Popular Pattern Research Articles

Unified Patterns for Realtime Interactive Simulation in Games and Digital Storytelling.

This paper discusses the state of the art in realtime interactive simulation patterns, which are essential in the development of game engines, digital storytelling, and many other graphical applications. We show how currently popular patterns have evolved from equivalent, or, at least, very similar concepts, and are deeply related. We show that game engines do not need to choose a particular pattern, but can easily combine all desired properties with little or no overhead.

Toward Edge-Assisted Video Content Intelligent Caching With Long Short-Term Memory Learning

Nowadays video content has contributed to the majority of Internet traffic, which brings great challenge to the network infrastructure. Fortunately, the emergence of edge computing has provided a promising way to reduce the video load on the network by caching contents closer to users.But caching replacement algorithm is essential for the cache efficiency considering the limited cache space under existing edge-assisted network architecture. To investigate the challenges and opportunities inside, we first measure the performance of five state-of-the-art caching algorithms based on three real-world datasets. Our observation shows that state-of-the-art caching replacement algorithms suffer from following weaknesses: 1) the rule-based replacement approachs (e.g., LFU,LRU) cannot adapt under different scenarios; 2) data-driven forecast approaches only work efficiently on specific scenarios or datasets, as the extracted features working on one dataset may not work on another one. Motivated by these observations and edge-assisted computation capacity, we then propose an edge-assisted intelligent caching replacement framework LSTM-C based on deep Long Short-Term Memory network, which contains two types of modules: 1) four basic modules manage the coordination among content requests, content replace, cache space, service management; 2) three learning-based modules enable the online deep learning to provide intelligent caching strategy. Supported by this design, LSTM-C learns the pattern of content popularity at long and short time scales as well as determines the cache replacement policy. Most important, LSTM-C represents the request pattern with built-in memory cells, thus requires no data pre-processing, pre-programmed model or additional information. Our experiment results show that LSTM-C outperforms state-of-the-art methods in cache hit rate on three real-traces of video requests. When the cache size is limited, LSTM-C outperforms baselines by 20%~32% in cache hit rate. We also show that the training and predicting time of one iteration are $8.6~ms$ and $300~\mu s$ on average respectively, which are fast enough for online operations.

Pattern Primitive Library Construction and Feature Analysis of Kirgiz Textile Pattern

Textile patterns in each minority which contain cultural, historical, artistical, and reginal features are precious because of its unique aesthetic characteristics. More than 78 typical classical patterns of Kirgiz Yormac which is a kind of Kirgiz home-made textile are collected through field investigation, and digitalized by CorelDraw based on the concept of pattern primitives. Three methods for obtaining the pattern primitive units are proposed, and a pattern primitive library is established. It is found that the most popular Yormac pattern items are the ram horn, the flying eagles, and geometric figures, with a symmetrical configuration and structure. Red is the most epidemic color with the proportion over 70% of the pattern collected. By statistically and literature analysis, it can be concluded that Kirgiz textile pattern comes from life, being used in life. The custom, implication, spirit of Kirgiz shown in pattern come down in one continuous line with national pattern, and are highly in conformity with the culture and tradition of Chinese nation. The feature analysis of Yormac pattern, and construction of pattern primitive library is done for better comprehension of Kirgiz culture, and thus protecting and inheriting the Kirgiz intangible culture heritage.

Deontologization of culture and identity transformation in the context of a consumer society

The article substantiates the point that deontologization of culture as a process of collapse of principal value foundations resulted in the absence of stable worldview guidelines and in the establishment of principles of relativism and pluralism. Cultivation of an unlimited choice in the absence of a value-semantic hierarchy, which is characteristic of the Western European culture organized around mass consumption, has led to the inability to create stable identification models. As a result, one of the main problems of contemporary society has become the loss of a stable, holistic identity by a person. The instability of existence, the loss of value-semantic foundations assigned a task of constant self-construction of identity to a person. The principle of consumption as the dominant feature of modern times characterizes an individual lifestyle of a person. In the process of the historical transition to a consumer society, the source of identity formation shifted from the labour sphere to the sphere of consumption, leisure, entertainment. Self-identification of a person is closely related to consumerism. To a large extent, identity began to be determined by the consumer role rather than the class affiliation. As a result, consumption has become one of the main sources of self-identity. In consumer culture, identity is deprived of a fixed centre and appears as an infinitely transformed, mobile thing shifting into the sphere of individual needs and desires; conscious feeling of oneself as a holistic identity becomes unclaimed. The ideology of consumption determines the lifestyle, preferences and character of a person. The media, which are the main identification tools in modern popular culture, offer models and patterns of behaviour that contribute to the formation of a consumer engaged in the search for the quality of his/her individual life.

HALALAN THAYYIBAN: THE KEY OF SUCCESSGUL HALAL FOOD INDUSTRY DEVELOPMENT

This study aims to see the role of halal and thayyib food which can support the success of the halal food industry. Because at this time the diversity of food circulating in the community began to a very popular pattern of life. Starting from local food, even imported food has become a public interest in consumption. However, this diversity raises doubts about whether the food consumed has met the standards of halal and thayyib. Presently, halal and thayyib food is not only a basic obligation for consumers to consume, but has become the basic key to the development of a global halal food industry considering halal and thayyib foods are not only sought after by Muslim consumers, but non-muslim consumers are interested in halal products because security is guaranteed. The research method used uses literature studies by searching for various literature that is relevant to this research. The implications of this research are as a strategy for food industries to use halal and thayyib standards in their industrial development so that they can survive and be able to compete with other industries.

FlatDTW – Dynamic Time Warping optimization for piecewise constant templates

The aim of this work is to construct and analyze a method of optimization of the Dynamic Time Warping (DTW) algorithm – a well-known and popular pattern recognition tool. DTW is typically used to evaluate the degree of similarity between time series subjected to non-linear time warping, which is a common problem i.a. in speech recognition, music information retrieval, motion analysis and gesture recognition. Based on the dynamic programming principle, DTW is computed efficiently in O(N2) time, which contributes to its popularity and makes it applicable in many real-time scenarios. In this paper we propose a method of DTW algorithm optimization in the case when one of the time series may be modeled as a piecewise constant sequence. Such sequences, composed of several “flat” sections, typically occur when real-world data are compared to some predefined templates, as in the task of matching a melody sung by a user to a MIDI-based template melody (a.k.a. Query-by-Singing/Humming, QbH). The modified DTW processes such templates in a more efficient way reducing the computational complexity. The obtained speed-up of the algorithm is not associated with any quality loss of the matching process results.

LBP and GLCM Based Image Forgery Recognition

The forgery of digital images became very easy and it’s very difficult to ascertain the authenticity of such images by naked eye. Among the various kinds of image forgeries, image splicing is a frequent and widely used technique. Even though various methods are available to detect image splicing forgery, authors have attempted to provide a novel hybrid method which can yield greater accuracy, sensitivity and specificity. In this method, gray level co-occurrence matrix (GLCM) features are extracted using local binary pattern (LBP) operator on the image and the detection of the splicing forged images among the authentic images is done using the popular pattern recognition algorithms such as combined k-NN (Comb-KNN), back propagation neural network (BPNN) and support vector machine (SVM). The recorded results are also compared with the existing results of the previous studies to ascertain the quality of the results.

A hybrid genetic algorithm with a neighborhood function for flexible job shop scheduling

One of the most important issues of manufacturing systems optimization is scheduling. In fact, it plays a great role in reducing the production time and minimizing the required resources for production. Recently, due to the furious competition between companies, manufacturers are pushed to ensure p roducts of high quality with a minimum amount of resources. In addition to that, they should satisfy the deadline of theirs customers. The Flexible Job Shop Scheduling Problem (FJSSP) is a very popular pattern in the real manufacturing systems. This problem is a generalization of the Classical Job Shop problem (JSP). FJJSP is called flexible because a machine can perform many types of operations. Each job in FJSSP has its own production sequence, composed of a set of operations. However, each machine can execute one operation at the same time. The problem is how to ensure the achievement of all jobs in the shortest time (Makespan). A hybrid genetic algorithm (HGA) to solve FJSSP is proposed. An Improved Tabu Search (ITS) algorithm with an original neighborhood function is designed, to improve the performance of GA. The approach was tested and validated using one of the most known benchmarks. The effectiveness of the proposed approach is proved by tests.

On political activism in digital games

This project investigates how players of digital games apply their own play with the intent to transmit political messages to other players. Acts of activism are collected from a sample of commercial multiplayer online games; three taxonomies are proposed of which one is used to present the findings, and popular patterns or structures of activism are identified. It is found that in-game activism often takes its cue from activism in everyday life, but that some original topics emerge, for example, the ownership of virtual worlds and practices of in-game political activism such as novel forms of rallies. Current political activism often appears to utilize generic and widely-shared game mechanics, rather than mechanics specific to individual games or genres. Games are therefore selected for their topics, availability, and costs, and popularity with the target audience

Human Epithelial Type-2 Cell Image Classification Using an Artificial Neural Network with Hybrid Descriptors

ABSTRACTAntinuclear antibody (ANA) testing is best performed using the indirect immunofluorescence (IIF) method with human epithelial type-2 (HEp-2) cells as the substrate. IIF is a subjective procedure in which HEp-2 patterns are analyzed manually from the microscope. Therefore, ANA test results greatly rely on the experience and expertise of pathologists. Hence, complete automation of the ANA test is required to avoid incorrect diagnoses. This paper represents an algorithm for the complex HEp-2 cell classification problem. The proposed algorithm used a small hybrid feature set that characterizes the texture and morphology of the HEp-2 cells along with artificial neural network (ANN). The hybrid features were extracted by breaking up the image into eight binary images. The proposed hybrid descriptors were more efficient than the popular co-occurrence matrix descriptor and local binary pattern descriptors for texture analysis. The proposed algorithm was evaluated on the ICPR 2016 IIF HEp-2 cell image dataset. The results indicated that the hybrid descriptor with an ANN approach achieved improved performance, with “96.8%” mean class accuracy.

Assessing ADHD symptoms in children and adults: evaluating the role of objective measures

BackgroundDiagnostic guidelines recommend using a variety of methods to assess and diagnose ADHD. Applying subjective measures always incorporates risks such as informant biases or large differences between ratings obtained from diverse sources. Furthermore, it has been demonstrated that ratings and tests seem to assess somewhat different constructs. The use of objective measures might thus yield valuable information for diagnosing ADHD. This study aims at evaluating the role of objective measures when trying to distinguish between individuals with ADHD and controls. Our sample consisted of children (n = 60) and adults (n = 76) diagnosed with ADHD and matched controls who completed self- and observer ratings as well as objective tasks. Diagnosis was primarily based on clinical interviews. A popular pattern recognition approach, support vector machines, was used to predict the diagnosis.ResultsWe observed relatively high accuracy of 79% (adults) and 78% (children) applying solely objective measures. Predicting an ADHD diagnosis using both subjective and objective measures exceeded the accuracy of objective measures for both adults (89.5%) and children (86.7%), with the subjective variables proving to be the most relevant.ConclusionsWe argue that objective measures are more robust against rater bias and errors inherent in subjective measures and may be more replicable. Considering the high accuracy of objective measures only, we found in our study, we think that they should be incorporated in diagnostic procedures for assessing ADHD.

Incremental Mining of Popular Patterns from Transactional Databases

From the day the mining of frequent pattern problem has been introduced the researchers have extended the frequent patterns to various helpful patterns like cyclic, periodic, regular patterns in emerging databases. In this paper, we get to know about popular pattern which gives the Popularity of every items between the incremental databases. The method that used for the mining of popular patterns is known as Incrpop-growth algorithm. Incrpop-tree structure is been applied in this algorithm. In incremental databases the event recurrence and the event conduct of the example changes at whatever point a little arrangement of new exchanges are added to the database. In this way proposes another calculation called Incrpop-tree to mine mainstream designs in incremental value-based database utilizing Incrpop-tree structure. At long last analyses have been done and comes about are indicated which gives data about conservativeness, time proficient and space productive.

COD: caching on demand in information-centric networking

By overcoming the well-known challenges of the current internet, great expectations are focused on information-centric networking (ICN). ICN extensively uses universal in-network caching. However, developing an efficient caching scheme remains an open question. To overcome the useless caching and duplication caching of previous caching schemes, we propose an adaptive caching scheme—caching on demand (COD). By following the change in potential demand from the consumer and the temporal patterns of content popularity, COD allows content to be cached only by some necessary nodes instead of all nodes on the path from content provider to content consumer. At the same time, in accordance with trading off bandwidth for cache (bandwidth-for-cache), content can be pushed to the adjacent node with more cache capacity. We present a theoretical model to evaluate cache usage for COD. Finally, we evaluate COD through extensive experiments and a wide range of performance metrics. The experimental results under diverse setting demonstrate that COD can yield a steady improvement of network performance and caching efficiency compared with CEE, EgoBetw and Probcache. Notably, COD improves performance with negligible overhead.

Евангелисты В. Л. Боровиковского как образец для церковных росписей XIX века в Тверской области

Vasily Borovikovsky is famous for his icons of Evangelists in the central iconostasis of the Holy Gates of the Cathedral of the Kazan Icon of the Mother of God, in St. Petersburg. It is well-known that the imprints of the icons were published in 1819 and served as a popular pattern for church art. Nevertheless, the nature and the scale of this phenomenon have not been duly researched so far.Vasily Borovikovsky is famous for his icons of Evangelists in the central iconostasis of the Holy Gates of the Cathedral of the Kazan Icon of the Mother of God, in St. Petersburg. It is well-known that the imprints of the icons were published in 1819 and served as a popular pattern for church art. Nevertheless, the nature and the scale of this phenomenon have not been duly researched so far.The study of the previously unknown church murals in the north-eastern Tver region revealed that the Evangelists were a very popular pattern in wall-paintings throughout the 19th century. The Evangelists became the first Russian academic prototype among the plenty of German engravings — also very popular prototypes for wall-painting. The images of Evangelists and their important role in iconographic programs remained since the ancient times till the beginning of the 20th century.The devices of the original imprints transference from the graphic images to the monumental form of murals are indicative of the painters’ craftsmanship. The analysis of the changes made by the regional painters in the metropolitan patterns allows us to value the artists’ degree of freedom and their deep understanding of the murals tasks.The life-cycles of the prototypes in different stylistic directions during the 19th century and the kinds and the peculiarities of Classicism and Eclecticism in wall-painting art have been analyzed. The present study of the icons by V. L. Borovikovsky permits us to demonstrate the variety of regional monumental artistic means of that time.

Adaptive Real-Time Predictive Collaborative Content Discovery and Retrieval in Mobile Disconnection Prone Networks

Emerging mobile environments motivate the need for the development of new distributed technologies which are able to support dynamic peer to peer content sharing, decrease high operating costs, and handle intermittent disconnections. In this paper, we investigate complex challenges related to the mobile disconnection tolerant discovery of content that may be stored in mobile devices and its delivery to the requesting nodes in mobile resource-constrained heterogeneous environments. We propose a new adaptive real-time predictive multi-layer caching and forwarding approach, CafRepCache, which is collaborative, resource, latency, and content aware. CafRepCache comprises multiple multi-layer complementary real-time distributed predictive heuristics which allow it to respond and adapt to time-varying network topology, dynamically changing resources, and workloads while managing complex dynamic tradeoffs between them in real time. We extensively evaluate our work against three competitive protocols across a range of metrics over three heterogeneous real-world mobility traces in the face of vastly different workloads and content popularity patterns. We show that CafRepCache consistently maintains higher cache availability, efficiency and success ratios while keeping lower delays, packet loss rates, and caching footprint compared to the three competing protocols across three traces when dynamically varying content popularity and dynamic mobility of content publishers and subscribers. We also show that the computational cost and network overheads of CafRepCache are only marginally increased compared with the other competing protocols.

Predicting Popularity of Online Videos Using Support Vector Regression

In this work, we propose a regression method to predict the popularity of an online video based on temporal and visual cues. Our method uses Support Vector Regression with Gaussian Radial Basis Functions. We show that modelling popularity patterns with this approach provides higher and more stable prediction results, mainly thanks to the non-linearity character of the proposed method as well as its resistance against overfitting. We compare our method with the state of the art on datasets containing over 14,000 videos from YouTube and Facebook. Furthermore, we show that results obtained relying only on the early distribution patterns, can be improved by adding social and visual metadata.

Distribution and Popularity Patterns of Chinese Music on YouTube: A Case Study of Local Music’s Representation on a Global Internet Platform

The Internet allows local music to be available to a global audience, blurring distinctions between ‘insider’ and ‘outsider’, and between ‘etic’ and ‘emic’. This study examines the interactive relationships between globalised ‘local music’ and a ‘global audience’, using Chinese music as found on YouTube as an example. Distribution and popularity patterns of Chinese music videos are studied, as well as their public perception, on the basis of user comments. The study is an attempt to push forward the boundaries of ethnomusicological research in the twenty-first-century Internet age. It constitutes a methodological proposal for the study of Internet music from a musicological perspective.

Effect of pattern elements of weft knitting on haptic preferences regarding winter garments

An empirical survey was conducted, manually evaluating the relative haptic preference of patterns in weft knitting for winter garments in Mönchengladbach, Germany. Thirty-six patterns were compared in a blind test using ranking orders with respect to six primary hand values (thickness, softness, warmth, weight, stiffness, and smoothness) and general preference for winter garments by 35 participants. Traits of popular patterns included softness and smoothness as well as tucked stitches, rib structures, and float stitches on the surface. On the other hand, unpopular patterns included lace, purl stitches, and ripples.

A survey of itemset mining

Itemset mining is an important subfield of data mining, which consists of discovering interesting and useful patterns in transaction databases. The traditional task of frequent itemset mining is to discover groups of items (itemsets) that appear frequently together in transactions made by customers. Although itemset mining was designed for market basket analysis, it can be viewed more generally as the task of discovering groups of attribute values frequently cooccurring in databases. Because of its numerous applications in domains such as bioinformatics, text mining, product recommendation, e‐learning, and web click stream analysis, itemset mining has become a popular research area. This study provides an up‐to‐date survey that can serve both as an introduction and as a guide to recent advances and opportunities in the field. The problem of frequent itemset mining and its applications are described. Moreover, main approaches and strategies to solve itemset mining problems are presented, as well as their characteristics are provided. Limitations of traditional frequent itemset mining approaches are also highlighted, and extensions of the task of itemset mining are presented such as high‐utility itemset mining, rare itemset mining, fuzzy itemset mining, and uncertain itemset mining. This study also discusses research opportunities and the relationship to other popular pattern mining problems, such as sequential pattern mining, episode mining, subgraph mining, and association rule mining. Main open‐source libraries of itemset mining implementations are also briefly presented.WIREs Data Mining Knowl Discov2017, 7:e1207. doi: 10.1002/widm.1207This article is categorized under:Algorithmic Development > Association RulesTechnologies > Association Rules

Building Regional Covariance Descriptors for Vehicle Detection

We study the question of building regional covariance descriptors (RCDs) for vehicle detection from high-resolution satellite images. A unified way is proposed to build RCD features by constant convolutional kernels in the forms of 2-D masks. Two novel formulas are designed to construct different RCD types based upon one or two convolutional masks, obtaining ten novel RCD features by four simple constant convolutional masks. Experiments show that such convolutional-mask-based RCDs outperform the previous image-derivative-based RCDs, the popular local binary patterns (LBPs), the histogram of oriented gradients (HOGs), and LBP+HOG. Furthermore, feeding to nonlinear support vector machines (SVMs) of two kernel types [L1 kernel and radial basis function (RBF)], these RCDs outperform four known deep convolutional neural networks: AlexNet, GoogLeNet, CaffeNet, and LeNet, as well as their fine-tuned models by their well-trained weights of imageNet classification. Among three popular classic classifiers we have tested in the experiments, nonlinear SVMs outperform BP and Adaboost obviously, and L1 kernel exceeds RBF slightly.