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Time series is a set of sequential data point in time order. The sizes and dimensions of the time series datasets are increasing day by day. Clustering is an unsupervised data mining technique that groups objects based on their similarities. It is used to analyze various datasets, such as finance, climate, and bioinformatics datasets. [Formula: see text]-means is one of the most used clustering algorithms. However, it is challenging to determine the value of [Formula: see text] parameter, which is the number of clusters. One of the most used methods to determine the number of clusters (such as [Formula: see text]) is cluster validity indexes. Several internal and external validity indexes are used to find suitable cluster numbers based on characteristics of datasets. In this study, we propose a hybrid validity index to determine the value of [Formula: see text] parameter of [Formula: see text]-means algorithm. The proposed hybrid validity index comprises four internal validity indexes, such as Dunn, Silhouette, C index, and Davies–Bouldin indexes. The proposed method was applied to nine real-life finance and benchmarks time series datasets. The financial dataset was obtained from Yahoo Finance, consisting of daily closing data of stocks. The other eight benchmark datasets were obtained from UCR time series classification archive. Experimental results showed that the proposed hybrid validity index is promising for finding the suitable number of clusters with respect to the other indexes for clustering time-series datasets.

RBC (Red Blood Cell) membrane is a highly elastic structure, and proper modelling of this elasticity is essential for biomedical applications that involve computational experiments with blood flow. In this work, we present a new method for estimating one of the key parameters of red blood cell elasticity, which uses a neural network trained on the simulation outputs. We test classic LSTM (Long-Short Term Memory) architecture for the time series regression task, and we also experiment with novel CNN-LSTM (Convolutional Neural Network) architecture. We paid special attention to investigating the impact of the way the three-dimensional training data are reduced to their two-dimensional projections. Such a comparison is possible thanks to working with simulation outputs that are equivalently defined for all dimensions and their combinations. The obtained results can be used as recommendations for an appropriate way to record real experiments for which the reduced dimension of the acquired data is essential.

Limited-area numerical weather prediction models currently run operationally in the United States and follow a “partially cycled” schedule, where sequential data assimilation is periodically interrupted by replacing model states with solutions interpolated from a global model. While this strategy helps overcome several practical challenges associated with real-time regional forecasting, it is no substitute for a robust sequential data assimilation approach for research-to-operations purposes. Partial cycling can mask systematic errors in weather models, data assimilation systems, and data preprocessing techniques, since it introduces information from a different prediction system. It also adds extra heuristics to the model initialization steps outside the general Bayesian filtering framework from which data assimilation methods are derived. This study uses a research-oriented modeling system, which is self-contained in the operational Hurricane Weather Research and Forecasting (HWRF) Model package, to illustrate why next-generation modeling systems should prioritize sequential data assimilation at early stages of development. This framework permits the rigorous examination of all model system components—in a manner that has never been done for the HWRF Model. Examples presented in this manuscript show how sequential data assimilation capabilities can accelerate model advancements and increase academic involvement in operational forecasting systems at a time when the United States is developing a new hurricane forecasting system.

We consider a new class of Data Envelopment Analysis (DEA) modeling, which we call ‘sequential DEA’. This new approach is a relatively simple generalization of the standard and popular in practice DEA. It allows for analyzing efficiency of the decision making units that consist of a sequence of sub-DMUs (e.g., branches of banks, hospital holding company running a number of hospitals at different locations, hotel chains, etc.). The approach is embedded in the Hilbert sequence space ( $$\ell ^{2}$$ ) and therefore it allows for potentially different numbers of the sub-DMUs as well as different numbers of inputs and outputs used by different decision making units. We hope this approach will open up a new stream of literature in the sense that many existing variations from the already rich literature on DEA can be adapted to this approach.

Patterns of strengths and weaknesses represent relatively novel methods for identifying specific learning disabilities (SLD) with proponents asserting that the incorporation of multiple sources of assessment data and professional judgment play a key role in their utility. In this study, we examined if the sequential presentation of assessment data impacted school psychologists’ ratings as to whether or not hypothetical students depicted in special education evaluation vignettes should be identified with SLD. Results showed that when participants viewed vignettes that were indicative of SLD (i.e., SLD positive), SLD likelihood ratings increased with the additional presentation of assessment data sources over time. However, when participants viewed vignettes that were indicative of a student not having SLD (i.e., SLD negative), SLD likelihood ratings were relatively consistent over time. Moreover, participants demonstrated relatively high levels of confidence in their SLD identification decisions, and in SLD negative vignettes, confidence increased after the fourth assessment data source was presented. Implications for SLD identification are discussed.

Objective High-throughput electronic phenotyping algorithms can accelerate translational research using data from electronic health record (EHR) systems. The temporal information buried in EHRs is often underutilized in developing computational phenotypic definitions. This study aims to develop a high-throughput phenotyping method, leveraging temporal sequential patterns from EHRs. Materials and Methods We develop a representation mining algorithm to extract 5 classes of representations from EHR diagnosis and medication records: the aggregated vector of the records (aggregated vector representation), the standard sequential patterns (sequential pattern mining), the transitive sequential patterns (transitive sequential pattern mining), and 2 hybrid classes. Using EHR data on 10 phenotypes from the Mass General Brigham Biobank, we train and validate phenotyping algorithms. Results Phenotyping with temporal sequences resulted in a superior classification performance across all 10 phenotypes compared with the standard representations in electronic phenotyping. The high-throughput algorithm’s classification performance was superior or similar to the performance of previously published electronic phenotyping algorithms. We characterize and evaluate the top transitive sequences of diagnosis records paired with the records of risk factors, symptoms, complications, medications, or vaccinations. Discussion The proposed high-throughput phenotyping approach enables seamless discovery of sequential record combinations that may be difficult to assume from raw EHR data. Transitive sequences offer more accurate characterization of the phenotype, compared with its individual components, and reflect the actual lived experiences of the patients with that particular disease. Conclusion Sequential data representations provide a precise mechanism for incorporating raw EHR records into downstream machine learning. Our approach starts with user interpretability and works backward to the technology.

We invented a wearable device that can measure voiding time and frequency by checking a habitual series of characteristic motions among men. This study collected and analyzed urination time data collected smart bands worn by patients to resolve the clinical issues posed by using voiding charts. By developing a smart band-based algorithm for assessing urination time in patients, this study aimed to explore the feasibility of urination management systems. This study aimed to assess urination time based on a patient's posture and changes in posture. Motion data were obtained from a smart band on the arm. An algorithm that identifies the three stages of urination (forward movement, urination, backward movement) was developed based on data collected from a 3-axis accelerometer and tilt angle data. Therefore, we analyze hidden Markov model (HMM)-based sequential data to determine urination time. Real-time data were acquired from the smart band. For data corresponding to a specific duration, the value of the signals was calculated and then compared with the set analysis model to calculate the time of urination. The final accuracy of the algorithm was calculated based on clinical guidelines for urologists. The experiment showed a high average accuracy of 92.5%, proving the robustness of the proposed algorithm. The proposed urination time recognition technology draws on acceleration data and tilt angle data collected via a smart band; these data were then analyzed using a classifier after applying the HMM method.

International audience; In application domains ranging from social networks to e-commerce, it is important to cluster users with respect to both their relationships (e.g., friendship or trust) and their actions (e.g., visited locations or rated products). Motivated by these applications, we introduce here the task of clustering the nodes of a sequence graph, i.e., a graph whose nodes are labeled with strings (e.g., sequences of users' visited locations or rated products). Both string clustering algorithms and graph clustering algorithms are inappropriate to deal with this task, as they do not consider the structure of strings and graph simultaneously. Moreover, attributed graph clustering algorithms generally construct poor solutions because they need to represent a string as a vector of attributes, which inevitably loses information and may harm clustering quality. We thus introduce the problem of clustering a sequence graph. We first propose two pairwise distance measures for sequence graphs, one based on edit distance and shortest path distance and another one based on SimRank. We then formalize the problem under each measure, showing also that it is NP-hard. In addition, we design a polynomial-time 2-approximation algorithm, as well as a heuristic for the problem. Experiments using real datasets and a case study demonstrate the effectiveness and efficiency of our methods.

Background: With the advent of data analysis and machine learning, there is a growing impetus of analyzing and generating models on historic data. The data comes in numerous forms and shapes with an abundance of challenges. The most sorted form of data for analysis is the numerical data. With the plethora of algorithms and tools it is quite manageable to deal with such data. Another form of data is of categorical nature, which is subdivided into, ordinal (order wise) and nominal (number wise). This data can be broadly classified as Sequential and Non-Sequential. Sequential data analysis is easier to preprocess using algorithms. Objective: The challenge of applying machine learning algorithms on categorical data of nonsequential nature is dealt in this paper. Methods: Upon implementing several data analysis algorithms on such data, we end up getting a biased result, which makes it impossible to generate a reliable predictive model. In this paper, we will address this problem by walking through a handful of techniques which during our research helped us in dealing with a large categorical data of non-sequential nature. In subsequent sections, we will discuss the possible implementable solutions and shortfalls of these techniques. Results: The methods are applied to sample datasets available in public domain and the results with respect to accuracy of classification are satisfactory. Conclusion: The best pre-processing technique we observed in our research is one hot encoding, which facilitates breaking down the categorical features into binary and feeding it into an Algorithm to predict the outcome. The example that we took is not abstract but it is a real – time production services dataset, which had many complex variations of categorical features. Our Future work includes creating a robust model on such data and deploying it into industry standard applications.

A method for analyzing sequential data sets, similar to the permutation entropy one, is discussed. The characteristic features of this method are as follows: it preserves information about equal values, if any, in the embedding vectors; it is exempt of combinatorics; it delivers the same entropy value as does the permutation method, provided the embedding vectors do not have equal components. In the latter case this method can be used instead of the permutation one. If embedding vectors have equal components this method could be more precise in discriminating between similar data sets., 17 pages, 2 figures, 23 references, 6 tables. This version is accepted in the Complexity (Hindawi)

We present an efficient score statistic, called the S 3 T statistic, to detect the emergence of a spatially and temporally correlated signal from either fixed-sample or sequential data. The signal ...

Publishing physical activity data can facilitate reproducible health-care research in several areas such as population health management, behavioral health research, and management of chronic health problems. However, publishing such data also brings high privacy risks related to re-identification which makes anonymization necessary. One of the challenges in anonymizing physical activity data collected periodically is its sequential nature. The existing anonymization techniques work sufficiently for cross-sectional data but have high computational costs when applied directly to sequential data. This article presents an effective anonymization approach, multi-level clustering-based anonymization to anonymize physical activity data. Compared with the conventional methods, the proposed approach improves time complexity by reducing the clustering time drastically. While doing so, it preserves the utility as much as the conventional approaches.

Human behavior has been always an important factor in social communication. The human activity and action recognition are all clues that facilitate the analysis of human behavior. Human action recognition is an important challenge in a variety of application including human-computer interaction and intelligent video surveillance to enhance security in different domains. The evaluation algorithm relies on the proper extraction and the learning data. The success of the deep learning led to many imposing results in several contexts that include neural network. Here the emergence of Gated Recurrent Neural Networks with increased computation powers is being adopted for sequential data and video classification. However, to have an efficient classifier for assigning the class label, it is very necessary to have a strong features vector. Features are the most important information in each data. Indeed, features extraction can influence on the performance of the algorithm and the computation complexity. This paper proposes a novel approach for human action recognition based on hybrid deep learning model. The proposed approach is evaluated on the challenging UCF Sports, UCF101 and KTH datasets. An average of 96.3% is obtained when we have tested on KTH dataset.

With the tremendous advance of technologies in data collection and storage, sequential data are becoming more and more ubiquitous in a wide spectrum of application scenarios. There are various embodiments of sequential data such as time series/video frames, biological data, and event data. It bears important practical utility for learning and understanding the dynamic behavior as well as the causality relationships across sequences, and it also calls for robust models to handle noisy and incomplete sequence data in real-world settings. Thus, machine-learning-based methods can be applied to efficiently analyze and model these sequential data.

Multiview learning is concerned with machine learning problems, where data are represented by distinct feature sets or views. Recently, this definition has been extended to accommodate sequential data, i.e., each view of the data is in the form of a sequence. Multiview sequential data pose major challenges for representation learning, including i) absence of sample correspondence information between the views , ii) complex relations among samples within each view , and iii) high complexity for handling multiple sequences . In this article, we first introduce a generalized deep learning model that can simultaneously discover sample correspondence and capture the cross-view relations among the data sequences. The model parameters can be optimized using a gradient descent-based algorithm. The complexity for computing the gradient is at most quadratic with respect to sequence lengths in terms of both computational time and space. Based on this model, we propose a second model by integrating the objective with reconstruction losses of autoencoders. This allows the second model to provide a better trade-off between view-specific and cross-view relations in the data. Finally, to handle multiple (more than two) data sequences, we develop a third model along with a convergence-guaranteed optimization algorithm. Extensive experiments on public datasets demonstrate the superior performances of our models over competing methods.

In recent years, the volume of clickstream and user data collected by news organizations has reached enormous proportions. As a result, news organizations—as well as journalism scholars—face novel methodological challenges to describe and analyze this wealth of information. To move forward, we demonstrate a computational approach to understand the news journeys Web users take to find the news they want to read. We propose the use of Markov chains. These models provide an effective and compact way to discover meaningful patterns in clickstream data. In particular, they capture the sequentiality in news use patterns. We illustrate this approach with an analysis of more than 1 million Web pages, from 175 websites (news websites, search engines, social media), collected over 8 months in 2017/18. The analysis of such data is of high interest to journalism scholars, but can also help news organizations to design sales strategies, provide more personalized content, and find the most effective structure for their website.

Due to the success and growing job market of deep learning (DL), students and researchers from many areas are interested in learning about DL technologies. Visualization has been used as a modern medium during this learning process. However, despite the fact that sequential data tasks, such as text and function analysis, are at the forefront of DL research, there does not yet exist an educational visualization that covers recurrent neural networks (RNNs). Additionally, the benefits and trade-offs between using visualization environments and conventional learning material for DL have not yet been evaluated. To address these gaps, we propose exploRNN, the first interactively explorable educational visualization for RNNs. exploRNNis accessible online and provides an overview of the training process of RNNs at a coarse level, as well as detailed tools for the inspection of data flow within LSTM cells. In an empirical between-subjects study with 37 participants, we investigate the learning outcomes and cognitive load of exploRNN compared to a classic text-based learning environment. While learners in the text group are ahead in superficial knowledge acquisition, exploRNN is particularly helpful for deeper understanding. Additionally, learning with exploRNN is perceived as significantly easier and causes less extraneous load. In conclusion, for difficult learning material, such as neural networks that require deep understanding, interactive visualizations such as exploRNN can be helpful., publishedVersion

This article describes the characteristics of two types of hybrid tools that are useful for obtaining both quantitative and qualitative data. It reviews how these tools have been used in previously published studies and identifies the advantages and disadvantages of their use. The two types of tools in question are life satisfaction diagrams and biographical grids. Both types of diagrams were originally designed as instruments for obtaining quantitative data within life course research, and later came to be used as either hybrid tools or only as qualitative tools. Both types of diagrams are evaluated mainly on the use made by the author in studies in which he has participated. The main advantages identified can be summarised in two points: (1) the ease of obtaining an accurate representation of complex facts and (2) the opportunity for this representation to stimulate personal reflection. One main limitation common to both diagrams has been identified: they can be difficult to understand unless interviewees receive a clear explanation. This type of diagrams can be seen as a bridge between the life course perspective and mixed methods research, a combination that has been seldom addressed in the methodological literature. En el artículo se describen las características de dos tipos de herramientas híbridas útiles para la obtención conjunta de datos cuantitativos y cualitativos, se revisa el tipo de uso que han tenido en trabajos publicados precedentemente y se identifican las ventajas e inconvenientes de su uso. Estos dos tipos de herramientas son los diagramas de satisfacción vital y las parrillas biográficas. Ambos tipos de diagramas fueron originariamente diseñados como instrumentos de obtención de datos cuantitativos en el marco de la investigación sobre el curso de vida y posteriormente han pasado a ser utilizados también como herramientas híbridas y como herramientas únicamente cualitativas. Ambos tipos de diagramas son evaluados principalmente a partir del uso realizado por el autor en investigaciones en las que ha participado. Las principales ventajas identificadas pueden resumirse en dos puntos: 1) la facilidad para obtener una representación fidedigna de hechos complejos, y 2) la posibilidad de estimular la reflexión personal a partir de dicha representación. Se identifica también una limitación principal común para los dos tipos de diagramas: pueden ser difíciles de entender, a menos que las personas entrevistadas reciban una explicación clara sobre cómo utilizarlos. En cualquier caso, este tipo de diagramas constituyen un puente entre la perspectiva del curso de vida y la investigación con métodos mixtos, una combinación muy poco tratada en la literatura metodológica. En l'article es descriuen les característiques de dos tipus d'eines híbrides útils per a l'obtenció conjunta de dades quantitatives i qualitatives, es revisa el tipus d'ús que han tingut en treballs publicats precedentment i s'identifiquen els avantatges i els inconvenients del seu ús. Aquests dos tipus d'eines són els diagrames de satisfacció vital i les graelles biogràfiques. Tots dos tipus de diagrames van ser originàriament dissenyats com a instruments d'obtenció de dades quantitatives en el marc de la recerca sobre el curs de vida i posteriorment han passat a ser utilitzats també com a eines híbrides i com a eines únicament qualitatives. Tots dos tipus de diagrames són avaluats principalment a partir de l'ús realitzat per l'autor en recerques en les quals ha participat. Els principals avantatges identificats poden resumir-se en dos punts: 1) la facilitat per obtenir una representació fidedigna de fets complexos, i 2) la possibilitat d'estimular la reflexió personal a partir d'aquesta representació. S'identifica també una limitació principal comuna per als dos tipus de diagrames: poden ser difícils d'entendre, tret que les persones entrevistades rebin una explicació clara sobre com utilitzar-los. En tot cas, aquest tipus de diagrames constitueixen un pont entre la perspectiva del curs de vida i la recerca amb mètodes mixtos, una combinació molt poc tractada en la literatura metodològica.

The purpose of this study was to determine the effect of revitalization and awareness of tourist preferences. Data collection is done by questionnaire using purposive sampling method. Respondents in this study were tourists who made tourist preferences to Bukit Pentulu Indah totaling 100 people. The variable in this study is revitalization, awareness of tourist preferences. The sampling technique used is the Nonprobability Sampling technique so that the selection of sample members takes into account the conditions set. The data used in this study are primary data and sequential data. The results of this study indicate that the revitalization variable does not affect tourist preferences, awareness has a positive and significant effect on tourist preferences.

Higher-order networks are efficient representations of sequential data. Unlike the classic first-order network approach, they capture indirect dependencies between items composing the input sequences by the use of \textit{memory-nodes}. We focus in this study on the variable-order network model introduced in [Xu et al. (2016);Saebi et al. (2020)]. Authors suggested that random-walk-based mining tools can be directly applied to these networks. We discuss the case of the PageRank measure. We show the existence of a bias due to the distribution of the number of representations of the items. We propose an adaptation of the PageRank model in order to correct it. Application on real-world data shows important differences in the achieved rankings. \keywords{Higher-order Networks, Sequential data, Random walks, PageRank, Comment: This article contains 11 pages, 5 figures, and is in submission process for the Complex Networks 2021 conference

The process of hiding information between images is called Steganography. The usage has been increased more in the current decade. Formed various methods and predictions to ad-lib the communication. It involves more with regards to the solid transmission of the information in the correspondence series. The data might be in any form like the text, picture, sound and even video records. Steganography all the more deeply utilizes the image as a medium to cover the first data. In the proposed strategy uses the adjusted type of Least Significant Bit (LSB) addition method. The first text record is first changed over into a parallel structure. The cover picture is changing over concerning the info information's comparing pixel game plan. The substitution happens so that one pixel worth will be altered and supplanted in each square. Therefore, the interloper can't discover the progressions that occurred inside the picture. This strategy utilizes the LSB procedure in a changed manner with pixel substitution. The trial results show that the proposed technique stays secure and is quick to process.

Convolutional neural networks have become one of the most powerful computing tools of artificial intelligence in recent years. They are especially suitable for the analysis of images and other data that have an inherent sequence structure, such as time series data. In the case of data in the form of vectors of features, the order of which does not matter, the use of convolutional neural networks is not justified. This paper presents a new method of representing non-sequential data as images that can be analyzed by a convolutional network. The well-known Kohonen network was used for this purpose. After training on non-sequential data, each example is represented by so-called U-image that can be used as input to a convolutional layer. A hybrid approach was also presented, where the neural network uses two types of input signals, both U-image representation and the original features. The results of the proposed method on traditional machine learning databases as well as on a difficult classification problem originating from the analysis of measurement data from experiments in particle physics are presented.

One of the main issues that potentially cause faults in ego-vehicle trajectory prediction is various styles of drivers. To deal with this problem, we propose Driving Style Attention Generative Adversarial Network (DSA-GAN), which can generate the trajectory of ego-vehicle conditioned on the driving style. This system can be adopted in many vehicles because it only needs CAN-bus data to predict the trajectory. The proposed architecture involves two stages, Driving style recognition and Trajectory prediction. In the Driving style recognition, Recurrence Plot (RP) transforms sequential data into images and the converted images are processed into the driving styles by Convolutional Neural Network (CNN). In the Trajectory prediction part, Conditional Generative Adversarial Network (CGAN) generates the multi-modal realistic trajectories from the distribution and these trajectories are conditioned by the driving style. In this paper, we predict more realistic and accurate trajectories than conventional prediction methods, even if a driver's driving style is not categorized by our defined classes.

Molecular networking of non-targeted tandem mass spectrometry data connects structurally related molecules based on similar fragmentation spectra. Here, we report the Chemical Proportionality (ChemProp) contextualization of molecular networks. ChemProp scores the changes of abundance between two connected nodes over sequential data series (e.g., temporal or spatial relationships), which can be displayed as a direction within the network to prioritize potential biological and chemical transformations or proportional changes of (biosynthetically) related compounds. We tested the ChemProp workflow on a ground truth data set of a defined mixture and highlighted the utility of the tool to prioritize specific molecules within biological samples, including bacterial transformations of bile acids, human drug metabolism, and bacterial natural products biosynthesis. The ChemProp workflow is freely available through the Global Natural Products Social Molecular Networking (GNPS) environment.

Many real-world phenomena can be represented as dynamic graphs, i.e., networks that change over time. The problem of dynamic graph summarization, i.e., to succinctly describe the evolution of a dynamic graph, has been widely studied. Existing methods typically use objective measures to find fixed structures such as cliques, stars, and cores. Most of the methods, however, do not consider the problem of online summarization, where the summary is incrementally conveyed to the analyst as the graph evolves, and (thus) do not take into account the knowledge of the analyst at a specific moment in time. We address this gap in the literature through a novel, generic framework for subjective interestingness for sequential data. Specifically, we iteratively identify atomic changes, called ‘actions’, that provide most information relative to the current knowledge of the analyst. For this, we introduce a novel information gain measure, which is motivated by the minimum description length (MDL) principle. With this measure, our approach discovers compact summaries without having to decide on the number of patterns. As such, we are the first to combine approaches for data mining based on subjective interestingness (using the maximum entropy principle) with pattern-based summarization (using the MDL principle). We instantiate this framework for dynamic graphs and dense subgraph patterns, and present DSSG, a heuristic algorithm for the online summarization of dynamic graphs by means of informative actions, each of which represents an interpretable change to the connectivity structure of the graph. The experiments on real-world data demonstrate that our approach effectively discovers informative summaries. We conclude with a case study on data from an airline network to show its potential for real-world applications.