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Description
Critical infrastructures in healthcare, power systems, and web services, incorporate cyber-physical systems (CPSes), where the software controlled computing systems interact with the physical environment through actuation and monitoring. Ensuring software safety in CPSes, to avoid hazards to property and human life as a result of un-controlled interactions, is essential and challenging. The principal hurdle in this regard is the characterization of the context driven interactions between software and the physical environment (cyber-physical interactions), which introduce multi-dimensional dynamics in space and time, complex non-linearities, and non-trivial aggregation of interaction in case of networked operations. Traditionally, CPS software is tested for safety either through experimental trials, which can be expensive, incomprehensive, and hazardous, or through static analysis of code, which ignore the cyber-physical interactions. This thesis considers model based engineering, a paradigm widely used in different disciplines of engineering, for safety verification of CPS software and contributes to three fundamental phases: a) modeling, building abstractions or models that characterize cyberphysical interactions in a mathematical framework, b) analysis, reasoning about safety based on properties of the model, and c) synthesis, implementing models on standard testbeds for performing preliminary experimental trials. In this regard, CPS modeling techniques are proposed that can accurately capture the context driven spatio-temporal aggregate cyber-physical interactions. Different levels of abstractions are considered, which result in high level architectural models, or more detailed formal behavioral models of CPSes. The outcomes include, a well defined architectural specification framework called CPS-DAS and a novel spatio-temporal formal model called Spatio-Temporal Hybrid Automata (STHA) for CPSes. Model analysis techniques are proposed for the CPS models, which can simulate the effects of dynamic context changes on non-linear spatio-temporal cyberphysical interactions, and characterize aggregate effects. The outcomes include tractable algorithms for simulation analysis and for theoretically proving safety properties of CPS software. Lastly a software synthesis technique is proposed that can automatically convert high level architectural models of CPSes in the healthcare domain into implementations in high level programming languages. The outcome is a tool called Health-Dev that can synthesize software implementations of CPS models in healthcare for experimental verification of safety properties.
ContributorsBanerjee, Ayan (Author) / Gupta, Sandeep K.S. (Thesis advisor) / Poovendran, Radha (Committee member) / Fainekos, Georgios (Committee member) / Maciejewski, Ross (Committee member) / Arizona State University (Publisher)
Created2012
Description
Security requirements are at the heart of developing secure, invulnerable software. Without embedding security principles in the software development life cycle, the likelihood of producing insecure software increases, putting the consumers of that software at great risk. For large-scale software development, this problem is complicated as there may be hundreds or thousands of security requirements that need to be met, and it only worsens if the software development project is developed by a distributed development team. In this thesis, an approach is provided for software security requirement traceability for large-scale and complex software development projects being developed by distributed development teams. The approach utilizes blockchain technology to improve the automation of security requirement satisfaction and create a more transparent and trustworthy development environment for distributed development teams. The approach also introduces immutability, auditability, and non-repudiation into the security requirement traceability process. The approach is evaluated against existing software security requirement solutions.
ContributorsKulkarni, Adi Deepak (Author) / Yau, Stephen S. (Thesis advisor) / Banerjee, Ayan (Committee member) / Wang, Ruoyu (Committee member) / Baek, Jaejong (Committee member) / Arizona State University (Publisher)
Created2022
Description
There has been an explosion in the amount of data on the internet because of modern technology – especially image data – as a consequence of an exponential growth in the number of cameras existing in the world right now; from more extensive surveillance camera systems to billions of people walking around with smartphones in their pockets that come with built-in cameras. With this sudden increase in the accessibility of cameras, most of the data that is getting captured through these devices is ending up on the internet. Researchers soon took leverage of this data by creating large-scale datasets. However, generating a dataset – let alone a large-scale one – requires a lot of man-hours. This work presents an algorithm that makes use of optical flow and feature matching, along with utilizing localization outputs from a Mask R-CNN, to generate large-scale vehicle datasets without much human supervision. Additionally, this work proposes a novel multi-view vehicle dataset (MVVdb) of 500 vehicles which is also generated using the aforementioned algorithm.There are various research problems in computer vision that can leverage a multi-view dataset, e.g., 3D pose estimation, and 3D object detection. On the other hand, a multi-view vehicle dataset can be used for a 2D image to 3D shape prediction, generation of 3D vehicle models, and even a more robust vehicle make and model recognition. In this work, a ResNet is trained on the multi-view vehicle dataset to perform vehicle re-identification, which is fundamentally similar to a vehicle make and recognition problem – also showcasing the usability of the MVVdb dataset.
ContributorsGuha, Anubhab (Author) / Yang, Yezhou (Thesis advisor) / Lu, Duo (Committee member) / Banerjee, Ayan (Committee member) / Arizona State University (Publisher)
Created2022
Description
Ontologies play an important role in storing and exchanging digitized data. As the need for semantic web information grows, organizations from around the globe has defined ontologies in different domains to better represent the data. But different organizations define ontologies of the same entity in their own way. Finding ontologies of the same entity in different fields and domains has become very important for unifying and improving interoperability of data between these multiple domains. Many different techniques have been used over the year, including human assisted, automated and hybrid. In recent years with the availability of many machine learning techniques, researchers are trying to apply these techniques to solve the ontology alignment problem across different domains. In this study I have looked into the use of different machine learning techniques such as Support Vector Machine, Stochastic Gradient Descent, Random Forest etc. for solving ontology alignment problem with some of the most commonly used datasets found from the famous Ontology Alignment Evaluation Initiative (OAEI). I have proposed a method OntoAlign which demonstrates the importance of using different types of similarity measures for feature extraction from ontology data in order to achieve better results for ontology alignment.
ContributorsNasim, Tariq M (Author) / Bansal, Srividya (Thesis advisor) / Mehlhase, Alexandra (Committee member) / Banerjee, Ayan (Committee member) / Arizona State University (Publisher)
Created2022
Description
With the advent of new advanced analysis tools and access to related published data, it is getting more difficult for data owners to suppress private information from published data while still providing useful information. This dual problem of providing useful, accurate information and protecting it at the same time has been challenging, especially in healthcare. The data owners lack an automated resource that provides layers of protection on a published dataset with validated statistical values for usability. Differential privacy (DP) has gained a lot of attention in the past few years as a solution to the above-mentioned dual problem. DP is defined as a statistical anonymity model that can protect the data from adversarial observation while still providing intended usage. This dissertation introduces a novel DP protection mechanism called Inexact Data Cloning (IDC), which simultaneously protects and preserves information in published data while conveying source data intent. IDC preserves the privacy of the records by converting the raw data records into clonesets. The clonesets then pass through a classifier that removes potential compromising clonesets, filtering only good inexact cloneset. The mechanism of IDC is dependent on a set of privacy protection metrics called differential privacy protection metrics (DPPM), which represents the overall protection level. IDC uses two novel performance values, differential privacy protection score (DPPS) and clone classifier selection percentage (CCSP), to estimate the privacy level of protected data. In support of using IDC as a viable data security product, a software tool chain prototype, differential privacy protection architecture (DPPA), was developed to utilize the IDC. DPPA used the engineering security mechanism of IDC. DPPA is a hub which facilitates a market for data DP security mechanisms. DPPA works by incorporating standalone IDC mechanisms and provides automation, IDC protected published datasets and statistically verified IDC dataset diagnostic report. DPPA is currently doing functional, and operational benchmark processes that quantifies the DP protection of a given published dataset. The DPPA tool was recently used to test a couple of health datasets. The test results further validate the IDC mechanism as being feasible.
Contributorsthomas, zelpha (Author) / Bliss, Daniel W (Thesis advisor) / Papandreou-Suppappola, Antonia (Committee member) / Banerjee, Ayan (Committee member) / Shrivastava, Aviral (Committee member) / Arizona State University (Publisher)
Created2023
Description
In recent years, deep learning systems have outperformed traditional machine learning systems in most domains. There has been a lot of research recently in the field of hand gesture recognition using wearable sensors due to the numerous advantages these systems have over vision-based ones. However, due to the lack of extensive datasets and the nature of the Inertial Measurement Unit (IMU) data, there are difficulties in applying deep learning techniques to them. Although many machine learning models have good accuracy, most of them assume that training data is available for every user while other works that do not require user data have lower accuracies. MirrorGen is a technique which uses wearable sensor data and generates synthetic videos using hand movements and it mitigates the traditional challenges of vision based recognition such as occlusion, lighting restrictions, lack of viewpoint variations, and environmental noise. In addition, MirrorGen allows for user-independent recognition involving minimal human effort during data collection. It also helps leverage the advances in vision-based recognition by using various techniques like optical flow extraction, 3D convolution. Projecting the orientation (IMU) information to a video helps in gaining position information of the hands. To validate these claims, we perform entropy analysis on various configurations such as raw data, stick model, hand model and real video. Human hand model is found to have an optimal entropy that helps in achieving user independent recognition. It also serves as a pervasive option as opposed to a video-based recognition. The average user independent recognition accuracy of 99.03% was achieved for a sign language dataset with 59 different users, 20 different signs with 20 repetitions each for a total of 23k training instances. Moreover, synthetic videos can be used to augment real videos to improve recognition accuracy.
ContributorsRamesh, Arun Srivatsa (Author) / Gupta, Sandeep K S (Thesis advisor) / Banerjee, Ayan (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2018
Description
The advent of commercial inexpensive sensors and the advances in information and communication technology (ICT) have brought forth the era of pervasive Quantified-Self. Automatic diet monitoring is one of the most important aspects for Quantified-Self because it is vital for ensuring the well-being of patients suffering from chronic diseases as well as for providing a low cost means for maintaining the health for everyone else. Automatic dietary monitoring consists of: a) Determining the type and amount of food intake, and b) Monitoring eating behavior, i.e., time, frequency, and speed of eating. Although there are some existing techniques towards these ends, they suffer from issues of low accuracy and low adherence. To overcome these issues, multiple sensors were utilized because the availability of affordable sensors that can capture the different aspect information has the potential for increasing the available knowledge for Quantified-Self. For a), I envision an intelligent dietary monitoring system that automatically identifies food items by using the knowledge obtained from visible spectrum camera and infrared spectrum camera. This system is able to outperform the state-of-the-art systems for cooked food recognition by 25% while also minimizing user intervention. For b), I propose a novel methodology, IDEA that performs accurate eating action identification within eating episodes with an average F1-score of 0.92. This is an improvement of 0.11 for precision and 0.15 for recall for the worst-case users as compared to the state-of-the-art. IDEA uses only a single wrist-band which includes four sensors and provides feedback on eating speed every 2 minutes without obtaining any manual input from the user.
ContributorsLee, Junghyo (Author) / Gupta, Sandeep K.S. (Thesis advisor) / Banerjee, Ayan (Committee member) / Li, Baoxin (Committee member) / Chiou, Erin (Committee member) / Kudva, Yogish C. (Committee member) / Arizona State University (Publisher)
Created2019
Description
Languages, specially gestural and sign languages, are best learned in immersive environments with rich feedback. Computer-Aided Language Learning (CALL) solu- tions for spoken languages have successfully incorporated some feedback mechanisms, but no such solution exists for signed languages. Computer Aided Sign Language Learning (CASLL) is a recent and promising field of research which is made feasible by advances in Computer Vision and Sign Language Recognition(SLR). Leveraging existing SLR systems for feedback based learning is not feasible because their decision processes are not human interpretable and do not facilitate conceptual feedback to learners. Thus, fundamental research is needed towards designing systems that are modular and explainable. The explanations from these systems can then be used to produce feedback to aid in the learning process.
In this work, I present novel approaches for the recognition of location, movement and handshape that are components of American Sign Language (ASL) using both wrist-worn sensors as well as webcams. Finally, I present Learn2Sign(L2S), a chat- bot based AI tutor that can provide fine-grained conceptual feedback to learners of ASL using the modular recognition approaches. L2S is designed to provide feedback directly relating to the fundamental concepts of ASL using an explainable AI. I present the system performance results in terms of Precision, Recall and F-1 scores as well as validation results towards the learning outcomes of users. Both retention and execution tests for 26 participants for 14 different ASL words learned using learn2sign is presented. Finally, I also present the results of a post-usage usability survey for all the participants. In this work, I found that learners who received live feedback on their executions improved their execution as well as retention performances. The average increase in execution performance was 28% points and that for retention was 4% points.
In this work, I present novel approaches for the recognition of location, movement and handshape that are components of American Sign Language (ASL) using both wrist-worn sensors as well as webcams. Finally, I present Learn2Sign(L2S), a chat- bot based AI tutor that can provide fine-grained conceptual feedback to learners of ASL using the modular recognition approaches. L2S is designed to provide feedback directly relating to the fundamental concepts of ASL using an explainable AI. I present the system performance results in terms of Precision, Recall and F-1 scores as well as validation results towards the learning outcomes of users. Both retention and execution tests for 26 participants for 14 different ASL words learned using learn2sign is presented. Finally, I also present the results of a post-usage usability survey for all the participants. In this work, I found that learners who received live feedback on their executions improved their execution as well as retention performances. The average increase in execution performance was 28% points and that for retention was 4% points.
ContributorsPaudyal, Prajwal (Author) / Gupta, Sandeep (Thesis advisor) / Banerjee, Ayan (Committee member) / Hsiao, Ihan (Committee member) / Azuma, Tamiko (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2020
Description
Humans have an excellent ability to analyze and process information from multiple domains. They also possess the ability to apply the same decision-making process when the situation is familiar with their previous experience.
Inspired by human's ability to remember past experiences and apply the same when a similar situation occurs, the research community has attempted to augment memory with Neural Network to store the previously learned information. Together with this, the community has also developed mechanisms to perform domain-specific weight switching to handle multiple domains using a single model. Notably, the two research fields work independently, and the goal of this dissertation is to combine their capabilities.
This dissertation introduces a Neural Network module augmented with two external memories, one allowing the network to read and write the information and another to perform domain-specific weight switching. Two learning tasks are proposed in this work to investigate the model performance - solving mathematics operations sequence and action based on color sequence identification. A wide range of experiments with these two tasks verify the model's learning capabilities.
Inspired by human's ability to remember past experiences and apply the same when a similar situation occurs, the research community has attempted to augment memory with Neural Network to store the previously learned information. Together with this, the community has also developed mechanisms to perform domain-specific weight switching to handle multiple domains using a single model. Notably, the two research fields work independently, and the goal of this dissertation is to combine their capabilities.
This dissertation introduces a Neural Network module augmented with two external memories, one allowing the network to read and write the information and another to perform domain-specific weight switching. Two learning tasks are proposed in this work to investigate the model performance - solving mathematics operations sequence and action based on color sequence identification. A wide range of experiments with these two tasks verify the model's learning capabilities.
ContributorsPatel, Deep Chittranjan (Author) / Ben Amor, Hani (Thesis advisor) / Banerjee, Ayan (Committee member) / McDaniel, Troy (Committee member) / Arizona State University (Publisher)
Created2020
Description
The mobile crowdsensing (MCS) applications leverage the user data to derive useful information by data-driven evaluation of innovative user contexts and gathering of information at a high data rate. Such access to context-rich data can potentially enable computationally intensive crowd-sourcing applications such as tracking a missing person or capturing a highlight video of an event. Using snippets and pictures captured from multiple mobile phone cameras with specific contexts can improve the data acquired in such applications. These MCS applications require efficient processing and analysis to generate results in real time. A human user, mobile device and their interactions cause a change in context on the mobile device affecting the quality contextual data that is gathered. Usage of MCS data in real-time mobile applications is challenging due to the complex inter-relationship between: a) availability of context, context is available with the mobile phones and not with the cloud, b) cost of data transfer to remote cloud servers, both in terms of communication time and energy, and c) availability of local computational resources on the mobile phone, computation may lead to rapid battery drain or increased response time. The resource-constrained mobile devices need to offload some of their computation.
This thesis proposes ContextAiDe an end-end architecture for data-driven distributed applications aware of human mobile interactions using Edge computing. Edge processing supports real-time applications by reducing communication costs. The goal is to optimize the quality and the cost of acquiring the data using a) modeling and prediction of mobile user contexts, b) efficient strategies of scheduling application tasks on heterogeneous devices including multi-core devices such as GPU c) power-aware scheduling of virtual machine (VM) applications in cloud infrastructure e.g. elastic VMs. ContextAiDe middleware is integrated into the mobile application via Android API. The evaluation consists of overheads and costs analysis in the scenario of ``perpetrator tracking" application on the cloud, fog servers, and mobile devices. LifeMap data sets containing actual sensor data traces from mobile devices are used to simulate the application run for large scale evaluation.
This thesis proposes ContextAiDe an end-end architecture for data-driven distributed applications aware of human mobile interactions using Edge computing. Edge processing supports real-time applications by reducing communication costs. The goal is to optimize the quality and the cost of acquiring the data using a) modeling and prediction of mobile user contexts, b) efficient strategies of scheduling application tasks on heterogeneous devices including multi-core devices such as GPU c) power-aware scheduling of virtual machine (VM) applications in cloud infrastructure e.g. elastic VMs. ContextAiDe middleware is integrated into the mobile application via Android API. The evaluation consists of overheads and costs analysis in the scenario of ``perpetrator tracking" application on the cloud, fog servers, and mobile devices. LifeMap data sets containing actual sensor data traces from mobile devices are used to simulate the application run for large scale evaluation.
ContributorsPore, Madhurima (Author) / Gupta, Sandeep K. S. (Thesis advisor, Committee member) / Banerjee, Ayan (Committee member) / Reisslein, Martin (Committee member) / CERIN, CHRISTOPHE (Committee member) / Arizona State University (Publisher)
Created2019