Vol. 40 · No. 5

In 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

Recognition of facial expressions is critical for successful social interactions and relationships. Facial expressions transmit emotional information, which is critical for human-machine interaction; therefore, significant research in computer vision has been conducted, with promising findings in using facial expression detection in both academia and industry. 3D pictures acquired enormous popularity owing to their ability to overcome some of the constraints inherent in 2D imagery, such as lighting and variation. We present a method for recognizing facial expressions in this article by combining features extracted from 2D textured pictures and 3D geometric data using the Local Binary Pattern (LBP) and the 3D Voxel Histogram of Oriented Gradients (3DVHOG), respectively. We performed various pre-processing operations using the MDPA-FACE3D and Bosphorus datasets, then we carried out classification process to classify images into seven universal emotions, namely anger, disgust, fear, happiness, sadness, neutral, and surprise. Using Support Vector Machine classifier, we achieved the accuracy of 88.5

The exponential growth of the number of documents in digital libraries and on the Web calls for very intensive development of retrieval systems. One possible architectural approach to IRS, an architecture with hidden verticals, is proposed in this paper. In IRS with hidden verticals, documents from the searched corpus are stored into a predefined set of classes. The user's query is classified before the search, and searching is done only within the corresponding class. The performance of the proposed system is compared to the performance of standard IRS (that contains a unique inverted index) and IRS with cluster pruning (in which searching corpus is clustered and query is compared to the clusters' centroids first, then search is done only in the most similar cluster). Search time in the proposed system is 7.9 times shorter than in the standard IRS and 1.7 times shorter than in the system with cluster pruning. The precision of the proposed system is 2.59 times higher than the precision of the standard IRS, and 1.68 times better compared to the IRS with cluster pruning. The recall of the proposed system is 1.09 times smaller than the recall of the standard IRS, but it is 1.28 times better than the recall of the IRS with cluster pruning. Based on the above results, we can say that proposed approach reduces search time and increases search precision with a minimal reduction in recall.

Redocumentation is an approach that is used to recover knowledge from raw software artifacts by using alternative presentations. Several legacy systems have been developed based on event-driven programming which require redocumentation. However, these existing repository and query techniques emphasize only on lexical and syntactical based queries which come with limitations in providing the semantic relationship for program understanding. We are using ontology based approach that uses both ontology reasoning and querying techniques to generate software documentation from the knowledge repository. We present a controlled experiment for the empirical evaluation on the proposed ontology based approach and implemented in a tool called Ontology Based Software Redocumentation (OBSR). In this experiment, two existing tools namely Universal Report (UR) and Microsoft Visual Studio specifically for Visual Basic (VB) programming environment have been selected to be compared with the OBSR tool. The goal is to provide experimental evidence of the viability of our approach in the context of program understanding using HTML based semantic software documentation. The experiment shows that the software maintainers are able to understand and provide significant improvement in program understanding to accomplish the maintenance task easily. We describe in detail the experiment performed, discuss its results and reflect the lesson learned from the experiment.

Extended Partial Dynamic Backtracking (EPDB) is a repair algorithm based on PDB. It deals with Dynamic CSPs based on ordering heuristics and retroactive data structures, safety conditions, and nogoods which are saved during the search process. In this paper, we show that the drawback of both EPDB and PDB is the exhaustive verification of orders, saved in safety conditions and nogoods, between variables. This verification affects remarkably search time, especially since orders are often indirectly deduced. Therefore, we propose a new approach for dynamically changing environments, the Lazy Repairing Backtracking (LRB), which is a fast version of EPDB insofar as it deduces orders directly through the used ordering heuristic. We evaluate LRB on various kinds of problems, and compare it, on the one hand, with EPDB to show its effectiveness compared to this approach, and, on the other hand, with MAC-2001 in order to conclude, from what perturbation rate resolving a DCSP with an efficient approach can be more advantageous than repair.

Business 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.

Nowadays, one of the most powerful side channel attacks (SCA) is profiled attack. Machine learning algorithms, for example support vector machine, are currently used for improving the effectiveness of the attack. One issue when using SVM-based profiled attack is extracting points of interest, or features from power traces. So far, studies in SCA domain have selected the points of interest (POIs) from the raw power trace for the classifiers. Our work proposes a novel method for finding POIs that based on the combining variational mode decomposition (VMD) and Gram-Schmidt orthogonalization (GSO). That is, VMD is used to decompose the power traces into sub-signals (modes) of different frequencies and POIs selection process based on GSO is conducted on these sub-signals. As a result, the selected POIs are used for SVM classifier to conduct profiled attack. This attack method outperforms other profiled attacks in the same attack scenario. Experiments were performed on a trace data set collected from the Atmega8515 smart card run on the side channel evaluation board Sakura-G/W and the data set of DPA contest v4 to verify the effectiveness of our method in reducing number of power traces for the attacks, especially with noisy power traces.

In conventional Ciphertext-Policy Attribute-Based Encryption (CP-ABE), the access policy appears in plaintext form that might reveal confidential user information and violate user privacy. CP-ABE with hidden access policies hides all attributes, but the computational burden increases due to the attribute hiding. In this paper, we present a Linear Secret Sharing Scheme (LSSS) access structure CP-ABE scheme that hides only sensitive attributes, rather than all attributes, in the access policy. We also provide an attribute selection method to choose these sensitive attributes and use an Attribute Bloom Filter (ABF) to hide them. Compared with the existing major CP-ABE schemes with hidden access policies, our proposed scheme is flexible in selecting attributes to hide. This scheme enhances the efficiency of policy hiding while still protecting policy privacy. Test results show that our approach is reasonable and feasible.

In some countries, bicycles are often used to access public transit stations, but the proportion of out-of-the-way travel is much smaller due to the limited availability of bicycles. Public bicycles are innovative rental or free bicycle schemes in urban areas that can be used for day-to-day mobility as one-way use is possible and can be considered as part of a public transport system. Different from traditional, mostly leisure bike rental services, they provide fast and easy access and have a variety of organizational layout, business models and useful technology for smart bikes (rented via smart cards or mobile phones). We find that bicycle-sharing systems that complement the traditional public transport system could potentially increase the competitiveness and attractiveness of sustainable modes of urban transport and thus help cities to promote sustainable daily mobility. Finally, we emphasize that the availability of open sources of urban transport information, such as public transport in our case, is crucial for analyzing urban mobility patterns. The aim of the research is to analyze and model PPP bicycle rentals using mathematical and computer methods. The article presents the application of the statistical and topological properties of bicycle rental and return network theory in city Novo mesto. The article uses swarm intelligence, a colony of ants to optimize the development of wheels across 14 stations. The wider city Novo mesto region with a population of almost 30 000 people, as a key industrial center, is heavily dependent on urban transport.

Computation Tree Logic of Knowledge (CTLK) can specify many requirements of privacy and security of multi-agent systems (MAS). In our previous papers, we defined Knowledge-oriented Petri Net (KPN) to model MAS, proposed similar reachability graph to verify CTLK, gave their model checking algorithms and developed a related tool. In this paper, we use the technique of Ordered Binary Decision Diagrams (OBDD) to encode similar reachability graph in order to alleviate the state explosion problem, and verify more epistemic operators of CTLK. We design the corresponding symbolic model checking algorithms and improve our tool. We compare our model and method with MCMAS that is the state-of-the-art CTLK model checker, and experiments illustrate the advantages of our model and method. We also explain the reasons why our model and method can obtain better performances.