Computing and Informatics (E-Journal - Institute of Informatics, SAS, Bratislava)
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Using Probabilistic Temporal Logic PCTL and Model Checking for Context Prediction
Get PDFContext prediction is a promoting research topic with a lot of challenges and opportunities. Indeed, with the constant evolution of context-aware systems, context prediction remains a complex task due to the lack of formal approach. In this paper, we propose a new approach to enhance context prediction using a probabilistic temporal logic and model checking. The probabilistic temporal logic PCTL is used to provide an efficient expressivity and a reasoning based on temporal logic in order to fit with the dynamic and non-deterministic nature of the system's environment. Whereas, the probabilistic model checking is used for automatically verifying that a probabilistic system satisfies a property with a given likelihood. Our new approach allows a formal expressivity of a multidimensional context prediction. Tested on real data our model was able to achieve 78% of the future activities prediction accuracy
Multi-Carrier Steganographic Algorithm Using File Fragmentation of FAT FS
Get PDFSteganography is considered to be not only a science, but also a craft of concealing ongoing communication by hiding messages in unsuspicious cover documents, such as texts, digital images, audio and video sequences. Its essential feature is the constant search for - often exceptionally creative - possibilities of concealing information. In computers, steganography often uses secondary memory and exchangeable memory media utilising file systems. This paper deals with the current state of the issues related to information hiding by means of hard disks, being the most important source of forensic data. This paper focuses on information hiding using the File Allocation Table (FAT) file system. It also proposes a novel multi-carrier algorithm of hiding information in file fragmentation. The algorithm provides flexibility of encoding the information to be hidden and makes steps toward optimization that allows reduction of interference with the current state of the file system, represented by the statistical values of the file fragmentation parameters
Exploiting the Use of Cooperation in Self-Organizing Reliable Multiagent Systems
Get PDFIn this paper, a novel and cooperative approach is exploited introducing a self-organizing engine to achieve high reliability and availability in multiagent systems. The Adaptive Multiagent Systems theory is applied to design adaptive groups of agents in order to build reliable multiagent systems. According to this theory, adaptiveness is achieved via the cooperative behaviors of agents and their ability to change the communication links autonomously. In this approach, there is not a centralized control mechanism in the multiagent system and there is no need of global knowledge of the system to achieve reliability. This approach was implemented to demonstrate its performance gain in a set of experiments performed under different operating conditions. The experimental results illustrate the effectiveness of this approach
An Experimental Study on Virtual Machine Live Migration Impact on Services Performance
Get PDFOne important benefit of servers' virtualization is the reduction of the maintenance complexity of infrastructures. A key feature is servers' live migration which allows virtual servers to be exchanged between physical machines without stopping their services. However, virtualization also has some drawbacks caused by the overhead generated. Our research evaluated live migration process overhead, on real and virtual environments, noticed from the client's side regarding two different services: web and database. YCSB and ab Benchmark were adopted as workloads. Almost all tests on real environment overcame those on virtual, with both benchmarks. The impact of the live migration in the services was evident, proving to be more effective on real machines than on virtual machines. We found the DB service accommodated better to the virtual environment and to migration than Web service. We also considered an environment with multiple migrations which presented a higher degradation than when only one migration is performed
A New Dynamic Load Balancing Algorithm for Multi-ROIA
Get PDFReal-time Online Interactive Application (ROIA) is an emerging class of large-scale distributed application which can support millions of concurrent users around the world. Due to the dynamic changes in the number of concurrent users as well as the uncertainty of user operations, the dynamic load balancing is a key issue for ROIA. However, most of previous works are dedicated to the load balancing in a single ROIA without considering the variety of different type ROIAs. We take the advantage of differences between ROIAs and propose a new load balancing algorithm for multi-ROIA to improve the scalability of ROIA and increase the resource utilization of system. This paper firstly describes the motivation of the new load balancing algorithm, then presents the dynamic load balancing algorithm for multi-ROIA. Finally, the simulation results are also presented to show the efficiency and feasibility of the new algorithm
Specific Parameter-Free Global Optimization to Speed Up Setting and Avoid Factors Interactions
Get PDFMeta-heuristics utilizing numerous parameters are more complicated than meta-heuristics with a couple of parameters for various reasons. In essence, the effort expected to tune the strategy-particular parameters is far more prominent as the quantity of parameters increases and furthermore, complex algorithms are liable for the presence of further parameter interactions. Jaya meta-heuristic does not involve any strategy-specific parameters and is a one-stage technique. It has demonstrated its effectiveness compared to major types of meta-heuristics and it introduces various points of interest, such as its easy deployment and set-up in industrial applications and its low complexity to be studied. In this work, a new meta-heuristic, Enhanced Jaya (EJaya) is proposed to overcome the inconsistency of Jaya in diverse situations, introducing coherent attraction and repulsion movements and restrained intensity for flight. Comparative results of EJaya in a set of benchmark problems including statistical tests show that it is feasible to increase the accuracy, scalability and exploitation capability of Jaya while keeping its specific parameter-free feature. EJaya is especially suitable for a priori undefined characteristics optimization functions or applications where the set-up time of the optimization process is critical and parameters tuning and interactions must be avoided
Learned Spatio-Temporal Texture Descriptors for RGB-D Human Action Recognition
Get PDFDue to the recent arrival of Kinect, action recognition with depth images has attracted researchers' wide attentions and various descriptors have been proposed, where Local Binary Patterns (LBP) texture descriptors possess the properties of appearance invariance. However, the LBP and its variants are most artificially-designed, demanding engineers' strong prior knowledge and not discriminative enough for recognition tasks. To this end, this paper develops compact spatio-temporal texture descriptors, i.e. 3D-compact LBP (3D-CLBP) and local depth patterns (3D-CLDP), for color and depth videos in the light of compact binary face descriptor learning in face recognition. Extensive experiments performed on three standard datasets, 3D Online Action, MSR Action Pairs and MSR Daily Activity 3D, demonstrate that our method is superior to most comparative methods in respects of performance and can capture spatial-temporal texture cues in videos
Chaotic Encryption and Privilege Based Visual Secret Sharing Model for Color Images
Get PDFIn the Privilege-based Visual Secret Sharing Model (PVSSM), each share has a unique privilege and a higher-privilege share contributes with more privilege to reveal the secret image. However, in PVSSM, when several shares with the higher priority are stacked, the secret image can be visibly displayed. This security problem is solved by applying a two-dimensional Logistic-Adjusted Sine Map (2D-LASM) to each share. This method is called Chaotic Encryption-based PVSSM. In this paper, we aim to present how Chaotic Encryption-based PVSSM is applied to color images. In order to assess the efficiency of this method, histogram analysis, data loss attack, salt-pepper noise attack, differential attack, chi-square analysis and correlation analysis tests were applied. The performance of this method has been evaluated according to NCPR, UACI, PSNR, SSIM and CQM. The proposed method achieved a good test values and showed better results compared to similar studies in literature
Incorporating Stratified Negation into Query-Subquery Nets for Evaluating Queries to Stratified Deductive Databases
Get PDFMost of the previously known evaluation methods for deductive databases are either breadth-first or depth-first (and recursive). There are cases when these strategies are not the best ones. It is desirable to have an evaluation framework for stratified DatalogN that is goal-driven, set-at-a-time (as opposed to tuple-at-a-time) and adjustable w.r.t. flow-of-control strategies. These properties are important for efficient query evaluation on large and complex deductive databases. In this paper, by incorporating stratified negation into so-called query-subquery nets, we develop an evaluation framework, called QSQNSTR, with such properties for evaluating queries to stratified DatalogN databases. A variety of flow-of-control strategies can be used for QSQNSTR. The generic evaluation method QSQNSTR for stratified DatalogN is sound, complete and has a PTIME data complexity
RDGC: A Reuse Distance-Based Approach to GPU Cache Performance Analysis
Get PDFIn the present paper, we propose RDGC, a reuse distance-based performance analysis approach for GPU cache hierarchy. RDGC models the thread-level parallelism in GPUs to generate appropriate cache reference sequence. Further, reuse distance analysis is extended to model the multi-partition/multi-port parallel caches and employed by RDGC to analyze GPU cache memories. RDGC can be utilized for architectural space exploration and parallel application development through providing hit ratios and transaction counts. The results of the present study demonstrate that the proposed model has an average error of 3.72 % and 4.5 % (for L1 and L2 hit ratios, respectively). The results also indicate that the slowdown of RDGC is equal to 47 000 times compared to hardware execution, while it is 59 times faster than GPGPU-Sim simulator