Pattern Detection Approach Research Articles

Semantic Pattern Detection in Covid-19 using Contextual Clustering and Intelligent Topic Modeling

The Covid-19 pandemic is the deadliest outbreak in our living memory. So, it is need of hour, to prepare the world with strategies to prevent and control the impact of the epidemics. In this paper, a novel semantic pattern detection approach in the Covid-19 literature using contextual clustering and intelligent topic modeling is presented. For contextual clustering, three level weights at term level, document level, and corpus level are used with latent semantic analysis. For intelligent topic modeling, semantic collocations using pointwise mutual information(PMI) and log frequency biased mutual dependency(LBMD) are selected and latent dirichlet allocation is applied. Contextual clustering with latent semantic analysis presents semantic spaces with high correlation in terms at corpus level. Through intelligent topic modeling, topics are improved in the form of lower perplexity and highly coherent. This research helps in finding the knowledge gap in the area of Covid-19 research and offered direction for future research.

Dynamics of Agricultural Land Systems in Western Mediterranean Areas: A Pattern-Detection Approach Based on Som

Dynamics of Agricultural Land Systems in Western Mediterranean Areas: A Pattern-Detection Approach Based on Som

Feature-based software design pattern detection

Software design patterns are standard solutions to common problems in software design and architecture. Knowing that a particular module implements a design pattern is a shortcut to design comprehension. Manually detecting design patterns is a time consuming and challenging task, therefore, researchers have proposed automatic design pattern detection techniques. However, these techniques show low performance for certain design patterns. In this work, we introduce a design pattern detection approach, DPDF that improves the performance over the state-of-the-art by using code features with machine learning classifiers to automatically train a design pattern detector. DPDF creates a semantic representation of Java source code using the code features and the call graph, and applies the Word2Vec algorithm on the semantic representation to construct the word-space geometric model of the Java source code. DPDF then builds a machine learning classifier trained on a labelled dataset and identifies software design patterns with over 80% Precision and over 79% Recall. Additionally, we have compared DPDF with two existing design pattern detection techniques namely FeatureMaps &MARPLE-DPD. Empirical results demonstrate that our approach outperforms the existing approaches by approximately 35% and 15% respectively in terms of Precision. The run-time performance also supports the practical applicability of our classifier.

Semantic Pattern Detection in COVID-19 Using Contextual Clustering and Intelligent Topic Modeling

The Covid-19 pandemic is the deadliest outbreak in our living memory. So, it is need of hour, to prepare the world with strategies to prevent and control the impact of the epidemics. In this paper, a novel semantic pattern detection approach in the Covid-19 literature using contextual clustering and intelligent topic modeling is presented. For contextual clustering, three level weights at term level, document level, and corpus level are used with latent semantic analysis. For intelligent topic modeling, semantic collocations using pointwise mutual information(PMI) and log frequency biased mutual dependency(LBMD) are selected and latent dirichlet allocation is applied. Contextual clustering with latent semantic analysis presents semantic spaces with high correlation in terms at corpus level. Through intelligent topic modeling, topics are improved in the form of lower perplexity and highly coherent. This research helps in finding the knowledge gap in the area of Covid-19 research and offered direction for future research.

A General Framework to Detect Design Patterns by Combining Static and Dynamic Analysis Techniques

Design pattern detection can provide useful insights to support software comprehension. Accurate and complete detection of pattern instances are extremely important to enable software usability improvements. However, existing design pattern detection approaches and tools suffer from the following problems: incomplete description of design pattern instances, inaccurate behavioral constraint checking, and inability to support novel design patterns. This paper presents a general framework to detect design patterns while solving these issues by combining static and dynamic analysis techniques. The framework has been instantiated for typical behavioral and creational patterns, such as the observer pattern, state pattern, strategy pattern, and singleton pattern to demonstrate the applicability. Based on the open-source process mining toolkit ProM, we have developed an integrated tool that supports the whole detection process for these patterns. We applied and evaluated the framework using software execution data containing around 1,000,000 method calls generated from eight synthetic software systems and three open-source software systems. The evaluation results show that our approach can guarantee a higher precision and recall than existing approaches and can distinguish state and strategy patterns that are indistinguishable by the state-of-the-art.

Detecting Pattern Anomalies in Hydrological Time Series with Weighted Probabilistic Suffix Trees

Anomalous patterns are common phenomena in time series datasets. The presence of anomalous patterns in hydrological data may represent some anomalous hydrometeorological events that are significantly different from others and induce a bias in the decision-making process related to design, operation and management of water resources. Hence, it is necessary to extract those “anomalous” knowledge that can provide valuable and useful information for future hydrological analysis and forecasting from hydrological data. This paper focuses on the problem of detecting anomalous patterns from hydrological time series data, and proposes an effective and accurate anomalous pattern detection approach, TFSAX_wPST, which combines the advantages of the Trend Feature Symbolic Aggregate approximation (TFSAX) and weighted Probabilistic Suffix Tree (wPST). Experiments with different hydrological real-world time series are reported, and the results indicate that the proposed methods are fast and can correctly detect anomalous patterns for hydrological time series analysis, and thus promote the deep analysis and continuous utilization of hydrological time series data.

Design pattern detection approaches: a systematic review of the literature

Nowadays, software engineers have a great tendency to use design patterns (DPs) because they are considered to have an important role in software engineering in the sense that they can make the understanding of nonentities easier. However, most of the systems have no document helping engineers recognize DPs from the codes. As a result, different approaches for design pattern detection have been suggested. The current paper reviews different available literature on design pattern detection and reports a number of different aspects of them such as data representation, type of design pattern, advantages and disadvantages for different approaches, quantitative results, etc. The current paper reviews research studies published between 2008 until 2019 and represents a list of datasets used for evaluations. The present investigation paper is not only to lay the ground for the selection of the optimal design patterns, but also hopes to guide the future studies through raising awareness about the potential defects in the previous researches.

Detection of time series patterns and periodicity of cloud computing workloads

Workload pattern detection can be used as part of a proactive decision-making approach to optimize resource provisioning strategies and anticipate any performance problem in cloud computing environments. Existing workload pattern detection approaches suffer from two essential limitations which restrict their effectiveness in such environments. Specifically, they require massive human intervention, and are specific to particular types of workload data. To overcome these limitations, we propose in this paper a generic workload pattern and periodicity detection technique that employs the prefix transposition approach from the molecular biology domain to detect workload periodicity in node-specific and aggregated cloud environments. The strengths of the proposed technique compared to the state of the art lie in its ability to be applied to any type of workload, and also to detect patterns of varying lengths, amplitudes, and shapes. Experiments conducted on cloud server nodes and aggregated CPU and throughput workload datasets collected from Information Technology (IT) and Telecom domains reveal that our solution improves the accuracy of the detections, especially in harsh environments, where the lengths, shapes, and amplitudes of patterns vary, as compared to the autocorrelation technique. Other findings show that the proposed approach is also highly efficient at detecting multiple short-term and long-term periodic patterns on any type of time series-based cloud computing workloads of different time granularity.

Software Metrics and tree-based machine learning algorithms for distinguishing and detecting similar structure design patterns

Design patterns are general reusable solutions for recurrent occurring problems. When software systems become more complicated due to the lack of documentation of design patterns in software and the maintenance and evolution costs become a challenge. Design pattern detection is used to reduce the complexity and to increase the understandability of the design in the software. In this paper, we propose a design pattern detection approach based on tree-based machine learning algorithms and software metrics to study the effectiveness of software metrics in distinguishing between similar structural design patterns. We build our datasets using P-MARt repository by extracting the roles of design patterns and calculating the metrics for each role. We used parameter optimization techniques based on the Grid search algorithm to define the optimal parameter of each algorithm. We used two feature selection methods based on a genetic algorithm to find features that influence the most in the distinguishing process. Through our experimental study, we showed the effectiveness of machine learning and software metrics when distinguishing similar structure design patterns. Moreover, we extracted the essential metrics in each dataset that supported the machine learning model to take its decision. We presented the detection conditions for each role in the design pattern by extracting them from the decision tree model.

A Bayesian unsupervised learning approach for identifying soil stratification using cone penetration data

This paper presents a novel perspective to understanding the spatial and statistical patterns of a cone penetration dataset and identifying soil stratification using these patterns. Both local consistency in physical space (i.e., along depth) and statistical similarity in feature space (i.e., logQt–logFrspace, where Qtis the normalized tip resistance and Fris the normalized friction ratio, or the Robertson chart) between data points are considered simultaneously. The proposed approach, in essence, consists of two parts: (i) a pattern detection approach using the Bayesian inferential framework and (ii) a pattern interpretation protocol using the Robertson chart. The first part is the mathematical core of the proposed approach, which infers both spatial pattern in physical space and statistical pattern in feature space from the input dataset; the second part converts the abstract patterns into intuitive spatial configurations of multiple soil layers having different soil behavior types. The advantages of the proposed approach include probabilistic soil classification and identification of soil stratification in an automatic and fully unsupervised manner. The proposed approach has been implemented in MATLAB R2015b and Python 3.6, and tested using various datasets including both synthetic and real-world cone penetration test soundings. The results show that the proposed approach can accurately and automatically detect soil layers with quantified uncertainty and reasonable computational cost.

Trajectory based vehicle counting and anomalous event visualization in smart cities

Motion pattern analysis can be performed automatically on the basis of object trajectories by means of tracking videos; an effective approach to analyse and to model the traffic behaviour; is important to describe motion by taking the whole trajectory whereas it’s more essential to identify and evaluate object behaviour online. In this paper, pattern detection approach is presented which takes spatio-temporal characteristic of vehicle trajectories. A real time system is built to infer and track the object behaviour quickly by online performing trajectory analysis. Every independent vehicle in the video frame is tracked over time. As the anomaly behaviour occurs, glyph is generated to show it occurrences. Vehicle counting is done by estimating the trajectories and compared with Hungarian tracker. Several surveillance videos are taken into account for the performance checking of system. Experimental results demonstrated that proposed method in comparison with the state of the art algorithms, provides robust vehicle density estimation and event information i.e., lane change information.

SMR neurofeedback for improving sleep and memory – Two studies in primary insomnia

ming and subject reporting errors. Unlike with traditional data collection, error identification required a systematic, non-qualitative approach. A method for discerning between errors and outliers was also necessary. However, these challenges were met through developing a pattern detection approach within single and multiple fields. In addition, because of the sheer quantity of errors and outliers, they could be analyzed to identify trends to both promote data quality and to answer new, substantive questions of the data. For example, benefits included the possibility of categorizing types of users through patterns of responses, and the identification of learning curves for diary entry through patterns of errors. Other improvements to data quality and analysis included reducedmissing data, improved data integrity, and substantial increases in data capture. Conclusion: The development of newmethodologies for addressing insomnia-specific issues in the automated, large scale collection of data is needed in the age of Big Data trials. We propose methods for addressing data quality andmanagement issues, and discuss possible new directions in the examination of insomnia data. Acknowledgements: Funding for this study came from the National Institutes of Health/National Institute of Mental Health (R01MH086758).

Discovering Preferential Patterns in Sectoral Trade Networks.

We analyze the patterns of import/export bilateral relations, with the aim of assessing the relevance and shape of “preferentiality” in countries’ trade decisions. Preferentiality here is defined as the tendency to concentrate trade on one or few partners. With this purpose, we adopt a systemic approach through the use of the tools of complex network analysis. In particular, we apply a pattern detection approach based on community and pseudocommunity analysis, in order to highlight the groups of countries within which most of members’ trade occur. The method is applied to two intra-industry trade networks consisting of 221 countries, relative to the low-tech “Textiles and Textile Articles” and the high-tech “Electronics” sectors for the year 2006, to look at the structure of world trade before the start of the international financial crisis. It turns out that the two networks display some similarities and some differences in preferential trade patterns: they both include few significant communities that define narrow sets of countries trading with each other as preferential destinations markets or supply sources, and they are characterized by the presence of similar hierarchical structures, led by the largest economies. But there are also distinctive features due to the characteristics of the industries examined, in which the organization of production and the destination markets are different. Overall, the extent of preferentiality and partner selection at the sector level confirm the relevance of international trade costs still today, inducing countries to seek the highest efficiency in their trade patterns.

Using metric-based filtering to improve design pattern detection approaches

Design patterns represent high-level recurring abstractions that reflect the know-how of experts. Their detection is a key task in the context of software engineering; it is very useful in program comprehension, design recovery and also for re-documenting object-oriented systems. Despite their usefulness, current design pattern detection approaches have a high time complexity that hinders their application. This complexity is further aggravated with the absence of guiding principles in determining which pattern to look for first among the various patterns. To overcome this practical limit, we propose to optimize any pattern detection approach by foregoing it by a filtering phase that detects possible existence of patterns and that orders the candidate patterns in terms of their degree of resemblance to the analyzed design fragments. The herein proposed filtering approach exploits semantic and structural design metrics to look for the semantic and structural symptoms of design pattern instances. Its performance is experimentally demonstrated through our design pattern detection method MAPeD (Multi-phase Approach for Pattern Discovery) applied on the open source system JUnit.

EVALUATION OF AN AUTOMATED MULTI-PHASE APPROACH FOR PATTERNS DISCOVERY

Design patterns capitalize the knowledge of expert designers and offer reuse that provides for higher design quality and overall faster development. To attain these advantages, a designer must, however, overcome the difficulties in understanding design patterns and determining those appropriate for his/her particular application. On the other hand, one way to benefit from design patterns is to assist inexperienced designers in pattern detection during the design elaboration. Such detection should tolerate variations between the design and the pattern since the exact instantiation of a pattern is infrequent in a design. However, not all variations of a pattern are tolerated. In particular, some structural variations may result in non-optimal instantiations where the requirements are respected but the structure is different; such variations are called spoiled patterns and should also be detected and transformed into acceptable pattern instantiations. This paper first presents an improvement of our design/spoiled pattern detection approach, named MAPeD (Multi-phase Approach for Pattern Discovery). The latter uses an XML information retrieval technique to identify design/spoiled pattern occurrences in a design using, first, static and semantic information and, secondly, dynamic information. This multi-phase detection approach tolerates structural differences between the examined design and the identified design pattern. Furthermore, thanks to the matching information it collects, our identification technique can offer assistance for the improvement of a design. In its second contribution, this paper evaluates MAPeD by comparing its recall and precision rates for five open source systems: JHotDraw, JUnit, JRefactory, MapperXML, QuickUML. The latter were used by other approaches in experimental evaluations. Our evaluation shows that our design pattern identification approach has an average improvement of 9.98% in terms of precision over the best known approach.

Detection of Concealed Individuals Based on Their Vital Signs by Using a See-Through-Wall Imaging System With a Self-Injection-Locked Radar

This paper presents a self-injection-locked (SIL) radar with ranging and tracking capabilities to see through walls for discovering hidden people. Characterized by low complexity and high sensitivity, the proposed SIL radar merges the frequency-modulated continuous-wave and sum-difference pattern detection approaches to determine the distance and azimuth from the radar to each individual in a scene behind a wooden partition wall. An individual can be distinguished from a stationary object by using dynamic spectral subtraction to extract human motions or vital signs. Additionally, two or more individuals can be distinguished from each other by decomposing the Doppler signal into contributions of individuals in a polar domain. Consequently, a see-through-wall imaging system is constructed with the proposed SIL radar to position different individuals concealed behind a wall.

Repeated Web Page Visits and the Scanpath Theory: A Recurrent Pattern Detection Approach

This paper investigates the eye movement sequences of users visiting web pages repeatedly. We are interested in potential habituation due to repeated exposure. The scanpath theory posits that every person learns an idiosyncratic gaze sequence on first exposure to a stimulus and re-applies it on subsequent exposures. Josephson and Holmes (2002) tested the applicability of this hypothesis to web page revisitation but results were inconclusive. With a recurrent temporal pattern detection technique, we examine additional aspects and expose scanpaths. Results do not suggest direct applicability of the scanpath theory. While repetitive scan patterns occurred and were individually distinctive, their occurrence was variable, there were often several different patterns per person, and patterns were not primarily formed on the first exposure. However, extensive patterning occurred for some participants yet not for others which deserves further study into its determinants.

Methods to Window Data to Differentiate Between Markov Models

In this paper, we consider how we can detect patterns in data streams that are serial Markovian, where target behaviors are Markovian, but targets may switch from one Markovian behavior to another. We want to reliably and promptly detect behavior changes. Traditional Markov-model-based pattern detection approaches, such as hidden Markov models, use maximum likelihood techniques over the entire data stream to detect behaviors. To detect changes between behaviors, we use statistical pattern matching calculations performed on a sliding window of data samples. If the window size is very small, the system will suffer from excessive false-positive rates. If the window is very large, change-point detection is delayed. This paper finds both necessary and sufficient bounds on the window size. We present two methods of calculating window sizes based on the state and transition structures of the Markov models. Two application examples are presented to verify our results. Our first example problem uses simulations to illustrate the utility of the proposed approaches. The second example uses models extracted from a database of consumer purchases to illustrate their use in a real application.