Relevant bibliographies by topics / Node importance estimation

Academic literature on the topic 'Node importance estimation'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Node importance estimation"

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Fatemi, Zahra, and Elena Zheleva. "Minimizing Interference and Selection Bias in Network Experiment Design." Proceedings of the International AAAI Conference on Web and Social Media 14 (May 26, 2020): 176–86. http://dx.doi.org/10.1609/icwsm.v14i1.7289.

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Current approaches to A/B testing in networks focus on limiting interference, the concern that treatment effects can "spill over" from treatment nodes to control nodes and lead to biased causal effect estimation. Prominent methods for network experiment design rely on two-stage randomization, in which sparsely-connected clusters are identified and cluster randomization dictates the node assignment to treatment and control. Here, we show that cluster randomization does not ensure sufficient node randomization and it can lead to selection bias in which treatment and control nodes represent different populations of users. To address this problem, we propose a principled framework for network experiment design which jointly minimizes interference and selection bias. We introduce the concepts of edge spillover probability and cluster matching and demonstrate their importance for designing network A/B testing. Our experiments on a number of real-world datasets show that our proposed framework leads to significantly lower error in causal effect estimation than existing solutions.
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Jia, Shijie, Tianyin Wang, Xiaoyan Su, and Liuke Liang. "A Novel Video Propagation Strategy Fusing User Interests and Social Influences Based on Assistance of Key Nodes in Social Networks." Electronics 12, no. 3 (January 19, 2023): 532. http://dx.doi.org/10.3390/electronics12030532.

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Accurate video launching and propagation is significant for promotion and distribution of videos. In this paper, we propose a novel video propagation strategy that fuses user interests and social influences based on the assistance of key nodes in social networks (VPII). VPII constructs an estimation model for video distribution capacities in the process of video propagation by investigating interest preference and influence of social users: (1) An estimation method of user preferences for video content is designed by integrating a comparative analysis between current popular videos and historical popular videos. (2) An estimation method to determine the distribution capacities of videos is designed according to scale and importance of neighbor nodes covered. VPII further designs a multi-round video propagation strategy with the assistance of the selected key nodes, which enables these nodes to implement accurate video launching by estimating weighted levels based on available bandwidth and node degree centrality. The video propagation can effectively promote the scale and speed of video sharing and efficiently utilize network resources. Simulations-based testing shows how VPII outperforms other state-of-the-art solutions in terms of startup delay, caching hit ratio, caching cost and higher control overhead.
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Geier, Christian, and Klaus Lehnertz. "Which Brain Regions are Important for Seizure Dynamics in Epileptic Networks? Influence of Link Identification and EEG Recording Montage on Node Centralities." International Journal of Neural Systems 27, no. 01 (November 8, 2016): 1650033. http://dx.doi.org/10.1142/s0129065716500337.

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Nodes in large-scale epileptic networks that are crucial for seizure facilitation and termination can be regarded as potential targets for individualized focal therapies. Graph-theoretical approaches based on centrality concepts can help to identify such important nodes, however, they may be influenced by the way networks are derived from empirical data. Here we investigate evolving functional epileptic brain networks during 82 focal seizures with different anatomical onset locations that we derive from multichannel intracranial electroencephalographic recordings from 51 patients. We demonstrate how the various methodological steps (from the recording montage via node and link inference to the assessment of node centralities) affect importance estimation and discuss their impact on the interpretability of findings in the context of pathophysiological aspects of seizure dynamics.
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Murayama, Toru. "Articulation Node Importance Estimation and Its Correctness for Robustness of Multi-robot Network." IFAC-PapersOnLine 51, no. 22 (2018): 166–71. http://dx.doi.org/10.1016/j.ifacol.2018.11.536.

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Liu, Chao, and Zhongshan Zhang. "Towards a robust FANET: Distributed node importance estimation-based connectivity maintenance for UAV swarms." Ad Hoc Networks 125 (February 2022): 102734. http://dx.doi.org/10.1016/j.adhoc.2021.102734.

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Islam, Tariq, and Yong Kyu Lee. "A Two-Stage Localization Scheme with Partition Handling for Data Tagging in Underwater Acoustic Sensor Networks." Sensors 19, no. 9 (May 8, 2019): 2135. http://dx.doi.org/10.3390/s19092135.

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Knowledge about the geographic coordinates of underwater sensor nodes is of primary importance for many applications and protocols of under water sensor networks (UWSNs) thus making localization of sensor nodes a crucial part of underwater network design. In case of mobile underwater sensor network, location estimation becomes challenging not only due to the need for periodic tracking of nodes, but also due to network partitioning caused by the pseudo-random mobility of nodes. Our proposed technique accomplishes the task of localization in two stages: (1) relative localization of sensor nodes with respect to a reference node at regular intervals during sensing operation. (2) Offline absolute localization of sensor nodes using absolute coordinates of the reference node and relative locations estimated during stage 1. As our protocol deals with mobile underwater sensor networks that may introduce network partitioning, we also propose a partition handling routine to deal with network partitions to achieve high localization coverage. The major design goal of our work is to maximize localization coverage while keeping communication overhead minimum, thus achieving better energy efficiency. Major contributions of this paper are: (1) Two energy efficient relative localization techniques, and (2) A partition handling strategy that ensures localization of partitioned nodes.
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Wang, Zheng Jun, Xiao Ou Jin, Guo Qiang Fu, and Dian Min Sun. "Experimental Method and Research Evolution of Strength Prediction of Cement Concrete." Advanced Materials Research 250-253 (May 2011): 1071–76. http://dx.doi.org/10.4028/www.scientific.net/amr.250-253.1071.

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That griping and predicting concrete strength is crucial importance for shorten design cycle of cement concrete proportion and control efficiently key construction node in time. The paper exposited systematically experimental method of strength estimation of cement concrete and corresponding research progress at present, and the prospect was also analyzed. It is very necessary to promote research work of experiment and analysis method because strength estimation of cement concrete is paramount. It has important meaning for delving into the aspect through above discussion in the future.
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Jokic, Aleksandar, Jovana Grahovac, Jelena Dodic, Zoltan Zavargo, Sinisa Dodic, Stevan Popov, and Damjan Vucurovic. "Interpreting the neural networkfor prediction of fermentation of thick juice from sugar beet processing." Acta Periodica Technologica, no. 42 (2011): 241–49. http://dx.doi.org/10.2298/apt1142241j.

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Methods that can provide adequate accuracy in the estimation of variables from incomplete information are desirable for the prediction of fermentation processes. A feed-forward back-propagation artificial neural network was used for modelling of thick juice fermentation. Fermentation time and starting sugar content were usedas input variables, i.e. nodes. Neural network had one output node (ethanol content, yeast cell number or sugar content). The hidden layer had nine neurons. Garson's algorithm and connection weights were used for interpreting neural network. The inadequacy of Garson's algorithm can be seen by comparing with the results of regression analysis, which indicates that the influence of the fermentation time is higher. A better agreement of the results was obtained using network connection weights, a method that can be used to determine the relative importance of input variables.
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Liu, Li, A. Sankarasubramanian, and S. Ranji Ranjithan. "Logistic regression analysis to estimate contaminant sources in water distribution systems." Journal of Hydroinformatics 13, no. 3 (October 28, 2010): 545–57. http://dx.doi.org/10.2166/hydro.2010.106.

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Accidental or intentional contamination in a water distribution system (WDS) has recently attracted attention due to the potential hazard to public health and the complexity of the contaminant characteristics. The accurate and rapid characterization of contaminant sources is necessary to successfully mitigate the threat in the event of contamination. The uncertainty surrounding the contaminants, sensor measurements and water consumption underscores the importance of a probabilistic description of possible contaminant sources. This paper proposes a rapid estimation methodology based on logistic regression (LR) analysis to estimate the likelihood of any given node as a potential source of contamination. Not only does this algorithm yield location-specific probability information, but it can also serve as a prescreening step for simulation–optimization methods by reducing the decision space and thus alleviating the computational burden. The applications of this approach to two example water networks show that it can efficiently rule out numerous nodes that do not yield contaminant concentrations to match the observations. This elimination process narrows down the search space of the potential contamination locations. The results also indicate that the proposed method efficiently yields a good estimation even when some noise is incorporated into the measurements and demand values at the consumption nodes.
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Stroeven, Piet, Nghi L. B. Le, Lambertus J. Sluys, and Huan He. "POROSIMETRY BY RANDOM NODE STRUCTURING IN VIRTUAL CONCRETE." Image Analysis & Stereology 31, no. 2 (May 17, 2012): 79. http://dx.doi.org/10.5566/ias.v31.p79-87.

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Two different porosimetry methods are presented in two successive papers. Inspiration for the development came from the rapidly-exploring random tree (RRT) approach used in robotics. The novel methods are applied to virtual cementitious materials produced by a modern concurrent algorithm-based discrete element modeling system, HADES. This would render possible realistically simulating all aspects of particulate matter that influence structure-sensitive features of the pore network structure in maturing concrete, namely size, shape and dispersion of the aggregate and cement particles. Pore space is a complex tortuous entity. Practical methods conventionally applied for assessment of pore size distribution may fail or present biased information. Among them, mercury intrusion porosimetry and 2D quantitative image analysis are popular. The mathematical morphology operator “opening” can be applied to sections and even provide 3D information on pore size distribution, provided isotropy is guaranteed. However, aggregate grain surfaces lead to anisotropy in porosity. The presented methods allow exploration of pore space in the virtual material, after which pore size distribution is derived from star volume measurements. In addition to size of pores their continuity is of crucial importance for durability estimation. Double-random multiple tree structuring (DRaMuTS), introduced earlier in IA&S (Stroeven et al., 2011b) and random node structuring (RaNoS) provide such information.
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Book chapters on the topic "Node importance estimation"

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Ntalaperas, Dimitris, Iosif Angelidis, Giorgos Vafeiadis, and Danai Vergeti. "A Decision-Support System for the Digitization of Circular Supply Chains." In New Business Models for the Reuse of Secondary Resources from WEEEs, 97–107. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-74886-9_8.

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AbstractAs it has been already explained, it is very important for circular economies to minimize the wasted resources, as well as maximize the utilization value of the existing ones. To that end, experts can evaluate the materials and give an accurate estimation for both aspects. In that case, one might wonder, why is a decision support system employing machine learning necessary? While a fully automated machine learning model rarely surpasses a human’s ability in such tasks, there are several advantages in employing one. For starters, human experts will be more expensive to employ, rather than use an algorithm. One could claim that research towards developing an efficient and fully automated decision support system would end up costing more than employing actual human experts. In this instance, it is paramount to think long-term. Investing in this kind of research will create systems which are reusable, extensible, and scalable. This aspect alone more than remedies the initial costs. It is also important to observe that, if the number of wastes to be processed is more than the human experts can process in a timely fashion, they will not be able to provide their services, even if employment costs were not a concern. On the contrary, a machine learning model is perfectly capable of scaling to humongous amounts of data, conducting fast data processing and decision making. For power plants with particularly fast processing needs, an automated decision support system is an important asset. Moreover, a decision support system can predict the future based on past observations. While not always entirely spot on, it can give a future estimation about aspects such as energy required, amounts of wastes produced etc. in the future. Therefore, processing plants can plan of time and adapt to specific needs. A human expert can provide this as well to some degree, but on a much smaller scale. Especially in time series forecasting, it is interesting to note that, even if a decision support model does not predict exact values, it is highly likely to predict trends of the value increasing or decreasing in certain ranges. In the next sections, we are going to describe the four machine learning models that were developed and which compose the Decision Support System of FENIX. Section 8.1 describes how we predict the quality of the extracted materials based on features such as temperature, extruder speed, etc. Section 8.2 describes the process of extracting heuristic rules based on existing results. Section 8.3 describes how FENIX provides time-series forecasting to predict the future of a variable based on past observations. Finally, Sect. 8.4 describes the process of classifying materials based on images.
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Khan, Shabnam, and Prabhjot Singh. "A Novel Hybrid GWO-FA to Locate Unknown Node in 2D Environment." In Advances in Transdisciplinary Engineering. IOS Press, 2023. http://dx.doi.org/10.3233/atde221250.

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Grey wolf optimizer (GWO) is a meta-heuristic algorithm adopted by grey wolf leadership theory and hunting swarm intelligence algorithm. Besides that, three basic hunting trials are used seeking prey encircling pray and attacking prey as a result, that GWO has attracted a large research involvement from a variety of areas in a less period. The existing GWO devotes half of its iteration to exploration and another half to exploitation ignoring the importance of finding the accurate balance among the two to ensure correct estimation of the optimum solution. To address this problem a 2D based optimization of gray wolf using the Firefly algorithm (GWO-FA) is introduced to overcome this problem and obtain to exact location. The GWO-FA algorithm and other meta-heuristics are used in this study to examine the 2D locations in an anisotropy atmosphere utilizing a different approach of placing the virtual anchors with parasol projection across the moving targeted nodes. In compared to certain known algorithms, simulations results show that this approach can deliver successful outcomes. The finding of the node localization challenge shows that the suggested approach is efficient in tracking actual situation involving uncertain solution space.
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McGuire, Michael L., and Konstantinos N. Plataniotis. "Accuracy Bounds for Wireless Localization Methods." In Localization Algorithms and Strategies for Wireless Sensor Networks, 380–405. IGI Global, 2009. http://dx.doi.org/10.4018/978-1-60566-396-8.ch015.

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Node localization is an important issue for wireless sensor networks to provide context for collected sensory data. Sensor network designers need to determine if the desired level of localization accuracy is achievable from their network configuration and available measurements. The Cramér-Rao lower bound is used extensively for this purpose. This bound is loose since it uses only information from measurements in its calculations. Information, such as that from the sensor selection process, is not considered. In addition, non-line-of-sight radio propagation causes the regularity conditions of the Cramér-Rao lower bound to be violated. This chapter demonstrates the Weinstein-Weiss and extended Ziv-Zakai lower bounds for localization error which remain valid with non-line-of-sight propagation. These bounds also use all available information for bound calculations. It is demonstrated that these bounds are tight to actual estimator performance and may be used determine the available accuracy of location estimation from survey data collected in the network area.
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Monroy, Javier, and Javier Gonzalez-Jimenez. "Towards Odor-Sensitive Mobile Robots." In Electronic Nose Technologies and Advances in Machine Olfaction, 244–63. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-3862-2.ch012.

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Out of all the components of a mobile robot, its sensorial system is undoubtedly among the most critical ones when operating in real environments. Until now, these sensorial systems mostly relied on range sensors (laser scanner, sonar, active triangulation) and cameras. While electronic noses have barely been employed, they can provide a complementary sensory information, vital for some applications, as with humans. This chapter analyzes the motivation of providing a robot with gas-sensing capabilities and also reviews some of the hurdles that are preventing smell from achieving the importance of other sensing modalities in robotics. The achievements made so far are reviewed to illustrate the current status on the three main fields within robotics olfaction: the classification of volatile substances, the spatial estimation of the gas dispersion from sparse measurements, and the localization of the gas source within a known environment.
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Zhang, Jiaming, Baotian Dong, and Pengcheng Li. "Vehicle Track Reconstruction and Section Speed Evaluation Based on Low-Frequency Data." In Advances in Transdisciplinary Engineering. IOS Press, 2022. http://dx.doi.org/10.3233/atde220059.

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Considering the cost considerations and data maintenance ability in the traffic field, traffic managers only collect only the traffic data of a few important road network nodes and road network sections, and the relevant data of the missing secondary sections will affect the fine research of the road network. So this paper proposes a method based on multiple source data, which firstly performs the vehicle trajectory reconstruction and location positioning with the idea of minimum cost and maximum flow, and then calculates the average speed based on this, and all the results are tested by actual data. The experimental results show that the trajectory reconstruction algorithm is about 90% efficient, and the road average speed estimation result meets the actual situation.
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Zhang, Guoying, Alan J. Dubinsky, and Yong Tan. "Impact of Blogs on Sales Revenue." In Studies in Virtual Communities, Blogs, and Modern Social Networking, 106–20. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-4022-1.ch008.

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In this study, blog data were collected and network parameters were captured to represent three common measurements of online Word-Of-Mouth: intensity, influence level, and dispersion. These parameters were then analyzed using a General Estimating Equation (GEE) model to test their effects on average weekly movie box office receipts. Findings indicated that all three parameters were significant in the model. The aggregated degree, representing WOM intensity, was positively significant, which was consistent with results from extant research. Further, diameter of a network, representing WOM dispersion, was observed to be positively significant, which validated the importance of spreading WOM as far as possible. Counter-intuitively, the aggregated size node, representing WOM influence level, was ascertained to be negatively significant, which might be explained by the possible negative stance from opinion leaders with high influence level. Applying network analysis methodology to blog entries, the present work differentiated itself from extant WOM literature that has focused chiefly on content analysis. The findings also provided managerial insights to companies interested in utilizing blogs as online WOM for marketing initiatives and implications for future research.
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Kamalanathan, Selvakumar, Sai Ramesh Lakshmanan, and Kannan Arputharaj. "Fuzzy-Clustering-Based Intelligent and Secured Energy-Aware Routing." In Handbook of Research on Fuzzy and Rough Set Theory in Organizational Decision Making, 24–37. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-1008-6.ch002.

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In many applications such as disaster management, temperature control, weather forecasting, industrial control system and forest fire detection, it is very difficult for a human to monitor and control each and every event in real time. Even with advancement in technology, this issue has remained a challenging task. The existing Wireless Network may not be suitable for data communication with human network. Hence, to monitor and control the physical parameters of the environment, a special device with needed functionalities is required. The network which is formed with these devices is known as sensor network. This is used to monitor, control and send the collected information to the end user. These networks are formed with a large number of sensor nodes with limitation such as self-energized, low computation power, infra-structure less, multi-hop communication and without central administrator control. Due to the ad hoc nature, the nodes are deployed unevenly over a geographical region, it is necessary to provide some mechanism to manage and control the topology of the sensor nodes to prolong their life time. Clustering algorithms are useful for data mining, compression, probability density estimation and many other important tasks like IDS. Clustering algorithm utilize a distance metric in order to partition network traffic patterns so that patterns within a single group have same network characteristics than in a different group. The proposed system builds a Fuzzy logic clustering model that can perform three different types of clusters in order to achieve the secure and energy aware routing of packets.
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Payne, Geoff. "The pessimism of earlier academic mobility analysis." In The New Social Mobility. Policy Press, 2017. http://dx.doi.org/10.1332/policypress/9781447310662.003.0007.

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A review of three key mobility studies demonstrates that all the blame for under-estimating mobility does not lie with the ‘Westminster Bubble’. The highly influential LSE study in 1954 produced figures now recognised to be implausible, due to reasons revealed here for the first time. The Nuffield Mobility Study in the 1970s had a ‘Marxisant character’ strongly favouring greater openness, and used analytical techniques which inadvertently gave an impression of less mobility and change, than there was. Despite its huge impact since 2005, the work by LSE economists on income mobility has severe technical flaws. In none of these most important studies, representing the old approach to mobility, was there adequate discussion of gender, ethnicity, geography, or the significance of labour market dynamics.
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Lin, Kai, Min Chen, Joel J. P. C. Rodrigues, and Hongwei Ge. "System Design and Data Fusion in Body Sensor Networks." In Advances in Healthcare Information Systems and Administration, 1–25. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-4666-0888-7.ch001.

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Body Sensor Networks (BSNs) are formed by the equipped or transplanted sensors in the human body, which can sense the physiology and environment parameters. As a novel e-health technology, BSNs promote the deployment of innovative healthcare monitoring applications. In the past few years, most of the related research works have focused on sensor design, signal processing, and communication protocol. This chapter addresses the problem of system design and data fusion technology over a bandwidth and energy constrained body sensor network. Compared with the traditional sensor network, miniaturization and low-power are more important to meet the requirements to facilitate wearing and long-running operation. As there are strong correlations between sensory data collected from different sensors, data fusion is employed to reduce the redundant data and the load in body sensor networks. To accomplish the complex task, more than one kind of node must be equipped or transplanted to monitor multi-targets, which makes the fusion process become sophisticated. In this chapter, a new BSNs system is designed to complete online diagnosis function. Based on the principle of data fusion in BSNs, we measure and investigate its performance in the efficiency of saving energy. Furthermore, the authors discuss the detection and rectification of errors in sensory data. Then a data evaluation method based on Bayesian estimation is proposed. Finally, the authors verify the performance of the designed system and the validity of the proposed data evaluation method. The chapter is concluded by identifying some open research issues on this topic.
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Salhi, Afef, Fahmi Ghozzi, and Ahmed Fakhfakh. "Approximation Algorithm for Scheduling a Chain of Tasks for Motion Estimation on Heterogeneous Systems MPSoC." In Engineering Problems - Uncertainties, Constraints and Optimization Techniques [Working Title]. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.97676.

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Co-design embedded system are very important step in digital vehicle and airplane. The multicore and multiprocessor SoC (MPSoC) started a new computing era. It is becoming increasingly used because it can provide designers much more opportunities to meet specific performances. Designing embedded systems includes two main phases: (i) HW/SW Partitioning performed from high-level (eclipse C/C++ or python (machine learning and deep learning)) functional and architecture models (with virtual prototype and real prototype). And (ii) Software Design performed with significantly more detailed models with scheduling and partitioning tasks algorithm DAG Directed Acyclic Graph and GGEN Generation Graph Estimation Nodes (there are automatic DAG algorithm). Partitioning decisions are made according to performance assumptions that should be validated on the more refined software models for ME block and GGEN algorithm. In this paper, we focus to optimize a execution time and amelioration for quality of video with a scheduling and partitioning tasks in video codec. We show how they can be modeled the video sequence test with the size of video in height and width (three models of scheduling tasks in four processor). This modeling with DAG and GGEN are partitioning at different platform in OVP (partitioning, SW design). We can know the optimization of consumption energy and execution time in SoC and MPSoC platform.
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Conference papers on the topic "Node importance estimation"

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Huang, Han, Leilei Sun, Bowen Du, Chuanren Liu, Weifeng Lv, and Hui Xiong. "Representation Learning on Knowledge Graphs for Node Importance Estimation." In KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3447548.3467342.

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Murayama, Toru. "Distributed Estimation of Articulation Node Importance for Robustness of Multi-Robot Systems." In 2018 57th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE). IEEE, 2018. http://dx.doi.org/10.23919/sice.2018.8492689.

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Rai, Ajit, Rene C. Valenzuela, Bruno Tuffin, Gerardo Rubino, and Pierre Dersin. "Approximate Zero-Variance Importance Sampling for static network reliability estimation with node failures and application to rail systems." In 2016 Winter Simulation Conference (WSC). IEEE, 2016. http://dx.doi.org/10.1109/wsc.2016.7822352.

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Huang, Chenji, Yixiang Fang, Xuemin Lin, Xin Cao, Wenjie Zhang, and Maria Orlowska. "Estimating Node Importance Values in Heterogeneous Information Networks." In 2022 IEEE 38th International Conference on Data Engineering (ICDE). IEEE, 2022. http://dx.doi.org/10.1109/icde53745.2022.00068.

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Tariq, Hasan, and Farid Touati. "Environmentally-Powered WSN for Urban-Scale Mapping and Assessment of Air Quality in Qatar." In Qatar University Annual Research Forum & Exhibition. Qatar University Press, 2020. http://dx.doi.org/10.29117/quarfe.2020.0056.

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Environmental monitoring has gained significant importance in outdoor air quality measurement and assessment for fundamental survival as well as ambient assisted living. In real-time outdoor urban scale, instantaneous air quality index estimation, the electrochemical sensors warm-up time, cross-sensitivity computation-error, geo-location typography, instantaneous capacity or back up time; and energy efficiency are the six major challenges. These challenges lead to real-time gradient anomalies that effect the accuracy and pro-longed lags in air quality index mapping campaigns for state and environmental/meteorological agencies. In this work, a gradient-aware, multi-variable air quality-sensing node is proposed with event-triggered sensing based on position, gas magnitudes, and cross-sensitivity interpolation. In this approach, temperature, humidity, pressure, geo-position, photovoltaic power, volatile organic compounds, particulate matter (2.5), ozone, Carbon mono-oxide, Nitrogen dioxide, and Sulphur dioxide are the principle variables. Results have shown that the proposed system optimized the real-time air quality monitoring for the chosen geo-spatial cluster (Qatar University).
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Park, Namyong, Andrey Kan, Xin Luna Dong, Tong Zhao, and Christos Faloutsos. "Estimating Node Importance in Knowledge Graphs Using Graph Neural Networks." In KDD '19: The 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3292500.3330855.

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Lagerkvist, Johan, Peter Simonsson, Mats Karlsson, Rasmus Rempling, Petra Bosch-Sijtsema, and Ola Lædre‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌. "Climate impact estimation – from feasibility study to handover." In IABSE Congress, Ghent 2021: Structural Engineering for Future Societal Needs. Zurich, Switzerland: International Association for Bridge and Structural Engineering (IABSE), 2021. http://dx.doi.org/10.2749/ghent.2021.0622.

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<p>Responsible for 1/5 of the total CO2-equivalents emissions and 50% of the materials resources used globally, the construction industry plays a vital role for a sustainable future. All parties in the construction industry address the challenge from their perspective and national transport administrations are often considered as the driver toward a fossil free industry. In this study, three Swedish infrastructure projects are studied by means of interviews, focusing on the usage and acceptance of the recently implemented climate estimation process. From the interviews, it is found that currently there is a lack of knowledge regarding climate estimations among bridge and environmental specialists as well as project managers. To address this challenge, it is important to educate the industry and increase the knowledge about climate estimations and declarations as well as inform which actions have the largest impact to reduce the CO2-equivalents emissions.</p><p><br clear="none"/></p>
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Lagerkvist, Johan, Peter Simonsson, Mats Karlsson, Rasmus Rempling, Petra Bosch-Sijtsema, and Ola Lædre‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌. "Climate impact estimation – from feasibility study to handover." In IABSE Congress, Ghent 2021: Structural Engineering for Future Societal Needs. Zurich, Switzerland: International Association for Bridge and Structural Engineering (IABSE), 2021. http://dx.doi.org/10.2749/ghent.2021.0622.

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<p>Responsible for 1/5 of the total CO2-equivalents emissions and 50% of the materials resources used globally, the construction industry plays a vital role for a sustainable future. All parties in the construction industry address the challenge from their perspective and national transport administrations are often considered as the driver toward a fossil free industry. In this study, three Swedish infrastructure projects are studied by means of interviews, focusing on the usage and acceptance of the recently implemented climate estimation process. From the interviews, it is found that currently there is a lack of knowledge regarding climate estimations among bridge and environmental specialists as well as project managers. To address this challenge, it is important to educate the industry and increase the knowledge about climate estimations and declarations as well as inform which actions have the largest impact to reduce the CO2-equivalents emissions.</p><p><br clear="none"/></p>
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Tran, Dzung, Tansel Yucelen, and Sarangapani Jagannathan. "A New Result on Distributed Input and State Estimation for Heterogeneous Sensor Networks." In ASME 2017 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/dscc2017-5061.

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Abstract:
An important research area in sensor networks is the design and analysis of distributed estimation algorithms for dynamic information fusion in the presence of heterogeneity resulting from (i) nonidentical information roles of nodes and (ii) nonidentical modalities of nodes. In particular, (i) implies that both active (i.e., subject to observations of a process of interest) and passive (i.e., subject to no observations) nodes can be present in the sensor network. Furthermore, (ii) implies that active nodes can observe different measurements from a process (e.g., a subset of active nodes can observe position measurements and the rest can observe velocity measurements for a target tracking problem). In this paper, we focus on heterogeneous sensor networks, sensor networks with (i) and (ii), and present a new distributed input and state estimation approach. In addition to the presented theoretical contribution including the stability and performance of the proposed estimation approach, an illustrative numerical example is also given to demonstrate its efficacy.
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10

Wang, Ruohui, and Dahua Lin. "Scalable Estimation of Dirichlet Process Mixture Models on Distributed Data." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/646.

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We consider the estimation of Dirichlet Process Mixture Models (DPMMs) in distributed environments, where data are distributed across multiple computing nodes. A key advantage of Bayesian nonparametric models such as DPMMs is that they allow new components to be introduced on the fly as needed. This, however, posts an important challenge to distributed estimation -- how to handle new components efficiently and consistently. To tackle this problem, we propose a new estimation method, which allows new components to be created locally in individual computing nodes. Components corresponding to the same cluster will be identified and merged via a probabilistic consolidation scheme. In this way, we can maintain the consistency of estimation with very low communication cost. Experiments on large real-world data sets show that the proposed method can achieve high scalability in distributed and asynchronous environments without compromising the mixing performance.
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