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Assessment of the quantum detection efficiency of X-ray detectors, digital mammography complexes MADIS and SIM

The aim of work: To develop a methodology to assess the quantum detection efficiency, and modulation transfer function for the X-ray detector, built by the scheme RLE - optics - PDCC-matrix. The methods: In paper describes a revised technique for evaluation of the detective quantum efficiency and modulation transfer function for X‑ray detectors of digital mammography systems SYMA and MADIS. The detectors are assembled according to the scheme: X-ray luminescent screen – lens – photosensitive CCD. The physical and technical conditions of generating input data sets for the evaluation program of parameters for mammography systems SYMA and MADIS and the principle of operation of the systems were considered. The results: The detailed description of input data obtained by receiving X-ray images via X-ray detectors was given. This quantity of information allows making calculations of investigated dependencies with sufficient accuracy. Stages of the program algorithm for calculating the values of the detective quantum efficiency and modulation transfer function of the detectors were described. The diagrams that show dependencies of the detective quantum efficiency and modulation transfer function on spatial frequencies which were received for X-ray detectors of commercially available samples of mammography systems SYMA and MADIS were given. The quality characteristics which were received satisfy the requirements of normative documents for the X-ray detectors. The conclusions: The method of estimating the quantum detection efficiency, and modulation transfer function for the X-ray detector, built by the scheme RLE - optics - PDCC matrix. Such detectors are used in the production of mammography complexes Madis and SIM is described. The quality characteristics of radiation detectors mammography complexes SYMA and MADIS performance meet the requirements of regulatory documents to the X-ray detectors.

Model-based testing for building reliable realtime interactive music systems

The role of an Interactive Music System (IMS) is to accompany musicians during live performances, acting like a real musician. It must react in realtime to audio signals from musicians, according to a timed high-level requirement called mixed score, written in a domain specific language. Such goals imply strong requirements of temporal reliability and robustness to unforeseen errors in input, yet not much addressed by the computer music community.We present the application of Model-Based Testing techniques and tools to a state-of-the-art IMS, including in particular: offline and on-the-fly approaches for the generation of relevant input data for testing (including timing values), with coverage criteria, the computation of the corresponding expected output, according to the semantics of a given mixed score, the black-box execution of the test data on the System Under Test and the production of a verdict. Our method is based on formal models in a dedicated intermediate representation, compiled directly from mixed scores (high-level requirements), and either passed, to the model-checker Uppaal (after conversion to Timed Automata) in the offline approach, or executed by a virtual machine in the online approach. Our fully automatic framework has been applied to real mixed scores used in concerts and the results obtained have permitted to identify bugs in the target IMS.

Mechanical performance of physical-contact, multi-fiber optical connectors: Finite element analysis and semi-analytical model

Abstract Three-dimensional finite element analysis of physical-contact, multi-fiber optical connector was used to characterize fiber-to-fiber contact and support the development and validation of a semi-analytical model (SAM) for the contact force. This contact behavior is determined by the elastic deformation of the system components (ferrule, fibers, and bonding adhesive) and the classical Hertzian contact at the fiber tips – effects that ultimately define the axial compliance of the system. Two 3-D finite element models for a 12-fiber connector are constructed to study the contact of two connectors, and the specific numerical simulations are carried out to generate input data to SAM, confirm the main assumptions made in its development, and numerically validate the predictions for the contact force. These simulations mainly consider non-uniform fiber height profiles and different end-face fiber tip geometries characterized by their radius of curvature. The numerically validated SAM is then used to study some performance aspects of multi-fiber connectors as related to the required contact force, namely, finding fiber height profiles that require minimum contact force and evaluating the throughput of polishing processes assuming a target contact force. Predictions are supported by Monte Carlo simulations and associated with current profile geometry metrics.

Hydrologic Modeling Using SWAT and GIS, Application to Subwatershed Bab-Merzouka (Sebou, Morocco)

Water management in morocco faces significant challenges in both levels, quality and quantity, in order to address these challenges and to secure the rational and efficient management of water resources, it is necessary to try to understand the different issues involved in the water cycle works! The large number of hydrological studies and techniques used at the Sebou watershed are limited to water quality, and the use of the simpler empirical models, that, do not permit to take into consideration all the factors affecting flow and cycle of water. In the purpose of using the hydrological model SWAT to study the hydrological functioning of the Sebou watershed (Morocco) which faces many problems regarding the management of its water resources like all the other watersheds in our country, this study focuses on the creation, organization and generation of input data (soil, soil occupation, climate…) using a GIS (ARCGIS) then, the configuration and calibration of the model and after that, the evaluation of the results. The main reason behind this work is to measure the adaptability of this model to the selected area in order to generalize the model to the whole Sebou watershed. The results are satisfying; they show that this model can represent the overall hydrological regime of the pilot basin Bab-Merzouka. This model can open up new perspectives regarding the effects of climate change, and the changes caused by humans on water resources, in terms of both quantity and quality.

Combination and mutation strategies to support test data generation in the context of autonomous vehicles

The software used to control autonomous vehicles is a type of embedded system that needs to undergo strenuous testing before deployment. Field testing is the final stage of testing ensuring that autonomous vehicles show the intended behaviour. It usually does not take into consideration the code structure. In this context, a previously proposed testing model and a software tool to support structural testing in the context of autonomous vehicle field testing have been improved to support the generation of new input data from logs collected during field testing using strategies of combination and mutation. We present in this paper three combination strategies and five mutation strategies with the objective of being used in a search-based algorithm for structural data testing generation. A study to assess their coverage according to the criteria all-nodes and all-edges is also shown.

Вычисление входных данных для достижения определенной функции в программе методом итеративного динамического анализа

Dynamic symbolic execution is a well-known technique used for different tasks of program analysis: input generation for increasing test coverage for program, inputs of death generation, exploit generation and etc. But huge time costs of program analysis during dynamic symbolic execution for any real-life program is a well-known problem caused by path explosion and necessity of path constraint solving for every path with different SAT/SMT techniques which is a NP-complete task in general case. Brute force analysis of every path in program has limited practical sense for time limited analysis; instead different techniques and heuristics are used to improve analysis performance and reduce space of analysis for specific needs of analyst or while solving specific problem under analysis. We present our approach which combines static analysis of program binary code based on binutils library with dynamic symbolic execution tool based on Avalanche - an iterative dynamic analysis tool to perform targeted input data generation for reaching specific function in the program. As the first step of our algorithm we extract reduced program call graph which contains only calls to functions which ends with the function of interest, then we amplify this call graph with control flow graph inside of functions included into reduced call graph. Using the reduced control-flow graph of program which contain only calls and conditional jumps directions which lead to the function of interest we built the metric of best next analysis direction. This approach allows us to significantly (up to twelve times for some real world programs) reduce the time of reaching function of interest comparatively to brute force program paths analysis with inversion of every conditional jump at the execution path dependent on tainted data.

A GA based Software Test Data to Generator Suitable Test Cases

This work presents a new concept that is applied in genetic algorithm based tester. Genetic algorithm is sort of evolutionary algorithm that uses in searching problem for optimal solution. In presented test input data generation application, the solution sought by genetic algorithm is a set of test data that causes execution of all possible paths of a given program under test. The proposed concept, repetition frequency is a tool to measure high frequented and also dead parts of program. To experiment with proposed genetic generator, random test data generator was implemented too. Both of these generators tested three programs that are benchmark for many researchers in order to show our proposed tester efficiency.

Modeling the search for vulnerabilities via the fuzzing method using an automation representation of network protocols

Currently, fuzzing is one of the most popular methods of revealing security problems of programs, information systems, network protocols, and web resources. A formal description of network protocols as a set of the processes of switching between states has been proposed, as well as an approach to fuzzing process modeling that allows one to detect network protocol vulnerabilities based on input data generation and the analysis of network protocol process states.

Investigation of the accuracy of close-range photogrammetry – a 3d printing case study

3D scanning of physical objects is one of the frequently used methods for generating input data for 3D printing process. Close-range photogrammetry represents a cost-efficient alternative to conventional 3D scanning. However, one of the basic problems in application of this method is accuracy, espe-cially in the case of small objects with complex geometry. In this case study, a 3D-printed object of small dimensions was used to test the accuracy and precision of close-range photogrammetry. CAD Inspection was used to obtain measurements of the scanned model and compare it with the original CAD model, while the results were statistically analyzed. The results of statistical analysis showed that the scanning accuracy in this experiment did not depend on the particular cross-section of the model, while the precision of 3D scanning depended on the selection of cross-sectional profile curve.

MOtoNMS: A MATLAB toolbox to process motion data for neuromusculoskeletal modeling and simulation.

BackgroundNeuromusculoskeletal modeling and simulation enable investigation of the neuromusculoskeletal system and its role in human movement dynamics. These methods are progressively introduced into daily clinical practice. However, a major factor limiting this translation is the lack of robust tools for the pre-processing of experimental movement data for their use in neuromusculoskeletal modeling software.ResultsThis paper presents MOtoNMS (matlab MOtion data elaboration TOolbox for NeuroMusculoSkeletal applications), a toolbox freely available to the community, that aims to fill this lack. MOtoNMS processes experimental data from different motion analysis devices and generates input data for neuromusculoskeletal modeling and simulation software, such as OpenSim and CEINMS (Calibrated EMG-Informed NMS Modelling Toolbox). MOtoNMS implements commonly required processing steps and its generic architecture simplifies the integration of new user-defined processing components. MOtoNMS allows users to setup their laboratory configurations and processing procedures through user-friendly graphical interfaces, without requiring advanced computer skills. Finally, configuration choices can be stored enabling the full reproduction of the processing steps. MOtoNMS is released under GNU General Public License and it is available at the SimTK website and from the GitHub repository. Motion data collected at four institutions demonstrate that, despite differences in laboratory instrumentation and procedures, MOtoNMS succeeds in processing data and producing consistent inputs for OpenSim and CEINMS.ConclusionsMOtoNMS fills the gap between motion analysis and neuromusculoskeletal modeling and simulation. Its support to several devices, a complete implementation of the pre-processing procedures, its simple extensibility, the available user interfaces, and its free availability can boost the translation of neuromusculoskeletal methods in daily and clinical practice.

Investigations on the formation of straightness deviation in MQL deep-hole drilling of thin-walled aluminium components

One of the most challenging machining operation, particularly with regard to the difficult chip evacuation, is the deep-hole drilling process. Due to the high length-to-diameter ratio of the drill holes and of the tools no complete dry machining is possible, thus minimum quantity lubrication is used to provide a lubrication film at the contact surface of the tribological partners and to ensure a reliable chip removal capability. This paper presents a fundamental comparison of the in-process heat input into the workpiece and the resulting straightness deviations for two different tool concepts—a single-lip drill and a twist drill. In particular, the deep-hole drilling of components with small wall thicknesses, down to a minimum value of $${s_{{\mathrm {w}}}} = 1\, \hbox {mm}$$ , was analysed to gather information about the mechanisms leading to straightness deviations. Due to the conclusions, particularly using a single-lip drill, a novel experimental approach for the determination of the force distribution over the drill radius was developed. It enables the incremental force measurement along the cutting edge and has been used to generate input data for a finite element analysis of the tool deflection and the contact conditions between tool and workpiece. The results indicate that the single-lip drill is a competitive alternative to the twist drill, but the asymmetric cutting edge design and the required guide pad support limit its productivity, in particular with regard to the heat input and the straightness deviation in the manufacturing of drill holes in thin-walled workpieces.

Human Pluripotent Stem Cell Based Developmental Toxicity Assays for Chemical Safety Screening and Systems Biology Data Generation.

Efficient protocols to differentiate human pluripotent stem cells to various tissues in combination with -omics technologies opened up new horizons for in vitro toxicity testing of potential drugs. To provide a solid scientific basis for such assays, it will be important to gain quantitative information on the time course of development and on the underlying regulatory mechanisms by systems biology approaches. Two assays have therefore been tuned here for these requirements. In the UKK test system, human embryonic stem cells (hESC) (or other pluripotent cells) are left to spontaneously differentiate for 14 days in embryoid bodies, to allow generation of cells of all three germ layers. This system recapitulates key steps of early human embryonic development, and it can predict human-specific early embryonic toxicity/teratogenicity, if cells are exposed to chemicals during differentiation. The UKN1 test system is based on hESC differentiating to a population of neuroectodermal progenitor (NEP) cells for 6 days. This system recapitulates early neural development and predicts early developmental neurotoxicity and epigenetic changes triggered by chemicals. Both systems, in combination with transcriptome microarray studies, are suitable for identifying toxicity biomarkers. Moreover, they may be used in combination to generate input data for systems biology analysis. These test systems have advantages over the traditional toxicological studies requiring large amounts of animals. The test systems may contribute to a reduction of the costs for drug development and chemical safety evaluation. Their combination sheds light especially on compounds that may influence neurodevelopment specifically.

Applying Genetic Algorithm to Generation of High-Dimensional Item Response Data

The item response data is thenm-dimensional data based on the responses made bymexaminees to the questionnaire consisting ofnitems. It is used to estimate the ability of examinees and item parameters in educational evaluation. For estimates to be valid, the simulation input data must reflect reality. This paper presents the effective combination of the genetic algorithm (GA) and Monte Carlo methods for the generation of item response data as simulation input data similar to real data. To this end, we generated four types of item response data using Monte Carlo and the GA and evaluated how similarly the generated item response data represents the real item response data with the item parameters (item difficulty and discrimination). We adopt two types of measurement, which are root mean square error and Kullback-Leibler divergence, for comparison of item parameters between real data and four types of generated data. The results show that applying the GA to initial population generated by Monte Carlo is the most effective in generating item response data that is most similar to real item response data. This study is meaningful in that we found that the GA contributes to the generation of more realistic simulation input data.

Применение статической инструментации байт-кода языка Java для динамического анализа программ

This paper focuses on dynamic analysis of Java programs. We consider the following limitations: analysis tool may not have access to target program source code, and the program may be interpreted by a non-standard virtual machine with bytecode format different from Java Virtual Machine specifications. The paper describes an approach to bytecode instrumentation which is used to perform iterative dynamic analysis for new execution path discovery. Path discovery is performed through automatic input data generation by tracing tainted data, collecting path conditions, and satisfiability checking. The proposed approach is based on static bytecode instrumentation. The main advantages of this approach are analysis speedup (because of one-time instrumentation) and explicit access to statically generated instrumented bytecode which makes it possible to run instrumented program on different virtual machines with different bytecode formats. Proposed approaches were implemented in the Coffee Machine tool. Paper sections dedicated to this tool provide a detailed description of taint data tracing and automatic branch traversing techniques as well as a set of instrumentation utilities based on Coffee Machine allowing executed instructions printing, taint trace dumping, and synchronization events trace generation. Coffee Machine uses BCEL (bytecode instrumentation library) for instrumentation. The paper concludes with an overview of practical restrictions existing for discussed methods and possible future work directions. Main disadvantage of proposed approach is the inability to access dynamic data at run-time and instrument a set of system class methods. It may be resolved by method simulation and execution environment modifications.

Systematic Poisoning Attacks on and Defenses for Machine Learning in Healthcare.

Machine learning is being used in a wide range of application domains to discover patterns in large datasets. Increasingly, the results of machine learning drive critical decisions in applications related to healthcare and biomedicine. Such health-related applications are often sensitive, and thus, any security breach would be catastrophic. Naturally, the integrity of the results computed by machine learning is of great importance. Recent research has shown that some machine-learning algorithms can be compromised by augmenting their training datasets with malicious data, leading to a new class of attacks called poisoning attacks. Hindrance of a diagnosis may have life-threatening consequences and could cause distrust. On the other hand, not only may a false diagnosis prompt users to distrust the machine-learning algorithm and even abandon the entire system but also such a false positive classification may cause patient distress. In this paper, we present a systematic, algorithm-independent approach for mounting poisoning attacks across a wide range of machine-learning algorithms and healthcare datasets. The proposed attack procedure generates input data, which, when added to the training set, can either cause the results of machine learning to have targeted errors (e.g., increase the likelihood of classification into a specific class), or simply introduce arbitrary errors (incorrect classification). These attacks may be applied to both fixed and evolving datasets. They can be applied even when only statistics of the training dataset are available or, in some cases, even without access to the training dataset, although at a lower efficacy. We establish the effectiveness of the proposed attacks using a suite of six machine-learning algorithms and five healthcare datasets. Finally, we present countermeasures against the proposed generic attacks that are based on tracking and detecting deviations in various accuracy metrics, and benchmark their effectiveness.

Roadside levels and traffic emission rates of PM2.5 and BTEX in Ho Chi Minh City, Vietnam

A monitoring program was designed and implemented to characterize roadside levels of PM2.5 and BTEX in Ho Chi Minh City, Vietnam, and to generate input data for CALINE (California LINE Source Dispersion Model) inverse modeling. Monitoring was done during Dec 2007–Jan 2008, on both weekdays and weekends, and yielded 284 hourly BTEX samples (adsorption tubes), 24 samples of 8 h-PM2.5 and 42 samples of 24 h-PM2.5 (by MiniVol samplers). The air sampling was done at 8 points on both sides of one street that had an average traffic flow, simultaneously meteorology data and vehicle flows were recorded. Roadside 24 h-PM2.5 levels were 97 ± 31 (53–151) μg m−3, higher on weekdays than weekends. Diurnal BTEX variation patterns were consistent with the diurnal flows of 6 vehicle categories moving on the street. BTEX levels were reduced with the increase in downwind distance from traffic lanes (approximately by 15% for each 5 m increment). Principal component analysis also confirmed the association between roadside pollution levels and traffic. A calculation algorithm was developed to remove the urban background, contributed by other sources than traffic in the selected street, from the roadside measured pollution levels. Urban background contributed a majority of PM2.5 (90–98%) and hourly BTEX (67–97%) with higher contributions at upwind side of the street and at late evening hours when less traffic was observed. CALINE inverse modeling produced explainable fleet hourly emission rates (g km−1 h−1) and vehicle emission factors (EF, mg veh−1 km−1). The obtained EF for gasoline and diesel vehicles were comparable with recent measurements made in Asian cities, as well as with calculated EFs for European and US urban fleets about 10–15 years ago. To minimize the collinearity problem encountered in inverse traffic modeling it is essential that the monitoring is done at different times to capture significant variations in the street traffic compositions.

Automatic generation of valid and invalid test data for string validation routines using web searches and regular expressions

Classic approaches to automatic input data generation are usually driven by the goal of obtaining program coverage and the need to solve or find solutions to path constraints to achieve this. As inputs are generated with respect to the structure of the code, they can be ineffective, difficult for humans to read, and unsuitable for testing missing implementation. Furthermore, these approaches have known limitations when handling constraints that involve operations with string data types.This paper presents a novel approach for generating string test data for string validation routines, by harnessing the Internet. The technique uses program identifiers to construct web search queries for regular expressions that validate the format of a string type (such as an email address). It then performs further web searches for strings that match the regular expressions, producing examples of test cases that are both valid and realistic. Following this, our technique mutates the regular expressions to drive the search for invalid strings, and the production of test inputs that should be rejected by the validation routine.The paper presents the results of an empirical study evaluating our approach. The study was conducted on 24 string input validation routines collected from 10 open source projects. While dynamic symbolic execution and search-based testing approaches were only able to generate a very low number of values successfully, our approach generated values with an accuracy of 34% on average for the case of valid strings, and 99% on average for the case of invalid strings. Furthermore, whereas dynamic symbolic execution and search-based testing approaches were only capable of detecting faults in 8 routines, our approach detected faults in 17 out of the 19 validation routines known to contain implementation errors.

ISTSOS, SENSOR OBSERVATION MANAGEMENT SYSTEM: A REAL CASE APPLICATION OF HYDRO-METEOROLOGICAL DATA FOR FLOOD PROTECTION

Abstract. istSOS (Istituto scienze della Terra Sensor Observation Service) is an implementation of the Sensor Observation Service standard from Open Geospatial Consortium (OGC). The development of istSOS started in 2009 in order to provide a simple implementation of the Sensor Observation Service (SOS) standard for the management, provision and integration of hydro-meteorological data collected in Canton Ticino (Southern Switzerland). istSOS is entirely written in Python and is based on reliable open source software like PostgreSQL/PostGIS and Apache/mod_wsgi. The authors during this presentation want to illustrate the latest software enhancements together with a real case in a production environment. Latest software enhancement includes the development of a RESTful service and of a Web-based graphical user interface that allows hydrologists a better interaction with measurements. This includes the ability of new services creation, addition of new sensors and relative metadata, visualization and manipulation of stored observations, registration of new measures and setting of system properties like observable properties and data quality codes. The study will show a real case application of the system for the provision of data to interregional partners and to a hydrological model for lake level forecasting and flooding hazard assessment. The hydrological model uses a combination of WPS (Web Processing Service) and SOS for the generation of model input data. This system is linked with a dedicated geo-portal used by the civil protection for the management, alert and protection of population and assets of the Locarno area (Verbano Lake flooding). Practical considerations and technical issues will be presented and discussed.

Динамический анализ программ с целью поиска ошибок и уязвимостей при помощи целенаправленной генерации входных данных

This paper describes the principles of program dynamic analysis for defect detection using input data generation. Techniques of program transformation allowing execution trace extraction, data flow tracing and input data generation for execution path coverage approaches are considered. We clarify in what way such an approach allows us to perform fully automatic analysis using executable or interpretable code. This paper also presents dynamic analysis tools developed at Institute for System Programming RAS---Avalanche (Valgrind-based tool) and a prototype tool for Java applications. The paper concludes with an evaluation of practical results of applying Avalanche tool to a set of open source projects as well as results of applying Java analysis tool to detect concurrency defects.

A Model-Data Framework for Dynamic Vehicle Routing Models

We propose a Model-Data Framework (MoDaF) to consider delivery-related static and dynamic contexts to support delivery service. In general, Static Vehicle Routing Models (SVRMs) usually use Static Modeling-Context (SMOC) such as Immediate Pickup and Delivery Requirements (IPDR) and resources related to idle vehicles at depots. On the other hand, Dynamic Vehicle Routing Models (DVRMs) need to consider Dynamic Modeling-Context (DMOC) such as unfulfilled Advanced Pickup and Delivery Requirements (APDR) on advanced vehicle routes. Therefore, it is getting more important to manage Modeling-Context (MOC) including both SMOC and DMOC and to specify the MOC into input data used in DVRMs to find a solution. Recently, the generation of input data for a specific IP (Integer Programming) model becomes a significant issue as the MOC is getting dynamic. Hence, it needs systematic approach. In this research, we focus on the generation of input data from the MOC and Input Data Inference Rules (IDIRs) based on forward chaining for a dynamic-MDPDPTW as an illustration of a specific DVRM. To prove the availability of a MoDaF, we implement a prototype to generate input data from MOC effectively and show that it is significantly useful to solve DVRM in the realistic vehicle routing system through the experimentation.