Solving game problems using recursion

The article describes the methodology to game problems of Olympiads in informatics using recursive numbers generation. The study is based on the sequential solution of increasingly complex tasks. For each task, the following materials are given: the condition of the task, the idea of the solution with a proposal to come up with an independent implementation, the solution in the Pascal programming language. Distance learning system DL.GSU.BY is the effective technical base for teaching. It allows offer the student a condition of the task; send the solution for review; get a verdict from the system — a correct or incorrect solution; for incorrect solutions, the number of the test on which the solution did not pass is indicated. A student can take a test (input and output data), on which his solution did not pass, figure out what the error is in his program, correct and send the solution again. In addition, for each problem there is a link on it to the topic in the forum at site, where you can ask a question on solving this problem and / or read the answer if the questions have already been asked before.

Remote Subthreshold Stimulation Enhances Skin Sensitivity in the Lower Extremity

Foot sole skin interfaces with the ground and contributes to successful balance. In situations with reduced sensitivity in the glabrous foot skin, stochastic resonance (SR) improves skin sensitivity by adding tactile noise. Some situations, however, involve an interface comprised of hairy skin, which has higher thresholds for sensitivity. For example, in lower extremity amputation the residual limb is comprised of hairy leg skin. The main objective of this study was to determine if SR improves skin sensitivity in hairy skin, and whether a specific intensity of noise is most effective. Secondary objectives were to compare the effect between locations, ages and modalities. In 60 healthy participants a vibrotactile (test) input was delivered at the lower extremity concurrently with a second, noisy stimulus applied more proximally. The presence of a remote SR effect was tested in 15 young participants using electrotactile noise at the calf. Secondary objectives were tested in separate groups of 15 subjects and differed by substituting for one of the three variables: vibrotactile noise, heel site, and with older participants. A forced-choice protocol was used to determine detection ability of the subthreshold vibration test input with varying noise levels applied simultaneously (0, 20, 40, 60, 80, and 100% of perceptual threshold). An SR effect was identified when increased detection of the input was obtained at any level of noise versus no noise. It was found that all four test groups demonstrated evidence of SR: 33–47% of individuals showed better detection of the input with added noise. The SR effect did not appear consistently at any specific noise level for any of the groups, and none of the variables showed a superior ability to evoke SR. Interestingly, in approximately 33% of cases, threshold values fluctuated throughout testing. While this work has provided evidence that SR can enhance the perception of a vibrotactile input in hairy skin, these data suggest that the ability to repeatably show an SR effect relies on maintaining a consistent threshold.

A Conformal Predictive System for Distribution Regression with Random Features

Distribution regression is the regression case where the input objects are distributions. Many machine learning problems can be analysed in this framework, such as multi-instance learning and learning from noisy data. This paper attempts to build a conformal predictive system(CPS) for distribution regression, where the prediction of the system for a test input is a cumulative distribution function(CDF) of the corresponding test label. The CDF output by a CPS provides useful information about the test label, as it can estimate the probability of any event related to the label and be transformed to prediction interval and prediction point with the help of the corresponding quantiles. Furthermore, a CPS has the property of validity as the prediction CDFs and the prediction intervals are statistically compatible with the realizations. This property is desired for many risk-sensitive applications, such as weather forecast. To the best of our knowledge, this is the first work to extend the learning framework of CPS to distribution regression problems. We first embed the input distributions to a reproducing kernel Hilbert space using kernel mean embedding approximated by random Fourier features, and then build a fast CPS on the top of the embeddings. While inheriting the property of validity from the learning framework of CPS, our algorithm is simple, easy to implement and fast. The proposed approach is tested on synthetic data sets and can be used to tackle the problem of statistical postprocessing of ensemble forecasts, which demonstrates the effectiveness of our algorithm for distribution regression problems.

Testing Deep Learning-based Visual Perception for Automated Driving

Due to the impressive performance of deep neural networks (DNNs) for visual perception, there is an increased demand for their use in automated systems. However, to use deep neural networks in practice, novel approaches are needed, e.g., for testing. In this work, we focus on the question of how to test deep learning-based visual perception functions for automated driving. Classical approaches for testing are not sufficient: A purely statistical approach based on a dataset split is not enough, as testing needs to address various purposes and not only average case performance. Additionally, a complete specification is elusive due to the complexity of the perception task in the open context of automated driving. In this article, we review and discuss existing work on testing DNNs for visual perception with a special focus on automated driving for test input and test oracle generation as well as test adequacy. We conclude that testing of DNNs in this domain requires several diverse test sets. We show how such tests sets can be constructed based on the presented approaches addressing different purposes based on the presented methods and identify open research questions.

Test Oracle Generation Based on BPNN by Using the Values of Variables at Different Breakpoints for Programs

Automatic test oracle generation is a bottleneck in realizing full automation of the entire software testing process. This study proposes a new method for automatically generating a test oracle for a new test input on the basis of several historical test cases by using a backpropagation neural network (BPNN) model. The new method is different from existing test oracle techniques. Specifically, our method has two steps. First, the values of variables are collected as training data when several historical test inputs are used to execute the program at different breakpoints. The test oracles (pass or fail) of these test cases are utilized to classify and label the training data. Second, a new test input is used to execute the program at different breakpoints, where the trained BPNN prediction model automatically generates its test oracle on the basis of the collected values of the variables involved. We conduct an experiment to validate our method. In the experiment, 113 faulty versions of seven types of programs are used as experimental objects. Results show that the average prediction accuracy rate of 74,651 test oracles is 95.8%. Although the failed test cases in the training data account for less than 5%, the overall average recall rate (prediction accuracy of test case execution failure) of all programs is 78.9%. Furthermore, the trained BPNN can reveal not only the impact of the values of variables but also the impact of the logical correspondence between variables in test oracle generation.

Electric Vehicle Modelling and Simulation of a Light Commercial Vehicle Using PMSM Propulsion

Even though the Internal Combustion Engine (ICE) used in conventional vehicles is one of the major causes of global warming and air pollution, the emission of toxic gases is also harmful to living organisms. Electric propulsion has been developed in modern electric vehicles to replace the ICE.The aim of this research is to use both the Simulink and Simscape toolboxes in MATLAB to model the dynamics of a light commercial vehicle powered by electric propulsion. This research focuses on a Volkswagen Crafter with a diesel propulsion engine manufactured in 2020. A rear-wheel driven electric powertrain based on a Permanent Magnet Synchronous Motor was designed to replace its front-wheel driven diesel engine in an urban environment at low average speeds.In this research, a Nissan Leaf battery with a nominal voltage of 360 V and a capacity of 24 kWh was modelled to serve as the energy source of the electric drivetrain. The New European Driving Cycle was used in this research to evaluate the electric propulsion. Another test input such as a speed ramp was also used to test the vehicle under different road conditions. A Proportional Integral controller was applied to control the speed of both the vehicle and synchronous motor. Different driving cycles were used to test the vehicle. The vehicle demonstrated a good tracking capability in each type of test. In addition, this research determined that the fuel economy of electric vehicles is approximately 19% better than that of conventional vehicles.

Application of Basketball Training System Based on Dynamic Intelligent Fog Computing Network

Although the development of the mobile Internet and the Internet of Things has greatly promoted the progress and development of society, it has also created many problems for people on the road of scientific and technological exploration. In order to meet the problems and requirements of high bandwidth, high load, and low latency in the current network development, the emergence of the concept of mobile edge computing has attracted extensive attention from the academic community. This article focuses on the representative mode of mobile edge computing-fog computing (in this model, data, (data)processing, and applications are concentrated in devices at the edge of the network, instead of being stored almost entirely in the cloud). By applying it to the development and operation of basketball training system, it explores the performance of dynamic intelligent fog computing in intelligent end user services. This paper proposes a fog resource scheduling scheme based on linear weighted genetic algorithm, which converts the problem of multiobjective optimization into a single-objective optimization problem. When applying the genetic algorithm based on weighted sum, preference is given to delay, communication load, and service cost. Value is integrated into an objective function to perform genetic operations to get a better solution. From the experimental data, the system can support 20 DCTU terminals with a pressure request of 10 messages per second per terminal under the pressure environment created by the pressure test input data. The barrier-free transmission distance is 200 m, and the barrier transmission distance is 50 m. It has strong fault tolerance.

Generating Test Inputs from String Constraints with an Automata-Based Solver

Software testing is an integral part of the software development process. To test certain parts of software, developers need to identify inputs that reach those parts. Data and control dependencies make this a non-trivial task, and as the complexity of software increases it becomes more difficult to manually derive such inputs. Due to complex data manipulations, this process is even more challenging for programs with string inputs, such as security applications. Thus, automated reachability test input generation for string data types is an important research area. Symbolic Execution is a path-sensitive static program analysis technique that can automatically generate conditions for inputs that reach a given program location. Commonly, such conditions are encoded as automata that describe a set of strings at that location. Automata result from string operations applied to inputs along that path. However, these automata do not necessarily correspond to string inputs that result in string values at the program location. To find those input values, we need to undo the effects of string operations through backward analysis. The intricate relationships between symbolic string values complicate this process. These relationships are due to non-injective string operations and data-flow dependencies of string values. This thesis presents a novel method for test input generation for automatabased string constraints. It uses single-track automata along with novel computational techniques to perform inverse string operations. Empirical evaluations on a set of benchmarks have shown this method to be effective in solving automatabased string constraints from real-world applications.

Tracing CVE Vulnerability Information to CAPEC Attack Patterns Using Natural Language Processing Techniques

For effective vulnerability management, vulnerability and attack information must be collected quickly and efficiently. A security knowledge repository can collect such information. The Common Vulnerabilities and Exposures (CVE) provides known vulnerabilities of products, while the Common Attack Pattern Enumeration and Classification (CAPEC) stores attack patterns, which are descriptions of common attributes and approaches employed by adversaries to exploit known weaknesses. Due to the fact that the information in these two repositories are not linked, identifying related CAPEC attack information from CVE vulnerability information is challenging. Currently, the related CAPEC-ID can be traced from the CVE-ID using Common Weakness Enumeration (CWE) in some but not all cases. Here, we propose a method to automatically trace the related CAPEC-IDs from CVE-ID using three similarity measures: TF–IDF, Universal Sentence Encoder (USE), and Sentence-BERT (SBERT). We prepared and used 58 CVE-IDs as test input data. Then, we tested whether we could trace CAPEC-IDs related to each of the 58 CVE-IDs. Additionally, we experimentally confirm that TF–IDF is the best similarity measure, as it traced 48 of the 58 CVE-IDs to the related CAPEC-ID.