| 1. |
- Javed, Salman, 1982-, et al.
(author)
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A Smart Manufacturing Ecosystem for Industry 5.0 using Cloud-based Collaborative Learning at the Edge
- 2023
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In: NOMS 2023-2023 IEEE/IFIP Network Operations and Management Symposium. - : IEEE. - 9781665477178 - 9781665477161
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Conference paper (peer-reviewed)abstract
- In the modern manufacturing industry, collaborative architectures are growing in popularity. We propose an Industry 5.0 value-driven manufacturing process automation ecosystem in which each edge automation system is based on a local cloud and has a service-oriented architecture. Additionally, we integrate cloud-based collaborative learning (CCL) across building energy management, logistic robot management, production line management, and human worker Aide local clouds to facilitate shared learning and collaborate in generating manufacturing workflows. Consequently, the workflow management system generates the most effective and Industry 5.0-driven workflow recipes. In addition to managing energy for a sustainable climate and executing a cost-effective, optimized, and resilient manufacturing process, this work ensures the well-being of human workers. This work has significant implications for future work, as the ecosystem can be deployed and tested for any industrial use case.
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| 2. |
- Javed, Saleha, 1990-, et al.
(author)
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Cloud-based Collaborative Learning (CCL) for the Automated Condition Monitoring of Wind Farms
- 2022
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In: Proceedings 2022 IEEE 5th International Conference on Industrial Cyber-Physical Systems (ICPS). - : Institute of Electrical and Electronics Engineers (IEEE).
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Conference paper (peer-reviewed)abstract
- Modeling Industrial Internet of Things (IIoT) architectures for the automation of wind turbines and farms(WT/F), as well as their condition monitoring (CM) is a growing concept among researchers. Several end-to-end automated cloud-based solutions that digitize CM operations intelligently to reduce manual efforts and costs are being developed. However, establishing robust and secure communication across WT/F is still difficult for the wind energy industry. We propose a fully automated cloud-based collaborative learning (CCL) architecture using the Eclipse Arrowhead Framework and an unsupervised dictionary learning (USDL) CM approach. The scalability of the framework enabled digitization and collaboration across the WT/Fs. Collaborative learning is a novel approach that allows all WT/Fs to learn from each other in real-time. Each turbine has CCL based CM using USDL as micro-services that autonomously perform feature selection and failure prediction to optimize cost, computation, and resources. The fundamental essence of the USDA approach is to enhance the WT/F’s learning and accuracy. We use dictionary distances as a metric for analyzing the CM of WT in our proposed USDL approach. A dictionary indicates an anomaly if its distances increased from the dictionary computed at a healthy state of that WT. Using CCL, a WT/F learns all types of failures that could occur in a similar WT/F, predicts any machinery failure, and sends alerts to the technicians to ensure guaranteed proactive maintenance. The results of our research support the notion that when testing a turbine with dictionaries of all the other turbines, every dictionary converges to similar behavior and captures the fault that occurs in that turbine.
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| 3. |
- Javed, Saleha, 1990-, et al.
(author)
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Deep Ontology Alignment with BERT_INT: Improvements and Industrial Internet of Things (IIoT) Case Study
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Other publication (other academic/artistic)abstract
- “He who knows no foreign languages knows nothing of his own.” Johann Wolfgang emphasized the worth of languages for expanding ones learning horizons. This work instills the same notion into the industrial internet of things (IIoT) sensory devices paradigm. We study the interoperability problem setting with a new perspective of envisioning knowledge graphs (KGs) modeling for the device to device ontology alignment. Ontology alignment is structured as entity alignment in which similar entities are linked from two heterogeneous knowledge graphs. The novelty is conceiving the IIoT ontology graph as a language of the sensory device and then addressing it through the natural language processing (NLP) language translation approach. The IIoT ontology graph nodes have unique URIs so they act as words (sentences) for the NLP model and the schema of the graph is depicted as the language structure. Existing methods give less attention to the importance of structural information which ignores the fact of even when a node pair has similar entity labels it may not refer to a similar context and vice versa. To deal with these issues, we propose a novel solution using a modified BERT_INT model on graph Triplets for ontology alignment among heterogeneous IIoT devices. Moreover, an iterative framework is designed to leverage the alignments within nodes as well as among relations. As the first attempt at this problem, the proposed model is tested on a contemporary language dataset of DBP15K and compared with the best state-of-the-art results. The proposed model outperforms the target baseline BERT_INT model by 2.1% in terms of HR@1, HR@10, and MRR. Next, a dataset on ontology instances is constructed on smart building sensors using two W3C standardized IIoT ontologies i.e. SSN and SOSA. Comprehensive experiments and analysis with ablation study on language and structural encoders demonstrate the effectiveness of our model.
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| 4. |
- Javed, Saleha, 1990-, et al.
(author)
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Player Profiling and Quality Assessment of Dynamic Car Racing Tracks using Entertainment Quantifier
- 2018
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In: Computational Intelligence. - United States : Wiley. - 1467-8640 .- 0824-7935. ; 34:4, s. 1046-1071
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Journal article (peer-reviewed)abstract
- Interactive games have been an interesting area of research and have many challenges. With the advancement in technology, games have been revolutionizing at each step as per the emerging and variant interests of players. Recently, machine learning techniques are used for the generation of game content based on players experience. The Dynamic Content Generation (DCG) in computer games based on players experience and feedback is still a challenging task. This requires measurement of entertainment factor achieved by a player during a game. In order to measure entertainment factor, we need to incorporate Human Computer Interaction (HCI) by evolution of game content with respect to players response. Optimization techniques can be used for the measurement of entertainment factor as well as for the generation of dynamic game content. The use of computational intelligence techniques in game development can lead to a new domain called Computational Intelligence in Games (CIG). This research is focused on car racing game genre and the paradigm selected for dynamicity is track generation of car racing game. It requires player profiling and classification of players. The optimization of track generation has been performed by using single and multi-objective Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). Initially, classification of players rank based on data and theory driven approaches has been performed. Moreover, three different techniques of defining ranges or boundaries of race parameters for players rank classification are studied. The techniques are based on crisp values, neural network and fuzzy inference process. Then an Entertainment Quantifier (EQ) technique is proposed for a player after playing a certain number of games based on dynamic content generation using multi-objective genetic algorithm (MOGA) using standard Pareto optimal front as well as an Epsilon ("€") front. In conclusion, the method proposed for quantifying entertainment can be used to analyze and classify the trend in interests of a player according to which the game itself can dynamically generate. This will keep the interest of player intact and provides maximum entertainment experience as per the interest of an individual. The proposed solution can easily be used in generation of any game content and can effectively be used in accurate measurement of entertaining factor of any game.
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| 5. |
- Javed, Saleha, 1990-
(author)
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Towards Digitization and Machine learning Automation for Cyber-Physical System of Systems
- 2022
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Licentiate thesis (other academic/artistic)abstract
- Cyber-physical systems (CPS) connect the physical and digital domains and are often realized as spatially distributed. CPS is built on the Internet of Things (IoT) and Internet of Services, which use cloud architecture to link a swarm of devices over a decentralized network. Modern CPSs are undergoing a foundational shift as Industry 4.0 is continually expanding its boundaries of digitization. From automating the industrial manufacturing process to interconnecting sensor devices within buildings, Industry 4.0 is about developing solutions for the digitized industry. An extensive amount of engineering efforts are put to design dynamically scalable and robust automation solutions that have the capacity to integrate heterogeneous CPS. Such heterogeneous systems must be able to communicate and exchange information with each other in real-time even if they are based on different underlying technologies, protocols, or semantic definitions in the form of ontologies. This development is subject to interoperability challenges and knowledge gaps that are addressed by engineers and researchers, in particular, machine learning approaches are considered to automate costly engineering processes. For example, challenges related to predictive maintenance operations and automatic translation of messages transmitted between heterogeneous devices are investigated using supervised and unsupervised machine learning approaches.In this thesis, a machine learning-based collaboration and automation-oriented IIoT framework named Cloud-based Collaborative Learning (CCL) is developed. CCL is based on a service-oriented architecture (SOA) offering a scalable CPS framework that provides machine learning-as-a-Service (MLaaS). Furthermore, interoperability in the context of the IIoT is investigated. I consider the ontology of an IoT device to be its language, and the structure of that ontology to be its grammar. In particular, the use of aggregated language and structural encoders is investigated to improve the alignment of entities in heterogeneous ontologies. Existing techniques of entity alignment are based on different approaches to integrating structural information, which overlook the fact that even if a node pair has similar entity labels, they may not belong to the same ontological context, and vice versa. To address these challenges, a model based on a modification of the BERT_INT model on graph triples is developed. The developed model is an iterative model for alignment of heterogeneous IIoT ontologies enabling alignments within nodes as well as relations. When compared to the state-of-the-art BERT_INT, on DBPK15 language dataset the developed model exceeds the baseline model by (HR@1/10, MRR) of 2.1%. This motivated the development of a proof-of-concept for conducting an empirical investigation of the developed model for alignment between heterogeneous IIoT ontologies. For this purpose, a dataset was generated from smart building systems and SOSA and SSN ontologies graphs. Experiments and analysis including an ablation study on the proposed language and structural encoders demonstrate the effectiveness of the model.The suggested approach, on the other hand, highlights prospective future studies that may extend beyond the scope of a single thesis. For instance, to strengthen the ablation study, a generalized IIoT ontology that is designed for any type of IoT devices (beyond sensors), such as SAREF can be tested for ontology alignment. Next potential future work is to conduct a crowdsourcing process for generating a validation dataset for IIoT ontology alignment and annotations. Lastly, this work can be considered as a step towards enabling translation between heterogeneous IoT sensor devices, therefore, the proposed model can be extended to a translation module in which based on the ontology graphs of any device, the model can interpret the messages transmitted from that device. This idea is at an abstract level as of now and needs extensive efforts and empirical study for full maturity.
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| 6. |
- Javed, Saleha, 1990-, et al.
(author)
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Understanding the Role of Objectivity in Machine Learning and Research Evaluation
- 2021
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In: Philosophies. - Switzerland : MDPI. - 2409-9287. ; 6:1
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Journal article (peer-reviewed)abstract
- This article makes the case for more objectivity in Machine Learning (ML) research. Any research work that claims to hold benefits has to be scrutinized based on many parameters, such as the methodology employed, ethical considerations and its theoretical or technical contribution. We approach this discussion from a Naturalist philosophical outlook. Although every analysis may be subjective, it is important for the research community to keep vetting the research for continuous growth and to produce even better work. We suggest standardizing some of the steps in ML research in an objective way and being aware of various biases threatening objectivity. The ideal of objectivity keeps research rational since objectivity requires beliefs to be based on facts. We discuss some of the current challenges, the role of objectivity in the two elements (product and process) that are up for consideration in ML and make recommendations to support the research community.
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| 7. |
- Nilsson, Jacob, et al.
(author)
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AI Concepts for System of Systems Dynamic Interoperability
- 2024
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In: Sensors. - : MDPI. - 1424-8220. ; 24:9
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Journal article (peer-reviewed)abstract
- Interoperability is a central problem in digitization and sos engineering, which concerns the capacity of systems to exchange information and cooperate. The task to dynamically establish interoperability between heterogeneous cps at run-time is a challenging problem. Different aspects of the interoperability problem have been studied in fields such as sos, neural translation, and agent-based systems, but there are no unifying solutions beyond domain-specific standardization efforts. The problem is complicated by the uncertain and variable relations between physical processes and human-centric symbols, which result from, e.g., latent physical degrees of freedom, maintenance, re-configurations, and software updates. Therefore, we surveyed the literature for concepts and methods needed to automatically establish sos with purposeful cps communication, focusing on machine learning and connecting approaches that are not integrated in the present literature. Here, we summarize recent developments relevant to the dynamic interoperability problem, such as representation learning for ontology alignment and inference on heterogeneous linked data; neural networks for transcoding of text and code; concept learning-based reasoning; and emergent communication. We find that there has been a recent interest in deep learning approaches to establishing communication under different assumptions about the environment, language, and nature of the communicating entities. Furthermore, we present examples of architectures and discuss open problems associated with ai-enabled solutions in relation to sos interoperability requirements. Although these developments open new avenues for research, there are still no examples that bridge the concepts necessary to establish dynamic interoperability in complex sos, and realistic testbeds are needed.
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| 8. |
- Rakesh, Sumit, 1987-, et al.
(author)
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Sign Gesture Recognition from Raw Skeleton Information in 3D Using Deep Learning
- 2021
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In: Computer Vision and Image Processing. - Singapore : Springer Nature. ; , s. 184-195
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Conference paper (peer-reviewed)abstract
- Sign Language Recognition (SLR) minimizes the communication gap when interacting with hearing impaired people, i.e. connects hearing impaired persons and those who require to communicate and don’t understand SLR. This paper focuses on an end-to-end deep learning approach for the recognition of sign gestures recorded with a 3D sensor (e.g., Microsoft Kinect). Typical machine learning based SLR systems require feature extractions before applying machine learning models. These features need to be chosen carefully as the recognition performance heavily relies on them. Our proposed end-to-end approach eradicates this problem by eliminating the need to extract handmade features. Deep learning models can directly work on raw data and learn higher level representations (features) by themselves. To test our hypothesis, we have used two latest and promising deep learning models, Gated Recurrent Unit (GRU) and Bidirectional Long Short Term Memory (BiLSTM) and trained them using only raw data. We have performed comparative analysis among both models and also with the base paper results. Conducted experiments reflected that proposed method outperforms the existing work, where GRU successfully concluded with 70.78% average accuracy with front view training.
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| 9. |
- Usman, Muhammad, et al.
(author)
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A Blockchain Based Scalable Domain Access Control Framework for Industrial Internet of Things
- 2024
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In: IEEE Access. - : IEEE. - 2169-3536. ; 12, s. 56554-56570
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Journal article (peer-reviewed)abstract
- Industrial Internet of Things (IIoT) applications consist of resource constrained interconnected devices that make them vulnerable to data leak and integrity violation challenges. The mobility, dynamism, and complex structure of the network further make this issue more challenging. To control the information flow in such environments, access control is critical to make collaboration and communication safe. To deal with these challenges, recent studies employ attribute-based access control on top of blockchain technology. However, the attribute-based access control frameworks suffer due to high computational overhead. In this paper, we propose an improved role-based access control framework using hyperledger blockchain to deal with IIoT requirements with less computational overhead making the information control process more efficient and real-time. The proposed framework leverages a layered architecture of chaincodes to implement the improved access control framework that handles the permission delegation and conflict management to deal with the dynamism of the IIoT network. The system uses a Policy Contract, Device Contract, and Access Contract to manage the workflow of the whole access control process. Each chaincode in the proposed framework is isolated in terms of its responsibilities to make the design low coupled. The integration of improved access control with blockchain enables the proposed framework to provide a highly scalable solution, tamper-proof, and flexible to manage conflicting scenarios. The proposed system outperforms the recent studies significantly in computational overhead in extensive simulation results. To verify the scalability and efficiency, the proposed is evaluated against a large number of concurrent virtual clients in simulation and statistical analysis proves that the proposed system is promising for further research in this domain.
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| 10. |
- Usman, Muhammad, et al.
(author)
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Automatic Hybrid Access Control in SCADA-Enabled IIoT Networks Using Machine Learning
- 2023
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In: Sensors. - : MDPI. - 1424-8220. ; 23:8
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Journal article (peer-reviewed)abstract
- The recent advancements in the Internet of Things have made it converge towards critical infrastructure automation, opening a new paradigm referred to as the Industrial Internet of Things (IIoT). In the IIoT, different connected devices can send huge amounts of data to other devices back and forth for a better decision-making process. In such use cases, the role of supervisory control and data acquisition (SCADA) has been studied by many researchers in recent years for robust supervisory control management. Nevertheless, for better sustainability of these applications, reliable data exchange is crucial in this domain. To ensure the privacy and integrity of the data shared between the connected devices, access control can be used as the front-line security mechanism for these systems. However, the role engineering and assignment propagation in access control is still a tedious process as its manually performed by network administrators. In this study, we explored the potential of supervised machine learning to automate role engineering for fine-grained access control in Industrial Internet of Things (IIoT) settings. We propose a mapping framework to employ a fine-tuned multilayer feedforward artificial neural network (ANN) and extreme learning machine (ELM) for role engineering in the SCADA-enabled IIoT environment to ensure privacy and user access rights to resources. For the application of machine learning, a thorough comparison between these two algorithms is also presented in terms of their effectiveness and performance. Extensive experiments demonstrated the significant performance of the proposed scheme, which is promising for future research to automate the role assignment in the IIoT domain.
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