Determination of temporal information granules to improve forecasting in fuzzy time series

Partitioning the universe of discourse and determining intervals containing useful temporal information and coming with better interpretability are critical for forecasting in fuzzy time series. In the existing literature, researchers seldom consider the effect of time variable when they partition the universe of discourse. As a result, and there is a lack of interpretability of the resulting temporal intervals. In this paper, we take the temporal information into account to partition the universe of discourse into intervals with unequal length. As a result, the performance improves forecasting quality. First, time variable is involved in partitioning the universe through Gath–Geva clustering-based time series segmentation and obtain the prototypes of data, then determine suitable intervals according to the prototypes by means of information granules. An effective method of partitioning and determining intervals is proposed. We show that these intervals carry well-defined semantics. To verify the effectiveness of the approach, we apply the proposed method to forecast enrollment of students of Alabama University and the Taiwan Stock Exchange Capitalization Weighted Stock Index. The experimental results show that the partitioning with temporal information can greatly improve accuracy of forecasting. Furthermore, the proposed method is not sensitive to its parameters.     

Leveraging clustering approaches to solve the gray-sheep users problem in recommender systems

Recommender systems apply data mining and machine learning techniques for filtering unseen information and can predict whether a user would like a given item. This paper focuses on gray-sheep users problem responsible for the increased error rate in collaborative filtering based recommender systems. This paper makes the following contributions: we show that (1) the presence of gray-sheep users can affect the performance – accuracy and coverage – of the collaborative filtering based algorithms, depending on the data sparsity and distribution; (2) gray-sheep users can be identified using clustering algorithms in offline fashion, where the similarity threshold to isolate these users from the rest of community can be found empirically. We propose various improved centroid selection approaches and distance measures for the K-means clustering algorithm; (3) content-based profile of gray-sheep users can be used for making accurate recommendations. We offer a hybrid recommendation algorithm to make reliable recommendations for gray-sheep users. To the best of our knowledge, this is the first attempt to propose a formal solution for gray-sheep users problem. By extensive experimental results on two different datasets (MovieLens and community of movie fans in the FilmTrust website), we showed that the proposed approach reduces the recommendation error rate for the gray-sheep users while maintaining reasonable computational performance.     

三聚氰胺的改性及其应用

综述了三聚氰胺对脲醛树脂、酚醛树脂的改性以及改性三聚氰胺在涂料、板材等方面的应用。三聚氰胺可以和其它聚合物发生聚合反应,形成立体网状结构,提高聚合物的强度、韧性、阻燃性等。三聚氰胺可作为改性剂对许多聚合物进行改性,以提高其性能。     

牛物法PDO合成PTT结构与性能的研究

采用生物法1，3-丙二醇（PDO）直接酯化缩聚合成聚对苯二甲酸丙二醇酯（PTT），通过IR、^1H NMR、DSC和TG方法对其进行了表征，并与化学法PDO合成的PTT进行比较。结果表明，生物法PDO合成的产物是PTT；生物法PDO合成的肿比同一纯度的化学法PDO合成的肿色泽好、粘度大、摩尔质量高，且随PDO纯度提高，肿粘度、摩尔质量增大；生物法PDO合成的肿熔点与化学法PDO合成的肿相差不大，熔融峰比化学法PDO合成的肿尖锐，熔融热大，结晶度高；不同PDO合成的肿树脂热失重相差不大，表明PDO不同对肿热分解行为影响不大。     

Co~(60)辐照交联聚氯乙烯性能的研究

本文详细讨论了影响Co~(60)辐照交联聚氯乙烯邵氏硬度、拉伸强度及医用化学性能的因素.发现,交联剂的种类、含量、辐照剂量、辐照后处理以及辐照前热历史对其性能都有着不同程度的影响;适当地控制这些因素,可以得到具有较大硬度变化和较高拉伸强度的材料.     

Impact of class clustering in a multiclass FCFS queue with order-dependent service times

In multi-class queueing systems, customers of different classes can enter the system. When studying such systems, it is traditionally assumed that the different classes of customers occur randomly and independently in the arrival stream of customers in the system. This is often in contrast to the actual situation. Therefore, we study a multi-class system with so-called class clustering in the customer arrival stream, i.e., (Markovian) correlation occurs in the classes of consecutive customers. The system under investigation consists of one server that is able to serve two classes of customers. In addition, the service-time distribution of a customer depends on the equality or non-equality of its class with the class of the previous customer. This latter feature occurs frequently in practice. For instance, execution of the same task again can lead to both faster or slower processing times. The first case can occur when the execution of a different task entails resetting a machine, or loading new data, et cetera. The opposite situation appears, for instance, when execution of the same task requires postprocessing (such as cooling down or reinitialization of a machine). We deduce the probability generating function (pgf) of the system content, from which we can extract various performance measures, among which the mean values of the system content and the customer delay. We demonstrate that class clustering has a tremendous impact on the system performance, which highlights the necessity to include it in the performance assessment of any system in which it occurs.     

Detecting similarities in virtual machine behavior for cloud monitoring using smoothed histograms

The growing size and complexity of cloud systems determine scalability issues for resource monitoring and management. While most existing solutions consider each Virtual Machine (VM) as a black box with independent characteristics, we embrace a new perspective where VMs with similar behaviors in terms of resource usage are clustered together. We argue that this new approach has the potential to address scalability issues in cloud monitoring and management. In this paper, we propose a technique to cluster VMs starting from the usage of multiple resources, assuming no knowledge of the services executed on them. This innovative technique models VMs behavior exploiting the probability histogram of their resources usage, and performs smoothing-based noise reduction and selection of the most relevant information to consider for the clustering process. Through extensive evaluation, we show that our proposal achieves high and stable performance in terms of automatic VM clustering, and can reduce the monitoring requirements of cloud systems.     

A Framework for Interactive t-SNE Clustering

In this paper, we describe our progress in creating the framework for an interactive application that allows humans to actively participate in a t-SNE clustering process. t-SNE (t-Distributed Stochastic Neighbor Embedding) is a dimensionality reduction technique that maps high dimensional data sets to lower dimensions that can then be visualized for human interpretation. By prompting users to monitor outlying points during the t-SNE clustering process, we hypothesize that users may be able to make clustering faster and more accurate than purely algorithmic methods. Further research would test these hypotheses directly. We would also attempt to decrease the lag time between the various components of our application and develop an intuitive approach for humans to aid in clustering unlabeled data. Research into human assisted clustering can combine the strengths of both humans and computer programs to improve the results of data analysis.     

改性硝酸铵的工业制备方法及其性能

硝酸铵饱和溶液中加入表面活性剂辛基三甲基溴化铵，采用真空膨胀结晶技术工业制备一种新型改性硝酸铵，并介绍其性能特点。     

尼龙/聚丙烯合金的性能与制备

介绍国外各种尼龙/聚丙烯合金的性能和制备工艺。聚合物合金的相容性原理,以及PA/PP合金适用的各种相容剂。