A Portfolio Selection Method Based on Pattern Matching with Dual Information of Direction and Distance

Pattern matching method is one of the classic classifications of existing online portfolio selection strategies. This article aims to study the key aspects of this method—measurement of similarity and selection of similarity sets, and proposes a Portfolio Selection Method based on Pattern Matching with Dual Information of Direction and Distance (PMDI). By studying different combination methods of indicators such as Euclidean distance, Chebyshev distance, and correlation coefficient, important information such as direction and distance in stock historical price information is extracted, thereby filtering out the similarity set required for pattern matching based investment portfolio selection algorithms. A large number of experiments conducted on two datasets of real stock markets have shown that PMDI outperforms other algorithms in balancing income and risk. Therefore, it is suitable for the financial environment in the real world.

Exact Graph Pattern Matching:Applications,Progress and Prospects

Graph pattern matching(GPM)can be used to mine the key information in graphs.Exact GPM is one of the most commonly used methods among all the GPM-related methods,which aims to exactly find all subgraphs for a given query graph in a data graph.The exact GPM has been widely used in biological data analyses,social network analyses and other fields.In this paper,the applications of the exact GPM were first introduced,and the research progress of the exact GPM was summarized.Then,the related algorithms were introduced in detail,and the experiments on the state-of-the-art exact GPM algorithms were conducted to compare their performance.Based on the experimental results,the applicable scenarios of the algorithms were pointed out.New research opportunities in this area were proposed.

Spectral matching algorithm based on nonsubsampled contourlet transform and scale-invariant feature transform

A new spectral matching algorithm is proposed by us- ing nonsubsampled contourlet transform and scale-invariant fea- ture transform. The nonsubsampled contourlet transform is used to decompose an image into a low frequency image and several high frequency images, and the scale-invariant feature transform is employed to extract feature points from the low frequency im- age. A proximity matrix is constructed for the feature points of two related images. By singular value decomposition of the proximity matrix, a matching matrix （or matching result） reflecting the match- ing degree among feature points is obtained. Experimental results indicate that the proposed algorithm can reduce time complexity and possess a higher accuracy.

New multi-pattern matching algorithm

The traditional multiple pattern matching algorithm, deterministic finite state automata, is implemented by tree structure. A new algorithm is proposed by substituting sequential binary tree for traditional tree. It is proved by experiment that the algorithm has three features, its construction process is quick, its cost of memory is small. At the same time, its searching process is as quick as the traditional algorithm. The algorithm is suitable for the application which requires preprocessing the patterns dynamically.

Fast Multi-Pattern Matching Algorithm on Compressed Network Traffic

Pattern matching is a fundamental approach to detect malicious behaviors and information over Internet, which has been gradually used in high-speed network traffic analysis. However, there is a performance bottleneck for multi-pattern matching on online compressed network traffic(CNT), this is because malicious and intrusion codes are often embedded into compressed network traffic. In this paper, we propose an online fast and multi-pattern matching algorithm on compressed network traffic(FMMCN). FMMCN employs two types of jumping, i.e. jumping during sliding window and a string jump scanning strategy to skip unnecessary compressed bytes. Moreover, FMMCN has the ability to efficiently process multiple large volume of networks such as HTTP traffic, vehicles traffic, and other Internet-based services. The experimental results show that FMMCN can ignore more than 89.5% of bytes, and its maximum speed reaches 176.470MB/s in a midrange switches device, which is faster than the current fastest algorithm ACCH by almost 73.15 MB/s.

POINT PATTERN MATCHING ALGORITHM BASED ON POINT PAIR TOPOLOGICAL CHARACTERISTICS AND SPECTRAL MATCHING

Most of the Point Pattern Matching (PPM) algorithm performs poorly when the noise of the point's position and outliers exist. This paper presents a novel and robust PPM algorithm which combined Point Pair Topological Characteristics (PPTC) and Spectral Matching (SM) together to solve the afore mentioned issues. In which PPTC, a new shape descriptor, is firstly proposed. A new comparability measurement based on PPTC is defined as the matching probability. Finally, the correct matching results are achieved by the spectral matching method. The synthetic data experiments show its robustness by comparing with the other state-of-art algorithms and the real world data experiments show its effectiveness.

A Novel Mathematical Model for Similarity Search in Pattern Matching Algorithms

Modern applications require large databases to be searched for regions that are similar to a given pattern. The DNA sequence analysis, speech and text recognition, artificial intelligence, Internet of Things, and many other applications highly depend on pattern matching or similarity searches. In this paper, we discuss some of the string matching solutions developed in the past. Then, we present a novel mathematical model to search for a given pattern and it’s near approximates in the text.

Modified Multi-Resolution Telescopic Search Algorithm for Block-Matching Motion Estimation

This paper presents a modified multi-resolution telescopic search algorithm (MRTlcSA) for the block-matching motion estimation. A novel inverse telescopic search is substituted for the conventional telescopic search, that reduces the on-chip memory size and memory bandwidth for VLSI implementation. In addition, strategies of motion track and adaptive search window are applied to reduce the computational complexity of motion estimation. Simulation results show that, compared with the MRTleSA, the proposed algorithm reduces the computational load to only 30% while preserving almost the same image quality. Comparisons on hardware cost and power consumption of the VLSI implementations using the two algorithms are also presented in the paper.

Assessing Risk in Chemical Plant with Pattern Matching

This paper discusses potential application of fuzzy set theory,more specifically, pattern matching, in assessing risk in chemicalplants. Risk factors have been evaluated using linguisticrepresentations of the quantity of the hazardous substance involved,its frequency of interaction with the environment, severity of itsimpact and the uncertainty involved in its detection in advance. Foreach linguistic value there is a corresponding membership functionranging over a universe of discourse. The risk scenario created by ahazard/hazardous situation having highest degree of featural value istaken as the known pattern.

A Stream Pattern Matching Method for Traffic Analysis

In order to identify any traces of suspicious activities for the networks security, Network Traffic Analysis has been the basis of network security and network management. With the continued emergence of new applications and encrypted traffic, the currently available approaches can not perform well for all kinds of network data. In this paper, we propose a novel stream pattern matching technique which is not only easily deployed but also includes the advantages of different methods. The main idea is： first, defining a formal description specification, by which any series of data stream can be unambiguously descrbed by a special stream pattern; then a tree representation is constructed by parsing the stream pattern; at last, a stream pattern engine is constructed with the Non-t-mite automata （S-CG-NFA） and Bit-parallel searching algorithms. Our stream pattern analysis system has been fully prototyped on C programming language and Xilinx Vn-tex2 FPGA. The experimental results show the method could provides a high level of recognition efficiency and accuracy.

Fast algorithm on string cross pattern matching

Given a set U which is consisted of strings defined on alphabet Σ, string cross pattern matching is to find all the matches between every two strings in U. It is utilized in text processing like removing the duplication of strings. This paper presents a fast string cross pattern matching algorithm based on extracting high frequency strings. Compared with existing algorithms including single-pattern algorithms and multi-pattern matching algorithms, this algorithm is featured by both low time complexity and low space complexity. Because Chinese alphabet is large and the average length of Chinese words is much short, this algorithm is more suitable to process the text written by Chinese, especially when the size of Σ is large and the number of strings is far more than the maximum length of strings of set U.

A Single Pattern Matching Algorithm Based on Character Frequency

Based on the study of single pattern matching, MBF algorithm is proposed by imitating the string searching procedure of human. The algorithm preprocesses the pattern by using the idea of Quick Search algorithm and the already-matched pattern psefix and suffix information. In searching phase, the algorithm makes use of the!character using frequency and the continue-skip idea. The experiment shows that MBF algorithm is more efficient than other algorithms.

Parallel Quick Search Algorithm for the Exact String Matching Problem Using OpenMP

String matching is seen as one of the essential problems in computer science. A variety of computer applications provide the string matching service for their end users. The remarkable boost in the number of data that is created and kept by modern computational devices influences researchers to obtain even more powerful methods for coping with this problem. In this research, the Quick Search string matching algorithm are adopted to be implemented under the multi-core environment using OpenMP directive which can be employed to reduce the overall execution time of the program. English text, Proteins and DNA data types are utilized to examine the effect of parallelization and implementation of Quick Search string matching algorithm on multi-core based environment. Experimental outcomes reveal that the overall performance of the mentioned string matching algorithm has been improved, and the improvement in the execution time which has been obtained is considerable enough to recommend the multi-core environment as the suitable platform for parallelizing the Quick Search string matching algorithm.

A Fast Pattern Matching Algorithm Using Changing Consecutive Characters

Pattern matching is a very important algorithm used in many applications such as search engine and DNA analysis. They are aiming to find a pattern in a text. This paper proposes a Pattern Matching Algorithm Using Changing Consecutive Characters (PMCCC) to make the searching pro- cess of the algorithm faster. PMCCC enhances the shift process that determines how the pattern moves in case of the occurrence of the mismatch between the pattern and the text. It enhances the Berry Ravindran (BR) shift function by using m consecutive characters where m is the pattern length. The formal basis and the algorithms are presented. The experimental results show that PMCCC made enhancements in searching process by reducing the number of comparisons and the number of attempts. Comparing the results of PMCCC with other related algorithms has shown significant enhancements in average number of comparisons and average number of attempts.

Location estimation of autonomous driving robot and 3D tunnel mapping in underground mines using pattern matched LiDAR sequential images

In this study,a machine vision-based pattern matching technique was applied to estimate the location of an autonomous driving robot and perform 3D tunnel mapping in an underground mine environment.The autonomous driving robot continuously detects the wall of the tunnel in the horizontal direction using the light detection and ranging(Li DAR)sensor and performs pattern matching by recognizing the shape of the tunnel wall.The proposed method was designed to measure the heading of the robot by fusion with the inertial measurement units sensor according to the pattern matching accuracy;it is combined with the encoder sensor to estimate the location of the robot.In addition,when the robot is driving,the vertical direction of the underground mine is scanned through the vertical Li DAR sensor and stacked to create a 3D map of the underground mine.The performance of the proposed method was superior to that of previous studies;the mean absolute error achieved was 0.08 m for the X-Y axes.A root mean square error of 0.05 m^(2)was achieved by comparing the tunnel section maps that were created by the autonomous driving robot to those of manual surveying.

Hierarchical particle filter tracking algorithm based on multi-feature fusion

A hierarchical particle filter（HPF） framework based on multi-feature fusion is proposed.The proposed HPF effectively uses different feature information to avoid the tracking failure based on the single feature in a complicated environment.In this approach,the Harris algorithm is introduced to detect the corner points of the object,and the corner matching algorithm based on singular value decomposition is used to compute the firstorder weights and make particles centralize in the high likelihood area.Then the local binary pattern（LBP） operator is used to build the observation model of the target based on the color and texture features,by which the second-order weights of particles and the accurate location of the target can be obtained.Moreover,a backstepping controller is proposed to complete the whole tracking system.Simulations and experiments are carried out,and the results show that the HPF algorithm with the backstepping controller achieves stable and accurate tracking with good robustness in complex environments.

Discovering Association Rules with Graph Patterns in Temporal Networks

Discovering regularities between entities in temporal graphs is vital for many real-world applications(e.g.,social recommendation,emergency event detection,and cyberattack event detection).This paper proposes temporal graph association rules(TGARs)that extend traditional graph-pattern association rules in a static graph by incorporating the unique temporal information and constraints.We introduce quality measures(e.g.,support,confidence,and diversification)to characterize meaningful TGARs that are useful and diversified.In addition,the proposed support metric is an upper bound for alternative metrics,allowing us to guarantee a superset of patterns.We extend conventional confidence measures in terms of maximal occurrences of TGARs.The diversification score strikes a balance between interestingness and diversity.Although the problem is NP-hard,we develop an effective discovery algorithm for TGARs that integrates TGARs generation and TGARs selection and shows that mining TGARs is feasible over a temporal graph.We propose pruning strategies to filter TGARs that have low support or cannot make top-k as early as possible.Moreover,we design an auxiliary data structure to prune the TGARs that do not meet the constraints during the TGARs generation process to avoid conducting repeated subgraph matching for each extension in the search space.We experimentally verify the effectiveness,efficiency,and scalability of our algorithms in discovering diversified top-k TGARs from temporal graphs in real-life applications.

Skipping Undesired High-Frequency Content to Boost DPI Engine

Deep Packet Inspection(DPI)at the core of many monitoring appliances,such as NIDS,NIPS,plays a major role.DPI is beneficial to content providers and censorship to monitor network traffic.However,the surge of network traffic has put tremendous pressure on the performance of DPI.In fact,the sensitive content being monitored is only a minority of network traffic,that is to say,most is undesired.A close look at the network traffic,we found that it contains many undesired high frequency content(UHC)that are not monitored.As everyone knows,the key to improve DPI performance is to skip as many useless characters as possible.Nevertheless,researchers generally study the algorithm of skipping useless characters through sensitive content,ignoring the high-frequency non-sensitive content.To fill this gap,in this literature,we design a model,named Fast AC Model with Skipping(FAMS),to quickly skip UHC while scanning traffic.The model consists of a standard AC automaton,where the input traffic is scanned byte-by-byte,and an additional sub-model,which includes a mapping set and UHC matching model.The mapping set is a bridge between the state node of AC and UHC matching model,while the latter is to select a matching function from hash and fingerprint functions.Our experiments show promising results that we achieve a throughput gain of 1.3-2.6 times the original throughput and 1.1-1.3 times Barr’s double path method.

The Improve of BM Algorithm Base on First Character

The article analyzes the classical BM algorithm and an improved algorithm, and then it puts forward a new improved algorithm which called I_BM algorithm according to the characteristics of the string matching. The I_BM algorithm determines the right distance according to the first character of the pattern string and the distance between the matching windows, so it fasts matching. The matching way of I_BM algorithm is from right to left. In order to verify the IBM algorithm＇ s performance, it does experiments on I_BM algorithm from two aspects of the matching times and the numbers of matching characters under the condition of the same text strings and pattem string. The experimental results show that I_BM algorithm is more quickly and more efficient because it reduces greatly the number of matching and character comparison for maximizing to skip the bad characters.

Mobile crowd sensing task optimal allocation： a mobility pattern matching perspective

With the proliferation of sensor-equipped portable mobile devices, Mobile CrowdSensing （MCS） using smart devices provides unprecedented opportunities for collecting enormous surrounding data. In MCS applications, a crucial issue is how to recruit appropriate participants from a pool of available users to accomplish released tasks, satisfying both resource efficiency and sensing quality. In order to meet these two optimization goals simultaneously, in this paper, we present a novel MCS task allocation framework by aligning existing task sequence with users＇ moving regularity as much as possible. Based on the process of mobility repetitive pattern discovery, the original task allocation problem is converted into a pattern matching issue, and the involved optimization goals are transformed into pattern matching length and support degree indicators. To determine a trade-off between these two competitive metrics, we propose greedy- based optimal assignment scheme search approaches, namely MLP, MDP, IU1 and IU2 algorithm, with respect to matching length-preferred, support degree-preferred and integrated utility, respectively. Comprehensive experiments on real- world open data set and synthetic data set clearly validate the effectiveness of our proposed framework on MCS task optimal allocation.