On the versatility of radial basis function neural networks: A case study in the field of intrusion detection

Classifiers based on radial basis function neural networks have a number of useful properties that can be exploited in many practical applications. Using sample data, it is possible to adjust their parameters (weights), to optimize their structure, and to select appropriate input features (attributes). Moreover, interpretable rules can be extracted from a trained classifier and input samples can be identified that cannot be classified with a sufficient degree of “certainty”. These properties support an analysis of radial basis function classifiers and allow for an adaption to “novel” kinds of input samples in a real-world application. In this article, we outline these properties and show how they can be exploited in the field of intrusion detection (detection of network-based misuse). Intrusion detection plays an increasingly important role in securing computer networks. In this case study, we first compare the classification abilities of radial basis function classifiers, multilayer perceptrons, the neuro-fuzzy system NEFCLASS, decision trees, classifying fuzzy-k-means, support vector machines, Bayesian networks, and nearest neighbor classifiers. Then, we investigate the interpretability and understandability of the best paradigms found in the previous step. We show how structure optimization and feature selection for radial basis function classifiers can be done by means of evolutionary algorithms and compare this approach to decision trees optimized using certain pruning techniques. Finally, we demonstrate that radial basis function classifiers are basically able to detect novel attack types. The many advantageous properties of radial basis function classifiers could certainly be exploited in other application fields in a similar way.     

Personalized text snippet extraction using statistical language models

In knowledge discovery in a text database, extracting and returning a subset of information highly relevant to a user's query is a critical task. In a broader sense, this is essentially identification of certain personalized patterns that drives such applications as Web search engine construction, customized text summarization and automated question answering. A related problem of text snippet extraction has been previously studied in information retrieval. In these studies, common strategies for extracting and presenting text snippets to meet user needs either process document fragments that have been delimitated a priori or use a sliding window of a fixed size to highlight the results. In this work, we argue that text snippet extraction can be generalized if the user's intention is better utilized. It overcomes the rigidness of existing approaches by dynamically returning more flexible start-end positions of text snippets, which are also semantically more coherent. This is achieved by constructing and using statistical language models which effectively capture the commonalities between a document and the user intention. Experiments indicate that our proposed solutions provide effective personalized information extraction services.     

无人机侧向系统LPV 模型降阶

无人机线性参变(LPV)模型能准确描述其非线性动态特性,但初始建立的LPV模型阶数较高,控制过程计算量较大.为此,提出一种基于平衡截断的LPV模型降阶方法.首先给出LPV系统的适定性、稳定性和平衡实现的定义;然后,提出LPV模型的平衡截断降阶方法.针对无人机侧向系统LTI模型,通过多项式拟合来建立LPV模型,并实现模型降阶.仿真结果表明,降阶模型的阶跃响应满足输出响应的精度要求.     

Using Scrum to guide the execution of software process improvement in small organizations

For software process improvement - SPI - there are few small organizations using models that guide the management and deployment of their improvement initiatives. This is largely because a lot of these models do not consider the special characteristics of small businesses, nor the appropriate strategies for deploying an SPI initiative in this type of organization. It should also be noted that the models which direct improvement implementation for small settings do not present an explicit process with which to organize and guide the internal work of the employees involved in the implementation of the improvement opportunities. In this paper we propose a lightweight process, which takes into account appropriate strategies for this type of organization. Our proposal, known as a “Lightweight process to incorporate improvements”, uses the philosophy of the Scrum agile method, aiming to give detailed guidelines for supporting the management and performance of the incorporation of improvement opportunities within processes and their putting into practice in small companies. We have applied the proposed process in two small companies by means of the case study research method, and from the initial results, we have observed that it is indeed suitable for small businesses.     

Activity labeling in process modeling: Empirical insights and recommendations

Few studies have investigated the factors contributing to the successful practice of process modeling. In particular, studies that contribute to the act of developing process models that facilitate communication and understanding are scarce. Although the value of process models is not only dependent on the choice of graphical constructs but also on their annotation with textual labels, there has been hardly any work on the quality of these labels. Accordingly, the research presented in this paper examines activity labeling practices in process modeling. Based on empirical data from process modeling practice, we identify and discuss different labeling styles and their use in process modeling praxis. We perform a grammatical analysis of these styles and use data from an experiment with process modelers to examine a range of hypotheses about the usability of the different styles. Based on our findings, we suggest specific programs of research towards better tool support for labeling practices. Our work contributes to the emerging stream of research investigating the practice of process modeling and thereby contributes to the overall body of knowledge about conceptual modeling quality.     

Integrating cognitive load theory and concepts of human–computer interaction

With the continually increasing complexity of e-learning environments, there is a need for integrating concepts of cognitive load theory (CLT) with concepts of human–computer interaction (HCI). Basic concepts of both fields were reviewed and contrasted. A literature review was conducted within the literature database “The Guide to Computing Literature,” searching for “cognitive load theory” and “Sweller.” Sixty-five publications contained “cognitive load” in their titles or abstracts. Each publication was checked to see whether it contained the concepts of intrinsic, extraneous, or germane cognitive load. The review showed that CLT concepts have been adopted in HCI. However, the concept of germane cognitive load has attracted less attention up to the present time. Two conceptual models are proposed. The first model divides extraneous cognitive load into load induced by the instructional design and load caused by software usage. The model clarifies the focus of traditional usability principles and of existing instructional design principles derived from CLT. The second model fits CLT concepts into the basic components of user-centered design. The concept of germane cognitive load illustrates that an increase of cognitive load can be desirable when designing e-learning environments. Areas for future interdisciplinary research are sketched.     

Distributed bandwidth allocation based on alternating evolution algorithm

In a network, end nodes have to compete for bandwidth through some distributed congestion control algorithms. It is a great challenge to ensure the efficiency and fairness of the distributed control algorithms. TCP congestion control algorithms do not perform well in terms of their efficiency and fairness in high speed networks. In this paper, we propose a novel asymptotic evolution algorithm based on the Logistic Model to allocate limited bandwidth resource. The algorithm introduces an explicit bandwidth pre-allocation factor. The factor is carried by the packet and is computed in routers based on the information of the router capacity, the aggregate load, and the instantaneous queue length; therefore the algorithm does not require the routers to keep the per-flow state. According to this pre-allocation bandwidth factor, the senders asymptotically adjust their sending rate and the bandwidth factor changes asymptotically along with the variation of the aggregate load and the queue length in the routers; therefore the sending rate and the pre-allocation bandwidth factor form alternating evolution and eventually reach a steady state.     

Does stock repurchase declaration affect stock price? Differences between the electrics industry and other industries

This study is to discuss the impact of stock repurchase declaration and purpose of repurchase on the stock price in the backdrop of listed companies on Taiwan’s stock market. Event Study Method is employed to discuss stock price fluctuations while GARCH (Generalized Autoregressive Conditional Heteroscedasticity) is applied to estimate the Market Model regressive coefficients. The samples consisted of companies declaring first stock repurchase are selected from August 9, 2000 to December 31, 2005 with a precondition that all the companies shall be listed ones 150 days prior to declaration. The study results reveal that companies from other industries have considerably bigger average CAR than companies of the electrics industry before and after the declaration of stock repurchase. Companies with application purpose of “maintain stockholders’ equities and corporate credit” have considerably bigger average CAR than companies with application purpose of “transferring stocks to employees”. In industries other than electrics, companies with application purpose of “maintain stockholders’ equities and corporate credit” have bigger accumulated abnormal return response than companies with application purpose of “transferring stocks to employees”. In case of “maintain stockholders’ equities and corporate credit” as the application purpose of stock repurchase, companies from industries other than electrics have relatively higher average CAR response. The empirical study results can serve as a reference for the listed company management and to related academic studies.     

谈智能科学多媒体辅助教学设计

在多媒体辅助教学中,教学设计直接影响教学效果。认知心理学的发展,尤其是R.E.Mayer提出的多媒体教学理论给我们提供了一个研究教学设计规则的依据。描述同多媒体教学相关的认知心理学的几个基本理论,在此基础上提出一些关于提高多媒体辅助教学效果的教学设计方法。     

RTO: An overview and assessment of current practice

The practice of implementing real-time optimization (RTO) using a rigorous steady-state model, in conjunction with model predictive control (MPC), dates back to the late 1980s. Since then, numerous projects have been implemented in refinery and chemical plants, and RTO has received significant attention in the industrial and academic literature. This history affords us the opportunity to assess the impact and success of RTO technology in the process industries. We begin with a discussion of the role RTO serves in the hierarchy of control and optimization decision making in the plant, and outline the key steps of the RTO layer and the coordination with MPC. Where appropriate, we point out the different approaches that have been used in practice and discuss the success factors that directly relate to the success of RTO within an organization. We also discuss alternative approaches that have been used to alleviate some of the challenges associated with implementing RTO and which may be appropriate for those unwilling to commit to the traditional RTO approach. Lastly, we provide suggestions for improvement to motivate further research.