Malicious User Experience Design Research for Cybersecurity

This paper explores the factors and theory behind the user-centered research that is necessary to create a successful game-like prototype, and user experience, for malicious users in a cybersecurity context. We explore what is known about successful addictive design in the fields of video games and gambling to understand the allure of breaking into a system, and the joy of thwarting the security to reach a goal or a reward of data. Based on the malicious user research, game user research, and using the GameFlow framework, we propose a novel malicious user experience design approac

Photoshop (CS6) Intelligent Tutoring System

In this paper, we designed and developed an intelligent tutoring system for teaching Photoshop. We designed the lessons, examples, and questions in a way to teach and evaluate student understanding of the material. Through the feedback provided by this tool, you can assess the student's understanding of the material, where there is a minimum overshoot questions stages, and if the student does not pass the level of questions he is asked to return the lesson and read it again. Eventually this administration is a special teacher for the students and can continue with him until he fully understands the lesson without weariness or boredom, regardless of the level of student

AI for the Common Good?! Pitfalls, challenges, and Ethics Pen-Testing

Recently, many AI researchers and practitioners have embarked on research visions that involve doing AI for "Good". This is part of a general drive towards infusing AI research and practice with ethical thinking. One frequent theme in current ethical guidelines is the requirement that AI be good for all, or: contribute to the Common Good. But what is the Common Good, and is it enough to want to be good? Via four lead questions, I will illustrate challenges and pitfalls when determining, from an AI point of view, what the Common Good is and how it can be enhanced by AI. The questions are: What is the problem / What is a problem?, Who defines the problem?, What is the role of knowledge?, and What are important side effects and dynamics? The illustration will use an example from the domain of "AI for Social Good", more specifically "Data Science for Social Good". Even if the importance of these questions may be known at an abstract level, they do not get asked sufficiently in practice, as shown by an exploratory study of 99 contributions to recent conferences in the field. Turning these challenges and pitfalls into a positive recommendation, as a conclusion I will draw on another characteristic of computer-science thinking and practice to make these impediments visible and attenuate them: "attacks" as a method for improving design. This results in the proposal of ethics pen-testing as a method for helping AI designs to better contribute to the Common Good.Comment: to appear in Paladyn. Journal of Behavioral Robotics; accepted on 27-10-201

The Prescription Opioid Epidemic: an Evidence-Based Approach

A group of experts, led by researchers at the Johns Hopkins Bloomberg School of Public Health, issued this report aimed at stemming the prescription opioid epidemic, a crisis that kills an average of 44 people a day in the U.S. The report calls for changes to the way medical students and physicians are trained, prescriptions are dispensed and monitored, first responders are equipped to treat overdoses, and those with addiction are identified and treated. The report grew out of discussions that began last year at a town hall co-hosted by the Bloomberg School and the Clinton Health Matters Initiative, an initiative of the Clinton Foundation. The recommendations were developed by professionals from medicine, pharmacy, injury prevention and law. Patient representatives, insurers and drug manufacturers also participated in developing the recommendations. The report breaks its recommendations into seven categories:Prescribing GuidelinesPrescription Drug Monitoring Programs (PDMPs)Pharmacy Benefit Managers (PBMs) and PharmaciesEngineering Strategies (i.e., packaging)Overdose Education and Naloxone Distribution ProgramsAddiction TreatmentCommunity-Based Prevention Strategie

Spartan Daily, November 8, 2017

Volume 149, Issue 33https://scholarworks.sjsu.edu/spartan_daily_2017/1074/thumbnail.jp

Acquiring and Using Limited User Models in NLG

It is a truism of NLG that good knowledge of the reader can improve the quality of generated texts, and many NLG systems have been developed that exploit detailed user models when generating texts. Unfortunately, it is very difficult in practice to obtain detailed information about users. In this paper we describe our experiences in acquiring and using limited user models for NLG in four different systems, each of which took a different approach to this issue. One general conclusion is that it is useful if imperfect user models are understandable to users or domain experts, and indeed perhaps can be directly edited by them; this agrees with recent thinking about user models in other applications such as intelligent tutoring systems (Kay, 2001)

Comparison of smoothing filters in analysis of EEG data for the medical diagnostics purposes

This paper covers a brief review of both the advantages and disadvantages of the implementation of various smoothing filters in the analysis of electroencephalography (EEG) data for the purpose of potential medical diagnostics. The EEG data are very prone to the occurrence of various internal and external artifacts and signal distortions. In this paper, three types of smoothing filters were compared: smooth filter, median filter and Savitzky-Golay filter. The authors of this paper compared those filters and proved their usefulness, as they made the analyzed data more legible for diagnostic purposes. The obtained results were promising, however, the studies on finding perfect filtering methods are still in progress.Web of Science203art. no. 80