Evaluating deadlock detection methods for concurrent software

Threats to construct validity should be reduced to a minimum. If true, sources of bias, namely raters, items, tests as well as gender, age, race, language background, culture, and socio-economic status need to be spotted and removed. This study investigates raters’ experience, language background, and the choice of essay prompt as potential sources of biases. Eight raters, four native English speakers and four Persian L1 speakers of English as a Foreign Language (EFL), scored 40 essays on one general and one field-specific topic. The raters assessed these essays based on Test of English as a Foreign Language (TOEFL) holistic and International English Language Testing System (IELTS) analytic band scores. Multifaceted Rasch Measurement (MFRM) was run to find extant biases. In spite of not finding statistically significant biases, several interesting results emerged illustrating the influence of construct-irrelevant factors such as raters’ experience, L1, and educational background. Further research is warranted to investigate these factors as potential sources of rater bias.

Introduction“Big data” – including linked administrative data – can be exploited to evaluate interventions for maternal and child health, providing time- and cost-effective alternatives to randomised controlled trials. However, using these data to evaluate population-level interventions can be challenging.ObjectivesWe aimed to inform future evaluations of complex interventions by describing sources of bias, lessons learned, and suggestions for improvements, based on two observational studies using linked administrative data from health, education and social care sectors to evaluate the Family Nurse Partnership (FNP) in England and Scotland.MethodsWe first considered how different sources of potential bias within the administrative data could affect results of the evaluations. We explored how each study design addressed these sources of bias using maternal confounders captured in the data. We then determined what additional information could be captured at each step of the complex intervention to enable analysts to minimise bias and maximise comparability between intervention and usual care groups, so that any observed differences can be attributed to the intervention.ResultsLessons learned include the need for i) detailed data on intervention activity (dates/geography) and usual care; ii) improved information on data linkage quality to accurately characterise control groups; iii) more efficient provision of linked data to ensure timeliness of results; iv) better measurement of confounding characteristics affecting who is eligible, approached and enrolled.ConclusionsLinked administrative data are a valuable resource for evaluations of the FNP national programme and other complex population-level interventions. However, information on local programme delivery and usual care are required to account for biases that characterise those who receive the intervention, and to inform understanding of mechanisms of effect. National, ongoing, robust evaluations of complex public health evaluations would be more achievable if programme implementation was integrated with improved national and local data collection, and robust quasi-experimental designs.

Health hazards of obesity have been recognized for centuries, appearing, for example, in writings attributed to Hippocrates. From the later decades of the 20th century through the present, there have been numerous epidemiological studies of the relationship between excess weight and the total, or all-cause, mortality rate,1 a critical cumulative measure of the public health impact of any health condition. Using body mass index (BMI), an indicator of relative weight for height (weight [kg]/height [m]2) and a frequently used surrogate for assessment of excess body fat, these studies have found linear, U-shaped, or J-shaped relationships between total mortality and BMI. That is, in some studies, both the thin and the obese were more likely to die than those in between. There is, however, always a point at which increasing BMI is associated with increasing mortality risk, but the BMI at which this occurs varies across studies and populations.2 Currently,3 overweight in adults is defined as a BMI of 25.0 to <30.0 kg/m2 and obesity as a BMI of ≥30.0 kg/m2 (Table 1). A number of studies have found no significant relationship between BMI in the overweight range and mortality rate4 and have shown the nadir of mortality risk to be in the overweight range. In particular, commentaries in both the lay press5–7 and scientific literature2,8,9 subsequent to recent reports from National Health and Nutrition Examination Surveys (NHANES)10,11 have highlighted the confusion and controversy regarding this issue. Some have interpreted the recent data to mean that overweight is not detrimental to health and is not in itself a public health concern and that drawing attention to the need for weight loss in this range will have negative effects on the health and well-being of the general population.8 Others have argued …

The testing of Ajax (Asynchronous JavaScript and XML) web applications poses novel challenges for testers because Ajax constructs dynamic web applications by using Asynchronous communication and run time Document Object Model (DOM) manipulation. Ajax involves extreme dynamism, which induces novel kind of issues like state explosion, triggering state changes and unreachable states etc. that require more demanding web-testing methods. Model based testing is amongst the effective approaches to detect faults in web applications. However, the state model generated for an Ajax application can be enormous and may be hit by state explosion problem for large number of user action based changes and for immense dynamism. Recent research has not addressed this issue comprehensively because existing techniques either apply partial reduction or compromise the effectiveness of testing. This research uses soft computing based Fuzzy C Means (FCM) clustering algorithm to generate state machine model of an Ajax web application. The focus is on devising a framework to avoid the state explosion problem. The framework prioritizes the requirements and use cases based on requirements weightage, stakeholder weightage and user session based use case frequency. FCM uses this data to reduce the state space by identifying the most pivotal usage areas. The resultant DOM mutations for only these usage areas are considered to induce the finite state machine thus avoiding the state explosion. Experimental results show that the framework controls the size of generated state machine without compromising the effectiveness of testing.

This paper describes a reduction technique which is very useful against the state explosion problem which occurs when model checking many distributed systems. Timestamps are often used to keep track of the relative order of events. They are usually implemented with very large counters and therefore they generate state explosion. The aim of this paper is to present a very efficient reduction of the state space generated by a model checker when using timestamps. The basic idea is to map the timestamps values to the smallest possible range. This is done dynamically and on-the-fly by adding to the model checker a call to a reduction function after each newly generated state. Our reduction works for model checkers using explicit state enumeration and does not require any change in the model. Our method has been applied to an industrial example and the reduction obtained was spectacular.

Dynamic program analysis is extremely successful both in code debugging and in malicious code attacks. Fuzzing, concolic, and monkey testing are instances of the more general problem of analysing programs by dynamically executing their code with selected inputs. While static program analysis has a beautiful and well established theoretical foundation in abstract interpretation, dynamic analysis still lacks such a foundation. In this paper, we introduce a formal model for understanding the notion of precision in dynamic program analysis. It is known that in sound-by-construction static program analysis the precision amounts to completeness. In dynamic analysis, which is inherently unsound, precision boils down to a notion of coverage of execution traces with respect to what the observer (attacker or debugger) can effectively observe about the computation. We introduce a topological characterisation of the notion of coverage relatively to a given (fixed) observation for dynamic program analysis and we show how this coverage can be changed by semantic preserving code transformations. Once again, as well as in the case of static program analysis and abstract interpretation, also for dynamic analysis we can morph the precision of the analysis by transforming the code. In this context, we validate our model on well established code obfuscation and watermarking techniques. We confirm the efficiency of existing methods for preventing control-flow-graph extraction and data exploit by dynamic analysis, including a validation of the potency of fully homomorphic data encodings in code obfuscation.

Model checking is a technique that can make a verification for finite state systems absolutely automatic. We propose a method for automatic verification of fault-tolerant systems using this technique. Unlike other related work, which is tailored to specific systems, we are aimed at providing a general approach to verification of fault tolerance. The main obstacle in model checking is state explosion. To avoid the problem, we design this method so that it can use SMV, a symbolic model checking tool. Symbolic model checking can overcome the problem by expressing the state space and the transition relation by Boolean functions. Assuming that a system to be verified is specified by guarded commands, we define a modeling language suited for describing guarded command programs and propose a translation method from the modeling language to the input language of SMV. We show the results of applying the proposed method to various examples to demonstrate the usefulness.

Model checking is widely used in verifying properties of concurrent systems. As an improvement, symbolic model checking uses OBDDs to alleviate the state explosion problem. However, the size of OBDDs greatly increases with the system becoming larger and more complex. In symbolic computation Wu's method is efficient to solve algebraic equations, and has succeeded in geometry theorem proving. In this paper, we present a framework to apply Wu's method to symbolic model checking. Briefly, symbolic model checking is carried out by using Wu's method to calculate the characteristic sets of polynomials that represent Kripke structures as well as CTL formulas. This is a new approach to model checking, and is a new successful application of Wu's method.

ABSTRACTPotential Adélie penguin Pygoscelis adeliae breeding habitat in the Australian Antarctic Territory (AAT: 45°E–136°E, 142°E–160°E) was mapped using a geographic information system (GIS), and the literature reviewed for evidence of the mapped habitat being searched for presence or absence of breeding Adélie penguins. It is concluded that incomplete search effort is a possible source of substantial negative bias of Adélie penguin breeding abundance derived from published count data in some regions of the AAT. The extent of search effort in other regions of Antarctica could be determined using the same approach applied here, because GIS data required for mapping potential habitat are available for the entire continent. We would expect that regions with more scientific and tourist activity, such as the Antarctic Peninsula, are likely to have greater search effort than the AAT.

Recommender Systems (RS) tend to recommend more popular items instead of the relevant long-tail items. Mitigating such popularity bias is crucial to ensure that less popular but relevant items are part of the recommendation list shown to the user. In this work, we study the phenomenon of popularity bias in session-based RS (SRS) obtained via deep learning (DL) models. We observe that DL models trained on the historical user-item interactions in session logs (having long-tailed item-click distributions) tend to amplify popularity bias. To understand the source of this bias amplification, we consider potential sources of bias at two distinct stages in the modeling process: i. the data-generation stage (user-item interactions captured as session logs), ii. the DL model training stage. We highlight that the popularity of an item has a causal effect on i. user-item interactions via conformity bias, as well as ii. item ranking from DL models via biased training process due to class (target item) imbalance. While most existing approaches in literature address only one of these effects, we consider a comprehensive causal inference framework that identifies and mitigates the effects at both stages. Through extensive empirical evaluation on simulated and real-world datasets, we show that our approach improves upon several strong baselines from literature for popularity bias and long-tailed classification. Ablation studies show the advantage of our comprehensive causal analysis to identify and handle bias in data generation as well as training stages.

The pure-play technique for estimating divisional cost of capital relies on the use of betas of undiversified firms as proxy divisional betas. This article identifies in this procedure three potential sources of bias that arise from differences between diversified and undiversified firms. A new procedure for using diversified firm data to obtain product-line betas that avoids these sources of bias is developed and tested.

The purpose of a negative control is to reproduce a condition that cannot involve the hypothesized causal mechanism, but does involve the same sources of bias and confounding that may distort the primary association of interest. Observational studies suggest physical inactivity is a major risk factor for cardiovascular disease (CVD), although potential sources of bias, including reverse causation and residual confounding, make it difficult to infer causality. The aim was to employ a negative control outcome to explore the extent to which the association between physical activity and CVD mortality is explained by confounding. The sample comprised 104 851 participants (aged 47 ± 17 years; 45.4% male) followed up over mean (SD) 9.4 ± 4.5 years, recruited from the Health Survey for England and the Scottish Health Survey. There were 10 309 deaths, of which 3109 were attributed to CVD and 157 to accidents (negative control outcome). Accidental death was related to age, male sex, smoking, longstanding illness and psychological distress, with some evidence of social patterning. This confounding structure was similar to that seen with CVD mortality, suggesting that our negative control outcome was appropriate. Physical activity (per SD unit increase in MET-hr-wk) was inversely associated with CVD [hazard ratio (HR) = 0.75; 95% confidence interval (CI), 0.70, 0.80]; the point estimate between physical activity and accidental death was in the same direction but of lesser magnitude (HR = 0.86; 95% CI: 0.69, 1.07). A linear dose-response pattern was observed for physical activity and CVD but not with the negative control. Inverse associations between physical activity and risk of CVD mortality are likely causal but of a smaller magnitude than commonly observed. Negative control studies have the potential to improve causal inference within the physical activity field.

Large language models (LLMs) are increasingly adopted in educational contexts to provide personalized support to students and teachers. The unprecedented capacity of LLM‐based applications to understand and generate natural language can potentially improve instructional effectiveness and learning outcomes, but the integration of LLMs in education technology has renewed concerns over algorithmic bias, which may exacerbate educational inequalities. Building on prior work that mapped the traditional machine learning life cycle, we provide a framework of the LLM life cycle from the initial development of LLMs to customizing pre‐trained models for various applications in educational settings. We explain each step in the LLM life cycle and identify potential sources of bias that may arise in the context of education. We discuss why current measures of bias from traditional machine learning fail to transfer to LLM‐generated text (eg, tutoring conversations) because text encodings are high‐dimensional, there can be multiple correct responses, and tailoring responses may be pedagogically desirable rather than unfair. The proposed framework clarifies the complex nature of bias in LLM applications and provides practical guidance for their evaluation to promote educational equity. Practitioner notes What is already known about this topic The life cycle of traditional machine learning (ML) applications which focus on predicting labels is well understood. Biases are known to enter in traditional ML applications at various points in the life cycle, and methods to measure and mitigate these biases have been developed and tested. Large language models (LLMs) and other forms of generative artificial intelligence (GenAI) are increasingly adopted in education technologies (EdTech), but current evaluation approaches are not specific to the domain of education. What this paper adds A holistic perspective of the LLM life cycle with domain‐specific examples in education to highlight opportunities and challenges for incorporating natural language understanding (NLU) and natural language generation (NLG) into EdTech. Potential sources of bias are identified in each step of the LLM life cycle and discussed in the context of education. A framework for understanding where to expect potential harms of LLMs for students, teachers, and other users of GenAI technology in education, which can guide approaches to bias measurement and mitigation. Implications for practice and/or policy Education practitioners and policymakers should be aware that biases can originate from a multitude of steps in the LLM life cycle, and the life cycle perspective offers them a heuristic for asking technology developers to explain each step to assess the risk of bias. Measuring the biases of systems that use LLMs in education is more complex than with traditional ML, in large part because the evaluation of natural language generation is highly context‐dependent (eg, what counts as good feedback on an assignment varies). EdTech developers can play an important role in collecting and curating datasets for the evaluation and benchmarking of LLM applications moving forward.

Asymmetric dominance as a potential source of bias in hedonic testing

Analysing electronic health records: The benefits of target trial emulation