Computer Science Journal (AGH University of Science and Technology, Krakow)
Not a member yet
476 research outputs found
Sort by
Memoization method for storing of minimum-weight triangulation of a convex polygon
Get PDFThis study presents a practical view of dynamic programming, specifically in the context of the application of finding the optimal solutions for the polygon triangulation problem. The problem of the optimal triangulation of polygon is considered to be as a recursive substructure. The basic idea of the constructed method lies in finding to an adequate way for a rapid generation of optimal triangulations and storing - them in as small as possible memory space. The upgraded method is based on a memoization technique, and its emphasis is in storing the results of the calculated values and returning the cached result when the same values again occur. The significance of the method is in the generation of the optimal triangulation for a large number of n. All the calculated weights in the triangulation process are stored and performed in the same table. Results processing and implementation of the method was carried out in the Java environment and the experimental results were compared with the square matrix and Hurtado-Noy method
Sensor Based Cyber Attack Detections in Critical Infrastructures Using Deep Learning Algorithms
Get PDFThe technology that has evolved with innovations in the digital world has also caused an increase in many security problems. Day by day the methods and forms of the cyber-attacks began to become complicated, and therefore their detection became more difficult. In this work we have used the datasets which have been prepared in collaboration with Raymond Borges and Oak Ridge National Laboratories. These datasets include measurements of the Industrial Control Systems related to chewing attack behavior. These measurements include synchronized measurements and data records from Snort and relays with the simulated control panel. In this study, we developed two models using this datasets. The first is the model we call the DNN Model which was build using the latest Deep Learning algorithms. The second model was created by adding the AutoEncoder structure to the DNN Model. All of the variables used when developing our models were set parametrically. A number of variables such as activation method, number of hidden layers in the model, the number of nodes in the layers, number of iterations were analyzed to create the optimum model design. When we run our model with optimum settings, we obtained better results than related studies. The learning speed of the model has 100\% accuracy rate which is also entirely satisfactory. While the training period of the dataset containing about 4 thousand different operations lasts about 90 seconds, the developed model completes the learning process at the level of milliseconds to detect new attacks. This increases the applicability of the model in real world environment
Detecting Gaze Direction Using Robot-Mounted and Mobile-Device Cameras
Get PDFTwo common channels through which humans communicate are speech andgaze. Eye gaze is an important mode of communication: it allows people tobetter understand each others’ intentions, desires, interests, and so on. The goalof this research is to develop a framework for gaze triggered events which canbe executed on a robot and mobile devices and allows to perform experiments.We experimentally evaluate the framework and techniques for extracting gazedirection based on a robot-mounted camera or a mobile-device camera whichare implemented in the framework. We investigate the impact of light on theaccuracy of gaze estimation, and also how the overall accuracy depends on usereye and head movements. Our research shows that the light intensity is im-portant, and the placement of light source is crucial. All the robot-mountedgaze detection modules we tested were found to be similar with regard to ac-curacy. The framework we developed was tested in a human-robot interactionexperiment involving a job-interview scenario. The flexible structure of thisscenario allowed us to test different components of the framework in variedreal-world scenarios, which was very useful for progressing towards our long-term research goal of designing intuitive gaze-based interfaces for human robotcommunication
A Client-Based Encryption Model for Secure Data Storing in Publicly Available Storage Systems
Get PDFThis document presents a conceptual model of a system for protecting the data stored in publicly available data storage systems. The main idea was to apply encryption on both the client and server sides that would consequently have a significant impact on data security. The compatibility with existing systems allows us to deploy the solution fast and at a low cost. The tests conducted on a simplified implementation have confirmed the solution’s validity, and they have shown some possible performance issues as compared to the classical system (which can be easily bypassed)
A novel approach for big data classification based on hybrid parallel dimensionality reduction using spark cluster
No full textThe big data concept has elicited studies on how to accurately and efficiently extract valuable information from such huge dataset. The major problem during big data mining is data dimensionality due to a large number of dimensions in such datasets. This major consequence of high data dimensionality is that it affects the accuracy of machine learning (ML) classifiers; it also results in time wastage due to the presence of several redundant features in the dataset. This problem can be possibly solved using a fast feature reduction method. Hence, this study presents a fast HP-PL which is a new hybrid parallel feature reduction framework that utilizes spark to facilitate feature reduction on shared/distributed-memory clusters. The evaluation of the proposed HP-PL on KDD99 dataset showed the algorithm to be significantly faster than the conventional feature reduction techniques. The proposed technique required >1 minute to select 4 dataset features from over 79 features and 3,000,000 samples on a 3-node cluster (total of 21 cores). For the comparative algorithm, more than 2 hours was required to achieve the same feat. In the proposed system, Hadoop’s distributed file system (HDFS) was used to achieve distributed storage while Apache Spark was used as the computing engine. The model development was based on a parallel model with full consideration of the high performance and throughput of distributed computing. Conclusively, the proposed HP-PL method can achieve good accuracy with less memory and time compared to the conventional methods of feature reduction. This tool can be publicly accessed at https://github.com/ahmed/Fast-HP-PL
Flow caching effectiveness in packet forwarding applications
Get PDFRouting algorithms are known to be a potential bottleneck for packet processing. Network flow caching can function as a genneral acceleration technique for packet processing workloads. The goal of this article is to evaluate the effectiveness of packet flow caching techniques in high-speed networks. The area of focus is data distribution characteristics that lead to effectiveness of caching of network flows (connections). Based on statistical analysis and simulations the article sets necessary conditions for effective use of caches in packet forwarding applications. Public domain network traces were examined and measured for data locality. Software simulations show a strong correlation between flow packet distance metric and cache hit rate
Towards a Distributed Solution to the Multi-Robot Task Allocation Problem with Energetic and Spatiotemporal Constraints
Get PDFThe multi-robot task allocation problem consists of two distinct sets: a set of tasks (requiring resources) and a set of robots (offering resources); then tasks are allocated to robots; while optimizing a certain objective function, subject to some constraints: e.g. allocate the maximum number of tasks, minimize the distances travelled by robots, etc. In this paper, we propose some objective functions, and our main contribution is the introduction of energetic constraints on multi-robot task allocation problems. In addition, we propose an allocation method (based on parallel distributed guided genetic algorithms) and compare it to two state-of-the-art algorithms. Performed simulations and obtained results show the effectiveness and scalability of our solution, even with a large number of robots and tasks
Towards textual data augmentation for neural networks: synonyms and maximum loss
Get PDFData augmentation is one of the ways of dealing with labeled data scarcity and overfitting. Both these problems are crucial for modern deep learning algorithms which require massive amounts of data. The problem is better explored in the context of image analysis than for text. This work is a step forward to close this gap. We propose a method for augmenting textual data when training convolutional neural networks for sentence classification. The augumentation is based on the substitution of words using a thesaurus as well as the Princeton WordNet. Our method improves upon the baseline in almost all cases. In terms of accuracy the best of the variants is 1.2% (pp.) better than the baseline
Hypergraph grammar based multi-thread multi-frontal direct solver with Galois scheduler
No full textIn this paper we analyze two dimensional grids with point and edge singularities in order to develop an efficient graph grammar based multi-frontal direct solver algorithm. We express these grids by hypergraph models. For these meshes we define a sequence of graph grammar productions expressing the construction of frontal matrices, elimination of fully assembled nodes, merging of resulting Schur complements, and repeating the process of elimination and merging until a single frontal matrix remains. The dependency relation between graph grammar productions is analyzed, and the dependency graph is plot, which is equivalent to the elimination tree of the multi-frontal solver algorithm. We utilize classical multi-frontal solver algorithm, and the graph grammar productions allows us to construct an efficient elimination tree, based on the graph representation of the computational mesh, and not the global matrix itself. The graph grammar productions are assigned to nodes of the dependency graph, and they are implemented as tasks in the GALOIS system and scheduled according to the developed dependency graph over the shared memory parallel machine. We show that our graph grammar based solver outperforms parallel MUMPS solver
The application of modified Chebyshev polynomials in asymmetric cryptography
No full textBased on Chebyshev polynomials, you can create an asymmetric cryptosystem that allows secure communication. Such a cryptosystem uses the fact that these polynomials form a semi-group due to the composition operation. This article presents new cryptosystems that use other than semi-group property dependencies. Based on these dependencies as well as modifications of Chebyshev\u27s polynomials, two cryptosystems have been proposed. The presented analysis shows that their security is the same as in the case of algorithms associated with the problem of discrete logarithms. The article also shows methods that allow faster calculation of Chebyshev polynomials