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2022

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Leveraging Initial Cognitive Load To Predict User Response To Complex Visual Tasks, Reem Albaghli, Yaman Jandali, Sarah Almahmid, Nada Attar Jan 2022

Leveraging Initial Cognitive Load To Predict User Response To Complex Visual Tasks, Reem Albaghli, Yaman Jandali, Sarah Almahmid, Nada Attar

Faculty Research, Scholarly, and Creative Activity

In this study, we were able to show how cognitive load measurement during the initiation of a complex visual task can predict user response. We measured cognitive load using pupil size and microsaccade rate. The initial phase of task was defined as the first 25 percent of the trial Reaction Time (RT), which was variable up to 50 seconds. The complex visual task entailed a set of twelve words that could be grouped based into 1, 2, or 3 categories or sets, e.g., the words bed, pillow, headboard, etc. can be grouped into a single set that is bedroom. We …

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Benchmarking Compositionality With Formal Languages, Josef Valvoda, Naomi Saphra, Jonathan Rawski, Adina Williams, Ryan Cotterell Jan 2022

Benchmarking Compositionality With Formal Languages, Josef Valvoda, Naomi Saphra, Jonathan Rawski, Adina Williams, Ryan Cotterell

Faculty Research, Scholarly, and Creative Activity

Recombining known primitive concepts into larger novel combinations is a quintessentially human cognitive capability. Whether large neural models in NLP can acquire this ability while learning from data is an open question. In this paper, we investigate this problem from the perspective of formal languages. We use deterministic finite-state transducers to make an unbounded number of datasets with controllable properties governing compositionality. By randomly sampling over many transducers, we explore which of their properties contribute to learnability of a compositional relation by a neural network. We find that the models either learn the relations completely or not at all. The …

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An Investigation Of The Relationship Between The Use Of Modern Digital Technologies, Language Learning Strategies, And Development Of Second Language Skills, Volodymyr Lazar Jan 2022

An Investigation Of The Relationship Between The Use Of Modern Digital Technologies, Language Learning Strategies, And Development Of Second Language Skills, Volodymyr Lazar

Theses and Dissertations

Like many other areas of human knowledge, the field of language learning has undergone changes as a consequence of the application of digital technologies. Extensive exposure and anytime and anywhere access availability to data in a second or foreign language (L2) bring almost unlimited learning opportunities for digital age students, which affects their learning behaviors also known as language learning strategies (LLS). The purpose of the present study is to define preferred LLS patterns of digitally native L2 learners and to establish relationships between types of existing digital technologies, learners’ demographic characteristics, and the use of learning strategies to support …

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Ksat: Knowledge-Infused Self Attention Transformer - Integrating Multiple Domain-Specific Contexts, Kaushik Roy, Yuxin Zi, Vignesh Narayanan, Manas Gaur, Amit P. Sheth Jan 2022

Ksat: Knowledge-Infused Self Attention Transformer - Integrating Multiple Domain-Specific Contexts, Kaushik Roy, Yuxin Zi, Vignesh Narayanan, Manas Gaur, Amit P. Sheth

Publications

Domain-specific language understanding requires integrating multiple pieces of relevant contextual information. For example, we see both suicide and depression related behavior (multiple contexts) in the text “I have a gun and feel pretty bad about my life, and it wouldn’t be the worst thing if I didn’t wake up tomorrow”. Domain specificity in self-attention architectures is handled by fine-tuning on excerpts from relevant domain specific resources (datasets and external knowledge - medical textbook chapters on mental health diagnosis related to suicide and depression). We propose a modified self-attention architecture Knowledge infused Self Attention Transformer (KSAT) that achieves the integration of …

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Wise Causal Models: Wisdom Infused Semantics Enhanced Causal Models - A Study In Suicidality Diagnosis, Kaushik Roy, Yuxin Zi, Vignesh Narayanan, Manas Gaur, Sanjay Chandrasekar, Amit Sheth Jan 2022

Wise Causal Models: Wisdom Infused Semantics Enhanced Causal Models - A Study In Suicidality Diagnosis, Kaushik Roy, Yuxin Zi, Vignesh Narayanan, Manas Gaur, Sanjay Chandrasekar, Amit Sheth

Publications

The COVID-19 Pandemic has highlighted the gap between the number of mental health care seekers and care providers. Netizens have taken to internet-based platforms such as Reddit to express their experiences. Mental illness diagnosis processes have clinically accepted causal interpretations and semantics. Curiously, mental illness diagnosis accuracy is low relative to similar well-studied illnesses. Motivated by this discrepancy, we propose Wisdom Infused Semantics Enhanced (WISE) causal models, inspired by the wisdom of the crowd idea that learns from a collective agreement among causal models and their semantics for mental illness diagnoses. We use suicidality diagnosis task descriptions, datasets, and baseline …

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Knowledge-Infused Reinforcement Learning, Kaushik Roy, Manas Gaur, Qi Zhang, Amit Sheth Jan 2022

Knowledge-Infused Reinforcement Learning, Kaushik Roy, Manas Gaur, Qi Zhang, Amit Sheth

Publications

Virtual health agents (VHAs) have received considerable attention, but the early focus has been on collecting data, helping patients follow generic health guidelines, and providing reminders for clinical appointments. While presenting the collected data and frequency of visits to the clinician is useful, further context and personalization are needed for a VHA to interpret and understand what the data means in clinical terms. This has made their use in managing health limited. Such understanding enables patient empowerment and self-appraisal – i.e., aiding the patient in interpreting the data to understand the changes in the patient’s health conditions, and self-management – …

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Application Of Value Stream Mapping In E-Commerce: A Case Study On An Amazon Retailer, Yanfang Qin, Hongrui Liu Jan 2022

Application Of Value Stream Mapping In E-Commerce: A Case Study On An Amazon Retailer, Yanfang Qin, Hongrui Liu

Faculty Research, Scholarly, and Creative Activity

In recent years, the e-commerce market has grown significantly, and the online retail market has become very competitive. Online retailers strive to improve their supply chain operations to reduce costs and to improve customer satisfaction. Value stream mapping (VSM), a tool created by the lean production movement to identify and reduce errors, losses, and lead time and to improve value-added activities, has been proven to be effective in many manufacturing processes. In this study, we investigate the application of value stream mapping (VSM) in the supply chain of an e-commerce retailer on Amazon. By visualizing the entire supply chain with …

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