Information Transmission Path Research Articles

Multi-target detection method based on YOLOv4 convolutional neural network

In order to solve the problems of low detection accuracy, false detection and high miss detection rate of small targets in target detection tasks, this paper is a multi-target detection method based on YOLOv4 convolutional neural network. The proposed method is based on YOLOv4. The semantic information of high-level features is first propagated to the low-level network through FPN sampling, and then it is fused with the high-resolution information of the underlying features to improve the detection effect of small target detection objects. The information transmission path from the bottom to the top is enhanced by downsampling the feature pyramid, and finally the feature maps of different layers are fused to achieve relevant predictions. Experiments prove that the method proposed in this paper has good results.

Fund Network Centrality, Hard-to-Value Portfolio, and Investment Performance

Based on the quarterly data of mutual funds in China from the fourth quarter of 2004 to the fourth quarter of 2019, this paper constructs a series of complex bipartite networks based on the overlapped portfolios of mutual funds and then explores the influences of fund network position on mutual fund’s investment behavior and performance. This paper finds that a mutual fund with shorter information transmission path to other entities in the fund network (i.e., having higher closeness centrality) or with stronger ties with those entities in important information positions (i.e., having higher eigenvector centrality) will achieve better investment performance. However, a stronger mediating role over the potential information flow of the fund network (i.e., having higher betweenness centrality) cannot help a mutual fund increase performance. The empirical results also indicate that a mutual fund holding stock portfolios with high valuation difficulties caused by the market or fundamental information uncertainty will achieve better investment performance, while holding hard-to-value portfolios caused by limited public information will reduce the performance of the fund. Furthermore, high closeness centrality or eigenvector centrality can help mutual funds deal with the disclose problems of public information, thus reducing the likelihood of a mutual fund holding hard-to-value portfolios caused by limited public information to achieve worse performance. Eigenvector centrality brings information advantages about company fundamentals, so it is easier for a mutual fund with high eigenvector centrality to profit from holding hard-to-value portfolios caused by the fundamental information uncertainty. The conclusions of this paper can enhance our understanding of the fund network and its information mechanism and shed new light on mutual fund’s information advantages and related asset allocation strategies.

Service‐oriented routing with Markov space‐time graph in low earth orbit satellite networks

AbstractLow earth orbit (LEO) satellite network has been more and more widely used because of its advantages of low delay, low overhead, and flexible networking. However, the reliability and effectiveness of data transmission are affected by the specific characteristics of LEO satellite networks (LSN), such as time‐varying topology, uneven distribution of service demands, and multiple types of service. In this article, a service‐oriented routing with Markov space‐time graph (SOR‐MSG) in LSN is proposed to improve the reliability and effectiveness of information transmission. First, for the time‐varying topology, a Markov space‐time graph (MSG) model is constructed by calculating the connectivity time and connectivity probability between satellite node states. Then, in light of the uneven distribution of service demands, the space‐time factor (STF) is defined to balance the network load by scheduling the intersatellite link with lower loads. After that, we formulate the general routing problem caused by different quality of service (QoS) requirements for multimedia services and uneven load distribution and propose a multi‐QoS optimization objective function according to delay, remaining bandwidth, packet loss rate, and STF. Following this, based on the established MSG, the state transition rule and pheromone updating rule are optimally adjusted to select the next hop node, so as to finally determine the best information transmission path. Finally, the simulation results show that the SOR‐MSG algorithm can not only adapt to dynamically changing topologies and meet multiservice QoS requirements but also well balance the network load.

Why do many a-share listed companies eagerly disclose food safety information in China?—from the perspective of financing constraints

ABSTRACT Recently, many listed food companies have started to eagerly disclose their food safety information; the question is, why? This study empirically tests the impacts of food safety information disclosure (FSID) on corporate financing constraints using a panel dataset. The results show that FSID is negatively correlated with equity financing constraint, while its degree is negatively correlated with debt financing constraint. FSID has a larger influence on the financing constraints of state-owned enterprises. Therefore, the standardized FSID is conducive to establishing a mechanism connecting an enterprise with government supervision, and to forming a bottom-up information transmission path in food safety production.

Measure User Intimacy by Mining Maximum Information Transmission Paths

The Internet has become an important carrier of information. Its data contain abundant information about hot events, user relations and attitudes, and so on. Many enterprises use high-impact Internet users to promote products, so it is very important to understand the mechanism of information transmission. Mining social network data can help people analyze the complex and changing relationships between users. The traditional method for doing this is to analyze information such as common interests and common friends, but this data cannot truly describe the degree of intimacy between users. What really connects different users on the Internet is the delivery of information. The algorithm proposed in this paper considers the dynamic characteristics of information transmission, finds maximum transmission paths from information transmission results, and finally calculates the intimacy degrees between users according to all the maximum information transmission paths within a certain period.

Research on algorithm of information transmission path planning in big data environment

The information transmission path planning is conducive to the improvement of efficiency of information transmission, which matches requests of development of era. However, most of the information transmission path plans select transmission paths based on ID format. Although this algorithm can make nodes in big data environment reach the optimal path of sink node, it has high computational complexity. Therefore, an information transmission path planning algorithm based on an ingress-priority under big data environment is proposed. Based on this algorithm, a method for evaluating the information transmission path planning is obtained. Then analysis model of the information transmission path planning is constructed. Based on these, dynamic information transmission path planning is implemented by utilising priority multi-actuators. Experiments show that our proposed method can effectively improve the efficiency of information transmission path planning, ensure the accuracy of information after transmission and improve the quality of information transmission. Therefore, our proposed method is significant in application.

Research on algorithm of information transmission path planning in big data environment

The information transmission path planning is conducive to the improvement of efficiency of information transmission, which matches requests of development of era. However, most of the information transmission path plans select transmission paths based on ID format. Although this algorithm can make nodes in big data environment reach the optimal path of sink node, it has high computational complexity. Therefore, an information transmission path planning algorithm based on an ingress-priority under big data environment is proposed. Based on this algorithm, a method for evaluating the information transmission path planning is obtained. Then analysis model of the information transmission path planning is constructed. Based on these, dynamic information transmission path planning is implemented by utilising priority multi-actuators. Experiments show that our proposed method can effectively improve the efficiency of information transmission path planning, ensure the accuracy of information after transmission and improve the quality of information transmission. Therefore, our proposed method is significant in application.

ResWnet for Retinal Small Vessel Segmentation

U-Net shows excellent performance in biomedical image segmentation tasks, Variants of U-Net have been proposed, such as U-Net with residual blocks, densely connected convolution, or deformed convolution. However, contracting path of these variants is containing four consecutive downsampling layers and an encoding-decoding structure with skip connections. The feature map transformation paths are limited. Therefore, ResWnet is proposed in this work, the number of downsampling layers of contracting path is modified to two, and the network is changed from the encoding-decoding structure to the encoding-decoding-encoding-decoding structure so that the network can retain more detailed features and extract deeper semantic information. The skip connections are used between the contracting path and the expansion path at the same scale to increase the information transmission path. The convergence speed of the network is accelerated by the residual block replacing convolution layer. The image is scaled to different scales and is inputted into the network from the left to enhance the sensitivity of the network for blood vessels at different scales. The feature maps extracted from different layers of the network are scaled to the same size as the inputted image for fusion and segmentation. The performance of ResWnet is evaluated on DRIVE and STARE databases. The AUC is 0.9799 and 0.9863, and the accuracy is 0.9554 and 0.9723, respectively. According to the results, ResWnet outperforms many other proposed methods in small vessel segmentation. For future research, it can be learned that pre-processing and post-processing are vital to the segmentation accuracy. Furthermore, ResWnet can also be applied to other semantic segmentation tasks such as lung nodule segmentation or cancer cell segmentation. For future research, the pre-processing and post-processing will be tried to further enhance the segmentation accuracy performance of ResWnet.

Information Security Risk Propagation Model Based on the SEIR Infectious Disease Model for Smart Grid

With the high integration of smart grid information and physical systems, the security of information systems must affect the safe and stable operation of physical systems. Risk assessment is an effectual means to objectively evaluate the information security threats of the smart grid. However, the existing risk assessment methods are aim at solving the threat of security risks in communication networks and information systems in the smart grid, but there is no in-depth study on how the spread of information security risks between information systems and physical systems in the smart grid. Therefore, based on the traditional infectious disease transmission theory, the information security risk propagation model based on the Susceptible–Exposed–Infected–Recovered (SEIR) infectious disease model for smart grid (ISRP-SEIRIDM) is proposed in this paper. In ISRP-SEIRIDM, we analyze the information interaction between information collection devices and define the connection of nature and the security risks between the information collection devices in the smart grid. At the same time, we also study the impact of the number of information acquisition devices and information interaction capabilities of these devices on the speed of security risk transmission between information systems and physics systems in the smart grid and the maximum risk range. Experimental results show that the risk propagation range can be significantly reduced by optimizing the data interaction capability and information transmission path between information collection devices in the smart grid; when a probability from a susceptible state to an exposed state reduces by 0.15, the maximum spread and average spread of security risk will be reduced by 7% and 1.96%, respectively.

An Energy-Efficient Clustering Routing Protocol Based on a High-QoS Node Deployment with an Inter-Cluster Routing Mechanism in WSNs.

Currently, wireless sensor network (WSN) protocols are mainly used to achieve low power consumption of the network, but there are few studies on the quality of services (QoS) of these networks. Coverage can be used as a measure of the WSN’s QoS, which can further reflect the quality of data information. Additionally, the coverage requirements of regional monitoring target points are different in real applications. On this basis, this paper proposes an energy-efficient clustering routing protocol based on a high-QoS node deployment with an inter-cluster routing mechanism (EECRP-HQSND-ICRM) in WSNs. First, this paper proposes formula definitions for information integrity, validity, and redundancy from the coverage rate and introduces a node deployment strategy based on twofold coverage. Then, in order to satisfy the uniformity of the distribution of cluster heads (CHs), the monitoring area is divided into four small areas centered on the base station (BS), and the CHs are selected in the respective cells. Finally, combined with the practical application of the WSN, this paper optimizes the Dijkstra algorithm, including: (1) nonessential paths neglecting considerations, and (2) a simultaneous introduction of end-to-end weights and path weights, achieving the selection of optimal information transmission paths between the CHs. The simulation results show that, compared with the general node deployment strategies, the deployment strategy of the proposed protocol has higher information integrity and validity, as well as lower redundancy. Meanwhile, compared with some classic protocols, this protocol can greatly reduce and balance network energy consumption and extend the network lifetime.

Reliability and Operating Analysis of Transmission of Alarm Signals of Distributed Fire Signaling System

Abstract The article presents basic issues regarding the operation process and analysis of the reliability of the fire alarm and transmission system in the distributed fire alarm system. Signals of fire and fault alarms generated from the fire alarm system (SSP) send fire monitoring. Fire monitoring consists of sending, with the confirmation, an automatic fire alarm and a fault signal to the appropriate alarm receiving centers (ACO). The reliability structure of the fire and fault alarm transmission system is presented. An analysis of reliability of independent SSP system information transmission paths was performed.

Routing Algorithm Based on Trajectory Prediction in Opportunistic Networks

Due to the dynamic change of the opportunistic network topology and the lack of stable information transmission paths between nodes, the traditional topology-based routing algorithm cannot achieve the desired routing performance. To address of this problem, this paper proposes a routing algorithm based on trajectory prediction (RATP). The routing protocol based on trajectory prediction can efficiently and quickly adapt to the network link quality instability and the dynamic changes of network topology. RATP algorithm constructs a node mobility model by analyzing the historical mobility characteristics of the nodes. According to the node prediction information, the metric value of the candidate node is calculated, and the node with the smaller metric value is selected as the data forwarding node, which can effectively reduce the packet loss rate and avoids excessive consumption. Simulation results show that compared with other algorithms, the proposed algorithm has higher data delivery ratio, and end-to-end data delay and routing overhead are significantly reduced.

Improved Ant Colony-Genetic Algorithm for Information Transmission Path Optimization in Remanufacturing Service System

The information transmission path optimization (ITPO) can often affect the efficiency and accuracy of remanufacturing service. However, there is a greater degree of uncertainty and complexity in information transmission of remanufacturing service system, which leads to a critical need for designing planning models to deal with this added uncertainty and complexity. In this paper, a three-dimensional (3D) model of remanufacturing service information network for information transmission is developed, which combines the physic coordinate and the transmitted properties of all the devices in the remanufacturing service system. In order to solve the basic ITPO in the 3D model, an improved 3D ant colony algorithm (Improved AC) was put forward. Moreover, to further improve the operation efficiency of the algorithm, an improved ant colony-genetic algorithm (AC-GA) that combines the improved AC and genetic algorithm was developed. In addition, by taking the transmission of remanufacturing service demand information of certain roller as example, the effectiveness of AC-GA algorithm was analyzed and compared with that of improved AC, and the results demonstrated that AC-GA algorithm was superior to AC algorithm in aspects of information transmission delay, information transmission cost, and rate of information loss.

An improved evaluating method of node spreading influence in complex network based on information spreading probability

How to evaluate the node spreading ability and how to find influential nodes in complex networks are crucial to controlling diseases and rumors, accelerating or hindering information from diffusing, and designing effective advertising strategies for viral marketing, etc. At present, many indicators based on the shortest path, such as closeness centrality, betweenness centrality and the (SP) index have been proposed to evaluate node spreading influence. The shortest path indicates that the information transmission path between nodes always selects the optimal mode. However, information does not know the ideal route from one place to another. The message does not flow only along geodesic paths in most networks, and information transmission path may be any reachable path between nodes. In the network with high clustering coefficient, the local high clustering of the nodes is beneficial to the large-scale dissemination of information. If only the information is transmitted according to the optimal propagation mode, which is the shortest path propagation, the ability to disseminate the node information would be underestimated, and thus the sorting precision of node spreading influence is reduced. By taking into account the transmission rate and the reachable path between a node and its three-step inner neighbors, we design an improved method named ASP to generate ranking list to evaluate the node spreading ability. We make use of the susceptible-infected-recovered (SIR) spreading model with tunable transmission rate to check the effectiveness of the proposed method on six real-world networks and three artificial networks generated by the Lancichinetii-Fortunato-Radicchi (LFR) benchmark model. In the real data sets, the proposed algorithm can achieve a better result than other metrics in a wide range of transmission rate, especially in networks with high clustering coefficients. The experimental results of the three LFR benchmark datasets show that the relative accuracy of ranking result of the ASP index and the SP index changes with the sparseness of the network and the information transmission rate. When the information dissemination rate is small, SP index is slightly better than the ASP index. The reason for this result is that when the transmission rate is small, the node influence is close to the degree. However, when the transmission rate is greater, the accuracy of the ASP index is higher than those of other indicators. This work can shed light on how the local clustering exerts an influence on the node propagation.

Exploration and Analysis of Sports Information Transmission Paths with Carrier Network Using Grey Model

Exploration and Analysis of Sports Information Transmission Paths with Carrier Network Using Grey Model

A complex network evolution model for network growth promoted by information transmission

In many real complex networks, information transmission occurs all the time. To study the effects of information transmission on the complex network evolution, we propose a new model for network growth promoted by the information transmission. The model includes three major steps: (i) New links attached to the nodes on the information transmission path, whose source point is chosen preferentially; (ii) the first link of the new node attached to the nodes in the local-world; (iii) other links of the new node attached to the nodes on the information transmission path, whose source point is the new node. The process of information transmission is simulated by self-avoiding random walk, and by considering the local information including its degree and distance; selective connection is established between the nodes on the information transmission path. Theoretical analysis and numerical simulation results show that the proposed model can not only reproduce small-world and scale-free network characteristics, but also indicate that shift power-law distribution and truncated power law function may form for different parameters which have some non-power-law features, such as exponential cutoff, and saturation for small variables. Moreover, in our model, the clustering coefficient is tunable without changing the degree distribution, and the model can also construct a network with assortative or disassortative mixed pattern.

The small world networks property and cognitive function in the frontal low-grade glioma patients: a pre-and-postoperative fMRI study

Objective To explore the property of brain functional networks and cognitive function changes in patients with frontal lobe low-grade gliomas ( LGG ). Methods 8 cases of suspected frontal lobe LGG patients were undergone with resting-fMRI scanning to analyze the small-world property of the LGG , meanwhile the LGG groups had Montreal (MoCA) cognitive score exam compared with the control group. Results The value of MoCA was 22.5±1.5 , 21.8±2.0, and 27.9±2.1 respectively with statistical significance (P<0.05) in the LGG groups and the control groups.The LGG group cognitive score was significantly lower than that in the control group with statistical significance (P<0.05 ). As to threshold, the two groups were consistent with the small world property.The LGG local efficiency was smaller than that of the controls, the postoperative small world properties(σ=2.49)were lower than that the pre-operative (σ=2.68), the largest brain function areas of preoperative information transmission were respectively the supramarginal gyrus, posterior cingulate, insula, and the postoperative being the precuneus, calcarine sulcus and superior frontal gyrus. The maximum cluster coefficient of the preoperative functional network were respectively the entorhinal cortex, transverse temporal gyrus and the calcarine sulcus, and postoperative were Wilson, transverse temporal gyri and occipital gyrus.Preoperative information transmission path was less than the postoperative, and the small world properties were positively correlated with MoCA. Conclusion LGG accompany by the changes of cognitive function, and with the small world network property pre-and post-operation. Key words: Low-grade gliomas; Small-world networks; Cognitive

0403 MATCHING FIRST EPISODE PSYCHOSIS PATIENT WITH EMPLOYMENT RESOURCES: CREATION OF A NEW TOOL

From the perspective of dynamic co-words network, for the first time this paper provide a unique flavored critical review on the evolution of the research topic system (RTS) of soil heavy metal pollution bioremediation (SHMPB). We built the dynamic co-words network-related measures of the RTS of SHMPB. Several main innovative findings were obtained. (i) We got the basic characteristics of the co-words network. The co-words network for the RTS had a relative wide and flat structure, which implies the SHMPB's RTS has spread in a broad range. This is differently from the usual radial structure of RTSs in many other scopes. (ii) We revealed the hotspots and development trends of the RTS: the contact degree between different hot fields gradually has increased as the years go by. We found that researchers tended to use plants to carry out SHMPB. The hotspots have shifted from hyperaccumulators to the plants like poplar. (iii) From the degree distribution of the dynamic co-words network, we revealed the evolution of the RTS had the scale free characteristics. The growth and preferential attachment mechanism of keywords were the driving force of the evolving RTS. (iv) By the calculations of the average path length and average clustering coefficients, we found that the evolution of the RTS had the small-world effect. (v) Basing on the obtained results, we gave some implications such as the confirmation of high efficient information transmission, the identification of potential destructive effect, the recognition of important nodes and the creation of new information transmission path within the RTS. Beyond the RTS of SHMPB, we would like to stress the importance of the proposed method, demonstrating its universal applicable merits for RTSs in wide scopes.