Physical Sciences and Mathematics Commons^™

Experience Constructing The Artifact Genome Project (Agp): Managing The Domain's Knowledge One Artifact At A Time, Cinthya Grajeda, Laura Sanchez, Ibrahim Baggili, Devon R. Clark, Frank Breitinger

Electrical & Computer Engineering and Computer Science Faculty Publications

While various tools have been created to assist the digital forensics community with acquiring, processing, and organizing evidence and indicating the existence of artifacts, very few attempts have been made to establish a centralized system for archiving artifacts. The Artifact Genome Project (AGP) has aimed to create the largest vetted and freely available digital forensics repository for Curated Forensic Artifacts (CuFAs). This paper details the experience of building, implementing, and maintaining such a system by sharing design decisions, lessons learned, and future work. We also discuss the impact of AGP in both the professional and academic realms of digital forensics. …

Model Ai Assignments 2018, Todd W. Neller, Zack Butler, Nate Derbinsky, Heidi Furey, Fred Martin, Michael Guerzhoy, Ariel Anders, Joshua Eckroth

Computer Science Faculty Publications

The Model AI Assignments session seeks to gather and disseminate the best assignment designs of the Artificial Intelligence (AI) Education community. Recognizing that assignments form the core of student learning experience, we here present abstracts of seven AI assignments from the 2018 session that are easily adoptable, playfully engaging, and flexible for a variety of instructor needs. Assignment specifications and supporting resources may be found at http://modelai.gettysburg.edu.

Ai Education Matters: Teaching Hidden Markov Models, Todd W. Neller

Computer Science Faculty Publications

In this column, we share resources for learning about and teaching Hidden Markov Models (HMMs). HMMs find many important applications in temporal pattern recognition tasks such as speech/handwriting/gesture recognition and robot localization. In such domains, we may have a finite state machine model with known state transition probabilities, state output probabilities, and state outputs, but lack knowledge of the states generating such outputs. HMMs are useful in framing problems where external sequential evidence is used to derive underlying state information (e.g. intended words and gestures). [excerpt]

Ai Education Matters: Lessons From A Kaggle Click-Through Rate Prediction Competition, Todd W. Neller

Computer Science Faculty Publications

In this column, we will look at a particular Kaggle.com click-through rate (CTR) prediction competition, observe what the winning entries teach about this part of the machine learning landscape, and then discuss the valuable opportunities and resources this commends to AI educators and their students. [excerpt]

Smart Cities And Urban Management, Singapore Management University

Research Collection Office of Research

In this booklet, read about SMU’s research and initiatives related to smart cities and urban management, and how we strive to make meaningful impact on business, government and society for Singapore and beyond.

Contents:

Liveability and quality of life

• Community participation through mobile crowdsourcing
• Smarter, healthier eating with Food AI
• Data-driven community eldercare platform for sustainable ageing-in-place
• A date with AI
• Smart mobility accessibility for barrier-free access
• Food security

Optimisation and resource management

• Collaborative urban delivery optimisation
• Seat occupancy detection through capacitance sensing
• Large-scale crowd simulation based on real-world data
• Gaining insights through Wi-Fi technology
• Taxi driver guidance system
• Efficiency …