Collaborating Authors

Modeling Privacy Requirements for Quality Manipulation of Information on Social Networking Sites

AAAI Conferences

The volume and diversity of information shared and exchanged within and across social networking sites is increasing. As a result new and challenging requirements are needed for quality manipulation of the information. An important requirement is information usability with privacy dimensions.\deleted{ Despite this situation}Existing social networking sites do not provide adequate functionalities to fulfill privacy requirements of information use. This is largely due to the lack of a privacy-by-design approach that conducts an effective privacy requirements analysis as a means to develop suitable models for social networking that protect privacy. To bridge this gap, this paper analyses and models privacy requirements for a recommendation service in social networking sites.

Is Data Scraping one of the most demanded skills by companies?


The Internet generates millions of useful data every day. All of this data is recorded and stored, making the Internet an easily accessible hub that hosts an overwhelming volume of data, generated at immense speed with every passing moment. This data can be extracted to study recurring patterns and trends to assist in the deduction of useful insights and predictions. When a large amount of information is aggregated in an organized manner, it can be used to help a company drive its business decisions. Of course, there is too much data online to do this manually and efficiently.

DeepPurpose: a Deep Learning Based Drug Repurposing Toolkit Machine Learning

With a few lines of code, DeepPurpose generates drug candidates based on aggregating five pretrained state-of-the-art models while offering flexibility for users to train their own models with 15 drug/target encodings and 50 novel architectures. We demonstrated DeepPurpose using case studies, including repurposing for COVID-19 where promising candidates under trials are ranked high in our results. Drug repurposing is about investigating existing drugs for new therapeutic purposes which can potentially speed up drug development 1 . With a large number of existing drugs, it is important to quickly and accurately identify promising candidates for new indications. Especially in facing COVID-19 pandemic today, drug repurposing become particularly relevant as a potentially much faster way to discover effective and safe drugs for treating COVID-19. Deep learning has recently demonstrated its superior performance than classic methods to assist computational drug discovery 2, 3, thanks to its expressive power in extracting, processing and extrapolating patterns in molecular data.

Exploring Mixed-Initiative Interaction for Learning with Situated Instruction in Cognitive Agents

AAAI Conferences

Human-agent interaction for learning with instruction can would involve pointing the tank in at the enemy tank be viewed on a continuum of instructor/agent control. The environment is partially observable to the instructor or imitation. The other extreme of the continuum is and the task is unknown to the agent, necessitating mixed occupied by systems where instructor interaction is limited initiative, bidirectional information transfer. Our agents are instantiated in Soar (Laird, 2008), a To be able to maintain the state of interactions with the symbolic, cognitive architecture based on the problemspace instructor while acting in the environment, and to be able to hypothesis. A Soar agent's current state is derived learn from these instructions in the context they were from its perceptions, its beliefs about the world and provided in, the agent needs a model of task-oriented knowledge in its long-term memories and is held in its interaction.

The eight types of AI you should know about


Artificial intelligence (AI) is a broadly-used term, akin to the word manufacturing, which can cover the production of cars, cupcakes or computers. Its use as a blanket term disguises how important it is to be clear about AI's purpose. Purpose impacts the choice of technology, how it is measured and the ethics of its application. At its root, AI is based on different meta-level purposes. As Bernard Marr comments in Forbes, there is a need to distinguish between "the ability to replicate or imitate human thought" that has driven much AI to more recent models which "use human reasoning as a model but not an end goal".