Although language model (LM) agents are demonstrating growing potential in many domains, their success in cybersecurity has been limited due to simplistic design and the lack of fundamental features for this domain. We present EnIGMA, an LM agent for autonomously solving Capture The Flag (CTF) challenges. EnIGMA introduces new Agent-Computer Interfaces (ACIs) to improve the success rate on CTF challenges. We establish the novel Interactive Agent Tool concept, which enables LM agents to run interactive command-line utilities essential for these challenges. Empirical analysis of EnIGMA on over 350 CTF challenges from three different benchmarks indicates that providing a robust set of new tools with demonstration of their usage helps the LM solve complex problems and achieves state-of-the-art results on the NYU CTF and Intercode-CTF benchmarks. Finally, we discuss insights on ACI design and agent behavior on cybersecurity tasks that highlight the need to adapt real-world tools for LM agents.

A general overview of current CP courses and instructional methods is presented, with a focus on online and virtually-delivered courses. This is followed by a discussion of the novel approach taken to introductory CP education for engineering students at large scale at the Georgia Institute of Technology (Georgia Tech) in Atlanta, GA, USA. The paper summarizes important takeaways from the Georgia Tech CP course and ends with a discussion on the future of CP education. Some ideas for instructional methods, promotional methods, and organizational changes are proposed to aid in the long-term growth of CP education.

Question answering systems are recognized as popular and frequently effective means of information seeking on the web. In such systems, information seekers can receive a concise response to their query by presenting their questions in natural language. Interactive question answering is a recently proposed and increasingly popular solution that resides at the intersection of question answering and dialogue systems. On the one hand, the user can ask questions in normal language and locate the actual response to her inquiry; on the other hand, the system can prolong the question-answering session into a dialogue if there are multiple probable replies, very few, or ambiguities in the initial request. By permitting the user to ask more questions, interactive question answering enables users to dynamically interact with the system and receive more precise results. This survey offers a detailed overview of the interactive question-answering methods that are prevalent in current literature. It begins by explaining the foundational principles of question-answering systems, hence defining new notations and taxonomies to combine all identified works inside a unified framework. The reviewed published work on interactive question-answering systems is then presented and examined in terms of its proposed methodology, evaluation approaches, and dataset/application domain. We also describe trends surrounding specific tasks and issues raised by the community, so shedding light on the future interests of scholars. Our work is further supported by a GitHub page with a synthesis of all the major topics covered in this literature study. https://sisinflab.github.io/interactive-question-answering-systems-survey/

Programs are read more often than they are written. Models are used more often than they are trained. The PyTorch, and the deep-learning ecosystem in general, abounds with tools for training models, and squeezing the best performance out of computational resources in doing this. In the life cycle of a model this is only the beginning of the journey. Once a model has been trained, it will be shared, and used in a multitude of contexts, often on a daily basis, in operations, evaluation, comparision and experimentation by data scientists.

Running your Python scripts is an important step in the development process, because it is in this manner that you'll get to find out if your code works as you intended it to. It is, also, often the case that we would need to pass information to the Python script for it to function. In this tutorial, you will discover various ways of running and passing information to a Python script. Running and Passing Information to a Python Script Photo by Andrea Leopardi, some rights reserved. The command-line interface is used extensively for running Python code.

The function was held recently at India Habitat Centre, New Delhi. SuperStartUps Asia Awards are selected through a rigorous 3-tier process. The award gives the start up an exposure to investors and helps boost the profile of the business among various stakeholders, vendors, employees, regulatory authorities and prospective customers. The award not only benefits the business but also helps the owners to have a better understanding of the trends and industry through brainstorming and interactive sessions. Mr Kiran Kalakuntla, founder and CEO of ekincare said, "ekincare is pleased to receive the SuperStartUps Asia 2019 award. This is an achievement of the ekincare team who supported a vision that I had. Not only for the team, it is also an assurance for our clients that they have trusted the right platform for their employee's wellness. For us, this award is a motivation to perform better and match up to the expectations of our clients. The insights by the industry experts during the interactive sessions were educative and we are looking forward to achieve more with their help."