Human Action Performance Using Deep Neuro-Fuzzy Recurrent Attention Model

Bendre, Nihar; Ebadi, Nima; Prevost, John J.; Rad, Paul

doi:10.1109/access.2020.2982364

Cited by 28 publications

(16 citation statements)

References 63 publications

(100 reference statements)

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“…Recent advancements in artificial intelligence (AI) have enabled real time human action performance [11], facial behavioral analysis [12], speech analysis [13], speech disfluency detection [14], stereotypical motor movement from sensory data [15], many more. Published research from the last 5 years shows the use of a wide variety of sensory inputs to predict human behavior, diseases, and cognitive states using AI methods, especially deep learning (DL).…”

Section: B Artificially Intelligent Methods In Behavioral Healthmentioning

confidence: 99%

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AI-Augmented Behavior Analysis for Children With Developmental Disabilities: Building Toward Precision Treatment

Ghafghazi

Carnett

Neely

et al. 2021

IEEE Syst. Man Cybern. Mag.

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Autism spectrum disorder is a developmental disorder characterized by significant social, communication, and behavioral challenges. Individuals diagnosed with autism, intellectual, and developmental disabilities (AUIDD) typically require long-term care and targeted treatment and teaching. Effective treatment of AUIDD relies on efficient and careful behavioral observations done by trained applied behavioral analysts (ABAs). However, this process overburdens ABAs by requiring the clinicians to collect and analyze data, identify the problem behaviors, conduct pattern analysis to categorize and predict categorical outcomes, hypothesize responsiveness to treatments, and detect the effects of treatment plans. Successful integration of digital technologies into clinical decision-making pipelines and the advancements in automated decision-making using Artificial Intelligence (AI) algorithms highlights the importance of augmenting teaching and treatments using novel algorithms and high-fidelity sensors. In this article, we present an AI-Augmented Learning and Applied Behavior Analytics (AI-ABA) platform to provide personalized treatment and learning plans to AUIDD individuals. By defining systematic experiments along with automated data collection and analysis, AI-ABA can promote self-regulative behavior using reinforcement-based augmented or virtual reality and other mobile platforms. Thus, AI-ABA could assist clinicians to focus on making precise data-driven decisions and increase the quality of individualized interventions for individuals with AUIDD.

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Section: B Artificially Intelligent Methods In Behavioral Healthmentioning

confidence: 99%

“…Problem behavior such as biting, hitting, kicking could be tracked using body tracking sensors and algorithms [11]. Vocal aggression such as screaming, shouting, use of foul language, etc.…”

Section: Problem Behaviormentioning

confidence: 99%

AI-Augmented Behavior Analysis for Children With Developmental Disabilities: Building Toward Precision Treatment

Ghafghazi

Carnett

Neely

et al. 2021

IEEE Syst. Man Cybern. Mag.

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“…The performance metrics for BERT present significant improvement over three other based lines (TD-IDF + {SVM, Logistic regression, Naïve Bayes}) [42] that are reported in Table 2. Due to the class imbalance, F1-micro and F1-Macro are better metrics to compare the models [43]. BERT model achieved F1-micro and F1-Macro of (0.9599, 0.9889), (0.9830, 0.9930) and (0.9433, 0.9723) respectively for privacy, security and convenience classes.…”

Section: Data Processingmentioning

confidence: 99%

Separating privacy and security in online decision-making process: The case of Venmo

Zanella¹,

Jozani²,

Pour³

2022

Proceedings of the Annual Hawaii International Conference on System Sciences

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The rise of peer-to-peer online financial services that attract users with social media features warrants a sharper distinction between security and privacy. While past research on online financial services focuses on the security of the transactions, the literature on online social media emphasizes the risks for the individual's privacy. Unfortunately, the two concepts are often considered as overlapping or, in some cases, as two dimensions of the same concept, thus making complex the study of the distinct roles of security and privacy in the decision-making process. We analyze the activity of 13, 338 accounts on Venmo to explore the different roles of the two concepts in the decision to disclose financial transactions on online platforms. The results show that security concerns cause the users to opt-out of any public feeds, while users address their privacy concerns by limiting the amount of information disclosed. The findings and their impact are discussed.

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“…The performance of these models is measured against the capacity of these networks to correctly predict the answer with proper reasoning thus eliminating the traditional black-box approach. In recent times, there has been a considerable increase in the number of datasets available to train models for VQA [3] like the DAQUAR dataset [18], COCOQA dataset [20], [21], CLEVR dataset [15], VQA and VQA2.0 datasets [22], [23], etc. The DAQUAR dataset has relatively few samples of indoor scenes thus restricting its usage towards a generic VQA model.…”

Section: A Visual Question Answeringmentioning

confidence: 99%

Show Why the Answer is Correct! Towards Explainable AI using Compositional Temporal Attention

Bendre¹,

Desai²,

Najafirad

2021

2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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Visual Question Answering (VQA) models have achieved significant success in recent times. Despite the success of VQA models, they are mostly black-box models providing no reasoning about the predicted answer, thus raising questions for their applicability in safety-critical such as autonomous systems and cyber-security. Current state of the art fail to better complex questions and thus are unable to exploit compositionality. To minimize the black-box effect of these models and also to make them better exploit compositionality, we propose a Dynamic Neural Network (DMN), which can understand a particular question and then dynamically assemble various relatively shallow deep learning modules from a pool of modules to form a network. We incorporate compositional temporal attention to these deep learning based modules to increase compositionality exploitation. This results in achieving better understanding of complex questions and also provides reasoning as to why the module predicts a particular answer. Experimental analysis on the two benchmark datasets, VQA2.0 and CLEVR, depicts that our model outperforms the previous approaches for Visual Question Answering task as well as provides better reasoning, thus making it reliable for mission critical applications like safety and security.

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Human Action Performance Using Deep Neuro-Fuzzy Recurrent Attention Model

Cited by 28 publications

References 63 publications

AI-Augmented Behavior Analysis for Children With Developmental Disabilities: Building Toward Precision Treatment

AI-Augmented Behavior Analysis for Children With Developmental Disabilities: Building Toward Precision Treatment

Separating privacy and security in online decision-making process: The case of Venmo

Show Why the Answer is Correct! Towards Explainable AI using Compositional Temporal Attention

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