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Wireless Sensor Networks (WSNs) comprise interconnected wireless nodes that receive and transmit data across applications and platforms. This paper addressed the problem of link failures in WSNs that potentially could lead to the loss of data packets while still in transit. This was achieved through the use of network coding which is known to address capacity bottleneck problems in WSNs. In particular, a technique called Ant Agent-Assisted Network Coding (AAANC) is proposed that employs the ant colony optimization technique in addition to network coding operations. The main aim of AAANC is to facilitate the successful delivery and decoding of coded data packets in the presence of link failures. AAANC employs a packet route selection technique that is inspired by the social behavior of natural ants. For natural ants, a strong pheromone trail along a path indicates a promising route to a food source, and this is analogous to a reliable communication link for routing data packets in this paper. Through simulations, AAANC was compared to diagonal pseudorandom network coding (DNC) and triangular pseudorandom network coding (TNC), and it proved to have a superior performance in terms of packet delivery ratio and number of decoded packets. Significant performance gain can be achieved if AAANC algorithm is made to dynamically adapt the ant colony and network coding parameters in response to traffic changes. [ABSTRACT FROM AUTHOR]

Introduced by Gutman & Trinajstić in 1972, the two well-known Zagreb indices efficiently determine the total π -electron energy of alternant hydrocarbons. The hyper-Zagreb index is a variant of the classical first Zagreb index. A tree is an acyclic graphical structure. A segment in a tree is equivalent to a vertex of degree two. This paper derives sharp upper and lower bounds on the hyper-Zagreb index of trees with given number of segments. We also characterize the corresponding trees achieving those bounds. The second part of the paper studies the predictive potential of the hyper-Zagreb index for determining physicochemical properties such as the enthalpies of combustion, formation, sublimation, and vaporization of monocarboxylic acids. Our statistical analysis asserts that the hyper-Zagreb index correlates these physicochemical properties of monocarboxylic acids with correlation coefficient ρ > 0.99. The strongest correlation by the hyper-Zagreb index was obtained with the enthalpy of combustion having correlation coefficient ρ = 0.999991. This warrants the further employability of the hyper-Zagreb index in QSPR modeling. This potential applicability justifies the construction of the hyper-Zagreb graphical index whose motivation of defining was purely mathematical. [ABSTRACT FROM AUTHOR]

A neural network is a computer system modeled on the nerve tissue and nervous system. In this sense, neural networks refer to systems of neurons, either organic or artificial in nature. Neural networks have diverse applications in computer graphics, artificial intelligence (AI), machine & deep learning, chemistry, material science, among others. Research on structural properties and their classification of favorable or unfavorable for neural networks has been started recently. Employing tools from mathematics and specifically graph theory has been a key research direction in this area. Different graph-theoretic parameters have potential applications in studying topological properties of neural networks. In this paper for the first time, we study some nondeterministic polynomial-time (NP)-complete and NP-hard problems on various neural networks by considering their graphs. We consider classes of 3- and 4-layered probabilistic neural networks (PNNs), cellular neural networks (CNNs), and Tickysym spiking neural networks (TSNNs). In order to study their topological properties, we study their structures of maximal cliques, minimal colorings, maximal independent sets, maximal and perfect matchings, and minimum dominating sets. Computational results on the clique number, chromatic number, independence number, matching ratio and the domination number are reported. For instance, the clique number of the probabilistic neural networks is 2, the Tickysym spiking neural network is 3, whereas, for the cellular neural network, it is 4. These numerical results quantify the incoming/outgoing traffic in these architectures. Similarly, the independence number of 25-nodal 3-layered probabilistic neural network is 15, whereas, the independence number of 25-nodal cellular neural network is just 9. Moreover, the asymptotic matching ratio for PNNs (resp. CNNs) is 1 2 (resp. 1 8 ), whereas, it is 1 6 for the case of TSNNs. This gives probabilistic neural networks a significant priority in controllability over cellular neural networks. Some open problems are proposed in the end to further this research area. [ABSTRACT FROM AUTHOR]

The extraction of the communities is reflected by the allocation of the nodes to the different communities and therefore a distribution, to which we can associate a score indicating its quality. Several methods have been proposed to extract existing communities in a network. The classification of these methods may be the subject of a separate study. The common point noted between most of these methods is the use of a function that measures the quality of a distribution. A metric called "Modularity" is considered to be the most efficient; it is used in most of the community extraction algorithms. Other metrics have been proposed such as "Z-Modularity", "Modularity density", "Exponential modularity". However, Modularity is still a competing metric. Our job is to establish an in-depth study of these quality measurement functions. For this purpose, considering the case of non-overlapping communities, we thought of a structural comparison between the results obtained, executing the "Leiden" algorithm, each time we change the metric among the four adopted. We chose this algorithm because it is an improved version of one of the most popular algorithms in this area called "Louvain". [ABSTRACT FROM AUTHOR]

Let Θ (G) denote the Shannon capacity of a graph G. We give an elementary proof of the equivalence, for any graphs G and H , of the inequalities Θ (G ⊔ H) > Θ (G) + Θ (H) and Θ (G ⊠ H) > Θ (G) Θ (H). This was shown independently by Wigderson and Zuiddam (2022) using Kadison–Dubois duality and the Axiom of choice. [ABSTRACT FROM AUTHOR]

The purpose of this article is to present how to solve critical path problems by simulation. The activities specified for the realization of the design of a naval engine injection cam having random durations, the probability distribution of the total realization duration is determined by Monte Carlo simulation method. The automatic statistical analysis module Analysis ToolPak of MS Excel will be used to generate uniformly distributed random numbers and the PERT/CPM module of WinQSB software will be used for simulation. [ABSTRACT FROM AUTHOR]

In smart manufacturing, an increasing amount of heterogeneous data inputs need to be integrated into a holistic traceability model. One key challenge is the coherent mapping of dynamic and event-based sensor data for real-time tracking purposes with static data, which are mostly needed for retrospective tracing purposes. In this research, we develop a graph-based traceability model, which integrates static and dynamic data sub-models in one connected traceability graph. For an automotive use case, the novel traceability model is implemented and tested using a hybrid architecture based on the graph database Neo4j and the event engine Apache Kafka. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new consensus protocol for networks of multiple mobile robots with fixed communication topology using a strategy based on an invariant manifold technique. The agents are subjected to non-holonomic constraints and transformed into Brockett integrator form. The objective is to asymptotically stabilize the non-holonomic model for each agent about a group decision value using distributed protocol. This approach solves the consensus problem in two cases: leader–follower consensus problem when the leader is static and leaderless consensus problem. Then, a design of test-bed is fully described for testing distributed protocols. The setup consists of a group of non-holonomic mobile robots moving on a platform with different ArUco markers on their tops, an overhead camera to determine the poses (positions and orientations) of these markers, and a desktop computer to provide the interaction between the robots. The design of this test-bed has relied on ROS framework as a software platform for offering ROS network architecture a solution for distributed communication. Finally, results were presented to show the performance of this design by applying the proposed protocol. [ABSTRACT FROM AUTHOR]

The problem of automatic analysis and processing of electroencephalogram (EEG) signals has been an attractive but a quite difficult research subject with many potential clinical applications. For this problem, one major goal is to find and locate EEG state change that is from a normal state to an abnormal state during monitoring period, which remains challenging due to the high complexity of EEG signals. This study proposes a dynamic modeling method based on the combination of dynamic graph analysis and hierarchical decomposition (HD). The method can take into account the difference of contribution of individual frequency components in the EEG by construction of a family of hierarchical graph models (HGMs) from frequency components decomposed by HD. Adaptive input weighting (AIW) fusion is subsequently adopted to utilize and assemble these differences from all constructed graphs to form a comprehensive indicator that can describe the behavioral characteristics of EEG signals. Finally, a common null hypothesis testing is allowed for producing a decision making. Experiments results, our method can successfully detect all seizures without false alarm outperforming representable methods, demonstrated the availability and great potentials of the proposed method in real scenarios. This work is an extension of a recent result (Lu et al., 2020), which introduces an automated system that can detect changes in a given EEG time series. • Innovative Hierarchical Graph Modeling This approach combines dynamic graph analysis with hierarchical decomposition to address the complexity and diverse frequency components of EEG signals. • Adaptive Input Weighting Fusion Utilizing adaptive input weighting (AIW), this approach integrates the diverse contributions of individual frequency components into a comprehensive indicator. • Superior Performance and Validation Through experiments on two public datasets, this approach is proven to be a powerful clinical tool for early disease diagnosis and monitoring. [ABSTRACT FROM AUTHOR]

A computational problem well studied in the field of Complexity Theory is Graph Isomorphism. Color Refinement is a classical technique for testing the non-existence of isomorphism between two given graphs. Tinhofer's algorithm is an extension of the Color Refinement algorithm and the same technique does not succeed for all graphs. We characterize Tinhofer graphs algebraically. We propose and study in detail, a new graph hierarchy based on Tinhofer's algorithm which also prove for all the new graph classes in the hierarchy a sufficient condition. We look at the relations of the new graph classes with each other and also with the already known classes. We also facilitate an effective graph isomorphism algorithm for the lower classes in the hierarchy. Getting an algebraic characterization for the new hierarchy classes, understanding the exact location of other graphs in this hierarchy and extending the efficient algorithm for higher graph classes are identified as potential areas of future interest. [ABSTRACT FROM AUTHOR]

• Transfer entropy is used to construct whole-brain effective connectivity networks. • Topological metrics of functional and effective networks were characterized. • Effective networks contain a subset of highly interactive regions. Based on magnetic resonance imaging (MRI), macroscopic structural and functional connectivity of human brain has been widely explored in the last decade. However, little work has been done on effective connectivity between individual brain parcels. In this preliminary study, we aim to investigate whole-brain effective connectivity networks from resting-state functional MRI (rs-fMRI) images. After the functional connectivity networks of 26 healthy subjects (aged from 25 to 35 years old) from Human Connectome Project database were derived from rs-fMRI images with dynamic time warping, proportional thresholding (PT) was performed on the functional connectivity matrices by retaining the PT% strongest functional connections. PT% ranges from 40% to 10% in steps of 5%. Then, effective connections corresponding to the PT% strongest functional connections, both bi-directional and unidirectional, were estimated with Renyi's 2-order transfer entropy (TE) method. Topological metrics of the built functional and effective connectivity networks were further characterized, including clustering coefficient, transitivity, and modularity. It is found that the effective connectivity networks exhibit small world attributes, and that the networks contain a subset of highly interactive regions, including right frontal pole (in-degree 6), left middle frontal gyrus (in-degree 8, out-degree 1), right precentral gyrus (out-degree 9), left precentral gyrus (out-degree 7), right posterior division of supramarginal gyrus (in-degree 2, out-degree 3), left angular gyrus (out-degree 6), left inferior division of lateral occipital cortex (out-degree 6), right occipital pole (in-degree 5), right cerebellum 7b parcel (in-degree 15), and right cerebellum 8 parcel (in-degree 7, out-degree 1). The observations in this study provide information about the casual interactions among brain parcels in resting state, helping reveal how different subregions of large-scale distributed neural networks are coupled together in performing cognitive functions. [ABSTRACT FROM AUTHOR]

Arabic handwritten words segmentation is considered a challenging step in Arabic optical character recognition. In this work, we propose a convolutional neural network-based segmentation of handwritten Arabic words. After generating candidate segmentation points, we applied a graph theory-based technique to divide the word into sub-words. Our approach reached a 96% correct sub-word segmentation rate. Additionally, we develop a Segmentation Hypothesis Graph (SHG) to generate candidate characters for each sub-word. We employ both a manually designed convolutional neural network and a transfer learning-based CNN using the pretrained AlexNet model. The accuracy, 96.97%, of the pretrained model outperformed the manually and state of the art models trained on the same dataset. Finally, we used recognition confidence to validate the segmentation points. Hence, our approach correctly segments 88% of the words into characters and successfully resolves 100% of the overlapping cases. [ABSTRACT FROM AUTHOR]

Let G=(V,E) be a graph. The complement of G is the graph G:=(V,[V]²\E) where [V]² is the set of pairs {x,y} of distinct elements of V. If K is a subset of V, the restriction of G to K is the graph G↾K:=(K,[K]²∩E). We prove that if G=(V,E) is a graph and k is an integer, 2 ≤ k ≤ v-2, then there is a k -element subset K of V such that e(G¯↾K)≠e(G↾K), moreover the condition k ≤ v-2 is optimal. We also study the case e(G↾K)e(GK)(mod p) where p is a prime number. Following a question from M.Pouzet, we show this: Let G=(V,E) be a graph with v vertices. If e(G) ≠ e(G) (resp. e(G)=e(G)) then there is an increasing family (Hn)2≤n≤v (resp. (Hn)2≤n≤v-2) of n -element subsets Hn of V such that e(G↾Hn)≠ e(G↾Hn for all n. Similarly if e(G)e(G)(modp) where p is a prime number, p>v-2, then there is an increasing family (Hn)2≤n≤v of n -element subsets Hn of V such that e(GlHn)e(G↾Hn)(mod p) for all integer n∈{2,3,...,v}. [ABSTRACT FROM AUTHOR]

The importance of Information Systems and Technology (IST) for organizations has been recognized numerous times, whether in facilitating the achievement of strategic goals, or in overcoming operational challenges, in addition to assisting managers in decision-making processes. The sustainable extraction of value from IST requires organizations to make efforts to maintain the use of IST aligned with the organization's objectives. IT-CMF consists of a comprehensive framework to guide the evolution of an organization's exploitation of IST in 36 management areas. Based on this framework, several evolutionary paths for the improvement of the IST capability can be derived. The prioritization and selection among the available paths may pose a problem for organizations, especially due to the intricate relationships between the different areas of intervention. In this study, we resort to Graph Theory to conduct a network analysis of IT-CMF, in order to assist organizations choosing the evolutionary path with the greatest potential for improving their use of IST, taking into account their strengths, weaknesses, and priorities. [ABSTRACT FROM AUTHOR]

Twitter is one of the most popular microblogging and social networking platforms where massive instant messages (i.e. tweets) are posted every day. Twitter sentiment analysis tackles the problem of analyzing users' tweets in terms of thoughts, interests and opinions in a variety of contexts and domains. Such analysis can be valuable for several researchers and applications that require understanding people views about a particular topic or event. The study carried out in this paper provides an overview of the algorithms and approaches that have been used for sentiment analysis in twitter. The reviewed articles are categories into four categories based on the approach they use. Furthermore, we discuss directions for future research on how twitter sentiment analysis approaches can utilize theories and technologies from other fields such cognitive science, semantic Web, big data and visualization. [ABSTRACT FROM AUTHOR]

The duality in China's traffic planning has given rise to the basic unit of urban form and function called the superblock, which is defined and bound by an arterial street network. The street network of China's superblock underpins the coexistence and interaction of global and local movement, the public and daily space, and affects place diversity and local characteristics. However, its configuration remains to be articulated because of the lack of a systematic representation method, and the associations between configuration and performance, cognition and design cannot be determined. This study proposes an improved representation method for the street network of China's superblocks based on Marshall's route structure analysis to explore the configurational characteristics and sustainability of the network. To fit local conditions, this study improves Marshall's route structure analysis from four perspectives, namely, the judgement of relative hierarchy, the node construction principle, and the deletion and addition of the original indicators. The improved method is then applied to calculate and compare the depth, connectivity, and complexity of the street networks of 10 sample superblocks in Nanjing, which are classified into six types by construction backgrounds, each having two scenarios differing by the level of publicity. Results indicate that the types formed in accordance with the " The Capital Plan" of the Republic of China, which presents a combination of fine orthogonal grids and radiations, and by the renewal of the traditional street-and-lane network, which has the "characteristic structure" defined by Marshall, perform best in terms of configurational sustainability. The analysis also reveals that the addition of semipublic streets formed mainly from the bottom up narrows the sustainability gap among the samples. This study provides a tool for elaborate urban study and design and provides insights into the cognitive and practical aspects of China's urban planning and design. [ABSTRACT FROM AUTHOR]

The graph understanding for mastering the natural science concepts through the process skills approach aims to improve student ability in lower secondary school. In this study, we conducted a series of quasi-experiments for participants by using pre-test and post-test. The research instruments consisted of two tests and the questionnaires. After learning through the process skills approach, the post-test was used to evaluate the graph understanding and the mastery of natural science concepts. The aspects of written communication, interpretation, conclusion, and prediction were evaluated for the graph understanding while the aspects of knowledge, comprehension, application, analysis, synthesis, and evaluation were evaluated for the mastery of the concepts. The data were analyzed quantitatively by average scores and level categories. The t-test formula was used with a 5% significance level for the treatment effectiveness. The results showed that the enhancement of graph understanding can increase the concept mastery. The difficulties in understanding the graphs were closely related to the student mastery of prerequisite skills such as the basic concepts of mathematics. Then, the prerequisite skills are very important to improve their knowledge. [ABSTRACT FROM AUTHOR]

Computer-aided engineering (CAE) applications generate vast quantities of heterogeneous data. Domain experts often fail to explore and analyze these data, because they are not integrated across different applications. Existing data management solutions are rather tailored to scientific applications. In our approach, we tackle this issue by combining a data lake solution with graph-based metadata management. This provides a holistic view of all CAE data and of the data-generating applications in one interconnected structure. Based on a prototypical implementation, we discuss how this eases the task of domain experts to explore and extract data for further analyses. [ABSTRACT FROM AUTHOR]

Intrusion detection has been the subject of numerous studies in industry and academia. Still, Cybersecurity analysts always want greater precision and global threat analysis to secure their systems in cyberspace. To improve the intrusion detection system, the visualization of the security events in the form of graphs and diagrams is significant to improve the accuracy of alerts. In this paper, we propose an approach of an IDS based on cloud computing, big data technique and, using a machine learning graph algorithm which can detect in real-time different attacks as early as possible. We use the MAWILab intrusion detection dataset. We choose Microsoft Azure as a unified cloud environment to load our dataset on Azure blob storage. We implement the k2 algorithm, which is a graphical machine learning algorithm to classify attacks. Our system showed a great performance due to the graphical machine learning algorithm and Apache Spark structured streaming engine. [ABSTRACT FROM AUTHOR]

The payment installment is an agreement between the debtor taxpayer and the tax authorities establishing that the amounts owed are to be paid within a period between 6 and 60 months, with monthly installments. The purpose of the staggering is to help the entities that are in financial difficulty to overcome this moment and to continue their activity. Also, the fiscal body collects debts from the taxpayer, monthly, without any other expenses. The debtor benefits from the payment exemption of the delay penalties if the scheduling schedule is respected. The delay increases are paid, monthly being included in the installment rates. In order to benefit from staggering the debtor constitutes guarantees in the form of money means, insurance seizure on the goods or gas contract or mortgage in favor of the fiscal body. The main condition for maintaining the validity of the payment installment is the declaration and payment of the tax obligations with payment terms starting with the date of the communication of the staggering decision, except in the case in which the payment scheduling is requested. During the period for which they were granted payment installments, interest is currently due of 0.02% for each day of delay. [ABSTRACT FROM AUTHOR]

In this paper, we redesign, implement, and evaluate ShareSafe (Based on SecGraph), an open-source secure graph data sharing/publishing platform. Within ShareSafe, we propose De-anonymization Quantification Module and Recommendation Module. Besides, we model the attackers' background knowledge and evaluate the relation between graph data privacy and the structure of the graph. To the best of our knowledge, ShareSafe is the first platform that enables users to perform data perturbation, utility evaluation, De-A evaluation, and Privacy Quantification. Leveraging ShareSafe, we conduct a more comprehensive and advanced utility and privacy evaluation. The results demonstrate that (1) The risk of privacy leakage of anonymized graph increases with the attackers' background knowledge. (2) For a successful de-anonymization attack, the seed mapping, even relatively small, plays a much more important role than the auxiliary graph. (3) The structure of graph has a fundamental and significant effect on the utility and privacy of the graph. (4) There is no optimal anonymization/de-anonymization algorithm. For different environment, the performance of each algorithm varies from each other. [ABSTRACT FROM AUTHOR]

In this paper, we introduce concepts of optimal and near optimal secret data hiding schemes. We present a new digital image steganography approach based on the Galois field d GF (pm) using graph and automata to design the data hiding scheme of the general form (k, N. log2 pmn for binary, gray and palette images with the given assumptions, where k,m,n,N are positive integers and p is prime, show the sufficient conditions for the existence and prove the existence of some optimal and near optimal secret data hiding schemes. These results are derived from the concept of the maximal secret data ratio of embedded bits, the module approach and the fastest optimal parity assignment method proposed by Huy et al. in 2011 and 2013. An application of the schemes to the process of hiding a finite sequence of secret data in an image is also considered. Security analyses and experimental results confirm that our approach can create steganographic schemes which achieve high efficiency in embedding capacity, visual quality, speed as well as security, which are key properties of steganography. [ABSTRACT FROM AUTHOR]

[Display omitted] • Computational models with networks look suitable for simulating an outbreak. • This systematic review answers five research questions about studies dealing with the spread of outbreaks. • There is a relation between computational model, spatial scale and type of network used. • There is little development of datasets containing individual movements or contacts. • Outdoor simulations with a day as time step validated with aggregated data are the most studied. To examine recent literature in order to present a comprehensive overview of the current trends as regards the computational models used to represent the propagation of an infectious outbreak in a population, paying particular attention to those that represent network-based transmission. a systematic review was conducted following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Papers published in English between 2010 and September 2021 were sought in the ACM Digital Library, IEEE Xplore, PubMed and Scopus databases. Upon considering their titles and abstracts, 832 papers were obtained, of which 192 were selected for a full content-body check. Of these, 112 studies were eventually deemed suitable for quantitative and qualitative analysis. Emphasis was placed on the spatial and temporal scales studied, the use of networks or graphs, and the granularity of the data used to evaluate the models. The models principally used to represent the spreading of outbreaks have been stochastic (55.36%), while the type of networks most frequently used are relationship networks (32.14%). The most common spatial dimension used is a region (19.64%) and the most used unit of time is a day (28.57%). Synthetic data as opposed to an external source were used in 51.79% of the papers. With regard to the granularity of the data sources, aggregated data such as censuses or transportation surveys are the most common. We identified a growing interest in the use of networks to represent disease transmission. We detected that research is focused on only certain combinations of the computational model, type of network (in both the expressive and the structural sense) and spatial scale, while the search for other interesting combinations has been left for the future. [ABSTRACT FROM AUTHOR]

Abstract In this paper, we show a very close relationship between the compaction, vertex-compaction, and retraction problems for reflexive and bipartite graphs. Similar to a long-standing open problem concerning whether the compaction and retraction problems are polynomially equivalent, the relationships that we present relate to our problems concerning whether the compaction and vertex-compaction problems are polynomially equivalent, and whether the vertex-compaction and retraction problems are polynomially equivalent. The relationships that we present also relate to the constraint satisfaction problem, providing evidence that similar to the compaction and retraction problems, it is also likely to be difficult to give a complete computational complexity classification of the vertex-compaction problem for every fixed reflexive or bipartite graph. In this paper, we however give a complete computational complexity classification of the vertex-compaction problem for all graphs, including even partially reflexive graphs, with four or fewer vertices, by giving proofs based on mostly just knowing the computational complexity classification results of the compaction problem for such graphs determined earlier by the author. Our results show that the compaction, vertex-compaction, and retraction problems are polynomially equivalent for every graph with four or fewer vertices. [ABSTRACT FROM AUTHOR]

In this paper we demonstrate that the result by Zelikovskij concerning Königs problem for abelian permutation groups, reported in a recent survey, is false. We propose in this place two results on 2-closed abelian permutation groups which concern the same topic in a more general setting. [ABSTRACT FROM AUTHOR]

In this paper we describe the automorphism groups of graphs and edge-colored graphs that are cyclic as permutation groups. In addition, we show that every such group is the automorphism group of a complete graph whose edges are colored with 3 colors, and we characterize those groups that are automorphism groups of simple graphs. [ABSTRACT FROM AUTHOR]

In this paper, we have designed a new algorithm for calculating the exact value of the chromatic number of a graph and dubbed it as R-coloring algorithm. Using this R-coloring algorithm, we have proved some results. We have discussed some application in different fields of our life by using this new algorithm. [ABSTRACT FROM AUTHOR]

In this paper, the definition of a jumping folding of a graph has been introduced. The relations between this type of jumping folding of the graph and its chromatic number will be obtained. Theorems governing these relations have been introduced. Mobile radio frequency assignment has been given as an application. [ABSTRACT FROM AUTHOR]

Currently, societal and technological trends make decision-making environments more and more complex. Linguistic variables are used to enrich the assessment of decision makers. This fact leads to the computational techniques of unbalanced linguistic terms for group decision making in the literature. First, we redefine the concept of unbalanced linguistic term set. We use three vertices in a graph based on a uniformly distributed term set to represent the left, central, and right parts of an unbalanced linguistic term set. Our definition makes unbalanced linguistic terms more arbitrary compared with previous definitions. We combine the 2-tuple linguistic model to construct an approximation representation model, which pursues computational convenience and concision at the expense of accuracy. Second, we propose a distance measure between two vertices on the graph and prove its reasonability. We extend this measure to aggregate preference of unbalanced linguistic terms provided by decision makers. Finally, a numerical example is given to illustrate the feasibility and validity of the proposed model. Our methodology allows a decision maker to use diversified assessment spaces to construct his/her preference. Furthermore, by using the aggregated geodesic distance to measure the ranking of alternatives, the slight difference in assessments can be noticed and reflected in the ranking. [ABSTRACT FROM AUTHOR]

Visual tools are implemented to help users in defining how to generate Linked Data from raw data. This is possible thanks to mapping languages which enable detaching mapping rules from the implementation that executes them. However, no thorough research has been conducted so far on how to visualize such mapping rules, especially if they become large and require considering multiple heterogeneous raw data sources and transformed data values. In the past, we proposed the RMLEditor, a visual graph-based user interface, which allows users to easily create mapping rules for generating Linked Data from raw data. In this paper, we build on top of our existing work: we (i) specify a visual notation for graph visualizations used to represent mapping rules, (ii) introduce an approach for manipulating rules when large visualizations emerge, and (iii) propose an approach to uniformly visualize data fraction of raw data sources combined with an interactive interface for uniform data fraction transformations. We perform two additional comparative user studies. The first one compares the use of the visual notation to present mapping rules to the use of a mapping language directly, which reveals that the visual notation is preferred. The second one compares the use of the graph-based RMLEditor for creating mapping rules to the form-based RMLx Visual Editor, which reveals that graph-based visualizations are preferred to create mapping rules through the use of our proposed visual notation and uniform representation of heterogeneous data sources and data values. [ABSTRACT FROM AUTHOR]

In Web Service Composition, services or APIs from multiple and independent providers are combined to create new functionality. Web Service Challenges have formalized the problem at a series of events where the community competed with various solutions. The composition problem has been incrementally extended adding more expressiveness and since 2006 the semantic aspect was addressed by the use of ontologies for service parameter description. In this paper, we propose a polynomial-time algorithm based on graph search, that solves the 2008 challenge. The algorithm uses a heuristic to address the NP-Hard problem of optimizing the number of services selected. Experimental results show that our solution is several times faster and generates shorter compositions on all evaluation tests compared with all winning solutions of the competition. Also, it is up to 50 times faster and very close to generate shortest compositions on the tests, compared with the solution published in 2011, that generates optimal compositions. [ABSTRACT FROM AUTHOR]

In Alberto and Mateo [2] , 2004, a graph-based structure used for manipulating populations of Multi-Objective Evolutionary Algorithms in a more efficient way than the structures existing at that point was defined. In this paper, an improvement of such tool is presented. It consists of the simultaneous insertion of a set of solutions (solution batch), instead of a single one, into the created graph structure. Furthermore, two experiments devoted to comparing the behavior of the new algorithms with the original version from Alberto and Mateo [2] and with a well-known non-dominated sorting algorithm are carried out. The first shows how the new version outperforms the original one in time and number of Pareto comparisons. The second experiment shows a reduction in the time needed in all the cases and an important reduction in the number of Pareto comparisons when inserting chains of dominated solutions. From these experiments it is verified that, in general, the new proposals save computational time and, in the majority of the cases, the number of Pareto comparisons carried out for the insertion. In addition, when the new proposals outperform the others, they increase their gain over them as the size of the population and/or the size of the batch increases. The new tool can also be used, for example, in parallel genetic algorithms such as the ones based on islands, to carry out the migrations of the solutions. [ABSTRACT FROM AUTHOR]

By Berge's theorem, finding a maximum matching in a graph relies on the use of augmenting paths . When no further constraint is added, Edmonds' algorithm allows to compute a maximum matching in polynomial time by sequentially augmenting such paths. Motivated by applications in the scheduling of airline operations, we consider a similar problem where only paths of bounded length can be augmented. Precisely, let k ≥ 1 be an odd integer, and M a matching of a graph G . What is the maximum size of a matching that can be obtained from M by using only augmenting paths of length at most k ? We first prove that this problem can be solved in polynomial time for k ≤ 3 in any graph and that it is NP-complete for any fixed k ≥ 5 in the class of planar bipartite graphs of degree at most 3 and arbitrarily large girth. We then prove that this problem is in P, for any k , in several subclasses of trees such as caterpillars or trees with all vertices of degree at least 3 “far appart”. Moreover, this problem can be solved in time O ( n ) in the class of n -node trees when k and the maximum degree are fixed parameters. Finally, we consider a more constrained problem where only paths of length exactly k can be augmented. We prove that this latter problem becomes NP-complete for any fixed k ≥ 3 and in trees when k is part of the input. [ABSTRACT FROM AUTHOR]

One factor that goes into the construction of a space–time model, in this case, the Generalized Space–Time Autoregressive (GSTAR) model, is the weight matrix, which acts as a spatial representation and illustrates the relationship that exists between different locations. The temporal and spatial correlations are both accounted for in this model simultaneously. The accuracy with which the weight matrix is chosen significantly impacts the prediction results. The distance inverse and uniform weight matrices are two examples of weight matrices utilized frequently. A recent study on the adjustment of the weight matrix uses distance with rail transit, queen contiguity matrices, and techniques with graph theory. This research aims to find a weight matrix that closely resembles the real-world spatial context by analyzing and contrasting the five weight matrices presented earlier. Afterward, the weight matrix is fed into the GSTAR model as an input variable. In this article, each weight matrix's various properties will be broken down and analyzed in detail. The case study used in preparation for the Christmas and New Year's celebrations in 2023 focused on the daily increase in Covid-19 cases across six provinces on Java Island, Indonesia. The Java island was selected as Indonesia's capital because it serves as the country's economic hub and connects all provinces to one another. This enables large-scale spatial links between sites with the same time lag or different time delays. According to the findings that were uncovered, the MST weight matrix is the one that should be utilized, and it should have a prediction error tolerance of 19%. [ABSTRACT FROM AUTHOR]

Tunnels are specific engineering structures, which are constructed in order to shorten transport routes and improve road safety. Therefore, safe operation of tunnel is very important. Tunnel Traffic & Operation Simulator of the University of Žilina in combination with unique softwares allows a research of the possible operating conditions during a normal service and model emergency situations. Risk analysis of road tunnels is in Slovakia managed by technical specifications TP 041 (TP 02/2011) “Risk analysis for Slovak road tunnels” where is defined precise methodology of risk analysis with regard to safety of road tunnels users. This technical specifications, based on the Austrian tunnel risk analysis model TuRisMo, defines and explaines exactly a risk model to calculate risk in road tunnels. [ABSTRACT FROM AUTHOR]

The increasing size of Big Data is often heralded but how data are transformed and represented is also profoundly important to knowledge discovery, and this is exemplified in Big Graph analytics. Much attention has been placed on the scale of the input graph but the product of a graph algorithm can be many times larger than the input. This is true for many graph problems, such as listing all triangles in a graph. Enabling scalable graph exploration for Big Graphs requires new approaches to algorithms, architectures, and visual analytics. A brief tutorial is given to aid the argument for thoughtful representation of data in the context of graph analysis. Then a new algebraic method to reduce the arithmetic operations in counting and listing triangles in graphs is introduced. Additionally, a scalable triangle listing algorithm in the MapReduce model will be presented followed by a description of the experiments with that algorithm that led to the current largest and fastest triangle listing benchmarks to date. Finally, a method for identifying triangles in new visual graph exploration technologies is proposed. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel approach for exploiting structural relations to track multiple objects that may undergo long-term occlusion and abrupt motion. We use a model-free approach that relies only on annotations given in the first frame of the video to track all the objects online, i.e. without knowledge from future frames. We initialize a probabilistic Attributed Relational Graph (ARG) from the first frame, which is incrementally updated along the video. Instead of using the structural information only to evaluate the scene, the proposed approach considers it to generate new tracking hypotheses. In this way, our method is capable of generating relevant object candidates that are used to improve or recover the track of lost objects. The proposed method is evaluated on several videos of table tennis, volleyball, and on the ACASVA dataset. The results show that our approach is very robust, flexible and able to outperform other state-of-the-art methods in sports videos that present structural patterns. [ABSTRACT FROM AUTHOR]

As a reliable and portable object-oriented programming language, Java has been extensively used in industries and taught in schools. To assist Java programming educations, we have developed a Web-based Java Programming Learning Assistant System (JPLAS). JPLAS provides a fill-inblank problem for novice students who have started learning Java programming including grammar and basic programming through code reading. In this problem, students are asked to fill in the blank elements in a high-quality Java code. In this paper, we propose a graph-based blank element selection algorithm to select as many blanks as possible that have grammatically correct and unique answers from a given code. First, the algorithm generates a graph by selecting each candidate element in the code as a vertex, and connecting any pair of vertices by an edge if they can be blanked together. Then, it extracts a maximal clique of the graph for a solution. For evaluations, the correctness of the algorithm is verified manually by applying it to 100 Java codes. Eventually, the educational effects in Java programming learning are confirmed by assigning generated fill-in-blank problems to students in our Java programming course. [ABSTRACT FROM AUTHOR]

The energy consumption is one of the most common issues in the Wireless Sensor Networks (WSNs). Since the communication usually accounts as a major power consumption, there is some techniques, such as topology control and network coding, to decrease the activity of sensors’ transceivers. If we utilize the techniques synchronously, then may overall performance do not increase as expected. This paper provides an optimization problem for energy consumption in WSNs, where the network employs both topology control and network-coding-based multi-cast simultaneously. This approach improves overall performance in comparison with employing them distinctly. The proposed optimization problem is transformed into a convex problem which leads to a numerous theoretical and conceptual advantages. Then the Karush-Kuhn-Tucker (KKT) optimality conditions are presented to derive analytical expressions of the globally optimal solution. Simulation results show that the proposed approach decrease end-to-end delay and has a significantly lower energy consumption than conventional ones. [ABSTRACT FROM AUTHOR]

We consider the single-source shortest paths problem in a digraph with negative edge costs allowed. A hybrid of the Bellman–Ford and Dijkstra algorithms is suggested, improving the running time bound of Bellman–Ford for graphs with a sparse distribution of negative cost edges. The algorithm iterates Dijkstra several times without re-initializing the tentative value d ( v ) at vertices. At most k + 2 iterations solve the problem, if for any vertex reachable from the source, there exists a shortest path to it with at most k negative cost edges. In addition, a new, straightforward proof is suggested that the Bellman–Ford algorithm produces a shortest paths tree. [ABSTRACT FROM AUTHOR]