Academic literature on the topic 'MAP REDUCE ARCHITECTURE'
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Journal articles on the topic "MAP REDUCE ARCHITECTURE"
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Rathore, Neeraj. "Map Reduce Architecture for Grid." i-manager's Journal on Software Engineering 10, no. 1 (September 15, 2015): 21–30. http://dx.doi.org/10.26634/jse.10.1.3629.
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Osero, Benard O., Elisha Abade, and Stephen Mburu. "Mobile Agent Based Distributed Network Architecture with Map Reduce Programming Model." Computer Science and Information Technology 7, no. 5 (November 2019): 129–61. http://dx.doi.org/10.13189/csit.2019.070501.
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Moharrer, Armin, and Stratis Ioannidis. "Distributing Frank–Wolfe via map-reduce." Knowledge and Information Systems 60, no. 2 (December 18, 2018): 665–90. http://dx.doi.org/10.1007/s10115-018-1294-7.
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K N, Manjunatha, and Vaibhav A Meshram. "Energy Efficient VLSI Architecture for Variable Iterative 4G LTE Turbo Decoder." International Journal of Engineering & Technology 7, no. 3 (July 16, 2018): 1535. http://dx.doi.org/10.14419/ijet.v7i3.12652.
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The Long Term Evolution (LTE) networks main objective is to support the next generation wireless communication systems. But most of the LTE approaches are suffer from decoding latency. Hence results in drop of data rate and this is not supported by the 4G LTE standards. To overcome this few parallel architectures has been introduced with the cost of power and silicon chip area. One promising decoding algorithm to overcome the decoding latency is Maximum a Posteriori (MAP) algorithm. The MAP has two computationally challenging α and β units. These two units have critical path and are to be reduced. A novel architecture for Add-Compare-Select (ACS) is proposed with clock gating techniques to reduce the unnecessary power dissipation across the recursive computational units. The proposed technique is applied with max-log MAP algorithm to precise the approximation. The overall design in implemented in a 45nm CMOS technology and results in 179.2mW of power dissipation which results in 34.6% less power compared to reported design while monitoring the moderate or same throughput level.
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THABTAH, FADI, and SUHEL HAMMOUD. "MR-ARM: A MAP-REDUCE ASSOCIATION RULE MINING FRAMEWORK." Parallel Processing Letters 23, no. 03 (September 2013): 1350012. http://dx.doi.org/10.1142/s0129626413500126.
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Association rule is one of the primary tasks in data mining that discovers correlations among items in a transactional database. The majority of vertical and horizontal association rule mining algorithms have been developed to improve the frequent items discovery step which necessitates high demands on training time and memory usage particularly when the input database is very large. In this paper, we overcome the problem of mining very large data by proposing a new parallel Map-Reduce (MR) association rule mining technique called MR-ARM that uses a hybrid data transformation format to quickly finding frequent items and generating rules. The MR programming paradigm is becoming popular for large scale data intensive distributed applications due to its efficiency, simplicity and ease of use, and therefore the proposed algorithm develops a fast parallel distributed batch set intersection method for finding frequent items. Two implementations (Weka, Hadoop) of the proposed MR association rule algorithm have been developed and a number of experiments against small, medium and large data collections have been conducted. The ground bases of the comparisons are time required by the algorithm for: data initialisation, frequent items discovery, rule generation, etc. The results show that MR-ARM is very useful tool for mining association rules from large datasets in a distributed environment.
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Malekimajd, Marzieh, Danilo Ardagna, Michele Ciavotta, Alessandro Maria Rizzi, and Mauro Passacantando. "Optimal Map Reduce Job Capacity Allocation in Cloud Systems." ACM SIGMETRICS Performance Evaluation Review 42, no. 4 (June 2, 2015): 51–61. http://dx.doi.org/10.1145/2788402.2788410.
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Khakimov, A., A. Suminov, and A. Muthanna. "DEVELOPMENT OF EDGE COMPUTING DISTRIBUTION METHOD IN VANET." Telecom IT 7, no. 2 (December 2019): 46–54. http://dx.doi.org/10.31854/2307-1303-2019-7-2-46-54.
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Research subject. Devoted the VANET network architecture, based on SDN / MEC (software-defined networks / mobile edge computing) systems that can reduce network load and traffic density. Method. The developed algorithm is considered. A testbed experiment based on a model network was done. Core results. The article examined the possibility of temporarily placing an application in RSU to reduce the load on the transit network. The condition for temporary placement of an application is based on data on congestion of road sections from Google Map & Yandex monitoring systems and current statistics on Internet traffic. Practical relevance. The proposed architecture allows to optimal use of RSU/MEC resources and significantly reduces the delay and load on data transmission.
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Shafighi, Nesa, and Babak Shirazi. "Ontological Map of Service Oriented Architecture Based on Zachman." Research in Economics and Management 2, no. 4 (July 18, 2017): 33. http://dx.doi.org/10.22158/rem.v2n4p33.
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<em>Service orientation is an approach in the field of enterprise architecture, business information systems and software application that its main element is the service. Shared services is an organization model of sharing, across an organization. It enables collaboration among the functions/departments. Main motivations for shared services are sharing, promote efficiency, reduce cost, and support scalability. Despite of the widespread use of these two approaches in information technology, there is no tool to optimize the management of them. The aim of this study is Ontological map of service oriented architecture based on zachman framework to adapt it in the reference enterprise architecture framework through implementation ontology views on system architect software and as well as equivalent ontology component with UML diagrams. After the implementation of the suggested model, the results showed that ontology is a formal description and explicit display of objects, concepts and other entities in the relationship between them. In other words, there is a model that describe all that is in fact in to understandable language for the system. Thus the proposed establishes have association between all aspects of zachman framework, also to create a clear description of business concepts in the management of shared services and is effective to provide a unified platform for enterprise modeling.</em>
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Khamaru, Ananda, and Tryambak Hiwarkar. "A Dynamics of Machine Learning on Map-Reduce Architecture for Enhancing Big Data Analysis Performance." International Journal of Computer Science and Mobile Computing 11, no. 11 (November 30, 2022): 109–30. http://dx.doi.org/10.47760/ijcsmc.2022.v11i11.009.
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Big data is a fantastic resource for disseminating system-generated insights to external stakeholders. However, automation is required to manage such a large body of information, and this has spurred the development of data processing and machine learning tools. Just as in other fields of study and business, the ICT industry is serving and developing platforms and solutions to help professionals treat their knowledge and learn automatically. Large companies like Google and Microsoft, as well as the Apache Foundation's incubator, are the primary providers of these platforms. Spark is an open-source platform for handling Big Data insights that have been tainted by contamination. This unified framework provides a variety of methods for dealing with unstructured or structured text data, graph data, and real-time streaming data. Spark relies on MLlib to create customised ML algorithms. To parallelize a huge cluster of machines for data analytics, these methods require less memory, less processing time, and, to a large extent, hand tuned specialized architecture. Data sets are analysed with machine learning methods including Linear Regression, Decision Tree, Random Forest, and Gradient Boosting Tree. In order to comprehend the data sets with the help of machine learning algorithms and to determine the best forecast value from the comparative study, the prediction model provided in this research is used. One key goal of this study is to use the proposed model to make the most accurate forecast possible utilising machine learning methods. The suggested model utilizes the Apache Spark framework to perform a comparative analysis of the various existing approaches that have implemented the supervised and unsupervised techniques utilizing the MapReduce approach. By comparing the temporal complexity of each method, this method calculates the best prediction from the model. This dissertation emphasizes the characteristics of datasets that are most useful for examining the most effective prediction using machine learning algorithms.
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AlZubi, Ahmad Ali. "Big data analytic diabetics using map reduce and classification techniques." Journal of Supercomputing 76, no. 6 (April 16, 2018): 4328–37. http://dx.doi.org/10.1007/s11227-018-2362-1.
Full textDissertations / Theses on the topic "MAP REDUCE ARCHITECTURE"
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Mehta, Dhananjay. "Building a scalable distributed data platform using lambda architecture." Kansas State University, 2017. http://hdl.handle.net/2097/35403.
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Master of ScienceDepartment of Computer Science
William H. Hsu
Data is generated all the time over Internet, systems sensors and mobile devices around us this is often referred to as ‘big data’. Tapping this data is a challenge to organizations because of the nature of data i.e. velocity, volume and variety. What make handling this data a challenge? This is because traditional data platforms have been built around relational database management systems coupled with enterprise data warehouses. Legacy infrastructure is either technically incapable to scale to big data or financially infeasible. Now the question arises, how to build a system to handle the challenges of big data and cater needs of an organization? The answer is Lambda Architecture. Lambda Architecture (LA) is a generic term that is used for scalable and fault-tolerant data processing architecture that ensures real-time processing with low latency. LA provides a general strategy to knit together all necessary tools for building a data pipeline for real-time processing of big data. LA comprise of three layers – Batch Layer, responsible for bulk data processing, Speed Layer, responsible for real-time processing of data streams and Service Layer, responsible for serving queries from end users. This project draw analogy between modern data platforms and traditional supply chain management to lay down principles for building a big data platform and show how major challenges with building a data platforms can be mitigated. This project constructs an end to end data pipeline for ingestion, organization, and processing of data and demonstrates how any organization can build a low cost distributed data platform using Lambda Architecture.
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Jiang, Wei. "A Map-Reduce-Like System for Programming and Optimizing Data-Intensive Computations on Emerging Parallel Architectures." The Ohio State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=osu1343677821.
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Buttar, Sarpreet Singh. "Applying Machine Learning to Reduce the Adaptation Space in Self-Adaptive Systems : an exploratory work." Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-77201.
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Self-adaptive systems are capable of autonomously adjusting their behavior at runtime to accomplish particular adaptation goals. The most common way to realize self-adaption is using a feedback loop(s) which contains four actions: collect runtime data from the system and its environment, analyze the collected data, decide if an adaptation plan is required, and act according to the adaptation plan for achieving the adaptation goals. Existing approaches achieve the adaptation goals by using formal methods, and exhaustively verify all the available adaptation options, i.e., adaptation space. However, verifying the entire adaptation space is often not feasible since it requires time and resources. In this thesis, we present an approach which uses machine learning to reduce the adaptation space in self-adaptive systems. The approach integrates with the feedback loop and selects a subset of the adaptation options that are valid in the current situation. The approach is applied on the simulator of a self-adaptive Internet of Things application which is deployed in KU Leuven, Belgium. We compare our results with a formal model based self-adaptation approach called ActivFORMS. The results show that on average the adaptation space is reduced by 81.2% and the adaptation time by 85% compared to ActivFORMS while achieving the same quality guarantees.
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Buttar, Sarpreet Singh. "Applying Artificial Neural Networks to Reduce the Adaptation Space in Self-Adaptive Systems : an exploratory work." Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-87117.
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Self-adaptive systems have limited time to adjust their configurations whenever their adaptation goals, i.e., quality requirements, are violated due to some runtime uncertainties. Within the available time, they need to analyze their adaptation space, i.e., a set of configurations, to find the best adaptation option, i.e., configuration, that can achieve their adaptation goals. Existing formal analysis approaches find the best adaptation option by analyzing the entire adaptation space. However, exhaustive analysis requires time and resources and is therefore only efficient when the adaptation space is small. The size of the adaptation space is often in hundreds or thousands, which makes formal analysis approaches inefficient in large-scale self-adaptive systems. In this thesis, we tackle this problem by presenting an online learning approach that enables formal analysis approaches to analyze large adaptation spaces efficiently. The approach integrates with the standard feedback loop and reduces the adaptation space to a subset of adaptation options that are relevant to the current runtime uncertainties. The subset is then analyzed by the formal analysis approaches, which allows them to complete the analysis faster and efficiently within the available time. We evaluate our approach on two different instances of an Internet of Things application. The evaluation shows that our approach dramatically reduces the adaptation space and analysis time without compromising the adaptation goals.
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NEGI, ROHIT. "K-MEANS CLUSTERING ALGORITHM ON MAP REDUCE ARCHITECTURE." Thesis, 2016. http://dspace.dtu.ac.in:8080/jspui/handle/repository/14749.
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The rapid development of the Internet and its impact on every aspect of life has resulted in the size of the data to increase from GB level to TB even PB level. This has brought about new technologies such as Hadoop for efficient storage and analysis the data. Hadoop is an open-source software framework for storing and processing big data in a distributed fashion on large clusters of commodity hardware. Cluster is a collection of data members having similar characteristics. The process of establishing a relation or deriving information from raw data by performing some operations on the data set like clustering is known as data mining. Data collected in practical scenarios is more often than not completely random and unstructured. Hence, there is always a need for analysis of unstructured data sets to derive meaningful information. This is where unsupervised algorithms come in to picture to process unstructured or even semi structured data sets by resultant. K-Means Clustering is one such technique used to provide a structure to unstructured data so that valuable information can be extracted. This paper discusses the implementation of the K-Means Clustering Algorithm over a distributed environment using ApacheTM Hadoop. The key to the implementation of the K-Means Algorithm is the design of the Mapper and Reducer routines which has been discussed in the later part of the paper. The steps involved in the execution of the K-Means Algorithm has also been described in this paper based on a small scale implementation of the K-Means Clustering Algorithm on an experimental setup to serve as a guide for practical implementations.
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Rigg, Michelle C. "TREE MITIGATION STRATEGIES TO REDUCE THE EFFECT OF URBAN HEAT ISLANDS IN CENTER TOWNSHIP, IN." Thesis, 2012. http://hdl.handle.net/1805/3184.
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Indiana University-Purdue University Indianapolis (IUPUI)The purpose of this study was to identify urban heat island locations within Center Township, Indiana and to develop a model to determine areas of high social vulnerability. In addition, an urban heat island mitigation strategy was developed for socially vulnerable and highest temperature locations. Land surface temperature was estimated using Landsat ETM+ satellite imagery. Social vulnerability was estimated using principal components analysis and spatial analysis methods such as kernel density functions. These methods incorporate various socioeconomic variables, land surface temperature, and tree canopy cover. Tree canopy cover was extracted using Quickbird imagery among other techniques. Areas with high social vulnerability, high temperature and low tree canopy cover were analyzed and plantable spaces were assessed. The findings of this study will be shared with Keep Indianapolis Beautiful, Inc. so that they can inform their tree planting campaigns that seek to reduce the effects of urban heat islands on socially vulnerable populations.
Books on the topic "MAP REDUCE ARCHITECTURE"
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Escolme, Bridget. Tragedy in Twentieth and Twenty-first Century Theatre Production. Edited by Michael Neill and David Schalkwyk. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780198724193.013.33.
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This chapter discusses the relationship between actor and scenography in twentieth and twenty-first century productions of Hamlet and King Lear, particularly the common theatrical trope of realist acting on abstract stage sets. It argues that whilst in some productions the notion of tragic hero as common man reduces the plays to a set of psychological problems, in others, contrasts and tensions between acting style and scenography or theatre architecture have created what the author calls a ‘politics of intimacy’. These productions have made it possible for detailed, realist acting on non-naturalistic stage sets to pose potent questions about the social and political meanings of human relations in the plays. They have allowed for an audience experience that involves both psychological intimacy and ideological critique.
Book chapters on the topic "MAP REDUCE ARCHITECTURE"
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Wu, Chenni, and Wei Zhang. "Business Process Execution Based on Map Reduce Architecture." In Advances in Intelligent and Soft Computing, 167–72. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28308-6_23.
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Hassen, Hamdi, Hajlaoui Nasreddine, and Khemak Maher. "DMRA-MCP: A Distributed Map-Reduce Applications Deployments in a Multi-Cloud Platform Based on Cloud Broker Architecture." In Intelligent Decision Technologies 2019, 275–85. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-8311-3_24.
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Martins, Pedro, Maryam Abbasi, José Cecílio, and Pedro Furtado. "Integrating Map-Reduce and Stream-Processing for Efficiency (MRSP)." In Beyond Databases, Architectures and Structures. Towards Efficient Solutions for Data Analysis and Knowledge Representation, 3–15. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-58274-0_1.
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Vasireddi, Hemanth Kumar, and K. Suganya Devi. "An Ideal Big Data Architectural Analysis for Medical Image Data Classification or Clustering Using the Map-Reduce Frame Work." In Lecture Notes in Electrical Engineering, 1481–94. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-7961-5_134.
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Moorthy, S. N., M. S. Sajeev, R. P. K. Ambrose, and R. J. Anish. "Conclusions and future prospects." In Tropical tuber starches: structural and functional characteristics, 264–70. Wallingford: CABI, 2021. http://dx.doi.org/10.1079/9781786394811.0264.
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Abstract This book chapter outlines the future steps in improving the status of the tropical tuber crops: (1) reduce the period of maturity of all the TTCs to 110-150 days, (2) improve the tuber shape and make them more definitive in shape, (3) improve plant architecture to attain a harvest index of 0.70, (4) eliminate antinutritional factors and improve nutritive values, (5) impart resistance to the three major biotic problems afflicting the TTCs: CMD (cassava mosaic disease), taro leaf blight and sweet potato weevil, (6) introduce determinacy in cassava and yams, (7) increase the post-harvest storage life, (8) reduce the loss during post-harvest storage, transportation and processing, (9) use biotechnological means to alter starch characteristics like amylose content, chain length and phosphorus content in starch, (10) it is also suggested that there should be a 'starch bank', which acts as a repository of different starches and provides information on all physicochemical and functional properties for various applications to researchers and industries, and (11) an organization to carry out exclusive research on tuber starches may be established in any of the TTC-growing countries.
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Bozzano, Marco, Alessandro Cimatti, Alberto Griggio, and Martin Jonáš. "Efficient Analysis of Cyclic Redundancy Architectures via Boolean Fault Propagation." In Tools and Algorithms for the Construction and Analysis of Systems, 273–91. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-99527-0_15.
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AbstractMany safety critical systems guarantee fault-tolerance by using several redundant copies of their components. When designing such redundancy architectures, it is crucial to analyze their fault trees, which describe combinations of faults of individual components that may cause malfunction of the system. State-of-the-art techniques for fault tree computation use first-order formulas with uninterpreted functions to model the transformations of signals performed by the redundancy system and an AllSMT query for computation of the fault tree from this encoding. Scalability of the analysis can be further improved by techniques such as predicate abstraction, which reduces the problem to Boolean case.In this paper, we show that as far as fault trees of redundancy architectures are concerned, signal transformation can be equivalently viewed in a purely Boolean way as fault propagation. This alternative view has important practical consequences. First, it applies also to general redundancy architectures with cyclic dependencies among components, to which the current state-of-the-art methods based on AllSMT are not applicable, and which currently require expensive sequential reasoning. Second, it allows for a simpler encoding of the problem and usage of efficient algorithms for analysis of fault propagation, which can significantly improve the runtime of the analyses. A thorough experimental evaluation demonstrates the superiority of the proposed techniques.
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Ersoysal, Samet, Niclas Hoffmann, Lennart Ralfs, and Robert Weidner. "Towards a Modular Elbow Exoskeleton: Concepts for Design and System Control." In Annals of Scientific Society for Assembly, Handling and Industrial Robotics 2021, 141–52. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-74032-0_12.
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AbstractIn industrial workplaces, strenuous, repetitive, and long-term tasks at head level or above as well as carrying heavy loads may lead to musculoskeletal disorders of different task dependent body parts. With an increasing trend towards wearable support systems, there is already a large quantity of exoskeletons that may support the user during movements, or stabilize postures, in order to reduce strain on various parts of the body. However, most commercially available exoskeletons mainly focus on the back and shoulder support. Only a few of them address the elbow joint, despite it being prone to injury. Therefore, this paper discusses different possible design and control concepts of modular elbow exoskeletons. The modular architecture potentially enables coupling to existing commercial- and research-associated systems, through appropriate interfaces. Different morphological structures and control mechanisms are assessed in respect to their ability to extend common exoskeletons for back and shoulder support. Based on these considerations, a first functional passive prototype is presented, which supports the flexion of the elbow joint and can be coupled to an existing exoskeleton. In future work, the prototype may be used for further elaboration and practical investigations in laboratory settings to evaluate its technical functionality and biomechanical effects on the user.
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Lockner, Yannik, Paul Buske, Maximilian Rudack, Zahra Kheirandish, Moritz Kröger, Stoyan Stoyanov, Seyed Ruhollah Dokhanchi, et al. "Improving Manufacturing Efficiency for Discontinuous Processes by Methodological Cross-Domain Knowledge Transfer." In Internet of Production, 1–33. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-030-98062-7_8-1.
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AbstractDiscontinuous processes face common tasks when implementing modeling and optimization techniques for process optimization. While domain data may be unequal, knowledge about approaches for each step toward the solution, e.g., data gathering, model reduction, and model optimization, may be useful across different processes. A joint development of methodologies for machine learning methods, among other things, ultimately supports fast advances in cross-domain production technologies. In this work, an overview of common maturation stages of data-intensive modeling approaches for production efficiency enhancement is given. The stages are analyzed and communal challenges are elaborated. The used approaches include both physically motivated surrogate modeling as well as the advanced use of machine learning technologies. Apt research is depicted for each stage based on demonstrator work for diverse production technologies, among them high-pressure die casting, surface engineering, plastics injection molding, open-die forging, and automated tape placement. Finally, a holistic and general framework is illustrated covering the main concepts regarding the transfer of mature models into production environments on the example of laser technologies.Increasing customer requirements regarding process stability, transparency and product quality as well as desired high production efficiency in diverse manufacturing processes pose high demands on production technologies. The further development of digital support systems for manufacturing technologies can contribute to meet these demands in various production settings. Especially for discontinuous production, such as injection molding and laser cutting, the joint research for different technologies helps to identify common challenges, ranging from problem identification to knowledge perpetuation after successfully installing digital tools. Workstream CRD-B2.II “Discontinuous Production” confronts this research task by use case-based joint development of transferable methods. Based on the joint definition of a standard pipeline to solve problems with digital support, various stages of this pipeline, such as data generation and collection, model training, optimization, and the development and deployment of assistance systems are actively being researched. Regarding data generation, e.g., for the high-pressure die-casting process, data acquisition and extraction approaches for machines and production lines using OPC UA are investigated to get detailed process insights. For diverse discontinuous processes and use cases, relevant production data is not directly available in sufficient quality and needs to be preprocessed. For vision systems, ptychographic methods may improve recorded data by enhancing the picture sharpness to enable the usage of inline or low-cost equipment to detect small defects. Further down the pipeline, several research activities concern the domain-specific model training and optimization tasks. Within the realm of surface technologies, machine learning is applied to predict process behavior, e.g., by predicting the particle properties in plasma spraying process or plasma intensities in the physical vapor deposition process. The injection molding process can also be modeled by data-based approaches. The modeling efficiency based on the used amount of data can furthermore be effectively reduced by using transfer learning to transfer knowledge stored in artificial neural networks from one process to the next. Successful modeling approaches can then be transferred prototypically into production. On the examples of vision-based defect classification in the tape-laying process and a process optimization assistance system in open-die forging, the realization of prototypical support systems is demonstrated. Once mature, research results and consequent digital services must be made available for integrated usage in specific production settings using relevant architecture. By the example of a microservice-based infrastructure for laser technology, a suitable and flexible implementation of a service framework is realized. The connectivity to production assets is guaranteed by state-of-the-art communication protocols. This chapter illustrates the state of research for use-case-driven development of joint approaches.
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Liu, Zhe, Mee Loong Yang, and Wei Qi Yan. "A Framework for Image Encryption on Frequency Domain." In Exploring Security in Software Architecture and Design, 247–59. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-6313-6.ch010.
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In this chapter, the authors propose an improved image encryption algorithm based on digital watermarking. The algorithm combines discrete wavelet transform (DWT), discrete cosine transform (DCT), and singular value decomposition (SVD) together in a DWT-DCT-SVD framework to improve the robust watermarking technique. The secret image is embedded into both high-frequency and low-frequency sub-bands of the host image; this makes it difficult to be attacked in all the sub-bands. To reduce the size of a secret key, the authors use a logistic map to generate random images so as to replace the host images. They tested the algorithm by using five types of attacks and the results indicate that the proposed algorithm has higher robustness than traditional chaotic scrambling method and the DRPE method. It shows strong resilience against the five types of attacks as well as statistical attacks.
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Cai, Guoyong, and Yumeng Cai. "Hierarchy Spatial-Temporal Transformer for Action Recognition in Short Videos." In Fuzzy Systems and Data Mining VI. IOS Press, 2020. http://dx.doi.org/10.3233/faia200754.
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Short videos action recognition based on deep learning has made a series of important progress; most of the proposed methods are based on 3D Convolution neural networks (3D CNN) and Two Stream architecture. However, 3D CNN has a large number of parameters and Two Stream networks cannot learn features well enough. This work aims to build a network to learn better features and reduce the scale of parameters. A Hierarchy Spatial-Temporal Transformer model is proposed, which is based on Two Stream architecture and hierarchy inference. The model is divided into three modules: Hierarchy Residual Reformer, Spatial Attention Module, and Temporal-Spatial Attention Module. In the model, each frame’s image is firstly transformed into a spatial visual feature map. Secondly, spatial feature learning is performed by spatial attention to generating attention spatial feature maps. Finally, the generated attention spatial feature map is incorporated with temporal feature vectors to generate a final representation for classification experiments. Experiment results in the hmdb51 and ucf101 data set showed that the proposed model achieved better accuracy than the state-of-art baseline models
Conference papers on the topic "MAP REDUCE ARCHITECTURE"
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Lohani, Bhanu Prakash, Ajit Singh, and Vimal Bibhu. "A hybrid optimization approach using Evolutionary Computing and Map Reduce Architecture." In 2019 International Conference on Advances in Computing and Communication Engineering (ICACCE). IEEE, 2019. http://dx.doi.org/10.1109/icacce46606.2019.9080013.
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Saravanan, S., and V. Venkatachalam. "Advance Map Reduce Task Scheduling algorithm using mobile cloud multimedia services architecture." In 2014 Sixth International Conference on Advanced Computing (ICoAC). IEEE, 2014. http://dx.doi.org/10.1109/icoac.2014.7229736.
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Ayma, V. A., R. S. Ferreira, P. N. Happ, D. A. B. Oliveira, G. A. O. P. Costa, R. Q. Feitosa, A. Plaza, and P. Gamba. "On the architecture of a big data classification tool based on a map reduce approach for hyperspectral image analysis." In IGARSS 2015 - 2015 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2015. http://dx.doi.org/10.1109/igarss.2015.7326066.
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Stump, Gary, Timothy W. Simpson, Stella Oggianu, and Ritesh Khire. "Toward an Interactive Visualization Environment for Architecting Microgrids in Ultra Low Energy Communities." In ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/detc2012-70535.
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In this paper we describe the development of an interactive visualization tool to support the design and evaluation of microgrid architectures in ultra low energy communities. The work is motivated by recent Department of Defense regulations to reduce energy costs at and increase energy conservation at military installations. Using two sets of energy analysis models derived from existing energy modeling software packages, we illustrate how such a design environment can be used to (1) run a fast, low fidelity model to support an initial trade space exploration, (2) understand key trends and relationships, (3) filter microgrid architectures based on desired constraints, (4) identify architectures of interest, (5) run high fidelity analyses for architectures of interest, and (6) select an architecture and use a map view to change device type locations. The process is demonstrated through a web-based design environment that we prototyped and applied to two design examples. In both cases, promising microgrid architectures are identified from an initial set of 500 randomly generated designs. Manual adjustments of the position and location of the device types were used to further improve system performance. The end result in each case was a microgrid architecture that offered low fixed and operating costs based on the assumed electrical and thermal loads. The prototype effectively illustrates how Visual Analysis might be performed during Steps 4 & 5 of the Army’s Real Property Master Planning Process. Future enhancements to support the design decision-making process are also discussed.
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Wang, Yuxin, Hongtao Xie, Zilong Fu, and Yongdong Zhang. "DSRN: A Deep Scale Relationship Network for Scene Text Detection." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/133.
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Nowadays, scene text detection has become increasingly important and popular. However, the large variance of text scale remains the main challenge and limits the detection performance in most previous methods. To address this problem, we propose an end-to-end architecture called Deep Scale Relationship Network (DSRN) to map multi-scale convolution features onto a scale invariant space to obtain uniform activation of multi-size text instances. Firstly, we develop a Scale-transfer module to transfer the multi-scale feature maps to a unified dimension. Due to the heterogeneity of features, simply concatenating feature maps with multi-scale information would limit the detection performance. Thus we propose a Scale Relationship module to aggregate the multi-scale information through bi-directional convolution operations. Finally, to further reduce the miss-detected instances, a novel Recall Loss is proposed to force the network to concern more about miss-detected text instances by up-weighting poor-classified examples. Compared with previous approaches, DSRN efficiently handles the large-variance scale problem without complex hand-crafted hyperparameter settings (e.g. scale of default boxes) and complicated post processing. On standard datasets including ICDAR2015 and MSRA-TD500, the proposed algorithm achieves the state-of-art performance with impressive speed (8.8 FPS on ICDAR2015 and 13.3 FPS on MSRA-TD500).
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Freitas, Vitor Yeso Fidelis, Richardson Santiago Teles Menezes, Francisco Vidal, and Helton Maia. "Estimation of Safety Distance Between Vehicles on Highways Using YOLOv4 from Aerial Images." In Congresso Brasileiro de Inteligência Computacional. SBIC, 2021. http://dx.doi.org/10.21528/cbic2021-148.
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Traffic accidents are among the most worrying problems in modern life, often caused by human operational errors such as inattention, distraction, and misbehavior. Vehicle speed detection and safety distance measurement can help reduce these accidents. In this study, the computational development conducted was based on Convolutional Neural Networks (CNNs) and the You Only Look Once (YOLO) algorithm to detect vehicles from aerial images and calculate the safe distance and the vehicle’s speed on Brazilian highways. The investigation was conducted to model the YOLO algorithm for detecting vehicles in different network architecture configurations. The best results were obtained with the YOLO Full-608, reaching a mean Average Precision (mAP) of 97.44%. Additional computer vision approaches have been developed to calculate the speed of the moving vehicle and the safe distance between them. Therefore, the developed system allows that, based on detecting the safe distance between moving vehicles on the highways, accidents are predicted and possibly avoided.
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Joseph, Binu Thomas, Roberto Astesani, and Harish Maliekkal. "An Innovative and Intelligent Journey Management System for the Energy Industry." In International Petroleum Technology Conference. IPTC, 2022. http://dx.doi.org/10.2523/iptc-22627-ea.
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Abstract Land transport management remains a significant HSE challenge for any organization involved in energy industry operations. With many remote operational locations and involvement of land transport contractors, achieving sustainable driving performance improvement is reliant on consistent implementation of robust journey management practices. This paper discusses an innovative journey management system app, which provides an intelligent and effective solution incorporating quality control assurance and optimization processes while providing the convenience of digital mobility. The features of this HSE digitally enabled solution include the use of a progressive web app (PWA)and artificial intelligence (AI) engine developed by in-house resources. The PWA was used to develop a user-friendly interface in seven different languages. AI and an interactive drag and drop map feature were introduced to reduce human error in workflow checks and provide functional flexibility. The solution provides corporate and personnel data security for any mobile user, whether internal to the company or contractors. Other incorporated features include an intelligent driver inactivity identifier and a simple to use pre-departure vehicle safety control checklist. The first release of the enhancements to the existing app was provided to approximately 15% of the global journey management mobile app users and additional contractor driver users. Use of the enhanced app is monitored using a dashboard to show adoption rate and trip information data. The app allows drivers to create their own trip in the app, reducing the reliance on company supervisors to create trips for them. In under a year of deployment, by incorporating contractor drivers into the digital journey management process there has been a reduction of 828,000 phone calls and associated manual trip management processing time, replaced by the same number of application transactions. The digital journey management app continues to enable effective and safety journey management processes are maintained in remote operations and pandemic conditions, in addition significant cost savings were achieved. The following are the key innovative functions the mobile apps brings: Flexibility and Security - Provides corporate and personnel data security for all users.Innovative architecture Allows easy app access and intuitive use.Multi-Language Support Enables use in multiple languages.Interactive drag and drop- Incorporated map feature, reduces data entry.Intelligent inactivity identifier- Drivers who have not used the app within a specified time period are automatically provided a refresher of the IOGP life-saving rule video.Safety Control Check User-friendly vehicle inspection checklist for every trip. The adoption and effective use of the new enhancements to the journey management app improve journey management efficiency and ultimately driving safety performance. The app is designed using resilient digital architecture to provide the user with additional features, and an intuitive user experience of these services to reduce the potential for human error in data entry.
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Khemissa, Hocine, Omar Imad Al Mutwali, Alwai Yaseen Al Safi, Ashraf Muhammad, Sundos Ibrahim Alabed, Ali Saeed Alfelasi, Aysha Alkhoori, et al. "Extra Deep Azimuthal Resistivity Application for Efficient Well Placement in a Long Horizontal Drain: Mitigating Challenges Due to Heterogeneity in Thin Target Carbonate Reservoirs Associated with Water Encroachment in a Mature Field – A Case History from Abu Dhabi Offshore." In ADIPEC. SPE, 2022. http://dx.doi.org/10.2118/211255-ms.
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Abstract This abstract demonstrates the application of an advance Extra Deep Azimuthal Resistivity tool to efficiently maximize reservoir exposure and map the reservoir architecture in an extended reach well of a mature field. The study shows how to maximise benefit from using advanced geosteering technology to successfully place the horizontal drain in an oil-saturated layer, map the water encroachment and design lower completion accordingly. The reservoir is composed mainly of Grainstone to Packstone, with rock analysis showing highly heterogeneous limestone of good to excellent porosity and permeability. Total thickness is about 30 to 40 ft. with the target zone delineated on the upper part of the layer, within 6 ft of the top. The challenges identified during the pre-well stage are: (1) Oil columns getting thinner as fields become more mature; (2) OWC exhibits less clear-cut boundaries and commonly displayed as transition zone profiles; (3) Resistivities within the transition zone vary along the lateral due either to proximity to other producing wells or to porosity variations within the geology; (4) Low resistivity environment; and (5) The increased uncertainty associated with conventional ‘deep’ resistivity tools to differentiate multiple resistivity layers. The reservoir is under water injection and consequently, water movement is irregular due to the presence of high permeability streaks. There is high risk of water encroachment, which the Conventional ‘Deep’ Resistivity tools with limited depth of detection would not be able to differentiate. Utilizing an Extra Deep Azimuthal Resistivity tool offered a solution to efficiently geosteer the drain within 6 ft. from the top boundary, and map the water saturation around it. A feasibility inversion model incorporating the geological setting of the area and the water encroaching scenario predicted that the Extra Deep Azimuthal Resistivity tool would map the denser top of the target zone, the transition and water encroachment intervals with high confidence at a remote distance from the wellbore. This reduced the uncertainty around well placement so the tool was included in a Triple combo BHA to enable informed and proactive geosteering decision-making process. The long horizontal drain was successfully placed along the porous oil-saturated zone and the water risk was mapped continuously below the wellbore. The Extra Deep Azimuthal Resistivity technology mapped multiple resistivity layers leading to better understanding of the structural model and fluid distribution. The delineation of the resistive dense layer above the wellbore also facilitated good well placement at the required stand-off from the roof. Furthermore, the tool demonstrated its ability to map the top of the transition zone and the resistivity below, which is valuable in identifying intervals where the transition zone was swept with water requiring isolation in the completion. As a direct result, the completion design was optimized to reduce water risk.
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Choi, Yuk-Ming, and Hayden Kwok-Hay So. "Map-reduce processing of k-means algorithm with FPGA-accelerated computer cluster." In 2014 IEEE 25th International Conference on Application-specific Systems, Architectures and Processors (ASAP). IEEE, 2014. http://dx.doi.org/10.1109/asap.2014.6868624.
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L .Vinagreiro, Michel Andre, Edson C. Kitani, Armando Antonio M. Lagana, and Leopoldo R. Yoshioka. "Using Multilinear Feature Space to Accelerate CNN Classification." In 2nd International Conference on Machine Learning &Trends (MLT 2021). AIRCC Publishing Corporation, 2021. http://dx.doi.org/10.5121/csit.2021.111109.
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Computer vision plays a crucial role in ADAS security and navigation, as most systems are based on deep CNN architectures the computational resource to run a CNN algorithm is demanding. Therefore, the methods to speed up computation have become a relevant research issue. Even though several works on acceleration techniques found in the literature have not yet been achieved satisfactory results for embedded real-time system applications. This paper presents an alternative approach based on the Multilinear Feature Space (MFS) method resorting to transfer learning from large CNN architectures. The proposed method uses CNNs to generate feature maps, although it does not work as complexity reduction approach. When the training process ends, the generated maps are used to create vector feature space. We use this new vector space to make projections of any new sample in order to classify them. Our method, named MFS-CNN, uses the transfer learning from pre trained CNN to reduce the classification time of new sample image, with minimal loss in accuracy. Our method uses the VGG-16 model as the base CNN architecture for experiments; however, the method works with any similar CNN model. Using the well-known Vehicle Image Database and the German Traffic Sign Recognition Benchmark we compared the classification time of original VGG-16 model with the MFS-CNN method and our method is, on average, 17 times faster. The fast classification time reduces the computational and memories demand in embedded applications that requires a large CNN architecture.
Reports on the topic "MAP REDUCE ARCHITECTURE"
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Kapulnik, Yoram, Maria J. Harrison, Hinanit Koltai, and Joseph Hershenhorn. Targeting of Strigolacatones Associated Pathways for Conferring Orobanche Resistant Traits in Tomato and Medicago. United States Department of Agriculture, July 2011. http://dx.doi.org/10.32747/2011.7593399.bard.
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This proposal is focused on examination of two plant interactions: parasitic with Orobanche, and symbiosis with arbuscular mycorrhiza fungi (AMF), and the involvement of a newly define plant hormones, strigolactones (SLs), in these plant interactions. In addition to strigolactones role in regulation of above-ground plant architecture, they are also known to be secreted from roots, and to be a signal for seed germination of the parasitic plants Orobanche. Moreover, secreted strigolactones were recognized as inducers of AMFhyphae branching. The present work was aimed at Generation of RNAi mutants of both tomato and Medicago, targeting multiple genes that may be involved in strigolactone production, carotenoid biosynthesis pathway, Pi signaling or other metabolic pathways, and hence affect AMF colonization and/or Orobanche resistance. Following the newly formed and existing RNAi mutants were examined for AMF colonization and Orobanche resistance. At the first phase of this project Orobanche seed germination assays and AMF colonization were examined in intact plants. These assays were shown to be effective and resulted with enhancement of Orobanche seed germination and AMF colonization in WT tomato plants, whereas roots of strigolactones impaired lines did not result with Orobanche seed germination and mycorrhiza colonization. Unexpectedly, root organ cultures (ROC) that were produced from the same wild type (WT) and mutant lines did not induce the Orobanche seed germination and AMFhyphal branching. This implies that under in vitro conditions ROC cultures are missing an important component for induction of Orobanche seed germination and AMFhyphal branching. In another line of experiments we have tested transgenic lines of Medicagotruncatula for AMFhuyphal branching and Orobanche seed germination assays. These lines included lines silenced for a GRAS transcription factor (RNAi 1845), an NBS-LRR type resistance gene (RNAi 1847), a kinase (RNAi 2403) and a protein of unknown function (RNAi 2417). In all cases, five independent transgenic root lines showed altered AMFphenotypes with reduced or aberrant colonization patterns. Following, we transformed tomato plants with the M. truncatulaTC 127050 PhosphoinositidekinaseRNAi construct. Transgenic lines that contained GUS constructs were used as control. All transgenic lines showed reduced level of Orobanche seed germination, masking any strigoalctones-specific effect. The research demonstrated that SLs production may not be examined in ROC –based bioassays. It was shown by the 3 independent assays employed in this project that none of the recognized characters of SLs may be reflected in these bioassays. However, when the whole plant root exudates were examined, SLs activity in root exudates was demonstrated. Hence, it can be concluded that the presence of an intact shoot, and possibly, shoot factors, may be necessary for production of SLs in roots. Another point of interest that rises from these results is that the presence of SLs is not necessary for AMF completion of life cycle. Hence, it may be concluded that SLs are important for AMFhyphal branching, before symbiosis, but not essential for AMF colonization and life cycle completion under ROC system conditions.
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Dubcovsky, Jorge, Tzion Fahima, and Ann Blechl. Molecular characterization and deployment of the high-temperature adult plant stripe rust resistance gene Yr36 from wheat. United States Department of Agriculture, November 2013. http://dx.doi.org/10.32747/2013.7699860.bard.
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Stripe rust, caused by Puccinia striiformis f. sp. tritici is one of the most destructive fungal diseases of wheat. Virulent races that appeared within the last decade caused drastic cuts in yields. The incorporation of genetic resistance against this pathogen is the most cost-effective and environmentally friendly solution to this problem. However, race specific seedling resistance genes provide only a temporary solution because fungal populations rapidly evolve to overcome this type of resistance. In contrast, high temperature adult plant (HTAP) resistance genes provide a broad spectrum resistance that is partial and more durable. The cloning of the first wheat HTAP stripe rust resistance gene Yr36 (Science 2009, 323:1357), funded by our previous (2007-2010) BARD grant, provided us for the first time with an entry point for understanding the mechanism of broad spectrum resistance. Two paralogous copies of this gene are tightly linked at the Yr36 locus (WKS1 and WKS2). The main objectives of the current study were to characterize the Yr36 (WKS) resistance mechanism and to identify and characterize alternative WKSgenes in wheat and wild relatives. We report here that the protein coded by Yr36, designated WKS1, that has a novel architecture with a functional kinase and a lipid binding START domain, is localized to chloroplast. Our results suggest that the presence of the START domain may affect the kinase activity. We have found that the WKS1 was over-expressed on leaf necrosis in wheat transgenic plants. When the isolated WKS1.1 splice variant transcript was transformed into susceptible wheat it conferred resistance to stripe rust, but the truncated variant WKS1.2 did not confer resistance. WKS1.1 and WKS1.2 showed different lipid binding profiling. WKS1.1 enters the chloroplast membrane, while WKS1.2 is only attached outside of the chloroplast membrane. The ascorbate peroxidase (APX) activity of the recombinant protein of TmtAPXwas found to be reduced by WKS1.1 protein in vitro. The WKS1.1 mature protein in the chloroplast is able to phosphorylate TmtAPXprotein in vivo. WKS1.1 induced cell death by suppressing APX activity and reducing the ability of the cell to detoxify reactive oxygen. The decrease of APX activity reduces the ability of the plant to detoxify the reactive H2O2 and is the possible mechanism underlying the accelerated cell death observed in the transgenic plants overexpressing WKS1.1 and in the regions surrounding a stripe rust infection in the wheat plants carrying the natural WKS1.1 gene. WKS2 is a nonfunctional paralog of WKS1 in wild emmer wheat, probably due to a retrotransposon insertion close to the alternative splicing site. In some other wild relatives of wheat, such as Aegilops comosa, there is only one copy of this gene, highly similar to WKS2, which is lucking the retrotransposon insertion. WKS2 gene present in wheat and WKS2-Ae from A. showed a different pattern of alternative splice variants, regardless of the presence of the retrotransposon insertion. Susceptible Bobwhite transformed with WKS2-Ae (without retrotansposon insertion in intron10), which derived from Aegilops comosaconferred resistance to stripe rust in wheat. The expression of WKS2-Ae in transgenic plants is up-regulated by temperature and pathogen infection. Combination of WKS1 and WKS2-Ae shows improved stripe rust resistance in WKS1×WKS2-Ae F1 hybrid plants. The obtained results show that WKS1 protein is accelerating programmed cell death observed in the regions surrounding a stripe rust infection in the wheat plants carrying the natural or transgenic WKS1 gene. Furthermore, characterization of the epistatic interactions of Yr36 and Yr18 demonstrated that these two genes have additive effects and can therefore be combined to increase partial resistance to this devastating pathogen of wheat. These achievements may have a broad impact on wheat breeding efforts attempting to protect wheat yields against one of the most devastating wheat pathogen.