Multi-Path Routing Algorithm for Wireless Sensor Network Based on Semi-Supervised Learning.

Multi-path transmission can well solve the data transmission reliability problems and life cycle problems caused by single-path transmission. However, the accuracy of the routing scheme generated by the existing multi-path routing algorithms is difficult to guarantee. In order to improve the accuracy of the multi-path routing scheme, this paper innovatively proposes a multi-path routing algorithm for a wireless sensor network (WSN) based on the evaluation. First, we design and implement the real-time evaluation algorithm based on semi-supervised learning (RESL). We prove that RESL is better in evaluation time and evaluation accuracy through comparative experiments. Then, we combine RESL to design and implement the multi-path routing algorithm for wireless sensor networks based on semi-supervised learning (MRSSL). Then, we prove that MRSSL has advantages in improving the accuracy of the multi-path routing scheme through comparative experiments.

QOGMP: QoS-oriented global multi-path traffic scheduling algorithm in software defined network.

According to the research status of Software Defined Network (SDN) control layer traffic scheduling, we find the current common problems, including single path, easy congestion, Quality of Service (QoS) requirements and high delay. To solve these four problems, we design and implement a QoS-oriented global multi-path traffic scheduling algorithm for SDN, referred to as QOGMP. First, we propose a link weight calculation algorithm based on the idea of traction links and deep reinforcement learning, and conduct experimental verifications related to traction links. The algorithm considers QoS requirements and alleviates the problems of easy congestion and high delay. Then, we propose a traffic scheduling algorithm based on link weight and multi-path scheme, which also considers QoS requirements and solves the problem of single path. Finally, we combined the link weight calculation algorithm and the traffic scheduling algorithm to implement QOGMP, and carried out comparative experiments in the built simulation environment. The experimental results show that QOGMP is better than the two comparison algorithms in terms of delay and rescheduling rate.

RMHIL: A Rule Matching Algorithm Based on Heterogeneous Integrated Learning in Software Defined Network.

To ensure the efficient operation of large-scale networks, the flow scheduling in the software defined network (SDN) requires the matching time and memory overhead of rule matching to be as low as possible. To meet the requirement, we solve the rule matching problem by integrating machine learning methods, including recurrent neural networks, reinforcement learning, and decision trees. We first describe the SDN rule matching problem and transform it into a heterogeneous integrated learning problem. Then, we design and implement an SDN flow forwarding rule matching algorithm based on heterogeneous integrated learning, referred to as RMHIL. Finally, we compare RMHIL with two existing algorithms, and the comparative experimental results show that RMHIL has advantages in matching time and memory overhead.

Meta-Knowledge and Multi-Task Learning-Based Multi-Scene Adaptive Crowd Counting.

In this paper, we propose a multi-scene adaptive crowd counting method based on meta-knowledge and multi-task learning. In practice, surveillance cameras are stationarily deployed in various scenes. Considering the extensibility of a surveillance system, the ideal crowd counting method should have a strong generalization capability to be deployed in unknown scenes. On the other hand, given the diversity of scenes, it should also effectively suit each scene for better performance. These two objectives are contradictory, so we propose a coarse-to-fine pipeline including meta-knowledge network and multi-task learning. Specifically, at the coarse-grained stage, we propose a generic two-stream network for all existing scenes to encode meta-knowledge especially inter-frame temporal knowledge. At the fine-grained stage, the regression of the crowd density map to the overall number of people in each scene is considered a homogeneous subtask in a multi-task framework. A robust multi-task learning algorithm is applied to effectively learn scene-specific regression parameters for existing and new scenes, which further improve the accuracy of each specific scenes. Taking advantage of multi-task learning, the proposed method can be deployed to multiple new scenes without duplicated model training. Compared with two representative methods, namely AMSNet and MAML-counting, the proposed method reduces the MAE by 10.29% and 13.48%, respectively.

Deep Reinforcement Learning-Based Resource Allocation for Satellite Internet of Things with Diverse QoS Guarantee.

Large-scale terminals' various QoS requirements are key challenges confronting the resource allocation of Satellite Internet of Things (S-IoT). This paper presents a deep reinforcement learning-based online channel allocation and power control algorithm in an S-IoT uplink scenario. The intelligent agent determines the transmission channel and power simultaneously based on contextual information. Furthermore, the weighted normalized reward concerning success rate, power efficiency, and QoS requirement is adopted to balance the performance between increasing resource efficiency and meeting QoS requirements. Finally, a practical deployment mechanism based on transfer learning is proposed to promote onboard training efficiency and to reduce computation consumption of the training process. The simulation demonstrates that the proposed method can balance the success rate and power efficiency with QoS requirement guaranteed. For S-IoT's normal operation condition, the proposed method can improve the power efficiency by 60.91% and 144.44% compared with GA and DRL_RA, while its power efficiency is only 4.55% lower than that of DRL-EERA. In addition, this method can be transferred and deployed to a space environment by merely 100 onboard training steps.

Multi-Objective Optimisation in Multi-QoS Routing Strategy for Software-Defined Satellite Network.

The satellite network plays an increasingly important role in the global communication. With the development of communication technology, quality of service requirements have become more and more complex and diverse and the quality of service routing strategy of software-defined satellite network has become a more and more hot and difficult issue. In this paper, an interval-type-2 fuzzy set routing algorithm is proposed. Firstly, the multi- quality of service routing problem in software-defined satellite network is modeled. Then, the interval-type-2 fuzzy set routing algorithm is proposed to make fuzzy decisions. A series of experiments conducted in Network Simulator (Version 2.35) have proved that the proposed interval type-2 fuzzy set routing algorithm can reduce average delay, increase total throughput and reduce packet drop rate.

Active Module Identification From Multilayer Weighted Gene Co-Expression Networks: A Continuous Optimization Approach.

Searching for active modules, i.e., regions showing striking changes in molecular activity in biological networks is important to reveal regulatory and signaling mechanisms of biological systems. Most existing active modules identification methods are based on protein-protein interaction networks or metabolic networks, which require comprehensive and accurate prior knowledge. On the other hand, weighted gene co-expression networks (WGCNs) are purely constructed from gene expression profiles. However, existing WGCN analysis methods are designed for identifying functional modules but not capable of identifying active modules. There is an urgent need to develop an active module identification algorithm for WGCNs to discover regulatory and signaling mechanism associating with a given cellular response. To address this urgent need, we propose a novel algorithm called active modules on the multi-layer weighted (co-expression gene) network, based on a continuous optimization approach (AMOUNTAIN). The algorithm is capable of identifying active modules not only from single-layer WGCNs but also from multilayer WGCNs such as cross-species and dynamic WGCNs. We first validate AMOUNTAIN on a synthetic benchmark dataset. We then apply AMOUNTAIN to WGCNs constructed from Th17 differentiation gene expression datasets of human and mouse, which include a single layer, a cross-species two-layer and a multilayer dynamic WGCNs. The identified active modules from WGCNs are enriched by known protein-protein interactions, and more importantly, they reveal some interesting and important regulatory and signaling mechanisms of Th17 cell differentiation.

Identifying influential nodes based on network representation learning in complex networks.

Identifying influential nodes is an important topic in many diverse applications, such as accelerating information propagation, controlling rumors and diseases. Many methods have been put forward to identify influential nodes in complex networks, ranging from node centrality to diffusion-based processes. However, most of the previous studies do not take into account overlapping communities in networks. In this paper, we propose an effective method based on network representation learning. The method considers not only the overlapping communities in networks, but also the network structure. Experiments on real-world networks show that the proposed method outperforms many benchmark algorithms and can be used in large-scale networks.

Prognostic Significance of microRNA-7 and its Roles in the Regulation of Cisplatin Resistance in Lung Adenocarcinoma.

BACKGROUND: Previously, microRNA (miR)-7 has been reported to function as a tumor suppressor in human cancers, but the correlations of miR-7 expression with prognosis and cisplatin (CDDP) resistance in lung adenocarcinoma (LA) are unclear. Here, our aim is to determine the prognostic significance of miR-7 and its roles in the regulation of CDDP resistance in LA. METHODS: Quantitative real-time PCR (qRT-PCR) assay was performed to determine miR-7 expression in 108 paired of LA tissues and analyze its correlations with clinicopathological factors of patients. The patient survival data were collected retrospectively by Kaplan-Meier analyses, and multivariate analysis was performed using the Cox proportional hazards model to determine the prognostic significance of miR-7 expression. The effects of miR-7 expression on the chemosensitivity of LA cells to CDDP and its possible mechanisms were evaluated by MTT, flow cytometry, Western blot and luciferase assays. RESULTS: It was observed that the relative expression level of miR-7 in LA tissues was significantly lower than that in the adjacent normal tissues and low miR-7 expression level was closely associated with poorer tumor differentiation, advanced pathological T-factor, higher incidence of lymph node metastasis and advanced p-TNM stage. Also, patients with low miR-7 expression showed a shorter overall survival than those with high miR-7 expression, and multivariate analysis indicated that status of miR-7 expression was an independent molecular biomarker for predicting the overall survival (OS) of LA patients. In addition, upregulation of miR-7 increases the sensitivity of LA cells to CDDP via induction of apoptosis by targeting Bcl-2. CONCLUSIONS: Our finding for the first time demonstrates that low miR-7 expression may be an independent poor prognostic factor and targeting miR-7 may be a potential strategy for the reversal of CDDP resistance in LA.

An efficient semi-supervised community detection framework in social networks.

Community detection is an important tasks across a number of research fields including social science, biology, and physics. In the real world, topology information alone is often inadequate to accurately find out community structure due to its sparsity and noise. The potential useful prior information such as pairwise constraints which contain must-link and cannot-link constraints can be obtained from domain knowledge in many applications. Thus, combining network topology with prior information to improve the community detection accuracy is promising. Previous methods mainly utilize the must-link constraints while cannot make full use of cannot-link constraints. In this paper, we propose a semi-supervised community detection framework which can effectively incorporate two types of pairwise constraints into the detection process. Particularly, must-link and cannot-link constraints are represented as positive and negative links, and we encode them by adding different graph regularization terms to penalize closeness of the nodes. Experiments on multiple real-world datasets show that the proposed framework significantly improves the accuracy of community detection.

Active module identification in intracellular networks using a memetic algorithm with a new binary decoding scheme.

BACKGROUND: Active modules are connected regions in biological network which show significant changes in expression over particular conditions. The identification of such modules is important since it may reveal the regulatory and signaling mechanisms that associate with a given cellular response. RESULTS: In this paper, we propose a novel active module identification algorithm based on a memetic algorithm. We propose a novel encoding/decoding scheme to ensure the connectedness of the identified active modules. Based on the scheme, we also design and incorporate a local search operator into the memetic algorithm to improve its performance. CONCLUSION: The effectiveness of proposed algorithm is validated on both small and large protein interaction networks.

Generalization Bounds Derived IPM-Based Regularization for Domain Adaptation.

Domain adaptation has received much attention as a major form of transfer learning. One issue that should be considered in domain adaptation is the gap between source domain and target domain. In order to improve the generalization ability of domain adaption methods, we proposed a framework for domain adaptation combining source and target data, with a new regularizer which takes generalization bounds into account. This regularization term considers integral probability metric (IPM) as the distance between the source domain and the target domain and thus can bound up the testing error of an existing predictor from the formula. Since the computation of IPM only involves two distributions, this generalization term is independent with specific classifiers. With popular learning models, the empirical risk minimization is expressed as a general convex optimization problem and thus can be solved effectively by existing tools. Empirical studies on synthetic data for regression and real-world data for classification show the effectiveness of this method.

Expression of microRNA-218 and its clinicopathological and prognostic significance in human glioma cases.

BACKGROUND: MicroRNAs are a class of noncoding RNAs which regulate multiple cellular processes during tumor development. The purpose of this report is to investigate the clinicopathological and prognostic significance of miR-218 in human gliomas. MATERIALS AND METHODS: Quantitative RT-PCR (qRT-PCR) was conducted to detect the expression of miR-218 in primary normal human astrocytes, three glioma cell lines and 98 paired glioma and adjacent normal brain tissues.Associations of miR-218 with clinicopathological variables of glioma patients were statistically analyzed. Finally, a survival analysis was performed using the Kaplan-Meier method and Cox's proportional hazards model. RESULTS: The expression level of miR-218 in primary normal human astrocytes was significantly higher than that in glioma cell lines (p<0.01). Also, the expression level of miR-218 in glioma tissues was significantly downregulated in comparison with that in the adjacent normal brain tissues (p<0.001). Statistical analyses demonstrated that low miR-218 expression was closely associated with advanced WHO grade (p=0.002) and low Karnofsky performance score (p=0.010) of glioma patients. Kaplan-Meier analysis with the log-rank test showed that patients with low-miR-218 expression had poorer disease-free survival and overall survival (p=0.0045 and 0.0124, respectively). Multivariate analysis revealed that miR-218 expression was independently associated with the disease-free survival (p=0.009) and overall survival (p=0.004) of glioma patients. CONCLUSIONS: Our results indicate that miR-218 is downregulated in gliomas and that its status might be a potential valuable biomarker for glioma patients.

Polybrominated diphenyl ethers in human hair from the college environment: comparison with indoor dust.

Eight Polybrominated diphenyl ether (PBDE) congeners (BDE 28, 47, 99, 100, 153, 154, 183 and 209) were determined to examine the hair burden at low concentrations, and the relationship between PBDE concentrations in human hair and indoor dust from a college environment (Shanghai University campus). Chemical analyses showed that the total concentrations of PBDEs in hair ranged from 4.04 to 99 ng/g dw, and were found to be fourfold higher in females than in males (p < 0.05). The total PBDEs concentrations in indoor dust samples ranged from 170 to 1,360 ng/g dw. Significantly positive correlations were observed between human hair and indoor dust for BDE 47 (r = 0.44, p = 0.048) and BDE 99 (r = 0.68, p = 0.025). However, no significant association was noted between other PBDE congeners in human hair and indoor dust in the present study.

Application of uncertainty reasoning based on cloud model in time series prediction.

Time series prediction has been successfully used in several application areas, such as meteorological forecasting, market prediction, network traffic forecasting, etc., and a number of techniques have been developed for modeling and predicting time series. In the traditional exponential smoothing method, a fixed weight is assigned to data history, and the trend changes of time series are ignored. In this paper, an uncertainty reasoning method, based on cloud model, is employed in time series prediction, which uses cloud logic controller to adjust the smoothing coefficient of the simple exponential smoothing method dynamically to fit the current trend of the time series. The validity of this solution was proved by experiments on various data sets.