SHADD: A scenario-based approach to software architectural defects detection

Although a good architecture is not sufficient to guarantee the success of a software product, undoubtedly it is essential to support the product quality. Evaluating software architecture provides early insight into product capabilities and limitations. The earlier in the life cycle the problems are detected, the cheaper it is to fix them. In this paper, at first, a review on well-known scenario-based methods to evaluate software architectures is presented, and their advantages and limitations are discussed. Then, a method named SHADD with different characteristics is introduced to detect architectural defects. Using a scenario-based approach, the proposed method finds out the critical aspects and potential problems threatening the system from the stakeholder’s point of view. Scenarios are then used as a basis for the process of architectural defects detection. SHADD and its elements are specified in a systematic form and an illustration of its application on the architecture of a real system is also presented. The results show that SHADD can be used to detect those architectural defects which may be uncovered during the application of conventional evaluation methods.     

The Cascade Vulnerability Problem: the detection problem and a simulated annealing approach for its correction

The Cascade Vulnerability Problem is a potential problem which must be faced when using the interconnected accredited system approach of the Trusted Network Interpretation. It belongs to a subset of the problem set that addresses the issue of whether the interconnection of secure systems via a secure channel results in a secure distributed system. The Cascade Vulnerability Problem appears when an adversary can take advantage of network connections to compromise information across a range of sensitivity levels that is greater than the accreditation range of any of the component systems s/he must defeat to do so. The general Cascade Vulnerability Problem is presented, the basic properties of the most important detection algorithms are described, a brief comparative analysis is conducted, and a new approach based on simulated annealing for its correction is presented.     

A new iterative approach for multi-objective fault detection observer design and its application to a hypersonic vehicle

This paper addresses the multi-objective fault detection observer design problems for a hypersonic vehicle. Owing to the fact that parameters’ variations, modelling errors and disturbances are inevitable in practical situations, system uncertainty is considered in this study. By fully utilising the orthogonal space information of output matrix, some new understandings are proposed for the construction of Lyapunov matrix. Sufficient conditions for the existence of observers to guarantee the fault sensitivity and disturbance robustness in infinite frequency domain are presented. In order to further relax the conservativeness, slack matrices are introduced to fully decouple the observer gain with the Lyapunov matrices in finite frequency range. Iterative linear matrix inequality algorithms are proposed to obtain the solutions. The simulation examples which contain a Monte Carlo campaign illustrate that the new methods can effectively reduce the design conservativeness compared with the existing methods.     

Risk assessment for build-operate-transfer projects: a dynamic multi-objective programming approach

This study uses a dynamic multi-objective programming approach to establish a risk assessment model, and develops an iterative algorithm for the model solution. The results obtained show that the sum of the interactive utility value could determine whether or not the interactive relationship is characterized by independence among negotiators. In addition, our numerical example shows that the risk measurement model developed can reflect risk assessment made by the negotiation group for certain events, and can analyze interaction characteristics among negotiators.     

Human lifting simulation using a multi-objective optimization approach

This paper presents a multiobjective optimization (MOO) approach to predicting dynamic lifting for a three-dimensional, highly redundant digital human model with 55 degrees of freedom. The optimization problem is formulated to optimize two objective functions simultaneously—dynamic effort and stability—subject to basic physical and kinematical constraints. The predictive dynamics approach is used to solve for the joint angles, torque profiles, and ground reaction forces. The weighted sum approach of MOO is used to aggregate the two objective functions, and the Pareto optimal set for the problem is generated by systematically varying the weighting parameters for the objective functions. Experimental data are used to validate the final simulation. Several examples are presented to demonstrate the effect of the weighting parameters for the two objective functions on the predicted box-lifting strategies. The results show that the proposed MOO approach improves the simulation results compared to the single objective optimization formulation. Also, the formulation is less sensitive to the weighting coefficient for the stability criterion.     

Smart mobile computation offloading: Centralized selective and multi-objective approach

Although mobile devices have been considerably upgraded to more powerful terminals, yet their lightness feature still impose intrinsic limitations in their computation capability, storage capacity and battery lifetime. With the ability to release and augment the limited resources of mobile devices, mobile cloud computing has drawn significant research attention allowing computations to be offloaded and executed on remote resourceful infrastructure. Nevertheless, circumstances like mobility, latency, applications execution overload and mobile device state; any can affect the offloading decision, which might dictate local execution for some tasks and remote execution for others. We present in this article a novel system model for computations offloading which goes beyond existing works with smart centralized, selective, and optimized approach. The proposition consists of (1)hotspots selection mechanism to minimize the overhead of the offloading evaluation process yet without jeopardizing the discovery of the optimal processing environment of tasks, (2)a multi-objective optimization model that considers adaptable metrics crucial for minimizing device resource usage and augmenting its performance, and (3)a tailored centralized decision maker that uses genetics to intelligently find the optimal distribution of tasks. The scalability, overhead and performance of the proposed hotspots selection mechanism and hence its effect on the decision maker and tasks dissemination are evaluated. The results show its ability to notably reduce the evaluation cost while the decision maker was able in turn to maintain optimal dissemination of tasks. The model is also evaluated and the experiments prove its competency over existing models with execution speedup and significant reduction in the CPU usage, memory consumption and energy loss.     

Conservative multi-objective optimization considering design robustness and tolerance: a quality engineering design approach

Methods of multi-objective optimization are proposed to account for tolerance of design variable and variation in problem parameter. The post-optimization effort is initiated from deterministic Pareto-optimal solutions that were obtained from NSGA-II. The successive process to determine search directions and step sizes toward conservative multi-objective solutions was conducted by design of experiments to determine the worst design that had the highest constraint violation. The signal-to-noise (S/N) ratio was also employed to represent the robustness of constrained objective functions under parameter variation. Structural optimization was explored to accommodate both design tolerance and parameter variation and further apply S/N ratio in conservative multi-objective optimization.     

PITAGORAS-PSP: Including domain knowledge in a multi-objective approach for protein structure prediction

Whenever evolutionary algorithms are used to solve certain classes of problems such as those that present a huge search space, the incorporation of problem-specific knowledge is required to achieve adequate levels of performance. In this paper, we propose a multi-objective optimization-based procedure that includes such a domain-specific knowledge to cope with a difficult problem, the protein structure prediction (PSP). This problem is considered to be an open problem as there is no recognized “best” procedure to find solutions. It presents a vast search space and the analysis of each protein conformation requires significant amount of computing time. In our procedure, we provide a reduction of the search space by using the dependent rotamer library and include new heuristics to improve a multi-objective approach to PSP based on the PAES algorithm. As it is shown in the paper, by using benchmark proteins from the CASP8 set, this hybrid PSP procedure provides competitive results when it is compared with some of the better proposals appeared up to now.     

新的基于图像显著性区域特征的织物疵点检测算法

鉴于织物疵点类型的多样性和传统人工检测方法的低效率,为更有效地检测织物疵点,提出一种新的基于图像显著性特征的织物疵点检测方法——SGE。将原织物图分成相同两份:一份利用改进的基于频率的显著性区域(FSR)方法提取区域特征,粗定位疵点位置。另一份先Gabor滤波,取Gabor模图为输出特征;再利用基于像素的显著性区域(PSR)方法进行区域特征提取,细定位疵点位置;然后利用最大熵分别对粗细定位的疵点图进行分割,再融合;最后描绘轮廓,计算周长和面积,去除孤立点,得最终检测结果。采用OpenCV算法库,选取了4种具有代表的织物疵点图片进行验证。实验结果表明,这种粗细定位疵点的方法能够获得较好的检测结果,无需事先学习,能够满足实时性要求。     

Feature interaction detection: a LOTOS-based approach

A methodology to detect feature interactions in the design of telephone systems is presented. First, the system with its features is specified in language of temporal ordering specification (LOTOS). With the use of tools, the specification is then expanded into a state transition model, representing either the entire global state space or significant portions of it. Properties describe proper system and feature behavior; violations to these properties are symptoms of feature interaction. Observers, watchdogs, and trace analysis tools are constructed to check for deviations from these properties. These processes are used to search the state transitions, both during and after the construction of the state transition model. Traces presenting violation of properties are printed as message sequence charts (MSCs).     

基于博弈论的排队网络业务分配算法

在排队网络中用户最优的用户均衡(UE)业务分配模型和全局代价最小化的系统最优(S0)业务分配模型之间存在着本质上的矛盾,很难同时实现.同时由于网络业务流的动态性和随机性,无法通过确定的模型 对其进行建模.为了解决这两个问题,提出了一种基于博弈论的排队网络业务分配算法.该算法将UE和SO之间的竞争建模为一种博弈,然后使用斯坦科尔伯格主从博弈理论对两者的收益进行均衡,并获得了更加可行的业务分配.仿真比较了该模型和非合作模型下SO及UE的性能,仿真结果验证了该模型的可行性,并且相对确定模型,该模型对网络业务流的建模更精确.     

Simulation of lifting motions using a novel multi-objective optimization approach

In this study, a novel lifting motion simulation model was developed based on a multi-objective optimization (MOO) approach. Two performance criteria, minimum physical effort and maximum load motion smoothness, were selected to define the multi-objective function in the optimization procedure using a weighted-sum MOO approach. Symmetric lifting motions performed by younger and older adults under varied task conditions were simulated. The results showed that the proposed MOO approach led to up to 18.9% reductions in the prediction errors compared to the single-objective optimization approach. This finding suggests that both minimum physical effort and maximum load motion smoothness play an important role in lifting motion planning. Age-related differences in the mechanisms for planning lifting motions were also investigated. In particular, younger workers tend to rely more on the criterion of minimizing physical effort during lifting motion planning, while maximizing load motion smoothness seems to be the dominant objective for older workers.Relevance to industryLifting tasks are closely associated with occupational low back pain (LBP). In this study, a novel lifting motion simulation model was developed to facilitate the analysis of lifting biomechanics and LBP prevention. Age-related differences in lifting motion planning were discussed for better understanding LBP injury mechanisms during lifting.     

Calibration of imprecise and inaccurate numerical models considering fidelity and robustness: a multi-objective optimization-based approach

Traditionally, model calibration is formulated as a single objective problem, where fidelity to measurements is maximized by adjusting model parameters. In such a formulation however, the model with best fidelity merely represents an optimum compromise between various forms of errors and uncertainties and thus, multiple calibrated models can be found to demonstrate comparable fidelity producing non-unique solutions. To alleviate this problem, the authors formulate model calibration as a multi-objective problem with two distinct objectives: fidelity and robustness. Herein, robustness is defined as the maximum allowable uncertainty in calibrating model parameters with which the model continues to yield acceptable agreement with measurements. The proposed approach is demonstrated through the calibration of a finite element model of a steel moment resisting frame.     

PGMA: An algorithmic approach for multi-objective hardware software partitioning

Designing embedded systems efficiently has always been of significant interest. This has been tremendously scaled-up for contemporary and high-end applications with their increasing complexity and the need to satisfy multiple conflicting constraints. This paper presents a high-speed Hardware Software Partitioning technique for the design of such systems. The partitioning problem has been modeled as a multi-dimensional optimization problem with the aim of minimizing the area utilization, power dissipation, time of execution and system memory requirement of the implementation. A two-phased algorithm (Phased Greedy Metaheuristic Algorithm or PGMA) has been proposed which also takes into consideration the communication costs between hardware and software Processing-Engines (PEs) while partitioning. Subsequently, a detailed empirical analysis of the proposed algorithm is presented to ascertain its efficiency, quality and speed. The execution time is as low as 18 ms for partitioning an algorithm consisting of 1000 blocks. Thereafter, the proposed algorithm is applied to a real-life embedded system, the Joint Photographic Expert-Group (JPEG) Encoder, to demonstrate its effectiveness. For a power constraint of 600 mW, an area utilization of 58.28% has been achieved, which is the maximum amongst all the reported works till date, to the best of our knowledge. This allowed for a decreased offloading of tasks to software, resulting in a memory usage of only 14 KB and execution time of 20 ms.     

Automatic generation of controllers for embodied legged organisms: a Pareto evolutionary multi-objective approach

In this paper, we investigate the use of a self-adaptive Pareto evolutionary multi-objective optimization (EMO) approach for evolving the controllers of virtual embodied organisms. The objective of this paper is to demonstrate the trade-off between quality of solutions and computational cost. We show empirically that evolving controllers using the proposed algorithm incurs significantly less computational cost when compared to a self-adaptive weighted sum EMO algorithm, a self-adaptive single-objective evolutionary algorithm (EA) and a hand-tuned Pareto EMO algorithm. The main contribution of the self-adaptive Pareto EMO approach is its ability to produce sufficiently good controllers with different locomotion capabilities in a single run, thereby reducing the evolutionary computational cost and allowing the designer to explore the space of good solutions simultaneously. Our results also show that self-adaptation was found to be highly beneficial in reducing redundancy when compared against the other algorithms. Moreover, it was also shown that genetic diversity was being maintained naturally by virtue of the system's inherent multi-objectivity.     

Solving a dial-a-ride problem with a hybrid evolutionary multi-objective approach: Application to demand responsive transport

Demand responsive transport allows customers to be carried to their destination as with a taxi service, provided that the customers are grouped in the same vehicles in order to reduce operational costs. This kind of service is related to the dial-a-ride problem. However, in order to improve the quality of service, demand responsive transport needs more flexibility. This paper tries to address this issue by proposing an original evolutionary approach. In order to propose a set of compromise solutions to the decision-maker, this approach optimizes three objectives concurrently. Moreover, in order to intensify the search process, this multi-objective evolutionary approach is hybridized with a local search. Results obtained on random and realistic problems are detailed to compare three state-of-the-art algorithms and discussed from an operational point of view.     

An interactive possibilistic programming approach for a multi-objective hub location problem: Economic and environmental design

This paper presents a new multi-objective mathematical model for a multi-modal hub location problem under a possibilistic-stochastic uncertainty. The presented model aims to minimize the total transportation and traffic noise pollution costs. Furthermore, it aims to minimize the maximum transportation time between origin-destination nodes to ensure a high probability of meeting the service guarantee. In order to cope with the uncertainties and the multi-objective model, we propose a two-phase approach, including fuzzy interactive multi-objective programming approach and an efficient method based on the Me measure. Due to the NP-hardness of the presented model, two meta-heuristic algorithms, namely hybrid differential evolution and hybrid imperialist competitive algorithm, are developed. Furthermore, a number of sensitivity analyses are provided to demonstrate the effectiveness of the presented model. Finally, the foregoing meta-heuristics are compared together through different comparison metrics.     

多目标约束下逆变器阻抗的电流矫正方法

弱电网下锁相环与电网阻抗间的耦合会造成系统不稳定.含前置低通滤波器锁相环的并网逆变器虽能改善并网系统稳定性,但存在并网电流相位滞后问题.本文通过将滤波器从锁相环支路“拆分”,提出了一种电流二阶校正的阻抗重塑法,以基波电流控制性能、相角裕度、低频幅值为约束建立了多目标约束函数,单独优化设计了二阶校正环节参数,提高了并网逆变器的弱网稳定性,实现弱电网下的单位功率因数并网.最后在StarSim实验平台验证了所提控制方法的有效性.     

Automated image-orientation detection: a scalable boosting approach

With the proliferation of digital cameras and self-publishing of photos, automatic detection of image orientation has become an important part of photo-management systems. In this paper, we present a novel system, based on combining the outputs of hundreds of classifiers trained with AdaBoost, to determine the upright orientation of an image. We thoroughly test our system on photos gathered from professional and amateur photo collections that have been taken with a variety of cameras (digital, film, camera phones). The test images include photos that are in color and black and white, realistic and abstract, and outdoor and indoor. As this system is intended for mass consumer deployment, efficiency in use and accessibility is paramount. Results show that the presented method surpasses similar methods based on Support Vector Machines, in terms of both accuracy and feasibility of deployment.

---