Toward a Formalism for Conservative Claims about the Dependability of Software-Based Systems

In recent work, we have argued for a formal treatment of confidence about the claims made in dependability cases for software-based systems. The key idea underlying this work is "the inevitability of uncertainty": It is rarely possible to assert that a claim about safety or reliability is true with certainty. Much of this uncertainty is epistemic in nature, so it seems inevitable that expert judgment will continue to play an important role in dependability cases. Here, we consider a simple case where an expert makes a claim about the probability of failure on demand (pfd) of a subsystem of a wider system and is able to express his confidence about that claim probabilistically. An important, but difficult, problem then is how such subsystem (claim, confidence) pairs can be propagated through a dependability case for a wider system, of which the subsystems are components. An informal way forward is to justify, at high confidence, a strong claim, and then, conservatively, only claim something much weaker: "I'm 99 percent confident that the pfd is less than 10-5, so it's reasonable to be 100 percent confident that it is less than 10-3." These conservative pfds of subsystems can then be propagated simply through the dependability case of the wider system. In this paper, we provide formal support for such reasoning

Detecting Fraud Patterns in Telecommunications Using Case Based Reasoning

All round the world, fraud situations are significantly causing huge revenue leakage in the telecommunication companies every year. The reuse of previous cases is an important issue in dealing with fraud pattern in a data with string features. Case Based Reasoning (CBR) systems have a set of cases inform of library used to facilitate the process of validation of new cases without the direct involvement of a domain expert. The proposed detection technique in this paper is based on Case Based Reasoning used to detect the occurrence of fraud with a meaningful confidence in telecommunication data. Experimental result on the fraud data indicates that the weight for all attribute used in this study needs to be set as 0.1 in order to get 98% similarity performance

Representation of Confidence in Assurance Cases Using the Beta Distribution

Assurance cases are used to document an argument that a system—such as a critical software system—satisfies some desirable property (e.g., safety, security, or reliability). Demonstrating high confidence that the claims made based on an assurance case can be trusted is crucial to the success of the case. Researchers have proposed quantification of confidence as a Baconian probability ratio of eliminated concerns about the assurance case to the total number of identified concerns. In this paper, we extend their work by mapping this discrete ratio to a continuous probability distribution—a beta distribution— enabling different visualizations of the confidence in a claim. Further, the beta distribution allows us to quantify and visualize the uncertainty associated with the expressed confidence. Additionally, by transforming the assurance case into a reasoning structure, we show how confidence calculations can be performed using beta distributions

mHealth optimisation for education and physical activity in Type 1 diabetes: MEDPAT1.

Aims: To develop and evaluate usability of prototype personalised prediction algorithms for people with Type 1 diabetes to optimise blood glucose control associated with physical activity using smart phone technology. To explore the potential to build a knowledge repository founded on case-based reasoning and linkage with an online structured education programme that will increase confidence and levels of participation in physical activity

Use of Case Studies to Promote Critical Thinking in Occupational Therapy Students

Critical thinking is a necessary component of clinical reasoning that should be addressed as part of the graduate curriculum for occupational therapists. For students to apply critical thinking in practice they must also have confidence and minimal anxiety about their abilities. Case-based learning was chosen to address critical thinking skill development, increase confidence, and decrease anxiety. The purpose of this mixed methods pilot study was to evaluate progressively independent engagement with case-based learning on student performance, confidence, and anxiety in applying critical thinking skills in the clinical setting. Critical thinking was measured using a rubric to assess students’ performance in analyzing the case studies with decreasing levels of instructor guidance over the semester. Confidence and anxiety levels were measured using a Likert confidence and anxiety scale at the start and end of the semester. Qualitative data was collected through telephone interviews to determine ways in which classroom activities translated to a clinical setting and to elicit student feedback about the lesson design and what they felt they learned in class after the first Level II fieldwork. All three areas of critical thinking, confidence, and anxiety improved, demonstrating a significance of

Investigating effort prediction of web-based applications using CBR on the ISBSG dataset

As web-based applications become more popular and more sophisticated, so does the requirement for early accurate estimates of the effort required to build such systems. Case-based reasoning (CBR) has been shown to be a reasonably effective estimation strategy, although it has not been widely explored in the context of web applications. This paper reports on a study carried out on a subset of the ISBSG dataset to examine the optimal number of analogies that should be used in making a prediction. The results show that it is not possible to select such a value with confidence, and that, in common with other findings in different domains, the effectiveness of CBR is hampered by other factors including the characteristics of the underlying dataset (such as the spread of data and presence of outliers) and the calculation employed to evaluate the distance function (in particular, the treatment of numeric and categorical data)

Assessing confidence in cased based reuse step

Case-Based Reasoning (CBR) is a learning approach that solves current situations by reusing previous solutions that are stored in a case base. In the CBR cycle the reuse step plays an important role into the problem solving process, since the solution for a new problem is based in the available solutions of the retrieved cases. In classification tasks a trivial reuse method is commonly used, which takes into account the most frequently solution proposed by the set of retrieved cases. We propose an alternative reuse process; we call confidence-reuse method, which make a qualitative assessment of the information retrieved. This approach is focused on measuring the solution accuracy, applying some confidence predictors based in a k-NN classifier with the aim of analyzing and evaluating the information offered by the retrieved cases.Peer Reviewe

Construction cost estimation using a case-based reasoning hybrid genetic algorithm based on local search method

Estimates of project costs in the early stages of a construction project have a significant impact on the operator\u27s decision-making in essential matters, such as the site\u27s decision or the construction period. However, it is not easy to carry out the initial stage with confidence, because information such as design books and specifications is not available. In previous studies, case-based reasoning (CBR) is used to estimate initial construction costs, and genetic algorithms are used to calculate the weight of the retrieve phase in CBR\u27s process. However, it is difficult to draw a better solution than the current one, because existing genetic algorithms use random numbers. To overcome these limitations, we reflect correlation numbers in the genetic algorithms by using the method of local search. Then, we determine the weights using a hybrid genetic algorithm that combines local search and genetic algorithms. A case-based reasoning model was developed using a hybrid genetic algorithm. Then, the model was verified with construction cost data that were not used for the development of the model. As a result, it was found that the hybrid genetic algorithm and case-based reasoning applied with the local search performed better than the existing solution. The detail mean error value was found to be 3.52%, 6.15%, and 0.33% higher for each case than the previous one

Rational Use of Antimicrobials-Improving Clinical Reasoning with Case Based Learning

Responsible antimicrobial use is not only a day one competency required by the Royal College of Veterinary Surgeons but is of vital importance in reducing the development of future antimicrobial resistance. Feedback from veterinary students in their final year of training at the University of Glasgow mirrored the observations of tutors, in that while all were aware of the increasingly important issue of antimicrobial resistance, most felt lacking in their ability to make rational clinical decisions regarding antimicrobial use in the field. To address this issue, a small group, two hour tutorial was implemented, using a case-based discussion format, with students having on-line access to a PowerPoint of the clinical scenarios, and links to additional printed resources prior to the tutorial. Student feedback was gathered after each tutorial with an optional questionnaire, and this data was used to evaluate the efficacy of this format in improving student confidence and clinical reasoning with regard to antimicrobial use. Results of the questionnaire were extremely positive, with a very high percentage of students agreeing that the tutorial had improved their confidence and decision making ability in this area. Several students requested more of the same, and others suggested that similar tutorials be given earlier in the course

PENGARUH SELF CONFIDENCE SISWA TERHADAP KEMAMPUAN PENALARAN MATEMATIS SISWA SMK NEGERI DI KOTA CIMAHI

This study aims to conduct an analysis in order to determine the effect of students' self confidence in the mathematical reasoning abilities of vocational students. The method carried out by researchers is a correlational research method based on a quantitative approach, which connects the presence or absence of influence in mathematical reasoning ability with self confidence. Sampling of research conducted is as many as 20 students in one of the State Vocational Schools in Cimahi City. In this case the two influences of cognitive and affective abilities consist of tests and non-tests. The test is done by using mathematical reasoning abilities using three indicators as many as 5 items, while the non test uses a self confidence questionnaire of 20 statement scale. Based on the results that have been obtained that students' self confidence has a positive influence on the ability to think mathematical reasoning of vocational students