 208 school staff completed a national online survey through the HAPPEN primary school network, consisting of questions about school closures, the phased reopening of schools and a return to full-time education. Thematic analysis of survey responses identified five recommendations; (i) prioritise the health and wellbeing of pupils and staff. This includes more focus on wellbeing activities and less focus on attainment/assessments and protecting staff breaks to promote workplace wellbeing; (ii) focus on enabling parental engagement and support. Introduce support sessions for parents and include regular check ins during periods of home learning; (iii) improve digital competence amongst pupils, teachers and parents. Ensure the provision of equipment and training in building digital skills. Offer a combination of paper-based and digital home learning activities; (iv) consider opportunities for smaller class sizes and additional staffing. Ensure support is directed to need whilst providing pastoral care; and (v) improve the mechanism of communication between schools and families, and between government and schools. Ensure that schools receive advance notice of local or national changes in guidance.
Trends in body mass index and blood pressure associations from 1989 to 2018: co-ordinated analysis of 145,399 participants

Background: High body mass index (BMI) is an important contributor to higher blood pressure (BP) levels and its deleterious consequences. However, the strength of this association may be context-specific and differ across time due to increases in medication use or secular changes in body composition. Thus, we utilised two independent data sources to investigate if associations between BMI and systolic BP (SBP) in Britain changed from 1989-2018. Methods: We used 23 repeated cross-sectional datasets - the Health Survey for England (HSE) at 25+ years (1994-2018; N=126,742); and three British birth cohorts (born 1946, 1958, and 1970) with outcomes available at 43-46 years (N=18,657). Anthropometry and BP were measured using standard protocols. We used linear and quantile regression to investigate cross-sectional associations between BMI and SBP. Results: In HSE, associations were weaker in subsequent years, and this trend was most pronounced amongst older adults - after accounting for sex, treatment and education, the mean difference in SBP per 1 kg/m2 increase in BMI amongst adults 55+ years was 0.75mmHg (95% CI: 0.60, 0.90) in 1994, 0.66mmHg (0.46, 0.85) in 2003, and 0.53mmHg (0.35, 0.71) in 2018. In cohorts, BMI and SBP associations were of similar magnitude in 1958 and 1970 cohorts and weaker in the 1946 cohort. Quantile regression analyses suggested that associations between BMI and SBP were present both below and above the hypertension threshold. Conclusion: The consequences of BMI may differ across time and by age - associations between BMI and SBP appear to have weakened in recent decades, particularly in older ages. Thus, at older ages, this weakening strength of association may partly offset the public health impacts of increases in obesity prevalence. However, BMI remains positively associated with SBP in all adult age groups, highlighting the potential adverse consequences of the ongoing obesity epidemic.
Scaling static taint analysis to industrial SOA applications: a case study at Alibaba

In Alibaba, we have seen a growing demand for tracing data flow for scenarios such as data leak detection, change governance, and data consistency checking. Static taint analysis is a technique for such problems, and many approaches are proposed for high scalability and precision. This paper shares our experience in applying taint analysis in Alibaba. In particular, we find that the state-of-the-art taint analysis tool, FlowDroid, does not work well in our cases because our applications make heavy use of libraries, native methods and enterprise-specific frameworks, which impose two major challenges, scalability and implicit dependency, to FlowDroid. This paper presents ANTaint to address these problems. ANTaint improves scalability by expanding the call graph and applying taint propagation on demand for libraries, which account for majority of the program execution but only a small fraction propagates taints. To improve accuracy, we ensure to build a sound call graph with its core part having certain accuracy, and providing a more precise taint propagation model. The practice of applying ANTaint in the company workload validates the idea. According to an experiment on 60 production cases, ANTaint is correct for 95% of the cases (precision: 95%, recall: 98%) while FlowDroid is 13%. ANTaint takes 65% less time and none of the cases run out of memory with 32 GB limitation.
Mining assumptions for software components using machine learning

Software verification approaches aim to check a software component under analysis for all possible environments. In reality, however, components are expected to operate within a larger system and are required to satisfy their requirements only when their inputs are constrained by environment assumptions. In this paper, we propose EPIcuRus, an approach to automatically synthesize environment assumptions for a component under analysis (i.e., conditions on the component inputs under which the component is guaranteed to satisfy its requirements). EPIcuRus combines search-based testing, machine learning and model checking. The core of EPIcuRus is a decision tree algorithm that infers environment assumptions from a set of test results including test cases and their verdicts. The test cases are generated using search-based testing, and the assumptions inferred by decision trees are validated through model checking. In order to improve the efficiency and effectiveness of the assumption generation process, we propose a novel test case generation technique, namely Important Features Boundary Test (IFBT), that guides the test generation based on the feedback produced by machine learning. We evaluated EPIcuRus by assessing its effectiveness in computing assumptions on a set of study subjects that include 18 requirements of four industrial models. We show that, for each of the 18 requirements, EPIcuRus was able to compute an assumption to ensure the satisfaction of that requirement, and further, ≈78% of these assumptions were computed in one hour.
SinkFinder: harvesting hundreds of unknown interesting function pairs with just one seed

Mastering the knowledge about security-sensitive functions that can potentially result in bugs is valuable to detect them. However, identifying this kind of functions is not a trivial task. Introducing machine learning-based techniques to do the task is a natural choice. Unfortunately, the approach also requires considerable prior knowledge, e.g., sufficient labelled training samples. In practice, the requirement is often hard to meet. In this paper, to solve the problem, we propose a novel and practical method called SinkFinder to automatically discover function pairs that we are interested in, which only requires very limited prior knowledge. SinkFinder first takes just one pair of well-known interesting functions as the initial seed to infer enough positive and negative training samples by means of sub-word word embedding. By using these samples, a support vector machine classifier is trained to identify more interesting function pairs. Finally, checkers equipped with the obtained knowledge can be easily developed to detect bugs in target systems. The experiments demonstrate that SinkFinder can successfully discover hundreds of interesting functions and detect dozens of previously unknown bugs from large-scale systems, such as Linux, OpenSSL and PostgreSQL.
Borrowing your enemy’s arrows: the case of code reuse in Android via direct inter-app code invocation

The Android ecosystem offers different facilities to enable communication among app components and across apps to ensure that rich services can be composed through functionality reuse. At the heart of this system is the Inter-component communication (ICC) scheme, which has been largely studied in the literature. Less known in the community is another powerful mechanism that allows for direct inter-app code invocation which opens up for different reuse scenarios, both legitimate or malicious. This paper exposes the general workflow for this mechanism, which beyond ICCs, enables app developers to access and invoke functionalities (either entire Java classes, methods or object fields) implemented in other apps using official Android APIs. We experimentally showcase how this reuse mechanism can be leveraged to “plagiarize" supposedly-protected functionalities. Typically, we were able to leverage this mechanism to bypass security guards that a popular video broadcaster has placed for preventing access to its video database from outside its provided app. We further contribute with a static analysis toolkit, named DICIDer, for detecting direct inter-app code invocations in apps. An empirical analysis of the usage prevalence of this reuse mechanism is then conducted. Finally, we discuss the usage contexts as well as the implications of this studied reuse mechanism.
Detecting and understanding JavaScript global identifier conflicts on the web

JavaScript is widely used for implementing client-side web applications, and it is common to include JavaScript code from many different hosts. However, in a web browser, all the scripts loaded in the same frame share a single global namespace. As a result, a script may read or even overwrite the global objects or functions in other scripts, causing unexpected behaviors. For example, a script can redefine a function in a different script as an object, so that any call of that function would cause an exception at run time. We systematically investigate the client-side JavaScript code integrity problem caused by JavaScript global identifier conflicts in this paper. We developed a browser-based analysis framework, JSObserver, to collect and analyze the write operations to global memory locations by JavaScript code. We identified three categories of conflicts using JSObserver on the Alexa top 100K websites, and detected 145,918 conflicts on 31,615 websites. We reveal that JavaScript global identifier conflicts are prevalent and could cause behavior deviation at run time. In particular, we discovered that 1,611 redefined functions were called after being overwritten, and many scripts modified the value of cookies or redefined cookie-related functions. Our research demonstrated that JavaScript global identifier conflict is an emerging threat to both the web users and the integrity of web applications.
Real-time incident prediction for online service systems

Incidents in online service systems could dramatically degrade system availability and destroy user experience. To guarantee service quality and reduce economic loss, it is essential to predict the occurrence of incidents in advance so that engineers can take some proactive actions to prevent them. In this work, we propose an effective and interpretable incident prediction approach, called eWarn, which utilizes historical data to forecast whether an incident will happen in the near future based on alert data in real time. More specifically, eWarn first extracts a set of effective features (including textual features and statistical features) to represent omen alert patterns via careful feature engineering. To reduce the influence of noisy alerts (that are not relevant to the occurrence of incidents), eWarn then incorporates the multi-instance learning formulation. Finally, eWarn builds a classification model via machine learning and generates an interpretable report about the prediction result via a state-of-the-art explanation technique (i.e., LIME). In this way, an early warning signal along with its interpretable report can be sent to engineers to facilitate their understanding and handling for the incoming incident. An extensive study on 11 real-world online service systems from a large commercial bank demonstrates the effectiveness of eWarn, outperforming state-of-the-art alert-based incident prediction approaches and the practice of incident prediction with alerts. In particular, we have applied eWarn to two large commercial banks in practice and shared some success stories and lessons learned from real deployment.
Effort-aware just-in-time defect identification in practice: a case study at Alibaba

Effort-aware Just-in-Time (JIT) defect identification aims at identifying defect-introducing changes just-in-time with limited code inspection effort. Such identification has two benefits compared with traditional module-level defect identification, i.e., identifying defects in a more cost-effective and efficient manner. Recently, researchers have proposed various effort-aware JIT defect identification approaches, including supervised (e.g., CBS+, OneWay) and unsupervised approaches (e.g., LT and Code Churn). The comparison of the effectiveness between such supervised and unsupervised approaches has attracted a large amount of research interest. However, the effectiveness of the recently proposed approaches and the comparison among them have never been investigated in an industrial setting. In this paper, we investigate the effectiveness of state-of-the-art effort-aware JIT defect identification approaches in an industrial setting. To that end, we conduct a case study on 14 Alibaba projects with 196,790 changes. In our case study, we investigate three aspects: (1) The effectiveness of state-of-the-art supervised (i.e., CBS+,OneWay, EALR) and unsupervised (i.e., LT and Code Churn) effortaware JIT defect identification approaches on Alibaba projects, (2) the importance of the features used in the effort-aware JIT defect identification approach, and (3) the association between projectspecific factors and the likelihood of a defective change. Moreover, we develop a tool based on the best performing approach and investigate the tool's effectiveness in a real-life setting at Alibaba.
Dimensions of software configuration: on the configuration context in modern software development

With the rise of containerization, cloud development, and continuous integration and delivery, configuration has become an essential aspect not only to tailor software to user requirements, but also to configure a software system’s environment and infrastructure. This heterogeneity of activities, domains, and processes blurs the term configuration, as it is not clear anymore what tasks, artifacts, or stakeholders are involved and intertwined. However, each re- search study and each paper involving configuration places their contributions and findings in a certain context without making the context explicit. This makes it difficult to compare findings, translate them to practice, and to generalize the results. Thus, we set out to evaluate whether these different views on configuration are really distinct or can be summarized under a common umbrella. By interviewing practitioners from different domains and in different roles about the aspects of configuration and by analyzing two qualitative studies in similar areas, we derive a model of configuration that provides terminology and context for research studies, identifies new research opportunities, and allows practitioners to spot possible challenges in their current tasks. Although our interviewees have a clear view about configuration, it substantially differs due to their personal experience and role. This indicates that the term configuration might be overloaded. However, when taking a closer look, we see the interconnections and dependencies among all views, arriving at the conclusion that we need to start considering the entire spectrum of dimensions of configuration.
PCA: memory leak detection using partial call-path analysis

Data dependence analysis underlies various applications in software quality assurance, yet existing frameworks/tools for this analysis commonly suffer scalability challenges. We present PCA, a static interprocedural data dependence analyzer for real-world C programs. PCA performs interprocedural points-to and data-flow analyses with a lightweight design. Most of all, it features a partial call-path (PCA) analysis that consists of optimization options to further speed up data dependence computation. As an example application of it, PCA readily supports memory leak detection, for which it helps achieve close or better performance and precision relative to the same application based on a state-of-the-art value flow analysis. In particular, it found four more memory leaks in an industry-scale system which have been fixed by the developers. Through the data dependence it computes, PCA can enable other applications (e.g., impact analysis and taint analysis).
DiffTech: a tool for differencing similar technologies from question-and-answer discussions

Developers can use different technologies for different software development tasks in their work. However, when faced with several technologies with comparable functionalities, it can be challenging for developers to select the most appropriate one, as trial and error comparisons among such technologies are time-consuming. Instead, developers resort to expert articles, read official documents or ask questions in Q&A sites for technology comparison. However, it is still very opportunistic whether they will get a comprehensive comparison, as online information is often fragmented, contradictory and biased. To overcome these limitations, we propose the DiffTech system that exploits the crowd sourced discussions from Stack Overflow, and assists technology comparison with an informative summary of different comparison aspects. We found 19,118 comparative sentences from 2,410 pairs of comparable technologies. We released our DiffTech website for public use. Our website attracts over 1800 users and we also receive some positive comments on social media. A walkthrough video of the tool demo: https://www.youtube. com/watch? v=ixX41DXRNsI Website link: https://difftech.herokuapp. com/
Java Ranger: statically summarizing regions for efficient symbolic execution of Java

Merging execution paths is a powerful technique for reducing path explosion in symbolic execution. One approach, introduced and dubbed “veritesting” by Avgerinos et al., works by translating abounded control flow region into a single constraint. This approach is a convenient way to achieve path merging as a modification to a pre-existing single-path symbolic execution engine. Previous work evaluated this approach for symbolic execution of binary code, but different design considerations apply when building tools for other languages. In this paper, we extend the previous approach for symbolic execution of Java. Because Java code typically contains many small dynamically dispatched methods, it is important to include them in multi-path regions; we introduce dynamic inlining of method-regions to do so modularly. Java’s typed memory structure is very different from the binary representation, but we show how the idea of static single assignment (SSA) form can be applied to object references to statically account for aliasing. We have implemented our algorithms in Java Ranger, an extension to the widely used Symbolic Pathfinder tool. In a set of nine benchmarks, Java Ranger reduces the running time and number of execution paths by a total of 38% and 71% respectively as compared to SPF. Our results are a significant improvement over the performance of JBMC, a recently released verification tool for Java bytecode. We also participated in a static verification competition at a top theory conference where other participants included state-of-the-art Java verifiers. JR won first place in the competition’s Java verification track.
The Effects of Maternal Metformin Treatment on Late Prenatal and Early Postnatal Development of the Offspring Are Modulated by Sex

Metformin is currently used to improve pregnancy outcome in women affected by polycystic ovary syndrome (PCOS) or diabetes. However, metformin may also be useful in pregnancies at risk of intrauterine growth restriction (IUGR) since it improves placental efficiency and the fetuses’ developmental competence. There is no data on the duration of the effect of this treatment from the prenatal up to the postnatal stages. Therefore, the present trial aimed at determining the impact of metformin treatment on the offspring neonatal traits and early postnatal development (i.e., during lactation) using an in vivo swine model. The results support that maternal metformin treatment during pregnancy induces protective changes in body shape and composition of the progeny (i.e., larger head size and body length at birth and higher total viscera weight at weaning). However, there were also major effects of the offspring sex (smaller corpulence in females and lower relative weight of main viscerae in males), which should be considered for further preclinical studies and when even the current clinical application in women affected by PCOS or diabetes is implemented.