Pedagogical Evaluation of Cognitive Accessibility Learning Lab in the Classroom

In a study conducted by Webaim, 98.1% of sites had a detectable accessibility issue. This poses a profound challenge to the 1 billion users across the world who have a disability. This indicates that developers either are not aware of how to make the sites accessible or aware of how critical it is to make the sites usable by all users. This problem is further compounded by the lack of available resources that can educate students and future developers in making their software accessible. To address current limitations/challenges, we have developed an all-in-one immersive learning experience known as the Accessibility Learning Labs (ALL). These modules are carefully crafted to provide students a better understanding of various accessibility topics and increase awareness. They incorporate the best of all learning methods, from case studies to hands-on activities and quizzes. In this paper, we focus specifically on the cognitive module developed under the Accessibility Learning Labs. This module strives to educate students on the importance of building accessible software for users with cognitive disabilities. We discuss the pedagogical approach used to craft the components of the cognitive module and the design rationale behind the experiential activity. We investigate how the order of the reading and experiential activity affect the students\u27 understanding of the material. To do this, we perform a study involving 28 students in 2 computer science-related courses. Our findings include: (I) The accessibility improvements made in the lab have a positive impact on the students\u27 performance when compared to the inaccessible version (II) When the reading material is presented after the experiential activity, students have a better understanding of the cognitive accessibility principles

Examining the Changing Role of Influencing Factors in the Association between Food Insecurity & Obesity

About 49 million Americans – roughly 15% of entire America - live in households that lack the means to get enough nutritious food on a regular basis. Past experiences and fear of food accessibility could affect the quality of diet and eating behavior in many ways. In this study we examine long-term trends in food insecurity and obesity over a 6-year period. We specifically examine the changing role of health behaviors in the association between food insecurity and obesity. Most studies on this topic have conducted cross-sectional analysis. Examining this association over time would help us make more careful considerations in making policies. Until recently, it was assumed that the only reason for being overweight was excessive eating. Food insecurity could also cause weight gain due to adverse social and physical environments with identifiable risk factors. It is imperative to know that food security and poverty are both forms of material deficit which bring about a range of detrimental results such as excess weight gain. Food insecurity is a continuum of experiences ranging from the most extreme form, starvation, to complete food security. Changes in food security status can be temporary, cyclical, medium or long term

The role of Artificial Intelligence in digital forensics:Case studies and future directions

The increase in digital evidence, especially in cases involving Indecent Images of Children (IIOC), presents a pressing challenge for law enforcement agencies. In this article, we discuss two of the most prominent types of Artificial Intelligence (AI) and how they can be used in digital forensic processes, providing examples, and highlighting potential challenges that are likely to be experienced in developing and adopting AI. The two main types are of Data-Driven Model (DDM) age classification and Model-Based Reasoning (MBR), and in this article, examples for both are provided and discussed in the contents of IIOC investigations

Agent-based modeling of a price information trading business

We describe an agent-based simulation of a fictional (but feasible) information trading business. The Gas Price Information Trader (GPIT) buys information about real-time gas prices in a metropolitan area from drivers and resells the information to drivers who need to refuel their vehicles. Our simulation uses real world geographic data, lifestyle-dependent driving patterns and vehicle models to create an agent-based model of the drivers. We use real world statistics of gas price fluctuation to create scenarios of temporal and spatial distribution of gas prices. The price of the information is determined on a case-by-case basis through a simple negotiation model. The trader and the customers are adapting their negotiation strategies based on their historical profits. We are interested in the general properties of the emerging information market: the amount of realizable profit and its distribution between the trader and customers, the business strategies necessary to keep the market operational (such as promotional deals), the price elasticity of demand and the impact of pricing strategies on the profit.Comment: Extended version of the paper published at Computer and Information Sciences, Proc. of ISCIS-26, 201

Clean Energy at the Crossroads of America: An Integrated Resource Plan for Northern Indiana Public Service Company, LLC (NIPSCO)

This paper outlines an Integrated Resource Plan (IRP) for the Indiana electric utility company, NIPSCO, looking forward to the year 2050 and evaluating different pathways to net zero emissions from the power generation sector. It is a plan for the future, identifying the most cost-effective and reliable mix of resources to meet the energy needs of NIPSCO\u27s customers and reaching decarbonization goals by mid-century

Pre-Screening of Ionic Liquids as Gas Hydrate Inhibitor via Application of COSMO-RS for Methane Hydrate

Ionic liquids (ILs) due to their potential dual functionality to shift hydrate equilibrium curve and retard hydrate nucleation are considered as a very promising gas hydrate inhibitor. However, experimental testing alone is insufficient to examine all potential ILs combinations due to a high number of cation and anion to form ILs. In this context, four fundamental properties of IL-hydrate system, namely, sigma profile, hydrogen bonding energies, activity coefficient, and solubility, were stimulated through conductor-like screening model for real solvent (COSMO-RS). ILs were then analyzed to determine if they can be correlated with IL inhibition ability. Among them, sigma profile and hydrogen bonding energies, which later upgraded to total interaction energies, exhibit a significant relationship with IL inhibition ability. Total interaction energies of ions, on the other hand, have successfully been applied to develop a model. The model can predict the thermodynamic inhibition ability in terms of average temperature depression. The correlation was further validated with experimental values from literature with an average error of 20.49%. Finally, using sigma profile graph and developed correlation, the inhibition ability of 20 ammonium-based ILs (AILs) have been predicted. Tetramethylammonium hydroxide (TMA-OH), due to its short alkyl chain length cation and highly electronegative anion, has shown the most promising inhibition ability among the considered system

Towards Automated Vulnerability Assessment

Vulnerability assessment (VA) is a well established method for determining security weaknesses within a system. The VA process is heavily reliant on expert knowledge, something that is attributed to being in short supply. This paper explores the possibility of automating VA and demonstrates an initial proof-of-concept involving decision-making skills comparable with a human-expert. This is achieved through encoding a domain model to represent expert-like capabilities, and then using model-based VA planning to determine VA tasks. Although security evaluation is a complex task, through the help of such models, we can determine the ways to find potential vulnerabilities without an expert present. This technique allows time constrained assessments, where a 'risk factor' is also encoded to represent the significance of each security flaw. The ultimate goal of this work-in-progress is to realistically simulate a human vulnerability auditor. This paper demonstrates the first step towards that goal; a systematic transformation of the VA knowledge into a PDDL representation, accommodating a broad range of time constrained investigative actions. The output plan and its analysis evidently evinces many potential benefits such as increased feasibility and productivity