基于B/S模式公共任选课网选系统的设计与应用

以动态网页技术(ASP.NET)及数据库为基础,采用浏览器/服务器(B/S)模式的公共任选课网选系统作为信息化教学管理平台一个分支投入运行使用,对提高我校教学管理水平,合理分配教学资源和提高学生选课兴趣有着重要意义。

Novel Active Learning Method for Speech Recognition

In speech recognition, acoustic modeling always requires tremendous transcribed samples, and the transcription becomes intensively time-consuming and costly. In order to aid this labor-intensive process, Active Learning （AL） is adopted for speech recognition, where only the most informative training samples are selected for manual annotation. In this paper, we propose a novel active learning method for Chinese acoustic modeling, the methods for initial training set selection based on Kullback-Leibler Divergence （KLD） and sample evaluation based on multi-level confusion networks are proposed and adopted in our active learning system, respectively. Our experiments show that our proposed method can achieve satisfying performances.

ICN Based Vehicle-to-Cloud Delivery for Multimedia Streaming in Urban Vehicular Networks

Mobile multimedia streaming is an open topic in vehicular environment. Due to the high intermittent links, it has become a critical challenge to deliver high quality video streaming in vehicular networks. In this paper, we reform the Information Centric Networking （ICN） concept for multimedia delivery in urban vehicular networks. By leveraging the 1CN perspective, we highlight that vehicular peers can obtain multimedia chunks via the vehicle-to-cloud （V2C） approach to improve the delivery quality. Based on this, we propose a lightweight multipath selection strategy to guide the network system to adaptively adjust the forwarding means. Extensive simulations show that the proposed solution can optimize the utilization of network paths, lighten network loads as well as avoid wasting resources.

Novel design concepts for network intrusion systems based on dendritic cells processes

An abstraction and an investigation to the worth of dendritic cells (DCs) ability to collect, process and present antigens are presented. Computationally, this ability is shown to provide a feature reduction mechanism that could be used to reduce the complexity of a search space, a mechanism for development of highly specialized detector sets as well as a selective mechanism used in directing subsets of detectors to be activated when certain danger signals are present. It is shown that DCs, primed by different danger signals, provide a basis for different anomaly detection pathways. Different antigen-peptides are developed based on different danger signals present, and these peptides are presented to different adaptive layer detectors that correspond to the given danger signal. Experiments are then undertaken that compare current approaches, where a full antigen structure and the whole repertoire of detectors are used, with the proposed approach. Experiment results indicate that such an approach is feasible and can help reduce the complexity of the problem by significant levels. It also improves the efficiency of the system, given that only a subset of detectors are involved during the detection process. Having several different sets of detectors increases the robustness of the resulting system. Detectors developed based on peptides are also highly discriminative, which reduces the false positives rates, making the approach feasible for a real time environment.

Fluidized bed control system based on inverse system method

The invertible of the Large Air Dense Medium Fluidized Bed (ADMFB) were studied by introducing the concept of the inverse system theory of nonlinear systems. Then the ADMFB, which was a multivariable, nonlinear and coupled strongly system, was decoupled into independent SISO pseudo-linear subsystems. Linear controllers were designed for each of subsystems based on linear systems theory. The practice output proves that this method improves the stability of the ADMFB obviously.

Using probabilistic cache scheme to construct the small world network

Recently some P2P systems have constructed the small world network using the small world model so as to improve the routing performance.In this paper,we propose a novel probabilistic cache scheme to construct the small world network based on the small world model and use it to improve CAN,that is,PCCAN(Probabilistic Cache-based CAN).PCCAN caches the long contact.It uses the worm routing replacing mechanism and probabilistic replacing strategy on the cache.The probabilistic cache scheme proves to be an efficient approach to model the small world phenomenon.Experiments in both the static and the dynamic network show that PCCAN can converge to the steady state with the cache scheme,and the routing performance is significantly improved with additional low overheads in the network compared with CAN.

An immunity-based technique to detect network intrusions

This paper briefly reviews other people’s works on negative selection algorithm and their shortcomings. With a view to the real problem to be solved, authors bring forward two assumptions, based on which a new immune algorithm, multi-level negative selection algorithm, is developed. In essence, compared with Forrest’s negative selection algorithm, it enhances detector generation efficiency. This algorithm integrates clonal selection process into negative selection process for the first time. After careful analyses, this algorithm was applied to network intrusion detection and achieved good results.

Always-optimally-coordinated candidate selection algorithm for peer-to-peer files sharing system in mobile self-organized networks

In order to improve the performance of peer-to-peer files sharing system under mobile distributed en- vironments, a novel always-optimally-coordinated （AOC） criterion and corresponding candidate selection algorithm are proposed in this paper. Compared with the traditional min-hops criterion, the new approach introduces a fuzzy knowledge combination theory to investigate several important factors that influence files transfer success rate and efficiency. Whereas the min-hops based protocols only ask the nearest candidate peer for desired files, the selection algorithm based on AOC comprehensively considers users＇ preferences and network requirements with flexible balancing rules. Furthermore, its advantage also expresses in the independence of specified resource discovering protocols, allowing for scalability. The simulation results show that when using the AOC based peer selection algorithm, system performance is much better than the rain-hops scheme, with files successful transfer rate improved more than 50% and transfer time re- duced at least 20%.

Network Selection Based on Context-Awareness Services

In this paper,we propose a systemic architecture of network selection based on context-awareness services,which gathers contextual information that includes such network information,user information and local information.This network selection strategy considers the Quality of Service(QoS) and user preferences.Also,it perceives contexts such as speed,coverage percentage and location,etc.,and it eventually performs network selection decision making and network execution based on multiple factors.From the perspective of network decision,it presents two network selection algorithms,namely the fuzzy mathematics evaluation method and multiple attribute decision making using the TOPSIS evaluation method.System simulations suggest that network selection based on the mathematics evaluation method is much faster than the TOPSIS evaluation method.However,the TOPSIS evaluation method is practically more efficient.The network selection method based on context-awareness provides an effective and flexible network vertical handover strategy,and ensures a good accuracy and efficiency.

Selecting pinning nodes to control complex networked systems

One of the fundamental problems in pinning control of complex networks is selecting appropriate pinning nodes, such that the whole system is controlled. This is particularly useful for complex networks with huge numbers of nodes. Recent research has yielded several pinning node selection strategies, which may be efficient. However, selecting a set of pinning nodes and identifying the nodes that should be selected first remain challenging problems. In this paper, we present a network control strategy based on left Perron vector. For directed networks where nodes have the same in-and out-degrees, there has so far been no effective pinning node selection strategy, but our method can find suitable nodes. Likewise, our method also performs well for undirected networks where the nodes have the same degree. In addition, we can derive the minimum set of pinning nodes and the order in which they should be selected for given coupling strengths. Our proofs of these results depend on the properties of non-negative matrices and M-matrices. Several examples show that this strategy can effectively select appropriate pinning nodes, and that it can achieve better results for both directed and undirected networks.