Articoli di riviste sul tema "Multidimensionnal network"
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1
Xuan, Qi, Xiaodi Ma, Chenbo Fu, Hui Dong, Guijun Zhang e Li Yu. "Heterogeneous multidimensional scaling for complex networks". International Journal of Modern Physics C 26, n. 02 (febbraio 2015): 1550023. http://dx.doi.org/10.1142/s0129183115500230.
Abstract (sommario):
Many real-world networks are essentially heterogeneous, where the nodes have different abilities to gain connections. Such networks are difficult to be embedded into low-dimensional Euclidean space if we ignore the heterogeneity and treat all the nodes equally. In this paper, based on a newly defined heterogeneous distance and a generalized network distance under the constraints of network and triangle inequalities, respectively, we propose a new heterogeneous multidimensional scaling method (HMDS) to embed different networks into proper Euclidean spaces. We find that HMDS behaves much better than the traditional multidimensional scaling method (MDS) in embedding different artificial and real-world networks into Euclidean spaces. Besides, we also propose a method to estimate the appropriate dimensions of Euclidean spaces for different networks, and find that the estimated dimensions are quite close to the real dimensions for those geometrical networks under study. These methods thus can help to better understand the evolution of real-world networks, and have practical importance in network visualization, community detection, link prediction and localization of wireless sensors.
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Abrahão, Felipe S., Klaus Wehmuth, Hector Zenil e Artur Ziviani. "Algorithmic Information Distortions in Node-Aligned and Node-Unaligned Multidimensional Networks". Entropy 23, n. 7 (29 giugno 2021): 835. http://dx.doi.org/10.3390/e23070835.
Abstract (sommario):
In this article, we investigate limitations of importing methods based on algorithmic information theory from monoplex networks into multidimensional networks (such as multilayer networks) that have a large number of extra dimensions (i.e., aspects). In the worst-case scenario, it has been previously shown that node-aligned multidimensional networks with non-uniform multidimensional spaces can display exponentially larger algorithmic information (or lossless compressibility) distortions with respect to their isomorphic monoplex networks, so that these distortions grow at least linearly with the number of extra dimensions. In the present article, we demonstrate that node-unaligned multidimensional networks, either with uniform or non-uniform multidimensional spaces, can also display exponentially larger algorithmic information distortions with respect to their isomorphic monoplex networks. However, unlike the node-aligned non-uniform case studied in previous work, these distortions in the node-unaligned case grow at least exponentially with the number of extra dimensions. On the other hand, for node-aligned multidimensional networks with uniform multidimensional spaces, we demonstrate that any distortion can only grow up to a logarithmic order of the number of extra dimensions. Thus, these results establish that isomorphisms between finite multidimensional networks and finite monoplex networks do not preserve algorithmic information in general and highlight that the algorithmic information of the multidimensional space itself needs to be taken into account in multidimensional network complexity analysis.
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Garcez, Thalles V., e Przemyslaw Szufel. "Multidimensional Risk Management for Underground Electricity Networks". Studies in Logic, Grammar and Rhetoric 37, n. 1 (8 agosto 2014): 51–69. http://dx.doi.org/10.2478/slgr-2014-0017.
Abstract (sommario):
Abstract In the paper we consider an electricity provider company that makes decision on allocating resources on electric network maintenance. The investments decrease malfunction rate of network nodes. An accidental event (explosion, fire, etc.) or a malfunctioning on underground system can have various consequences and in different perspectives, such as deaths and injuries of pedestrians, fires in nearby locations, disturbances in the flow of vehicular traffic, loss to the company image, operating and financial losses, etc. For this reason it is necessary to apply an approach of the risk management that considers the multidimensional view of the consequences. Furthermore an analysis of decision making should consider network dependencies between the nodes of the electricity distribution system. In the paper we propose the use of the simulation to assess the network effects (such as the increase of the probability of other accidental event and the occurrence of blackouts of the dependent nodes) in the multidimensional risk assessment in electricity grid. The analyzed effects include node overloading due to malfunction of adjacent nodes and blackouts that take place where there is temporarily no path in the grid between the power plant and a node. The simulation results show that network effects have crucial role for decisions in the network maintenance – outcomes of decisions to repair a particular node in the network can have significant influence on performance of other nodes. However, those dependencies are non-linear. The effects of network connectivity (number of connections between nodes) on its multidimensional performance assessment depend heavily on the overloading effect level. The simulation results do not depend on network type structure (random or small world) – however simulation outcomes for random networks have shown higher variance compared to small-world networks.
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Obrubov, M., e S. Kirillova. "USING LSTM NETWORK FOR SOLVING THE MULTIDIMENTIONAL TIME SERIES FORECASTING PROBLEM". National Association of Scientists 2, n. 68 (1 luglio 2021): 43–48. http://dx.doi.org/10.31618/nas.2413-5291.2021.2.68.450.
Abstract (sommario):
The article discusses using of the recurrent neural networks technology to the multidimensional time series prediction problem. There is an experimental determination of the neural network architecture and its main hyperparameters carried out to achieve the minimum error. The revealed network structure going to be used further to detect anomalies in multidimensional time series.
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Veiga, André, E. Glen Weyl e Alexander White. "Multidimensional Platform Design". American Economic Review 107, n. 5 (1 maggio 2017): 191–95. http://dx.doi.org/10.1257/aer.p20171044.
Abstract (sommario):
Successful platforms attract not just many users, but also those of the right kind. 'The right kind of user' is one who can either be directly monetized or who differentially attracts other valuable users. Bonacich centrality on the network of user sorting with direct value of monetization captures this feedback loop and thus characterizes the value of user characteristics. We use this value to determine optimal steady-state platform design and reliable means for platforms to reach such a steady state. We apply these results respectively to explain the dynamic growth strategy of social networks and urban development policies of cities.
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MANGAL, MANISH, e MANU PRATAP SINGH. "ANALYSIS OF MULTIDIMENSIONAL XOR CLASSIFICATION PROBLEM WITH EVOLUTIONARY FEEDFORWARD NEURAL NETWORKS". International Journal on Artificial Intelligence Tools 16, n. 01 (febbraio 2007): 111–20. http://dx.doi.org/10.1142/s0218213007003229.
Abstract (sommario):
This paper describes the application of two evolutionary algorithms to the feedforward neural networks used in classification problems. Besides of a simple backpropagation feedforward algorithm, the paper considers the genetic algorithm and random search algorithm. The objective is to analyze the performance of GAs over the simple backpropagation feedforward in terms of accuracy or speed in this problem. The experiments considered a feedforward neural network trained with genetic algorithm/random search algorithm and 39 types of network structures and artificial data sets. In most cases, the evolutionary feedforward neural networks seemed to have better of equal accuracy than the original backpropagation feedforward neural network. We found few differences in the accuracy of the networks solved by applying the EAs, but found ample differences in the execution time. The results suggest that the evolutionary feedforward neural network with random search algorithm might be the best algorithm on the data sets we tested.
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BLOCK, PER, e THOMAS GRUND. "Multidimensional homophily in friendship networks". Network Science 2, n. 2 (agosto 2014): 189–212. http://dx.doi.org/10.1017/nws.2014.17.
Abstract (sommario):
AbstractHomophily—the tendency for individuals to associate with similar others—is one of the most persistent findings in social network analysis. Its importance is established along the lines of a multitude of sociologically relevant dimensions, e.g. sex, ethnicity and social class. Existing research, however, mostly focuses on one dimension at a time. But people are inherently multidimensional, have many attributes and are members of multiple groups. In this article, we explore such multidimensionality further in the context of network dynamics. Are friendship ties increasingly likely to emerge and persist when individuals have an increasing number of attributes in common? We analyze eleven friendship networks of adolescents, draw on stochastic actor-oriented network models and focus on the interaction of established homophily effects. Our results indicate that main effects for homophily on various dimensions are positive. At the same time, the interaction of these homophily effects is negative. There seems to be a diminishing effect for having more than one attribute in common. We conclude that studies of homophily and friendship formation need to address such multidimensionality further.
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Li, Siming, Zhangxi Lin, Jiaxian Qiu, Roozmehr Safi e Zhongyi Xiao. "How friendship networks work in online P2P lending markets". Nankai Business Review International 6, n. 1 (2 marzo 2015): 42–67. http://dx.doi.org/10.1108/nbri-01-2014-0010.
Abstract (sommario):
Purpose – The purpose of this paper is to study the effects of multidimensional friendship networks on economic outcomes in the domain of online people-to-people (P2P) lending markets. Design/methodology/approach – The empirical analysis is based on the data set of transactions and friendship networks from PPDai.com market, the most prominent P2P lending market in China. A friendship hierarchy is proposed in this paper to conceptualize friendship network types. Furthermore, methodologies of t-test, logistic regression and ordinary least squares regression are implemented to measure the impact of multidimensional friendship network variables on the probability of successful funding, as well as the interest rates on funded loans. Findings – The study demonstrates significant effects of structural, relational and cognitive friendship networks using PPDai.com data. The results indicate that structural friendship network measured in terms of the number of friendship ties is a significant factor of funding performance. Additionally, borrowers, who are involved in higher-quality friendship networks, are more likely to be funded and pay lower interest rates on funded loans. Also, the deeper the level of the relationship is in the friendship hierarchy, the more significant will be the effect of friendship on the final economic results. Furthermore, quality is more important than quantity in determining funding performance. Originality/value – This paper is the first to study the effects of multidimensional friendship networks on economic outcome variables in the domain of online P2P lending, thus broadening the theory of multidimensional social capital, which can deepen our understanding about how social networks work and have significant implications practically and theoretically.
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Pavan, Elena. "Embedding Digital Communications Within Collective Action Networks: A Multidimensional Network Approach". Mobilization: An International Quarterly 19, n. 4 (1 dicembre 2014): 441–55. http://dx.doi.org/10.17813/maiq.19.4.w24rl524u074126k.
Abstract (sommario):
In this article, we conceive of digital media as embedded within social networks, and use this perspective to examine the role of online communications in collective action. We claim that the adoption of this perspective requires two shifts: first, rethinking the ontological separation between media and social networks of action that has, so far, characterized research in this domain; second, the adoption of flexible tools that enable us to account, simultaneously, for the multiplicity of relations underpinning collective efforts and the hybrid interplay between direct and technology-mediated interactions. After discussing the necessity and the implications of considering communication technologies as endogenous to social networks of collective action, we introduce multidimensional networks (MDNs) as a suitable perspective to advance the application of a relational approach to the study of collective action, thus meeting the challenges posed by the diffusion of interactive and networking digital media.
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Dařena, František, Alexander Troussov e Jan Žižka. "Simulating activation propagation in social networks using the graph theory". Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 58, n. 3 (2010): 21–28. http://dx.doi.org/10.11118/actaun201058030021.
Abstract (sommario):
The social-network formation and analysis is nowadays one of objects that are in a focus of intensive research. The objective of the paper is to suggest the perspective of representing social networks as graphs, with the application of the graph theory to problems connected with studying the network-like structures and to study spreading activation algorithm for reasons of analyzing these structures. The paper presents the process of modeling multidimensional networks by means of directed graphs with several characteristics. The paper also demonstrates using Spreading Activation algorithm as a good method for analyzing multidimensional network with the main focus on recommender systems. The experiments showed that the choice of parameters of the algorithm is crucial, that some kind of constraint should be included and that the algorithm is able to provide a stable environment for simulations with networks.
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Maltby, John, Sarah A. Hunt, Asako Ohinata, Emma Palmer e Simon Conroy. "Frailty and Social Isolation: Comparing the Relationship between Frailty and Unidimensional and Multifactorial Models of Social Isolation". Journal of Aging and Health 32, n. 10 (9 giugno 2020): 1297–308. http://dx.doi.org/10.1177/0898264320923245.
Abstract (sommario):
Objective: The aim of the study was to compare uni- and multidimensional models of social isolation to improve the specificity of determining associations between social isolation and frailty. Methods: The study included participants aged ≥60 years from the English Longitudinal Study of Ageing assessed for social isolation and frailty (frailty index and Fried phenotype) over a 4-year period. Factor analysis assessed whether social isolation was multidimensional. Multiple regression analysis was used to assess specificity in associations between social isolation and frailty over time. Results: Social isolation comprises social isolation from nuclear family, other immediate family, and wider social networks. Over time, social isolation from a wider social network predicted higher frailty index levels, and higher frailty index and Fried phenotype levels predicted greater social isolation from a wider social network. Discussion: Social isolation is multidimensional. The reciprocal relationship between social isolation from wider social networks and accumulating frailty deficits, and frailty as a clinical syndrome influencing social isolation from social networks is discussed.
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Cao, Lifeng, Xin Lu, Zhensheng Gao, Mengda Han e Xuehui Du. "Multilevel Security Network Communication Model Based on Multidimensional Control". Mathematical Problems in Engineering 2020 (12 maggio 2020): 1–18. http://dx.doi.org/10.1155/2020/3528439.
Abstract (sommario):
To solve the problems associated with the application of multilevel security to actual networks, such as flexibility, availability, security, and secure communication, this study proposes a multilevel security network communication model based on multidimensional control. In the model, access control is retained on the basis of security labels. In addition, relational restraints among protection domains, credibility degree restraints of subjects on security attributes, aggregation inference control restraints, and secure tunnel control restraints are introduced and applied. Thus, secure information exchange within a multilevel security network information system is ensured. Moreover, using this model, multilevel security virtual networks with logical and independent characteristics can be built to accomplish secure interconnection and communication between nonequivalent members, thereby reducing the probability of information leakage. Finally, the security of the model is confirmed by applying the nontransitive, noninterference theory, and the typical application of the model in actual networks is described.
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Bachmann, Ivana, Patricio Reyes, Javier Bustos e Alonso Silva. "Multidimensional Network Resilience Analysis". IEEE Latin America Transactions 14, n. 6 (giugno 2016): 2912–14. http://dx.doi.org/10.1109/tla.2016.7555274.
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Dalfó, C., F. Comellas e M. A. Fiol. "The Multidimensional Manhattan Network". Electronic Notes in Discrete Mathematics 29 (agosto 2007): 383–87. http://dx.doi.org/10.1016/j.endm.2007.07.063.
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Zheng, Yun, Chonghao Zhai, Dajian Liu, Jun Mao, Xiaojiong Chen, Tianxiang Dai, Jieshan Huang et al. "Multichip multidimensional quantum networks with entanglement retrievability". Science 381, n. 6654 (14 luglio 2023): 221–26. http://dx.doi.org/10.1126/science.adg9210.
Abstract (sommario):
Quantum networks provide the framework for quantum communication, clock synchronization, distributed quantum computing, and sensing. Implementing large-scale and practical quantum networks relies on the development of scalable architecture and integrated hardware that can coherently interconnect many remote quantum nodes by sharing multidimensional entanglement through complex-medium quantum channels. We demonstrate a multichip multidimensional quantum entanglement network based on mass-manufacturable integrated-nanophotonic quantum node chips fabricated on a silicon wafer by means of complementary metal-oxide-semiconductor processes. Using hybrid multiplexing, we show that multiple multidimensional entangled states can be distributed across multiple chips connected by few-mode fibers. We developed a technique that can efficiently retrieve multidimensional entanglement in complex-medium quantum channels, which is important for practical uses. Our work demonstrates the enabling capabilities of realizing large-scale practical chip-based quantum entanglement networks.
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Cheng, Weijie, Renli Cheng, Jinsheng Liu, Weizhe Ma, Jie Li, Weiling Guan, Daolu Zhang e Tao Yu. "Multidimensional Intelligent Distribution Network Load Analysis and Forecasting Management System Based on Multidata Fusion Technology". Mathematical Problems in Engineering 2021 (12 febbraio 2021): 1–24. http://dx.doi.org/10.1155/2021/6677842.
Abstract (sommario):
In order to improve the work efficiency of load characteristic analysis and realize lean management, scientific prediction, and reasonable planning of the distribution networks, this paper develops a multidimensional intelligent distribution network load analysis and prediction management system based on the fusion of multidimensional data for the application of multidimensional big data in the smart distribution network. First, the framework of the software system is designed, and the functional modules for multidimensional load characteristic analysis are designed. Then, the method of multidimensional user load characterization is introduced; furthermore, the application functions and the design process of some important function modules of the software system are introduced. Finally, an application example of the multidimensional user load characterization system is presented. Overall, the developed system has the features of interoperability of data links between functional modules, information support between different functions, and modular design concept, which can meet the daily application requirements of power grid enterprises and can respond quickly to the issued calculation requirements.
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Boyd, Reiko, Abigail Williams-Butler, Katarina Ploch e Kristen Slack. "Multidimensional Aspects of Social Networks: Implications for CPS Recurrence". Social Sciences 12, n. 4 (14 aprile 2023): 234. http://dx.doi.org/10.3390/socsci12040234.
Abstract (sommario):
This study examines the social network characteristics of 670 mothers reported to and investigated by the child protection system (CPS) in Milwaukee County, Wisconsin in 2016. Specifically, having a recurrent CPS investigation within one year of an index investigation that did not result in an ongoing CPS case is assessed as a function of positive social network ties, negative social network ties, and perceived neighborhood support. Few studies have explored these aspects of social networks comparatively and simultaneously in relation to CPS outcomes, or within this population. We used cluster analysis to identify particular combinations of network characteristics among mothers with recent investigations and then examined whether different cluster types are predictive of recurrent CPS involvement within one year. Clusters differed on the perceived levels of both positive and negative interpersonal ties as well as perceived neighborhood support and were associated with different levels of known child maltreatment risk factors. Clusters with lower levels of perceived neighborhood support were more likely to be associated with future CPS investigations, but this association becomes statistically insignificant when controlling for mothers’ depressive symptoms. The results of this study suggest that a more multi-faceted view of social networks can be helpful to understand the social contexts of mothers as they experience contact with CPS and raises questions about how these contexts interact with parental mental health in relation to CPS recurrence.
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Yang, Xin, Liping Cui e Yan Liu. "Application Analysis of Overlapping Community Detection Algorithms for Multidimensional Network Big Data and IoT". Wireless Communications and Mobile Computing 2022 (25 agosto 2022): 1–9. http://dx.doi.org/10.1155/2022/1172186.
Abstract (sommario):
Capture the design and elements of these layers. Each layer corresponds to an alternative connection type between hubs in the normal world and requires tracking down communities in multidimensional networks. Most community disclosure approaches for multidimensional networks, then again, may ignore the transaction between layers or a layer’s unmistakable topological construction. Moreover, most of them are just equipped for distinguishing nonoverlapping communities. In this exploration, we offer another multidimensional network community disclosure strategy that exploits the connection among layers and the extraordinary geography of each layer to track down overlapping communities. First, use an overall assessment of edge behavior within and between layers to calculate the similarity of edges from similar layers and cross layers. You can then use these similarities to build a dendrogram of a multidimensional network that takes into account both characteristic topology structures and basic transactions. Finally, you can remove the overlapping communities in these layers by splitting the dendrogram and adding another community thickness metric for the multidimensional network. We show that our strategy is precise in recognizing overlapping communities in multidimensional networks by applying it to both manufactured and genuine world datasets. In chart and enormous information examination, community detection is a commonplace issue. It involves finding groups of firmly associated hubs with little associations with hubs outside the bunch. Distinguishing communities in huge scope networks, specifically, is a basic errand in numerous logical fields. In the writing, community detection techniques have been demonstrated to be wasteful, bringing about the improvement of communities with uproarious communications. To defeat this requirement, a framework that decides the best community among multifaceted networks in light of important determination standards and substance dimensionality should be created, eliminating loud communications in a continuous setting. Our outcomes likewise show that it is vital to utilize integral measurements to assess the exhibition of overlapping community detection calculations. Performance metrics, such as the NMI or the Omega Index, only measure the overall quality of a detected cover, whereas complementary metrics give us more information about the behavior of each algorithm in detecting overlapping communities. Finally, while some algorithms perform well on synthetic networks, none of the algorithms can detect the community structure in real networks. This is due to the detected communities of the algorithms being substantially different from the communities defined by the meta-data.
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Kosovets, Mykola, e Lilia Tovstenko. "Development of a Cluster with Cloud Computing Based on Neural Networks With Deep Learning for Modeling Multidimensional Fields". Cybernetics and Computer Technologies, n. 4 (30 dicembre 2021): 80–88. http://dx.doi.org/10.34229/2707-451x.21.4.8.
Abstract (sommario):
Introduction. The modeling of multidimensional fields on multiprocessors, with a neural network architecture, which is rebuilt in the process of solving the problem by means of deep learning, is considered. This architecture of the calculator allows the device to be used to solve the problems of passive location, monitoring station, active LPI location station, base telecommunications station at the same time. Particular attention is paid to the use of bionic principles in the processing of multidimensional signals. A cluster computer with cloud computing is proposed for creating a modeling complex for processing multidimensional signals and debugging the target system. The cluster is made in the form of a multiprocessor based on neural network technology with deep learning. Biomimetic principles are used in the architecture of the modeling complex. The purpose of the work. Creation of a modeling complex as a cluster with cloud computing using neural networks with deep learning. The cluster is a neuromultiprocessor that is rebuilt in the process. Results. In the process, we managed to create a multiprocessor, which in the process of computing is rebuilt, to simulate a terahertz 3D Imager scanner using cloud computing. Conclusions. In the process of performing the work a complex for modeling multidimensional signals was created. As the basis of the computer used a cluster that is rebuilt in the process. The computing base consists of neural networks with cloud computing. Keywords: cognitive space, deep learning, convolutional neural network, neural network architectures, cluster.
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Yu, Qiusheng, Xiaoyong Wang, Depin Lv, Bin Qi, Yongjing Wei, Lei Liu, Pu Zhang, Weihong Zhu e Wensheng Zhang. "Data Fusion and Situation Awareness for Smart Grid and Power Communication Network Based on Tensor Computing and Deep Reinforcement Learning". Electronics 12, n. 12 (9 giugno 2023): 2606. http://dx.doi.org/10.3390/electronics12122606.
Abstract (sommario):
With the large-scale deployment of sensors, both the smart grid and the power communication network should jointly deal with different kinds of big data. The fusion of both networks should bring unpredictable accidents, even leading a catastrophic destruction in our lives. However, data fusion (DF) and coordination treatment for two networks will greatly improve system performance, reduce system complexity, and improve the precision and control ability of both networks. Situation awareness (SA) is the key function for DF and accident avoidance for both networks with different network structures, data types, system mechanisms, and so on. This paper use tensor computing to provide a general data model for heterogeneous and multidimensional big data generated from smart grid and power communication network. A novel data fusion scheme is designed with multidimensional tensors. Deep reinforcement learning (DRL) algorithms are utilized to construct an optimal SA strategy based on tensor big data. A multi-agent actor-critic (MAAC) algorithm is used to achieve an optimal SA policy and improve system performance. The proposed DF and SA schemes based on tensor computing and DRL provide useful guidance for smart grid and power communication networks from theory and practice.
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Li, Haifeng. "Multidimensional Information Network Big Data Mining Algorithm Relying on Finite Element Analysis". Computational Intelligence and Neuroscience 2022 (11 aprile 2022): 1–11. http://dx.doi.org/10.1155/2022/7156715.
Abstract (sommario):
In recent years, with the rapid development of the Internet, online social networks have been continuously integrated with traditional interpersonal networks and research on information dissemination in social networks has gradually increased. This article studies and analyzes the multidimensional information network big data mining algorithm based on the finite element analysis method. This paper firstly introduces the finite element analysis and calculation process, a finite element data mining simulation application software management system will integrate current data, calculation, and background data into one, then analyzes the data mining clustering algorithm, and conducts an experimental exploration of the influential node mining algorithm in complex networks. The experimental results show that the LIC algorithm is better than the CC algorithm, the DC algorithm, and the BC algorithm; its overall performance is improved by 30%, and the effect is better. The LIC algorithm can effectively and quickly determine the influential nodes, which is helpful for social network analysis.
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Marar, João Fernando, e Aron Bordin. "Multidimensional wavelet neural networks Based on polynomial powers of sigmoid". DAT Journal 1, n. 2 (27 dicembre 2016): 106–23. http://dx.doi.org/10.29147/2526-1789.dat.2016v1i2p106-123.
Abstract (sommario):
Wavelet functions have been used as the activation function in feed forward neural networks. An abundance of R&D has been produced on wavelet neural network area. Some successful algorithms and applications in wavelet neural network have been developed and reported in the literature. However, most of the aforementioned reports impose many restrictions in the classical back propagation algorithm, such as low dimensionality, tensor product of wavelets, parameters initialization, and, in general, the output is one dimensional, etc. In order to remove some of these restrictions, a family of polynomial wavelets generated from powers of sigmoid functions is presented. We described how a multidimensional wavelet neural networks based on these functions can be constructed, trained and applied in pattern recognition tasks. As examples of applications for the method proposed a framework for face verfication is presented.
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Heer, Jeffrey, e Adam Perer. "Orion: A system for modeling, transformation and visualization of multidimensional heterogeneous networks". Information Visualization 13, n. 2 (12 dicembre 2012): 111–33. http://dx.doi.org/10.1177/1473871612462152.
Abstract (sommario):
The study of complex activities such as scientific production and software development often requires modeling connections among heterogeneous entities including people, institutions, and artifacts. Despite advances in algorithms and visualization techniques for understanding such social networks, the process of constructing network models and performing exploratory analysis remains difficult and time-consuming. In this article, we present Orion, a system for interactive modeling, transformation, and visualization of network data. Orion’s interface enables the rapid manipulation of large graphs—including the specification of complex linking relationships—using simple drag-and-drop operations with desired node types. Orion maps these user interactions to statements in a declarative workflow language that incorporates both relational operators (e.g. selection, aggregation, and joins) and network analytics (e.g. centrality measures). We demonstrate how these features enable analysts to flexibly construct and compare networks in domains such as online health communities, electronic medical records, academic collaboration, and distributed software development.
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Satława, Tadeusz, Joanna Grabska-Chrząstowska e Przemysław Korohoda. "Application of multidimensional data analysis to chromatography". Image Processing & Communications 18, n. 2-3 (1 dicembre 2013): 101–8. http://dx.doi.org/10.2478/v10248-012-0084-1.
Abstract (sommario):
Abstract This work presents analysis of chromatographic signal used to identify substances in samples. First part consists of chromatography overview and description of three classification methods (neural network with backpropagation, probabilistic neural network with Parzen window and support vector machines). Designed algorithm consists of several stages: signal filtering, peak detection and its approximation with sum of two Gaussian functions. The parameters of that two curves are the features vectors describing the peak of the substance. The last step is classification, for which two types of supervised machine learning were compared, based on the whole signal and on features vectors. Both types were tested for different classificators and their parameters. Verification was based on 55 chromatography signals. The best results for both methods of learning were achieved for probabilistic neural networks. The correct classification rate was 82% for the whole signal and 93% for feature vectors.
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Amagata, Daichi, Yuya Sasaki, Takahiro Hara e Shojiro Nishio. "Efficient Multidimensional Top-kQuery Processing in Wireless Multihop Networks". Mobile Information Systems 2015 (2015): 1–20. http://dx.doi.org/10.1155/2015/657431.
Abstract (sommario):
Top-kqueries, which retrieve thekmost preferable data objects, have been receiving much attention. An emerging challenge is to support efficient top-kquery processing in a wireless distributed network. In this study, we investigated how to process multidimensional top-kqueries efficiently in a wireless multihop network. A major challenge for multidimensional top-kqueries is that answers for different users are typically different, because each user has a unique preference and search range. Meanwhile, it is desirable for wireless networks to reduce unnecessary traffic even if users issue top-kqueries with their own unique preferences. Therefore, we address the above problem and propose a top-kquery processing method in wireless multihop networks, calledClusTo. ClusTo performs a novel clustering scheme for multidimensional top-kquery processing and routes queries based on the cluster while guaranteeing the user’s specified search range. Moreover, ClusTo takes a dynamic threshold approach to suppress unnecessary query transmissions to nodes which do not contribute to top-kdata retrieval. Extensive experiments on both real and synthetic data have demonstrated that ClusTo outperforms existing methods in terms of traffic and delay.
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Wang, Mengmeng, Wanli Zuo e Ying Wang. "A Multidimensional Nonnegative Matrix Factorization Model for Retweeting Behavior Prediction". Mathematical Problems in Engineering 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/936397.
Abstract (sommario):
Today microblogging has increasingly become a means of information diffusion via user’s retweeting behavior. As a consequence, exploring on retweeting behavior is a better way to understand microblog’s transmissibility in the network. Hence, targeted at online microblogging, a directed social network, along with user-based features, this paper first built content-based features, which consisted of URL, hashtag, emotion difference, and interest similarity, based on time series of text information that user posts. And then we measure relationship-based factor in social network according to frequency of interactions and network structure which blend with temporal information. Finally, we utilize nonnegative matrix factorization to predict user’s retweeting behavior from user-based dimension and content-based dimension, respectively, by employing strength of social relationship to constrain objective function. The results suggest that our proposed method effectively increases retweeting behavior prediction accuracy and provides a new train of thought for retweeting behavior prediction in dynamic social networks.
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Comellas, F., C. Dalfó e M. A. Fiol. "Multidimensional Manhattan Street Networks". SIAM Journal on Discrete Mathematics 22, n. 4 (gennaio 2008): 1428–47. http://dx.doi.org/10.1137/07068446x.
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Friedman, Alexander, Joshua F. Slocum, Danil Tyulmankov, Leif G. Gibb, Alex Altshuler, Suthee Ruangwises, Qinru Shi et al. "Analysis of complex neural circuits with nonlinear multidimensional hidden state models". Proceedings of the National Academy of Sciences 113, n. 23 (24 maggio 2016): 6538–43. http://dx.doi.org/10.1073/pnas.1606280113.
Abstract (sommario):
A universal need in understanding complex networks is the identification of individual information channels and their mutual interactions under different conditions. In neuroscience, our premier example, networks made up of billions of nodes dynamically interact to bring about thought and action. Granger causality is a powerful tool for identifying linear interactions, but handling nonlinear interactions remains an unmet challenge. We present a nonlinear multidimensional hidden state (NMHS) approach that achieves interaction strength analysis and decoding of networks with nonlinear interactions by including latent state variables for each node in the network. We compare NMHS to Granger causality in analyzing neural circuit recordings and simulations, improvised music, and sociodemographic data. We conclude that NMHS significantly extends the scope of analyses of multidimensional, nonlinear networks, notably in coping with the complexity of the brain.
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Schönauer, Moritz. "BOCR-Modell automatisches Fahren in der Produktion/BOCR model for automatic driving in production systems – Methodology for systems engineering processing and display of multidimensional processes (part 3)". wt Werkstattstechnik online 111, n. 07-08 (2021): 539–47. http://dx.doi.org/10.37544/1436-4980-2021-07-08-71.
Abstract (sommario):
Um für eine multidimensionale systemtechnische Problemstellung wie das automatische Fahren in der Produktion Handlungsempfehlungen zu generieren, wird im letzten Beitrag des dreiteiligen Forschungsvorhabens mit dem Analytic Network Process ein Entscheidungsnetzwerk konzipiert. Im Fokus des Beitrags steht die Entwicklung eines Entscheidungstools der Produktionsplanung und -steuerung, um die kumulierten Erkenntnisse der vorherigen Veröffentlichungen zum automatischen Fahren in der Produktion individuell zugänglich zu machen und die jeweils passendste Handlungsalternative aufzuzeigen. The last contribution of the three-part research project designs a decision-making network using the analytic network process method to generate recommendations on how to solve multidimensional system-technical problems such as automatic driving in production systems. This paper focuses on developing a decision-making tool for production planning and control to make the accumulated findings of the previous publications on automatic driving in production individually accessible and to display the best options for problem solving.
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Anderson, Kevin M., Tian Ge, Ru Kong, Lauren M. Patrick, R. Nathan Spreng, Mert R. Sabuncu, B. T. Thomas Yeo e Avram J. Holmes. "Heritability of individualized cortical network topography". Proceedings of the National Academy of Sciences 118, n. 9 (23 febbraio 2021): e2016271118. http://dx.doi.org/10.1073/pnas.2016271118.
Abstract (sommario):
Human cortex is patterned by a complex and interdigitated web of large-scale functional networks. Recent methodological breakthroughs reveal variation in the size, shape, and spatial topography of cortical networks across individuals. While spatial network organization emerges across development, is stable over time, and is predictive of behavior, it is not yet clear to what extent genetic factors underlie interindividual differences in network topography. Here, leveraging a nonlinear multidimensional estimation of heritability, we provide evidence that individual variability in the size and topographic organization of cortical networks are under genetic control. Using twin and family data from the Human Connectome Project (n = 1,023), we find increased variability and reduced heritability in the size of heteromodal association networks (h2: M = 0.34, SD = 0.070), relative to unimodal sensory/motor cortex (h2: M = 0.40, SD = 0.097). We then demonstrate that the spatial layout of cortical networks is influenced by genetics, using our multidimensional estimation of heritability (h2-multi; M = 0.14, SD = 0.015). However, topographic heritability did not differ between heteromodal and unimodal networks. Genetic factors had a regionally variable influence on brain organization, such that the heritability of network topography was greatest in prefrontal, precuneus, and posterior parietal cortex. Taken together, these data are consistent with relaxed genetic control of association cortices relative to primary sensory/motor regions and have implications for understanding population-level variability in brain functioning, guiding both individualized prediction and the interpretation of analyses that integrate genetics and neuroimaging.
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Huang, Jih-Jeng, Gwo-Hshiung Tzeng e Chorng-Shyong Ong. "Multidimensional data in multidimensional scaling using the analytic network process". Pattern Recognition Letters 26, n. 6 (maggio 2005): 755–67. http://dx.doi.org/10.1016/j.patrec.2004.09.027.
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Dmytro, OSTRENKO,, e KOLLAROV O. "Search for the optimal topology of artificial neural networks based on multidimensional Legendre polynomials." Journal of Electrical and power engineering 24, n. 1 (21 maggio 2021): 51–58. http://dx.doi.org/10.31474/2074-2630-2021-1-51-58.
Abstract (sommario):
Earlier in [1], it was concluded that it is necessary to improve the learning algorithms of neural networks operating in systems that generate electricity using renewable energy sources. This article is intended to acquaint the reader with a new type of activation functions of artificial neural networks (ANN), namely - the use of Legendre polynomials, as well as a new method of learning ANN, when this process is not sequential, as usual, but in parallel. The accepted statements made it possible to make sure that the new, designed neural network has better properties (such as training time and less value of learning error) than the standard ones. The relevance of this topic lies in the following provisions: - improving the interaction between the solar station and artificial intelligence systems, through increased productivity; - taking into account the transients in the electrical network by means of intelligent control, through the use of neural networks of the proposed architecture. The developed neural networks have found their application in the work of a photovoltaic station. Their main purpose is to fulfill the forecast in the electrical networks of the amount of generated power. To successfully complete the task, the following goals were set and solved: to analyze and compare standard activation functions and algorithms for ANN training, to show methods and describe the improvement of networks, to demonstrate the application of developed ANN in photovoltaic problems. This article was designed to acquaint with the new method of building neural networks, which is based on seeing the transmission of signals in a non-sequential way, such as parallel, with certain features of the connection with which it was given in the text. The paper also demonstrates the use of the Legendre polynomial using qualitative neural network activation functions that work with solar panels. For confirmation in the article the answers to calculations are given. In future materials it is planned to streamline in more detail the process of modeling and compiling a mathematical calculation for the construction of neural networks.
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Zhao, Wanqing, Thomas H. Beach e Yacine Rezgui. "A systematic mixed-integer differential evolution approach for water network operational optimization". Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474, n. 2217 (settembre 2018): 20170879. http://dx.doi.org/10.1098/rspa.2017.0879.
Abstract (sommario):
The operational management of potable water distribution networks presents a great challenge to water utilities, as reflected by the complex interplay of a wide range of multidimensional and nonlinear factors across the water value chain including the network physical structure and characteristics, operational requirements, water consumption profiles and the structure of energy tariffs. Nevertheless, both continuous and discrete actuation variables can be involved in governing the water network, which makes optimizing such networks a mixed-integer and highly constrained decision-making problem. As such, there is a need to situate the problem holistically, factoring in multidimensional considerations, with a goal of minimizing water operational costs. This paper, therefore, proposes a systematic optimization methodology for (near) real-time operation of water networks, where the operational strategy can be dynamically updated using a model-based predictive control scheme with little human intervention. The hydraulic model of the network of interest is thereby integrated and successively simulated with different trial strategies as part of the optimization process. A novel adapted mixed-integer differential evolution (DE) algorithm is particularly designed to deal with the discrete-continuous actuation variables involved in the network. Simulation results on a pilot water network confirm the effectiveness of the proposed methodology and the superiority of the proposed mixed-integer DE in comparison with genetic algorithms. It also suggests that 23.69% cost savings can be achieved compared with the water utility's current operational strategy, if adaptive pricing is adopted for all the pumping stations.
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Varkevisser, T. M. C. K., J. G. Röttgering, P. de Witt Hamer, M. C. M. Kouwenhoven, J. C. Reijneveld, V. Belgers, M. van Lingen, M. Klein e L. Douw. "P11.01 Symptom networks in glioma: a novel approach to study multidimensional symptomatology in glioma patients". Neuro-Oncology 23, Supplement_2 (1 settembre 2021): ii28—ii29. http://dx.doi.org/10.1093/neuonc/noab180.097.
Abstract (sommario):
Abstract BACKGROUND Glioma patients experience a high symptom burden contributing to poorer quality of life. Symptoms include depression, cognitive impairment, and fatigue and vary throughout the disease. These symptoms are rarely studied from a comprehensive perspective, while their interdependence may be relevant for their development, perpetuation, and ultimately successful treatment. The emerging field of symptom network analysis uncovers the multidimensional symptom space. Nodes are the symptoms, and edges are operationalized as the full conditional association, or partial correlation, between two symptom severity scores across patients. Highly connected nodes are considered central and may be particularly relevant targets for treatment as disruption of these central nodes impact the entire network. We visualized the overall glioma symptom network, compared multidimensional results to known literature, and statistically compared networks between relevant patient subgroups. MATERIAL AND METHODS A dataset comprised of 355 observations of 180 glioma patients at different disease phases was analysed. Cognitive testing and questionnaires regarding health-related and glioma-specific quality of life, fatigue, depression, and cognition resulted in the definition of 30 symptom nodes. Symptom clusters were visually explored in the resulting networks, as were node strength, betweenness, and closeness centrality measures for each node. Networks were statistically compared between preoperative patients and patients during stable disease, as well as patients with low versus high-grade gliomas. Networks of patients with normal versus severe levels of fatigue were also compared as cancer-/glioma-related fatigue has a strong impact on quality of life and can correlate with other common symptoms such as pain, depression, and/or sleep disturbance. RESULTS Symptom clusters existed between: 1) bodily pain, headache and physical functioning; 2) concentration and motivation; and 3) fatigue and drowsiness. Fatigue and mental health were the most central nodes in the networks. Furthermore, the overall connectivity between symptoms was significantly higher in the severely fatigued patients than in patients with normal fatigue. No network differences were found between low versus high-grade, and preoperative versus stable disease networks. CONCLUSION Fatigue is a central node in glioma patients’ burden of disease, and symptoms are more tightly intercorrelated in patients experiencing severe fatigue. From our data, we hypothesize that fatigue co-exists with or perpetuates other symptoms. Thus, although these results are preliminary, the network approach may innovate hypothesis generation in symptom management.
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Zilong He, Zilong He, Wentao Liu Wentao Liu, Bailin Shen Bailin Shen, Xue Chen Xue Chen, Xiqing Gao Xiqing Gao, Sheping Shi Sheping Shi, Qi Zhang Qi Zhang, Dongdong Shang Dongdong Shang, Yongning Ji Yongning Ji e and Yingfeng Liu and Yingfeng Liu. "Flexible multidimensional modulation formats based on PM-QPSK constellations for elastic optical networks". Chinese Optics Letters 14, n. 4 (2016): 040602–40605. http://dx.doi.org/10.3788/col201614.040602.
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Fang, Shuheng, Zhengmin Kong, Ping Hu e Li Ding. "A novel topology identification method based on compressive sensing for multidimensional networks". International Journal of Modern Physics B 34, n. 30 (28 ottobre 2020): 2050294. http://dx.doi.org/10.1142/s021797922050294x.
Abstract (sommario):
In real-world scenarios, it is difficult to know about the complete topology of a huge network with different types of links. In this brief, we propose a method to identify the topology of multidimensional networks from information transmission data. We consider information propagating over edges of a two-dimensional (2D) network, where one type of links is known and the other type is unknown. Given the state of all nodes at each unit time, we can transform the topology identification problem into a compressive sensing framework. A modified reconstruction algorithm, called Sparsity Adaptive Matching Pursuit with Mixed Threshold Mechanism (SAMPMTM), is proposed to tackle the problem. Compared with the classical Sparsity Adaptive Matching Pursuit (SAMP) algorithm, the proposed SAMPMTM algorithm can reduce the conflict rate and improve the accuracy of network recovery. We further demonstrate the performance of this improved algorithm through Monte-Carlo simulations under different network models.
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Tan, H. S. "Fourier Neural Networks and Generalized Single Hidden Layer Networks in Aircraft Engine Fault Diagnostics". Journal of Engineering for Gas Turbines and Power 128, n. 4 (17 ottobre 2005): 773–82. http://dx.doi.org/10.1115/1.2179465.
Abstract (sommario):
The conventional approach to neural network-based aircraft engine fault diagnostics has been mainly via multilayer feed-forward systems with sigmoidal hidden neurons trained by back propagation as well as radial basis function networks. In this paper, we explore two novel approaches to the fault-classification problem using (i) Fourier neural networks, which synthesizes the approximation capability of multidimensional Fourier transforms and gradient-descent learning, and (ii) a class of generalized single hidden layer networks (GSLN), which self-structures via Gram-Schmidt orthonormalization. Using a simulation program for the F404 engine, we generate steady-state engine parameters corresponding to a set of combined two-module deficiencies and require various neural networks to classify the multiple faults. We show that, compared to the conventional network architecture, the Fourier neural network exhibits stronger noise robustness and the GSLNs converge at a much superior speed.
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Ribeiro, João, Júlio Garganta, Keith Davids e Daniel Barreira. "A multilevel hypernetworks approach to assess coordination and communication in player interactions in sports teams as co-evolutionary networks". Brazilian Journal of Motor Behavior 14, n. 5 (1 dicembre 2020): 167–70. http://dx.doi.org/10.20338/bjmb.v14i5.216.
Abstract (sommario):
BACKGROUND: This paper presents an introduction and brief appraisal of the use of hyper networks metrics and its potential practical application in examining team dynamics' coordination patterns collective sports. AIM: Throughout their critique piece, we highlighted that game analysis, including the hyper network concept, may help overcome the limitations of previous tools such as social network measures. FINDINGS AND CONCLUSIONS: While the social network analysis generally considers only dyadic interactions (e.g., between two players), the hyper networks also take into account a multidimensional perspective, including both player level and team level communication and coordination. We also evidenced that new studies using hyper network metrics are required in a range of team sports, mainly using data gathered from official competition matches.
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Neshati, Ramin, e Tugrul U. Daim. "Multidimensional Assessment of Emerging Technologies". International Journal of Information Systems and Social Change 1, n. 2 (aprile 2010): 49–71. http://dx.doi.org/10.4018/jissc.2010040104.
Abstract (sommario):
The Internet has changed the world in many ways. Online communications, financial and business-to-business transactions, electronic shopping, banking, and entertainment have become the norm in the digital age. The combined package of technologies that comprise the Internet—the information superhighway—have made all of this possible. The aging technological infrastructure that supports these webs of interconnected networks is being stressed to its performance limits. Recent advances in the backbone infrastructure that supports the Internet have helped alleviate some of these problems, but more challenges lie ahead for solving technology-related performance bottlenecks for many online applications, including high-definition interactive gaming. In this paper, the authors developed a technology assessment through multiple perspectives. While different components of the technology such as applications, protocols and network components are identified, other impact areas such as market and management are also evaluated. Elements of user behavior are evaluated within the market perspective. Evaluating technologies through these dimensions concurrently provides a balanced assessment among technical, economical, social and political factors.
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Güneş, F., H. Torpi e F. Gürgen. "Multidimensional signal-noise neural network model". IEE Proceedings - Circuits, Devices and Systems 145, n. 2 (1998): 111. http://dx.doi.org/10.1049/ip-cds:19981712.
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Sorokina, Mariia. "Multidimensional fiber echo state network analogue". Journal of Physics: Photonics 2, n. 4 (2 ottobre 2020): 044006. http://dx.doi.org/10.1088/2515-7647/abb584.
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Yang, Shengqi, Bin Wu e Bai Wang. "Multidimensional views on mobile call network". Frontiers of Computer Science in China 3, n. 3 (15 agosto 2009): 335–46. http://dx.doi.org/10.1007/s11704-009-0056-9.
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Barrau, Enora, e Mathias Glaus. "Structural and Environmental Performance of Evolving Industrial Symbiosis: A Multidimensional Analysis". Sustainability 15, n. 1 (30 dicembre 2022): 693. http://dx.doi.org/10.3390/su15010693.
Abstract (sommario):
Industrial symbiosis (IS) involves networks of organizations collaborating through flow exchanges. Scientific research has shown that such systems are able to provide benefits at the environmental level. Structural organization and stability were also studied, as they are linked to resilience (maintenance of activity over time), especially with ecological network analysis (ENA), which considers several dimensions in the assessment of a network organization. Studies combining ENA and environmental assessment are lacking in the literature; therefore, the links between the two dimensions are not well documented. The intention of this study was to fill this gap by analyzing structural and environmental performance simultaneously using ENA and a life-cycle-analysis-based approach focusing on the structural topology of IS. The results show that the two dimensions do not strictly influence each other. Structural performance was found to vary depending on the network structure topology, whereas environmental performance was influenced by the network complexity. To ensure the continuation of IS benefits, the two dimensions should be considered in the decision-making process in IS planification, even if they are independent evaluation criteria. Tradeoffs should be based on IS development possibilities and territorial needs.
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Zhang, Kai, Zheng Tan, Jianying Sun, Baoyu Zhu, Yuanbo Yang e Qunbo Lv. "A Multidimensional Spectral Transformer with Channel-Wise Correlation for Hyperspectral Image Classification". Applied Sciences 13, n. 9 (28 aprile 2023): 5482. http://dx.doi.org/10.3390/app13095482.
Abstract (sommario):
Convolutional neural networks (CNNs) have been developed as an effective strategy for hyperspectral image (HSI) classification. However, the lack of feature extraction by CNN networks is due to the network failing to effectively extract global features and poor capability in distinguishing between different feature categories that are similar. In order to solve these problems, this paper proposes a novel approach to hyperspectral image classification using a multidimensional spectral transformer with channel-wise correlation. The proposed method consists of two key components: an input mask and a channel correlation block. The input mask is used to extract relevant spectral information from hyperspectral images and discard irrelevant information, reducing the dimensionality of the input data and improving classification accuracy. The channel correlation block captures the correlations between different spectral channels and is integrated into the transformer network to improve the model’s discrimination power. The experimental results demonstrate that the proposed method achieves great performance with several benchmark hyperspectral image datasets. The input mask and channel correlation block effectively improve classification accuracy and reduce computational complexity.
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Li, Wenfeng. "Multidimensional Heterogeneous Network Link Adaptation Based on Mobile Environment". Computational Intelligence and Neuroscience 2022 (24 marzo 2022): 1–15. http://dx.doi.org/10.1155/2022/9450393.
Abstract (sommario):
With the development of communication technology, train control operation system develops gradually, which significantly improves the reliability and efficiency of train operation. The current mobile Internet has gradually highlighted the many limitations of the mobile Internet in the high-speed mobile environment, which seriously deteriorate the service quality and user experience, and cause a waste of resources. In order to meet the real-time requirements of network communication resource scheduling in the mobile environment, aiming at the multidimensional dynamic adaptation framework constructed in a mobile environment, a service and network adaptation mechanism based on link failure state prediction is proposed in the paper. First, cross-layer theoretical analysis and actual data analysis are combined to construct a wireless link failure probability model. Then, reliable transmission requirements and transmission overhead are applied to optimize goals. Finally, simulation experiments are carried out according to the railway network data to evaluate the E-GCF adaptation algorithm. The experiment results show that compared with the current mainstream algorithms, the prediction accuracy of this adaptation algorithm is improved by 25%. The execution time of the algorithm is reduced by 9.6 seconds and the successful submission rate is as high as 99.99%. The advantages of the algorithm are significantly superior other algorithms. It proves that the research method of this paper can effectively improve the satisfaction rate and utility value of reliable transmission, as well as enhance the throughput performance. It solves the adaptation problems of frequent switching and low utilization of heterogeneous networks in a mobile environment, which contributes to the high-quality communication service of mobile network.
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Gjerdingen, Robert O. "Categorization of Musical Patterns by Self-Organizing Neuronlike Networks". Music Perception 7, n. 4 (1990): 339–69. http://dx.doi.org/10.2307/40285472.
Abstract (sommario):
Simulations of self-organizing neuronlike networks are used to demonstrate how untrained listeners might be able to sort their perceptions of dozens of diverse musical features into stable, meaningful schemata. A presentation is first made of the salient characteristics of such networks, especially the adaptive- resonance-theory (ART) networks proposed by Stephen Grossberg. Then a discussion follows of how a computer simulation of a four-level ART network—a simulation dubbed L'ART pour l'art—independently categorized musical events in Mozart's six earliest compositions. The ability of the network to abstract significant voiceleading combinations from these pieces (and in fact to detect a possible error in the New Mozart Edition) suggests that this approach holds promise for the study of how ordinary listeners process music's multidimensional complexity. In addition, the categorizations produced by the network are suggestive of alternative conceptualizations of music's hierarchical structure.
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Jayashree, Devasagayam, V. Uma Rani e K. Soma Sundaram. "Trust Based Misbehavior Detection in Wireless Sensor Networks". Applied Mechanics and Materials 622 (agosto 2014): 191–98. http://dx.doi.org/10.4028/www.scientific.net/amm.622.191.
Abstract (sommario):
Due to emerging technology Wireless Sensor Network (WSN), it is necessary to monitor the behavior of sensor nodes and establish the secure communication in network. Security is a challenging task in wireless environment. Several encryption mechanisms are available to prevent outsider attacks, but no mechanism available for insider attacks. A trust model is a collection of rules used to establish co-operation or collaboration among nodes as well as monitoring misbehavior of wireless sensor networks. Trust model is necessary to enhance secure localization, communication or routing, aggregation, collaboration among nodes. In this paper, proposed a behavior based distributed trust model for wireless sensor network to effectively deal with self-ish or malicious nodes. Here, take multidimensional trust attributes derived from communications and networks to evaluate the overall trust of sensor nodes. It monitors the behavior of nodes and establishes secure communication among networks.
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Gnedash, Anna, e Veronika Katermina. "Abortion Ban in English Social Media in 2022: Pragmatic Linguistics of Online Communications". Virtual Communication and Social Networks 2022, n. 4 (22 dicembre 2022): 172–78. http://dx.doi.org/10.21603/2782-4799-2022-1-4-172-178.
Abstract (sommario):
The present research featured the conflict discourse of online communication in social networks on the topic of abortion ban. The empirical base included 3,000,000 Twitter messages in English. The sampling by keywords women and abortion covered the period from May 1 to July 31, 2022, which saw an outburst of online and offline civic activities regarding some national anti-abortion policies. The resulting web corpus of network linguistic data (datasets) was subjected to multidimensional analysis using such methods as Data Science, mathematical modeling, relational sociology, corpus analysis, discourse analysis, etc. All these procedures combined resulted in a multidimensional comprehensive analysis of the simulated English asynchronous multimodal discursive field in Twitter. The models made it possible to visualize online communications in social networks, as well as to describe the discourse of online communication between pro-choice and pro-life. The authors analyzed the pragmatic potential of network communities on the current political agenda. The method might help to identify the conflict potential that can evolve from online communication into offline socio-political actions.
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O., Savchuk, e Morgal O. "On usage of the neural network technologies in the it- structure components’ diagnosing." Artificial Intelligence 29, AI.2024.29(1) (20 marzo 2024): 87–97. http://dx.doi.org/10.15407/jai2024.01.087.
Abstract (sommario):
The idea of using neural network technologes to prove electrophysical diagnostic methods based on the integral physical effects of IT structure components is considered. It is proposed to transform the received information using a discrete Karhunen-Loeve expansion, which gives the minimum root mean square error of packing a priory vectors in multidimensional space. The use of neural networks: MLP, self-organizing (Kohonen Maps) and RBF in MATLAB environment is verified. The best result for microcircuits was obtained using probabilistic RBF-neural networks. A new neural network approach to diagnostics made it possible to perform individual sorting of elements and ststistical evaluation of the IT structure components batch.
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Contractor, Noshir. "The Emergence of Multidimensional Networks". Journal of Computer-Mediated Communication 14, n. 3 (aprile 2009): 743–47. http://dx.doi.org/10.1111/j.1083-6101.2009.01465.x.