updated alttext

Author: Utkarshdevd

_pages/about.md

@@ -21,4 +21,4 @@ I am a Ph.D. student at the School of Information Studies, Univerisity of Maryla
 
 Previously, I was a game designer and software engineer at StudyPad (Now SplashLearn) in 2017, designing and developing games for learning math. And before that, I was a Research Scientist at IBM Research Labs, New Delhi, India. Primarily, I worked on the intersection of HCI and ML algorithms. I designed and developed machine learning-based technologies for eye-tracking and collaborated with Sesame street for early childhood learning use cases.
 
-You can download his <a href="/pdfs/Utkarsh_Dwivedi_CV_2021.pdf" download>CV</a> here.
+You can download my <a href="/pdfs/Utkarsh_Dwivedi_CV_2021.pdf" download>CV</a> here.

_pages/publications.md

@@ -20,7 +20,7 @@ nav: true
     {% endif %} -->
       {% if project.img %}
       <div class="col-sm-3">
-          <img class="img-fluid" src="{{ project.img | relative_url }}" alt="project thumbnail">
+          <img class="img-fluid" src="{{ project.img | relative_url }}" alt="{{project.alttext}}">
       </div>
       {% endif %}
       <div class="col-sm-9">

_publications/8_project.markdown

@@ -7,6 +7,7 @@ importance: 2
 pdf: https://www.researchgate.net/publication/348844784_Data_Sharing_in_Wellness_Accessibility_and_Aging
 type: Poster
 abstract: Datasets sourced from people with disabilities and older adults play an important role in innovation, benchmarking, and mitigating bias for both assistive and inclusive AI-infused applications. However, they are scarce. We conduct a systematic review of 137 accessibility datasets manually located across different disciplines over the last 35 years. Our analysis highlights how researchers navigate tensions between benefits and risks in data collection and sharing. We uncover patterns in data collection purpose, terminology, sample size, data types, and data sharing practices across communities of focus. We conclude by critically reflecting on challenges and opportunities related to locating and sharing accessibility datasets calling for technical, legal, and institutional privacy frameworks that are more attuned to concerns from these communities.
+alttext: Image 
 ---
 
 Every project has a beautiful feature showcase page.

_publications/9_project.markdown

@@ -7,6 +7,7 @@ importance: 5
 type: Full Paper
 pdf: https://files.eric.ed.gov/fulltext/ED593200.pdf
 abstract: As education gets increasingly digitized, and intelligent tutoring systems gain commercial prominence, scalable assessment generation mechanisms become a critical requirement for enabling increased learning outcomes. Assessments provide a way to measure learners' level of understanding and difficulty, and personalize their learning. There have been separate efforts in di erent areas to solve this by looking at different parts of the problem. This paper is a  rst effort to bring together techniques from diverse areas such as knowledge representation and reasoning, machine learning, inference on graphs, and pedagogy to generate automated assessments at scale. In this paper, we speci cally address the problem of Multiple Choice Question (MCQ) generation for vocabulary learning assessments, specially catered to young learners (YL). We evaluate the e cacy of our approach by asking human annotators to annotate the questions generated by the system based on relevance. We also compare our approach with one baseline model and report high usability of MCQs generated by our system compared to the baseline.
+alttext: A flowchart describing the database of words that is used to predict the next best word to be taught to a child
 ---
 
 Every project has a beautiful feature showcase page.

_publications/assets21.md

@@ -7,6 +7,7 @@ importance: -1
 type: Full Paper
 pdf: https://arxiv.org/abs/2108.10665
 abstract: Iteratively building and testing machine learning models can help children develop creativity, flexibility, and comfort with machine learning and artificial intelligence. We explore how children use machine teaching interfaces with a team of 14 children (aged 7-13 years) and adult co-designers.Children trained image classifiers and tested each other's models for robustness. Our study illuminates how children reason about ML concepts, offering these insights for designing machine teaching experiences for children - (i) ML metrics (\eg confidence scores should be visible for experimentation; (ii) ML activities should enable children to exchange models for promoting reflection and pattern recognition; and (iii) the interface should allow quick data inspection (\eg images vs. gestures).
+alttext: A drawing showing the different datasets that were collected. Following are the depictions - Eye-tracking for children, typing patterns, hand gesture recognition, fingerspelling detector using images of hands, dysartic speech recognition where an adult is speaking to a mobile app, and sensors on feet of Blind people for tracing their path.
 ---
 
 Every project has a beautiful feature showcase page.

_publications/vlhcc21.md

@@ -7,6 +7,7 @@ abstract: Iteratively building and testing machine learning models can help chil
 importance: -1
 type: Full Paper
 pdf: https://arxiv.org/abs/2109.11434
+alttext: An image where children are demonstrating their trained models to a class. A child holds an origami while an adult holds the laptop and another adult is taking notes on a whiteboard.
 ---
 
 Every project has a beautiful feature showcase page.