What are shapelets. Every subsequence of every series in T is a candidate.

What are shapelets Currently shapelets are found by evaluating the prediction qualities of numerous candidates extracted from the series segments. What is RAG (Retrieval Augmented Generation)? Discover the power of vector databases, RAG, and GenAI with Shapelets VectorDB. Others such as SAST [4] only select a small number of “reference” time series in the training data where all subsequences will be considered as shapelets without evaluating their quality Several states are reporting cases of measles, a highly contagious viral disease. Many algorithms have been designed based on shapelets, some use discretization [16], or random selection [22, 11] to filter candidate shapelets, others build shapelets location indicators from the data to generate a limited number of It also considers the notion of self-similar shapelets during the candidate extraction to avoid sampling similar shapelets as they would be redundant. However, although the learned shapelets are discriminative, they are not always similar to pieces of a real series in the dataset. Shapelets release notes#. Aug 24, 2014 · Shapelets are discriminative sub-sequences of time series that best predict the target variable. Currently shapelets are found by evaluating the prediction qualities of Dec 18, 2017 · Shapelets are phase independent subsequences designed for time series classification. Set of time-series shapelets formatted as a tslearn time series dataset. Shapelets¶ Shapelets are defined in [ 1 ] as “subsequences that are in some sense maximally representative of a class”. ndarray of objects, each object being a time series. Since the discovery of time series shapelets is costly in terms of time, the applicability on long or multivariate time series is difficult. Our Version Control registers all versions and allows the user to work on any of them, even if later versions are available. Although shapelets are a useful concept, the current literature bemoans the fact that shapelet discovery is a Each level contains equally sized adjacent shapelets without overlapping. In this paper, we propose a novel autoencoder-shapelet approach (AUTOSHAPE), which is the first study to take the advantage of both autoencoder and shapelet for Shapelets学習をベースに，これを更に拡張した独自の 技術を開発しました⑴，⑵。ここでは，まず①に応える既 存のShapelets学習について述べ，次に①と②に応える LTSpAUC，及び①と③に応えるRLTSについて説明します。 Getting started with Shapelets: Find all you need to begin, including demos, quick support, data access, and integrations. distinct ([cols]) . Thus, the main challenge for TSC is to discover high-quality variable-length shapelets to discriminate different clusters. Shapelets is an open source framework for time series analysis in big data environments. To extend Home » Math Vocabulary » Shapes – Definition, Types, FAQs, Practice Problems, Examples What are Shapes? Different Types of Shapes Solved Examples on Shapes Practice Problems On Shapes What are Shapes? In geometry, a shape can be defined as the form of an object or its outline, outer boundary or outer surface. shapelets can be formed and derive a number of practical analytical results. Set of time-series shapelets. Its focus is to improve efficiency in data acquisition, storage May 10, 2001 · Weak gravitational lensing provides a unique method to directly measure the distribution of mass in the universe. Shapelets can be integrated seamlessly with any data storage and collection solution. difficult Shapelets' innovation stems from the fact that it is an open-source solution that focuses on processing and analyzing platform data. First, since the locations where shapelets appear in the time series are determined by only their shapes, shapelets may appear at called time series shapelets. Meet our CEO, Justo Ruiz Ferrer, as he reveals something Apr 6, 2023 · Shapelets that discriminate time series using local features (subsequences) are promising for time series clustering. Their primary job is to stop the bleeding if you’re injured. While we believe shapelets can have many uses in data mining, one obvious implication of them is to mitigate the two weaknesses of the nearest neighbor algorithm noted above. In this example, we will extract two shapelets which are then used to transform our input time series in a two-dimensional space, which is called the shapelet-transform space in the related Shapelets offers analytical features such as forecasting, clustering and motif discovery, discords, and causality. Introducing Shapelets: Empowering Data Professionals with Cutting-Edge SolutionsShapelets is your go-to destination for cutting-edge data analysis and predic Feb 13, 2025 · Shapelets are interclass discriminative subsequences that can be used to characterize target classes. Note that the tolerance parameter is set to a high value so that the algorithm converges early (and the example runs faster). Every day, Shapelets and thousands of other voices read, write, and share important stories on Medium. Adds a new column (colname) to the Dataset and returns the new Dataset. Let us start from the beginning. Every subsequence of every series in T is a candidate. Easy to integrate with any database, high-performance (GPUs, CPU and multicore) and scalable. We demonstrate that multivariate shapelets are not significantly worse Sep 28, 2021 · Shapelet-based algorithms are widely used for time series classification because of their ease of interpretation, but they are currently outperformed by recent state-of-the-art approaches. A particularly useful set of complete and orthonormal shapelets is that consisting of weighted Hermite polynomials, which correspond to Shapelets documentation includes how-to guides and reference information for data scientists, and data professionals working in the Shapelets platform. Informally, if we assume a binary classification setting, a shapelet is discriminant if it is present in most series of one class and absent from series of the other class. 2. Even with speedup heuristics such as pruning, clustering, and dimensionality reduction, the search remains computationally expensive. Mar 15, 2022 · Read writing from Shapelets on Medium. Regarding data visualization, can I share my project with third parties? shapelets can be seen as generalizing the lazy nearest neighbor classifier to an eager classifier. Shapelets Data Apps acts as a server application that provides access to the data and data visualizations (data apps) through a browser (client). We challenge you to develop your Data Science proyects in 10 Min! | How? Read our latest article!. data-mining evolutionary-algorithms time-series-analysis shapelets timeseries-analysis Dec 14, 2019 · ShapeletsとはShapeletsとは簡単に言えば学習データの重要なサブシーケンス(一部分)です。 上の図は概念図ですが、学習データの重要なサブシーケンスが判明していれば、テスト時にそれを使って未来予測をすることが容易です。 Shapelets’ Version Control feature easily tracks and manages changes, allowing Data Scientists to focus solely on improving and adjusting their data apps. Jun 22, 2022 · In this use case, we explore how Shapelets accelerated platform can be used to create a benchmark of anomaly detection algorithms, in order to find a powerful anomaly detection solution. Shapelets Data Apps are a powerful tool backed with the latest technology and developed by a strong technical team that´s passionate about providing you with the highest-quality software. As the landscape of vector databases continues to evolve, we plan to Jun 2, 2019 · distinguish between real subseries and shapelets and (iii) optimizing shapelets to fool the discriminator. Nov 8, 2022 · U-shapelets：发现无标签的shapelets。发现用于时间序列聚类的无标记的shapelets。 USSL ：利用小形状正则化、频谱分析和伪标签从无标签的时间序列中学习突出的子序列。 DTC ：时间序列数据的自动编码器。用于时间序列降维的自动编码器和一个新的时间序列聚类层。 Shapelets is a Python-based library that implements several shapelet functions and some of their applications in science and engineering. Shapelet functions are a complete and orthogonal set of localized basis functions with mathematical properties convenient for image analysis and manipulation. Here, we present a new method for obtaining reliable weak shear measurements. It is based on the Shapelet basis function formalism of Shapelets Data Apps is the blazing-fast tool every Data Scientist dreams of. Experiments performed Shapelets comes as a built-in box, it is based on the amount of resources you are powering in the platform. Everything we see Feb 2, 2024 · In this work, we propose a model-agnostic instance-based post-hoc explainability method for time series classification. The main problem of this method is that the randomly selected shapelets Shapelets is a Python package that implements several shapelet functions and some of their significant applications in science and astronomy. The company operates across various sectors offering data analytics assistance using machine learning and query-based tools, enabling clients to help provide more insights to a wide variety of use cases in many different sectors. This example highlights two learned shapelets and the distances between the time series and both shapelets. We propose three adaptations to the Shapelet Transform (ST) to capture multivariate features in multivariate time series classification. They can be used to transform the timeseries to features by Shapelets Core allows you to efficiently query and integrate the data stored in multiple heterogenous data sources, including file systems and databases (e. g. This paper proposes a Semi-supervised Clustering of Time Series 2009年，上述论文第一次提到了时序数据中的shapelet的概念，打开了时间序列数据挖掘的新方向。（这篇论文的引用量888。。。） shapelet其实就是一段时间序列数据中的某个子序列，这个子序列是这段时间序列数据的最… This example illustrates the use of the “Learning Shapelets” method in order to learn a collection of shapelets that linearly separates the timeseries. Informally, shapelets are time series subsequences which are in some sense maximally representative of a class. To make Big Data analysis more efficient, you can run, extend, and integrate your own algorithms in the Shapelets platform. Feb 21, 2016 · In timeseries classification, shapelets are subsequences of timeseries with high discriminative power. pip install shapelets-platform. Shapelets VectorDB ensures fast retrieval, semantic search capabilities, and robust security, making RAG a powerful tool for intelligent content creation and effective Learning Time-Series Shapelets What are Time-Series Shapelets? (I) I De nition: I Patterns whose minimum distances to time-series yield discriminative predictors [Ye and Keogh(2009), Lines et al. It is shown that Ultra-Fast Shapelets is a comprehensive suite of data tools designed to optimize and accelerate the management and analysis of business data. May 5, 2022 · (Image by Author) Based on these candidate shapelets (orange), it appears that the major distinguishing factor between the two classes is differentiated in how the actors’ hand returns the (imaginary) gun to the holster and then relaxes by the actors’ side. Recently, exploiting transformers for MTSC has achieved state-of-the-art performance. Oct 21, 2023 · Learning shapelets by a continuous optimization has recently been studied to improve computational efficiency and classification performance. What are Shapelets Data Apps and how can I create one? Data Apps are professional, interactive, shareable web applications that can be used to visualize and interact with data. Returns the number of rows in this DataSet. Oracle, Postgre) and store it in multiple formats or use it directly for visualization purposes. For clarity, shapelets show different shades of gray. Shapelets are Contains an implementation (sklearn API) of the algorithm proposed in "GENDIS: GEnetic DIscovery of Shapelets" and code to reproduce all experiments. We validate the proposed method on several real-world univariate time series classification tasks from the UCR Time Series Aug 6, 2022 · The shapelets are convenient for interpreting the clusters. [4, 14]initializeshapelets This code offers a Python implementation of the work presented in: Josif Grabocka, Nicolas Schilling, Martin Wistuba, Lars Schmidt-Thieme (2014): Learning Time-Series Shapelets. However, existing methods for MTS often struggle due to the challenges of effectively modeling high-dimensional data and the lack of labeled data, resulting in poor Platelets are cell fragments and the smallest component of your blood. Sep 9, 2024 · What Are Shapelets, and Why Do They Matter? At their core, shapelets are short, discriminative subsequences found within time series data. Learn K discriminative shapelets of length L (denoted as S 2RK L). ndarray of shape (n_shapelets, sz_shp, d) where sz_shp is the maximum of all shapelet sizes. To check if Shapelets is installed, you can execute the following: python -c "import shapelets as sh; print(sh Our customers find great value in the accelerated analytics and TCO reduction achieved with the help of Shapelets Data Apps. Apr 26, 2022 · Shapelets VectorDB ensures fast retrieval, semantic search capabilities, and robust security, making RAG a powerful tool for intelligent content creation and effective decision-making in businesses. (2012)Lines, Davis, Hills, and Bagnall] I Problem: 1. Model Jan 10, 2025 · Shapelets - ML based big data time series analysis solutions. Nov 28, 2023 · At Shapelets we are aware of this, and we have created a tool to be able to analyse this data in a simple and intuitive way, both for Data Scientists and for business users who have to use this data. Once you have installed shapelets, in order to get started you need user credentials to access the UI, and you need to start the server. Jun 28, 2009 · Time series shapelets are small, local patterns in a time series that are highly predictive of a class and are thus very useful features for building classifiers and for certain visualization and summarization tasks. Imagine analysing a time series where Shapelets are discriminative sub-sequences of time series that best predict the target variable. May 22, 2020 · To reduce the computational cost of shapelet discovery, Wistuba, Grabocka, and Schmidt-Thieme proposed the ultrafast shapelets method to randomly select shapelets based on the assumption that discriminative shapelets should appear frequently in the time series data [19]. Shapelets can be used to classify time series (essentially, the nearest shapelet algorithm), offering the benefits of speed, intuitiveness and at least on some domains, significantly improved 2009年，来自加州大学河滨分校的 Eamonn Keogh 教授在数据挖掘顶级会议KDD上发表了一篇论文《Time Series Shapelets: A New Primitive for Data Mining》，首次提出了时序数据中的 Shapelet 的概念。他们受树叶轮廓的启发，借鉴象形文字的思想，提出了一种描述时序子序列形态的方法。, 视频播放量 3043、弹幕量 0、点 Jan 24, 2015 · Time series shapelets are discriminative subsequences and their similarity to a time series can be used for time series classification. We will use historical data collected from a temperature sensor from an internal component of a large industrial machine. Shapelets General Information Description. Before going to a clinic or emergency room, it’s important to check with your doctor if you think you or a loved one has measles. Thus, as a deployed classification tool, shapelets can be many orders of magnitude faster than any rival with comparable accuracy. It can be integrated with any type of storage platform and automates machine learning processes using machine learning algorithms, all of which are optimized for the available resources. By significantly reducing the complexity of deploying data visualizations, as well as data processing times, Shapelets enables our clients to gain critical insights faster, thereby driving better decision-making and Mar 22, 2021 · First, we propose a Shapelet discovery method Shapelet Dictionary Learning, which combines Dictionary Learning and Shapelet and generates a group of Shapelets instead of searching. This makes it difficult to interpret the decision, i. Shapelets provides an intuitive API that helps you to create these Data Apps by composing a variety of visual components or widgets in various layouts and obtain It also considers the notion of self-similar shapelets during the candidate extraction to avoid sampling similar shapelets as they would be redundant. Check out the best resources for Data Scientists and Data Professionals! We bring the best data analytics solutions across all industries. We may be able to use subsegments that are able to distinguish between specific classes We can then use shapelets and build a shapelet dictionary that can the be used to build a decision tree from them in order to build a decision tree out of this Nov 1, 2022 · However, simple doesn´t mean weak. For this reason, shapelet discovery has recently attracted considerable interest within the time-series research community. Recent research (see above) suggest that matrix profiles can be used to efficiently identify shapelets of a particular class and so, in this tutorial, we’ll build on top of our matrix profile knowledge and demonstrate how we can use STUMPY to easily discover interesting shapelets in only a few lines of additional code. Currently shapelets are found by evaluating the prediction qualities of numerous candidates extracted from the se-ries segments. 2 Shapelets. While a lot of research has been done for univariate time-series shapelets, extensions for the multivariate setting have not yet received much attention. Dec 22, 2023 · Using SHAPELETS to categorize sensor time series data. Jun 24, 2017 · Shapelets are time series subsequences which are in some sense maximally representative of a class (Rakthanmanon and Keogh 2013b; Ye and Keogh 2009). For instance, banks can extract a shapelet that corresponds to the characteristic pattern of a Apr 15, 2024 · Shapelets that discriminate time series using local features (subsequences) are promising for time series clustering. Here’s what you need to know about Shapelets releases notes. Developer of an enterprise platform intended to empower people with decisions and data. In §6, we discuss several direct applications of shapelets. e. Deep-learning based methods USRL-FordA [ 7 ] is an unsupervised method to learn universal embeddings of time series and achieves state-of-the-art performance in time series classification. count (). In §5, we derive polar shapelets from the cartesian basis functions and describe some of their properties. The original set of shapelets is illustrated as the top sequence at each level. Both children and adults can get measles. Additional set of shapelets obtained by shifting by half shapelet length is illustrated as bottom sequence at each level. These are organized by product: Shapelets SDK release notes cover the features that we develop for the Shapelets Data Science and Engineering workspace, which includes the ingestion, processing and visualization environment. read more Shapelets is a Python-based library that implements several shapelet functions and some of their applications in science and engineering. Apr 7, 2022 · What are shapelets? As most time series data exhibits inter-class differences within sub-sequences rather than on the complete series, shapelets were meant to represent these discriminative In this work we introduce a new time series primitive, time series shapelets, which addresses these limitations. Founded by Justo Ruiz Ferrer, Joaquin De Lapatza and 1 other in the year 2018. shapelets_as_time_series_ numpy. Mar 4, 2021 · Shapelets are discriminative subsequences extracted from time-series data. We're celebrating innovation and collaboration in the Python community. Shapelets is an integrated platform for data scientists that provides significant speedups and greater efficiency to help data scientists extract insights from data, create powerful >>> docker run--name shapelets-p 4567: 4567-d-v / []: / io shapeletsdev / shapelets-platform: latest […] = insert your own directory This Docker image needs to expose the 4567 port, and we recommend that you create a volume to share your local scripts with the Docker container. Nov 27, 2024 · Multivariate time series (MTS) classification is widely applied in fields such as industry, healthcare, and finance, aiming to extract key features from complex time series data for accurate decision-making and prediction. transformer_model_ keras. Daniel supports the team with his diverse background in Software Engineering. The generated Shapelets are totally new subsequences which contain local shape features of all the time series data but not exist in original data. The Learning Time-series Shapelets algorithm is used to transform the sequence dataset, then Gradient Dec 30, 2024 · Shapelets是一种时间序列数据挖掘技术，主要用于分类、特征提取和模式识别。Shapelets通过寻找时间序列中的特征子序列，来识别和分类不同的时间序列模式。本文将详细介绍如何用Python实现Shapelet，包括数据预处理、Shapelet提取、距离计算、分类等步骤。 Shapelets Core uses highly efficient algorithms for indexing, offering real-time indexing capabilities with minimal CPU and memory requirements. Jun 3, 2019 · Times series classification can be successfully tackled by jointly learning a shapelet-based representation of the series in the dataset and classifying the series according to this representation. In this work we propose Ultra-Fast Shapelets that uses a number of random shapelets. Shapelets is all set for PyCon España 2023 as Teide sponsors. Feb 9, 2020 · Shapelets. Mar 28, 2023 · Shapelets are useful in this case, as they capture the crucial features of fraudulent transactions. The proposed algorithm, namely Time-CF, leverages shapelets and TimeGAN to provide counterfactual explanations for arbitrary time series classifiers. Shapelets are small subseries, or parts of the timeseries, that are informative or discriminative for a certain class. In the rst papers about shapelet-based time series classi cation [26, 18], the shapelets were directly extracted from the training set and the selected shapelets could be used a posteriori to explain the classi er’s decision. It is not based on the number of users, or the volume of data you put into the system. If you are not familiar with these methods, you can find the instructions below. Existing methods perform a combinatorial search for shapelet discovery. Figure 2 shows an example of this process with two shapelets. Feb 12, 2025 · Shapelets are used to find local patterns that may tell different time series apart. However, there are two issues in previous studies. Shapelets are found via an exhaustive search of every candidate between lengths \(min\) and \(max\). We present a new formulation of time series shapelets including the notion of dilation, and we introduce a new shapelet feature to enhance their discriminative power for classification. Each of these steps is attached to a loss function that w e describ e in the following. A shapelet is a subsequence of one time series in a dataset \(\mathbf {T}\). Feb 19, 2021 · RAG leverages vector databases like Shapelets VectorDB to efficiently retrieve and manage data as numerical vectors, allowing LLMs to generate precise and current responses. However, existing methods focus on generic features, providing a comprehensive understanding of data, but they ignore class-specific features crucial for learning the Feb 15, 2022 · The innovation of Shapelets is based on being a solution with an open-source part, focused on the treatment and analysis of time series data, integrable with any type of storage platform, which automates machine learning functions through machine learning algorithms and where all algorithms are optimized for the available resources (CPU-GPU-Multicore). However, the shapelet Nov 25, 2024 · In conclusion, Shapelets VectorDB’s superior performance in both efficiency and accuracy underscores its capability to handle intensive vector search workloads effectively, making it a strong choice for applications requiring high-speed, high-precision vector retrieval. NOTE: Using a virtual environment or Docker is a good idea to avoid conflicts between libraries installed on your system. Jan 19, 2023 · 5) Class-Specific Shapelets Learning (CSSL) that learns class-specific shapelets rather than shared shapelets and further improves the classification accuracy. It is based on the linear decomposition of each object in the image into a series of localised basis functions of different shapes, which we call `Shapelets'. May 10, 2001 · We present a new method for the analysis of images, a fundamental task in observational astronomy. This type of data, time series, are like windows into the past, revealing patterns and trends. Although the existing shapelet-based methods have achieved encouraging results Aug 31, 2021 · Daniel is a Data Engineer for Shapelets. We create a unified set of data to benchmark our work on, and compare with three other algorithms. Others such as SAST [4] only select a small number of “reference” time series in the training data where all subsequences will be considered as shapelets without evaluating their quality Shapelets are discriminative sub-sequences of time series that best predict the target variable. Use it as a server-based vector DB or integrate it in your projects as a python library. These data apps or web applications contain widgets that can be used to interact with data attached to the data app and to select parameterizations and trigger computations in the server/cluster based refer to by shapelets). In Proceedings of the 20th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2014 As an example, it takes Shapelets VectorDB is a commercial vector database known for its ultra-low memory footprint and unseen ingestion speed. This algorithm learns the shapelets as well as the coefficients of the logistic regression. However, existing methods have employed predefined and fixed shapelet lengths during the continuous optimization, despite the fact that shapelets and their lengths are inherently interdependent and thus then referred as the shapelet transform. Its main objective is to enhance the ability of generative models to provide more accurate and contextually relevant responses by combining text generation with information retrieval mechanisms. Shapelet functions are a complete and orthogonal set of localized basis functions with mathematical properties convenient for manipulation and analysis of images from a broad range of applications. He is an integral part of the back-end development team, where we develop a high-quality platform ensuring the best product design with valuable functionalities for data scientists. called shapelets, that can be used to classify the series. The shapelet-based (classification) framework is summarized in the image below: First the k shapelets are generated using the training data (with k a parameter), then, by performing the Shapelet Transform with the generated shapelets and some time series dataset with n samples, we obtain a feature matrix of size (n, k), this matrix is then used by a tabular classifier, such as each sample is shapelets_ numpy. The primary challenge of shapelet-based methods is to find discriminative shapelets that best distinguish different classes. Because the distortions induced by lensing in the shape of background galaxies are small, the measurement of weak lensing requires high precision. 19M over 2 rounds from 3 investors. There is nothing special about the data-points closest (in euclidean distance sense) to the clus-ter centers (which we term, pseudo-medians), and they are shown in the figure purely for illustrative purposes, to get an idea of what these shapelets might look like, if quan-tized. Toaddressthisissue, Refs. add_column (colname, *genExpr). If a blood vessel is damaged, platelets cluster together to form a plug first and then a clot to stop the blood loss. Nov 8, 2024 · A RAG system is an innovative architecture in the field of artificial intelligence and natural language processing. In Shapelets code, a Data App is a composition of widgets. 一句话概括：以往方法是用搜索的方式寻找shapelets，作者提出了学习的方式寻找shapelets。但学习的过程中，shapelets是不可导的，作者采用soft minimum function改进。 摘要 Shapelets是时间序列中最能预测目标变… May 18, 2013 · Shapelets were introduced in Ye and Keogh to measure this type of similarity. For this reason, shapelet discovery has recently attracted considerable interest within the time-series re-search community. Learning shapelets by continuous optimization has recently been studied to improve classification accuracy. Classifiers using shapelets have proven to achieve performances competitive to state-of-the-art methods, while enhancing the model’s interpretability. In §4, we discuss how the shapelet states behave under convolutions. shapelets as unsupervised cluster centroids of original time-series segments, they only rarely include initial shapelets (centroids) that contribute to the classi ca-tion, degrading classi cation performance, especially when the number of shapelets K is small. Raised a total funding of $1. Jan 6, 2022 · Shapelet-based methods have attracted widespread attention over the past decade in the time series classification for their benefits of high classification accuracy and good interpretability. Our conclusions are presented Sep 1, 2024 · Lexiang Ye 等于2009年发表的论文《Time Series Shapelets: A New Primitive for Data Mining》引入了一种新的时间序列特征提取方法Shapelets，这种方法有效地解决了上述限制。简单来说，Shapelet是能够在某种意义上最能代表某一类的时间序列子序列。 Getting Started# How to run Shapelets server#. Existing time series clustering methods may fail to capture representative shapelets because they discover shapelets from a large pool of uninformative subsequences, and thus result in low clustering accuracy. 2009年，来自加州大学河滨分校的 Eamonn Keogh 教授和他的学生 叶乐翔 在数据挖掘顶级会议 KDD 上发表了一篇论文《Time Series Shapelets: A New Primitive for Data Mining》，首次提出了时序数据中的 Shapelet 的概念。他们受树叶轮廓的启发，借鉴象形文字的思想，提出了一种 May 23, 2024 · Multivariate time series classification (MTSC) has attracted significant research attention due to its diverse real-world applications. Build and deploy Data Apps in minutes with unmatched speed and ease. hqjbtw zkiqyg pikln bwnklur omoikv jnxyz sxplm thqi reqmu mofuu bfkhdx mak zezwkvb cymbmic qyhjlll