In the first case, the signals used to reconstruct the networks are magnetoencephalo- graphic (MEG) recordings acquired from human subjects in resting–state. The dataset consisted in 15 MEG recordings of 6–minutes length stemming from five different subjects. After a proper preprocessing of the data, zero–delays cross–correlations were estimated on a set of 12 MEG time series corresponding to as many brain lo- cations. Ten regions of this set are deemed to belong to a well–known resting state functional network, the default mode network (DMN), whereas the other two are considered as external, according to the selection given in the work of De Pasquale et al. . Our method was applied to estimate the structure of the fully–connected brain networks at different time scale resolutions. A great attention was devoted to test the correlation significance by means of two different approaches: the first one assumed as a null hypothesis that the sequences producing the correlation val- ues are independent and identically distributed Gaussian white noise sources; the second one was based on the use of signal surrogates, starting from a much more realistic assumption, namely that each pair of sequences is generated by two inde- pendent noise sources with the same distribution of amplitudes and approximately the same power spectrum. Thanks to the larger conservativity of the second hy- pothesis, the surrogates method has been chosen as the best one to determine the significance of the correlation.
Each node regularly broadcasts a beacon containing all its state information. The (mean, as is shown later on) time between two successive beacons of a single node is a system parameter called the beacon interval (BI). The time between between the respective beacons of two neighbouring nodes is calle the inter beacon space (IBS). As nodes receive beacons from other nodes they can construct a view of their local topology. With every received beacon, the view of the network is updated. A beacon that has been received by a node is only valid at that node for a specified amount of time. After a period called the beacon timeout (BT) the beacon is no longer valid; any information contained in it that is not contained in beacons that a node has of other nodes is removed. BT is given as a ratio to BI. If two nodes move away from each other the beacons they have received from each other will eventually time out and the nodes will update their view without the other node in it. Table 5.1 shows the information contained in a beacon. It is almost fully based on the node attributes in the original algorithm. New attributes are introduced in their respective sections. Figure 5.1 shows an example of how nodes keep state of the network. Thanks to the beacons a node knows (with certain probability as new nodes may always come within transmission range and old nodes may always leave) its neighbours and for each neighbour its attributes: the identifier of the neighbour’s neighbours, its identifier, its chosen number, its parent, its distance to the DS, its distance to its most distant descendant and any broken links (explained below) the neighbour has included.
In this section, distributed MVE-PCA is examined in environments with differing network topologies. Two types of topologies are used; a fullyconnected network and random strongly connectednetworks with differing network densities. The data sets with a training set of 200 data instances or less were distributed over five and ten nodes, and the data sets with 400 data instances were distributed over twenty and forty nodes. The real- world data sets from the centralized approach are now used in a distributed setting. 10 Monte Carlo runs are performed to reduce the effect of random elements in the simulation. As the distributed learning approach should yield a classifier that is very close to that of the centralized approach, the optimal parameters for the centralized classifier are used for the distributed classifiers. The value of ρ was chosen using parameter selection, selecting the value that allowed convergence to occur quickly and accurately. The number of iterations was chosen so that convergence occurred.
range, with the result shown in Figure 7. In terms of the interdependent ER networks, the result from Figure 7(a) can be understood. With the decrease of p , the invulnerability of network systems declines and with the decrease of q , the invulnerability of network systems raises . When p = 1 and q = 0, the invul- nerability of network systems reaches its maximum with final attack number being about 305. When p = 0 and q = 1, the invulnerability of network systems reaches its minimum with final attack number being about 5. Meanwhile, the impacts on the invulnerability of network systems by increasing p or decreasing q are nearly equal. These results also meet well with Figure 7(b) and Figure 7(c). Therefore, it can be concluded that adding supportive relations is useful to improve the invulnerability of network systems and adding dependent relations can decline the invulnerability of network systems. The effect of raising the in- vulnerability of network systems by adding supportive relations almost offsets the effects on declining the invulnerability of network systems by adding the same number of dependent relations.
Mobile Delay Tolerant Networks (DTNs), also called as occasionally connected mobile networks, are wireless networks in which a fullyconnected path from source to destination is unlikely to exist. In these networks, for message delivery nodes use store-carry-and-forward paradigm to route the messages. The examples of this networks are wildlife tracking, military networks etc. However, efficient forwarding based on a partial knowledge of get in touch with performance of nodes is demanding. It becomes serious to recommend practiced resource portion and data storage protocols. Although the connectivity of nodes is not continuously maintained, it is still popular to allow contact among nodes ,. Each time the source meats a relay node, it chooses a frame i for transmission with prospect ui. In the basic scenario, the foundation has originally all the packets .Under this statement it was shown in that the transmission policy has a threshold structure: it is best possible to use all chances to spread packets till some time σ depending on the energy restriction and then stop. This policy resembles the well-known “Spray-and-Wait” policy . In this work we take for granted a more general arrival process of packets: they require to be at the same time obtainable for transmission originally i.e., when forwarding starts, as assumed in the case when large multimedia files are recorded at the source node that sends them out presently than waiting for the whole file reception. This paper focuses on general packet arrivals at the source and two-hop routing . We differentiate two cases: when the source can overwrite its own packets in the relay nodes, and when it cannot.
Whereas feed-in management can be regarded as a reactively used “ultima ratio” in the sense of the red phase of the grid traffic light concept, proactively using the flex- ibility of controllable loads (also known as demand response) and flexible DER can be a feasible option for congestion management. In order to implement an intelli- gent use of flexibility, however, two major obstacles have to be overcome: First, grid operation has to be proactive both regarding the forecasting of loads and renew- able energy production, and regarding the usage of power flow calculations and state estimation for the prediction of congestions. Second, the activation of flexibility, the documentation of its actual provision and the monetary compensation have to be flexible, scalable and highly automated in order to tap this potential in a feasible, eco- nomically and organizationally efficient way. The first topic has been in the focus of several national and international research projects within the last years, whereas the second topic has not been fully addressed yet. In the following sections, intelligent agents and distributed ledgers are introduced as a potential means to solve the second problem.
For all its success, Neural Machine Translation (NMT) presents a range of new challenges. While popular encoder-decoder models are attractively simple, recent literature and the results of shared evaluation tasks show that a signiﬁcant amount of engineering is required to achieve “production-ready” performance in both translation quality and computational efﬁciency. In a trend that carries over from Statistical Machine Translation (SMT), the strongest NMT systems beneﬁt from subtle architecture modiﬁcations, hyper-parameter tuning, and empirically effective heuristics. To address these challenges, we introduce S OCKEYE , a neural sequence-to-sequence toolkit written in Python and built on Apache MXN ET 2 [Chen et al., 2015]. To the best of our knowledge, S OCKEYE is the only toolkit that includes implementations of all three major neural translation architectures: attentional recurrent neural networks [Schwenk, 2012, Kalchbrenner and Blunsom, 2013, Sutskever et al., 2014, Bahdanau et al., 2014, Luong et al., 2015], self- attentional transformers [Vaswani et al., 2017], and fully convolutional networks [Gehring et al., 2017]. These implementations are supported by a wide and continually updated range of features reﬂecting the best ideas from recent literature. Users can easily train models based on the latest research, compare different architectures, and extend them by adding their own code. S OCKEYE
Fully training a new or existing CNN gives full control of the architecture and parameters, which tends to yield a more robust network. However, this strategy not only requires a large amount of computational resources to train CNN’s parameters, but also needs a large amount of annotated remote sensing data. Although this amount of annotated remote sensing data is unusual, there are many works, usually using reasonable datasets (more than 2000 images), that achieved promising results by proposing the full training of new CNN [4–6]. Nogueira et al.  fully trained a new CNN architecture to classify images from aerial and multispectral images. Yue et al.  proposed a hybrid method combining principal component analysis, CNN, and logistic regression to classify hyperspectral image using both spectral and spatial features. Maggiori et al.  proposed a CNN architecture that is fully convolutional that only involves a series of convolution and deconvolution operations to produce the output classification maps. Makantasis et al.  exploited a CNN to encode pixels’ spectral and spatial information and a multi-layer perceptron to conduct the classification task. Volpi  presented a CNN-based system relying on a down-sample-then-up-sample architecture for semantic labeling of sub-decimeter resolution images. However, some drawbacks exist in this strategy . For example, Nogueira et al.  and Castelluccio et al.  trained the networks by only using the existing satellite image dataset, which had lower classification accuracy compared with using the pre-trained networks as global feature extractors or fine-tuning the pre- trained networks. The reason for this may be because the large-scale networks usually contain millions of parameters to be learned. Thus, training them using small-scale satellite image datasets causes overfitting and local minimum problems. Consequently, some constructed a new smaller network and trained it from scratch using satellite images to better fit the satellite images .
With the constant development of computer networks and the increasing number of users grows and the number of new types of attacks to denial of service. DoS / DDoS / DRDoS attacks are characterized by a straightforward implementation complexity and resistance, which poses new problems of researchers, who are still not yet resolved. Analysis of recent publications shows that exercise is accompanied by attacks: interception of confidential information to unauthorized use of network bandwidth and computational resources, the spread of false information, violation of network administration (Apiecionek L. et al., 2015).
The performance of the proposed algorithm is compared with the algorithm proposed by S. S. Basu and A. Chaudhuri in [ 3 ] and non fuzzy algorithm of the authors [ 6 ] . In [ 3 ] an algo- rithm to elect coordinator and a separate algo- rithm for node movement have been proposed. This algorithm is the centralized one. The algo- rithm proposed in [ 6 ] is the distributed one, but the movement control algorithm is not fuzzy- based. To evaluate the performance of this fuzzy logic based distributed algorithm to maintain the connectivity of the network, a network of five nodes and their initial coordinates ( 1 . 5 , 3 ) , ( 0 . 5 , 2 ) , ( 2 , 3 ) , ( 3 , 1 ) , and ( 4 , 1 ) have been con- sidered. Velocity changes of the different nodes are shown graphically for the algorithm pro- posed in [ 3 ] , [ 6 ] and this paper in Figure 16a and Figure 16b and Figure 16c respectively. From the results presented in 16a, 16b and 16c, it is seen that the proposed algorithm is very effective in maintaining the connectivity, com- pared to others. This algorithm has the addi- tional feature of capability of restoring connec- tivity if it is hampered due to a faulty node. This feature is not available in other algorithms. Sta- bility of the nodes is hampered by the sudden change of velocity. But from the above com- parison it is clear that the algorithm proposed in this paper is giving better stability, compared to others.
This paper deals with a proposed model of realization of an interconnected Hopfield Artificial Neural Network (HANN). Hopfield Neural Network is a multiple loop feedback neural network which can be used as an associative memory. All the neurons in the network are connected to every other neuron but without any self-feedback. HANN can be used in Wireless Mesh Network (WMN). WMN adopts a multi-hop access method and expands network coverage by increasing the number of user-node and also enhances reliability of data transmission in both urban and rural areas. This paper is a humble attempt to derive the generic stability criteria of energy function for HANN which can be realised in WMN as well for better QoS.
The IDS includes two components: sensors and agents. Sensors are generally used for monitoring activities NIDS, WIDS, and NBA systems. Agents are mostly used in Hybrid IDSs to inspect and examine network events. Sensors and agents both are capable of relaying information to the individual servers responsible for different management and database operations. Management part id deals with the stored events and the database part handles the event data in centralized storage for future reuse. There are two categories of network architectures: the first is the Managed Network (MN) that is a secluded network and is deployed to disguise the presence of IDS from attackers. Managed Networks increase the hardware overheads and carry certain management inconveniences for network administrators. A Standard Network (SN) is another form of a network, mostly a public network without any defense mechanism. One way to improve the security of the standard network is to configure a virtual and isolated network by means of a virtual local area network. Alternatively, most IDS technologies facilitate four common capabilities for defense purpose: information collection, logging, attack detection, and prevention. The observed activities of users are used to collect information inside the hosts/networks. The logged data for detected events can be utilized for validation alerts and for research of intrusion-related incidents.
4.1. Expected number of spanning trees in random SP graphs. The first step in our proof of Theorem 1.1 is the enumeration of SP networks that carry a distinguished spanning tree. For the purpose of counting spanning trees in SP networks, we need to introduce the following auxiliary class. Let D denote the class of SP networks that carry a distinguished spanning forest with two components, each of which contains one of the poles. Let D(x, y) be its associated EGF. Recall that a network is either trivial, series or parallel. By convention, we assume that networks with a root edge are parallel. Therefore, we define the following classes of networks. Let S and S denote the class of series networks that carry a distinguished spanning tree, respectively a distinguished spanning forest with two components each of which contains one of the poles. We denote their associated EGFs by S(x, y) and S(x, y), respectively. Similarly, let P and P denote the class of parallel networks and which carry a distinguished spanning tree, respectively a distinguished spanning forest with two components, each of which contains one of the poles. Observe that in both families the root edge might be present. Their associated EGFs are denoted by P (x, y) and P (x, y), respectively. For the sake of readability we may omit the parameters whenever they are clear from the context.
Artificial Neural Networks (ANNs), under the broad field of Artificial Intelligence (AI), is an active area of research with wide impact across multiple disciplines. Historically, AI research goes through active and dormant phases which Nilsson in “Quest for Artificial Intelligence”  calls AI summer or winter. The current AI summer phase has renewed interest in ANNs, especially for the fields of computer vision, natural language processing and robotics. One inflection point for the current summer AI was is 2012, when Hinton  swept popular machine learning competitions with his innovative ANNs. Hardware capabilities, like cheap memory storage, increased processing power, etc. contributed to the current summer AI, but this report will focus on the software algorithms needed to scale ANNs in commercial environments. This report will explore distributed model training for ANNs which remains a stubborn problem.
The sensor nodes that are deployed in hostile environments are vulnerable to capture and cooperation. An opponent may obtain private information from these sensors and then easily make clone from it and cleverly position them in the network to launch a diversity of insider attacks. This kind of attack process is generally called as a clone attack. Presently, the defenses against clone attacks are not only very few, but also suffer from selective intermission of detection and high overhead. This paper, propose a new effective and efficient scheme, called SET to detect such clone attacks. The key idea behind of this SET is to sense clones by computing the set operations of exclusive subsets in the network. Firstly the SET securely forms restricted unit subsets among one-hop neighbors in the WSN in a distributed way. This secure subset formation also provides the authentication of nodes’ subset membership. After that, the SET then employs a tree structure to compute non-overlapped set operations and integrates interleaved substantiation to prevent unauthorized falsification of subset information during forwarding. The randomization is used to make further the exclusive subset and tree formation changeable to a challenger. We show the reliability and resilience of SET by analyzing the probability that an adversary may effectively obstruct the set operations. Performance analysis and simulations also demonstrate that the projected scheme is more efficient than existing schemes from both communication and memory cost standpoints.
Compared to wavelength switched optical networks (WSON), flexgrid optical networks provide higher spectrum efficiency and flexibility. To properly analyze, design, plan, and operate flexgrid networks, the routing and spectrum allocation (RSA) problem must be solved. The RSA problem involves two different constraints: the continuity constraint to ensure that the allocated spectral resources are the same along the links in the route and the contiguity constraint to guarantee that those resources are contiguous in the spectrum. As a consequence of its complexity, it is crucial that efficient methods are available to allow solving realistic problem instances in practical times. In this paper, we review different RSA-related optimization problems that arise within the life-cycle of flexgrid networks. Different methods to solve those optimization problems are reviewed along with the different requirements related to where those problems appear. Starting from its formulation, we analyze network life-cycle and indicate different solving methods for the kind of problems that arise at each network phase: from off-line to in-operation network planning. We tackle two representative use cases: i) a use case for off-line planning where a flexgrid network is designed and periodically upgraded, and ii) multilayer restoration as a use case for in-operation planning. Three solving methods are proposed for the off-line planning problem: mathematical programming, column generation and metaheuristics, whereas, as a result of its stringent required solving times, two heuristic methods are presented for the on-line problem.
Modularity is important because managers should consider the connectivity of com- ponents with other components when making important design decisions such as out- sourcing or redesigning product components. For example, we found that those components of the engine that were more (directly and indirectly) connected to “trans- mit forces” (an important engine functional requirement) to other components were the ones that exhibited higher levels of component redesign, whereas those components that were more connected due to spatial consideration (an important design constraint) were the ones that exhibited lower levels of component redesign. Hence, the modularity of a component in a complex product can be associated with both high and low levels of component redesign. Managers can use the knowledge of linkages among components to propagate design decisions or to avoid redesigning some components to prevent propagating design constraints that could disrupt certain functional requirements of the product (Baldwin and Clark 2000; Sosa, Eppinger, and Rowles 2007a).
The essential issue is to achieve the QoS in the cross breed Remote Networks with wobbly transmission limit and high compactness. In base station arranged frameworks, to give the QoS Support, some coordinating traditions (Interserv , differserv ,) are being used which needs incorporates joint effort of centers, booking of packages. Giving the QoS Support in MANET is troublesome on account of their stand-out components, for instance, adaptability of the centers and confinements of the channel information exchange limit.
The wireless sensors network (WSN) always needs some control information, command. It may call query information about nodes in networks. Once the information is received, all other nodes will be having these values. Information discovering and placing in the next node. According to sensors networks, It makes chain of nodes to the destination. This concept is widely used by Indian army of the borders. It gives sensual information about the authorised military commandos time to time from change of information. Here we use temperature, control information, etc. be for we can use Wireless sensors networks. This might be binary tens of kilo byte information about control or the necessary information and code dissemination. The information needs two byte is usually for data send. It uses the protocol SDDISWSN as Secure and Discovery of Distributed Information and Spreading In Wireless Sensor Networks for providing security in distributed approach.