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Exploring the opportunity for applying digital twins in the healthcare context is an emerging research area that has the potential to support more personalised care. A recognised aspect in cancer care is the need for more personalised treatment planning to complement the recent advances in precision medicine. In this article, we present a classification of digital twins into Grey Box, Surrogate and Black Box models using systems and mathematical modelling theory. We then explore one possible approach, namely a Black Box classification for incorporating the use of digital twins in the context of personalised uterine cancer care. This paper presents one of the first attempts to use digital twins in this capacity and represents an amalgamation of three key domains: clinical, digital health and computer science respectively.

Electrical devices in transmission and distribution networks are submitted to fast front transients. These transients are often a cause of a failure in the power system. To simulate these phenomena, it is necessary to detailly model all the components of the power system. This paper concentrates on studding transformer internal failures due to voltage stress that can occur when fast front electromagnetic wave travels through the transformer. To prevent a failure, an internal insulation of the transformer has to be dimensioned to sustain these transient phenomena. Therefore, it is necessary to use advanced wideband transformer models for its dimensioning. In this paper, a wide band transformer model based on limited transformer geometry is used to simulate evaluate the stresses on internal transformer insulation in the case of fast front transients. The model is validated for calculation of internal overvoltages with the test case from CIGRE brochure.

This paper describes the possibility of analytical friction factor generation using a Grey Box modelling approach. A preprocessor is used, which automatically collects measured data via a Smart Production Network and calculates the effective friction coefficient as a function of all significant process parameters of the Equal-Channel-Angular-Pressing (ECAP) process using a data-driven evaluation algorithm. The results of this calculation are then automatically implemented in an efficiently programmed finite element analysis. This Digital Shadow then serves as the basis for the implementation of a Digital Twin, which is described shortly at the end.

A grey-box model of an automated people mover system (APMS) is constructed in this paper. APMS is one of the urban transit constructions that is being constructed in Indonesia and used for connecting between terminals in the airport. The APMS model can be identified to obtain the mathematical representation of the full system by finding unknown parameters through the data measured from the installed system at the airport. Generalized reduced gradient (GRG) algorithm is operated to identify the model parameters. The model can then be used for verification and testing controller designs such as proportional-integralderivative (PID) or model predictive control (MPC), and energy-saving strategies. The obtained model is able to follow the dynamic of the real APMS with RMSE value 0.013.

Purpose This paper aims to report an interview study investigating knowledge protection practices in a collaborative research and innovation project centred around the semi-conductor industry. The authors explore which and how knowledge protection practices are applied and zoom in on a particular one to investigate the perspective of three stakeholders which collaborate: the SUPPLIER of a specialised machine, the APPLIER of this machine and a SCHOLAR who collaborates with both, in an effort to develop a grey-box model of the machine and its operation. Design/methodology/approach A total of 33 interviews have been conducted in two rounds: 30 interviews explore knowledge protection practices applied across a large project. Qualitative content analysis is applied to determine practices not well covered by the research community. A total of three follow-up interviews inspect one specific collaboration case of three partners. Quotes from all interviews are used to illustrate the participants’ viewpoints and motivation. Findings SCHOLAR and APPLIER communicate using a data-centric knowledge protection practice, in that concrete parameter values are sensitive and hidden by communicating data within a wider parameter range. This practice balances the benefit that all three stakeholders have from communicating about specifics of machine design and operations. The grey-box model combines engineering knowledge of both SUPPLIER and APPLIER. Practical implications The line of thought described in this study is applicable to comparable collaboration constellations of a SUPPLIER of a machine, an APPLIER of a machine and a SCHOLAR who analyses and draws insights out of data. Originality/value The paper fills a research gap by reporting on applied knowledge protection practices and characterising a data-centric knowledge protection practice around a grey-box model.

Model-based solutions for monitoring and control of chemical batch processes have been of interest in research for many decades. However, unlike in continuous processes, in which model-based tools such as Model Predictive Control (MPC) have become a standard in the industry, the reported use of models for batch processes, either for monitoring or control, is rather scarce. This limited use is attributed partly to the inherent complexity of the batch processes (e. g., dynamic, nonlinear, multipurpose) and partly to the lack of appropriate commercial tools in the past. In recent years, algorithms and commercial tools for model-based monitoring and control of batch processes have become more mature and in the era of Industry 4.0 and digitalization they are slowly but steadily gaining more interest in real-word batch applications. This contribution provides a practical example in this application field. Specifically, the use of a grey-box modeling approach, in which a multiway Projection to Latent Structure (PLS) model is combined with a first-principles model, to monitor the evolution of a batch polymerization process and predict in real-time the final batch quality is reported. The modeling approach is described, and the experimental results obtained from an industrial batch laboratory reactor are presented.

Predictive controllers can greatly improve the performance of energy systems in buildings. An important challenge of these controllers is the need of a building model accurate and simple enough for...

Modelling the effects of solar irradiation plays an important role in various applications. This paper describes a semi-parametric (combined grey-box and spline-based), data-driven technique that can be used to model systems in which the solar gain depends on the sun position. The solar gain factor is introduced, i.e. the absorbed fraction of measured solar irradiation, and estimated as a continuous non-parametric function of the sun position. The implementation of the spline-based solar gain factor in a grey-box model framework is described. The method is tested in two case studies—in a model of the internal temperature of a dwelling in Aalborg, Denmark, and a model of the return temperature of a solar collector field in Solrod, Denmark. It is shown that the solar gain factor as a function of sun position is able to account for structural variations in solar gain that may occur due to factors such as shading obstacles and window or absorber orientation. In both test cases, the spline-based solar gain function improved the model accuracy significantly, and largely reduced structural errors in prediction residuals. In addition, the shape of the estimated function provided insight into the dynamics of the system and the local solar input characteristics. Accurate representation of such site characteristics was not possible with any data-driven method found in the literature. Besides the grey-box models used in this study, the solar gain factor can be used in a variety of data-driven models, for example in linear regression models.

This study investigates which product characteristics and sourcing location conditions (global or local) support efficient supplier integration in New Product Development (NPD) processes. A research model is suggested to analyse the relationship between procurement product characteristics, buyer-supplier project organisation and NPD results. Moreover, the moderating impact of the geographical sourcing location on the causal relationships in the model is assessed. Data from 209 research-intensive firms in German-speaking countries were used to test the research hypotheses using structural equation modelling. The findings propose that the complexity and importance of the product procured are crucial to the form of project organisation between the parties. Buyers and suppliers tend to collaborate to a greater extent when the complexity and importance of the procurement object are high. Collaborative project organisation influences the result of NPD, and this relationship is more effective when a local supplier is involved in the process.

In this work, two approaches for grey-box thermal system and time-varying heat disturbance modeling of buildings are proposed. They combine machine-learning-based modeling with grey-box modeling to form a hybrid approach. The first approach simultaneously estimates the system model parameters and estimator gain for a system model augmented with an integrating disturbance model. With the estimator gain and collected input-output data, the disturbance is estimated, and a predictive recurrent neural network disturbance model is fit using the estimates. The second approach simultaneously estimates the system model parameters and the predictive disturbance model parameters. Both approaches are applied to a building zone example to demonstrate and analyze them.

The physiomic approach is now widely used in the diagnosis of cardiovascular diseases. There are two possible methods for cardiovascular physiome: the traditional mathematical model and the machine learning (ML) algorithm. ML is used in almost every area of society for various tasks formerly performed by humans. Specifically, various ML techniques in cardiovascular medicine are being developed and improved at unprecedented speed. The benefits of using ML for various tasks is that the inner working mechanism of the system does not need to be known, which can prove convenient in situations where determining the inner workings of the system can be difficult. The computation speed is also often higher than that of the traditional mathematical models. The limitations with ML are that it inherently leads to an approximation, and special care must be taken in cases where a high accuracy is required. Traditional mathematical models are, however, constructed based on underlying laws either proven or assumed. The results from the mathematical models are accurate as long as the model is. Combining the advantages of both the mathematical models and ML would increase both the accuracy and efficiency of the simulation for many problems. In this review, examples of cardiovascular physiome where approaches of mathematical modeling and ML can be combined are introduced.

We design and implement from scratch a new fuzzer called SIVO that refines multiple stages of grey-box fuzzing. First, SIVO refines data-flow fuzzing in two ways: (a) it provides a new taint inference engine that requires only logarithmic in the input size number of tests to infer the dependency of all program branches on the input bytes, and (b) it deploys a novel method for inverting branches by solving directly and efficiently systems of inequalities. Second, our fuzzer refines accurate tracking and detection of code coverage with simple and easily implementable methods. Finally, SIVO refines selection of parameters and strategies by parameterizing all stages of fuzzing and then dynamically selecting optimal values during fuzzing. Thus the fuzzer can easily adapt to a target program and rapidly increase coverage. We compare our fuzzer to 11 other state-of-the-art grey-box fuzzers on 27 popular benchmarks. Our evaluation shows that SIVO scores the highest both in terms of code coverage and in terms of number of found vulnerabilities.

Fuzzing is significantly evolved in analysing native code, but web applications, invariably, have received limited attention until now. This paper designs, implements and evaluates webFuzz, a gray-box fuzzing prototype for discovering vulnerabilities in web applications.

We design and implement from scratch a new fuzzer called Sivo that refines multiple stages of grey-box fuzzing. First, Sivo refines data-flow fuzzing in two ways: (a) it provides a new taint inference engine that requires only logarithmic number of tests in the input size to infer dependency of many program branches on the input bytes, and (b) it employs a novel method for inverting branches by solving a systems of inequalities efficiently. Second, our fuzzer refines accurate tracking and detection of code coverage with simple and easily implementable methods. Finally, Sivo refines selection of parameters and strategies by parameterizing all stages of fuzzing and then dynamically selecting optimal values during fuzzing. Thus the fuzzer can easily adapt to a target program and rapidly increase coverage. We compare our fuzzer to 11 other state-of-the-art grey-box fuzzers on 27 popular benchmarks. Our evaluation shows that Sivo scores the highest both in terms of code coverage and in terms of number of found vulnerabilities.

We introduce a grey-box adversarial attack and defence framework for sentiment classification. We address the issues of differentiability, label preservation and input reconstruction for adversarial attack and defence in one unified framework. Our results show that once trained, the attacking model is capable of generating high-quality adversarial examples substantially faster (one order of magnitude less in time) than state-of-the-art attacking methods. These examples also preserve the original sentiment according to human evaluation. Additionally, our framework produces an improved classifier that is robust in defending against multiple adversarial attacking methods. Code is available at: https://github.com/ibm-aur-nlp/adv-def-text-dist.

Grey-box fuzzing is widely used to discover program vulnerabilities. However, most existing grey-box fuzzers cannot be directed towards sensitive points and dangerous locations in the programs. In this paper, in order to improve the performance of directed Grey-box fuzzing, we develop two strategies to improve the original directed Grey-box fuzzer, AFLGo, and implement a new directed Grey-box fuzzer, Guided-Fuzzer, to reach dangerous positions in the program faster. In the process of directed grey-box fuzzing, Guided-Fuzzer evaluates the seeds based on the control flow graph information and the execution path of the seed. And it prioritizes each seed based on the seed’s distance from the target, seed’s energy, and the number of times the seed are executed. Based on the priorities of seeds, Guided-Fuzzer puts seeds in the appropriate position of the input queue, and adopts an adaptive mutation strategy that combines coarse-grained mutant actions, medium-grained mutant actions and fine-grained mutant actions according to the distances between the seeds and the target location. Experimental results show that Guided-Fuzzer can reach the target location faster than the original directed Grey-box fuzzer AFLGo. In addition, on some tested programs, Guided-Fuzzer can reduce the time to discover certain vulnerabilities from about 3.2 hours to 1.2 hours, and effectively improves the efficiency of directed Grey-box fuzzing.

In many aircraft applications, especially on an antiskid control design, it is important to understand and consider the gear walk phenomenon, which is characterized by the deflection on the landing gear structure due the high braking force acting at the tire contact with the ground. This phenomenon is observed on drop tests, and its prediction on landing gear design depends on an adequate evaluation of the equivalent stiffness and damping of the structure, which is difficult, since they depend on the mechanism configuration. In this paper, it is presented a grey-box identification methodology for estimating these parameters of the landing gear, based on simulated data of a drop test. As the drop tests are mandatory obligatory for certificating modern aircraft according to e.g. Federal Aviation Regulations (FARs) by the Federal Aviation Administration (FAA), we hope to introduce a method based on measurements that are available at the design phase. The method will be useful to decrease men/hour costs and increase reliability by enabling better and more accurate anti-skid design.

Mobile applications have become a core component of our day-to-day life. These mobile applications (or as Mobile Apps.) can be customized,few of them are free, i.e., open source or freeware and some are paid or proprietary ones. Users look for the ones that are error-free and fulfill the needs. The error-free solutions require proper testing. Proper testing requires more time and efforts. Testing of mobile app. is alike as of the testing of desktop applications, but is more sensitive, as mobile apps. have less resources and more demand, they being portable. As with the desktop applications, black box, white box and grey box testing can be applied to mobile applications too. However, to address mobile-specific issues, there is a need for research in automation testing of mobile applications. The objective of this paper is to introduce mobile application by its responsive UI functionality and accuracy in presentation. The responsive UI need for mobile application is given in the introduction section. Also the paper gives an account of testing in mobile application in Sect. 2. The section characterizes the testing specific for mobile application, also compares different testing techniques with their applications. The next section compares testing techniques as black and white box with grey box testing. The successive section illustrates grey box testing in mobile application. In consecutive sections significance of research in grey box testing in mobile app. development is introduced. The paper also reviews existing studies of apps. on grey box testing in mobile app. testing and find research gaps. The paper concludes with need for future works in grey box testing for mobile application.

The 16th International Building Simulation Association (Building Simulation 2019), Rome, Italy, 2-4 September 2019

Fuzzing technologies have evolved at a fast pace in recent years, revealing bugs in programs with ever increasing depth and speed. Applications working with complex formats are however more difficult to take on, as inputs need to meet certain format-specific characteristics to get through the initial parsing stage and reach deeper behaviors of the program. Unlike prior proposals based on manually written format specifications, in this paper we present a technique to automatically generate and mutate inputs for unknown chunk-based binary formats. We propose a technique to identify dependencies between input bytes and comparison instructions, and later use them to assign tags that characterize the processing logic of the program. Tags become the building block for structure-aware mutations involving chunks and fields of the input. We show that our techniques performs comparably to structure-aware fuzzing proposals that require human assistance. Our prototype implementation WEIZZ revealed 16 unknown bugs in widely used programs.

Many notorious disasters in the last few decades may have been correlated with fatigue or human error. Detecting the level of fatigue from a person, in order to monitor and predict possible risk situations, has become a major concern. A person alertness model is used to produce data in a realistic manner, similarly to a Karolinska Sleepiness Scale self–valuation or Psychomotor Vigilance Test, by considering white measurement noise and a non–uniform sampling rate that provides small data amounts during the day, with no data collected during sleep. An identification grey–box algorithm based upon several windows of data is developed to retrieve the real biological parameters of a person’s alertness model. The alertness parametric model that describes both awake and sleep periods is non–linear, so the problem is solved by splitting the model into linear representations, one for awake and another for sleep periods. The first is solved by representing the parametric model in a canonical state–space form that leads to a straightforward least–squares estimation problem. Due to the lack of data during sleep periods, the second is addressed with a non–linear least squares algorithm. The performance of the proposed algorithm is evaluated by analyzing the ability to recover the stipulated biological parameters.

Fuzzing is an effective technology in software testing and security vulnerability detection. Unfortunately, fuzzing is an extremely compute-intensive job, which may cause thousands of computing hours to find a bug. Current novel works generally improve fuzzing efficiency by developing delicate algorithms. In this paper, we propose another direction of improvement in this field, i.e., leveraging parallel computing to improve fuzzing efficiency. In this way, we develop P-fuzz, a parallel fuzzing framework that can utilize massive, distributed computing resources to fuzz. P-fuzz uses a database to share the fuzzing status such as seeds, the coverage information, etc. All fuzzing nodes get tasks from the database and update their fuzzing status to the database. Also, P-fuzz handles some data races and exceptions in parallel fuzzing. We compare P-fuzz with AFL and a parallel fuzzing framework Roving in our experiment. The result shows that P-fuzz can easily speed up AFL about 2.59&times, and Roving about 1.66&times, on average by using 4 nodes.

This paper presents a hybrid building modelling method with a reduced modelling and calibration effort. The method combines a physics-based model, which describes the general behaviour of the system, with a machine learning algorithm trained to correct the physics-based model’s systematic errors. To exemplify the method, a highly simplified grey-box model is used as the physics-based part and a Gaussian process as the machine learning part. It is shown that the hybrid model improves the temperature and energy predictions of the grey-box model while having a lower generalization error than the pure Gaussian process. Specifically, the hybrid approach achieved a day-ahead zone temperature prediction error ca. 0.1 K (RMSE) lower than the grey-box model. As for the energy prediction, the hybrid model obtained an error of 3% compared to 8% for the grey-box model. In comparison to the Gaussian process, the hybrid approach achieved better predictions in all cases. The improvements were especially high when the models were trained with small datasets: 0.7 K in the temperature prediction and 25 percentage points in the energy prediction.

There are three ways of developing parts for the end product in relation to the supplier: Black box, grey box, and white box. While in the black box approach suppliers are completely respon...