Computer Science

Agile Development Methods adalah sekelompok metodologi pengembangan perangkat lunak yang didasarkan pada prinsip-prinsip yang sama atau pengembangan sistem jangka pendek yang memerlukan adaptasi cepat dari pengembang terhadap perubahan dalam bentuk apapun. Agile development methods merupakan salah satu dari Metodologi pengembangan perangkat lunak yang digunakan dalam pengembangan perangkat lunak. Agile memiliki pengertian bersifat cepat, ringan, bebas bergerak, dan waspada. [1] Sehingga saat membuat perangkat lunak dengan menggunakan agile development methods diperlukan inovasi dan responsibiliti yang baik antara tim pengembang dan klien agar kualitas dari perangkat lunak yang dihasilkan bagus dan kelincahan dari tim seimbang.

Agile Development Methods adalah sekelompok metodologi pengembangan perangkat lunak yang didasarkan pada prinsip-prinsip yang sama atau pengembangan sistem jangka pendek yang memerlukan adaptasi cepat dari pengembang terhadap perubahan dalam bentuk apapun. Agile development methods merupakan salah satu dari Metodologi pengembangan perangkat lunak yang digunakan dalam pengembangan perangkat lunak. Agile memiliki pengertian bersifat cepat, ringan, bebas bergerak, dan waspada. [1] Sehingga saat membuat perangkat lunak dengan menggunakan agile development methods diperlukan inovasi dan responsibiliti yang baik antara tim pengembang dan klien agar kualitas dari perangkat lunak yang dihasilkan bagus dan kelincahan dari tim seimbang.

Since the use of computers in business world, data collection has become one of the most important issues due to the available knowledge in the data; such data has been stored in database. Database system was developed which led to the evolvement of hierarchical and relational database followed by Standard Query Language (SQL). As data size increases, the need for more control and information retrieval increase. These increases lead to the development of data mining systems and data warehouses.

الشاعر والمفكر والفيلسوف العلامة محمد اقبال واحد من اشهر الشعراء خلال حقبة النصف الاول من القرن العشرين, و هو من الهند المستعمرة من قبل  بريطانيا مسبقا و حاليا الباكستان .كتب العديد من القصائد العتيدة والمقالات حول الدين والاخلاقيات في الاسلام. قصائده ثورية الروحية  وبعض منها بالإمكان تناوله كرثاء لتقهقر الحضارة الاسلامية .هذه الورقة البحثية تلقي بالضوء على مسألتين مهمتين في الحياة و هما الدين والعلم, الشاعر يتعامل من الحقيقة من الدين و بفلسفته الخاصة و كذلك الباحث يقوم بتوضيح اراء الشاعر بالعلوم الغربية و علاقتها بالدين و بناء الشخصية المثالية للفرد

The application of metaheuristic search techniques in test data generation has been extensively investigated in recent years. Most studies, however, have concentrated on the application of such techniques in structural testing. The use of search-based techniques in functional testing is less frequent, the main cause being the implicit nature of the specification. On the other hand, such techniques could be employed in functional test generation if an explicit, graph-based, model, that describes the algorithm used to produce the required results, existed. However, the process of creating and validating such a model is usually a highly-specialized and time consuming task, which quite often cannot be economically justified in the case of non-safety-critical applications. In this paper we propose a framework for genetic model based testing. Under this framework, a graph-based model of the system under test is built using a genetic algorithm. Test data is then derived from the resulting model using (possibly) metaheuristic search techniques to provide the desired level of coverage. The approach is illustrated with a case study: an array sorting program.

In this paper, a hardware system for Sobel Edge Detection Algorithm is designed and simulated for a 128 pixel, 8-bit monochrome line-scan camera. The system is designed to detect objects as they move along a conveyor belt in a manufacturing environment, the camera will observe dark objects on a light conveyor belt. The edge detector is required to detect horizontal and vertical edges using Sobel edge detection method. The Sobel operator requires 3 lines and takes 3 pixels per line, thus using a 3×3 input block to process each pixel. The centre pixel of the 3×3 pixel block can be classified as an edge point or otherwise by thresholding the value from the operator. The FPGA based Sobel edge detector is designed and simulated using Altera Quartus II 8.1 web edition by targeting Cyclone II development boards.

Contour representation of binary object is increasingly used in image processing and pattern recognition. Chain code and crack code are popular methods of contour encoding. However, by using these methods, an accurate estimate of geometric features like area and perimeter of objects are difficult to obtain. Mid-crack code, another contour encoding method, can help to obtain more accurate estimate of the geometric features of objects. Though a considerable amount of reduction of the size of images is obtained by fixed-length mid-crack code, yet, more efficient encoding is possible by considering and applying variable-length encoding technique. In this paper, a compressed mid-crack code is proposed based on the Huffman code. Experiments performed on different images yield that the proposed representation reduces the number of bits require to encode the contour of an image with compared to the classical mid-crack code.

Over the past two decades, research has focused on simplifying dependability analysis by looking at how we can synthesise dependability information from system models automatically. This has led to the field of model-based safety assessment (MBSA), which has attracted a significant amount of interest from industry, academia, and government agencies. Different model-based safety analysis methods, such as Hierarchically Performed Hazard Origin & Propagation Studies (HiP-HOPS), are increasingly applied by industry for dependability analysis of safety-critical systems. Such systems may feature multiple modes of operation where the behaviour of the systems and the interactions between system components can change according to what modes of operation the systems are in.

MBSA techniques usually combine different classical safety analysis approaches to allow the analysts to perform safety analyses automatically or semi-automatically. For example, HiP-HOPS is a state-of-the-art MBSA approach which enhances an architectural model of a system with logical failure annotations to allow safety studies such as Fault Tree Analysis (FTA) and Failure Modes and Effects Analysis (FMEA). In this way it shows how the failure of a single component or combinations of failures of different components can lead to system failure. As systems are getting more complex and their behaviour becomes more dynamic, capturing this dynamic behaviour and the many possible interactions between the components is necessary to develop an accurate failure model.

One of the ways of modelling this dynamic behaviour is with a state-transition diagram. Introducing a dynamic model compatible with the existing architectural information of systems can provide significant benefits in terms of accurate representation and expressiveness when analysing the dynamic behaviour of modern large-scale and complex safety-critical systems. Thus the first key contribution of this thesis is a methodology to enable MBSA techniques to model dynamic behaviour of systems. This thesis demonstrates the use of this methodology using the HiP-HOPS tool as an example, and thus extends HiP-HOPS with state-transition annotations. This extension allows HiP-HOPS to model more complex dynamic scenarios and perform compositional dynamic dependability analysis of complex systems by generating Pandora temporal fault trees (TFTs). As TFTs capture state, the techniques used for solving classical FTs are not suitable to solve them. They require a state space solution for quantification of probability. This thesis therefore proposes two methodologies based on Petri Nets and Bayesian Networks to provide state space solutions to Pandora TFTs.

Uncertainty is another important (yet incomplete) area of MBSA: typical MBSA approaches are not capable of performing quantitative analysis under uncertainty. Therefore, in addition to the above contributions, this thesis proposes a fuzzy set theory based methodology to quantify Pandora temporal fault trees with uncertainty in failure data of components.

The proposed methodologies are applied to a case study to demonstrate how they can be used in practice. Finally, the overall contributions of the thesis are evaluated by discussing the results produced and from these conclusions about the potential benefits of the new techniques are drawn.

ISO 26262, titled Road Vehicles–Functional Safety, is the new automotive functional safety standard for passenger vehicle industry. In order to accomplish the goal of designing and developing dependable automotive systems , ISO 26262 uses the concept of Automotive Safety Integrity Levels (ASILs), the adaptation of Safety Integrity Levels. ASILs are allocated to the components and subsystems that can cause system failure and malfunctions that lead to hazards. ASILs allocation is a hard problem consists of finding the optimal allocation of safety levels to the system architecture which must guarantee that the highest safety requirements are met while development cost of the automotive system is kept minimum. There were many successful attempts to solve this problem using different techniques. However, it is worth pointing out that there is an absence of a review that provides an in-depth study of all the existing methods and highlights their merits and demerits. This paper presents an overview of different approaches that were used to solve ASILs allocation problem. The review provides an overview of safety requirements including the related standards followed by a study of the resolution methods of the existing approaches. The study of each approach provides a detailed explanation of the used methodology and a discussion of its strength and weaknesses including the main open challenges.

Critical technological systems exhibit complex dynamic characteristics such as time-dependent behaviour, functional dependencies among events, sequencing and priority of causes that may alter the effects of failure. Dynamic fault trees (DFTs) have been used in the past to model the failure logic of such systems, but the quantitative analysis of DFTs has assumed the existence of precise failure data and statistical independence among events, which are unrealistic assumptions. In this paper, we propose an improved approach to reliability analysis of dynamic systems, allowing for uncertain failure data and statistical and stochastic dependencies among events. In the proposed framework, DFTs are used for dynamic failure modelling. Quantitative evaluation of DFTs is performed by converting them into generalised stochastic Petri nets. When failure data are unavailable, expert judgment and fuzzy set theory are used to obtain reasonable estimates. The approach is demonstrated on a simplified model of a Cardiac Assist System.

Over the years, several approaches have been developed for the quantitative analysis of dynamic fault trees (DFTs). These approaches have strong theoretical and mathematical foundations; however, they appear to suffer from the state-space explosion and high computational requirements, compromising their efficacy. Modularisation techniques have been developed to address these issues by identifying and quantifying static and dynamic modules of the fault tree separately by using binary decision diagrams and Markov models. Although these approaches appear effective in reducing computational effort and avoiding state-space explosion, the reliance of the Markov chain on exponentially distributed data of system components can limit their widespread industrial applications. In this paper, we propose a hybrid modularisation scheme where independent sub-trees of a DFT are identified and quantified in a hierarchical order. A hybrid framework with the combination of algebraic solution, Petri Nets, and Monte Carlo simulation is used to increase the efficiency of the solution. The proposed approach uses the advantages of each existing approach in the right place (independent module). We have experimented the proposed approach on five independent hypothetical and industrial examples in which the experiments show the capabilities of the proposed approach facing repeated basic events and non-exponential failure distributions. The proposed approach could provide an approximate solution to DFTs without unacceptable loss of accuracy. Moreover, the use of modularised or hierarchical Petri nets makes this approach more generally applicable by allowing quantitative evaluation of DFTs with a wide range of failure rate distributions for basic events of the tree. INDEX TERMS Reliability analysis, fault tree analysis, dynamic fault trees, modularisation, petri nets.

Temporal fault trees (TFTs), an extension of classical Boolean fault trees, can model timedependent failure behaviour of dynamic systems. The methodologies used for quantitative analysis of TFTs include algebraic solutions, Petri nets (PN), and Bayesian networks (BN). In these approaches, precise failure data of components are usually used to calculate the probability of the top event of a TFT. However, it can be problematic to obtain these precise data due to the imprecise and incomplete information about the components of a system. In this paper, we propose a framework that combines intuitionistic fuzzy set theory and expert elicitation to enable quantitative analysis of TFTs of dynamic systems with uncertain data. Experts' opinions are taken into account to compute the failure probability of the basic events of the TFT as intuitionistic fuzzy numbers. Subsequently, for the algebraic approach, the intuitionistic fuzzy operators for the logic gates of TFT are defined to quantify the TFT. On the other hand, for the quantification of TFTs via PN and BN-based approaches, the intuitionistic fuzzy numbers are defuzzified to be used in these approaches. As a result, the framework can be used with all the currently available TFT analysis approaches. The effectiveness of the proposed framework is illustrated via application to a practical system and through a comparison of the results of each approach.

This article proposes an approach named SafeML II, which applies empirical cumulative distribution function-based statistical distance measures in a designed human-in-theloop procedure to ensure the safety of machine learning-based classifiers in autonomous vehicle software.