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DURRESI, ARJAN, VAMSI PARUCHURI, and RAJ JAIN. "GEOMETRIC BROADCAST PROTOCOL FOR HETEROGENEOUS SENSOR NETWORKS." Journal of Interconnection Networks 06, no. 03 (September 2005): 193–207. http://dx.doi.org/10.1142/s0219265905001381.
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We present Geometric Broadcast for Heterogeneous Sensor Networks (GBS), a novel broadcasting protocol for heterogeneous wireless sensor and actor networks. While broadcasting is a very energy expensive protocol, it is also widely used as a building block for a variety of other network layer protocols. Therefore, reducing the energy consumption by optimizing broadcasting is a major improvement in heterogenous sensor networking. GBS is a distributed algorithm where nodes make local decisions on whether to transmit based on a geometric approach. GBS does not need any neighborhood information and imposes very low communication overhead. GBS is scalable to the change in network size, node type, node density and topology. Furthermore it accommodates seamlessly such network changes, including the presence of actors in heterogeneous sensor networks. Indeed, GBS takes advantage of actor nodes, and uses their resources when possible, thus reducing the energy consumption by sensor nodes. Through simulation evaluations, we show that GBS is very scalable and its performance is improved by the presence of actors. At the best of our knowledge, GBS is the first broadcast protocol designed specifically for heterogeneous sensor and actor networks.
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Geerts, Robbe, Frédéric Vandermoere, and Stijn Oosterlynck. "The Functionality of Dissimilarity: Pro-Environmental Behavior through Heterogenous Networks." Social Sciences 9, no. 12 (December 1, 2020): 221. http://dx.doi.org/10.3390/socsci9120221.
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This study explores whether social interaction with dissimilar others can lead to pro-environmental behavior. Dissimilar others are people who differ from the person in question (e.g., in terms of lifestyle or culture). While most research focuses on homogenous social networks (e.g., spatial communities), we explore the potential of network heterophily. Specifically, using data (n = 1370) from the Flemish Survey on Sociocultural Shifts, we examine the relationship between network heterophily and pro-environmental behavior (i.e., shopping decisions and curtailment behavior). Building on Granovetter’s study on ‘the strength of weak ties’, we emphasize the importance of social ties that provide novel information and social expectations. Through interaction with dissimilar others, people may create a heterogeneous network in which a diversity of information and social expectations with regard to pro-environmental behavior circulates. We expect that network heterophily may foster pro-environmental behavior. Our findings indicate that pro-environmental behavior may indeed be positively related to interaction with dissimilar others, partly because people with many dissimilar ties know more about environmental problems and are more concerned about them. This study therefore shows that network heterophily promotes pro-environmental behavior. The paper concludes with a discussion of the functionality of dissimilarity and some avenues for future research.
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A. P, Kavya, and D. J. Ravi. "A Distributed Clustering Based Energy Management Scheme for Heterogenous Wireless Sensor Network." Journal of Communication Engineering and Its Innovations 9, no. 1 (January 23, 2023): 1–13. http://dx.doi.org/10.46610/jocei.2023.v09i01.001.
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The Internet of Things (IoT) is an emerging paradigm that offers a wide array of benefits for real-world applications. An IoT system is supported by a network of heterogeneous sensor nodes that collect information about the environment over time. The non-rechargeable batteries and energy constraints limit the network's lifespan. For this reason, controlling energy dissipation is one of the most important considerations when designing communication protocols for sensor networks. The clustering scheme is one of the most efficient ways to help networks be more energy-efficient. However, the majorities of existing protocols do not handle energy distribution among heterogeneous sensor nodes well and are inappropriate. The purpose of this paper is to emphasize cluster head selection criteria and to propose an energy management clustering mechanism that takes heterogeneity into account. A dense deployment of heterogeneous multi-level nodes is considered in the proposed scheme to improve the nodes' stability and power efficiency. The threshold probability for selecting optimal CH nodes is determined by an energy-balanced weighted parameter. The proposed scheme is implemented on a numerical computing tool MatLab. In terms of alive nodes, dead nodes, packets sent to the base station, and processing time, the simulation provides efficient results. When compared to similar existing approaches, the proposed scheme was more successful in terms of network lifetime, stability, delay, and packet delivery.
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Alawi, Mahmoud, Raed Alsaqour, Maha Abdelhaq, Reem Alkanhel, Baraa Sharef, Elankovan Sundararajan, and Mahamod Ismail. "Adaptive QoS-Aware Multi-Metrics Gateway Selection Scheme for Heterogenous Vehicular Network." Systems 10, no. 5 (September 7, 2022): 142. http://dx.doi.org/10.3390/systems10050142.
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A heterogeneous vehicular network (HetVNET) is a promising network architecture that combines multiple network technologies such as IEEE 802.11p, dedicated short-range communication (DSRC), and third/fourth generation cellular networks (3G/4G). In this network area, vehicle users can use wireless fidelity access points (Wi-Fi APs) to offload 4G long-term evolution (4G-LTE) networks. However, when using Wi-Fi APs, the vehicles must organize themselves and select an appropriate mobile gateway (MGW) to communicate to the cellular infrastructure. Researchers are facing the problem of selecting the best MGW vehicle to aggregate vehicle traffic and reduce LTE load in HetVNETs when the Wi-Fi APs are unavailable for offloading. The selection process utilizes extra network overhead and complexity due to the frequent formation of clusters in this highly dynamic environment. In this study, we proposed a non-cluster adaptive QoS-aware gateway selection (AQAGS) scheme that autonomously picks a limited number of vehicles to act as LTE gateways based on the LTE network’s load status and vehicular ad hoc network (VANET) application’s QoS requirements. The present AQAGS scheme focuses on highway scenarios. The proposed scheme was evaluated using simulation of Urban mobility (SUMO) and network simulator version 2 (NS2) simulators and benchmarked with the clustered and non-clustered schemes. A comparison was made based on the end-to-end delay, throughput, control packet overhead (CPO), and packet delivery ratio (PDR) performance metrics over Voice over Internet Protocol (VoIP) and File Transfer Protocol (FTP) applications. Using VoIP, the AQAGS scheme achieved a 26.7% higher PDR compared with the other schemes.
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Jankovics, Vince, Michael Garcia Ortiz, and Eduardo Alonso. "HetSAGE: Heterogenous Graph Neural Network for Relational Learning (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 18 (May 18, 2021): 15803–4. http://dx.doi.org/10.1609/aaai.v35i18.17898.
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This paper aims to bridge this gap between neuro-symbolic learning (NSL) and graph neural networks (GNN) approaches and provide a comparative study. We argue that the natural evolution of NSL leads to GNNs, while the logic programming foundations of NSL can bring powerful tools to improve the way information is represented and pre-processed for the GNN. In order to make this comparison, we propose HetSAGE, a GNN architecture that can efficiently deal with the resulting heterogeneous graphs that represent typical NSL learning problems. We show that our approach outperforms the state-of-the-art on 3 NSL tasks: CORA, MUTA188 and MovieLens.
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Chen, Xiaonan, and Suxia Zhang. "An SEIR model for information propagation with a hot search effect in complex networks." Mathematical Biosciences and Engineering 20, no. 1 (2022): 1251–73. http://dx.doi.org/10.3934/mbe.2023057.
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<abstract><p>We formulate an SEIR model for information propagation with the effect of a hot search in complex networks. Mathematical analysis is conducted in both a homogeneous network and heterogenous network. The results reveal that the dynamics are completely determined by the basic propagation number if the effect of a hot search is absent. On the other hand, when the effect of a hot search is taken into account, there exists no information-free equilibrium, and the information-propagating equilibrium is stable if the threshold is greater than 1. Numerical simulations were performed to examine the sensitivity of the parameters to the basic propagation number and the propagable nodes. Furthermore, the proposed model has been applied to fit the collected data for two types of information spreading in Sina Weibo, which confirmed the validity of our model and simulated the dynamical behaviors of information propagation.</p></abstract>
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Singh, Harmeet, Manju Bala, and Sukhvinder Singh Bamber. "Augmenting network lifetime for heterogenous WSN assisted IoT using mobile agent." Wireless Networks 26, no. 8 (July 10, 2020): 5965–79. http://dx.doi.org/10.1007/s11276-020-02422-z.
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Shao, Ruizhe, Chun Du, Hao Chen, and Jun Li. "SUNet: Change Detection for Heterogeneous Remote Sensing Images from Satellite and UAV Using a Dual-Channel Fully Convolution Network." Remote Sensing 13, no. 18 (September 18, 2021): 3750. http://dx.doi.org/10.3390/rs13183750.
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Change Detection in heterogeneous remote sensing images plays an increasingly essential role in many real-world applications, e.g., urban growth tracking, land use monitoring, disaster evaluation and damage assessment. The objective of change detection is to identify changes of geo-graphical entities or phenomena through two or more bitemporal images. Researchers have invested a lot in the homologous change detection and yielded fruitful results. However, change detection between heterogenous remote sensing images is still a great challenge, especially for change detection of heterogenous remote sensing images obtained from satellites and Unmanned Aerial Vehicles (UAV). The main challenges in satellite-UAV change detection tasks lie in the intensive difference of color for the same ground objects, various resolutions, the parallax effect and image distortion caused by different shooting angles and platform altitudes. To address these issues, we propose a novel method based on dual-channel fully convolution network. First, in order to alleviate the influence of differences between heterogeneous images, we employ two different channels to map heterogeneous remote sensing images from satellite and UAV, respectively, to a mutual high dimension latent space for the downstream change detection task. Second, we adopt Hough method to extract the edge of ground objects as auxiliary information to help the change detection model to pay more attention to shapes and contours, instead of colors. Then, IoU-WCE loss is designed to deal with the problem of imbalanced samples in change detection task. Finally, we conduct extensive experiments to verify the proposed method using a new Satellite-UAV heterogeneous image data set, named HTCD, which is annotated by us and has been open to public. The experimental results show that our method significantly outperforms the state-of-the-art change detection methods.
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Avirmed, Altankhuyag, Uranchimeg Erdenedalai, Selenge Erdenechimeg, Yansen Su, and Tseren-Onolt Ishdorj. "Biomolecular Network-based Study of a Parasitic Disease and Therapeutic Drugs." ICT Focus 1, no. 1 (September 29, 2022): 22–34. http://dx.doi.org/10.58873/sict.v1i1.31.
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Computational drug repurposing methods, particularly biomolecular network-based disease-drug-target interaction models, are essential tools for integrating large-scale heterogenous molecular information and revealing functional mechanisms, as well as for main regulatory modules of interactants which can be useful in developing new drugs. In the present study, a drug-centric network for a parasitic disease (Echinococcosis) and therapeutic drugs have been considered. A complex network with more than 12,000 vertices and more than 33,000 edges representing interactions of 84 echinococcosis-related genes with associated proteins was built and analyzed. The networks of disease similarity and drug similarity were constructed based on the complex network. As a result, three drugs (D08356, D00701, and D00506) associated with three candidate diseases through three pathways and a protein complex have been extracted. This effort tries to predict the anti-echinococcosis effects of the drugs’ combinations with benzimidazole.
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Du, Dehui, Tong Guo, and Yao Wang. "DSML4CS." International Journal of Web Services Research 17, no. 2 (April 2020): 59–75. http://dx.doi.org/10.4018/ijwsr.2020040104.
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Cyber physical systems (CPS's) are a kind of complex system with highly integrated interaction between computing resources and physical environment in a network environment. There are some challenges in modeling and simulation of heterogeneous CPS due to its hybrid and heterogenous characteristics. To address the issue, we propose an executable domain specific modeling language for co-simulation (DSML4CS) to model the co-simulation of CPS. According to the construction method of domain modeling language, we present the abstract syntax, concrete syntax and operational semantics of DSML4CS. We also propose a flexible co-simulation mechanism, which supports partial step revision of specific co-simulation process with the state event fault-tolerant mechanism. The co-simulation language for heterogeneous CPS is implemented based on the GEMOC platform. Our aim is to provide the co-simulation service in CPS. The usability of DSML4CS is illustrated with a case study of a temperature control system in an energy-aware building.
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Cwalina, Krzysztof K., Piotr Rajchowski, Alicja Olejniczak, Olga Błaszkiewicz, and Robert Burczyk. "Channel State Estimation in LTE-Based Heterogenous Networks Using Deep Learning." Sensors 21, no. 22 (November 19, 2021): 7716. http://dx.doi.org/10.3390/s21227716.
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Following the continuous development of the information technology, the concept of dense urban networks has evolved as well. The powerful tools, like machine learning, break new ground in smart network and interface design. In this paper the concept of using deep learning for estimating the radio channel parameters of the LTE (Long Term Evolution) radio interface is presented. It was proved that the deep learning approach provides a significant gain (almost 40%) with 10.7% compared to the linear model with the lowest RMSE (Root Mean Squared Error) 17.01%. The solution can be adopted as a part of the data allocation algorithm implemented in the telemetry devices equipped with the 4G radio interface, or, after the adjustment, the NB-IoT (Narrowband Internet of Things), to maximize the reliability of the services in harsh indoor or urban environments. Presented results also prove the existence of the inverse proportional dependence between the number of hidden layers and the number of historical samples in terms of the obtained RMSE. The increase of the historical data memory allows using models with fewer hidden layers while maintaining a comparable RMSE value for each scenario, which reduces the total computational cost.
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Yang, Yihao, Howard Gritton, Martin Sarter, Sara J. Aton, Victoria Booth, and Michal Zochowski. "Theta-gamma coupling emerges from spatially heterogeneous cholinergic neuromodulation." PLOS Computational Biology 17, no. 7 (July 30, 2021): e1009235. http://dx.doi.org/10.1371/journal.pcbi.1009235.
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Theta and gamma rhythms and their cross-frequency coupling play critical roles in perception, attention, learning, and memory. Available data suggest that forebrain acetylcholine (ACh) signaling promotes theta-gamma coupling, although the mechanism has not been identified. Recent evidence suggests that cholinergic signaling is both temporally and spatially constrained, in contrast to the traditional notion of slow, spatially homogeneous, and diffuse neuromodulation. Here, we find that spatially constrained cholinergic stimulation can generate theta-modulated gamma rhythms. Using biophysically-based excitatory-inhibitory (E-I) neural network models, we simulate the effects of ACh on neural excitability by varying the conductance of a muscarinic receptor-regulated K+ current. In E-I networks with local excitatory connectivity and global inhibitory connectivity, we demonstrate that theta-gamma-coupled firing patterns emerge in ACh modulated network regions. Stable gamma-modulated firing arises within regions with high ACh signaling, while theta or mixed theta-gamma activity occurs at the peripheries of these regions. High gamma activity also alternates between different high-ACh regions, at theta frequency. Our results are the first to indicate a causal role for spatially heterogenous ACh signaling in the emergence of localized theta-gamma rhythmicity. Our findings also provide novel insights into mechanisms by which ACh signaling supports the brain region-specific attentional processing of sensory information.
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Wang, Yiding, Zhenyi Wang, Chenghao Li, Yilin Zhang, and Haizhou Wang. "Online social network individual depression detection using a multitask heterogenous modality fusion approach." Information Sciences 609 (September 2022): 727–49. http://dx.doi.org/10.1016/j.ins.2022.07.109.
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Chen, Lin, and Wan-Yu Deng. "Instance-Wise Denoising Autoencoder for High Dimensional Data." Mathematical Problems in Engineering 2016 (2016): 1–13. http://dx.doi.org/10.1155/2016/4365372.
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Denoising Autoencoder (DAE) is one of the most popular fashions that has reported significant success in recent neural network research. To be specific, DAE randomly corrupts some features of the data to zero as to utilize the cooccurrence information while avoiding overfitting. However, existing DAE approaches do not fare well on sparse and high dimensional data. In this paper, we present a Denoising Autoencoder labeled here as Instance-Wise Denoising Autoencoder (IDA), which is designed to work with high dimensional and sparse data by utilizing the instance-wise cooccurrence relation instead of the feature-wise one. IDA works ahead based on the following corruption rule: if an instance vector of nonzero feature is selected, it is forced to become a zero vector. To avoid serious information loss in the event that too many instances are discarded, an ensemble of multiple independent autoencoders built on different corrupted versions of the data is considered. Extensive experimental results on high dimensional and sparse text data show the superiority of IDA in efficiency and effectiveness. IDA is also experimented on the heterogenous transfer learning setting and cross-modal retrieval to study its generality on heterogeneous feature representation.
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Zhou, Lu, Si-Xin Chen, Yi-Qing Ni, and Liu Jiang. "Pitch-catch UGW-based multiple damage inference: a heterogeneous graph interpretation." Smart Materials and Structures 31, no. 1 (November 19, 2021): 015005. http://dx.doi.org/10.1088/1361-665x/ac36b0.
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Abstract Ultrasonic guided waves (UGWs) have been extensively utilized in nondestructive testing and structural health monitoring (SHM) for detection and real-time monitoring of structural defects. By implementing multiple piezoelectric sensors onto a plane of the target structure to form a sensor network, damages within the sensing range can be detected or even visualized through a pitch-catch configuration. On the other hand, deep learning (DL) techniques have recently been widely used to aid UGW-based SHM when the waveform is over complicated to extract a specific mode of interest due to irregular structure or boundary reflections. However, not too much research work has been conducted to thoroughly combine sensor networks with DL. Existing research using DL approaches is mainly used to train and interpret waveforms from isolated sensor pairs. The topological structure of sensor layout and sensor-damage relative positions are hardly considered in the data-driven process. Motivated by these concerns, this study offers a first-of-its-kind perspective to interpret UGW data collected from a sensor network by mapping the physical sensor-damage layout into a graph, in which sensors and potential damages serve as graph vertices bearing heterogenous properties upon coming to UGWs and the process of UGW transmission between sensors are encapsulated as wavelike message passing between the vertices. A novel physics-informed end-to-end graph neural network model, named as WaveNet, was exquisitely and meticulously developed. By utilizing wave information and topological structure, WaveNet enables inference of multiple damages in terms of severity and location with satisfactory accuracy, even when the waveforms are chaotic, and the sensor arrangement is different at the training and testing stages. More importantly, beyond the SHM scenario, the present study is expected to enlighten new thinking on interconnecting physical wave propagation with virtual messaging passing in neural networks.
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Chicchi, Lorenzo, Gloria Cecchini, Ihusan Adam, Giuseppe de Vito, Roberto Livi, Francesco Saverio Pavone, Ludovico Silvestri, Lapo Turrini, Francesco Vanzi, and Duccio Fanelli. "Reconstruction scheme for excitatory and inhibitory dynamics with quenched disorder: application to zebrafish imaging." Journal of Computational Neuroscience 49, no. 2 (April 7, 2021): 159–74. http://dx.doi.org/10.1007/s10827-020-00774-1.
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AbstractAn inverse procedure is developed and tested to recover functional and structural information from global signals of brains activity. The method assumes a leaky-integrate and fire model with excitatory and inhibitory neurons, coupled via a directed network. Neurons are endowed with a heterogenous current value, which sets their associated dynamical regime. By making use of a heterogenous mean-field approximation, the method seeks to reconstructing from global activity patterns the distribution of in-coming degrees, for both excitatory and inhibitory neurons, as well as the distribution of the assigned currents. The proposed inverse scheme is first validated against synthetic data. Then, time-lapse acquisitions of a zebrafish larva recorded with a two-photon light sheet microscope are used as an input to the reconstruction algorithm. A power law distribution of the in-coming connectivity of the excitatory neurons is found. Local degree distributions are also computed by segmenting the whole brain in sub-regions traced from annotated atlas.
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Fan, Xiying, Chuanhe Huang, Junyu Zhu, and Bin Fu. "Replication-Based Data Dissemination in Connected Internet of Vehicles." Wireless Communications and Mobile Computing 2019 (April 4, 2019): 1–16. http://dx.doi.org/10.1155/2019/2150524.
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Due to the dynamically changing topology of Internet of Vehicles (IoV), it is a challenging issue to achieve efficient data dissemination in IoV. This paper considers strongly connected IoV with a number of heterogenous vehicular nodes to disseminate information and studies distributed replication-based data dissemination algorithms to improve the performance of data dissemination. Accordingly, two data replication algorithms, a deterministic algorithm and a distributed randomised algorithm, are proposed. In the proposed algorithms, the number of message copies spread in the network is limited and the network will be balanced after a series of average operations among the nodes. The number of communication stages needed for network balance shows the complexity of network convergence as well as network convergence speed. It is proved that the network can achieve a balanced status after a finite number of communication stages. Meanwhile, the upper and lower bounds of the time complexity are derived when the distributed randomised algorithm is applied. Detailed mathematical results show that the network can be balanced quickly in complete graph; thus highly efficient data dissemination can be guaranteed in dense IoV. Simulation results present that the proposed randomised algorithm outperforms the present schemes in terms of transmissions and dissemination delay.
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Al-omari, Motea Saleh, Mohammad Ahmed Alomari, Abdul Rahman Ramli, Aduwati Sali, Raja Syamsul Azmir, and M. Hafiz Yusoff. "Effects of Femtocell Ultradense Deployment on Downlink Performance in LTE Heterogeneous Networks." Wireless Communications and Mobile Computing 2021 (September 9, 2021): 1–21. http://dx.doi.org/10.1155/2021/2735935.
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With huge number of smart gadgets and wireless devices being interconnected to each other, the demand for very high data bandwidth is becoming critically challenging. With such density of nodes inside wireless networks, providing high-quality service as well as wide coverage in indoor environment is a real challenge, which is due to the limited radio frequency and intense interference between nodes. As a one way to solve such problem and improve indoor service quality, femtocells have been introduced as an extension to the existing macrocell stations. Although femtocell is a promising technology, the pervasive deployment of huge number of femtocells without very tight network planning as well as coverage strategy may worsen the problem and degrade the service quality. One important problem that needs to be addressed when deploying femtocell technology in heterogenous networks (HetNets) is mitigating the various types of cross-tier and cotier interferences in between wireless cells. This study investigates the effect of unplanned ultradensity femtocell deployment in the downlink performance of two-tier heterogeneous networks in urban area based on LTE system. Instead of deploying femtocells one by one, grids of size either ( 3 × 3 ) or ( 5 × 5 ) of neighboring femtocell will be deployed inside each macrocell sector area. The simulation results show that femtocell deployment improves overall average user throughput in case of low and medium density scenarios. However, for ultradensity scenario, there is no enhancement in terms of fairness and throughput. The results confirm that this leads to high degradation for macrocell and femtocell user performance due to the severe interference between macrocells and femtocells, as well as among neighboring femtocells in each grid.
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Zhang, Qi, Huibin Yu, Xiaofeng Li, Tiesheng Liu, and Junfeng Hu. "A New Upscaling Method for Fluid Flow Simulation in Highly Heterogeneous Unconventional Reservoirs." Geofluids 2020 (August 25, 2020): 1–11. http://dx.doi.org/10.1155/2020/6213183.
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High heterogeneity and nonuniformly distributed multiscale pore systems are two characteristics of the unconventional reservoirs, which lead to very complex transport mechanisms. Limited by inadequate computational capability and imaging field of view, flow simulation cannot be directly performed on complex pore structures. The traditional methods usually coarsen the grid to reduce the computational load but will lead to the missing microstructure information and inaccurate simulation results. To develop a better understanding of flow properties in unconventional reservoirs, this study proposed a new upscaling method integrated gray lattice Boltzmann method (GLBM) and pore network model (PNM), accounting for the fluid flow in heterogeneous porous media. This method can reasonably reduce the computational loads while preserving certain micropore characteristics. Verifications are conducted by comparing the simulation and experimental results on tight sandstones, and good agreements are achieved. The proposed method is proven to be capable of estimating bulk properties in highly heterogenous unconventional reservoirs. This method could contribute to the development of multiscale pore structure characterizations and enhance the understandings of fluid flow mechanisms in unconventional reservoirs.
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Fan, Joline, Kiwamu Kudo, Kamalini Ranasinghe, Hirofumi Morise, Anne Findlay, Heidi Kirsch, Andrew Krystal, and Srikantan Nagarajan. "073 Whole-brain network analysis of neural oscillations during light sleep." Sleep 44, Supplement_2 (May 1, 2021): A30—A31. http://dx.doi.org/10.1093/sleep/zsab072.072.
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Abstract Introduction Sleep is a highly stereotyped phenomenon that is ubiquitous across species. Although behaviorally appearing as a homogeneous process, sleep has been recognized as cortically heterogenous and locally dynamic. PET/fMRI studies have provided key insights into regional activation and deactivation with sleep onset, but they lack the high temporal resolution and electrophysiology for understanding neural interactions. Using simultaneous electrocorticography (EEG) and magnetoencephalography (MEG) imaging, we systematically characterize whole-brain neural oscillations and identify frequency specific, cortically-based patterns associated with sleep onset. Methods In this study, 14 healthy subjects underwent simultaneous EEG and MEG imaging. Sleep states were determined by scalp EEG. Eight 15s artifact-free epochs, e.g. 120s sensor time series, were selected to represent each behavioral state: N1, N2 and wake. Atlas-based source reconstruction was performed using adaptive beamforming methods. Functional connectivity measures were computed using imaginary coherence and across regions of interests (ROIs, segmentation of 210 cortical regions with Brainnetome Atlas) in multiple frequency bands, including delta (1-4Hz), theta (4-8Hz), alpha (8-12Hz), sigma (12-15Hz), beta (15-30Hz), and gamma (30-50Hz). Directional phase transfer entropy (PTE) was also evaluated to determine the direction of information flow with transition to sleep. Results We show that the transition to sleep is encoded in a spatially and temporally specific dynamic pattern of whole-brain functional connectivity. With sleep onset, there is increased functional connectivity diffusely within the delta frequency, while spatially specific profiles in other frequency bands, e.g. increased fronto-temporal connectivity in the alpha frequency band and fronto-occipital connectivity in the theta band. In addition, rather than a decoupling of anterior-posterior regions with transition to sleep, there is a spectral shift to delta frequencies observed in the synchrony and information flow of neural activity. Conclusion Sleep onset is cortically heterogeneous, composed of spatially and temporally specific patterns of whole-brain functional connectivity, which may play an essential role in the transition to sleep. Support (if any) Research reported in this publication was supported by the National Center for Advancing Translational Sciences of the NIH under Award Number (5TL1TR001871-05 to JMF). Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the NIH.
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Ward, Gerard, and Lech Janczewski. "Investigating Data Risk Considerations in Emergent Cyber Physical Production Systems." Journal of Systemics, Cybernetics and Informatics 20, no. 2 (March 2022): 51–62. http://dx.doi.org/10.54808/jsci.20.02.51.
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The Industrial Internet of Things (IIoT) describes a computing model where ubiquitous networks of heterogenous devices equipped with embedded sensors and actuators support innovative data-centric business models. Emergent IIoT use cases include Cyber Physical Production Systems (CPPS) to support asset optimization through self-organization of modular machines within production systems. In CPPS, raw materials, machines, and operations are interconnected to form a tightly integrated network. To ensure manufacturing continuity as CPPS networks evolve, asset managers will need to evaluate risk across multi-disciplinary domains. The domains have different architectures, lexicons, and priorities. To contribute to the eventual codification of data risk in CPPS, this research builds on previous literature to consider how data may traverse the CPPS model. The resulting models put forward in this research are informed by a transdisciplinary panel of experts drawn from disciplines including information and operational technology to bring greater specificity to the definition of business-critical data in supporting IIoT. Based on these expert views, a conceptual hierarchical automation architecture that may characterize many future state production processes is presented.
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Gautam, Shuva, Luc LeBel, and Baburam Rijal. "Integrating Analytical Hierarchical Process and Network Optimization Model to Support Decision-Making on Biomass Terminal Selection." Forests 13, no. 11 (November 11, 2022): 1898. http://dx.doi.org/10.3390/f13111898.
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Forest biomass is an appealing bioenergy feedstock due its renewability, availability and potential to stimulate local economies. It is, however, voluminous, with heterogenous fuel characteristics and uncertainties in its supply. The feasibility of a bioenergy facility is contingent on a secure supply of uniform feedstock; a terminal in the supply chain can be useful in this regard. Biomass can be treated in the terminal to meet quality specifications and stored to overcome seasonality and supply disruptions. Nonetheless, such terminals require a significant capital investment; thus, the decision to use a terminal needs to be made judiciously. The decision process must account for a diverse set of factors that influence the terminal’s effectiveness. These include both quantitative and qualitative factors. The objective of this study is to develop a multi-criteria decision-making framework that takes quantitative and qualitative factors into consideration while selecting a terminal. The framework consists of analytical hierarchy process to analyze qualitative information, and a mixed-integer programming model to evaluate quantitative information including fuel quality (moisture content and thermal value). This hybrid framework was implemented in a case study. It proved to be an effective tool for identifying terminals with the highest potential to generate value for the bioenergy supply chain.
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Popel, A. S., A. Liu, B. Dawant, A. Koller, and P. C. Johnson. "Distribution of vascular resistance in terminal arteriolar networks of cat sartorius muscle." American Journal of Physiology-Heart and Circulatory Physiology 254, no. 6 (June 1, 1988): H1149—H1156. http://dx.doi.org/10.1152/ajpheart.1988.254.6.h1149.
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Morphometric information on the terminal arteriolar networks (n = 10) in cat sartorius muscle [Koller et al., Am. J. Physiol. 253 (Heart Circ. Physiol. 22): H154-H164, 1987] is utilized in the calculations of distribution of vascular hindrance throughout the networks. These networks have tree-type geometry, i.e., they do not contain closed loops. The results are discussed in terms of simulated flow distribution. The flow calculations are based on the exact geometry of the arteriolar networks (the control and dilated diameter and the length of each vascular segment) and on assumed values of postarteriolar resistances. Three cases of postarteriolar resistances are considered: zero, constant, and randomly distributed. With zero postarteriolar resistances, the distribution of flow in the terminal arteriolar segments would be highly heterogenous. The simulated flow in each terminal segment is determined primarily by the number of bifurcations on the pathway leading to the terminal segment, with a slight compensation for the length of the pathways. The coefficient of variation of flow in the control state, CV(Qc), would be close to the value in the dilated state, CV(Qd). When each of the terminal segments is connected to a constant postarteriolar resistance, the CV's in both states decrease. The coefficient of variation in the dilated state becomes significantly smaller than in the control state. When postarteriolar resistances are randomly distributed, both CV's increase, and their values become closer to each other. These results suggest that postarteriolar resistances may play a very important role in distribution of flow in the microvascular network. This study formulates a framework for the quantification of the effect of arteriolar dilation on flow redistribution in the network.
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Deo, Ravin N., Nikhil Singh, Kaushal Kishore, and Jayantha Kodikara. "Numerical Study on Urban Infrastructure Diagnosis in Laterally Heterogenous Soils Using Resistivity and Ground Penetrating Radar Techniques." Journal of Environmental and Engineering Geophysics 27, no. 4 (December 2022): 233–40. http://dx.doi.org/10.32389/jeeg22-022.
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Urban environment can be considered a complex system consisting of the engineered pavement physical structure over the buried utilities (water, gas, sewer) network embedded in the background soil environment. Assessment of buried pipeline civil infrastructures using proximal geophysical methods in such instances has to consider possible interferences, difficulties, and incorrect inferences. In this study, we have conducted a numerical modelling investigation to understand and evaluate how electrical resistivity profiling (ERP) and ground penetrating radar (GPR) can be utilised to provide subsurface information that otherwise may not be possible if either one of the techniques is used. A model geometry consisting of a typical pavement structure (asphalt, base/subbase, and background soil) with a single 2 m pipe buried at a depth of 1 m was used. Strong lateral variations in soil type were incorporated over the short pipe section in order to understand the complexities that can arise, especially with ERP measurements. The 3D electrical resistivity measurements were simulated in Comsol using the 4-probe method, while the 2D GPR measurements were simulated in gprMax to obtain the subsurface information. The results from both ERP and GPR were used to develop a practical framework that can be utilised by relevant authorities for proximal condition assessment of their buried assets. It was suggested that ERP can be used as a first level screening tool over the whole pipeline length, followed by discretely selected GPR scans in order to further gain information on the pipe health. This is attractive practically since, following delineations of a large pipe section into shorter subsections, advanced condition assessment approaches that are generally intrusive in nature can then be economically deployed within the subsections suspected of experiencing significant corrosion damage.
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Ojeda-Linares, César, Gonzalo D. Álvarez-Ríos, Carmen Julia Figueredo-Urbina, Luis Alfredo Islas, Patricia Lappe-Oliveras, Gary Paul Nabhan, Ignacio Torres-García, Mariana Vallejo, and Alejandro Casas. "Traditional Fermented Beverages of Mexico: A Biocultural Unseen Foodscape." Foods 10, no. 10 (October 9, 2021): 2390. http://dx.doi.org/10.3390/foods10102390.
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Mexico is one of the main regions of the world where the domestication of numerous edible plant species originated. Its cuisine is considered an Intangible Cultural Heritage of Humanity and ferments are important components but have been poorly studied. Traditional fermented foods are still diverse, but some are endangered, requiring actions to promote their preservation. Our study aimed to (1) systematize information on the diversity and cultural history of traditional Mexican fermented beverages (TMFB), (2) document their spatial distribution, and (3) identify the main research trends and topics needed for their conservation and recovery. We reviewed information and constructed a database with biocultural information about TMFB prepared and consumed in Mexico, and we analyzed the information through network approaches and mapped it. We identified 16 TMFB and 143 plant species involved in their production, species of Cactaceae, Asparagaceae, and Poaceae being the most common substrates. Microbiological research has been directed to the potential biotechnological applications of Lactobacillus, Bacillus, and Saccharomyces. We identified a major gap of research on uncommon beverages and poor attention on the cultural and technological aspects. TMFB are dynamic and heterogenous foodscapes that are valuable biocultural reservoirs. Policies should include their promotion for conservation. The main needs of research and policies are discussed.
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Sun, Yizhou, Jiawei Han, Xifeng Yan, Philip S. Yu, and Tianyi Wu. "Heterogeneous information networks." Proceedings of the VLDB Endowment 15, no. 12 (August 2022): 3807–11. http://dx.doi.org/10.14778/3554821.3554901.
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In 2011, we proposed PathSim to systematically define and compute similarity between nodes in a heterogeneous information network (HIN), where nodes and links are from different types. In the PathSim paper, we for the first time introduced HIN with general network schema and proposed the concept of meta-paths to systematically define new relation types between nodes. In this paper, we summarize the impact of PathSim paper in both academia and industry. We start from the algorithms that are based on meta-path-based feature engineering, then move on to the recent development in heterogeneous network representation learning, including both shallow network embedding and heterogeneous graph neural networks. In the end, we make the connection between knowledge graphs and HINs and discuss the implication of meta-paths in the symbolic reasoning scenario. Finally, we point out several future directions.
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Liu, Shi, Kaiyang Li, Yaoying Wang, Tianyou Zhu, Jiwei Li, and Zhenyu Chen. "Knowledge graph embedding by fusing multimodal content via cross-modal learning." Mathematical Biosciences and Engineering 20, no. 8 (2023): 14180–200. http://dx.doi.org/10.3934/mbe.2023634.
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<abstract><p>Knowledge graph embedding aims to learn representation vectors for the entities and relations. Most of the existing approaches learn the representation from the structural information in the triples, which neglects the content related to the entity and relation. Though there are some approaches proposed to exploit the related multimodal content to improve knowledge graph embedding, such as the text description and images associated with the entities, they are not effective to address the heterogeneity and cross-modal correlation constraint of different types of content and network structure. In this paper, we propose a multi-modal content fusion model (MMCF) for knowledge graph embedding. To effectively fuse the heterogenous data for knowledge graph embedding, such as text description, related images and structural information, a cross-modal correlation learning component is proposed. It first learns the intra-modal and inter-modal correlation to fuse the multimodal content of each entity, and then they are fused with the structure features by a gating network. Meanwhile, to enhance the features of relation, the features of the associated head entity and tail entity are fused to learn relation embedding. To effectively evaluate the proposed model, we compare it with other baselines in three datasets, i.e., FB-IMG, WN18RR and FB15k-237. Experiment result of link prediction demonstrates that our model outperforms the state-of-the-art in most of the metrics significantly, implying the superiority of the proposed method.</p></abstract>
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Ji, Fujiao, Zhongying Zhao, Hui Zhou, Heng Chi, and Chao Li. "A comparative study on heterogeneous information network embeddings." Journal of Intelligent & Fuzzy Systems 39, no. 3 (October 7, 2020): 3463–73. http://dx.doi.org/10.3233/jifs-191796.
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Heterogeneous information networks are widely used to represent real world applications in forms of social networks, word co-occurrence networks, and communication networks, etc. However, It is difficult for traditional machine learning methods to analyze these networks effectively. Heterogeneous information network embedding aims to convert the network into low dimensional vectors, which facilitates the following tasks. Thus it is receiving tremendous attention from the research community due to its effectiveness and efficiency. Although numerous methods have been present and applied successfully, there are few works to make a comparative study on heterogeneous information network embedding, which is very important for developers and researchers to select an appropriate method. To address the above problem, we make a comparative study on the heterogeneous information network embeddings. Specifically, we first give the problem definition of heterogeneous information network embedding. Then the heterogeneous information networks are classified into four categories from the perspective of network type. The state-of-the-art methods for each category are also compared and reviewed. Finally, we make a conclusion and suggest some potential future research directions.
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Kunchok, Tenzin, and Prof Kirubanand V. B. "A lightweight hybrid encryption technique to secure IoT data transmission." International Journal of Engineering & Technology 7, no. 2.6 (March 11, 2018): 236. http://dx.doi.org/10.14419/ijet.v7i2.6.10776.
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Internet of Things(IOT) is the rising innovation without bounds is required to associate billions of devices. IoT is the future where many low power resources and constrained devices are connected by means of the internet for communication, compute process and take actions in the communication network. The increased number of communication is relied upon to produce heaps of information and the security of information can be a threat resulting a secure solution for communication is necessitates among heterogenous devices. Focus of the work is to provide confidentiality, authentication and integrity of data in transit between IoT edge devices and back-end systems. This paper proposes a lightweight hybrid encryption system using ECDH key exchange mechanism for generating keys and establishing connection, digital signature for authentication, thereafter AES algorithm for encryption and decryption of user data file. The proposed combination is referred to as “three way secured data encryption mechanism” which interpret all the 3 protection schemes of authentication, info security and verification with the characteristics of lower calculation cost and faster speed makes it robust for hackers to crack the security system, thereby protective data in transmission.
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Liang, Zhaohui, Jimmy Xiangji Huang, and Sameer Antani. "Image Translation by Ad CycleGAN for COVID-19 X-Ray Images: A New Approach for Controllable GAN." Sensors 22, no. 24 (December 8, 2022): 9628. http://dx.doi.org/10.3390/s22249628.
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We propose a new generative model named adaptive cycle-consistent generative adversarial network, or Ad CycleGAN to perform image translation between normal and COVID-19 positive chest X-ray images. An independent pre-trained criterion is added to the conventional Cycle GAN architecture to exert adaptive control on image translation. The performance of Ad CycleGAN is compared with the Cycle GAN without the external criterion. The quality of the synthetic images is evaluated by quantitative metrics including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Peak Signal-to-Noise Ratio (PSNR), Universal Image Quality Index (UIQI), visual information fidelity (VIF), Frechet Inception Distance (FID), and translation accuracy. The experimental results indicate that the synthetic images generated either by the Cycle GAN or by the Ad CycleGAN have lower MSE and RMSE, and higher scores in PSNR, UIQI, and VIF in homogenous image translation (i.e., Y → Y) compared to the heterogenous image translation process (i.e., X → Y). The synthetic images by Ad CycleGAN through the heterogeneous image translation have significantly higher FID score compared to Cycle GAN (p < 0.01). The image translation accuracy of Ad CycleGAN is higher than that of Cycle GAN when normal images are converted to COVID-19 positive images (p < 0.01). Therefore, we conclude that the Ad CycleGAN with the independent criterion can improve the accuracy of GAN image translation. The new architecture has more control on image synthesis and can help address the common class imbalance issue in machine learning methods and artificial intelligence applications with medical images.
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Rabia, Rahla, and Sathish Kumar. "BIM and GIS integrated utility supply station location optimization and possibilities." Journal of Applied Engineering Science 20, no. 4 (2022): 1384–94. http://dx.doi.org/10.5937/jaes0-40600.
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Optimal planning of utility supply station location is an integral part of infrastructural projects. In general, this is a multi-objective optimization process by considering engineering, financial and geographical constraints. A shift from conventional 2D-CAD, manual quantification and design application-based approach to Building Information Modelling (BIM)-Geographic Information System (GIS) integrated approach is found to be suitable for minimizing optimal planning time, cost and increasing automation. In this paper, an Autodesk Revit add-in tool is proposed aimed at integrating BIM and GIS for Genetic Algorithm (GA) based utility supply station location optimization and to assess the possibilities of this integration. From the case study it is observed that up to 90% of cost saving can be accomplished by this proposed approach. It is found that compared to the traditional multi-software approach with manual data transfer, this integration can be utilized for multi-stage optimization and is suitable for automating heterogenous data integration with increased accuracy. The platform in which the add-in tool is developed for the utility network can be at either BIM or GIS and this selection is influenced by the availability and ease of data retrieval from the respective semantic information system and the level of automation that is to be accomplished. Standardised BIM-based modelling combined with concepts like artificial intelligence and image processing techniques can be promising for attaining desired results in industrial applications.
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Wu, Jibing, Zhifei Wang, Yahui Wu, Lihua Liu, Su Deng, and Hongbin Huang. "A Tensor CP Decomposition Method for Clustering Heterogeneous Information Networks via Stochastic Gradient Descent Algorithms." Scientific Programming 2017 (2017): 1–13. http://dx.doi.org/10.1155/2017/2803091.
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Clustering analysis is a basic and essential method for mining heterogeneous information networks, which consist of multiple types of objects and rich semantic relations among different object types. Heterogeneous information networks are ubiquitous in the real-world applications, such as bibliographic networks and social media networks. Unfortunately, most existing approaches, such as spectral clustering, are designed to analyze homogeneous information networks, which are composed of only one type of objects and links. Some recent studies focused on heterogeneous information networks and yielded some research fruits, such as RankClus and NetClus. However, they often assumed that the heterogeneous information networks usually follow some simple schemas, such as bityped network schema or star network schema. To overcome the above limitations, we model the heterogeneous information network as a tensor without the restriction of network schema. Then, a tensor CP decomposition method is adapted to formulate the clustering problem in heterogeneous information networks. Further, we develop two stochastic gradient descent algorithms, namely, SGDClus and SOSClus, which lead to effective clustering multityped objects simultaneously. The experimental results on both synthetic datasets and real-world dataset have demonstrated that our proposed clustering framework can model heterogeneous information networks efficiently and outperform state-of-the-art clustering methods.
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Chatterjee, Ayan, Andreas Prinz, Martin Gerdes, and Santiago Martinez. "An Automatic Ontology-Based Approach to Support Logical Representation of Observable and Measurable Data for Healthy Lifestyle Management: Proof-of-Concept Study." Journal of Medical Internet Research 23, no. 4 (April 9, 2021): e24656. http://dx.doi.org/10.2196/24656.
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Background Lifestyle diseases, because of adverse health behavior, are the foremost cause of death worldwide. An eCoach system may encourage individuals to lead a healthy lifestyle with early health risk prediction, personalized recommendation generation, and goal evaluation. Such an eCoach system needs to collect and transform distributed heterogenous health and wellness data into meaningful information to train an artificially intelligent health risk prediction model. However, it may produce a data compatibility dilemma. Our proposed eHealth ontology can increase interoperability between different heterogeneous networks, provide situation awareness, help in data integration, and discover inferred knowledge. This “proof-of-concept” study will help sensor, questionnaire, and interview data to be more organized for health risk prediction and personalized recommendation generation targeting obesity as a study case. Objective The aim of this study is to develop an OWL-based ontology (UiA eHealth Ontology/UiAeHo) model to annotate personal, physiological, behavioral, and contextual data from heterogeneous sources (sensor, questionnaire, and interview), followed by structuring and standardizing of diverse descriptions to generate meaningful, practical, personalized, and contextual lifestyle recommendations based on the defined rules. Methods We have developed a simulator to collect dummy personal, physiological, behavioral, and contextual data related to artificial participants involved in health monitoring. We have integrated the concepts of “Semantic Sensor Network Ontology” and “Systematized Nomenclature of Medicine—Clinical Terms” to develop our proposed eHealth ontology. The ontology has been created using Protégé (version 5.x). We have used the Java-based “Jena Framework” (version 3.16) for building a semantic web application that includes resource description framework (RDF) application programming interface (API), OWL API, native tuple store (tuple database), and the SPARQL (Simple Protocol and RDF Query Language) query engine. The logical and structural consistency of the proposed ontology has been evaluated with the “HermiT 1.4.3.x” ontology reasoner available in Protégé 5.x. Results The proposed ontology has been implemented for the study case “obesity.” However, it can be extended further to other lifestyle diseases. “UiA eHealth Ontology” has been constructed using logical axioms, declaration axioms, classes, object properties, and data properties. The ontology can be visualized with “Owl Viz,” and the formal representation has been used to infer a participant’s health status using the “HermiT” reasoner. We have also developed a module for ontology verification that behaves like a rule-based decision support system to predict the probability for health risk, based on the evaluation of the results obtained from SPARQL queries. Furthermore, we discussed the potential lifestyle recommendation generation plan against adverse behavioral risks. Conclusions This study has led to the creation of a meaningful, context-specific ontology to model massive, unintuitive, raw, unstructured observations for health and wellness data (eg, sensors, interviews, questionnaires) and to annotate them with semantic metadata to create a compact, intelligible abstraction for health risk predictions for individualized recommendation generation.
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Wu, Jibing, Lianfei Yu, Qun Zhang, Peiteng Shi, Lihua Liu, Su Deng, and Hongbin Huang. "Multityped Community Discovery in Time-Evolving Heterogeneous Information Networks Based on Tensor Decomposition." Complexity 2018 (2018): 1–16. http://dx.doi.org/10.1155/2018/9653404.
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The heterogeneous information networks are omnipresent in real-world applications, which consist of multiple types of objects with various rich semantic meaningful links among them. Community discovery is an effective method to extract the hidden structures in networks. Usually, heterogeneous information networks are time-evolving, whose objects and links are dynamic and varying gradually. In such time-evolving heterogeneous information networks, community discovery is a challenging topic and quite more difficult than that in traditional static homogeneous information networks. In contrast to communities in traditional approaches, which only contain one type of objects and links, communities in heterogeneous information networks contain multiple types of dynamic objects and links. Recently, some studies focus on dynamic heterogeneous information networks and achieve some satisfactory results. However, they assume that heterogeneous information networks usually follow some simple schemas, such as bityped network and star network schema. In this paper, we propose a multityped community discovery method for time-evolving heterogeneous information networks with general network schemas. A tensor decomposition framework, which integrates tensor CP factorization with a temporal evolution regularization term, is designed to model the multityped communities and address their evolution. Experimental results on both synthetic and real-world datasets demonstrate the efficiency of our framework.
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Yuan, Peisen, Yi Sun, and Hengliang Wang. "Heterogeneous Information Network-Based Recommendation with Metapath Search and Memory Network Architecture Search." Mathematics 10, no. 16 (August 12, 2022): 2895. http://dx.doi.org/10.3390/math10162895.
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Recommendation systems are now widely used on the Internet. In recommendation systems, user preferences are predicted by the interaction of users with products, such as clicks or purchases. Usually, the heterogeneous information network is used to capture heterogeneous semantic information in data, which can be used to solve the sparsity problem and the cold-start problem. In a more complex heterogeneous information network, the types of nodes and edges are very large, so there are lots of types of metagraphs in a complex heterogeneous information network. At the same time, machine learning tasks on heterogeneous information networks have a large number of parameters and neural network architectures that need to be set artificially. The main goal is to find the optimal hyperparameter settings and neural network architectures for the performance of a task in the set of hyperparameter space. To address this problem, we propose a metapath search method for heterogeneous information networks based on a network architecture search, which can search for metapaths that are more suitable for different heterogeneous information networks and recommendation tasks. We conducted experiments on Amazon and Yelp datasets and compared the architecture settings obtained from an automatic search with manually set structures to verify the effectiveness of the algorithm.
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Lu, Yuanfu, Chuan Shi, Linmei Hu, and Zhiyuan Liu. "Relation Structure-Aware Heterogeneous Information Network Embedding." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 4456–63. http://dx.doi.org/10.1609/aaai.v33i01.33014456.
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Heterogeneous information network (HIN) embedding aims to embed multiple types of nodes into a low-dimensional space. Although most existing HIN embedding methods consider heterogeneous relations in HINs, they usually employ one single model for all relations without distinction, which inevitably restricts the capability of network embedding. In this paper, we take the structural characteristics of heterogeneous relations into consideration and propose a novel Relation structure-aware Heterogeneous Information Network Embedding model (RHINE). By exploring the real-world networks with thorough mathematical analysis, we present two structure-related measures which can consistently distinguish heterogeneous relations into two categories: Affiliation Relations (ARs) and Interaction Relations (IRs). To respect the distinctive characteristics of relations, in our RHINE, we propose different models specifically tailored to handle ARs and IRs, which can better capture the structures and semantics of the networks. At last, we combine and optimize these models in a unified and elegant manner. Extensive experiments on three real-world datasets demonstrate that our model significantly outperforms the state-of-the-art methods in various tasks, including node clustering, link prediction, and node classification.
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Wnęk, Karol, and Piotr Boryło. "A Data Processing and Distribution System Based on Apache Nifi." Photonics 10, no. 2 (February 15, 2023): 210. http://dx.doi.org/10.3390/photonics10020210.
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The monitoring of physical and logical networks is essential for the high availability of 5G/6G networks. This could become a challenge in 5G/6G deployments due to the heterogeneity of the optical layer. It uses equipment from multiple vendors, and, as a result, the protocols and methods for gathering monitoring data usually differ. Simultaneously, to effectively support 5G/6G networks, the optical infrastructure should also be dense and ensure high throughput. Thus, vast numbers of photonic transceivers operating at up to 400 Gbps are needed to interconnect network components. In demanding optical solutions for 5G and beyond, enterprise-class equipment will be used—for example, high-capacity and high-density optical switches based on the SONiC distribution. These emerging devices produce vast amounts of data on the operational parameters of each optical transceiver, which should be effectively collected, processed, and analyzed. The aforementioned circumstances may lead to the necessity of using multiple independent monitoring systems dedicated to specific optical hardware. Apache NiFi can be used to address these potential issues. Its high configurability enables the aggregation of unstandardized log files collected from heterogenous devices. Furthermore, it is possible to configure Apache NiFi to absorb huge data streams about each of the thousands of transceivers comprising high-density optical switches. In this way, data can be preprocessed by using Apache NiFi and later uploaded to a dedicated system. In this paper, we focus on presenting the tool, its capabilities, and how it scales horizontally. The proven scalability is essential for making it usable in optical networks that support 5G/6G networks. Finally, we propose a unique optimization process that can greatly improve the performance and make Apache NiFi suitable for high-throughput and high-density photonic devices and optical networks. We also present some insider information on real-life implementations of Apache NiFi in commercial 5G networks that fully rely on optical networks.
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Ross, Mindy K., Henry Zheng, Bing Zhu, Ailina Lao, Hyejin Hong, Alamelu Natesan, Melina Radparvar, and Alex A. T. Bui. "Accuracy of Asthma Computable Phenotypes to Identify Pediatric Asthma at an Academic Institution." Methods of Information in Medicine 59, no. 06 (December 2020): 219–26. http://dx.doi.org/10.1055/s-0041-1729951.
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Abstract Objectives Asthma is a heterogenous condition with significant diagnostic complexity, including variations in symptoms and temporal criteria. The disease can be difficult for clinicians to diagnose accurately. Properly identifying asthma patients from the electronic health record is consequently challenging as current algorithms (computable phenotypes) rely on diagnostic codes (e.g., International Classification of Disease, ICD) in addition to other criteria (e.g., inhaler medications)—but presume an accurate diagnosis. As such, there is no universally accepted or rigorously tested computable phenotype for asthma. Methods We compared two established asthma computable phenotypes: the Chicago Area Patient-Outcomes Research Network (CAPriCORN) and Phenotype KnowledgeBase (PheKB). We established a large-scale, consensus gold standard (n = 1,365) from the University of California, Los Angeles Health System's clinical data warehouse for patients 5 to 17 years old. Results were manually reviewed and predictive performance (positive predictive value [PPV], sensitivity/specificity, F1-score) determined. We then examined the classification errors to gain insight for future algorithm optimizations. Results As applied to our final cohort of 1,365 expert-defined gold standard patients, the CAPriCORN algorithms performed with a balanced PPV = 95.8% (95% CI: 94.4–97.2%), sensitivity = 85.7% (95% CI: 83.9–87.5%), and harmonized F1 = 90.4% (95% CI: 89.2–91.7%). The PheKB algorithm was performed with a balanced PPV = 83.1% (95% CI: 80.5–85.7%), sensitivity = 69.4% (95% CI: 66.3–72.5%), and F1 = 75.4% (95% CI: 73.1–77.8%). Four categories of errors were identified related to method limitations, disease definition, human error, and design implementation. Conclusion The performance of the CAPriCORN and PheKB algorithms was lower than previously reported as applied to pediatric data (PPV = 97.7 and 96%, respectively). There is room to improve the performance of current methods, including targeted use of natural language processing and clinical feature engineering.
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Wang, Ranran, Xiao Ma, Chi Jiang, Yi Ye, and Yin Zhang. "Heterogeneous information network-based music recommendation system in mobile networks." Computer Communications 150 (January 2020): 429–37. http://dx.doi.org/10.1016/j.comcom.2019.12.002.
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Xie, Fenfang, Yangjun Xu, Angyu Zheng, Liang Chen, and Zibin Zheng. "Service recommendation through graph attention network in heterogeneous information networks." International Journal of Computational Science and Engineering 25, no. 6 (2022): 643. http://dx.doi.org/10.1504/ijcse.2022.127186.
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Zheng, Zibin, Angyu Zheng, Liang Chen, Yangjun Xu, and Fenfang Xie. "Service recommendation through graph attention network in heterogeneous information networks." International Journal of Computational Science and Engineering 25, no. 6 (2022): 643. http://dx.doi.org/10.1504/ijcse.2022.10052326.
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Liu, Zhenguo, Chao Ma, Jimiao Zhao, Haiyang Hu, and Xiongyi Yin. "Graph Transformation Based on Heterogeneous Information Network for Graph Algorithms." Journal of Physics: Conference Series 2575, no. 1 (August 1, 2023): 012006. http://dx.doi.org/10.1088/1742-6596/2575/1/012006.
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Abstract In the finical risk control, the graph structure data increasingly show its unique charm, especially the heterogeneous information network (HIN). And, graph computing algorithms for the data mining based on network is the most popular way at the moment. But, most of them require the graph network must have homogeneity and could not be applied to heterogeneous networks. Although the neural network models have some work in the HIN, which like heterogeneous graph neural networks, but these ways cannot provide enough interpretability due to its operations in black-box. In this paper, we summarize strategies to transform the HIN into homogeneous network. And we propose methods aim to rebuild HIN as several homogeneous networks at fine-grained level and greatly retain the original network topological structure information compared the previous which easy to lose sight of. The effectiveness of our method is verified by real data and community detection algorithms. In the experiment, the analysis found that our approached consistently perform promising results compared with the coarse-grained data processing on HIN.
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Wang, Chen, Chengyi Zeng, Hongfu Liu, and Jing Chen. "Adversarial Hiding Deception Strategy and Network Optimization Method for Heterogeneous Network Defense." Electronics 10, no. 21 (October 26, 2021): 2614. http://dx.doi.org/10.3390/electronics10212614.
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Heterogeneous networks are powerful tools for describing different types of entities and relationships and are more relevant models of complex networks. The study of heterogeneous network defense is of great practical significance for protecting useful networks such as military combat networks and critical infrastructure networks. However, a large amount of current research on complex network defense focuses on homogeneous networks under complete information conditions, which often ignore the real conditions such as incomplete information and heterogeneous networks. In this paper, we propose firstly a new adversarial hiding deception strategy for heterogeneous network defense under incomplete information conditions. Secondly, we propose an adversarial hiding deception network optimization method based on a genetic algorithm and design node importance index and a fitness function, which take into account the graph structure information and information about the type of nodes. Finally, we conduct comparison experiments for different defense strategies, and the results show that the proposed strategy and network optimization method are effective at hiding the critical nodes and inducing the attacker to attack the non-important nodes. The generated adversarial hiding deception network has a similar graph structure to the real network.
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Beshley, H., Y. Shkoropad, M. Beshley, and M. Klymash. "CONVERGENCE OF HETEROGENEOUS WIRELESS NETWORKS FOR FUTURE COMMUNICATIONS: ARCHITECTURE, QOS AND RESOURCE MANAGEMENT." Information and communication technologies, electronic engineering 2, no. 2 (December 2022): 20–32. http://dx.doi.org/10.23939/ictee2022.02.020.
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Scientific and theoretical approaches to the implementation of a heterogeneous mobile communication network based on SDN/NFV and SDR technologies are described. The architecture of the future heterogeneous network is proposed, taking into account the evolution of emerging standards and key technologies. An algorithm for dynamic bandwidth allocation and reservation between several logical channels at a certain moment of time to provide QoS for information flows in future networks is created. A simulation model of network traffic service with parameters corresponding to real networks has been made. The study of femtocell SDR load, as the main convergent device at the level of heterogeneous network access by users of different generation mobile communication technologies has been conducted.
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Gaykar, Reshma S., V. Khanaa, and Shashank D. Joshi. "A Hybrid Supervised Learning Approach for Detection and Mitigation of Job Failure with Virtual Machines in Distributed Environments." Ingénierie des systèmes d information 27, no. 4 (August 31, 2022): 621–27. http://dx.doi.org/10.18280/isi.270412.
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Distributed data processing techniques are very popular nowadays due to high data generation from various resources. To increase work learning outcomes and to reduce consumption, modern massive computational systems divide jobs into several smaller tasks that perform in parallel. Nevertheless, responding with straggler processes, which are sluggish running processes that rise the total response time, is a typical performance issue in such platforms. In this paper, we proposed the detection of struggler nodes in a large distributed environment using a hybrid machine learning technique. Initially, the data has been collected from numerous virtual machine network logs. The entire data set has various fields such as Virtual Machine ID, CPU load, memory load, bandwidth utilization, etc. Memory utilization an input to the proposed system is collected from the garbage collection log files where the memory consumption on each VM and its timestamp is recorded. This is the most efficient way to get the memory consumption in web/desktop applications. Similarly, the CPU, I/O and bandwidth utilization is grabbed from the process monitoring functionality and SAR (System Activity Report) utility from the respective VM boxes. This data set is useful to identify weather-specific virtual machine is heated up or not. In this approach, we proposed three conventional machine learning algorithms and a hybrid machine learning algorithm for the identification of node status. Main purpose of the proposed system is to identify the slow performing node in an efficient way to prevent the other nodes from failures. This can provide effective load balancing and low response time for task execution from available VM’s in distributed cloud environments. To create its training program, several extractions of features approaches were used. TF-IDF, correlational co-occurrence, and density-based features have been mined from the whole data set. With extensive experimental analysis, we evaluate our system with our proposed classification algorithm. As a result, the system produces higher classification accuracy of 94.5% over the traditional machine learning classifiers. If the proposed system is tested against the data set fields, memory load and CPU load on the homogenous machine configurations, we see more efficiency while detecting the underperforming node than the heterogenous machine configurations.
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Zhang, Benhui, Maoguo Gong, Jianbin Huang, and Xiaoke Ma. "Clustering Heterogeneous Information Network by Joint Graph Embedding and Nonnegative Matrix Factorization." ACM Transactions on Knowledge Discovery from Data 15, no. 4 (June 2021): 1–25. http://dx.doi.org/10.1145/3441449.
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Many complex systems derived from nature and society consist of multiple types of entities and heterogeneous interactions, which can be effectively modeled as heterogeneous information network (HIN). Structural analysis of heterogeneous networks is of great significance by leveraging the rich semantic information of objects and links in the heterogeneous networks. And, clustering heterogeneous networks aims to group vertices into classes, which sheds light on revealing the structure–function relations of the underlying systems. The current algorithms independently perform the feature extraction and clustering, which are criticized for not fully characterizing the structure of clusters. In this study, we propose a learning model by joint <underline>G</underline>raph <underline>E</underline>mbedding and <underline>N</underline>onnegative <underline>M</underline>atrix <underline>F</underline>actorization (aka GEjNMF ), where feature extraction and clustering are simultaneously learned by exploiting the graph embedding and latent structure of networks. We formulate the objective function of GEjNMF and transform the heterogeneous network clustering problem into a constrained optimization problem, which is effectively solved by l 0 -norm optimization. The advantage of GEjNMF is that features are selected under the guidance of clustering, which improves the performance and saves the running time of algorithms at the same time. The experimental results on three benchmark heterogeneous networks demonstrate that GEjNMF achieves the best performance with the least running time compared with the best state-of-the-art methods. Furthermore, the proposed algorithm is robust across heterogeneous networks from various fields. The proposed model and method provide an effective alternative for heterogeneous network clustering.
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Zhuo, Wei, Qianyi Zhan, Yuan Liu, Zhenping Xie, and Jing Lu. "Context Attention Heterogeneous Network Embedding." Computational Intelligence and Neuroscience 2019 (August 21, 2019): 1–15. http://dx.doi.org/10.1155/2019/8106073.
Full textAbstract:
Network embedding (NE), which maps nodes into a low-dimensional latent Euclidean space to represent effective features of each node in the network, has obtained considerable attention in recent years. Many popular NE methods, such as DeepWalk, Node2vec, and LINE, are capable of handling homogeneous networks. However, nodes are always fully accompanied by heterogeneous information (e.g., text descriptions, node properties, and hashtags) in the real-world network, which remains a great challenge to jointly project the topological structure and different types of information into the fixed-dimensional embedding space due to heterogeneity. Besides, in the unweighted network, how to quantify the strength of edges (tightness of connections between nodes) accurately is also a difficulty faced by existing methods. To bridge the gap, in this paper, we propose CAHNE (context attention heterogeneous network embedding), a novel network embedding method, to accurately determine the learning result. Specifically, we propose the concept of node importance to measure the strength of edges, which can better preserve the context relations of a node in unweighted networks. Moreover, text information is a widely ubiquitous feature in real-world networks, e.g., online social networks and citation networks. On account of the sophisticated interactions between the network structure and text features of nodes, CAHNE learns context embeddings for nodes by introducing the context node sequence, and the attention mechanism is also integrated into our model to better reflect the impact of context nodes on the current node. To corroborate the efficacy of CAHNE, we apply our method and various baseline methods on several real-world datasets. The experimental results show that CAHNE achieves higher quality compared to a number of state-of-the-art network embedding methods on the tasks of network reconstruction, link prediction, node classification, and visualization.
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Pentikousis, Kostas, Ramon Agüero, and Symeon Papavassiliou. "Mobility and Network Management in Heterogeneous Networks." Mobile Networks and Applications 16, no. 4 (June 8, 2011): 409–11. http://dx.doi.org/10.1007/s11036-011-0324-4.
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Zhang, Linchao, Lei Hang, Wenquan Jin, and Dohyeun Kim. "Interoperable Multi-Blockchain Platform Based on Integrated REST APIs for Reliable Tourism Management." Electronics 10, no. 23 (December 1, 2021): 2990. http://dx.doi.org/10.3390/electronics10232990.
Full textAbstract:
The tourism industry can significantly benefit from the blockchain since its implementation can build trust among stakeholders and improve customer satisfaction. However, most of the existing tourism-specified blockchain platforms are single-chains that provide business support for enterprises without guaranteeing transaction information privacy. Besides, these platforms are specified to a single use case and lack interoperability with other platforms to support heterogenous tourism services. This paper aims to address this issue by introducing a multi-chain architecture that utilizes multiple blockchains to enhance processing capability and provide various business services for the tourism industry. The proposed multi-chain architecture improves the interoperability between the activities in different chains by providing functional requirements in practical applications and supports the inter-ledger application. In addition, the private blockchain will be made available to allow users to access the network through central authorization. It also increases the transaction processing capability by distributing multiple tasks across the chains for large-scale applications. To demonstrate the usability and efficiency of the developed approach, a case study on hotel booking is conducted using the blockchain frameworks Winding Tree and Hyperledger Fabric. A comprehensive evaluation experiment is conducted, and the results show the significance of the proposed system.
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Gao, Hongwei, Han Qiao, Artem Sedakov, and Lei Wang. "A Dynamic Formation Procedure of Information Flow Networks." Journal of Systems Science and Information 3, no. 2 (April 25, 2015): 97–110. http://dx.doi.org/10.1515/jssi-2015-0097.
Full textAbstract:
AbstractA characterization of the equilibrium of information flow networks and the dynamics of network formation are studied under the premise of local information flow. The main result of this paper is that it gives the dynamic formation procedure in the local information flow network. The research shows that core-periphery structure is the most representative equilibrium network in the case of the local information flow without information decay whatever the cost of information is homogeneous or heterogeneous. If the profits and link costs of local information flow networks with information decay are homogeneous empty network and complete network are typical equilibrium networks, which are related to the costs of linking.