The migration from MATLAB to Python (M-to-PY) has gained significant traction in recent computati... more The migration from MATLAB to Python (M-to-PY) has gained significant traction in recent computational research. While MATLAB has long served as a linchpin in myriad scientific endeavors, there's an emerging trend to rejuvenate these projects using Python's extensive AI tools and libraries. This study presents a semi-automated process for M-to-PY conversion, using a detailed case study of an image skeletonization project comprising fifteen MATLAB files and a 1404-image dataset. Skeletonization is foundational for ongoing 3D motion detection research using AI transformers, predominantly developed in Python. The utilization of ChatGPT-4, acting as an AI co-programmer, is pivotal in this conversion. By leveraging the public OpenAI API, we developed an M-to-PY converter prototype, evaluated its efficacy using test cases from the Bard bot, and subsequently employed the converted code in an AI application. The dual contributions encompass a well-tested M-to-PY converter and a Skele...
2020 IEEE Conference on Big Data and Analytics (ICBDA), 2020
Deep Learning (DL) becomes a mainstream technique for Artificial Intelligence (AI) machine learni... more Deep Learning (DL) becomes a mainstream technique for Artificial Intelligence (AI) machine learning because of its success in performing many tasks, such as image recognition, speech interpretation, language prediction and translation. We are investigating the underlying principles of DL Neural Networks (NN) to design optimal DL NN for predicting human multi-linguistic conversations with a chatterbot. This research attempts to tackle the well-known open problem of finding optimal NN designs for data of various characteristics. We are in particular focusing on Recurrent Neural Networks (RNN) models with time progression, which takes into consideration the results from the previous steps plus the current input to predict the next step, i.e. it ‘remembers’ what it has previously learnt. Through the experiments of tuning an RNN to achieve an optimal performance in terms of accuracy and training time, we found that characteristics such as word counts and layers of neurons could affect the training performance. We applied the tuned optimal model to a game implementation, inspired by IBM Watson, where users can guess the words to be generated by a computer, called “Beat AI” to have human predict the machine prediction.
This paper highlights the explicit description about concept of ontology which is concerned with ... more This paper highlights the explicit description about concept of ontology which is concerned with the development and methodology involved in building ontology. The concept of ontologies has contributed to the development of Semantic Web where Semantic Web is an extension of the current World Wide Web in which information is given in a well-defined meaning that translates the given unstructured data into knowledgeable representation data thus enabling computers and people to work in cooperation. Thus, we can say that Semantic Web is information in machine understandable form. It is also called as Global Information Mesh (GIM). Semantic Web technology can be used to deal with challenges including traditional search engines and retrieval techniques within given organizations or for e-commerce applications whose initial focus is on professional users. Ontology represents information in a manner so that this information can also be used by machines not only for displaying, but also for automating, integrating, and reusing the same information across various applications which may include Artificial Intelligence, Information Retrieval (IR) and many more. Ontology is defined as a collection of set of concepts, their definitions and the relationships among them represented in a hierarchical manner that is termed as Taxonomy. There are various tools available for developing ontologies like Hozo, DOML, and AltovaSemantic Works etc. We have used proté gé which is one of the most widely used ontology development editor that defines ontology concepts (classes), properties, taxonomies, various restrictions and class instances. It also supports several ontology representation languages, including OWL. There are various versions of proté gé available like WebProtege 2.0 beta, Proté gé 3.4.8, Proté gé 4.1 etc. In this paper, we have illustrated ontology development using proté gé 3.1 by giving an example of Computer Science Department of University System. It may be useful for future researchers in making ontology on proté gé version 3.1.
This paper presents an innovative testing framework, testFAILS, designed for the rigorous evaluat... more This paper presents an innovative testing framework, testFAILS, designed for the rigorous evaluation of AI Linguistic Systems, with a particular emphasis on various iterations of ChatGPT. Leveraging orthogonal array coverage, this framework provides a robust mechanism for assessing AI systems, addressing the critical question, "How should we evaluate AI?" While the Turing test has traditionally been the benchmark for AI evaluation, we argue that current publicly available chatbots, despite their rapid advancements, have yet to meet this standard. However, the pace of progress suggests that achieving Turing test-level performance may be imminent. In the interim, the need for effective AI evaluation and testing methodologies remains paramount. Our research, which is ongoing, has already validated several versions of ChatGPT, and we are currently conducting comprehensive testing on the latest models, including ChatGPT-4, Bard and Bing Bot, and the LLaMA model. The testFAILS f...
2016 IEEE 2nd International Conference on Big Data Security on Cloud (BigDataSecurity), IEEE International Conference on High Performance and Smart Computing (HPSC), and IEEE International Conference on Intelligent Data and Security (IDS), 2016
This paper presents a new approach to predictive data analytics, called Radius of Neighbors (RN),... more This paper presents a new approach to predictive data analytics, called Radius of Neighbors (RN), and its mobile application, a multilingual RN-Chatter, devoted to improve communication among people, speaking different languages. RN is a modeless method of unsupervised machine learning, what makes it a fairly simple but effective way of analyzing big amounts of data while keeping acceptable speed of execution and taking up little run-time space. In the first preparatory stage of our research, we discovered that RN gives better results than well-known K-Nearest Neighbors (KNN) in some cases. We then extended our research to simulating the adjustments of floating radiuses, various volumes of the training data sets and ups and downs of the dimensions to tune RN for its optimum accuracy. We took experimental approach of not only extending the number of dimensions, but, instead, shrinking and modifying them in order to keep the predicted value' neighbors close by.
We study a dynamic allocation problem that arises in various scenarios where mobile clients joini... more We study a dynamic allocation problem that arises in various scenarios where mobile clients joining and leaving the system have to communicate with static stations via radio transmissions. Restrictions are a maximum delay, or laxity, between consecutive client transmissions and a maximum bandwidth that a station can share among its clients. We study the problem of assigning clients to stations so that every client transmits to some station, satisfying those restrictions. We consider reallocation algorithms, where clients are revealed at its arrival time, the departure time is unknown until they leave, and clients may be reallocated to another station, but at a cost proportional to the reciprocal of the client’s laxity. We present negative results for previous related protocols that motivate the study; we introduce new protocols that expound trade-offs between station usage and reallocation cost; we determine experimentally a classification of the clients attempting to balance those ...
Proceedings of the 53rd ACM Technical Symposium on Computer Science Education
Within computer science education, a growth mindset is encouraged. However, faculty development o... more Within computer science education, a growth mindset is encouraged. However, faculty development on the use of growth mindset in the classroom is rare and resources to support the use of a growth mindset are limited. A framework for a computer science growth mindset classroom, which includes faculty development, lesson plans, and vocabulary for use with students, has been developed. The objective is to determine if faculty development in growth mindset and active use of the growth mindset cues in the CS0 and CS1 classroom result in superior academic outcomes. Comparative study results are presented for two semesters of virtual classroom environments: one semester without Growth Mindset, and one semester with Growth Mindset. Female students demonstrated the most growth, as measured by academic grades, in CS0, and maintained that growth in CS1. Males demonstrated growth as well, with both males and females converging at the same high point of accomplishment at the end of CS1. Race and ethnicity gaps between students were reduced, improving academic equity.
Proceedings of the 52nd ACM Technical Symposium on Computer Science Education, 2021
A growth mindset encourages the development of intelligence, in contrast to a fixed mindset, whic... more A growth mindset encourages the development of intelligence, in contrast to a fixed mindset, which considers intelligence to be fixed and unable to be changed. Within computer science education, there is an awareness of a growth mindset, but resources to support the use of a growth mindset in the computer science classroom are limited, and faculty development in the use of growth mindset in the classroom is rare. Researchers have developed a framework for a computer science growth mindset classroom, which includes faculty development, lesson plans, and vocabulary for use with students. In addition, the faculty have formed a community of practice which identifies areas where growth mindset techniques can be used and has implemented these strategies in their classrooms. The framework and materials developed are presented here, with early results from this ongoing work. The objective is to determine if faculty development on growth mindset and active use of the framework for a growth mindset classroom results in superior academic outcomes in CS0 and CS1. Preliminary results are for faculty in both face-to-face and virtual classroom environments.
The 2022 International Conference on Computational Science and Computational Intelligence (CSCI), 2022
Transformers with neural networks (NN) have become a dominant technology in AI/ML to achieve bett... more Transformers with neural networks (NN) have become a dominant technology in AI/ML to achieve better accuracy in classification and prediction. We experimentally compare transformer-based NN and convolutional NN using various applications such as pollen detection and weather ITCZ prediction. Using machine learning in geoscience, earth sciences, and other natural sciences is not entirely new, but applying transformers to data from these environments creates many new opportunities. We apply Facebook's detection transformer (DETR) neural network, developed in 2020, to pollen and weather data to detect forty-four types of pollen and classify earth snapshots into two categories: having vs. not having the phenomena of double Intertropical Convergence Zones (ITCZs) in them. As we conduct our trials, we observe and document how the model performs on both datasets, detect the biases present in each layer of the network, and mitigate them as we tune the model and improve classification results even further.
The migration from MATLAB to Python (M-to-PY) has gained significant traction in recent computati... more The migration from MATLAB to Python (M-to-PY) has gained significant traction in recent computational research. While MATLAB has long served as a linchpin in myriad scientific endeavors, there's an emerging trend to rejuvenate these projects using Python's extensive AI tools and libraries. This study presents a semi-automated process for M-to-PY conversion, using a detailed case study of an image skeletonization project comprising fifteen MATLAB files and a 1404-image dataset. Skeletonization is foundational for ongoing 3D motion detection research using AI transformers, predominantly developed in Python. The utilization of ChatGPT-4, acting as an AI co-programmer, is pivotal in this conversion. By leveraging the public OpenAI API, we developed an M-to-PY converter prototype, evaluated its efficacy using test cases from the Bard bot, and subsequently employed the converted code in an AI application. The dual contributions encompass a well-tested M-to-PY converter and a Skele...
2020 IEEE Conference on Big Data and Analytics (ICBDA), 2020
Deep Learning (DL) becomes a mainstream technique for Artificial Intelligence (AI) machine learni... more Deep Learning (DL) becomes a mainstream technique for Artificial Intelligence (AI) machine learning because of its success in performing many tasks, such as image recognition, speech interpretation, language prediction and translation. We are investigating the underlying principles of DL Neural Networks (NN) to design optimal DL NN for predicting human multi-linguistic conversations with a chatterbot. This research attempts to tackle the well-known open problem of finding optimal NN designs for data of various characteristics. We are in particular focusing on Recurrent Neural Networks (RNN) models with time progression, which takes into consideration the results from the previous steps plus the current input to predict the next step, i.e. it ‘remembers’ what it has previously learnt. Through the experiments of tuning an RNN to achieve an optimal performance in terms of accuracy and training time, we found that characteristics such as word counts and layers of neurons could affect the training performance. We applied the tuned optimal model to a game implementation, inspired by IBM Watson, where users can guess the words to be generated by a computer, called “Beat AI” to have human predict the machine prediction.
This paper highlights the explicit description about concept of ontology which is concerned with ... more This paper highlights the explicit description about concept of ontology which is concerned with the development and methodology involved in building ontology. The concept of ontologies has contributed to the development of Semantic Web where Semantic Web is an extension of the current World Wide Web in which information is given in a well-defined meaning that translates the given unstructured data into knowledgeable representation data thus enabling computers and people to work in cooperation. Thus, we can say that Semantic Web is information in machine understandable form. It is also called as Global Information Mesh (GIM). Semantic Web technology can be used to deal with challenges including traditional search engines and retrieval techniques within given organizations or for e-commerce applications whose initial focus is on professional users. Ontology represents information in a manner so that this information can also be used by machines not only for displaying, but also for automating, integrating, and reusing the same information across various applications which may include Artificial Intelligence, Information Retrieval (IR) and many more. Ontology is defined as a collection of set of concepts, their definitions and the relationships among them represented in a hierarchical manner that is termed as Taxonomy. There are various tools available for developing ontologies like Hozo, DOML, and AltovaSemantic Works etc. We have used proté gé which is one of the most widely used ontology development editor that defines ontology concepts (classes), properties, taxonomies, various restrictions and class instances. It also supports several ontology representation languages, including OWL. There are various versions of proté gé available like WebProtege 2.0 beta, Proté gé 3.4.8, Proté gé 4.1 etc. In this paper, we have illustrated ontology development using proté gé 3.1 by giving an example of Computer Science Department of University System. It may be useful for future researchers in making ontology on proté gé version 3.1.
This paper presents an innovative testing framework, testFAILS, designed for the rigorous evaluat... more This paper presents an innovative testing framework, testFAILS, designed for the rigorous evaluation of AI Linguistic Systems, with a particular emphasis on various iterations of ChatGPT. Leveraging orthogonal array coverage, this framework provides a robust mechanism for assessing AI systems, addressing the critical question, "How should we evaluate AI?" While the Turing test has traditionally been the benchmark for AI evaluation, we argue that current publicly available chatbots, despite their rapid advancements, have yet to meet this standard. However, the pace of progress suggests that achieving Turing test-level performance may be imminent. In the interim, the need for effective AI evaluation and testing methodologies remains paramount. Our research, which is ongoing, has already validated several versions of ChatGPT, and we are currently conducting comprehensive testing on the latest models, including ChatGPT-4, Bard and Bing Bot, and the LLaMA model. The testFAILS f...
2016 IEEE 2nd International Conference on Big Data Security on Cloud (BigDataSecurity), IEEE International Conference on High Performance and Smart Computing (HPSC), and IEEE International Conference on Intelligent Data and Security (IDS), 2016
This paper presents a new approach to predictive data analytics, called Radius of Neighbors (RN),... more This paper presents a new approach to predictive data analytics, called Radius of Neighbors (RN), and its mobile application, a multilingual RN-Chatter, devoted to improve communication among people, speaking different languages. RN is a modeless method of unsupervised machine learning, what makes it a fairly simple but effective way of analyzing big amounts of data while keeping acceptable speed of execution and taking up little run-time space. In the first preparatory stage of our research, we discovered that RN gives better results than well-known K-Nearest Neighbors (KNN) in some cases. We then extended our research to simulating the adjustments of floating radiuses, various volumes of the training data sets and ups and downs of the dimensions to tune RN for its optimum accuracy. We took experimental approach of not only extending the number of dimensions, but, instead, shrinking and modifying them in order to keep the predicted value' neighbors close by.
We study a dynamic allocation problem that arises in various scenarios where mobile clients joini... more We study a dynamic allocation problem that arises in various scenarios where mobile clients joining and leaving the system have to communicate with static stations via radio transmissions. Restrictions are a maximum delay, or laxity, between consecutive client transmissions and a maximum bandwidth that a station can share among its clients. We study the problem of assigning clients to stations so that every client transmits to some station, satisfying those restrictions. We consider reallocation algorithms, where clients are revealed at its arrival time, the departure time is unknown until they leave, and clients may be reallocated to another station, but at a cost proportional to the reciprocal of the client’s laxity. We present negative results for previous related protocols that motivate the study; we introduce new protocols that expound trade-offs between station usage and reallocation cost; we determine experimentally a classification of the clients attempting to balance those ...
Proceedings of the 53rd ACM Technical Symposium on Computer Science Education
Within computer science education, a growth mindset is encouraged. However, faculty development o... more Within computer science education, a growth mindset is encouraged. However, faculty development on the use of growth mindset in the classroom is rare and resources to support the use of a growth mindset are limited. A framework for a computer science growth mindset classroom, which includes faculty development, lesson plans, and vocabulary for use with students, has been developed. The objective is to determine if faculty development in growth mindset and active use of the growth mindset cues in the CS0 and CS1 classroom result in superior academic outcomes. Comparative study results are presented for two semesters of virtual classroom environments: one semester without Growth Mindset, and one semester with Growth Mindset. Female students demonstrated the most growth, as measured by academic grades, in CS0, and maintained that growth in CS1. Males demonstrated growth as well, with both males and females converging at the same high point of accomplishment at the end of CS1. Race and ethnicity gaps between students were reduced, improving academic equity.
Proceedings of the 52nd ACM Technical Symposium on Computer Science Education, 2021
A growth mindset encourages the development of intelligence, in contrast to a fixed mindset, whic... more A growth mindset encourages the development of intelligence, in contrast to a fixed mindset, which considers intelligence to be fixed and unable to be changed. Within computer science education, there is an awareness of a growth mindset, but resources to support the use of a growth mindset in the computer science classroom are limited, and faculty development in the use of growth mindset in the classroom is rare. Researchers have developed a framework for a computer science growth mindset classroom, which includes faculty development, lesson plans, and vocabulary for use with students. In addition, the faculty have formed a community of practice which identifies areas where growth mindset techniques can be used and has implemented these strategies in their classrooms. The framework and materials developed are presented here, with early results from this ongoing work. The objective is to determine if faculty development on growth mindset and active use of the framework for a growth mindset classroom results in superior academic outcomes in CS0 and CS1. Preliminary results are for faculty in both face-to-face and virtual classroom environments.
The 2022 International Conference on Computational Science and Computational Intelligence (CSCI), 2022
Transformers with neural networks (NN) have become a dominant technology in AI/ML to achieve bett... more Transformers with neural networks (NN) have become a dominant technology in AI/ML to achieve better accuracy in classification and prediction. We experimentally compare transformer-based NN and convolutional NN using various applications such as pollen detection and weather ITCZ prediction. Using machine learning in geoscience, earth sciences, and other natural sciences is not entirely new, but applying transformers to data from these environments creates many new opportunities. We apply Facebook's detection transformer (DETR) neural network, developed in 2020, to pollen and weather data to detect forty-four types of pollen and classify earth snapshots into two categories: having vs. not having the phenomena of double Intertropical Convergence Zones (ITCZs) in them. As we conduct our trials, we observe and document how the model performs on both datasets, detect the biases present in each layer of the network, and mitigate them as we tune the model and improve classification results even further.
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