Large-scale monitoring of snow cover and runoff simulation in Himalayan river basins using remote sensing

Various remote sensing products are used to identify spatial-temporal trends in snow cover in river basins originating in the Himalayas and adjacent Tibetan-Qinghai plateau. It is shown that remote sensing allows detection of spatial-temporal patterns of snow cover across large areas in inaccessible terrain, providing useful information on a critical component of the hydrological cycle. Results show large variation in snow cover between years while an increasing trend from west to east is observed. Of all river basins the Indus basin is, for its water resources, most dependent on snow and ice melt and large parts are snow covered for prolonged periods of the year. A significant negative winter snow cover trend was identified for the upper Indus basin. For this basin a hydrological model is used and forced with remotely sensed derived precipitation and snow cover. The model is calibrated using daily discharges from 2000 to 2005 and stream flow in the upper Indus basin can be predicted with a high degree of accuracy. From the analysis it is concluded that there are indications that regional warming is affecting the hydrology of the upper Indus basin due to accelerated glacial melting during the simulation period. This warming may be associated with global changes in air temperature resulting from anthropogenic forcings. This conclusion is primarily based on the observation that the average annual precipitation over a five year period is less than the observed stream flow and supported by positive temperature trends in all seasons.     

Discovering hybrid temporal patterns from sequences consisting of point- and interval-based events

Previous sequential pattern mining studies have dealt with either point-based event sequences or interval-based event sequences. In some applications, however, event sequences may contain both point-based and interval-based events. These sequences are called hybrid event sequences. Since the relationships among both kinds of events are more diversiform, the information obtained by discovering patterns from these events is more informative. In this study we introduce a hybrid temporal pattern mining problem and develop an algorithm to discover hybrid temporal patterns from hybrid event sequences. We carry out an experiment using both synthetic and real stock price data to compare our algorithm with the traditional algorithms designed exclusively for mining point-based patterns or interval-based patterns. The experimental results indicate that the efficiency of our algorithm is satisfactory. In addition, the experiment also shows that the predicting power of hybrid temporal patterns is higher than that of point-based or interval-based patterns.     

A data mining technique for discovering distinct patterns of hand signs: implications in user training and computer interface design

Hand signs are considered as one of the important ways to enter information into computers for certain tasks. Computers receive sensor data of hand signs for recognition. When using hand signs as computer inputs, we need to (1) train computer users in the sign language so that their hand signs can be easily recognized by computers, and (2) design the computer interface to avoid the use of confusing signs for improving user input performance and user satisfaction. For user training and computer interface design, it is important to have a knowledge of which signs can be easily recognized by computers and which signs are not distinguishable by computers. This paper presents a data mining technique to discover distinct patterns of hand signs from sensor data. Based on these patterns, we derive a group of indistinguishable signs by computers. Such information can in turn assist in user training and computer interface design.     

A data mining technique for discovering distinct patterns of hand signs: implications in user training and computer interface design

Hand signs are considered as one of the important ways to enter information into computers for certain tasks. Computers receive sensor data of hand signs for recognition. When using hand signs as computer inputs, we need to (1) train computer users in the sign language so that their hand signs can be easily recognized by computers, and (2) design the computer interface to avoid the use of confusing signs for improving user input performance and user satisfaction. For user training and computer interface design, it is important to have a knowledge of which signs can be easily recognized by computers and which signs are not distinguishable by computers. This paper presents a data mining technique to discover distinct patterns of hand signs from sensor data. Based on these patterns, we derive a group of indistinguishable signs by computers. Such information can in turn assist in user training and computer interface design.     

Exact computation of pattern probabilities in random sequences generated by Markov chains

Observed patterns in macromolecular sequences are often considered as words and compared with their probabilities of occurring in random sequences. Calculation of these probabilities, however, often lacks rigour. We have developed an algorithm for exact computation of such probabilities for stochastic sequences that follow a Markov chain model. The method is applicable to the case that a random sequence contains one out of two given patterns P and Q, or both simultaneously. Another application yields the probability function P(x) that a sequence contains pattern P exactly x times. An application to patterns that include wild-card characters yields probabilities for homonucleotide clusters of a given length. We prove the probability of multiple runs of single nucleotides in the SV40 genome to be in accordance with the dinucleotide composition of the sequence, although it is in conflict with mononucleotide composition.     

Fast incremental mining of web sequential patterns with PLWAP tree

Point and click at web pages generate continuous data sequences, which flow into the web log data, causing the need to update previously mined web sequential patterns. Algorithms for mining web sequential patterns from scratch include WAP, PLWAP and Apriori-based GSP. Reusing old patterns with only recent additional data sequences in an incremental fashion, when updating patterns, would achieve fast response time with reasonable memory space usage. This paper proposes two algorithms, RePL4UP (Revised PLWAP For UPdate), and PL4UP (PLWAP For UPdate), which use the PLWAP tree structure to incrementally update web sequential patterns efficiently without scanning the whole database even when previous small items become frequent. The RePL4UP concisely stores the position codes of small items in the database sequences in its metadata during tree construction. During mining, RePL4UP scans only the new additional database sequences, revises the old PLWAP tree to restore information on previous small items that have become frequent, while it deletes previous frequent items that have become small using the small item position codes. PL4UP initially builds a bigger PLWAP tree that includes all sequences in the database using a tolerance support, t, that is lower than the regular minimum support, s. The position code features of the PLWAP tree are used to efficiently mine these trees to extract current frequent patterns when the database is updated. These approaches more quickly update old frequent patterns without the need to re-scan the entire updated database.     

Visual Analysis of Proximal Temporal Relationships of Social and Communicative Behaviors

Developmental psychology researchers examine the temporal relationships of social and communicative behaviors, such as how a child responds to a name call, to understand early typical and atypical development and to discover early signs of autism and developmental delay. These related behaviors occur together or within close temporal proximity, forming unique patterns and relationships of interest. However, the task of finding these early signs, which are in the form of atypical behavioral patterns, becomes more challenging when behaviors of multiple children at different ages need to be compared with each other in search of generalizable patterns. The ability to visually explore the temporal relationships of behaviors, including flexible redefinition of closeness, over multiple social interaction sessions with children of different ages, can make such knowledge extraction easier. We have designed a visualization tool called TipoVis that helps psychology researchers visually explore the temporal patterns of social and communicative behaviors. We present two case studies to show how TipoVis helped two researchers derive new understandings of their data.     

A general measure of similarity for categorical sequences

Measuring the similarity between categorical sequences is a fundamental process in many data mining applications. A key issue is extracting and making use of significant features hidden behind the chronological and structural dependencies found in these sequences. Almost all existing algorithms designed to perform this task are based on the matching of patterns in chronological order, but such sequences often have similar structural features in chronologically different order. In this paper we propose SCS, a novel, effective and domain-independent method for measuring the similarity between categorical sequences, based on an original pattern matching scheme that makes it possible to capture chronological and non-chronological dependencies. SCS captures significant patterns that represent the natural structure of sequences, and reduces the influence of those which are merely noise. It constitutes an effective approach to measuring the similarity between data in the form of categorical sequences, such as biological sequences, natural language texts, speech recognition data, certain types of network transactions, and retail transactions. To show its effectiveness, we have tested SCS extensively on a range of data sets from different application fields, and compared the results with those obtained by various mainstream algorithms. The results obtained show that SCS produces results that are often competitive with domain-specific similarity approaches.     

CASE-BASED REASONING AND KNOWLEDGE DISCOVERY IN MEDICAL APPLICATIONS WITH TIME SERIES

This paper discusses the role and integration of knowledge discovery (KD) in case-based reasoning (CBR) systems. The general view is that KD is complementary to the task of knowledge retaining and it can be treated as a separate process outside the traditional CBR cycle. Unlike knowledge retaining that is mostly related to case-specific experience, KD aims at the elicitation of new knowledge that is more general and valuable for improving the different CBR substeps. KD for CBR is exemplified by a real application scenario in medicine in which time series of patterns are to be analyzed and classified. As single pattern cannot convey sufficient information in the application, sequences of patterns are more adequate. Hence it is advantageous if sequences of patterns and their co-occurrence with categories can be discovered. Evaluation with cases containing series classified into a number of categories and injected with indicator sequences shows that the approach is able to identify these key sequences. In a clinical applica-tion and a case library that is representative of the real world, these key sequences would improve the classification ability and may spawn clinical research to explain the co-occurrence between certain sequences and classes.     

Modelling and recognition of the linguistic components in American Sign Language

The manual signs in sign languages are generated and interpreted using three basic building blocks: handshape, motion, and place of articulation. When combined, these three components (together with palm orientation) uniquely determine the meaning of the manual sign. This means that the use of pattern recognition techniques that only employ a subset of these components is inappropriate for interpreting the sign or to build automatic recognizers of the language. In this paper, we define an algorithm to model these three basic components form a single video sequence of two-dimensional pictures of a sign. Recognition of these three components are then combined to determine the class of the signs in the videos. Experiments are performed on a database of (isolated) American Sign Language (ASL) signs. The results demonstrate that, using semi-automatic detection, all three components can be reliably recovered from two-dimensional video sequences, allowing for an accurate representation and recognition of the signs.     

甲骨文字库与智能知识库的建立

文章介绍了用于计算机处理的甲骨文字库、句法分析和综合智能知识库的建立方法以及计算机甲骨文辅助辨识分析的工作原理,论述了计算机信息处理技术在甲骨文研究中的重要作用。目前的字库已收入三千多甲骨文字,可采用区位码和拼音输入,对与现代汉字有对应关系的一千多甲骨文字进行现代汉字、音、意、词性、属性等方面作出详尽的标注解释,用VC++实现了它们之间的互查功能。     

甲骨文识别的图同构方法

甲骨文是我国目前所能看到的最早而又比较完备的文字。为了使用计算机辅助识别和检索甲骨文,给出了一种将甲骨文转化为无向标号图的方法;使用无向标号图的拟邻接矩阵对甲骨文进行编码,重码较少;给出一种基于图同构的甲骨文识别算法,实例证明了该算法是有效的。     

浅析汉字在书籍封面设计中的应用

文字的出现使人类进入文明社会,它见证了人类的发展历程,使得各种重要信息得以保存,为后世留下了可考的依据。文字促进着文化发展,思想交流,丰富了人类的精神世界,让人类方便了生活。从甲骨文、金文到大篆、小篆再到隶书、楷书,这些符号的演变成就了中华文明的璀璨成果——汉字。文章将着重对汉字进行字体设计研究,从汉字的视觉传达角度探讨字体设计的原则及在书籍封面设计中的运用。     

基于数学形态学的甲骨拓片字形特征提取方法

甲骨拓片字形特征提取是对计算机辅助进行甲骨拓片复原、识别和断代等工作都非常关键的第一步。为了尽可能准确地把甲骨拓片从背景噪声中分离出来,首先对原始甲骨拓片图形进行预处理,然后再应用数学形态学方法对甲骨拓片进行图像处理和分析,提取出12项指标用于表现甲骨拓片字形特征,并构造了一个基于数学形态学方法的甲骨拓片字形特征提取系统。通过对《甲骨文合集》实验数据进行基于字形特征的甲骨拓片图像匹配验证,实验结果表明 数学形态学处理方法能有效地提取出较好地反映甲骨文字的笔画形态和结构的字形特征。     

Path testing of computer programs with loops using a tool for simple loop patterns

The purpose of this paper is to present a method for testing computer programs with iteration loops. Given such programs, we have shown that for classes of program paths, identified as sequences of simple loop paths, there is a characterizing function called a simple loop pattern. The key idea of simple loop patterns is that these special functions form a base set which can represent any path computation in the given program. A software tool called SILOP has been developed to automatically generate these simple loop patterns, and each corresponding sequence of simple loop paths can be considered as a test case. The tester uses each test case, and with knowledge of the application program, can generate corresponding test data. This paper also presents a method for selecting the specific paths and test data to determine the simple loop pattern reliably. The tester can use this selection method to predict the number of tests required. In order to apply this selection method, the given program must be a linear computer program. The SILOP tool and this test selection method have been applied to commercial software; in this paper, this computational experience is reported and several examples are given to demonstrate the approach.     

Identifying the signs of fraudulent accounts using data mining techniques

In today’s technological society there are various new means to commit fraud due to the advancement of media and communication networks. One typical fraud is the ATM phone scams. The commonality of ATM phone scams is basically to attract victims to use financial institutions or ATMs to transfer their money into fraudulent accounts. Regardless of the types of fraud used, fraudsters can only collect victims’ money through fraudulent accounts. Therefore, it is very important to identify the signs of such fraudulent accounts and to detect fraudulent accounts based on these signs, in order to reduce victims’ losses. This study applied Bayesian Classification and Association Rule to identify the signs of fraudulent accounts and the patterns of fraudulent transactions. Detection rules were developed based on the identified signs and applied to the design of a fraudulent account detection system. Empirical verification supported that this fraudulent account detection system can successfully identify fraudulent accounts in early stages and is able to provide reference for financial institutions.     

基于Capsule网络的甲骨文构件识别方法

甲骨文作为中国最早的成形文字系统,具有重要的文化和学术价值。研究甲骨文构件和其构形系统是破译未识别的甲骨文的重要方向,但是甲骨文构件的标记工作只能由资深专家来完成,并且需要耗费大量时间和精力。针对这些问题,提出了一种基于Capsule网络和迁移学习的模型OracleNet,可以自动识别并标记甲骨文字形中包含的构件;同时,构建了包含标记的甲骨文字形和构件数据集,用于模型的训练和评估。实验结果显示,OracleNet模型对甲骨文构件的预测精确度达到了60%以上,其中Top5精确度达到了71.56%,验证了模型的有效性。