Syed Fawad Hussain presented a talk on multi-view clustering algorithms and applications at the 13th International Conference on Frontiers of Information Technology in Islamabad, Pakistan from December 14-16, 2015. The talk discussed how data can be described by multiple views or feature sets, and how multi-view clustering can identify natural groupings by combining information from different views. Experimental results on several datasets showed that multi-view clustering achieved better performance than single view clustering. Multi-view clustering has applications in areas like recommender systems, question answering systems, and self-driving cars where combining information from multiple sources leads to more accurate analysis.
Clustering Arabic Tweets for Sentiment AnalysisMustafa Jarrar
Diab Abuaiadah, Dileep Rajendran, Mustafa Jarrar: Clustering Arabic Tweets for Sentiment Analysis. Proceedings of the 2017 IEEE/ACS 14th International Conference on Computer Systems and Applications. IEEE Computer Society. DOI 10.1109/AICCSA.2017.162
The document summarizes a thesis defense presentation on contextual analysis for trust evaluations using ConTED. It discusses analyzing context from sources like location, identity, time and activity to evaluate trust. ConTED uses a hybrid context model and weighted context information expressed on context scales to make decentralized, context-aware decisions. It was tested on scenarios where the number of agents, information exchanged, and scenario size increased. Future work includes improving adaptive extraction, weights, and testing on larger scenarios.
This document provides an overview of using deep learning techniques for recommender systems. It begins with establishing the need for recommender systems due to increasing information overload. It then gives a basic introduction and agenda for the talk, covering motivation, basics, deep learning for vehicle recommendations, and scalability/production. The talk discusses using deep learning approaches like wide and deep learning as well as sequential models to improve recommendation relevance for applications like vehicle recommendations. It provides details on preprocessing, training a classifier, candidate generation and ranking for recommendations. The document concludes with discussing deploying such a system at scale and current trends in recommender system research.
Recommender systems support the decision making processes of customers with personalized suggestions. These widely used systems influence the daily life of almost everyone across domains like ecommerce, social media, and entertainment. However, the efficient generation of relevant recommendations in large-scale systems is a very complex task. In order to provide personalization, engines and algorithms need to capture users’ varying tastes and find mostly nonlinear dependencies between them and a multitude of items. Enormous data sparsity and ambitious real-time requirements further complicate this challenge. At the same time, deep learning has been proven to solve complex tasks like object or speech recognition where traditional machine learning failed or showed mediocre performance.
Join Marcel Kurovski to explore a use case for vehicle recommendations at mobile.de, Germany’s biggest online vehicle market. Marcel shares a novel regularization technique for the optimization criterion and evaluates it against various baselines. To achieve high scalability, he combines this method with strategies for efficient candidate generation based on user and item embeddings—providing a holistic solution for candidate generation and ranking.
The proposed approach outperforms collaborative filtering and hybrid collaborative-content-based filtering by 73% and 143% for MAP@5. It also scales well for millions of items and users returning recommendations in tens of milliseconds.
Event: O'Reilly Artificial Intelligence Conference, New York, 18.04.2019
Speaker: Marcel Kurovski, inovex GmbH
Mehr Tech-Vorträge: inovex.de/vortraege
Mehr Tech-Artikel: inovex.de/blog
Retrieval, Crawling and Fusion of Entity-centric Data on the WebStefan Dietze
Stefan Dietze gave a keynote presentation covering three main topics:
1) Challenges in entity retrieval from heterogeneous linked datasets and knowledge graphs due to diversity and lack of standardization.
2) Approaches for enabling discovery and search through dataset recommendation, profiling, and entity retrieval methods that cluster entities to address link sparsity.
3) Going beyond linked data to exploit semantics embedded in web markup, with case studies in data fusion for entity reconciliation and retrieval.
The document provides information about data analytics using R. It discusses how R is a widely used open-source statistical programming language and software environment for data analysis and visualization. It also discusses key concepts in R like importing and transforming data, conducting statistical analysis through functions like mean, median, and plotting graphs. The document further explains important R packages like dplyr for data manipulation and clustering algorithms for analyzing hidden patterns in data. Finally, it mentions some example projects and applications of R in areas like psychology, business, and machine learning.
Federated Learning of Neural Network Models with Heterogeneous Structures.pdfKundjanasith Thonglek
Federated learning trains a model on a centralized server using datasets distributed over a large number of edge devices. Applying federated learning ensures data privacy because it does not transfer local data from edge devices to the server. Existing federated learning algorithms assume that all deployed models share the same structure. However, it is often infeasible to distribute the same model to every edge device because of hardware limitations such as computing performance and storage space. This paper proposes a novel federated learning algorithm to aggregate information from multiple heterogeneous models. The proposed method uses weighted average ensemble to combine the outputs from each model. The weight for the ensemble is optimized using black box optimization methods. We evaluated the proposed method using diverse models and datasets and found that it can achieve comparable performance to conventional training using centralized datasets. Furthermore, we compared six different optimization methods to tune the weights for the weighted average ensemble and found that tree parzen estimator achieves the highest accuracy among the alternatives.
Information Technology in Industry(ITII) - November Issue 2018ITIIIndustries
IT Industry publishes original research articles, review articles, and extended versions of conference papers. Articles resulting from research of both theoretical and/or practical natures performed by academics and/or industry practitioners are welcome. IT in Industry aims to become a leading IT journal with a high impact factor.
Clustering Arabic Tweets for Sentiment AnalysisMustafa Jarrar
Diab Abuaiadah, Dileep Rajendran, Mustafa Jarrar: Clustering Arabic Tweets for Sentiment Analysis. Proceedings of the 2017 IEEE/ACS 14th International Conference on Computer Systems and Applications. IEEE Computer Society. DOI 10.1109/AICCSA.2017.162
The document summarizes a thesis defense presentation on contextual analysis for trust evaluations using ConTED. It discusses analyzing context from sources like location, identity, time and activity to evaluate trust. ConTED uses a hybrid context model and weighted context information expressed on context scales to make decentralized, context-aware decisions. It was tested on scenarios where the number of agents, information exchanged, and scenario size increased. Future work includes improving adaptive extraction, weights, and testing on larger scenarios.
This document provides an overview of using deep learning techniques for recommender systems. It begins with establishing the need for recommender systems due to increasing information overload. It then gives a basic introduction and agenda for the talk, covering motivation, basics, deep learning for vehicle recommendations, and scalability/production. The talk discusses using deep learning approaches like wide and deep learning as well as sequential models to improve recommendation relevance for applications like vehicle recommendations. It provides details on preprocessing, training a classifier, candidate generation and ranking for recommendations. The document concludes with discussing deploying such a system at scale and current trends in recommender system research.
Recommender systems support the decision making processes of customers with personalized suggestions. These widely used systems influence the daily life of almost everyone across domains like ecommerce, social media, and entertainment. However, the efficient generation of relevant recommendations in large-scale systems is a very complex task. In order to provide personalization, engines and algorithms need to capture users’ varying tastes and find mostly nonlinear dependencies between them and a multitude of items. Enormous data sparsity and ambitious real-time requirements further complicate this challenge. At the same time, deep learning has been proven to solve complex tasks like object or speech recognition where traditional machine learning failed or showed mediocre performance.
Join Marcel Kurovski to explore a use case for vehicle recommendations at mobile.de, Germany’s biggest online vehicle market. Marcel shares a novel regularization technique for the optimization criterion and evaluates it against various baselines. To achieve high scalability, he combines this method with strategies for efficient candidate generation based on user and item embeddings—providing a holistic solution for candidate generation and ranking.
The proposed approach outperforms collaborative filtering and hybrid collaborative-content-based filtering by 73% and 143% for MAP@5. It also scales well for millions of items and users returning recommendations in tens of milliseconds.
Event: O'Reilly Artificial Intelligence Conference, New York, 18.04.2019
Speaker: Marcel Kurovski, inovex GmbH
Mehr Tech-Vorträge: inovex.de/vortraege
Mehr Tech-Artikel: inovex.de/blog
Retrieval, Crawling and Fusion of Entity-centric Data on the WebStefan Dietze
Stefan Dietze gave a keynote presentation covering three main topics:
1) Challenges in entity retrieval from heterogeneous linked datasets and knowledge graphs due to diversity and lack of standardization.
2) Approaches for enabling discovery and search through dataset recommendation, profiling, and entity retrieval methods that cluster entities to address link sparsity.
3) Going beyond linked data to exploit semantics embedded in web markup, with case studies in data fusion for entity reconciliation and retrieval.
The document provides information about data analytics using R. It discusses how R is a widely used open-source statistical programming language and software environment for data analysis and visualization. It also discusses key concepts in R like importing and transforming data, conducting statistical analysis through functions like mean, median, and plotting graphs. The document further explains important R packages like dplyr for data manipulation and clustering algorithms for analyzing hidden patterns in data. Finally, it mentions some example projects and applications of R in areas like psychology, business, and machine learning.
Federated Learning of Neural Network Models with Heterogeneous Structures.pdfKundjanasith Thonglek
Federated learning trains a model on a centralized server using datasets distributed over a large number of edge devices. Applying federated learning ensures data privacy because it does not transfer local data from edge devices to the server. Existing federated learning algorithms assume that all deployed models share the same structure. However, it is often infeasible to distribute the same model to every edge device because of hardware limitations such as computing performance and storage space. This paper proposes a novel federated learning algorithm to aggregate information from multiple heterogeneous models. The proposed method uses weighted average ensemble to combine the outputs from each model. The weight for the ensemble is optimized using black box optimization methods. We evaluated the proposed method using diverse models and datasets and found that it can achieve comparable performance to conventional training using centralized datasets. Furthermore, we compared six different optimization methods to tune the weights for the weighted average ensemble and found that tree parzen estimator achieves the highest accuracy among the alternatives.
Information Technology in Industry(ITII) - November Issue 2018ITIIIndustries
IT Industry publishes original research articles, review articles, and extended versions of conference papers. Articles resulting from research of both theoretical and/or practical natures performed by academics and/or industry practitioners are welcome. IT in Industry aims to become a leading IT journal with a high impact factor.
Emerging Dynamic TUW-ASE Summer 2015 - Distributed Systems and Challenges for...Hong-Linh Truong
This is a lecture from the advanced service engineering course from the Vienna University of Technology. See http://dsg.tuwien.ac.at/teaching/courses/ase/
Turning Data into Knowledge (KESW2014 Keynote)Stefan Dietze
The document discusses turning data into knowledge through profiling and interlinking web datasets. It covers recent work on linked data exploration, discovery, and search including entity and dataset interlinking recommendations and dataset profiling. It also discusses ensuring data consistency and resolving conflicts. The document then examines challenges with reusing and interlinking the long tail of linked datasets and issues regarding structure, semantics, interlinking, and persistence of linked data on the web.
The document discusses exploring big data landscapes using elastic displays. It presents a layer concept for examining the number of clusters in data, selecting algorithm parameters, and choosing different clustering algorithms. The comparison concept allows zooming into clusters and comparing algorithms at different levels of detail. The elastic displays provide intuitive interaction through stacking of views, natural zooming, and gestural interaction with force feedback.
Deepti Khanna is an Associate Professor in the IT department at a college. She has 14 years of teaching experience and a PhD in progress. Her research focuses on query optimization and she has published papers on the topic in several national and international journals and conferences between 2009-2016. She teaches subjects related to IT including databases, software engineering, and multimedia.
The document summarizes the plans and activities of DRESD, a research group on dynamic reconfigurability in embedded system design at Politecnico di Milano. It discusses DRESD's research objectives, collaboration with other universities, involvement in teaching courses, and plans to hold workshops and become an official association to support its research vision.
All Things Open 2014 - Day 1
Wednesday, October 22nd, 2014
Dan Bedard
Market Development Manager for iRODS Consortium, RENCI at UNC Chapel Hill
Lunch Session
Building the iRODS Consortium
“Semantic Technologies for Smart Services” diannepatricia
Rudi Studer, Full Professor in Applied Informatics at the Karlsruhe Institute of Technology (KIT), Institute AIFB, presentation “Semantic Technologies for Smart Services” as part of the Cognitive Systems Institute Speaker Series, December 15, 2016.
An overview of the workshop as presented at the 1st International Workshop on Benchmarking Linked Data (BLINK).
(HOBBIT project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 688227.)
The document outlines plans to create an interactive 3D model of the Karnak archaeological site in Egypt to reinvent how research is presented. It will include narratives by Professor Sullivan exploring the site's landmarks. Users can freely explore the immersive environment and learn through pop-ups and multimedia resources. Information will be easily accessible to users of varying technical skills. Independent exploration will be encouraged without promoting one navigation method over others. Clear graphics and annotations will guide users and help share information citations and narratives.
The document summarizes a presentation given at the 17th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD2013) about a loss prediction model called NARGES. The model uses a hybrid approach combining symbolic delay forecasting and machine learning to predict packet loss. It consists of a forecasting module called HDAX that uses linguistic variables and a predictive module using multilayer perceptron artificial neural networks. The model was evaluated on real network data and found to more accurately predict packet loss compared to autoregressive moving average models. Future work is planned to implement the model for online evaluation in co-simulation environments.
TUW-ASE-Summer 2014: Advanced service-based data analytics: concepts and designsHong-Linh Truong
This document discusses concepts and designs for advanced service-based data analytics. It begins by outlining principles of elasticity for data analytics and discussing data analytics within a single system. Data analytics within a single system is complex but has limitations as it operates within a single domain and infrastructure. The document goes on to discuss performing data analytics across multiple systems and composable cost evaluation.
This document provides an overview of Roberto Casadei's research activities and interests. It summarizes his background, publications, collaborations, and prospective research directions. Casadei's main research area is aggregate computing (AC), a programming paradigm for self-organizing collective systems. He has published works on AC programming languages and frameworks, distributed algorithms, and applications involving crowds, robot swarms, and IoT systems. Going forward, Casadei intends to further his research in programming languages and paradigms for autonomic and multi-agent systems, as well as collective artificial intelligence.
New Research Articles 2020 November Issue International Journal of Software E...ijseajournal
The International Journal of Software Engineering & Applications (IJSEA) is a bi-monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Software Engineering & Applications. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on understanding Modern software engineering concepts & establishing new collaborations in these areas.
Stefano Nativi presents the RDA.
Workshop title: Organising high-quality research data management services
Workshop abstract:
Open science needs high quality data management where researchers can create, use and share data according to well defined standards and practices. this is one of the pillars of Open Science. In the data management landscape we find quite a few organisations that aim at achieving this, however to get it right, a collaboration is called for where all can play a suitable role and present this in a consistent way to the researcher.
The proposed workshop brings together representatives of standard organisation (RDA), eInfrastructures (EUDAT) and Libraries (LIBER) that together can organise the high quality data management for research.
DAY 1 - PARALLEL SESSION 2
http://opensciencefair.eu/workshops/organising-high-quality-research-data-management-services
Rainfall intensity duration frequency curve statistical analysis and modeling...bijceesjournal
Using data from 41 years in Patna’ India’ the study’s goal is to analyze the trends of how often it rains on a weekly, seasonal, and annual basis (1981−2020). First, utilizing the intensity-duration-frequency (IDF) curve and the relationship by statistically analyzing rainfall’ the historical rainfall data set for Patna’ India’ during a 41 year period (1981−2020), was evaluated for its quality. Changes in the hydrologic cycle as a result of increased greenhouse gas emissions are expected to induce variations in the intensity, length, and frequency of precipitation events. One strategy to lessen vulnerability is to quantify probable changes and adapt to them. Techniques such as log-normal, normal, and Gumbel are used (EV-I). Distributions were created with durations of 1, 2, 3, 6, and 24 h and return times of 2, 5, 10, 25, and 100 years. There were also mathematical correlations discovered between rainfall and recurrence interval.
Findings: Based on findings, the Gumbel approach produced the highest intensity values, whereas the other approaches produced values that were close to each other. The data indicates that 461.9 mm of rain fell during the monsoon season’s 301st week. However, it was found that the 29th week had the greatest average rainfall, 92.6 mm. With 952.6 mm on average, the monsoon season saw the highest rainfall. Calculations revealed that the yearly rainfall averaged 1171.1 mm. Using Weibull’s method, the study was subsequently expanded to examine rainfall distribution at different recurrence intervals of 2, 5, 10, and 25 years. Rainfall and recurrence interval mathematical correlations were also developed. Further regression analysis revealed that short wave irrigation, wind direction, wind speed, pressure, relative humidity, and temperature all had a substantial influence on rainfall.
Originality and value: The results of the rainfall IDF curves can provide useful information to policymakers in making appropriate decisions in managing and minimizing floods in the study area.
Emerging Dynamic TUW-ASE Summer 2015 - Distributed Systems and Challenges for...Hong-Linh Truong
This is a lecture from the advanced service engineering course from the Vienna University of Technology. See http://dsg.tuwien.ac.at/teaching/courses/ase/
Turning Data into Knowledge (KESW2014 Keynote)Stefan Dietze
The document discusses turning data into knowledge through profiling and interlinking web datasets. It covers recent work on linked data exploration, discovery, and search including entity and dataset interlinking recommendations and dataset profiling. It also discusses ensuring data consistency and resolving conflicts. The document then examines challenges with reusing and interlinking the long tail of linked datasets and issues regarding structure, semantics, interlinking, and persistence of linked data on the web.
The document discusses exploring big data landscapes using elastic displays. It presents a layer concept for examining the number of clusters in data, selecting algorithm parameters, and choosing different clustering algorithms. The comparison concept allows zooming into clusters and comparing algorithms at different levels of detail. The elastic displays provide intuitive interaction through stacking of views, natural zooming, and gestural interaction with force feedback.
Deepti Khanna is an Associate Professor in the IT department at a college. She has 14 years of teaching experience and a PhD in progress. Her research focuses on query optimization and she has published papers on the topic in several national and international journals and conferences between 2009-2016. She teaches subjects related to IT including databases, software engineering, and multimedia.
The document summarizes the plans and activities of DRESD, a research group on dynamic reconfigurability in embedded system design at Politecnico di Milano. It discusses DRESD's research objectives, collaboration with other universities, involvement in teaching courses, and plans to hold workshops and become an official association to support its research vision.
All Things Open 2014 - Day 1
Wednesday, October 22nd, 2014
Dan Bedard
Market Development Manager for iRODS Consortium, RENCI at UNC Chapel Hill
Lunch Session
Building the iRODS Consortium
“Semantic Technologies for Smart Services” diannepatricia
Rudi Studer, Full Professor in Applied Informatics at the Karlsruhe Institute of Technology (KIT), Institute AIFB, presentation “Semantic Technologies for Smart Services” as part of the Cognitive Systems Institute Speaker Series, December 15, 2016.
An overview of the workshop as presented at the 1st International Workshop on Benchmarking Linked Data (BLINK).
(HOBBIT project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 688227.)
The document outlines plans to create an interactive 3D model of the Karnak archaeological site in Egypt to reinvent how research is presented. It will include narratives by Professor Sullivan exploring the site's landmarks. Users can freely explore the immersive environment and learn through pop-ups and multimedia resources. Information will be easily accessible to users of varying technical skills. Independent exploration will be encouraged without promoting one navigation method over others. Clear graphics and annotations will guide users and help share information citations and narratives.
The document summarizes a presentation given at the 17th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD2013) about a loss prediction model called NARGES. The model uses a hybrid approach combining symbolic delay forecasting and machine learning to predict packet loss. It consists of a forecasting module called HDAX that uses linguistic variables and a predictive module using multilayer perceptron artificial neural networks. The model was evaluated on real network data and found to more accurately predict packet loss compared to autoregressive moving average models. Future work is planned to implement the model for online evaluation in co-simulation environments.
TUW-ASE-Summer 2014: Advanced service-based data analytics: concepts and designsHong-Linh Truong
This document discusses concepts and designs for advanced service-based data analytics. It begins by outlining principles of elasticity for data analytics and discussing data analytics within a single system. Data analytics within a single system is complex but has limitations as it operates within a single domain and infrastructure. The document goes on to discuss performing data analytics across multiple systems and composable cost evaluation.
This document provides an overview of Roberto Casadei's research activities and interests. It summarizes his background, publications, collaborations, and prospective research directions. Casadei's main research area is aggregate computing (AC), a programming paradigm for self-organizing collective systems. He has published works on AC programming languages and frameworks, distributed algorithms, and applications involving crowds, robot swarms, and IoT systems. Going forward, Casadei intends to further his research in programming languages and paradigms for autonomic and multi-agent systems, as well as collective artificial intelligence.
New Research Articles 2020 November Issue International Journal of Software E...ijseajournal
The International Journal of Software Engineering & Applications (IJSEA) is a bi-monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Software Engineering & Applications. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on understanding Modern software engineering concepts & establishing new collaborations in these areas.
Stefano Nativi presents the RDA.
Workshop title: Organising high-quality research data management services
Workshop abstract:
Open science needs high quality data management where researchers can create, use and share data according to well defined standards and practices. this is one of the pillars of Open Science. In the data management landscape we find quite a few organisations that aim at achieving this, however to get it right, a collaboration is called for where all can play a suitable role and present this in a consistent way to the researcher.
The proposed workshop brings together representatives of standard organisation (RDA), eInfrastructures (EUDAT) and Libraries (LIBER) that together can organise the high quality data management for research.
DAY 1 - PARALLEL SESSION 2
http://opensciencefair.eu/workshops/organising-high-quality-research-data-management-services
Rainfall intensity duration frequency curve statistical analysis and modeling...bijceesjournal
Using data from 41 years in Patna’ India’ the study’s goal is to analyze the trends of how often it rains on a weekly, seasonal, and annual basis (1981−2020). First, utilizing the intensity-duration-frequency (IDF) curve and the relationship by statistically analyzing rainfall’ the historical rainfall data set for Patna’ India’ during a 41 year period (1981−2020), was evaluated for its quality. Changes in the hydrologic cycle as a result of increased greenhouse gas emissions are expected to induce variations in the intensity, length, and frequency of precipitation events. One strategy to lessen vulnerability is to quantify probable changes and adapt to them. Techniques such as log-normal, normal, and Gumbel are used (EV-I). Distributions were created with durations of 1, 2, 3, 6, and 24 h and return times of 2, 5, 10, 25, and 100 years. There were also mathematical correlations discovered between rainfall and recurrence interval.
Findings: Based on findings, the Gumbel approach produced the highest intensity values, whereas the other approaches produced values that were close to each other. The data indicates that 461.9 mm of rain fell during the monsoon season’s 301st week. However, it was found that the 29th week had the greatest average rainfall, 92.6 mm. With 952.6 mm on average, the monsoon season saw the highest rainfall. Calculations revealed that the yearly rainfall averaged 1171.1 mm. Using Weibull’s method, the study was subsequently expanded to examine rainfall distribution at different recurrence intervals of 2, 5, 10, and 25 years. Rainfall and recurrence interval mathematical correlations were also developed. Further regression analysis revealed that short wave irrigation, wind direction, wind speed, pressure, relative humidity, and temperature all had a substantial influence on rainfall.
Originality and value: The results of the rainfall IDF curves can provide useful information to policymakers in making appropriate decisions in managing and minimizing floods in the study area.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Discover the latest insights on Data Driven Maintenance with our comprehensive webinar presentation. Learn about traditional maintenance challenges, the right approach to utilizing data, and the benefits of adopting a Data Driven Maintenance strategy. Explore real-world examples, industry best practices, and innovative solutions like FMECA and the D3M model. This presentation, led by expert Jules Oudmans, is essential for asset owners looking to optimize their maintenance processes and leverage digital technologies for improved efficiency and performance. Download now to stay ahead in the evolving maintenance landscape.
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
Comparative analysis between traditional aquaponics and reconstructed aquapon...bijceesjournal
The aquaponic system of planting is a method that does not require soil usage. It is a method that only needs water, fish, lava rocks (a substitute for soil), and plants. Aquaponic systems are sustainable and environmentally friendly. Its use not only helps to plant in small spaces but also helps reduce artificial chemical use and minimizes excess water use, as aquaponics consumes 90% less water than soil-based gardening. The study applied a descriptive and experimental design to assess and compare conventional and reconstructed aquaponic methods for reproducing tomatoes. The researchers created an observation checklist to determine the significant factors of the study. The study aims to determine the significant difference between traditional aquaponics and reconstructed aquaponics systems propagating tomatoes in terms of height, weight, girth, and number of fruits. The reconstructed aquaponics system’s higher growth yield results in a much more nourished crop than the traditional aquaponics system. It is superior in its number of fruits, height, weight, and girth measurement. Moreover, the reconstructed aquaponics system is proven to eliminate all the hindrances present in the traditional aquaponics system, which are overcrowding of fish, algae growth, pest problems, contaminated water, and dead fish.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Applications of artificial Intelligence in Mechanical Engineering.pdfAtif Razi
Historically, mechanical engineering has relied heavily on human expertise and empirical methods to solve complex problems. With the introduction of computer-aided design (CAD) and finite element analysis (FEA), the field took its first steps towards digitization. These tools allowed engineers to simulate and analyze mechanical systems with greater accuracy and efficiency. However, the sheer volume of data generated by modern engineering systems and the increasing complexity of these systems have necessitated more advanced analytical tools, paving the way for AI.
AI offers the capability to process vast amounts of data, identify patterns, and make predictions with a level of speed and accuracy unattainable by traditional methods. This has profound implications for mechanical engineering, enabling more efficient design processes, predictive maintenance strategies, and optimized manufacturing operations. AI-driven tools can learn from historical data, adapt to new information, and continuously improve their performance, making them invaluable in tackling the multifaceted challenges of modern mechanical engineering.
An improved modulation technique suitable for a three level flying capacitor ...IJECEIAES
This research paper introduces an innovative modulation technique for controlling a 3-level flying capacitor multilevel inverter (FCMLI), aiming to streamline the modulation process in contrast to conventional methods. The proposed
simplified modulation technique paves the way for more straightforward and
efficient control of multilevel inverters, enabling their widespread adoption and
integration into modern power electronic systems. Through the amalgamation of
sinusoidal pulse width modulation (SPWM) with a high-frequency square wave
pulse, this controlling technique attains energy equilibrium across the coupling
capacitor. The modulation scheme incorporates a simplified switching pattern
and a decreased count of voltage references, thereby simplifying the control
algorithm.
Use PyCharm for remote debugging of WSL on a Windo cf5c162d672e4e58b4dde5d797...shadow0702a
This document serves as a comprehensive step-by-step guide on how to effectively use PyCharm for remote debugging of the Windows Subsystem for Linux (WSL) on a local Windows machine. It meticulously outlines several critical steps in the process, starting with the crucial task of enabling permissions, followed by the installation and configuration of WSL.
The guide then proceeds to explain how to set up the SSH service within the WSL environment, an integral part of the process. Alongside this, it also provides detailed instructions on how to modify the inbound rules of the Windows firewall to facilitate the process, ensuring that there are no connectivity issues that could potentially hinder the debugging process.
The document further emphasizes on the importance of checking the connection between the Windows and WSL environments, providing instructions on how to ensure that the connection is optimal and ready for remote debugging.
It also offers an in-depth guide on how to configure the WSL interpreter and files within the PyCharm environment. This is essential for ensuring that the debugging process is set up correctly and that the program can be run effectively within the WSL terminal.
Additionally, the document provides guidance on how to set up breakpoints for debugging, a fundamental aspect of the debugging process which allows the developer to stop the execution of their code at certain points and inspect their program at those stages.
Finally, the document concludes by providing a link to a reference blog. This blog offers additional information and guidance on configuring the remote Python interpreter in PyCharm, providing the reader with a well-rounded understanding of the process.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
1. December 14-16, 2015, Serena Hotel, Islamabad
13th International Conference on Frontiers of Information Technology (FIT), 2015
Multi-View Clustering
Algorithms and Applications
Presented by
Syed Fawad Hussain, PhD
Ghulam Ishaq Khan Institute of Engineering Sciences
and Technology.
Invited Talk, FIT 2015
2. Outline
13th Internaitonal Conference on Frontiers of IT, December 14-16, 2015
Syed Fawad Hussain, PhD
Multi-View Clustering: Algorithms and Applications
2
1. Introduction
1. Data generation
2. Motivation
2. Clustering and Co-Clustering
1. Traditional Clustering
2. Co-clustering
3. Multi-View Multi-Dimensional Clustering
1. Multiview data
2. Knowledge transfer between views
3. Experimental results
4. Application Areas of Multi-View Clustering
3. 13th Internaitonal Conference on Frontiers of IT, December 14-16, 2015
Information Generation
A huge percentage of information is
generated (mostly un-structured)
documents, journals, web pages, emails...
Information is usually generated
from different sources
Different languages (for web pages)
Different feature extractors (e.g. images)
Different links (citation data)
Different sections (movie data from imdb)
Etc.
1. Introduction
Syed Fawad Hussain, PhD
4. 13th Internaitonal Conference on Frontiers of IT, December 14-16, 2015
Views
Data is described by a set of variables/features
Words describing documents
Keywords describing movies
Links describing webpages
Actors describing movies
Features describing images
Sound describing video clips, etc.
A view?
A set of features/attributes/variables describing a set of
objects/instances.
Is independent, and individually sufficient for learning
4
1. Introduction
Syed Fawad Hussain, PhD
5. Clustering
5
2. Clustering and Co-Clustering
13th Internaitonal Conference on Frontiers of IT, December 14-16, 2015
Syed Fawad Hussain, PhD
Division of data into groups of ‘similar objects’
Classical clustering algorithms are based on “similarities” and
organize data into classes such that there is
high intra-class similarity
low inter-class similarity
Example:
P1(1,2), P2(2,2)
P3(4,5), P4(5,7),
P1 P2 P3 P4
P1 0 1 18 41
P2 1 0 13 34
P3 18 13 0 5
P4 41 34 5 0
C1 {P1,P2}
C2 {P3,P4}
6. Co-Clustering
6
2. Clustering and Co-Clustering
13th Internaitonal Conference on Frontiers of IT, December 14-16, 2015
Syed Fawad Hussain, PhD
How to automatically find semantic relationship in the data?
How to calculate similarity between documents?
Basic Idea:
Two documents are similar if they contain similar words
Two words are similar if they occur in similar documents
Solution?
Create similarity matrices R – between docs, and C – between words
Iteratively update R and C using the other.
Boeing recently unveiled its
new B787 aircraft dubbed
the “Dreamliner”.
Airbus’ latest A350 is a
next generation plane is
due to fly in 2013
d1 d2
7. Co-Clustering
7
2. Clustering and Co-Clustering
13th Internaitonal Conference on Frontiers of IT, December 14-16, 2015
Syed Fawad Hussain, PhD
Hussain et al, 2010
The algorithm is as follows
Step 1 - Given A, define R(0)=I, C(0)=I
Step 2 – for k=1 to t, do
Step 3: Output R(t) and C(t)
8. Co-Clustering
8
2. Clustering and Co-Clustering
13th Internaitonal Conference on Frontiers of IT, December 14-16, 2015
Syed Fawad Hussain, PhD
Bipartite Graph
G=(V1,V2,E)
V1={d1,d2,…,dm}
V2={w1,w2,…,wn}
E =Aij , iV1, j V2
Practically 4 iterations are enough
Iteration 1:
R(1) : Sim(d1,d2), Sim(d1,d3), …
C(1): Sim(w1,w2), Sim(w1,w3), …
Iteration 2:
R(2) : Sim(d1,d4) via C24 and C34 …
…
Successive iterations means
paths of increasing length
d1 d2 d3 d4
w1
w2 w3 w4 w5 w6
Aij
10. Single view vs Multiple views
Are these “researchers” similar?
Are their publication text similar?
Do they often cite the same (group of) authors?
Do they often publish in the same venue?
Are these “movies” similar?
Are they described by similar text in their plot?
Do they have similar/same actors?
Are they being described by similar keywords (genre)?
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3. Multi-view Clustering
13th Internaitonal Conference on Frontiers of IT, December 14-16, 2015
Syed Fawad Hussain, PhD
11. What are the natural grouping in this data?
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3. Multi-view Clustering
13th Internaitonal Conference on Frontiers of IT, December 14-16, 2015
Syed Fawad Hussain, PhD
12. Single view vs Multiple views
12
3. Multi-view Clustering
13th Internaitonal Conference on Frontiers of IT, December 14-16, 2015
Syed Fawad Hussain, PhD
Movie: Titanic
Leonardo diCaprio Kate Winslet … …
ship Iceberg europe voyage …
romantic
tragedy
adventure
…
…
Movie by Actors
Movie by plot
Movie by genre
Source: imdb
13. Multi-view data
13
3. Multi-view Clustering
13th Internaitonal Conference on Frontiers of IT, December 14-16, 2015
Syed Fawad Hussain, PhD
Movies-by-Actors Matrix
Movies/
actors
DiCaprio Kate Keanu Jolie
Titanic 1 1 0 0
Matrix 0 0 1 0
… … … … …
Movies-by-Keywords Matrix
Movies/
plot
ship iceberg Sci-fi murder
Titanic 1 1 0 0
Matrix 0 0 1 1
… … … … …
Movies-by-Genre Matrix
Movies/
genre
romantic tragedy war Sci-fi
Titanic 1 1 0 0
Matrix 0 0 0 1
… … … … …
Rows are similar across all views!
14. Clustering on multiple views
14
3. Multi-view Clustering
13th Internaitonal Conference on Frontiers of IT, December 14-16, 2015
Syed Fawad Hussain, PhD
Movies-by-Keywords Matrix
Movies
Clustering 2
Intermediate result
Movies-by-Actors Matrix
Clustering 1
Intermediate result
Movies-by-Genre Matrix
Clustering 3
Intermediate result
Combined Clustering
Better than each
individual clustering
15. Multi-View Learning
SIAM-Similar dataset: containing 1690 articles published in SIAM J MATRIX
ANAL A, SIAM J NUMER ANAL and SIAM J SCI COMPUT.
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3. Multi-view Clustering
13th Internaitonal Conference on Frontiers of IT, December 14-16, 2015
Syed Fawad Hussain, PhD
View Spectral Sum LMF
Abstract 0.2037
0.630 0.714
Title 0.2021
Keywords 0.2502
Authors 0.0017
citation 0.0078
[Wang et al, 2010]
16. Why it works?
The probability of disagreement is bound by the probability of error in the
individual views
Each view (must) have complementary information
A single view is quite sparse (curse of dimensionality)
The more informative the single views, the better the results.
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3. Multi-view Clustering
13th Internaitonal Conference on Frontiers of IT, December 14-16, 2015
Syed Fawad Hussain, PhD
17. Multi-view co-clustering
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3. Multi-view Clustering
13th Internaitonal Conference on Frontiers of IT, December 14-16, 2015
Syed Fawad Hussain, PhD
M: a single data view
R: row-row similarity matrix
C: col-col similarity matrix
χ-SIM : Co-clustering Algo
[Hussain et al, 2015]
18. Experimental setup
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3. Multi-view Clustering
13th Internaitonal Conference on Frontiers of IT, December 14-16, 2015
Syed Fawad Hussain, PhD
Dataset used
Experiments:
Single view clustering
Single view co-clustering
Multi-view co-clustering
19. Results
19
3. Multi-view Clustering
13th Internaitonal Conference on Frontiers of IT, December 14-16, 2015
Syed Fawad Hussain, PhD
Single View Co-Clustering Multi-View
𝐑(𝐭+𝟏)
= 𝐑𝐀
𝐭
𝐑(𝐭+𝟏)
= 𝐑𝐁
𝐭
VA VB VA VB VA VB VA VB
Cora 0.3209 0.3678 0.6004 0.3109 0.6004 0.7146 0.4453 0.3109
Citeseer 0.2503 0.3489 0.3783 0.3998 0.3783 0.5047 0.5897 0.3998
Cornell 0.3487 0.58974 0.3846 0.6051 0.3846 0.6051 0.4872 0.6051
Movies 0.2561 0.19125 0.2723 0.2253 0.2723 0.2853 0.2771 0.2253
Texas 0.3623 0.4670 0.4813 0.6791 0.4813 0.5508 0.6578 0.6791
20. Results
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3. Multi-view Clustering
13th Internaitonal Conference on Frontiers of IT, December 14-16, 2015
Syed Fawad Hussain, PhD
0.3678
0.3489
0.58974
0.2561
0.467
0.6004
0.3998
0.6051
0.2723
0.6791
0.7754
0.7135
0.7231
0.363
0.7754
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
C ORA C IT E S E E R C ORNE LL MOVIE S T E X A S
NMI
SCORE
DATASET
SINGLE VS MULTI-VIEW CLUSTERING
Single Co-clustering Multi-View
110.82 104.5 22.61 41.74 66.04
%
Increase
21. Co-Clustering of multi-view data
21
3. Multi-view Clustering
13th Internaitonal Conference on Frontiers of IT, December 14-16, 2015
Syed Fawad Hussain, PhD
Original Matrix Cora Dataset Co-Cluster
Mideast Politics Motorcycles Baseball Computer
Graphics
Space
Jewish Ride Pitching Graphics Nasa
Israel Harleys Players Image Flight
Arab Camping Season Color Shuttle
Palestinian Bikers yankees display orbital
22. Success Stories
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4. Application Areas of Multi-View Data
13th Internaitonal Conference on Frontiers of IT, December 14-16, 2015
Syed Fawad Hussain, PhD
• Million-dollar prize
– Improve the baseline movie
recommendation approach of
Netflix by 10% in accuracy
– The top submissions all combine
several teams and algorithms as
an ensemble
23. Information Retrieval
23
4. Application Areas of Multi-View Data
13th Internaitonal Conference on Frontiers of IT, December 14-16, 2015
Syed Fawad Hussain, PhD
24. IBM’s Watson
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4. Application Areas of Multi-View Data
13th Internaitonal Conference on Frontiers of IT, December 14-16, 2015
Syed Fawad Hussain, PhD
Watson uses a variety of techniques like deep learning as
just one element in a very complicated ensemble of
techniques, ranging from the statistical technique of Bayesian
inference to deductive reasoning.
Keanu Reeves had a Nokia phone, but it took a land line to slip in & out
of this, the title of a 1999 sci-fi flick
Watson – Around 6 million rules, Access to 10 billion web pages, Massively
parallel Computing power (6000 computers), complex machine learning
algorithms.
25. Self Driving Google Cars
25
4. Application Areas of Multi-View Data
13th Internaitonal Conference on Frontiers of IT, December 14-16, 2015
Syed Fawad Hussain, PhD
Can so far driven
300,000 miles
without accident
An average American
has an accident at
165,000 miles
Uses multiple sources of information,
- Many Cameras ( for situational awareness),
- laser range finder ( for other traffic) ,
- GPS,
- Google maps, radar sensor, etc
26. Conclusion
Data is growing at an enormous rate
Capturing data is easy…using it is not!
26
5. Conclusion
13th Internaitonal Conference on Frontiers of IT, December 14-16, 2015
Syed Fawad Hussain, PhD
“There are known knowns i.e. things we know that
we know; then there are known unknowns i.e.
things we know that we don’t know; and then we
have the unknown unknowns i.e. things we do not
know that we do not know.”
Donald Rumsfield
Former US Secretary of Defence
27. Conclusion
No Free-Lunch theorem
There is a lack of inherent superiority of any classifier
If we make no prior assumption about the nature of the classification task, is any
classification method superior overall?
Is any algorithm overall superior to random guessing?
Answer is to both questions… NO!
The Ugly-duckling theorem
In the absence of assumptions there is no “best” feature representation.
You need to try with a variety of methods, and
You need to know your data, and
You need to experiment a bit,
and finally
You need to contact and work with a machine learning expert
27
5. Conclusion
13th Internaitonal Conference on Frontiers of IT, December 14-16, 2015
Syed Fawad Hussain, PhD
29. References
[Xu,2013] C. Xu, D. Tao and C. Xu, A survey on multi-view learning, arXiv
preprint arXiv:1304.5634 (2013).
[Andew et. al, 2013] G. Andrew, R. Arora, J. Bilmes, and K. Livescu. Deep
canonical correlation analysis. In ICML, pp. 1247–1255, 2013
[Wang, 2009] W. Tang, Z. Lu and I. Dhillon, Clustering with multiple graphs, Data
Mining, 2009. ICDM'09. Ninth IEEE International Conference on. IEEE,
2009.
[Wang, ]W. Wang, R. Arora, K. Livescu, and J. Bilmes, On Deep Multi-View
Representation Learning, ” in Proc. of the 30th Int. Conf. Machine Learning
(ICML 2013), 2013, pp. 1247–1255.
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Multi-view clustering
30. References
[Hussain, 2010] S.F. Hussain, C. Grimal, G. Bisson, An improved co-similarity
measure for document clustering. Machine Learning and Applications
(ICMLA), 2010 Ninth International Conference on. IEEE, 2010.
[Hussain, 2011] S.F. Hussain. "Bi-clustering gene expression data using co-
similarity." Advanced Data Mining and Applications. Springer Berlin
Heidelberg, 2011. 190-200.
[Hussain, 2015] Hussain, Syed Fawad, and Shariq Bashir. "Co-clustering of multi-
view datasets." Knowledge and Information Systems (2015): 1-26.
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Multi-view clustering
31. Co-Clustering
31
3. Multi-View Multi-Dimensional Clustering
13th Internaitonal Conference on Frontiers of IT, December 14-16, 2015
Syed Fawad Hussain, PhD
Traditional clustering equates to finding groups in data “ under all
features/attributes”. In co-clustering (also called bi-clustering), the
pattern/behavior is usually observed under “a specified subset of
attributes/conditions”
Preferred when
Things behave different under
different subsets e.g. gene
expression data
To improve clustering results
To minimize the effect of “curse
of dimensionality”
32. Direct multi-view constrained clustering
Factorize all matrices at the same time under some constraint
where A(m) is a single view, P is the common factor shared between
all graphs, and Λ(m) captures the characteristics of each graph, α is a
weighting factor
Deep Canonical Correlation Analysis[Andew et. al, 2013]
Deep multi-view learning representation[Wang et al, 2015]
Survey of Multi-View Clustering [Xu et. al., 2013]
32
2. Techniques to knowledge transfer
13th Internaitonal Conference on Frontiers of IT, December 14-16, 2015
Syed Fawad Hussain, PhD
[Wang et. al, 2009]
33. Clustering on multiple views
33
1. Introduction
13th Internaitonal Conference on Frontiers of IT, December 14-16, 2015
Syed Fawad Hussain, PhD
Movies-by-Actors Matrix Movies-by-genre Matrix
Movies-by-keywords Matrix
Movies
Clustering 1 Clustering 3
Clustering 2
Intermediate result Intermediate result Intermediate result
34. Using Intermediate Integration
Combine information between views at the intermediate step
Combine intermediate results (e.g. similarity matrices) from the views
34
2. Techniques to knowledge transfer
13th Internaitonal Conference on Frontiers of IT, December 14-16, 2015
Syed Fawad Hussain, PhD
35. Using Late Integration
Combine information between views at the intermediate step
Given 2 views of the data, X(1) and X(2)
Cluster the views to generate two predictions P(1) and P(2)
Use P(1) as a training label for next iteration of X(2) and vice versa
35
2. Techniques to knowledge transfer
13th Internaitonal Conference on Frontiers of IT, December 14-16, 2015
Syed Fawad Hussain, PhD