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Journal articles on the topic 'Black-box Analysis'

Author: Grafiati

Published: 4 June 2021

Last updated: 20 June 2025

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1

NAILA, FIRDAUS, and MANISHA DAWRA Ms. "Requirement Analysis of Black Box Testing." International Journal of Advances in Engineering & Scientific Research 1, no. 5 (2014): 19–23. https://doi.org/10.5281/zenodo.10721079.

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<strong><em>Abstract</em></strong> <em>                        </em><em>Black Box Testing is used when code of the module is not available. Black box testing treats the system as a “black-box”, so it doesn’t explicitly use knowledge of the internal structure or code. Or in other words the Test engineer need not know the internal working of the “Black box” or application. In such situations appropriate priorities can be given to different test cases, so that the quality of software is not compromised, if testing is to be stopped prematurely. This paper briefly sketches a general strategy for black-box testing, different methods involved and the advantages and disadvantages associated with them. The study of this paper would be beneficial to both researchers and practitioners alike in having an understanding of black box testing. </em> <em> </em> <strong><em>Keywords:  </em></strong><em> Black-Box Testing, White-Box Testing, Equivalence Class Portioning, Boundary Value Analysis, Decision Table.</em>

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2

Leshkevich, Tatiana G. "Metaphors of the digital age and the Black Box Problem." Philosophy of Science and Technology 27, no. 1 (2022): 34–48. http://dx.doi.org/10.21146/2413-9084-2022-27-1-34-48.

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The article discusses the effects of the digital era, the driver of which is AI. The main goal is to focus on the Black Box Problem, “opacity of AI” and the possibility of Malicious Use of Artificial Intelligence. Three interconnected directions are interfaced. Firstly, in the context of the analysis of the digital age, the potential of metaphors is used, which makes it possible to describe digital transformations figuratively. Secondly, due to the growing demand for high technologies, the negative consequences of using AI are considered and a number of paradoxes of scientific and technological progress are formulated. Thirdly, the article examines the widespread practice of trust in intelligent systems, as well as the prospects for technological symbiosis. The analysis is based on the Russian and English-language literature. The author analyzes metaphors that indicate the type of modern existence – “face-to-screen” or “face-to-device” (1); features of the subject of the digital age – “content viewer” (2); the specifics of digital rationality – “knowledge rent”, digital multitasking (3). Attention is drawn to the process of “convergence of subjectivity”. The issue of malicious use of AI is discussed. The author draws conclusions about the need for “algorithmic responsibility” and expanding the field of reflective analysis aimed at studying the consequences of using AI.

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3

Resende, David Nunes, Xiaowen Yu, Sílvia Gaia, and David V. Gibson. "Inside the Black Box." International Journal of Information Systems and Social Change 8, no. 4 (2017): 1–22. http://dx.doi.org/10.4018/ijissc.2017100101.

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This article uses an original three-phase approach for empirical assessment and comparative evaluation of the efficiency of university technology transfer. It is based on analysis of inputs and outputs of a disclosure phase followed by a value-add phase and a final license phase, using a multidimensional framework. The objective is to find university Technology Transfer Office (TTO) efficiency and effectiveness patterns for each phase as well as overall TT processes. A network Data Envelopment Analysis (DEA) model was used to analyze and describe the complicated TT operational processes using Association of University Technology Managers data collected from 90 US university TTOs for the period 2007-2013. It was concluded that the sampled TTOs were most inefficient in their value-add (2nd) phase and that the average overall efficiency as well as the efficiency of disclosure (1st) and license (3rd) phases decreased during the last five years of data analyzed. In addition, in line with other studies, analysis supports the contention that the presence of medical school does not increase TT efficiency or effectiveness. The present research's contributions focus on three areas: (1) Analysing and modelling TTO valorization and commercialization process with a UML activity diagram to provide a clear picture of TT procedures and processes; (2) Proposing a three-phase DEA framework showing input/output indicators closely related to each phase of processes rather than a black box or separated activities; (3) Offering a strategy to conduct empirical studies on TTO's operational efficiency thereby helping to better understand future research operational problems.

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4

Mickelborough, Jane. "Unpacking the black box." Reviews in Clinical Gerontology 7, no. 4 (1997): 287–88. http://dx.doi.org/10.1017/s0959259897007417.

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There are two black boxes. One is Pandora's, stuff of myth and legend, and remembered for releasing into the world all its cares and troubles, leaving only hope behind in the bottom of the box. The other, more recent and less mythical, is the in-flight recorder, used to monitor the events in an aircraft's flight, for later analysis in case of difficulty. Both these boxes have their analogy when we consider the role and current state of rehabilitation research in physiotherapy.

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Kim, Byol, and Rina Foygel Barber. "Black-box tests for algorithmic stability." Information and Inference: A Journal of the IMA 12, no. 4 (2023): 2690–719. http://dx.doi.org/10.1093/imaiai/iaad039.

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Abstract Algorithmic stability is a concept from learning theory that expresses the degree to which changes to the input data (e.g. removal of a single data point) may affect the outputs of a regression algorithm. Knowing an algorithm’s stability properties is often useful for many downstream applications—for example, stability is known to lead to desirable generalization properties and predictive inference guarantees. However, many modern algorithms currently used in practice are too complex for a theoretical analysis of their stability properties, and thus we can only attempt to establish these properties through an empirical exploration of the algorithm’s behaviour on various datasets. In this work, we lay out a formal statistical framework for this kind of black-box testing without any assumptions on the algorithm or the data distribution, and establish fundamental bounds on the ability of any black-box test to identify algorithmic stability.

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6

Lamb, Anthony R. "Computer analysis: avoiding the ‘black box’ syndrome." Proceedings of the Institution of Civil Engineers - Civil Engineering 157, no. 3 (2004): 134–39. http://dx.doi.org/10.1680/cien.2004.157.3.134.

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Lamb, Anthony R. "Computer analysis: avoiding the ‘black box’ syndrome." Civil Engineering Innovation 1, no. 2 (2007): 93–99. http://dx.doi.org/10.1680/einn.2007.1.2.93.

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8

Lamb, Anthony R. "Computer analysis: avoiding the ‘black box’ syndrome." Civil Engineering Innovation 1, no. 2 (2007): 93–99. http://dx.doi.org/10.1680/jciei.2007.13258.

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9

Birkmeyer, John D., and Jean Y. Liu. "Decision analysis models: Opening the black box." Surgery 133, no. 1 (2003): 1–4. http://dx.doi.org/10.1067/msy.2003.21.

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10

Rossi, Dario, Elisa Sottile, and Paolo Veglia. "Black-box analysis of Internet P2P applications." Peer-to-Peer Networking and Applications 4, no. 2 (2010): 146–64. http://dx.doi.org/10.1007/s12083-010-0072-4.

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11

Hileman, L. C. "Penetrating the black box of phylogenetic analysis." Development 136, no. 24 (2009): 4067–68. http://dx.doi.org/10.1242/dev.038182.

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12

Nellaivadivelu, Guruswamy, Fabio Di Troia, and Mark Stamp. "Black box analysis of android malware detectors." Array 6 (July 2020): 100022. http://dx.doi.org/10.1016/j.array.2020.100022.

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13

Karadas, Georgiana, and Osman M. Karatepe. "Unraveling the black box." Employee Relations 41, no. 1 (2019): 67–83. http://dx.doi.org/10.1108/er-04-2017-0084.

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Purpose The purpose of this paper is to investigate the potential mediators that operate in the black box between high-performance work systems (HPWS) and employee outcomes. Design/methodology/approach The relationships the authors developed were assessed via data obtained from a time-lagged sample of customer-contact employees and their direct supervisors in the Romanian hotel industry. The study employed bias-corrected bootstrapping analysis to gauge the mediating effects. Findings The findings reveal that psychological capital mediates the impact of HPWS on work engagement. As hypothesized, both psychological capital and work engagement mediate the impact of HPWS on quitting intentions, creative performance and extra-role performance. In short, the findings underscore both psychological capital and work engagement as the two mediators that operate in the black box between HPWS and the aforesaid employee outcomes. In addition, the empirical data support the impact of work engagement in the intermediate linkage between psychological capital and these outcomes. Originality/value The study enhances current knowledge on HPWS by examining the potential mediators between HPWS and motivational outcomes and job outcomes.

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Wu, Yaqiong, Chunhong Zhang, Wenlong Wu, Weilin Li, and Lianfei Lyu. "Genome-wide identification and analysis of the MADS-box gene family and its potential role in fruit ripening in black raspberry (Rubus occidentalis L.)." Journal of Berry Research 11, no. 2 (2021): 301–15. http://dx.doi.org/10.3233/jbr-200679.

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BACKGROUND: Black raspberry is a vital fruit crop with a high antioxidant function. MADS-box genes play an important role in the regulation of fruit development in angiosperms. OBJECTIVE: To understand the regulatory role of the MADS-box family, a total of 80 MADS-box genes were identified and analyzed. METHODS: The MADS-box genes in the black raspberry genome were analyzed using bioinformatics methods. Through an analysis of the promoter elements, the possible functions of different members of the family were predicted. The spatiotemporal expression patterns of members of the MADS-box family during black raspberry fruit development and ripening were systematically analyzed. RESULTS: The genes were classified into type I (Mα: 33; Mβ: 6; Mγ: 10) and type II (MIKC*: 2; MIKCC: 29) genes. We also obtained a complete overview of the RoMADS-box gene family through phylogenetic, gene structure, conserved motif, and cis element analyses. The relative expression analysis showed different expression patterns, and most RoMADS-box genes were more highly expressed in fruit than in other tissues of black raspberry. CONCLUSIONS: This finding indicates that the MADS-box gene family is involved in the regulation of fruit ripening processes in black raspberry.

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Akshaya, S., V. Panicker Allan, Johnson Joel, Asok Karthik, and Ravi Chinnu. "Black Box for Cars." Research and Applications of Web Development and Design 7, no. 2 (2024): 39–44. https://doi.org/10.5281/zenodo.12066354.

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<em>As vehicle accidents have being increased day by day it is high time to resolve this issue. To solve this issue and improve road safety, a "Black Box" system has been proposed. This system involves the installation of a device in vehicles that records various data, including engine temperature, speed, and location. Collected data, such as snapshots and sensor readings, are transmitted via GSM GPS module to authorities, enabling quick response and assistance in the event of accidents. This system aims to monitor accidents on roads and provide immediate help to casualties if needed. This system also stores vehicle information for further accident investigation. Integrating ultrasound sensors into a car's black box or telematics system can offer several benefits, particularly in terms of enhancing safety and providing valuable data .It can be employed to detect obstacles around the vehicle in real-time. If an obstacle is detected, the black box can log this information, which might be useful for accident [9] analysis or to provide context in case of collisions. Drowsiness [10] on a car black box can be equipped with various sensors to detect signs of drowsiness and alert the driver or record relevant. </em>

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Schiller, Devon. "The Black Box Grimaces Back." Grimace, Vol. 2, no. 1 (2017): 72–81. http://dx.doi.org/10.47659/m2.072..art.

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With the algorithmic age of computable emotions, an increasing number of digital artists base the form of their Internet or sculptural installation on Automated Facial Expression Analysis (AFEA), and its functionality achieved via the photographic documentation in face databases. These contemporary artists make visible a digital habit of thought that objectivates the human face into a plastic grotesque of grimacing extremis, and the self inside out into the universal or utilitarian. Yet, most AFEA systems – a term little clarified and much confused with facial recognition or biometrics – are “black box” frameworks. Introduced by the technological industry and scientific experts, such proprietary closed source algorithms veil the majority of program functionality input from available data output, hiding how it works from immediate observation by artist and audience. By problematizing Julius von Bismarck’s Public Face (2008-14) and its intermedial genealogies, I probe the extent to which AFEA represents the face and its expression of emotion from a technostalgic view that reduces scientific complexity, while informing how we think about what we feel today. Keywords: automated facial expression analysis, biometrics, digital art and science, facial recognition algorithms, photography

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Schiller, Devon. "The Black Box Grimaces Back." Grimace, Vol. 2, no. 1 (2017): 72–81. http://dx.doi.org/10.47659/m2.072.art.

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With the algorithmic age of computable emotions, an increasing number of digital artists base the form of their Internet or sculptural installation on Automated Facial Expression Analysis (AFEA), and its functionality achieved via the photographic documentation in face databases. These contemporary artists make visible a digital habit of thought that objectivates the human face into a plastic grotesque of grimacing extremis, and the self inside out into the universal or utilitarian. Yet, most AFEA systems – a term little clarified and much confused with facial recognition or biometrics – are “black box” frameworks. Introduced by the technological industry and scientific experts, such proprietary closed source algorithms veil the majority of program functionality input from available data output, hiding how it works from immediate observation by artist and audience. By problematizing Julius von Bismarck’s Public Face (2008-14) and its intermedial genealogies, I probe the extent to which AFEA represents the face and its expression of emotion from a technostalgic view that reduces scientific complexity, while informing how we think about what we feel today. Keywords: automated facial expression analysis, biometrics, digital art and science, facial recognition algorithms, photography

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18

Larson, Jeffrey, Matt Menickelly, and Stefan M. Wild. "Derivative-free optimization methods." Acta Numerica 28 (May 1, 2019): 287–404. http://dx.doi.org/10.1017/s0962492919000060.

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In many optimization problems arising from scientific, engineering and artificial intelligence applications, objective and constraint functions are available only as the output of a black-box or simulation oracle that does not provide derivative information. Such settings necessitate the use of methods for derivative-free, or zeroth-order, optimization. We provide a review and perspectives on developments in these methods, with an emphasis on highlighting recent developments and on unifying treatment of such problems in the non-linear optimization and machine learning literature. We categorize methods based on assumed properties of the black-box functions, as well as features of the methods. We first overview the primary setting of deterministic methods applied to unconstrained, non-convex optimization problems where the objective function is defined by a deterministic black-box oracle. We then discuss developments in randomized methods, methods that assume some additional structure about the objective (including convexity, separability and general non-smooth compositions), methods for problems where the output of the black-box oracle is stochastic, and methods for handling different types of constraints.

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Chen, Jie, JunWei Su, and JingYin Li. "Self-Coupling Black Box Model of a Dynamic System Based on ANN and Its Application." Mathematical Problems in Engineering 2020 (March 27, 2020): 1–12. http://dx.doi.org/10.1155/2020/5724831.

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The black box model of a dynamic system usually consists of just input and output. There is no correlation or coupling between the input and output. This paper proposes a self-coupling black box model method to realize the coupling between its input and output by introducing “virtual variables” to the black box model of a dynamic system considering the advantages of artificial neural network (ANN) in the system. The ANN is used for black box modeling. The modeling process of the self-coupling black box is illustrated through the simulation models and simulation analysis of the particle settlement process and the Unmanned Undersea Vehicle (UUV) launching process. By comparing its result with the result of the standard black box model, the advantages and disadvantages of the self-coupling and standard black box models in the calculation of accuracy are analyzed.

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Chitic, Raluca, Ali Osman Topal, and Franck Leprévost. "Empirical Perturbation Analysis of Two Adversarial Attacks: Black Box versus White Box." Applied Sciences 12, no. 14 (2022): 7339. http://dx.doi.org/10.3390/app12147339.

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Through the addition of humanly imperceptible noise to an image classified as belonging to a category ca, targeted adversarial attacks can lead convolutional neural networks (CNNs) to classify a modified image as belonging to any predefined target class ct≠ca. To achieve a better understanding of the inner workings of adversarial attacks, this study analyzes the adversarial images created by two completely opposite attacks against 10 ImageNet-trained CNNs. A total of 2×437 adversarial images are created by EAtarget,C, a black-box evolutionary algorithm (EA), and by the basic iterative method (BIM), a white-box, gradient-based attack. We inspect and compare these two sets of adversarial images from different perspectives: the behavior of CNNs at smaller image regions, the image noise frequency, the adversarial image transferability, the image texture change, and penultimate CNN layer activations. We find that texture change is a side effect rather than a means for the attacks and that ct-relevant features only build up significantly from image regions of size 56×56 onwards. In the penultimate CNN layers, both attacks increase the activation of units that are positively related to ct and units that are negatively related to ca. In contrast to EAtarget,C’s white noise nature, BIM predominantly introduces low-frequency noise. BIM affects the original ca features more than EAtarget,C, thus producing slightly more transferable adversarial images. However, the transferability with both attacks is low, since the attacks’ ct-related information is specific to the output layers of the targeted CNN. We find that the adversarial images are actually more transferable at regions with sizes of 56×56 than at full scale.

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Hartono, Firman Dwi, and Yuni Sugiarti. "PERBANDINGAN METODE EQUIVALENCE PARTITIONS DAN BOUNDARY VALUE ANALYSIS PADA PENGUJIAN BLACK BOX Literatur Review." Majalah Ilmiah METHODA 12, no. 2 (2022): 153–59. http://dx.doi.org/10.46880/methoda.vol12no2.pp153-159.

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This review literature reviews the Equivalence Partitioning and Boundary Value Analysis methods published between 2019 and 2020. Insights that arise from this review can guide researchers in their ongoing investigation of the implementation of Black Box, especially the Equivalence Partitioning and Boundary Value Analysis. From the review of 10 journals, this study found several substantive changes in the field of Black Box research. This includes an increase in the use of theory in research. This review literature aims to study software testing techniques in black box and improve better quality jeminan goals.

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Koga, Kazuki, and Kazuhiro Takemoto. "Simple Black-Box Universal Adversarial Attacks on Deep Neural Networks for Medical Image Classification." Algorithms 15, no. 5 (2022): 144. http://dx.doi.org/10.3390/a15050144.

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Universal adversarial attacks, which hinder most deep neural network (DNN) tasks using only a single perturbation called universal adversarial perturbation (UAP), are a realistic security threat to the practical application of a DNN for medical imaging. Given that computer-based systems are generally operated under a black-box condition in which only input queries are allowed and outputs are accessible, the impact of UAPs seems to be limited because well-used algorithms for generating UAPs are limited to white-box conditions in which adversaries can access model parameters. Nevertheless, we propose a method for generating UAPs using a simple hill-climbing search based only on DNN outputs to demonstrate that UAPs are easily generatable using a relatively small dataset under black-box conditions with representative DNN-based medical image classifications. Black-box UAPs can be used to conduct both nontargeted and targeted attacks. Overall, the black-box UAPs showed high attack success rates (40–90%). The vulnerability of the black-box UAPs was observed in several model architectures. The results indicate that adversaries can also generate UAPs through a simple procedure under the black-box condition to foil or control diagnostic medical imaging systems based on DNNs, and that UAPs are a more serious security threat.

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Juhász, László, and Irén Juhász Junger. "Spectral Analysis and Parameter Identification of Textile-Based Dye-Sensitized Solar Cells." Materials 11, no. 9 (2018): 1623. http://dx.doi.org/10.3390/ma11091623.

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Linearized equivalent electrical-circuit representation of dye-sensitized solar cells is helpful both for the better understanding of the physical processes in the cell as well as for various optimizations of the cells. White-box and grey-box modelling approaches are well-known and they are widely used for standard cell types. However, in the case of new cell types or the lack of deep knowledge of the cell’s physic such approaches may not be applicable immediately. In this article a black-box approach for such cases is presented applied together with spectral analysis. The spectral analysis and the black-box approach were as first validated with a standard glass-based dye-sensitized solar cell and thereafter applied for the characterization of a new type of textile-based dye-sensitized solar cells. Although there are still improvement potentials, the results are encouraging and the authors believe that the black-box method with spectral analysis may be used particularly for new types of dye-sensitized solar cells.

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Billes, Marina, Anders Møller, and Michael Pradel. "Systematic black-box analysis of collaborative web applications." ACM SIGPLAN Notices 52, no. 6 (2017): 171–84. http://dx.doi.org/10.1145/3140587.3062364.

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Schroeder, Patrick J., and Bogdan Korel. "Black-box test reduction using input-output analysis." ACM SIGSOFT Software Engineering Notes 25, no. 5 (2000): 173–77. http://dx.doi.org/10.1145/347636.349042.

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Chen, Kai, DengGuo Feng, PuRui Su, and YingJun Zhang. "Black-box testing based on colorful taint analysis." Science China Information Sciences 55, no. 1 (2011): 171–83. http://dx.doi.org/10.1007/s11432-011-4291-y.

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Doerr, Benjamin, Carola Doerr, and Jing Yang. "Optimal parameter choices via precise black-box analysis." Theoretical Computer Science 801 (January 2020): 1–34. http://dx.doi.org/10.1016/j.tcs.2019.06.014.

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Banerjee, Ipsita, Siladitya Pal, and Spandan Maiti. "Computationally efficient black-box modeling for feasibility analysis." Computers & Chemical Engineering 34, no. 9 (2010): 1515–21. http://dx.doi.org/10.1016/j.compchemeng.2010.02.016.

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Vogt, Nina. "Unraveling the black box in neural signal analysis." Nature Methods 22, no. 5 (2025): 894. https://doi.org/10.1038/s41592-025-02715-1.

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Martynenko, S. I. "Robust Multigrid Technique for Black Box Software." Computational Methods in Applied Mathematics 6, no. 4 (2006): 413–35. http://dx.doi.org/10.2478/cmam-2006-0026.

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Abstract This paper presents a new robust multigrid technique for solving boundary value problems in a black box manner. To overcome the problem of robustness, the technique is based on the incorporation of adaptation of boundary value problems to numerical methods, control volume discretization and a new multigrid solver into a united computational algorithm. The special multiple coarse grid correction strategy makes it possible to obtain problem-independent transfer operators. As a result, most modes are approximated on coarse grids to make the task of the smoother on the finest grid the least demanding. A detailed description of the robust multigrid technique and examples of its application for solving benchmark problems are given in the paper.

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Pramudita, Rully. "Pengujian Black Box pada Aplikasi Ecampus Menggunakan Metode Equivalence Partitioning." INFORMATICS FOR EDUCATORS AND PROFESSIONAL : Journal of Informatics 4, no. 2 (2020): 193. http://dx.doi.org/10.51211/itbi.v4i2.1347.

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Salah satu tahapan penting dalam pengembangan sebuah perangkat lunak yaitu tahap pengujian. Teknik testing dibagi menjadi dua yaitu black box testing dan white box testing. Pada penelitian akan menerapkan teknik pengujian black box. Teknik pengujian black box terdiri dari, Equivalence Partitioning, Boundary Value Analysis / Limit Testing, Comparison Testing, Sample Testing, Robustness Testing, Behavior Testing, Requirement Testing, Performance Testing, Endurance Testing, Cause-Effect Relationship Testing. Salah satu yang akan diterapkan dalam penelitian ini yaitu teknik equivalence partitioning. Teknik ini ujicoba yang dilakukan berdasarkan tujuh tahapan, yaitu menentukan use case yang diuji, tentukan kriteria, definisikan partisi, buat data uji, buat kasus uji, lakukan pengujian dan evaluasi. Hasil pengujian nantinya akan menghasilkan feedback untuk masukan bagi pengelola ecampus. Kata kunci: Aplikasi, Black box, Ecampus, Ekuivalence partitioning, Pengujian  Abstract: One important stage in the development of a software is the testing phase. Testing techniques are divided into two namely black box testing and white box testing. In the study will apply black box testing techniques. Black box testing techniques consist of, Equivalence Partitioning, Boundary Value Analysis / Limit Testing, Comparison Testing, Sample Testing, Robustness Testing, Behavior Testing, Requirement Testing, Performance Testing, Endurance Testing, Cause-Effect Relationship Testing. One that will be applied in this research is equivalence partitioning technique. This technique is based on seven stages of testing, namely determining the tested use case, determining criteria, defining partitions, creating test data, making test cases, testing and evaluating. The test results will later produce feedback for input for ecampus managers.  Keywords: Application, Black box, Ecampus, Ekuivalence partitioning, Testing

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Pham, Cong, Trung-Nguyen Hoang, Cao-Truong Tran, and Viet-Binh Do. "GanTextKnockoff: stealing text sentiment analysis model functionality using synthetic data." Journal of Military Science and Technology, CSCE8 (December 30, 2024): 76–86. https://doi.org/10.54939/1859-1043.j.mst.csce8.2024.76-86.

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Today, black-box machine learning models are often subject to extraction attacks that aim to retrieve their internal information. Black-box model extraction attacks are typically conducted by providing input data and, based on observing the output results, constructing a new model that functions equivalently to the original. This process is usually carried out by leveraging available data from public repositories or synthetic data generated by generative models. Most model extraction attack methods using synthetic data have been concentrated in the field of computer vision, with minimal research focused on model extraction in natural language processing. In this paper, we propose a method that utilizes synthetic textual data to construct a new model with high accuracy and similarity to the original black-box sentiment analysis model.

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Pintelas, Emmanuel, Ioannis E. Livieris, and Panagiotis Pintelas. "A Grey-Box Ensemble Model Exploiting Black-Box Accuracy and White-Box Intrinsic Interpretability." Algorithms 13, no. 1 (2020): 17. http://dx.doi.org/10.3390/a13010017.

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Machine learning has emerged as a key factor in many technological and scientific advances and applications. Much research has been devoted to developing high performance machine learning models, which are able to make very accurate predictions and decisions on a wide range of applications. Nevertheless, we still seek to understand and explain how these models work and make decisions. Explainability and interpretability in machine learning is a significant issue, since in most of real-world problems it is considered essential to understand and explain the model’s prediction mechanism in order to trust it and make decisions on critical issues. In this study, we developed a Grey-Box model based on semi-supervised methodology utilizing a self-training framework. The main objective of this work is the development of a both interpretable and accurate machine learning model, although this is a complex and challenging task. The proposed model was evaluated on a variety of real world datasets from the crucial application domains of education, finance and medicine. Our results demonstrate the efficiency of the proposed model performing comparable to a Black-Box and considerably outperforming single White-Box models, while at the same time remains as interpretable as a White-Box model.

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Petrick, Elizabeth R. "Building the Black Box: Cyberneticians and Complex Systems." Science, Technology, & Human Values 45, no. 4 (2019): 575–95. http://dx.doi.org/10.1177/0162243919881212.

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In the 1950s and 1960s, cyberneticians defined and utilized a concept previously described by electronic engineers: the black box. They were interested in how it might aid them, as both a metaphor and as a physical or mathematical model, in their analysis of complex human-machine systems. The black box evolved as they applied it in new ways, across a range of scientific fields, from an unnamed concept involving inputs and outputs, to digital representations of the human brain, to white boxes that might be used to replicate black boxes. The diversity of understandings of the black box reflected the diversity of scientific perspectives and goals brought under the label of cybernetics. In this paper, I examine how cyberneticians drew upon the black box in their personal writings and publications. My goal is to unpack what the black box meant to these theorists as a starting framework from which we may understand the initial shape of the black box.

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Pasaribu, Johni Setiady. "Perbandingan Pengujian Boundary Value Analysis, Equivalence Partitioning dan Error Guessy (Studi Kasus Indeks Nilai)." Jurnal ICT : Information Communication & Technology 20, no. 2 (2021): 210–17. http://dx.doi.org/10.36054/jict-ikmi.v20i2.388.

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Software testing is a critical and time-consuming element of the software development life cycle (SDLC). Software testing determines the quality of a software before it is used by end users. The main goal is to find software errors or defects so that these errors or defects can be corrected in this testing phase. There are two general techniques for detecting software errors, namely: Black Box and White Box Testing. Black Box testing technique is used for validation, namely whether you have built the right software and White Box testing is used for verification, namely whether the software made is correct. This study aims to test the value index software using three methods of Black Box testing techniques, namely Equivalence partitioning (EP), Boundary Value Analysis (BVA) and Error Guessing. The testing process is carried out to find out how much error occurs in the value index software. The research method is qualitative descriptive and analytical. The results of this study indicate that the value index application has no shortcomings (error of 0%) both with BVA, EP and Error Guessing techniques.

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Dobslaw, Felix, Robert Feldt, and Francisco Gomes de Oliveira Neto. "Automated black-box boundary value detection." PeerJ Computer Science 9 (November 7, 2023): e1625. http://dx.doi.org/10.7717/peerj-cs.1625.

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Software systems typically have an input domain that can be subdivided into sub-domains, each of which generates similar or related outputs. Testing it on the boundaries between these sub-domains is critical to ensure high-quality software. Therefore, boundary value analysis and testing have been a fundamental part of the software testing toolbox for a long time and are typically taught early to software engineering students. Despite its many argued benefits, boundary value analysis for a given software specification or application is typically described in abstract terms. This allows for variation in how testers apply it and in the benefits they see. Additionally, its adoption has been limited since it requires a specification or model to be analysed. We propose an automated black-box boundary value detection method to support software testers in performing systematic boundary value analysis. This dynamic method can be utilized even without a specification or model. The proposed method is based on a metric referred to as the program derivative, which quantifies the level of boundariness of test inputs. By combining this metric with search algorithms, we can identify and rank pairs of inputs as good boundary candidates, i.e., inputs that are in close proximity to each other but with outputs that are far apart. We have implemented the AutoBVA approach and evaluated it on a curated dataset of example programs. Furthermore, we have applied the approach broadly to a sample of 613 functions from the base library of the Julia programming language. The approach could identify boundary candidates that highlight diverse boundary behaviours in over 70% of investigated systems under test. The results demonstrate that even a simple variant of the program derivative, combined with broad sampling and search over the input space, can identify interesting boundary candidates for a significant portion of the functions under investigation. In conclusion, we also discuss the future extension of the approach to encompass more complex systems under test cases and datatypes.

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Mansour, Nizar, Emna Gara, and Chiha Gaha. "Getting inside the black box." Personnel Review 43, no. 4 (2014): 490–514. http://dx.doi.org/10.1108/pr-03-2013-0052.

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Purpose – The purpose of this paper is to explore, and eventually unlocking, the “black box” problem by addressing the potential mediating role of human capital and organizational commitment in the relationship between high performance work systems (HPWS) and perceived firm performance in the Tunisian financial industry. Design/methodology/approach – Based on the strategic human resource management (SHRM) theory, the authors developed a model that links HPWS to perceived organizational performance through human capital and employee organizational commitment. Data collected from 351 respondents was considered. Multiple regression analysis was then used to assess the research hypotheses. Findings – Data collected from 351 respondents suggest that HPWS positively affect perceived firm performance through first, enhancing the firms’ human capital; and second, developing positive organizational commitment attitude among employees. In addition, a direct relationship between HPWS and firm performance was found. Research limitations/implications – The research focussed on the perceived performance of the organization rather than financial measures. Also, because data were collected from a sample of Tunisian financial companies, results of this study are not generalizable. Originality/value – More than two decades after the earliest contributions, the SHRM scholars believe that the “black box” problem is still accurate and, therefore, needs to be addressed in an effective way. At a general and broad level, the authors believe this study contributes to the SHRM literature by successfully addressing two critical “black box” elements, i.e. human capital and organizational commitment. Also, since there is a lack in such research in Tunisia, an icon country of the Arab spring, this paper provides theoretical basis for future research and managerial implications for Tunisian business leaders and HR managers. Finally, this research is extending the current empirical SHRM literature by addressing the critical role of HRM in the largely understudied field of financial services industry.

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Gerber, B. S., T. G. Tape, R. S. Wigton, and P. S. Heckerling. "Entering the Black Box of Neural Networks." Methods of Information in Medicine 42, no. 03 (2003): 287–96. http://dx.doi.org/10.1055/s-0038-1634363.

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Summary Objectives: Artificial neural networks have proved to be accurate predictive instruments in several medical domains, but have been criticized for failing to specify the information upon which their predictions are based. We used methods of relevance analysis and sensitivity analysis to determine the most important predictor variables for a validated neural network for community-acquired pneumonia. Methods: We studied a feed-forward, back-propagation neural network trained to predict pneumonia among patients presenting to an emergency department with fever or respiratory complaints. We used the methods of full retraining, weight elimination, constant substitution, linear substitution, and data permutation to identify a consensus set of important demographic, symptom, sign, and comorbidity predictors that influenced network output for pneumonia. We compared predictors identified by these methods to those identified by a weight propagation analysis based on the matrices of the network, and by logistic regression. Results: Predictors identified by these methods were clinically plausible, and were concordant with those identified by weight analysis, and by logistic regression using the same data. The methods were highly correlated in network error, and led to variable sets with errors below bootstrap 95% confidence intervals for networks with similar numbers of inputs. Scores for variable relevance tended to be higher with methods that precluded network retraining (weight elimination) or that permuted variable values (data permutation), compared with methods that permitted retraining (full retraining) or that approximated its effects (constant and linear substitution). Conclusion: Methods of relevance analysis and sensitivity analysis are useful for identifying important predictor variables used by artificial neural networks.

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Hu, Ran, Nan Ma, Bing Li, et al. "Black-Box Modelling of Active Distribution Network Devices Based on Neural ODEs." Journal of Physics: Conference Series 2826, no. 1 (2024): 012029. http://dx.doi.org/10.1088/1742-6596/2826/1/012029.

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Abstract The morphological structure and dynamic characteristics of power distribution systems are rapidly evolving due to the widespread application of distributed renewable energy and the rapid advancement of power electronics technology. The stability analysis of these complex new distribution systems depends on electromagnetic transient (EMT) simulation. However, the intellectual property rights protection by manufacturers leads to many distribution network devices that can only use black-box models with missing parameters, which challenges the simulation. This reduces the accuracy of the dynamic analysis of these new distribution systems. To address this problem, this paper proposes a black-box modelling method based on neural Ordinary Differential Equation (ODE) for active distribution equipment. The method uses port measurement data to construct a data-driven model that accurately captures the black-box device characteristics, transforming the uncertainties in the user-side black-box devices into observable, controllable, and analysable models. The paper also builds an IEEE-33 node system that includes the energy storage black-box model for validation, and the results confirm the accuracy of the model.

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Mani, Vaidyanath, and Robert E. Hartwig. "Duality and Black Box Interpolation I: The One Variable Nonderogatory Case." SIAM Journal on Matrix Analysis and Applications 16, no. 1 (1995): 21–28. http://dx.doi.org/10.1137/s0895479892234922.

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Koshy, Roshan, and Vineetha V. Nair. "Black Box for Accident Analysis Using MATLAB-Image Processing." International Journal of Computer Applications Technology and Research 6, no. 5 (2017): 229–33. http://dx.doi.org/10.7753/ijcatr0605.1003.

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Siordia, O. S., I. M. de Diego, C. Conde, and E. Cabello. "Wireless In-Vehicle Compliant Black Box: For Accident Analysis." IEEE Vehicular Technology Magazine 7, no. 3 (2012): 80–89. http://dx.doi.org/10.1109/mvt.2012.2203692.

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Takefuji, Yoshiyasu. "Black box analysis with linear regression on global warming." Hygiene and Environmental Health Advances 12 (December 2024): 100109. http://dx.doi.org/10.1016/j.heha.2024.100109.

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Carabantes, Manuel. "Black-box artificial intelligence: an epistemological and critical analysis." AI & SOCIETY 35, no. 2 (2019): 309–17. http://dx.doi.org/10.1007/s00146-019-00888-w.

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Lim, Eldin Wee Chuan, and Jin Rou New. "Black-Box Optimization by Fourier Analysis and Swarm Intelligence." JOURNAL OF CHEMICAL ENGINEERING OF JAPAN 45, no. 6 (2012): 417–28. http://dx.doi.org/10.1252/jcej.12we002.

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Yang, Homer. "Intraoperative automated ST segment analysis: a reliable ‘Black Box’?" Canadian Journal of Anaesthesia 43, no. 10 (1996): 1041–51. http://dx.doi.org/10.1007/bf03011907.

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Filiol, Eric. "Malware Pattern Scanning Schemes Secure Against Black-box Analysis." Journal in Computer Virology 2, no. 1 (2006): 35–50. http://dx.doi.org/10.1007/s11416-006-0009-x.

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Priyadarshini, MS V. Nisha, D. R. Lakshmi D R Lakshmi, Sirani Sunitha, et al. "Automobile Black Box System for Accident Analysis using IOT." International Journal of Advances in Engineering and Management 7, no. 3 (2025): 572–79. https://doi.org/10.35629/5252-0703572579.

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The Automobile Black Box System for Accident Analysis using Io Tis amodern solution aimed atim proving road safety through real-time monitoring and data collection.Inspired by the flight data recorders in aviation,this systemis designed to monitorand analyzecrucialvehicleparameters to detect,record, and report accidents or mishaps. The system integrates multiple sensors such as alcohol sensors, CO2 sensors, temperature sen- sors, ultra sonic sensors, collision sensors, anddoorsensors, which continuously track the vehicle’s environment and the driver’s behavior. Data from these sensors is processed by a central control unit, typically an Arduino or microcontroller, which communicatesthefindings through wireless networks usingGSM or Wi Fi technology. In case of an accident or abnormal event (such as alcohol detection or a collision), the system immediately triggers emergency notifications to predefined contacts and the relevant authorities, providing them with real-time information about thevehicle’s status,location,and sensorreadings.Furthermore,thesystemstoresalldata,enablingathoroughanal ysis oftheeventafter the incident.The collecteddatacanbe used for accident reconstruction, determining the cause of accidents, improving road safety measures, and even aiding insurance claims. The application of IoT in this context significantly enhancesthevehicle’ssafetybyenablingpreventiveact ions,quick accident responses, and comprehensive post-incident analysis. This system can be expanded to include advanced features suchas autonomous driving assistance, predictive maintenance, and vehicle health monitoring, ultimatelycontributingtosmarterand saferroads.

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Zhang, Chao, and Yu Wang. "Research on the Structure of Authentication Protocol Analysis Based on MSCs/Promela." Advanced Materials Research 989-994 (July 2014): 4698–703. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.4698.

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To discover the existing or possibly existing vulnerability of present authentication protocol, we proposed a structure of authentication protocol analysis, which consist of white box analysis, black box analysis and indicator system as the three main functional components. White box analysis makes use of the transformation from MSCs (Message Sequence Charts) to Promela (Process Meta Language), the input language of the remarkable model checker SPIN; black box analysis is based on the attack platform of authentication protocol analysis; indicator system is decided by deducibility restraint methods. Compared with the UML modeling methods based on Promela, the MSC2Promela methods we figured out on the context have more advantages than its disadvantages. Finally, we proposed a structure of authentication protocol analysis based on the transformation from MSC2Promela, which makes sense to the later work on the area of authentication protocol analysis.

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Greshoff, Rainer. "Relational Sociology–A Black Box Conception?" Analyse & Kritik 41, no. 1 (2019): 175–82. http://dx.doi.org/10.1515/auk-2019-410111.

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Abstract The article comments on Peetz’ concept of relational mechanisms. This concept is an alternative to mechanistical explanations of analytical sociology, conceptualized as based on human agents. Peetz criticises this foundation, juxtaposing it with the idea of the analytical primacy of relations. This perspective does not necessarily presuppose agents but can explain their emergence. To demonstrate the efficiency of his concept, he presents an explanation of a concrete mechanism. The analysis of this explanation shows that a crucial point is missing from the concept of relational mechanisms: the steps that produce a social process are never spelt out. Peetz thus presents a black box explanation, which is contrary to the demands of mechanistical explanations. His preference for black box argumentation is owed to his concepts. Unlike an enlightened methodological individualist, he is not in a position to explain the productions necessary for the formation of mechanistical processes.

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