Version Of Implementation Research Articles

A constrained version for the stereology inverse problem: Honoring power law and persistences of the fracture traces exposed on arbitrary surfaces

We present a stereological study in a cave setting that is part of a karstic carbonate system located in the São Francisco Craton, Brazil. Using a Lagrangian approach, a constrained version of the nonlinear inverse problem of stereology is solved. Besides the classical demand of fitting the histogram of fracture traces measured on arbitrary exposed surface, it is imposed that the solution honors also measures of surface intensity ( P 21 ) and power law exponent obtained from fracture traces on the same exposed surfaces. Estimates of volumetric intensities ( P 32 ) of conjugate fracture pairs might be also imposed to be close values. The resulting cost functional is minimized using the Particle Swarm Optimization (PSO) method. The implemented version of PSO furnishes the best solution and a set of suboptimal quasisolutions, from which the solution uncertainty is evaluated. A key aspect of the implemented approach is that all terms composing the cost functional are normalized, obtaining as a result, robustness for the weighting parameters to changes in the input data. The resulting stochastically simulated discrete fracture networks honor all statistical observations but, in general, do not reproduce the positions of the observed fracture traces. The methodology is applied to synthetic and field data examples. All obtained solutions are stable and geologically reliable.

Natural Language Processing through the Subtractive Mountain Clustering Algorithm - A Medication Intake Chatbot

In this work, the subtractive mountain clustering algorithm has been adapted to the problem of natural languages processing in view to construct a chatbot that answers questions posed by the user. The implemented algorithm version allosws for the association of a set of words into clusters. After finding the centre of every cluster — the most relevant word, all the others are aggregated according to a defined metric adapted to the language processing realm. All the relevant stored information (necessary to answer the questions) is processed, as well as the questions, by the algorithm. The correct processing of the text enables the chatbot to produce answers that relate to the posed queries. Since we have in view a chatbot to help elder people with medication, to validate the method, we use the package insert of a drug as the available information and formulate associated questions. Errors in medication intake among elderly people are very common. One of the main causes for this is their loss of ability to retain information. The high amount of medicine intake required by the advanced age is another limiting factor. Thence, the design of an interactive aid system, preferably using natural language, to help the older population with medication is in demand. A chatbot based on a subtractive cluster algorithm is the chosen solution.

The optimality of the option of abolishing serfdom in Russia, implemented in 1861

The article discusses the controversial issues related to the abolition of serfdom in Russia in 1861: its causes, features of preparation and implementation. The authors focus on the question of whether the implemented version of the abolition of serfdom in Russia was optimal. For this purpose, a comparative analysis of the abolition of serfdom in Russia is carried out with similar reforms in European countries, which could serve as a reference point, primarily in Austria and Prussia. It is concluded that the peasant reform in Russia in 1861 (in the final version) was carried out primarily in the interests of the state and not of individual social groups (landowners and peasants). It is the state that has benefited most from the implementation of this particular version of the reform, both financially and politically. Among the losers there were both peasants (to a greater extent) and landowners (to a lesser extent). The main thing was that the reform provoked the problem of the lack of land of the majority of peasants, which in the future became one of the main reasons for the social explosion and revolutions at the beginning of the XX century.

Consensus in anonymous asynchronous systems with crash-recovery and omission failures

In anonymous distributed systems, processes are indistinguishable because they have no identity and execute the same algorithm. Currently, anonymous systems are receiving a lot of attention mainly because they preserve privacy, which is an important property when we want to avoid impersonation attacks. On the other hand, Consensus is a fundamental problem in distributed computing. It is well-known that Consensus cannot be deterministically solved in pure asynchronous anonymous systems if processes can crash (the so-called crash-stop failure model). This impossibility holds even if message losses never occur in transmission. Failure detectors are an elegant and powerful abstraction for achieving deterministic Consensus in asynchronous distributed systems. A failure detector is a distributed object that gives the processes information about crashed processes. Failure detectors have attracted so much attention in the crash-stop failure model because they provide a totally independent abstraction. varOmega is the weakest failure detector to solve Consensus in classic asynchronous systems when a majority of processes never crash, and AvarOmega ' is its implementable version for anonymous systems. As far as we know, there is a lack of works in the literature which tackle Consensus in anonymous asynchronous systems where crashed process can recover (the so-called crash-recovery failure model) and also assuming errors in transmission operations (the so-called omission failure model). Extending failure models in the system allows us to design more realistic systems and solve more practical security problems (i.e., fair exchange and the secure multiparty computation). We present, in this paper, an algorithm to solve Consensus using AvarOmega ' in anonymous asynchronous systems under the crash-recovery and omission failure models. Another important contribution of this paper is a communication-efficient and latency-efficient implementation of AvarOmega ' for these new failure models.

Stratified Restricted Mean Survival Time Model for Marginal Causal Effect in Observational Survival Data

Time to event outcomes is commonly encountered in epidemiologic research. Multiple papers have discussed the inadequacy of using the hazard ratio as a causal effect measure due to its noncollapsibility and the time-varying nature. In this paper, we further clarified that the hazard ratio might be used as a conditional causal effect measure, but it is generally not a valid marginal effect measure, even under randomized design. We proposed to use the restricted mean survival time (RMST) difference as a causal effect measure, since it essentially measures the mean difference over a specified time horizon and has a simple interpretation as the area under survival curves. For observational studies, propensity score adjustment can be implemented with RMST estimation to remove observed confounding bias. We proposed a propensity score stratified RMST estimation strategy, which performs well in our simulation evaluation and is relatively easy to implement for epidemiologists in practice. Our stratified RMST estimation includes two different versions of implementation, depending on whether researchers want to involve regression modeling adjustment, which provides a powerful tool to examine the marginal causal effect with observational survival data.

Prospects for using gated lidars in autonomous mobile robots

The analysis of the implementation problems of technical vision systems based on the use of time-of-flight laser lidars is carried out. It is concluded that the implementation of vision systems with acceptable parameters dictates an excessively high cost of the lidar. An alternative version of the lidar implementation is considered – a gated lidar based on a laser vision system. Replacing the broadband detector and high-speed scanning system with a gated CCD-matrix can significantly reduce the cost of the lidar while ensuring the high resolution of the lidar. The analysis of the dependence of the signal-to-noise ratio for gated lidar with and without an electron-optical converter has shown that in bad weather conditions the decrease in the gain of the useful signal when the image intensifier is excluded is compensated by the exclusion of the EOC's noise factor, so that the loss in the observation distance is less than 15%.

An amalgamation of YOLOv4 and XGBoost for next-gen smart traffic management system.

The traffic congestion and the rise in the number of vehicles have become a grievous issue, and it is focused worldwide. One of the issues with traffic management is that the traffic light’s timer is not dynamic. As a result, one has to remain longer even if there are no or fewer vehicles, on a roadway, causing unnecessary waiting time, fuel consumption and leads to pollution. Prior work on smart traffic management systems repurposes the use of Internet of things, Time Series Forecasting, and Digital Image Processing. Computer Vision-based smart traffic management is an emerging area of research. Therefore a real-time traffic light optimization algorithm that uses Machine Learning and Deep Learning Techniques to predict the optimal time required by the vehicles to clear the lane is presented. This article concentrates on a two-step approach. The first step is to obtain the count of the independent category of the class of vehicles. For this, the You Only Look Once version 4 (YOLOv4) object detection technique is employed. In the second step, an ensemble technique named eXtreme Gradient Boosting (XGBoost) for predicting the optimal time of the green light window is implemented. Furthermore, the different implemented versions of YOLO and different prediction algorithms are compared with the proposed approach. The experimental analysis signifies that YOLOv4 with the XGBoost algorithm produces the most precise outcomes with a balance of accuracy and inference time. The proposed approach elegantly reduces an average of 32.3% of waiting time with usual traffic on the road.

Mean–variance combining rules that outperform naïve diversification

The mean–variance framework developed by Markowitz (1952). Portfolio selection, The Journal of Finance, 7(1), 77–91 is still the major model used nowadays in asset allocation and active portfolio management. However, the estimated mean–variance rules often fail to deliver superior performance compared with the simple naïve rule (the equally weighted portfolio) due to the problem of estimation errors. In this paper, I propose a portfolio construction method that is effective in dealing with estimation errors in the optimization process. Particularly, I specify the portfolio weights as an optimal combination of the equally weighted portfolio and a sample zero-investment portfolio. I show analytically that the proposed method alleviates the problem of estimation errors and dominates naïve diversification. I suggest two implementable versions of the combining method and show, empirically, their good performances relative to the naïve rule. The newly developed rules work well, particularly, for portfolios with a medium and high number of assets. Moreover, the outperformance persists generally even in the presence of transaction costs. Since the combinations are theory-based, my study may be interpreted as reaffirming the usefulness of the Markowitz portfolio theory in practice.

A hybrid ant colony optimization-variable neighborhood descent approach for the cumulative capacitated vehicle routing problem

In this paper, we present two swarm intelligence algorithms for the solution of the Cumulative Capacitated Vehicle Routing Problem. In particular, two hybrid algorithms of the Ant Colony Optimization family have been implemented, the Ant Colony System-Variable Neighborhood Decent and the Max-Min Ant System-Variable Neighborhood Decent. In this novel implementation, the ant-solution population, in both algorithms, is generated by applying local search operators on a single solution generated by the ant transition rules. This method of generating the population is compared to the traditional ACO population generation method. Their effectiveness is tested against well known benchmark instances in the literature and the results are compared to other approaches. The Ant Colony System-Variable Neighborhood Decent provided the best results among the two implemented versions and was able to find a new best known solution for two instances. Overall, on the 112 instances tested, best known solutions were reached in 92 of them. From the 20 instances in which the best known solution was not reached, 19 are instances with over 220 customers. The average gap from the best known solution in those instances is 0.35% and the maximum gap is 0.98%.

Modelling of power consumption for Advanced Encryption Standard and PRESENT ciphers

Paper presents a result of modelling by ELMO tool and measuring on hardware stand power consumption traces for encryption algorithms Advanced Encryption Standard and PRESENT. Power consumption traces for different version of AES (basic one-round AES algorithm are compared with AESMasked_R1 and MBedAES versions) and PRESENT (with various round numbers) are generated. Assessment of instructions with leaks were made by comparison power consumption traces for fixed-input and power consumption traces for random-inputs (total quantity of traces is 104). Numbers of total and potentially leakage instructions for each examined version of AES and PRESENT ciphers implementation are estimated.

COMBI LANGUAGE - A MEANS OF PRESENTING OPTIMIZATION MODELS IN THE IPC OF ANALYSIS AND FORECASTING OF SPATIAL ECONOMY

One of the effective tools for solving applied problems of analysis and modeling of the spatial economy are optimization interregional intersectoral models. Currently, IEIE SB RAS has developed and implemented several versions of the Model-Program Complex of the Optimization Interregional Interindustry Model "MPK OMMM", which are used to coordinate macroeconomic, sectoral and regional long-term forecasts on a regular basis and for experimental calculations of forecast options for the development of the national economy of the Russian Federation . This paper discusses one of the ways to represent optimization models of the OMMM type, based on the use of the COMBI-2000 language (Complex for processing matrix information blocks, version 2000), which is a tool for describing pre- and post-optimization calculations, a means of communication with the optimization package and reflecting a database and recording mathematical models in a fairly convenient and simplified structure for users.

PID Controllers with a Constant Speed Actuator: Implementation Versions and Self-Tuning of Pulse Duration

The article considers three implementation versions of controllers operating with constant speed actuators that are used in automation systems of a technological process: with a relay-pulse control unit (RPCU) with a lagging feedback and topping differentiator (a real differentiating element, RD), with calculating the output according to a PID algorithm and using the actuator model for its implementation, and with calculating the control output speed and using a pulse-width modulator. Formulas for calculating the parameters of the RPCU and RD primary components from the condition of approaching the equivalent PID algorithm are given. The system version with a PID controller with the actuator control proceeding from its model is considered in two options: with individual integrators for the controller’s integral component and the actuator model, and with a common integrator for the controller’s integral component and the actuator model. In the latter option, the signal from the actuator model is subtracted from the controller’s integral component, due to which the possibility of the integrator’s output to reach the integral component’s limit values is ruled out. The versions are compared by analyzing the automatic control system performance in the pulsed actuator’s operation mode based on simulation for the superheated steam temperature control section of a steam boiler equipped with a surface steam desuperheater. The analysis was carried out taking into account both the usual dynamic accuracy indicators and the number of actuators switched into operation during the transient. It is shown that the advantage of the version with the PID algorithm and common integrator for the integral component and the actuator model is that there is no need in this case to calculate the controlled variable second derivative; in addition, this version is free from the problem connected with accumulation of the difference between the signals from the actuator and its model. This version can be considered to be more preferred in implementing a PID algorithm in the software of programmed controllers. Recommendations for self-tuning of pulse duration and the control unit relay element parameters during the control process depending on the algorithm’s individual components are given. By using the proposed recommendations, it becomes possible to achieve an essentially lower actuator switching frequency during the control process.

Design of Secure Authenticated Key Management Protocol for Cloud Computing Environments

With the maturity of cloud computing technology in terms of reliability and efficiency, a large number of services have migrated to the cloud platform. To convenient access to the services and protect the privacy of communication in the public network, three-factor Mutual Authentication and Key Agreement (MAKA) protocols for multi-server architectures gain wide attention. However, most of the existing three-factor MAKA protocols don't provide a formal security proof resulting in various attacks on the related protocols, or they have high computation and communication costs. And most of the three-factor MAKA protocols haven't a dynamic revocation mechanism, which leads to malicious users can not be promptly revoked. To address these drawbacks, we propose a provable dynamic revocable three-factor MAKA protocol that achieves the user dynamic management using Schnorr signatures and provides a formal security proof in the random oracle. Security analysis shows that our protocol can meet various demands in the multi-server environments. Performance analysis demonstrates that the proposed scheme is well suited for computing resource constrained smart devices. The full version of the simulation implementation proves the feasibility of the protocol.

Implementation and Analysis of Fractals Shapes using GPU-CUDA Model

The rapid evolution of floating-point computing capacity and memory in recent years has resulted graphics processing units (GPUs) an increasingly attractive platform to speed scientific applications and are popular rapidly due to the large amount of data that processes the data on time. Fractals have many implementations that involve faster computation and massive amounts of floating-point computation. In this paper, constructing the fractal image algorithm has been implemented both sequential and parallel versions using fractal Mandelbrot and Julia sets. CPU was used for the execution in sequential mode while GPUarray and CUDA kernel was used for the parallel mode. The evaluation of the performance of the constructed algorithms for sequential structure using CPUs (2.20 GHz and 2.60 GHz) and parallelism structure for various models of GPU (GeForce GTX 1060 and GeForce GTX 1660 Ti ) devices, calculated in terms of execution time and speedup to compare between CPU and GPU maximum ability. The results showed that the execution on GPU using GPUArray or GUDA kernel is faster than its sequential implementation using CPU. And the execution using the GUDA kernel is faster than the execution using GPUArray, and the execution time between GPU devices was different, GPU with (Ti) series execute faster than the other models.

Enhanced Resolution Analysis for Water Molecules in MCM-41 and SBA-15 in Low-Field T2 Relaxometric Spectra.

Mesoporous silica materials are the subjects for relaxometric NMR studies in which we obtain information on the properties of molecules in confined geometries. The signal analysis in such investigations is generally carried out with the help of the Inverse Laplace Transform (ILT), which is accompanied by a regularization procedure. The appropriate selection of the regularization method may positively affect the resolution of the spectrum and the essence of the final conclusions. In this work, we examined the MCM-41 and SBA-15 model systems in various saturation states, using L-Curve regularization for relaxation spectra based on our own version of the fast fast ILT implementation. In a single relaxometric spectrum, the water contributions from the internal volume in the pores and between the silica particles were identified, which allowed us to trace the dynamics of the corresponding drying trends during the removal of water from the sample as a function of total water saturation.

Trajectory Outlier Detection

This article introduces two new problems related to trajectory outlier detection: (1) group trajectory outlier (GTO) detection and (2) deviation point detection for both individual and group of trajectory outliers. Five algorithms are proposed for the first problem by adapting DBSCAN , k nearest neighbors (kNN) , and feature selection (FS) . DBSCAN-GTO first applies DBSCAN to derive the micro clusters , which are considered as potential candidates. A pruning strategy based on density computation measure is then suggested to find the group of trajectory outliers. kNN-GTO recursively derives the trajectory candidates from the individual trajectory outliers and prunes them based on their density. The overall process is repeated for all individual trajectory outliers. FS-GTO considers the set of individual trajectory outliers as the set of all features, while the FS process is used to retrieve the group of trajectory outliers. The proposed algorithms are improved by incorporating ensemble learning and high-performance computing during the detection process. Moreover, we propose a general two-phase-based algorithm for detecting the deviation points, as well as a version for graphic processing units implementation using sliding windows. Experiments on a real trajectory dataset have been carried out to demonstrate the performance of the proposed approaches. The results show that they can efficiently identify useful patterns represented by group of trajectory outliers, deviation points, and that they outperform the baseline group detection algorithms.

A revision of the Combined Drought Indicator (CDI) used in the European Drought Observatory (EDO)

Abstract. Building on almost 10 years of expertise and operational application of the Combined Drought Indicator (CDI), which is implemented within the European Commission's European Drought Observatory (EDO) for the purposes of early warning and monitoring of agricultural droughts in Europe, this paper proposes a revised version of the index. The CDI conceptualizes drought as a cascade process, where a precipitation shortage (WATCH stage) develops into a soil water deficit (WARNING stage), which in turn leads to stress for vegetation (ALERT stage). The main goal of the revised CDI proposed here is to improve the indicator's performance for those events that are currently not reliably represented, without altering either the modelling conceptual framework or the required input datasets. This is achieved by means of two main modifications: (a) use of the previously occurring CDI value to improve the temporal consistency of the time series and (b) introduction of two temporary classes – namely TEMPORARY RECOVERY for soil moisture and vegetation greenness, respectively – to avoid brief discontinuities in a stage. The efficacy of the modifications is tested by comparing the performances of the revised and currently implemented versions of the indicator for actual drought events in Europe during the last 20 years. The revised CDI reliably reproduces the evolution of major droughts, outperforming the current version of the indicator, especially for long-lasting events, and reducing the overall temporal inconsistencies in stage sequencing of about 70 %. Since the revised CDI does not need supplementary input datasets, it is suitable for operational implementation within the EDO drought monitoring system.

РАЗРАБОТКА МЕТОДОВ ОПРЕДЕЛЕНИЯ И ОБЕСПЕЧЕНИЯ НАДЕЖНОСТИ ИСКУССТВЕННЫХ НЕЙРОННЫХ СЕТЕЙ НА БАЗЕ МЕМРИСТИВНЫХ УСТРОЙСТВ

The article is devoted to the reliability of hardware implementation of artificial neural networks based on memristive devices (ANNM). On the basis of the system engineering methodology, the authors have developed a universal general approach to determining the reliability of ANNM “from the accuracy of functioning through fault tolerance”. The active and passive methods of ensuring the reliability of ANNM are described. An example of determining and ensuring the reliability of a specific version of the hardware implementation of ANNM is given.

A cost-effective adaptive random testing algorithm for object-oriented software testing

The rapid development of object oriented programming (OOP) technology has made it one of the mainstream programming technologies that has been widely used in the design and development of object oriented software (OOS). The inheritance, encapsulation and polymorphism properties of object-oriented language can improve the reusability, scalability and interoperability of software while increasing the difficulty of testing OOS. Researchers have proposed a variety of testing methods to test OOS among which random testing (RT) has been widely used due to its simplicity and ease of use. An OMISS-ARTsum algorithm is proposed in this paper that uses improved OMISS random test FSCS-ART with max-sum standard, which is an implementation version of fixed-sized-candidate-set ART. The OMISS-ARTsum algorithm calculates the total distance between a candidate test case and the executed test case set before the next test case is selected from the set of candidate test cases. Unlike the traditional max-sum based FSCS-ART algorithm, OMISS-ARTsum does not calculate the distance between each executed test case and the candidate case and then sum up the total distance, but uses the method of summing up all the executed test cases and the candidate cases. The information of executing test cases is saved as a whole and the distance between the executed test case set and candidate cases is calculated at the same time. Experiment shows that compared to the OMISS-ART algorithm, the proposed OMISS-ARTsum algorithm can reduce the time overhead.

On a Semismooth* Newton Method for Solving Generalized Equations

In the paper, a Newton-type method for the solution of generalized equations (GEs) is derived, where the linearization concerns both the single-valued and the multivalued part of the considered GE. The method is based on the new notion of semismoothness${}^*$, which, together with a suitable regularity condition, ensures the local superlinear convergence. An implementable version of the new method is derived for a class of GEs, frequently arising in optimization and equilibrium models.