With the rapid expansion of smart technologies, people using devices such as smartphones and tablets are becoming ubiquitous. This growth has also expanded communication methods, such as social media platforms, that can be utilized for seeking support. However, parents of autistic children often receive inadequate support from existing technology. In this paper, we report findings from a qualitative study with 14 parents of autistic children from five different countries. We analyzed these parents’ experience with receiving and utilizing social support, whether they consider received support adequate, challenges faced, and the types of support these parents seek. Our findings reveal a complex dynamic in the way parents of autistic children seek social support. For instance, the higher stress levels experienced by these parents motivate them to seek support from their families first. However, a lack of autism awareness among family members often leads them to seek support elsewhere, especially from online autism groups. Interestingly, unhealthy competition among parents of autistic children in many online autism groups led some parents to discontinue interacting in these groups and work independently. We provide guidelines for designing new interventions and offer guidelines to redesign existing technologies that may better align with the needs of parents of autistic children.

This research examines the use of advanced Natural Language Processing (NLP) technology, specifically GPT-3.5/4 models, to enhance text-based learning for neurodiverse students within higher education. Recognizing that conventional pedagogical resources often fail to meet the distinct needs of learners with neurodevelopmental differences, our research explores the hypothesis that NLP-enabled personalization of academic content can facilitate effective, equitable, and engaging educational experiences for college students. We present AImpathizer, a tool designed to adapt academic content into representations more congruent with the cognitive styles of neurodiverse students. AImpathizer is designed using a user-centric approach, highlighting the challenges and needs of neurodiverse college students. AImpathizer aims to incorporate more accessibility modifications in comparison to traditional academic content. The outcomes of this research aim to provide empirical evidence supporting the integration of NLP tools into educational environments in synergy with Universal Design for Learning (UDL) framework, paving the way for more inclusive educational practices.

Artificial Intelligence (AI) is quickly becoming an integral part of everyday life. AI is no longer limited to advanced applications; it is embedded in applications and tools we use daily to streamline tasks and solve problems. Large Language Models (LLMs), such as ChatGPT and Claude, are designed to help users make decisions, produce recommendations, and provide more personalized solutions to user inquiries. The immense reach these systems have had in such a short period has significantly impacted education, work, and interpersonal interactions. AI systems are only becoming more common, and it is imperative that developers work to address bias within them to promote fair use for all, not only the neurotypical. Research investigating the bias harbored by LLM platforms is particularly limited when it comes to addressing bias against individuals with autism. We applied inductive thematic analysis, followed by deductive thematic analysis, to analyze 34 scientific research papers gathered from two major academic databases: ACM Digital Library and IEEE Xplore. This method helped us learn more about the current empirical understanding of LLM bias as it relates to the context of autism. We found that bias within LLMs has the potential to harm individuals with autism who are looking for answers to problems for which LLMs have been postulated as a solution. LLMs also have the potential to reinforce negative stereotypes, leading users with autism who already face bias in daily life to continually struggle when faced with this new technological frontier.

With the rapid growth and availability of smart technology, the idea of households using smart technology (smartphones, computers) is becoming popular. This availability of smart technology inside homes can potentially help with critical problems such as understanding the emotions of people with autism. Despite their high potential in solving complex problems, such technological solutions are largely unexplored. We take a first step in this direction by conducting a systematic literature review using three major databases: Google Scholar, ACM Digital Library, and IEEE Xplore. Our findings indicate that most of the existing approaches only utilize facial expressions for model development and collect data from children with autism, indicating a need for utilizing technology to capture more than facial expressions, capture contextual and other available information, and collect data from a diverse group of individuals with autism as it will improve the effectiveness of the machine learning models.

In recent years an increasing number of autistic students enrolled in college, many choosing computer science (CS) and related majors. However, the retention and graduation rates for autistic students are considerably lower than for neurotypical students. Research investigating factors that influence autistic students' success in college is sparse. More importantly, there is a scarcity of research that examines assessment techniques used and factors that influence the success of autistic CS students. Here, we report findings based on a systematic literature review$(\mathrm{N}=44)$that aims to examine the experience of autistic CS students to identify factors that influence their success given current assessment techniques. Our findings indicate that traditional accommodations, which in general are difficult to access and lack personalization, fall short of addressing many of the challenges autistic students face. The absence of a common vocabulary and communication style (e.g., verbal, instructions, teaching materials) and differences in learning style and cognitive processing between autistic students and neurotypical instructors hinders autistic students' academic success. Executive functioning (EF) challenges impact both academic and social aspects. However, accommodations targeted at addressing challenges related to EF are overlooked. Finally, despite many autistic students' affinity for technological interventions, their incorporation in accommodation is almost non-existent. We propose a set of actionable guidelines that could aid many of the challenges autistic students experience in CS programs, including designing a curriculum inspired by universal design, designing personalized accommodations, and incorporating technological interventions as part of the accommodation process.

Music Therapy (MT) has been well established as beneficial for people with Autism Spectrum Condition (ASC), commonly known as Autism Spectrum Disorder (ASD), by improving attention, motor synchrony, and prosocial behaviors. It has been shown to be increasing in popularity as an intervention choice for ASC due to its relative accessibility. Recent research has also shown promising results with integrating technology into traditional music therapy interventions to broaden and improve therapeutic modalities. In this paper, we conduct a systematic literature review on the relationship between MT and ASC, as well as current MT practices and use this to inform a set of design recommendations on technology designed for autistic people. This design framework will guide developers and therapists working to incorporate technology into a therapeutic setting with a strength-based (SB) approach that centers participants as stakeholders. Our review indicates that much of the current research focuses on the medical treatment of autistic behaviors. The research generally supports the efficacy of MT for autism and identifies it as a particular strength of the autistic community. However, it also highlights concerns regarding common outcome measures and standardization. We then put forth three design implications, supported by various findings, to further promote accessible MT for autistic people across the spectrum.

For many students, attending college is a dramatic but necessary change. To gain a better understanding of experiences that are unique to autistic college students, we conducted a mixed-method study with 20 students (10 autistic and 10 neurotypical). We collected physiological, contextual, experience, and environmental data from their natural environment using Fitbit and smartphones. We found that stress patterns, emotional states, and physical states are similar for both groups. Our autistic participants prioritized academic success over everything else, often intentionally confining their movements among academic, resident, and work locations to engage themselves with academic work as much as possible. They had a small number of friends, always preferring quality over quantity and sometimes regarding friends as close as family members. To maintain a better social life, they extensively used social media. They slept more than neurotypical participants per day; however, they experienced lower sleep quality.

As one of the most important and stressful steps towards gaining employment, job interviews are a social barrier that poses a unique challenge for individuals with Autism Spectrum Disorder (ASD). There are existing tools and in-person training available that show promise. However, these solutions are limited in the degree to which they can collect performance data, provide specific feedback, offer options to intelligently customize training, and simulate a variety of interviews in a realistic manner. InterViewR is a mixed reality job interview training simulator that addresses these shortcomings to reduce barriers in entering the workforce. The integration of Virtual Reality and wearable smart technology enables users to practice customizable interview simulations and receive both real-time and retrospective feedback. Findings are reported from a cognitive walkthrough (N=33) that highlights areas that need to be considered when designing such interview training platforms for individuals with autism.

Autistic college students face significant challenges in college settings and have a higher dropout rate than neurotypical college students. High physiological distress, depression, and anxiety are identified as critical challenges that contribute to this less than optimal college experience. In this paper, we leverage affordable mobile and wearable devices to collect large amounts of physiological and contextual data (biomarkers) and leverage a data-driven analysis approach for building stress prediction models. Such models can be used to provide real-time intervention for better stress management. We conducted a mixed-method study where we collected physiological and contextual data from 20 college students (10 neurotypical and 10 autistic). Our proposed data-driven analysis pipeline leverages an unsupervised representation learning technique with a semi-supervised label approximation method to predict the onset of stress based on biomarkers for autistic students, neurotypical students, and both populations with accuracies 69%, 72%, and 70%, respectively.

College students with autism have an estimated retention rate of 38.8% in the USA, which is attributed to challenges resulting from mental and behavioral problems, lack of adequate support from colleges, and a lack of awareness of their unique needs. We propose visualizations guided by the self-determination theory to help understand the experiences and needs of college students with autism and to empower them to manage their behavior. The visualizations are created based on physiological and contextual data collected from 20 college students (10 with autism, 10 neurotypical) using Fitbit and smartphone, and are incorporated in a dashboard to offer seamless access. We evaluated the effectiveness of these visualizations by conducting a lab study (N=18) and learned that such visualizations can highlight experiences of students with autism, can enhance awareness about individuals’ behaviors and habits, and can help to identify areas that can potentially improve the college experience.

This paper aims to understand the educational settings of children with autism (CWA) in the specialized schools of Bangladesh from their parents' perspective. Sparse research in the same context to understand teachers' perceptions has been found. Though parents and teachers are an integral part of these children's educational experience, limited research has been conducted to deeply understand and facilitate parent-teacher relationships to offer CWA an improved learning experience in Bangladesh. Our in-depth qualitative study on urban parents (N=10) showed current challenges and opportunities relating to the schools designed for CWA. The work takes a closer look at the existing concerns and confirms that parent-teacher communication in autism schools here is a complex one because of other interferences (e.g., social, and familial stigma, lack of transparency from administrators, insufficient qualified teachers) as felt by the parents. Nevertheless, the remarkable coverage of cellular networks and increasing usage of mobile phones in Bangladesh places us in a unique position to expect positive changes by employing ICT, which we believe will open avenues for future researchers and guide them in implementing robust technology to support an improved learning environment for the CWA and their families.

Job interviews are uniquely challenging for individuals with Autism Spectrum Disorder. While digital interview training tools have shown promising results for improving vocational outcomes for individuals with Autism, existing solutions are largely limited in the degree to which they can simulate a realistic interview environment, collect performance data from the user, and provide actionable feedback for continued improvement. To address these shortcomings and understand how to create an effective training tool, we designed InterViewR, a simulation-based interview training system that combines virtual reality and wearable smart technology in an integrated platform. Our design emphasizes an immersive user experience for training effectiveness and utilizes physiological sensing to provide intelligent affective biofeedback. We report findings from a usability study (N=11) where participants evaluated InterViewR on feasibility, usability, and perceived usefulness.

Tobacco addiction is one of the most challenging behavioral health problems with successful cessation rates remaining in single digits. With increased availability of commercially available mobile and wearable devices we have the opportunity to infer lapse vulnerability and intervene in near real-time. In this work we present findings from a mixed-method study where we collected around 398.2 hours of physiological data from regular smokers in the field (N is 5) using commercial wearable devices along with contextual data using EMAs. We applied quantitative and qualitative analysis techniques to identify key factors contributing to smoking lapse and developed a statistical model capable of inferring lapse vulnerability from physiological and contextual signals collected from the natural environment. Our methodology and findings show promise in designing practical, near real-time smoking intervention systems that can be used by regular smokers in their everyday living situation.

While sociocommunicative behaviors of the autistic population are frequently pathologized, the researchers find evidence supporting strength-based (SB) approaches which utilize the natural talents, strengths, interests and communication styles of individuals with autism, resulting in higher degrees of well-being. Information and Communication Technologies (ICTs) founded in SB approaches are visually designed, simple to use, and are also complex in functionality. Because of the heterogeneity of individuals with autism, personalization and customization are key features for applications to be accessible to a wide variety of user-experiences and needs. By using natural autistic communication-orientation and design preferences, it is possible that learned skills will become more generalizable and that other outcome types will be encouraged. As a key feature of SB approaches, ICT development must incorporate and collaborate with the autistic community these technologies seek to support.

Individuals with Autism Spectrum Disorder (ASD) experience difficulty in social interaction resulting in a lack of close social connections. Online social networking sites show promise in enabling individuals with ASD to make connections as the media of communication is often void of complexities present in real-life situations (e.g., real-time interpretation of complex social and emotional cues). We conducted a systematic content analysis of the Autism and Asperger's subreddits to understand user types, interaction patterns and trends, and user's experience, expectations, and unmet needs pertaining to these online communities. Our analysis revealed several interesting findings including the presence of 15 different topic areas, the tone and preference of the community, the difference in communication style between individuals with ASD and other related members (e.g., parents, therapists). Our analysis signified a need for an accessible and inclusive social networking site design that will cater to the specific needs of this community.

Smart technologies (wearable and mobile devices) show tremendous potential in the detection, diagnosis, and management of Autism Spectrum Disorder (ASD) by enabling continuous real-time data collection, identifying effective treatment strategies, and supporting intervention design and delivery. Though promising, effective utilization of smart technology in aiding ASD is still limited. We propose a set of implications to guide the design of ASD-support technology by analyzing 149 peer-reviewed articles focused on children with autism from ACM Digital Library, IEEE Xplore, and PubMed. Our analysis reveals that technology should facilitate real-time detection and identification of points-of-interest, adapt its behavior driven by the real-time affective state of the user, utilize familiar and unfamiliar features depending on user-context, and aid in revealing even minuscule progress made by children with autism. Our findings indicate that such technology should strive to blend-in with everyday objects. Moreover, gradual exposure and desensitization may facilitate successful adaptation of novel technology.

We present the design, implementation, and evaluation of Bangladesh Emergency Services (BES), a mobile application that provides 911-like services in Bangladesh. The involvement of A2I, a government agency, helped in the nationwide deployment of BES. To date, 120,382 unique users downloaded BES and 27,117 users are actively using it. Analysis of user ratings (N=3,788) and reviews (N=1,231) from Google Play Store, an online survey (N=137), and face-to-face interviews (N=10) revealed that users considered BES effective. Currently, BES is used to report aggressive behavior, street fight, harassment, fire-related incidents, and for locating nearby hospitals for medical emergencies. Our findings highlight the importance of trustworthiness, scalability of service, and iterative and participatory design for successful deployment of such services. We believe our research will inspire the design of mobile-based emergency applications in other developing countries that lack 911-like services.

There has been a dramatic increase in computer science undergraduate research activity at colleges and universities in recent years. However, developing a research culture that is explicitly designed to empower undergraduates (student-centered research) requires different models and objectives than those traditionally employed at more research-oriented universities. The goal of this BOF is to explore what effective techniques are employed by other primarily undergraduate institutions to build a culture of research that benefits undergraduate students. Some of the key issues covered in this BOF will be: defining student-centered research and its impact (How does student-centered research differ from traditional research? What secondary effects in the classroom and community might undergraduate research have?), redefining success metrics in student-centered research (How can we capture impact beyond publications and grants? How can we define measures that align with student impact?), exploring issues of accessibility and participation (How might student-centered research change models of student selection? How might it change faculty's scope and focus of research?), and structural mechanisms to empower student-centered research (Given constraints on time and/or resources, how can faculty enable undergraduate research?). Through this BOF, we also plan to build a sustainable community of interested academics leaders (using private Google+ or Facebook group) interested to share and collaborate on future undergraduate research efforts.

In this paper, we present the design and implementation of a location-aware mobile-based emergency service for Bangladesh, a developing country, which lacks any central 911-like emergency service. Our goal was to investigate the feasibility and acceptability of such services that do not require building any new infrastructure or changing the existing infrastructure available in Bangladesh. To achieve our goal, we iteratively designed and deployed two location-aware mobile-based emergency services in Dhaka, the capital city of Bangladesh. These deployments provided a deep insight about user experience, acceptance, and sustainability issues. We learned that users considered these services effective, felt comfortable using these during emergencies, and expressed a need for integration of additional services. Our findings indicate that it is feasible to design a location-aware mobile-based emergency service in Bangladesh. We believe that our proposed design will help to provide a low-cost alternative to the central 911-services, especially for the developing countries.

Smoking is one of the most challenging behavioral health problems. In the past, failed quit attempts have been attributed to factors including stress, presence of smoking cues, and negative affect – most of which were self-reported and prone to recall-bias. The first step in designing effective smoking cessation systems is to objectively identify factors that contribute to lapse. In our research, we collected physiological data utilizing wearable sensors from a four day pre-quit, post-quit study (N=55). We also collected self-report measures (n=3120), which offer rich contextual information about users' social, emotional, geographical, and physiological conditions. Analysis of collected data informed the design of MyQuitPal, a participant-centric cessation support system, which aims to assist individuals to better understand their smoking behavior. The design of MyQuitPal is also grounded on theories of long term health-behavior change. We believe our research advances understanding of complexities and opportunities surrounding the design of smoking cessation systems.

WiFi localization and tracking of indoor moving objects is an important problem in many contexts of ubiquitous buildings, first responder environments, and others. Previous approaches in WiFi-based indoor localization and tracking either assume prior knowledge of indoor environment or assume many data samples from location-fixed WiFi sources (i.e. anchor points). However, such assumptions are not always true, especially in emergency scenarios. This paper explores the possibility of real-time indoor localization and tracking without any knowledge of indoor environment and with real-time data samples from only few anchor points outside the building. By using a small set of synchronized directional antennas as outdoor anchor points to actively scan in various directions, a moving device inside the building can be localized and tracked from the received signal in real-time manner. The paper proposes an angle-of-arrival estimator for accurate localization and adaptive per-antenna angular scheduling for real-time indoor tracking. The validation results from real experiment and simulation yield effectiveness and accuracy of the proposed schemes.

To address the challenges posed by device capacity and capability, and also the nature of ad-hoc network of pervasive computing, a resource discovery model is needed that can resolve security and privacy issues with simple solutions. The use of complex algorithms and powerful fixed infrastructure is infeasible due to the volatile nature of pervasive environment and tiny pervasive devices. In this paper, we present a risk- aware trust based secure resource discovery model, RTSRD (risk-aware trust based secure resource discovery) for a truly pervasive environment. Our model is an adaptive hybrid one that allows both secure and non-secure discovery of services on adaptive trust. RTSRD also incorporates a risk model for sharing resources with unknown devices. Hence, the two contributions of this paper are: adaptive trust, and risk model for resource discovery in pervasive computing environments.

SSRD is a secure resource discovery model for devices running in a pervasive computing environment. SSRD is based on a lightweight trust model. SSRD+ is an extension of the existing SSRD model. In SSRD+, we enhance the trust model by adding dynamic trust relationship and also specifying behavioral characteristics that determine the level of trust among devices. We also add a risk model to address challenges posed by the pervasive and ad hoc nature of the network. These models work together to make the entire discovery process lightweight and secure. In this paper we present details of the trust and risk models. We illustrate the design and implementation of SSRD+ as a whole that optimally explores resources without degrading the performance of the devices while ensuring user security and privacy

A secure resource discovery model for devices running in pervasive computing environments has become significant. Due to the constrained nature of these devices, the need for a model that allows discovery and sharing of resources without putting much overhead on the operation of devices is needed. The dependency on fixed powerful machines for ensuring security is not desired and also not feasible. In this paper, we present a resource discovery model that provides security whenever needed without degrading the performance of the device. We also propose a trust based service-oriented adaptive security mechanism named SSRD (simple and secure resource discovery)

In this paper, primarily, we delineate the numerous challenges that can arise in mobile-business (m-business) to provide the highest degree of proliferation of pervasive advertisement. Later, we demonstrate the feasibility of PerAd-Service, an integral part of, MARKS (middleware adaptability for knowledge usability, resource discovery, and self-healing) to address those challenges.

Usability of pervasive computing features along with the detection of a meeting is very effective for the users. In this paper, we present the design of a smart meeting room (SMR) that not only detect the starting and ending of a meeting but also provide the support of participant's context information, knowledge usability and ephemeral group communication. Our approach will play a vital role for not only for meeting detection but other closely related applications of pervasive computing.