Black Box Perspective Research Articles

Demonstrating TabEE: Tabular Embedding Explanations

We present TabEE, Tab ular E mbedding E xplanations, a framework designed to generate explanations for interpreting tabular embedding models. Our framework aims to furnish both local and global explanations for the original data, facilitating the detection of potential flaws in embedding models. TabEE is versatile and compatible with any tabular embedding algorithm, as it adheres to the black box perspective of embedding models. The generated explanations also enable comparisons between multiple embedding models. This demonstration illustrates the effectiveness of TabEE in providing interpretable insights into tabular embedding models, contributing to improved model understanding and credibility assessment.

SFIDMT-ART: A metamorphic group generation method based on Adaptive Random Testing applied to source and follow-up input domains

Context:The performance of metamorphic testing relates strongly to the quality of test cases. However, most related research has only focused on source test cases, ignoring follow-up test cases to some extent. In this paper, we identify a potential problem that may be encountered with existing metamorphic group generation algorithms. We then propose a possible solution to address this problem. Based on this solution, we design a new algorithm for generating effective source and follow-up test cases. Objective:To improve the performance (test effectiveness and efficiency) of metamorphic testing. Methods:We introduce the concept of the input-domain difference problem, which is likely to affect the performance of metamorphic group generation algorithms. We propose a new test-case distribution criterion for metamorphic testing to address this problem. Based on our proposed criterion, we further present a new metamorphic group generation algorithm, from a black-box perspective, with new distance metrics to facilitate this algorithm. Results:Our algorithm performs significantly better than existing algorithms, in terms of test effectiveness, efficiency and test-case diversity. Conclusions:Through experiments, we find that the input-domain difference problem is likely to affect the performance of metamorphic group generation algorithms. The experimental results demonstrate that our algorithm can achieve good test efficiency, effectiveness, and test-case diversity.

ICVTest: A Practical Black-Box Penetration Testing Framework for Evaluating Cybersecurity of Intelligent Connected Vehicles

Intelligent connected vehicles (ICVs) are equipped with extensive electronic control units which offer convenience but also pose significant cybersecurity risks. Penetration testing, recommended in ISO/SAE 21434 “Road vehicles—Cybersecurity engineering”, is an effective approach to identify cybersecurity vulnerabilities in ICVs. However, there is limited research on vehicle penetration testing from a black-box perspective due to the complex architecture of ICVs. Additionally, no penetration testing framework has been proposed to guide security testers on conducting penetration testing for the whole vehicle. The lack of framework guidance results in the inexperienced security testers being uncertain about the processes to follow for conducting penetration testing. Moreover, the inexperienced security testers are unsure about which tests to perform in order to systematically evaluate the vehicle’s cybersecurity. To enhance the penetration testing efficiency of ICVs, this paper presents a black-box penetration testing framework, ICVTest. ICVTest proposes a standardized penetration testing process to facilitate step-by-step completion of the penetration testing, thereby addressing the issue of inexperienced testers lacking guidance on how to initiate work when confronted with ICV. Also, ICVTest includes 10 sets of test cases covering hardware and software security tests. Testers can select appropriate test cases based on the specific cybersecurity threats faced by the target object, thereby reducing the complexity of penetration testing tasks. Furthermore, we have developed a vehicle cybersecurity testing platform for ICVTest that seamlessly integrates various testing tools. The platform enables even novice testers to conduct vehicle black-box penetration testing in accordance with the given guidance which addresses the current industry’s challenge of an overwhelming number of testing tasks coupled with a shortage of skilled professionals. For the first time, we propose a comprehensive black-box penetration testing framework and implement the framework in the form of a cybersecurity testing platform. We apply ICVTest to evaluate an electric vehicle manufactured in 2021 for assessing the framework’s availability. With the aid of ICVTest, even testers with limited experience in automotive penetration can effectively evaluate the security risks of ICVs. In our experiments, numerous cybersecurity vulnerabilities were identified involving in-vehicle sensors, remote vehicle control systems, and in-vehicle controller area network (CAN) bus.

The contribution of different face parts to deep face recognition

The development of face recognition improvements still lacks knowledge on what parts of the face are important. In this article, the authors present face parts analysis to obtain important recognition information in a certain area of the face, more than just the eye or eyebrow, from the black box perspective. In addition, the authors propose a more advanced way to select parts without introducing artifacts using the average face and morphing. Furthermore, multiple face recognition systems are used to analyze the face component contribution. Finally, the results show that the four deep face recognition systems produce a different behavior for each experiment. However, the eyebrows are still the most important part of deep face recognition systems. In addition, the face texture played an important role deeper than the face shape.

Visual Identification of Mobile App GUI Elements for Automated Robotic Testing.

Automated robotic testing is an emerging testing approach for mobile apps that can afford complete black-box testing. Compared with other automated testing approaches, automatic robotic testing can reduce the dependence on the internal information of apps. However, capturing GUI element information accurately and effectively from a black-box perspective is a critical issue in robotic testing. This study introduces object detection technology to achieve the visual identification of mobile app GUI elements. First, we consider the requirements of test implementation, the feasibility of visual identification, and the external image features of GUI comprehensively to complete the reasonable classification of GUI elements. Subsequently, we constructed and optimized an object detection dataset for the mobile app GUI. Finally, we implement the identification of GUI elements based on the YOLOv3 model and evaluate the effectiveness of the results. This work can serve as the basis for vision-driven robotic testing for mobile apps and presents a universal approach that is not restricted by platforms to identify mobile app GUI elements.

Side Channel Leakages Against Financial IC Card of the Republic of Korea

Integrated circuit (IC) chip cards are commonly used in payment system applications since they can provide security and convenience simultaneously. More precisely, Europay, MasterCard, and VISA (EMV) are widely known to be well equipped with security frameworks that can defend against malicious attacks. On the other hand, there are other payment system applications at the national level. In the case of the Republic of Korea, standards for financial IC card specifications are established by the Korea Financial Telecommunications and Clearings Institute. Furthermore, security features defending against timing analysis, power analysis, electromagnetic analysis, and TEMPEST are required. This paper identifies side channel leakages in the financial IC cards of the Republic of Korea, although there may be side channel countermeasures. Side channel leakages in the financial IC cards of the Republic of Korea are identified for the first time since the side channel countermeasures were included in the standards. The countermeasure that is applied to the IC card from a black box perspective is estimated to measure security features against power analysis. Then, in order to investigate whether an underlying countermeasure is applied, first-order and second-order power analyses are performed on the main target, e.g., a S-box of the block cipher SEED that is employed in the financial system. Furthermore, the latest proposal in ICISC 2017 is examined to apply block cipher SEED to the financial IC card protocol. As a result, it is possible to identify some side channel leakages while expanding the lemma of the paper accepted in ICISC 2017. Algebraic logic is also constructed to recover the master key from some round keys. Finally, it is found that only 20,000 traces are required to find the master key.

Contextualising contexts – Scholarship of Teaching and Learning and cultural difference

SOTL has an originary moment and a well-rehearsed history. It has been constructed as an approach to understanding teaching and learning with a focus on reflective practice to improve practice and student learning, but it excludes sociocultural influences. This paper argues that by concentrating on the ‘classroom’, and cognitive measures of success, SOTL conforms to a Western view of what is valuable in education. This instrumentalist perspective allows the process of SOTL to seem universally applicable. It is argued that this instrumentalist approach is a black-box perspective, analogous to the black box of the mind seen in behaviourism. Using Chakrabarty’s notion of “provincializing Europe”, some structural elements of SOTL history are analysed with an eye to provincialising SOTL. Following this, a personal teaching experience across two cultures is described to demonstrate the inability of SOTL to comprehend the behaviour of students across different cultures without providing more contextual information. Differences between East Asian and Western views of cognition are presented to suggest that cultural differences in conceptions of learning have a significant effect on what happens in the classroom. In conclusion, if SOTL is to be meaningful internationally, it needs both to embrace and be explicit about sociocultural influences and, crucially, it needs to be decentred from Western hegemonic practice.
 How to cite this article:
 LOOKER, Peter. Contextualising contexts – Scholarship of Teaching and Learning and cultural difference. Scholarship of Teaching and Learning in the South, v. 2, n. 1, p. 112-128, Apr. 2018. Available at: http://sotl-south-journal.net/?journal=sotls&page=article&op=view&path%5B%5D=32
 
 This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

Fuzzy Systems Tuned By Swarm Based Optimization Algorithms for Predicting Stream flow

River flow prediction is an important phenomenon in water resources for which different methods and perspective have been used. Using fuzzy system with black box perspective is one of them. Fuzzy systems have some parameters and properties that have to be determined. This is an optimization problem that can be solved by swarm optimization techniques among several techniques. Swarm optimization are developed by inspiring from the behavior of the animals living as swarm. The study presents two achievements fuzzy system that tuned by swarm optimization algorithms can be used for prediction of monthly mean streamflow and which swarm optimization algorithm is better than the others for tuning fuzzy systems. Three swarm optimization algorithms, hunter search, firefly, artificial bee colony are used in this study. These algorithms are compared with mean performance values and convergence speed. Monthly streamflow data of three stream gauging stations in Susurluk Basin are used for the case study. The results show, swarm optimization algorithms can be used for prediction of monthly mean streamflow and ABC algorithm has better performance values than other optimization algorithms.

ENTERPRISE ARCHITECTURE: ENABLING INTEGRATION, AGILITY AND CHANGE

Three core imperatives are essential for modern businesses and organizations: seamless integration of customer and operational processes, agility, and the ability to change. These imperatives are relevant in view of successfully executing strategic choices, but all too often not satisfied. Businesses and organizations are complex adaptive socio-technical systems and can be viewed from two fundamentally different perspectives: the functional (black-box) perspective and the constructional (white-box) perspective. Management and governance of businesses and organizations regard the functional, black-box perspective, which is inherently ill-suited for addressing the imperatives mentioned. It will be argued that establishing system integration, agility and change requires a focus on the system's design, hence necessitates the constructional perspective. The concept of architecture is considered fundamental for operationalizing the constructional perspective. Next to the more familiar notion of technology architecture, the concepts of business, organizational and information architecture are formally introduced and elucidated. Various domains within these architectures will be highlighted, whereby the importance of coherence and consistency is stressed, especially in view of the ability to change. Collectively, the four architectures are labeled Enterprise Architecture. Finally, enterprise architecture will be positioned as a crucial means for linking strategy development and execution.

NONLINEAR IDENTIFICATION USING PRIOR KNOWLEDGE OF FIXED POINTS: A MULTIOBJECTIVE APPROACH

This paper is devoted to the problem of model building from data produced by a nonlinear dynamical system. Unlike most published works that address the problem from a black-box perspective, in the present paper a procedure is developed that permits the use of prior knowledge about the location of fixed-points in addition to the data thus resulting in a gray-box approach. Numerical results using Chua's double-scroll attractor and the sine map are presented. As discussed, the suggested procedure is useful as a means to partially compensate for the loss of information due to noise and to improve dynamical performance in the presence of model structure mismatches. Preliminary results have indicated that the procedure outlined in this paper is a systematic way of searching for models in the vicinity of the black-box solution. This could have important consequences not only in model building but also in model validation.