Zeitschriftenartikel: „Behavioral graph“

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RAFE, VAHID, and ADEL T. RAHMANI. "A NOVEL APPROACH TO VERIFY GRAPH SCHEMA-BASED SOFTWARE SYSTEMS." International Journal of Software Engineering and Knowledge Engineering 19, no. 06 (2009): 857–70. http://dx.doi.org/10.1142/s0218194009004398.

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Graph Grammars have recently become more and more popular as a general formal modeling language. Behavioral modeling of dynamic systems and model to model transformations are a few well-known examples in which graphs have proven their usefulness in software engineering. A special type of graph transformation systems is layered graphs. Layered graphs are a suitable formalism for modeling hierarchical systems. However, most of the research so far concentrated on graph transformation systems as a modeling means, without considering the need for suitable analysis tools. In this paper we concentrate on how to analyze these models. We will describe our approach to show how one can verify the designed graph transformation systems. To verify graph transformation systems we use a novel approach: using Bogor model checker to verify graph transformation systems. The AGG-like graph transformation systems are translated to BIR — the input language of Bogor — and Bogor verifies that model against some properties defined by combining LTL and special purpose graph rules. Supporting schema-based and layered graphs characterize our approach among existing solutions for verification of graph transformation systems.

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Heckel, Reiko, and Sebastian Thöne. "Behavioral Refinement of Graph Transformation-Based Models." Electronic Notes in Theoretical Computer Science 127, no. 3 (2005): 101–11. http://dx.doi.org/10.1016/j.entcs.2004.08.037.

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DU, XUEHONG, JIANXIN JIAO, and MITCHELL M. TSENG. "Product family modeling and design support: An approach based on graph rewriting systems." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 16, no. 2 (2002): 103–20. http://dx.doi.org/10.1017/s0890060402020097.

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Earlier research on product family design (PFD) often highlights isolated and successful empirical studies with a limited attempt to explore the modeling and design support issues surrounding this economically important class of engineering design problems. This paper proposes a graph rewriting system to organize product family data according to the underpinning logic and to model product derivation mechanisms for PFD. It represents the structural and behavioral aspects of product families as family graphs and related graph operations, respectively. The derivation of product variants becomes a graph rewriting process, in which family graphs are transformed to variant graphs by applying appropriate graph rewriting rules. The system is developed in the language of programmed graph rewriting systems or PROGRES, which supports the specification of hierarchical graph schema and parametric rewriting rules. A meta model is defined for family graphs to factor out those entities common to all product families. A generic model is defined to describe all specific entities relevant to particular families. An instance model describes all product variants for individual customer orders. A prototype of a graph-based PFD system for office chairs is also developed. The system can provide an interactive environment for customers to make choices among product offerings. It also facilitates design automation of product families and enhances interactions and negotiations among sales, design, and manufacturing.

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Wang, Yingxu, and Aderemi Adewumi. "The Formal Design Models of Digraph Architectures and Behaviors." International Journal of Software Science and Computational Intelligence 4, no. 1 (2012): 100–129. http://dx.doi.org/10.4018/jssci.2012010105.

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Graphs are one of the most fundamental and widely used non-linear hierarchical structures of linked nodes. Problems in sciences and engineering can be formulated and solved by the graph model. This paper develops a comprehensive design pattern of formal digraphs using the Doubly-Linked List (DLL) architecture. The most complicated form of graphs known as the weighted digraph is selected as a general graph model, based on it simple graphs such as nondirected and/or nonweighted ones can be easily derived and tailored. A rigorous denotational mathematics, Real-Time Process Algebra (RTPA), is adopted, which allows both architectural and behavioral models of digraphs to be rigorously designed and implemented in a top-down approach. The architectural models of digraphs are created using RTPA architectural modeling methodologies known as the Unified Data Models (UDMs). The physical model of digraphs is implemented using nodes of DLL dynamically created in the memory. The behavioral models of digraphs are specified and refined by a set of 18 Unified Process Models (UPMs) in three categories namely the management operations, traversal operations, and node manipulation operations. This work has been applied in a number of real-time and nonreal-time system designs and specifications such as a Real-Time Operating System (RTOS+), graph-based and tree-based applications, and the ADT library for an RTPA-based automatic code generation tool.

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Garnier, Elodie M., Nastasia Fouret, and Médéric Descoins. "ViSiElse: an innovative R-package to visualize raw behavioral data over time." PeerJ 8 (February 3, 2020): e8341. http://dx.doi.org/10.7717/peerj.8341.

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The scientific community encourages the use of raw data graphs to improve the reliability and transparency of the results presented in articles. However, the current methods used to visualize raw data are limited to one or two numerical variables per graph and/or small sample sizes. In the behavioral sciences, numerous variables must be plotted together in order to gain insight into the behavior in question. In this article, we present ViSiElse, an R-package offering a new approach in the visualization of raw data. ViSiElse was developed with the open-source software R to visualize behavioral observations over time based on raw time data extracted from visually recorded sessions of experimental observations. ViSiElse gives a global overview of a process by creating a visualization of the timestamps for multiple actions and all participants into a single graph; individual or group behavior can then be easily assessed. Additional features allow users to further inspect their data by including summary statistics and time constraints.

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Xiao, Fei, Yi Sun, Donggao Du, Xuelei Li, and Min Luo. "A Novel Malware Classification Method Based on Crucial Behavior." Mathematical Problems in Engineering 2020 (March 21, 2020): 1–12. http://dx.doi.org/10.1155/2020/6804290.

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Recently, some graph-based methods have been proposed for malware detection. However, current malware is generally characterized by sophisticated behaviors, which makes graph-based malware detection extremely challenging. To address this issue, we propose a graph repartition algorithm by transforming API call graphs into fragment behaviors based on programs’ dynamic execution traces. The proposed algorithm relies on the N-order subgraph (NSG) for constructing the appropriate fragment behavior. Moreover, we improve the term frequency-inverse document frequency- (TF-IDF-) like measure and information gain (IG) to extract the crucial N-order subgraph (CNSG). This novel behavioral representation and improved extraction method can accurately represent crucial behaviors of malware. Experiments on 4,400 samples demonstrate that the proposed method achieves a high accuracy of 99.75% in malware detection and promising performance of 95.27% in malware classification.

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Kong, Jun, Kang Zhang, Jing Dong, and Dianxiang Xu. "Specifying behavioral semantics of UML diagrams through graph transformations." Journal of Systems and Software 82, no. 2 (2009): 292–306. http://dx.doi.org/10.1016/j.jss.2008.06.030.

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Wang, Junjie, Keith W. Hipel, Liping Fang, Haiyan Xu, and D. Marc Kilgour. "Behavioral Analysis in the Graph Model for Conflict Resolution." IEEE Transactions on Systems, Man, and Cybernetics: Systems 49, no. 5 (2019): 904–16. http://dx.doi.org/10.1109/tsmc.2017.2689004.

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Ali, Nadia, and David Peebles. "The Effect of Gestalt Laws of Perceptual Organization on the Comprehension of Three-Variable Bar and Line Graphs." Human Factors: The Journal of the Human Factors and Ergonomics Society 55, no. 1 (2012): 183–203. http://dx.doi.org/10.1177/0018720812452592.

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Objective: We report three experiments investigating the ability of undergraduate college students to comprehend 2 × 2 “interaction” graphs from two-way factorial research designs. Background: Factorial research designs are an invaluable research tool widely used in all branches of the natural and social sciences, and the teaching of such designs lies at the core of many college curricula. Such data can be represented in bar or line graph form. Previous studies have shown, however, that people interpret these two graphical forms differently. Method: In Experiment 1, participants were required to interpret interaction data in either bar or line graphs while thinking aloud. Verbal protocol analysis revealed that line graph users were significantly more likely to misinterpret the data or fail to interpret the graph altogether. Results: The patterns of errors line graph users made were interpreted as arising from the operation of Gestalt principles of perceptual organization, and this interpretation was used to develop two modified versions of the line graph, which were then tested in two further experiments. One of the modifications resulted in a significant improvement in performance. Conclusion: Results of the three experiments support the proposed explanation and demonstrate the effects (both positive and negative) of Gestalt principles of perceptual organization on graph comprehension. Application: We propose that our new design provides a more balanced representation of the data than the standard line graph for nonexpert users to comprehend the full range of relationships in two-way factorial research designs and may therefore be considered a more appropriate representation for use in educational and other nonexpert contexts.

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Vodyaho, Alexander, Nataly Zhukova, Igor Kulikov, and Saddam Abbas. "Using the Context-Sensitive Policy Mechanism for Building Data Acquisition Systems in Large Scale Distributed Cyber-Physical Systems Built on Fog Computing Platforms." Computers 10, no. 8 (2021): 101. http://dx.doi.org/10.3390/computers10080101.

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The article deals with the use of context-sensitive policies in the building of data acquisition systems in large scale distributed cyber-physical systems built on fog computing platforms. It is pointed out that the distinctive features of modern cyber-physical systems are their high complexity and constantly changing structure and behavior, which complicates the data acquisition procedure. To solve this problem, it is proposed to use an approach according to which the data acquisition procedure is divided into two phases: model construction and data acquisition, which allows parallel realization of these procedures. A distinctive feature of the developed approach is that the models are built in runtime automatically. As a top-level model, a multi-level relative finite state operational automaton is used. The automaton state is described using a multi-level structural-behavioral model, which is a superposition of four graphs: the workflow graph, the data flow graph, the request flow graph and the resource graph. To implement the data acquisition procedure using the model, the context-sensitive policy mechanism is used. The article discusses possible approaches to implementation of suggested mechanisms and describes an example of application.

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Obert, James, and Adrian Chavez. "Graph Theory and Classifying Security Events in Grid Security Gateways." International Journal of Semantic Computing 14, no. 01 (2020): 93–105. http://dx.doi.org/10.1142/s1793351x2040005x.

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In recent years, the use of security gateways (SG) located within the electrical grid distribution network has become pervasive. SGs in substations and renewable distributed energy resource aggregators (DERAs) protect power distribution control devices from cyber and cyber-physical attacks. When encrypted communications within a DER network is used, TCP/IP packet inspection is restricted to packet header behavioral analysis which in most cases only allows the SG to perform anomaly detection of blocks of time-series data (event windows). Packet header anomaly detection calculates the probability of the presence of a threat within an event window, but fails in such cases where the unreadable encrypted payload contains the attack content. The SG system log (syslog) is a time-series record of behavioral patterns of network users and processes accessing and transferring data through the SG network interfaces. Threatening behavioral pattern in the syslog are measurable using both anomaly detection and graph theory. In this paper, it will be shown that it is possible to efficiently detect the presence of and classify a potential threat within an SG syslog using light-weight anomaly detection and graph theory.

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Tripathi, Dipty, Shreya Banerjee, and Anirban Sarkar. "Formalization of business workflow with typed attributed graph." International Journal of Web Information Systems 14, no. 3 (2018): 372–98. http://dx.doi.org/10.1108/ijwis-05-2017-0044.

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Purpose Business process workflow is a design conceptualization to automate the sequence of activities to achieve a business goal with involved participants and a predefined set of rules. Regarding this, a formal business workflow model is a prime requisite to implement a consistent and rigorous business process. In this context, majority of the existing research works are formalized structural features and have not focused on functional and behavioral design aspects of business processes. To address this problem, this paper aims to propose a formal model of business process workflow called as business process workflow using typed attributed graph (BPWATG) enriched with structural, functional and behavioral characteristics of business processes. Design/methodology/approach Typed attributed graph (ATG) and first-order logic have been used to formalize proposed BPWATG to provide rigorous syntax and semantics towards business process workflows. This is an effort to execute a business workflow on an automated machine. Further, the proposed BPWATG is illustrated using a case study to show the expressiveness of proposed model. Besides, the proposed graph is initially validated using generic modelling environment (GME) case tool. Moreover, a comparative study is performed with existing formal approaches based on several crucial features to exhibit the effectiveness of proposed BPWATG. Findings The proposed model is capable of facilitating structural, functional and behavioral aspects of business process workflows using several crucial features such as dependency conceptualization, timer concepts, exception handling and deadlock detection. These features are used to handle real-world problems and ensure the consistency and correctness of business workflows. Originality/value BPWATG is proposed to formalize a business workflow that is required to make a model of business process machine-readable. Besides, formalizations of dependency conceptualization, exception handling, deadlock detection and time-out concepts are specified. Moreover, several non-functional properties (reusability, scalability, flexibility, dynamicity, reliability and robustness) are supported by the proposed model.

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Bou-Harb, Elias, Mourad Debbabi, and Chadi Assi. "Big Data Behavioral Analytics Meet Graph Theory: On Effective Botnet Takedowns." IEEE Network 31, no. 1 (2017): 18–26. http://dx.doi.org/10.1109/mnet.2016.1500116nm.

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Panigrahi, Satya Sobhan, and Ajay Kumar Jena. "Optimization of Test Cases in Object-Oriented Systems Using Fractional-SMO." International Journal of Open Source Software and Processes 12, no. 1 (2021): 41–59. http://dx.doi.org/10.4018/ijossp.2021010103.

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This paper introduces the technique to select the test cases from the unified modeling language (UML) behavioral diagram. The UML behavioral diagram describes the boundary, structure, and behavior of the system that is fed as input for generating the graph. The graph is constructed by assigning the weights, nodes, and edges. Then, test case sequences are created from the graph with minimal fitness value. Then, the optimal sequences are selected from the proposed fractional-spider monkey optimization (fractional-SMO). The developed fractional-SMO is designed by integrating fractional calculus and SMO. Thus, the efficient test cases are selected based on the optimization algorithm that uses fitness parameters, like coverage and fault. Simulations are performed via five synthetic UML diagrams taken from the dataset. The performance of the proposed technique is computed using coverage and the number of test cases. The maximal coverage of 49 and the minimal number of test cases as 2,562 indicate the superiority of the proposed technique.

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Karuza, Elisabeth A., Ari E. Kahn, and Danielle S. Bassett. "Human Sensitivity to Community Structure Is Robust to Topological Variation." Complexity 2019 (February 11, 2019): 1–8. http://dx.doi.org/10.1155/2019/8379321.

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Despite mounting evidence that human learners are sensitive to community structure underpinning temporal sequences, this phenomenon has been studied using an extremely narrow set of network ensembles. The extent to which behavioral signatures of learning are robust to changes in community size and number is the focus of the present work. Here we present adult participants with a continuous stream of novel objects generated by a random walk along graphs of 1, 2, 3, 4, or 6 communities comprised of N = 24, 12, 8, 6, and 4 nodes, respectively. Nodes of the graph correspond to a unique object and edges correspond to their immediate succession in the stream. In short, we find that previously observed processing costs associated with community boundaries persist across an array of graph architectures. These results indicate that statistical learning mechanisms can flexibly accommodate variation in community structure during visual event segmentation.

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Zhang, Guohua, and Weijia Wang. "Hierarchic Entropy: An Information Theoretic Measure of Evolutionary Robotic Behavioral Diversity." International Journal of Pattern Recognition and Artificial Intelligence 31, no. 04 (2017): 1751002. http://dx.doi.org/10.1142/s0218001417510028.

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This paper investigates the design of information theoretic-based fitness function for embedded evolutionary robotics (ERs). Such fitness relies on the assumption that interesting behaviors result in a high sensorimotor (individual) diversity. The current simple entropy as a diversity metric only considers individuals’ difference but ignores their spatial relationship. The sensorimotor stream can be analyzed to construct a simple directed graph that has unique entry and exit nodes. This paper proposes a hierarchic entropy as a diversity metric by incorporating the simple entropy and the spatial relationship based graph entropy. Maximizing the hierarchic entropy, achieved by on-board evolutionary algorithm, thus defines a self-driven fitness function enforcing the controller visiting diverse sensorimotor states. The proposed algorithm achieves better performance than the published results of other entropy-based methods only relying on simple entropy, without requiring additional computational resources.

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Reading Turchioe, Meghan, Lisa V. Grossman, Annie C. Myers, Dawon Baik, Parag Goyal, and Ruth M. Masterson Creber. "Visual analogies, not graphs, increase patients' comprehension of changes in their health status." Journal of the American Medical Informatics Association 27, no. 5 (2020): 677–89. http://dx.doi.org/10.1093/jamia/ocz217.

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Abstract Objectives Patients increasingly use patient-reported outcomes (PROs) to self-monitor their health status. Visualizing PROs longitudinally (over time) could help patients interpret and contextualize their PROs. The study sought to assess hospitalized patients' objective comprehension (primary outcome) of text-only, non-graph, and graph visualizations that display longitudinal PROs. Materials and Methods We conducted a clinical research study in 40 hospitalized patients comparing 4 visualization conditions: (1) text-only, (2) text plus visual analogy, (3) text plus number line, and (4) text plus line graph. Each participant viewed every condition, and we used counterbalancing (systematic randomization) to control for potential order effects. We assessed objective comprehension using the International Organization for Standardization protocol. Secondary outcomes included response times, preferences, risk perceptions, and behavioral intentions. Results Overall, 63% correctly comprehended the text-only condition and 60% comprehended the line graph condition, compared with 83% for the visual analogy and 70% for the number line (P = .05) conditions. Participants comprehended the visual analogy significantly better than the text-only (P = .02) and line graph (P = .02) conditions. Of participants who comprehended at least 1 condition, 14% preferred a condition that they did not comprehend. Low comprehension was associated with worse cognition (P &lt; .001), lower education level (P = .02), and fewer financial resources (P = .03). Conclusions The results support using visual analogies rather than text to display longitudinal PROs but caution against relying on graphs, which is consistent with the known high prevalence of inadequate graph literacy. The discrepancies between comprehension and preferences suggest factors other than comprehension influence preferences, and that future researchers should assess comprehension rather than preferences to guide presentation decisions.

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Zhang, Zun-dong, Yan-fang Yang, Wenjiao Qi, Abderrahim Chariete, and Xing-xiang Lin. "A Cellular Automata Traffic Flow Model considering Bus Lane Changing Behavior with Scheduling Parameters." Discrete Dynamics in Nature and Society 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/348582.

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According to different driving behavioral characteristics of bus drivers, a cellular automata traffic model considering the bus lane changing behavior with scheduling parameters is proposed in this paper. Traffic bottleneck problems caused by bus stops are simulated in multiple lanes roads with no-bay bus stations. With the mixed traffic flow composed of different bus arrival rate, flow-density graph, density distribution graph, and temporal-spatial graph are presented. Furthermore, the mixed traffic flow characteristics are analyzed. Numerical experiment results show that the proposed model can generate a variety of complicated realistic phenomena in the traffic system with bus stops and provide theoretical basis for better using of traffic flow model.

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Yu, Xiaomin, Weibin Liu, and Weiwei Xing. "Behavioral segmentation for human motion capture data based on graph cut method." Journal of Visual Languages & Computing 43 (December 2017): 50–59. http://dx.doi.org/10.1016/j.jvlc.2017.09.001.

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Ebert, Timothy A., Elaine A. Backus, Miguel Cid, Alberto Fereres, and Michael E. Rogers. "A new SAS program for behavioral analysis of electrical penetration graph data." Computers and Electronics in Agriculture 116 (August 2015): 80–87. http://dx.doi.org/10.1016/j.compag.2015.06.011.

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Kubina, Richard M., Douglas E. Kostewicz, and Shawn M. Datchuk. "An initial survey of fractional graph and table area in behavioral journals." Behavior Analyst 31, no. 1 (2008): 61–66. http://dx.doi.org/10.1007/bf03392161.

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Nigro, Salvatore, Benedetta Tafuri, Daniele Urso, et al. "Brain Structural Covariance Networks in Behavioral Variant of Frontotemporal Dementia." Brain Sciences 11, no. 2 (2021): 192. http://dx.doi.org/10.3390/brainsci11020192.

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Recent research on behavioral variant frontotemporal dementia (bvFTD) has shown that personality changes and executive dysfunctions are accompanied by a disease-specific anatomical pattern of cortical and subcortical atrophy. We investigated the structural topological network changes in patients with bvFTD in comparison to healthy controls. In particular, 25 bvFTD patients and 20 healthy controls underwent structural 3T MRI. Next, bilaterally averaged values of 34 cortical surface areas, 34 cortical thickness values, and six subcortical volumes were used to capture single-subject anatomical connectivity and investigate network organization using a graph theory approach. Relative to controls, bvFTD patients showed altered small-world properties and decreased global efficiency, suggesting a reduced ability to combine specialized information from distributed brain regions. At a local level, patients with bvFTD displayed lower values of local efficiency in the cortical thickness of the caudal and rostral middle frontal gyrus, rostral anterior cingulate, and precuneus, cuneus, and transverse temporal gyrus. A significant correlation was also found between the efficiency of caudal anterior cingulate thickness and Mini-Mental State Examination (MMSE) scores in bvFTD patients. Taken together, these findings confirm the selective disruption in structural brain networks of bvFTD patients, providing new insights on the association between cognitive decline and graph properties.

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Tripathi, Akshay, Ankush Kumar Gaur, and Sweta Sri. "Implementation and Analysis of Social Network Graph in Interpersonal Network." Jurnal Ilmu Komputer 13, no. 2 (2020): 5. http://dx.doi.org/10.24843/jik.2020.v13.i02.p03.

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Social graph describes the graphical model of users and how they are related to each other online. Social network consists of a set of nodes (sometimes referred to as actors or vertices in graph theory) connected via some type of relations which are known as edges. Actors are the smallest unit of the network. It can be Persons, Organizations, and Families etc. Relations can be of many types such as directed, undirected, and weighted. Social network analysis consists of two phases. One is data collection phase and another is analysis phase. Data is collected with the help of surveys, Social sites such as face book, LinkedIn. We first input the user information in form of two dimensional matrices. Then we construct a graph based on the relationships among users from adjacency matrix. We can draw a directed graph or a simple graph based on the user input information from adjacency matrix. After analyzing the graph properties based on degree of node, centrality and other parameters we will give effective solution. There are many applications of analyzing social network for example examine a network of farm animals, to analyze how disease spread from one cow to another, discover emergent communities of interest among faculty at various universities, Some public sector uses include development of leader engagement strategies, analysis of individual and group engagement and media use, and community-based problem solving etc. Social network analysis is used widely in the social and behavioral sciences, as well as in economics, marketing, and industrial engineering. The social network perspective focuses on the relationships among social entities and is an important addition to standard social and behavioral research which is primarily concerned with attributes of the social units.

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Banerjee, A., and P. Banerjee. "A behavioral scene graph for rule enforcement in interactive virtual assembly sequence planning." Computers in Industry 42, no. 2-3 (2000): 147–57. http://dx.doi.org/10.1016/s0166-3615(99)00067-6.

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TARI, Z., and X. LI. "A FRAMEWORK FOR METHOD EVOLUTION AND BEHAVIOR CONSISTENCY IN OBJECT-ORIENTED DATABASES." International Journal of Software Engineering and Knowledge Engineering 06, no. 02 (1996): 279–305. http://dx.doi.org/10.1142/s0218194096000132.

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This paper addresses the problem of method evolution in object-oriented databases. We propose a set of evolutionary operations that affect all parts of methods, the signature and the implementation. When a restructuring operation is applied to a schema, behavioral consistency is checked by constructing a proof of program correctness. Two levels of granularity for behavioral consistency are described. The first level relates to the evolution of methods in the context of class inheritance hierarchy and is concerned with the semantics of the polymorphism of methods. The second level relates to behavioral evolution in which the chain of calling relationships between methods is considered. Behavioral consistency is checked with a graph-based approach that deals with problems such as run-time type errors, side-effects, redundant methods, and unexpected behaviors.

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Vasung, Lana, Hyuk Jin Yun, Henry A. Feldman, Patricia Ellen Grant, and Kiho Im. "An Atypical Sulcal Pattern in Children with Disorders of the Corpus Callosum and Its Relation to Behavioral Outcomes." Cerebral Cortex 30, no. 9 (2020): 4790–99. http://dx.doi.org/10.1093/cercor/bhaa067.

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Abstract Hypogenesis (hCC) and dysgenesis (dCC) of the corpus callosum (CC) are characterized by its smaller size or absence. The outcomes of these patients vary considerably and are unrelated to the size of the CC abnormality. The aim of the current study was to characterize the sulcal pattern in children with hCC and dCC and to explore its relation to clinical outcome. We used quantitative sulcal pattern analysis that measures deviation (similarity index, SI) of the composite or individual sulcal features (position, depth, area, and graph topology) compared to the control group. We calculated SI for each hemisphere and lobe in 11 children with CC disorder (hCC = 4, dCC = 7) and 15 controls. hCC and dCC had smaller hemispheric SI compared to controls. dCC subjects had smaller regional SI in the frontal and occipital lobes, which were driven by a smaller SI in a position or a graph topology. The significantly decreased SI gradient was found across groups only in the sulcal graph topology of the temporal lobes (controls &gt; hCC &gt; dCC) and was related to clinical outcome. Our results suggest that careful examination of sulcal pattern in hCC and dCC patients could be a useful biomarker of outcome.

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RAFE, VAHID, ADEL T. RAHMANI, and REZA RAFEH. "FORMAL ANALYSIS OF UML 2.0 ACTIVITIES USING GRAPH TRANSFORMATION SYSTEMS." International Journal of Software Engineering and Knowledge Engineering 20, no. 05 (2010): 679–94. http://dx.doi.org/10.1142/s0218194010004918.

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Graph transformation is a general visual modeling language which is suitable for stating the dynamic semantics of the designed models formally. We present a highly understandable yet precise approach to formally define the behavioral semantics of UML 2.0 Activity diagrams by using graph transformation. In our approach we take into account control flow and data flow semantics. Our proposed semantics is based on token-like semantics and traverse-to-completion. The main advantage of our approach is automated formal verification and analysis of UML Activities. We use AGG to design Activities and we use our previous approach to model checking graph transformation system. Hereby, designers can verify and analyze designed Activity diagrams. Since workflow modeling is one of the main application areas of the Activities, we use our proposed semantics for modeling and verification of workflows to illustrate our approach.

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Reyes, P., M. P. Ortega-Merchan, A. Rueda, et al. "Functional Connectivity Changes in Behavioral, Semantic, and Nonfluent Variants of Frontotemporal Dementia." Behavioural Neurology 2018 (2018): 1–10. http://dx.doi.org/10.1155/2018/9684129.

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Frontotemporal dementia (FTD) affects behavior, language, and personality. This study aims to explore functional connectivity changes in three FTD variants: behavioral (bvFTD), semantic (svPPA), and nonfluent variant (nfvPPA). Seventy-six patients diagnosed with FTD by international criteria and thirty-two controls were investigated. Functional connectivity from resting functional magnetic resonance imaging (fMRI) was estimated for the whole brain. Two types of analysis were done: network basic statistic and topological measures by graph theory. Several hubs in the limbic system and basal ganglia were compromised in the behavioral variant apart from frontal networks. Nonfluent variants showed a major disconnection with respect to the behavioral variant in operculum and parietal inferior. The global efficiency had lower coefficients in nonfluent variants than behavioral variants and controls. Our results support an extensive disconnection among frontal, limbic, basal ganglia, and parietal hubs.

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Seyal, Afzaal H. "Determinants of E-Banking Among Bruneian Corporate Customers." International Journal of E-Adoption 3, no. 4 (2011): 13–28. http://dx.doi.org/10.4018/jea.2011100102.

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E-banking has become a new way of doing business and an important alternative for many banks and their customers. The acceptance of e-banking is affected by several factors, including the personal attitude of the users that influences normality and the context in which it is used. This study examines 150 customers of four major banks in Brunei Darussalam to explore the behavioral intent of the corporate customers toward e-banking through their attitudes. The Theory of Planned Behavior (TPB) is used as a reference framework to understand the intention toward using e-banking. The data is analyzed using PLS smart graph; analysis suggests that customers’ attitudes toward e-banking are a significant predictor of behavioral intention, whereas subjective norms and perceived behavioral control are insignificant. The causal link between perceived behavioral control and behavior (use of e-banking) is also insignificant. However, use of e-banking is further supported by the behavioral intention. The implications of these findings are discussed and conclusions are drawn.

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JeongHoJin, Eunjoo Kang, and KIM JIN HEE. "Behavioral approach system (BAS) sensitivity and functional brain networks during rest: graph-theory analysis." Korean Journal of Cognitive and Biological Psychology 29, no. 2 (2017): 105–25. http://dx.doi.org/10.22172/cogbio.2017.29.2.001.

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Maffei-Almodovar, Lindsay, Gina Feliciano, Daniel M. Fienup, and Peter Sturmey. "The Use of Behavioral Skills Training to Teach Graph Analysis to Community Based Teachers." Behavior Analysis in Practice 10, no. 4 (2017): 355–62. http://dx.doi.org/10.1007/s40617-017-0199-3.

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Chen, Shuhong, Guojun Wang, and Weijia Jia. "Cluster-group based trusted computing for mobile social networks using implicit social behavioral graph." Future Generation Computer Systems 55 (February 2016): 391–400. http://dx.doi.org/10.1016/j.future.2014.06.005.

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Rezaee, M., E. Shakeri, A. Ardeshir, and H. Malekitabar. "Optimizing travel distance of construction workers considering their behavioral uncertainty using fuzzy graph theory." Automation in Construction 124 (April 2021): 103574. http://dx.doi.org/10.1016/j.autcon.2021.103574.

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Schutte, Nienke M., Narelle K. Hansell, Eco J. C. de Geus, Nicholas G. Martin, Margaret J. Wright, and Dirk J. A. Smit. "Heritability of Resting State EEG Functional Connectivity Patterns." Twin Research and Human Genetics 16, no. 5 (2013): 962–69. http://dx.doi.org/10.1017/thg.2013.55.

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We examined the genetic architecture of functional brain connectivity measures in resting state electroencephalographic (EEG) recordings. Previous studies in Dutch twins have suggested that genetic factors are a main source of variance in functional brain connectivity derived from EEG recordings. In addition, qualitative descriptors of the brain network derived from graph analysis — network clustering and average path length — are also heritable traits. Here we replicated previous findings for connectivity, quantified by the synchronization likelihood, and the graph theoretical parameters cluster coefficient and path length in an Australian sample of 16-year-old twins (879) and their siblings (93). Modeling of monozygotic and dizygotic twins and sibling resemblance indicated heritability estimates of the synchronization likelihood (27–74%) and cluster coefficient and path length in the alpha and theta band (40–44% and 23–40% respectively) and path length in the beta band frequency (41%). This corroborates synchronization likelihood and its graph theoretical derivatives cluster coefficient and path length as potential endophenotypes for behavioral traits and neurological disorders.

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Beer, Randall D. "Bittorio revisited: structural coupling in the Game of Life." Adaptive Behavior 28, no. 4 (2019): 197–212. http://dx.doi.org/10.1177/1059712319859907.

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The notion of structural coupling plays a central role in Maturana and Varela’s biology of cognition framework and strongly influenced Varela’s subsequent enactive elaboration of this framework. Building upon previous work using a glider in the Game of Life (GoL) cellular automaton as a toy model of a minimal autopoietic system with which to concretely explore these theoretical frameworks, this article presents an analysis of structural coupling between a glider and its environment. Specifically, for sufficiently small GoL universes, we completely characterize the nonautonomous dynamics of both a glider and its environment in terms of interaction graphs, derive the set of possible glider lives determined by the mutual constraints between these interaction graphs, and show how such lives are embedded in the state transition graph of the entire GoL universe.

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Miyashiro, C. H. K., P. Bertti, J. Tejada, N. Garcia-Cairasco, and J. A. Baranauskas. "049 — (MIY0133) Analysis of behavioral seizures in patients with temporal lobe epilepsy by graph theory." Epilepsy & Behavior 38 (September 2014): 202–3. http://dx.doi.org/10.1016/j.yebeh.2014.08.082.

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Liu, Yang, Qingguo Zeng, Joaquín Ordieres Meré, and Huanrui Yang. "Anticipating Stock Market of the Renowned Companies: A Knowledge Graph Approach." Complexity 2019 (August 7, 2019): 1–15. http://dx.doi.org/10.1155/2019/9202457.

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An increasing number of the renowned company’s investors are turning attention to stock prediction in the search for new efficient ways of hypothesizing about markets through the application of behavioral finance. Accordingly, research on stock prediction is becoming a popular direction in academia and industry. In this study, the goal is to establish a model for predicting stock price movement through knowledge graph from the financial news of the renowned companies. In contrast to traditional methods of stock prediction, our approach considers the effects of event tuple characteristics on stocks on the basis of knowledge graph and deep learning. The proposed model and other feature selection models were used to perform feature extraction on the websites of Thomson Reuters and Cable News Network. Numerous experiments were conducted to derive evidence of the effectiveness of knowledge graph embedding for classification tasks in stock prediction. A comparison of the average accuracy with which the same feature combinations were extracted over six stocks indicated that the proposed method achieves better performance than that exhibited by an approach that uses only stock data, a bag-of-words method, and convolutional neural network. Our work highlights the usefulness of knowledge graph in implementing business activities and helping practitioners and managers make business decisions.

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Chan, Jan Y. K., Zhihao Wang, Yunbo Xie, et al. "Identifying Potential Managerial Personnel Using PageRank and Social Network Analysis: The Case Study of a European IT Company." Applied Sciences 11, no. 15 (2021): 6985. http://dx.doi.org/10.3390/app11156985.

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Behavioral theory assumes that leaders can be identified by their daily behaviors. Social network analysis helps to understand behavioral patterns within their social networks. This work considers leaders as the managerial personnel of the organization and differentiates managements from non-managerial staff by their behavior with five different types of interactions with PageRank and their attributes in modern organizations. PageRank and word embedding using word2vec with phrases from features are adopted to extract new features for the identification of managerial staff. Both traditional machine learning methods and graph neural networks are utilized with real-world data from an Austrian IT company called Knapp System Integration. Our experimental results show that the proposed new features extracted using PageRank with different types of interactions and word2vec with phrases significantly improve the identification accuracy. We also propose to use graph neural networks as an effective learning algorithm to identify managers from organizations. Our approach can identify managerial staff with an accuracy of around 80%, which demonstrates that managers could be identified through social network analysis. By analyzing the behaviors of members, the proposed method is effective as a performance appraisal tool for organizations. The study facilitates sustainable management by helping organizations to retain managerial talents or to invite potential talents to join the management team.

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Goldfarb, M., and N. Celanovic. "A Lumped Parameter Electromechanical Model for Describing the Nonlinear Behavior of Piezoelectric Actuators." Journal of Dynamic Systems, Measurement, and Control 119, no. 3 (1997): 478–85. http://dx.doi.org/10.1115/1.2801282.

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A lumped-parameter model of a piezoelectric stack actuator has been developed to describe actuator behavior for purposes of control system analysis and design, and in particular for control applications requiring accurate position tracking performance. In addition to describing the input-output dynamic behavior, the proposed model explains aspects of nonintuitive behavioral phenomena evinced by piezoelectric actuators, such as the input-output rate-independent hysteresis and the change in mechanical stiffness that results from altering electrical load. Bond graph terminology is incorporated to facilitate the energy-based formulation of the actuator model. The authors propose a new bond graph element, the generalized Maxwell resistive capacitor, as a lumped-parameter causal representation of rate-independent hysteresis. Model formulation is validated by comparing results of numerical simulations to experimental data.

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Uren, Kenneth R., George van Schoor, Martin van Eldik, and Johannes J. A. de Bruin. "An Energy Graph-Based Approach to Fault Diagnosis of a Transcritical CO2 Heat Pump." Energies 13, no. 7 (2020): 1783. http://dx.doi.org/10.3390/en13071783.

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The objective of this paper is to describe an energy-based approach to visualize, identify, and monitor faults that may occur in a water-to-water transcritical CO 2 heat pump system. A representation using energy attributes allows the abstraction of all physical phenomena present during operation into a compact and easily interpretable form. The use of a linear graph representation, with heat pump components represented as nodes and energy interactions as links, is investigated. Node signature matrices are used to present the energy information in a compact mathematical form. The resulting node signature matrix is referred to as an attributed graph and is populated in such a way as to retain the structural information, i.e., where the attribute points to in the physical system. To generate the energy and exergy information for the compilation of the attributed graphs, a descriptive thermal–fluid model of the heat pump system is developed. The thermal–fluid model is based on the specifications of and validated to the actual behavioral characteristics of a physical transcritical CO 2 heat pump test facility. As a first step to graph-matching, cost matrices are generated to represent a characteristic residual between a normal system node signature matrix and a faulty system node signature matrix. The variation in the eigenvalues and eigenvectors of the characteristic cost matrices from normal conditions to a fault condition was used for fault characterization. Three faults, namely refrigerant leakage, compressor failure and gas cooler fouling, were considered. The paper only aims to introduce an approach, with the scope limited to illustration at one operating point and considers only three relatively large faults. The results of the proposed method show promise and warrant further work to evaluate its sensitivity and robustness for small faults.

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Osmanlioglu, Yusuf, Drew Parker, Steven Brem, Ali Shokoufandeh, and Ragini Verma. "NIMG-69. PERSONALIZED CONNECTOMIC SIGNATURES: BRIDGING THE GAP BETWEEN NEURO-ONCOLOGY AND CONNECTOMICS." Neuro-Oncology 22, Supplement_2 (2020): ii163. http://dx.doi.org/10.1093/neuonc/noaa215.682.

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Abstract PURPOSE Connectomics has led to significant neuroscientific findings within the last two decades, eventually making impact in the clinics. Neuro-oncology can benefit immensely from connectomics in evaluating structural connectivity of brains with tumor for pre- and post-treatment planning, as a tumor connectome along with derived network measures will make it possible to determine the cognitive effects of treatment and quantify the effect of surgery on quality of life. However, generating connectomes in the presence of tumor is a challenging task. Specifically, registration of an atlas to the brain, which is essential in parcellating the brain into regions of interest, fails around the tumor due to mass effect and infiltration related distortions which are not present in the atlas that comes from a healthy brain. We aim to tackle this problem by introducing a novel atlas registration method. METHOD Although tumor deforms the geometrical shape of its surrounding regions, it does not violate the connectivity of displaced cortical voxels to the rest of the brain. Leveraging this fact, we represent the brain as an annotated graph with nodes representing ROIs encoding geometric features of regions and weighted edges representing the connectivity between regions. In encoding the surroundings of the tumor into the graph, we subsample the region into smaller patches to represent the area with multiple nodes. We then calculate many-to-one graph matching between the graphs of a tumor patient and a healthy control to associate surroundings of tumor with healthy ROIs. OUTCOME A tumor connectome showing how the connectivity is morphed around the tumor, which can further be extended to creating connectomes of recurrence. CLINICAL IMPLICATIONS Use of connectomes can revolutionize neuro-oncology by helping surgeons in estimating structural, functional, and behavioral outcomes of resection prior to surgery and in predicting recovery after the surgery, potentially suggesting subject specific treatment plans.

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Yu, Drakin A., Potapov L. A., and Shkolin A. N. "Determination of transient thermal characteristics for thermal electric behavioral models of integrated circuits." Bulletin of Electrical Engineering and Informatics 9, no. 3 (2020): 933–42. http://dx.doi.org/10.11591/eei.v9i3.1725.

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In the current study, it was tried to describe a method for determining thermal characteristics of integrated micro-circuits to identify thermal parameters of multidisciplinary (thermal-electric) behavioral models. The problem is solved on the example of high-frequency pulse voltage converters. A solution was proposed to refine the minimum structure of the thermoelectric model based on an iterative least squares method using the Levenberg-Marquardt algorithm, as well as a graph of the spectral den-sity of time constants. This made it possible to reduce the influence of the filtering factor in the deconvolution operation when building a thermal model using the structural function of the thermal characteristic transition. Also, the results obtained can be used to build integrated circuits (IC) behavioral models, taking into account the thermal processes occurring in them.

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Peebles, David, and Peter C. H. Cheng. "Modeling the Effect of Task and Graphical Representation on Response Latency in a Graph Reading Task." Human Factors: The Journal of the Human Factors and Ergonomics Society 45, no. 1 (2003): 28–46. http://dx.doi.org/10.1518/hfes.45.1.28.27225.

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We report an investigation into the processes involved in a common graph-reading task using two types of Cartesian graph. We describe an experiment and eye movement study, the results of which show that optimal scan paths assumed in the task analysis approximate the detailed sequences of saccades made by individuals. The research demonstrates the computational inequivalence of two sets of informationally equivalent graphs and illustrates how the computational advantages of a representation outweigh factors such as user unfamiliarity. We describe two models, using the ACT rational perceptual motor (ACT-R/PM) cognitive architecture, that replicate the pattern of observed response latencies and the complex scan paths revealed by the eye movement study. Finally, we outline three guidelines for designers of visual displays: Designers should (a) consider how different quantities are encoded within any chosen representational format, (b) consider the full range of alternative varieties of a given task, and (c) balance the cost of familiarization with the computational advantages of less familiar representations. Actual or potential applications of this research include informing the design and selection of appropriate visual displays and illustrating the practice and utility of task analysis, eye tracking, and cognitive modeling for understanding interactive tasks with external representations.

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Wang, Hongxia, Yan Sun, Fan Lan, and Yan Liu. "Altered brain network topology related to working memory in internet addiction." Journal of Behavioral Addictions 9, no. 2 (2020): 325–38. http://dx.doi.org/10.1556/2006.2020.00020.

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AbstractBackground and aimsThe working memory (WM) ability of internet addicts and the topology underlying the WM processing in internet addiction (IA) are poorly understood. In this study, we employed a graph theoretical framework to characterize the topological properties of the IA brain network in the source cortical space during WM task.MethodsA sample of 24 subjects with IA and 23 matched healthy controls (HCs) performed visual 2-back task. Exact Low Resolution Electromagnetic Tomography was adopted to project the pre-processed EEG signals into source space. Subsequently, Lagged phase synchronization was calculated between all pairs of Brodmann areas, the graph theoretical approaches were then employed to estimate the brain topological properties of all participants during the WM task.ResultsWe found better WM behavioral performance in IA subjects compared with the HCs. Moreover, compared to the HC group, more integrated and hierarchical brain network was revealed in the IA subjects in alpha band. And altered regional centrality was mainly resided in frontal and limbic lobes. In addition, significant relationships between the IA severity and the significant altered graph indices were found.ConclusionsIn conclusion, these findings provide evidence to support the notion that altered topological configuration may underline changed WM function observed in IA.

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Anderson, John A. E., Saman Sarraf, Tarek Amer, et al. "Task-linked Diurnal Brain Network Reorganization in Older Adults: A Graph Theoretical Approach." Journal of Cognitive Neuroscience 29, no. 3 (2017): 560–72. http://dx.doi.org/10.1162/jocn_a_01060.

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Testing older adults in the morning generally improves behavioral performance relative to afternoon testing. Morning testing is also associated with brain activity similar to that of young adults. Here, we used graph theory to explore how time of day (TOD) affects the organization of brain networks in older adults across rest and task states. We used nodes from the automated anatomical labeling atlas to construct participant-specific correlation matrices of fMRI data obtained during 1-back tasks with interference and rest. We computed pairwise group differences for key graph metrics, including small-worldness and modularity. We found that older adults tested in the morning and young adults did not differ on any graph metric. Both of these groups differed from older adults tested in the afternoon during the tasks—but not rest. Specifically, the latter group had lower modularity and small-worldness (indices of more efficient network organization). Across all groups, higher modularity and small-worldness strongly correlated with reduced distractibility on an implicit priming task. Increasingly, TOD is seen as important for interpreting and reproducing neuroimaging results. Our study emphasizes how TOD affects brain network organization and executive control in older adults.

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Fang, Hui, Qiaorong Wu, Yun Li, et al. "Structural networks in children with autism spectrum disorder with regression: A graph theory study." Behavioural Brain Research 378 (January 2020): 112262. http://dx.doi.org/10.1016/j.bbr.2019.112262.

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Greenfield, Patricia M. "Errata." Behavioral and Brain Sciences 15, no. 2 (1992): 423. http://dx.doi.org/10.1017/s0140525x00069429.

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Due to a printer error, the wrong graph was printed on p. 578 of the Author's Response for “Language, Tools and Brain,” by Patricia M. Greenfield, BBS (1991), 14(4):531–95. The correct graph is presented below. Note that, at 16 months, Strategy 2, the pot strategy, was dominant for five children, whereas Strategy 1, the pairing strategy, was dominant for three children.

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Agrawal, Sakshi. "Financial Statistics and its Behavioral Implications- A Case Study of Select Hospitality Industry." IRA-International Journal of Management & Social Sciences (ISSN 2455-2267) 5, no. 3 (2016): 491. http://dx.doi.org/10.21013/jmss.v5.n3.p12.

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<em>The paper deals on Financial Statistics of Hospitality Industry vis Indian Hotels Ltd., Benaras Ltd, Sinclairs Ltd and The Grand Bhagwati Ltd. Looking at their share price and Holding period return did their portfolio and Risk Analysis. Taking their standard deviation, variance and the calculation of Sharpe Ratio did the risk analysis. The time period analyzed was from March 2010 to March 2015. The financial Statistics gives a comfortable position for the investors in terms of Returns and so a comfortable Portfolio Return Risk graph. However, a deeper analysis shows that the Profit after taxes of the respective firms are broadly not in congruence with the statistics and so the respective Returns differs. The paper takes an insight into the Behavioral implications of the Financial Statistics.</em>

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Hajizadeh, Roghiyeh, and Nima Jafari Navimipour. "A method for trust evaluation in the cloud environments using a behavior graph and services grouping." Kybernetes 46, no. 7 (2017): 1245–61. http://dx.doi.org/10.1108/k-02-2017-0070.

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Purpose Cloud services have become very popular among researchers and people recently. In such a scenario, identifying reliable cloud services has become very important. The trust value plays a significant role in recognizing reliable providers. The purpose of this paper is to propose a new method to evaluate the trust metric among the cloud providers. The main goal is to increase the precision and accuracy of the trust evaluation method in the cloud environments. Design/methodology/approach This paper evaluates the trust metric among the cloud providers and entities by grouping the services and using a behavioral graph. Four parameters, availability, reliability, interaction evolution and identity, are used for evaluating the trust value. The performance of the proposed method is assessed using a simulator which is programmed in the cloud Azure 2013 based on C# codes. Findings The method is evaluated through various experiments in terms of precision, recall, error-hit, reliability and availability. The obtained results show that the proposed method has better reliability and availability than the FIFO and QoS models. Also, the results show that increasing the number of groups leads to increasing values of trust, precision and availability, and decreasing values of error-hit. Originality/value This paper proposes a trust evaluation method in the cloud environment by grouping the services and using a behavioral graph for improving the amount of availability, error-hit, precision and reliability values.

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Eldawlatly, Seif, Rong Jin, and Karim G. Oweiss. "Identifying Functional Connectivity in Large-Scale Neural Ensemble Recordings: A Multiscale Data Mining Approach." Neural Computation 21, no. 2 (2009): 450–77. http://dx.doi.org/10.1162/neco.2008.09-07-606.

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Identifying functional connectivity between neuronal elements is an essential first step toward understanding how the brain orchestrates information processing at the single-cell and population levels to carry out biological computations. This letter suggests a new approach to identify functional connectivity between neuronal elements from their simultaneously recorded spike trains. In particular, we identify clusters of neurons that exhibit functional interdependency over variable spatial and temporal patterns of interaction. We represent neurons as objects in a graph and connect them using arbitrarily defined similarity measures calculated across multiple timescales. We then use a probabilistic spectral clustering algorithm to cluster the neurons in the graph by solving a minimum graph cut optimization problem. Using point process theory to model population activity, we demonstrate the robustness of the approach in tracking a broad spectrum of neuronal interaction, from synchrony to rate co-modulation, by systematically varying the length of the firing history interval and the strength of the connecting synapses that govern the discharge pattern of each neuron. We also demonstrate how activity-dependent plasticity can be tracked and quantified in multiple network topologies built to mimic distinct behavioral contexts. We compare the performance to classical approaches to illustrate the substantial gain in performance.

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