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To Patch or Not to Patch: Motivations, Challenges, and Implications for Cybersecurity

Nurse, Jason R. C.

arXiv.org Artificial Intelligence

As technology has become more embedded into our society, the security of modern-day systems is paramount. One topic which is constantly under discussion is that of patching, or more specifically, the installation of updates that remediate security vulnerabilities in software or hardware systems. This continued deliberation is motivated by complexities involved with patching; in particular, the various incentives and disincentives for organizations and their cybersecurity teams when deciding whether to patch. In this paper, we take a fresh look at the question of patching and critically explore why organizations and IT/security teams choose to patch or decide against it (either explicitly or due to inaction). We tackle this question by aggregating and synthesizing prominent research and industry literature on the incentives and disincentives for patching, specifically considering the human aspects in the context of these motives. Through this research, this study identifies key motivators such as organizational needs, the IT/security team's relationship with vendors, and legal and regulatory requirements placed on the business and its staff. There are also numerous significant reasons discovered for why the decision is taken not to patch, including limited resources (e.g., person-power), challenges with manual patch management tasks, human error, bad patches, unreliable patch management tools, and the perception that related vulnerabilities would not be exploited. These disincentives, in combination with the motivators above, highlight the difficult balance that organizations and their security teams need to maintain on a daily basis. Finally, we conclude by discussing implications of these findings and important future considerations.


Text2BIM: Generating Building Models Using a Large Language Model-based Multi-Agent Framework

Du, Changyu, Esser, Sebastian, Nousias, Stavros, Borrmann, André

arXiv.org Artificial Intelligence

The conventional BIM authoring process typically requires designers to master complex and tedious modeling commands in order to materialize their design intentions within BIM authoring tools. This additional cognitive burden complicates the design process and hinders the adoption of BIM and model-based design in the AEC (Architecture, Engineering, and Construction) industry. To facilitate the expression of design intentions more intuitively, we propose Text2BIM, an LLM-based multi-agent framework that can generate 3D building models from natural language instructions. This framework orchestrates multiple LLM agents to collaborate and reason, transforming textual user input into imperative code that invokes the BIM authoring tool's APIs, thereby generating editable BIM models with internal layouts, external envelopes, and semantic information directly in the software. Furthermore, a rule-based model checker is introduced into the agentic workflow, utilizing predefined domain knowledge to guide the LLM agents in resolving issues within the generated models and iteratively improving model quality. Extensive experiments were conducted to compare and analyze the performance of three different LLMs under the proposed framework. The evaluation results demonstrate that our approach can effectively generate high-quality, structurally rational building models that are aligned with the abstract concepts specified by user input. Finally, an interactive software prototype was developed to integrate the framework into the BIM authoring software Vectorworks, showcasing the potential of modeling by chatting.


Product Owner, Data Strategy at NBCUniversal - Englewood Cliffs, New Jersey, United States

#artificialintelligence

NBCUniversal owns and operates over 20 different businesses across 30 countries including a valuable portfolio of news and entertainment television networks, a premier motion picture company, significant television production operations, a leading television stations group, world-renowned theme parks and a premium ad-supported streaming service. Here you can be your authentic self. As a company uniquely positioned to educate, entertain and empower through our platforms, Comcast NBCUniversal stands for including everyone. We strive to foster a diverse and inclusive culture where our employees feel supported, embraced and heard. We believe that our workforce should represent the communities we live in, so that together, we can continue to create and deliver content that reflects the current and ever-changing face of the world.


Agile Scrum Master Training : Case Studies And Confessions

#artificialintelligence

Includes Narration from Randal Shaffer. Agile scrum is a simple method for managing and completing even the most complex project, even in difficult situations . Based on my experience, it is the number one most popular way to deliver projects on-time while maintaining a high degree of quality. Who should take is course? Whether you are acrum Master, Project Manager, Product Owner or Team Member or simply someone who wants the answer to the question "how do I deal with difficult/challenging situations using scrum", this is definitely the class is for you.


Product Owner & Team lead - Data Ops (NO or DK) at Nets - Stovner, Norway

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We are a part of Nexi Group - The European PayTech. Handling billions of transactions annually, Nets, is among the solid payment processors in Europe. We keep a tight focus on making it even easier and more intuitive for our customers to handle digital payments and related services. This has made us a trusted partner to more than 700,000 merchant outlets, including 140,000 online merchant outlets, more than 260,000 enterprises and over 250 banks across Europe. Changing the future of payments takes great personalities At Nets, you'll develop in a fast-growing tech company in a high-paced, high-impact market.


Product Owner, Data Engineering at Lucid Motors - Beaverton, OR

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Find open roles in Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), Computer Vision (CV), Data Engineering, Data Analytics, Big Data, and Data Science in general, filtered by job title or popular skill, toolset and products used.


Product Owner - BI Analytics - Remote

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Inspectorio is a cloud-based SaaS solution focused on creating a dynamic and risk-assessment based Quality and Compliance program with the goal of generating more sustainable and transparent supply chains. Our network is a one-stop-shop platform where all key stakeholders in the production process can connect to execute, monitor, and report on Quality and Compliance activities. Our products provide digitization, automation, transparency, and traceability, with a strong focus on advanced analytics & Machine Learning. This enables us to leverage customer data for predictive insights and dynamic risk-based interventions. Founded in 2016, Inspectorio set out to revolutionize the supply chain industry.


Remote Drupal openings in Austin, United States on August 18, 2022 – Web Development Tech Jobs

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Role requiring'No experience data provided' months of experience in None My name is Avdhesh Kumar and I am a Staffing Specialist at Intellectt Inc. I am reaching out to you on an exciting job opportunity with one of our clients.


Analytics Translators: Fact or Fiction? - DataScienceCentral.com

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It's been two years since Mckinsey invented the term analytics translator, called it the'new must-have role' and predicted we'd need around 5 million of them. For the past ten years, we've struggled with the ambiguous title'data scientist', then'citizen data scientist'. Although I've seen many'data scientists' change their Linkedin titles to'analytics translator', the problem remains that no one knows what'analytics translator' really means. Mckinsey seems to have slipped this term into a Harvard Business Review article, and it has somehow taken root. What's more, people seem truly excited by the term.


Building a solid data team - KDnuggets

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With more digital data entering the world every day, the first jobs were the ones of data scientists. Today there are so much data that not only Artificial Intelligence is growing, but it is also getting smarter each day. When building data-driven products, you need a data science team. Therefore, you will need data scientists, data engineers, and product owners, to name a few. Every role has its own focus, and they are all equally important.