Computing and Informatics (E-Journal - Institute of Informatics, SAS, Bratislava)
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Modelling and Control of Resource Allocation Systems within Discrete Event Systems by Means of Petri Nets – Part 1: Invariants, Siphons and Traps in Deadlock Avoidance
Get PDFSolving the deadlocks avoidance problem in Resource Allocation Systems (RAS) in Discrete-Event Systems (DES) is a rife problem, especially in Flexible Manufacturing Systems (FMS), alias Automated Manufacturing Systems (AMS). Petri Nets (PN) are an effectual tool often used at this procedure. In principle, there are two basic approaches how to deal with deadlocks in RAS based on PN. They are listed and illustrated here. First of the approaches is realized by means of the supervisor based on P-invariants of PN, while the second one is realized by means of the supervisor based on PN siphons. While the first approach needs to know the reachability graph/tree (RG/RT) expressing the causality of the development of the PN model of RAS, in order to find (after its thorough analysis) the deadlocks, the second approach needs the thorough analysis of the PN model structure by means of finding siphons and traps. Next, both approaches will be applied on the same PN model of RAS and the effectiveness of the achievement of their results will be compared and evaluated. Several simple illustrative examples will be introduced. For the in-depth analysis of the problem of deadlock avoiding, next Part 2 of this paper is prepared, where the newest research will be introduced and illustrated on more complicated examples. If necessary (because of the limited length of particular papers), also the third part – Part 3, will be prepared
Learning to Translate Kannada and English Queries for Mixed Script Information Retrieval
Get PDFDue to increase in the availability of numerous languages in the Web, cross language information retrieval is one of the happening issues in the field of natural language processing and information retrieval. Nowadays, people are habituated to combine two or more language words during oral or written discourse. Speakers have also employed intermixing of different languages and scripts in digital media while querying, blogging and on social media platforms. The way of representing two different language words of an utterance in their native scripts is known as mixed scripting. In the present work, we attempted to translate mixed script queries of Kannada and English languages into monolingual queries. We proposed three approaches for translation by constructing bilingual dictionary, word embeddings and Google translate. The proposed method outperforms the conventional dictionary based approach, when word embeddings were combined with the translations learnt from Google Translate and Dictionary
Real Time Mobile Ad Investigator: An Effective and Novel Approach for Mobile Click Fraud Detection
Get PDFToday, mobile advertising is considered as the most effective medium to convey promotional messages to customers because of the excessive usage of mobile phones and tablets all around the world. However, this ecosystem has severely been affected by fraudulent activities due to a large sum of money circulated in the advertising industry. The term ad fraud is referred to as any kind of fraudulent activities that are executed by fraudulent users either a human or an automated script. The combat between researchers and fraudulent users never ends because more smarter strategies are being used by the fraudsters to bypass the significant number of detection and prevention solutions. The Real Time Mobile Ad Investigator-RTMAI is proposed as a software solution to address this problem where a novel supervised learning algorithm based on the hidden Markov model along with a rule engine have been proposed to classify fraudulent impressions in real time. Furthermore, RTMAI proposed a solution to address the class imbalance problem which is generic to most of the classification datasets. The experimental results show the significance of the proposed approach to classify the fraud or non-fraud clicks/events, impressions and even user sessions more confidently in real time
Formalization and Model Checking of BPMN Collaboration Diagrams with DD-LOTOS
Get PDFBusiness Process Model and Notation (BPMN) is a standard graphical notation for modeling complex business processes. Given the importance of business processes, the modeling analysis and validation stage for BPMN is essential. In recent years, BPMN notation has become a widespread practice in business process modeling because of these intuitive diagrams. BPMN diagrams are built from basic elements. The major challenge of BPMN diagrams is the lack of formal semantics, which leads to several interpretations of the concerned diagrams. Hence, this work aims to propose an approach for checking BPMN collaboration diagrams to guarantee some properties of smooth functioning of systems modeled by BPMN notation. The verification approach used in this work is based on model checking techniques. The approach proposes as a first step a formal semantics of the collaboration diagrams in terms of the formal language DD-LOTOS, i.e., a phase of the transformation of collaboration diagrams into DD-LOTOS. This transformation is guided by applying the inference rules of the formal semantics of the DD-LOTOS formal language, and we then use the UPPAAL model checker to check the absence of deadlock, safety properties, and liveness properties
Fault Tolerance in Reversible Logic Circuits and Quantum Cost Optimization
Get PDFEnergy dissipation is a prominent factor for the very large scale integrated circuit (VLSI). The reversible logic-based circuit was capable to compute the logic without energy dissipation. Accordingly, reversible circuits are an emerging domain of research based on the low value of energy dissipation. At nano-level design, the critical factor in the logic computing paradigm is the fault. The proposed methodology of fault coverage is powerful for testability. In this article, we target three factors such as fault tolerance, fault coverage and fault detection in the reversible KMD Gates. Our analysis provides good evidence that the minimum test vector covers the 100 % fault coverage and 50 % fault tolerance in KMD Gate. Further, we show a comparison between the quantum equivalent and controlled V and V+ gate in all the types of KMD Gates. The proposed methodology mentions that after controlled V and V+ gate based ALU, divider and Vedic multiplier have a significant reduction in quantum cost. The comparative results of designs such as Vedic multiplier, division unit and ALU are obtained and they are analyzed showing significant improvement in quantum cost
A MapReduce Algorithm for Minimum Vertex Cover Problems and Its Randomization
Get PDFMapReduce is a programming paradigm for large-scale distributed information processing.
This paper proposes a MapReduce algorithm for the minimum vertex cover problem, which is known to be NP-hard.
The MapReduce algorithm can efficiently obtain a minimal vertex cover in a small number of rounds.
We show the effectiveness of the algorithm, through experimental evaluation and comparison with exact and approximate algorithms that it demonstrates high quality in a small number of MapReduce rounds.
We also confirm from experimentation that the algorithm has good scalability,
allowing high-quality solutions under restricted computation times due to increased graph size.
Moreover, we extend our algorithm to randomized one to obtain good expected approximate ratio
Breast Histopathology with High-Performance Computing and Deep Learning
Get PDFThe increasingly intensive collection of digitalized images of tumor tissue over the last decade made histopathology a demanding application in terms of computational and storage resources. With images containing billions of pixels, the need for optimizing and adapting histopathology to large-scale data analysis is compelling. This paper presents a modular pipeline with three independent layers for the detection of tumoros regions in digital specimens of breast lymph nodes with deep learning models. Our pipeline can be deployed either on local machines or high-performance computing resources with a containerized approach. The need for expertise in high-performance computing is removed by the self-sufficient structure of Docker containers, whereas a large possibility for customization is left in terms of deep learning models and hyperparameters optimization. We show that by deploying the software layers in different infrastructures we optimize both the data preprocessing and the network training times, further increasing the scalability of the application to datasets of approximatively 43 million images. The code is open source and available on Github
Detection of Macula and Recognition of Aged-Related Macular Degeneration in Retinal Fundus Images
Get PDFIn aged people, the central vision is affected by Age-Related Macular Degeneration (AMD). From the digital retinal fundus images, AMD can be recognized because of the existence of Drusen, Choroidal Neovascularization (CNV), and Geographic Atrophy (GA). It is time-consuming and costly for the ophthalmologists to monitor fundus images. A monitoring system for automated digital fundus photography can reduce these problems. In this paper, we propose a new macula detection system based on contrast enhancement, top-hat transformation, and the modified Kirsch template method. Firstly, the retinal fundus image is processed through an image enhancement method so that the intensity distribution is improved for finer visualization. The contrast-enhanced image is further improved using the top-hat transformation function to make the intensities level differentiable between the macula and different sections of images. The retinal vessel is enhanced by employing the modified Kirsch's template method. It enhances the vasculature structures and suppresses the blob-like structures. Furthermore, the OTSU thresholding is used to segment out the dark regions and separate the vessel to extract the candidate regions. The dark region and the background estimated image are subtracted from the extracted blood vessels image to obtain the exact location of the macula. The proposed method applied on 1349 images of STARE, DRIVE, MESSIDOR, and DIARETDB1 databases and achieved the average sensitivity, specificity, accuracy, positive predicted value, F1 score, and area under curve of 97.79 %, 97.65 %, 97.60 %, 97.38 %, 97.57 %, and 96.97 %, respectively. Experimental results reveal that the proposed method attains better performance, in terms of visual quality and enriched quantitative analysis, in comparison with eminent state-of-the-art methods
Probabilistic Memory Model for Visual Images Categorization
Get PDFDuring the past decades, numerous memory models have been proposed, which focused mainly on how spoken words are studied, whereas models on how visual images are studied are still limited. In this study, we propose a probabilistic memory model (PMM) for visual images categorization which is able to mimic the workings of the human brain during the image storage and retrieval. First, in the learning phase, the visual images are represented by the feature vectors extracted with convolutional neural network (CNN) and each feature component is assumed to conform to a Gaussian distribution and may be incompletely copied with a certain probability or randomly produced in accordance to an exponential distribution. Then, in the test phase, the likelihood ratio between the test image and each studied image is calculated based on the probabilistic inference theory, and an odd value in favor of an old item over a new one is obtained based on all likelihood values. Finally, if the odd value is above a certain threshold, the Bayesian decision rule is applied for image classification. Experimental results on two benchmark image datasets demonstrate that the proposed PMM can perform well on categorization tasks for both studied and non-studied images
Formal Approach Based on Petri Nets for Modeling and Verification of Video Games
Get PDFVideo games are complex systems that combine technical and artistic processes. The specification of this type of system is not a trivial task, making it necessary to use diagrams and charts to visually specify sets of requirements. Therefore, the underlying proposal of this work is to present an approach based on the formalism of Petri nets for aiding in the design process of video games. The activities of the game are represented by a specific type of Petri net called WorkFlow net. The definition of a topological map can be represented by state graphs. Using Colored Petri nets, it is possible to define formal communication mechanisms between the model of activity and the model of the map. The simulation of the timed models allows then to produce an estimated time that corresponds to the effective duration a player will need to complete a level of a game. Furthermore, a kind of Soundness property related to gameplay in a game Quest can be verified through state space analysis. For a better understanding of the approach, the video game Silent Hill II is used