When executed well, project-based learning (PBL) engages students' intrinsic motivation, encourages students to learn far beyond a course's limited curriculum, and prepares students to think critically and maturely about the skills and tools at their disposal. However, educators experience mixed results when using PBL in their classrooms: some students thrive with minimal guidance and others flounder. Early evaluation of project proposals could help educators determine which students need more support, yet evaluating project proposals and student aptitude is time-consuming and difficult to scale. In this work, we design, implement, and conduct an initial user study ( n = 36) for a software system that collects project proposals and aptitude information to support educators in determining whether a student is ready to engage with PBL. We find that (1) users perceived the system as helpful for writing project proposals and identifying tools and technologies to learn more about, (2) educator ratings indicate that users with less technical experience in the project topic tend to write lower-quality project proposals, and (3) GPT-4o's ratings show agreement with educator ratings. While the prospect of using LLMs to rate the quality of students' project proposals is promising, its long-term effectiveness strongly hinges on future efforts at characterizing indicators that reliably predict students' success and motivation to learn.

Finding the perfect match between a job proposal and a set of freelancers is not an easy task to perform at scale, especially in multiple languages. In this paper, we propose a novel neural retriever architecture that tackles this problem in a multilingual setting. Our method encodes project descriptions and freelancer profiles by leveraging pre-trained multilingual language models. The latter are used as backbone for a custom transformer architecture that aims to keep the structure of the profiles and project. This model is trained with a contrastive loss on historical data. Thanks to several experiments, we show that this approach effectively captures skill matching similarity and facilitates efficient matching, outperforming traditional methods.

Despite the continued research and progress in building secure systems, Android applications continue to be ridden with vulnerabilities, necessitating effective detection methods. Current strategies involving static and dynamic analysis tools come with limitations like overwhelming number of false positives and limited scope of analysis which make either difficult to adopt. Over the past years, machine learning based approaches have been extensively explored for vulnerability detection, but its real-world applicability is constrained by data requirements and feature engineering challenges. Large Language Models (LLMs), with their vast parameters, have shown tremendous potential in understanding semnatics in human as well as programming languages. We dive into the efficacy of LLMs for detecting vulnerabilities in the context of Android security. We focus on building an AI-driven workflow to assist developers in identifying and rectifying vulnerabilities. Our experiments show that LLMs outperform our expectations in finding issues within applications correctly flagging insecure apps in 91.67% of cases in the Ghera benchmark. We use inferences from our experiments towards building a robust and actionable vulnerability detection system and demonstrate its effectiveness. Our experiments also shed light on how different various simple configurations can affect the True Positive (TP) and False Positive (FP) rates.

We propose to build a reinforcement learning prover of independent components: a deductive system (an environment), the proof state representation (how an agent sees the environment), and an agent training algorithm. To that purpose, we contribute an additional Vampire-based environment to $\texttt{gym-saturation}$ package of OpenAI Gym environments for saturation provers. We demonstrate a prototype of using $\texttt{gym-saturation}$ together with a popular reinforcement learning framework (Ray $\texttt{RLlib}$). Finally, we discuss our plans for completing this work in progress to a competitive automated theorem prover.

DonorsChoose.org is a United States-based nonprofit organization that allows individuals to donate directly to public school classroom projects. At any given time, there are thousands of classroom requests that can be brought to life with a gift of any amount. Right now, a large number of volunteers is needed to manually screen each submission before it's approved to be posted on the DonorsChoose.org The goal of the competition is to predict whether or not a DonorsChoose.org The competition dataset contains information from teachers' project applications to DonorsChoose.org

All the Data Scientists hide at least on Data Science idea in their heart. However, due to time constraints or lack of money, they do not transform their ideas into a project. In this article, I propose a strategy to turn your idea into a Data Science project. The first step towards funding your project involves writing a first draft your project. The summary is an overview of the project.

CIBO, the science-based technology company that supports growers and enterprises on their journey to regenerative agriculture, is issuing an open invitation to partner with enterprises and organizations interested in submitting a project proposal to the USDA's new Climate-Smart Commodities Partnership Initiative. The initiative, part of the USDA's Climate Smart Agriculture & Forestry Project, includes a $1 billion fund for applicants but requires certain criteria to be met upon application. The CSAF partnership initiative will fund pilot projects that promote and incentivize on-farm conservation practices that sequester carbon or reduce greenhouse gas (GHG) emissions. In order to apply for funding, all applications must include the ability to measure/quantify, monitor, and verify the carbon and GHG benefits associated with those practices, which is where CIBO uniquely supports partnership project proposals. CIBO's proprietary technology platform provides detailed impact quantification, reporting, and verification (MRV) of farming practices using AI-enhanced computer vision from satellite imagery, advanced, mechanistic crop modeling, and scaled cloud infrastructure that combines to deliver in real-time a current carbon footprint, the future carbon impact of practices, and historic and in-season management practices at a field or portfolio level.

Let's start with the one minute version: I was part of the EF12 London cohort in 2019, where I met my co-founder. A privacy-preserving medical-data marketplace and AI platform built around federated deep learning. The purpose of the platform would have been to allow data scientists to train deep learning models on highly sensitive healthcare data without that data ever leaving the hospitals. At the same time, thanks to a novel data monetization strategy and marketplace component, hospitals would have been empowered to make money from the data they are generating. We received pre-seed funding, valued at $1 million. Then the race for demo day began with frantic product building and non-stop business development.

As announced by the International Telecommunication Union (ITU), the United Nations specialised agency for information and communication technology (ICT), its annual AI for Good Global Summit has successfully generated thirty-five innovative project proposals leveraging the power of artificial intelligence (AI) for good. "Leveraging the power of ICTs, including artificial intelligence, is imperative if we are to improve the livelihoods of all people, everywhere, through achievement of the United Nations Sustainable Development Goals," said ITU Secretary-General Mr Houlin Zhao. "This year, we hope to spur action to ensure that artificial intelligence accelerates progress towards the Sustainable Development Goals (SDGs)," Mr Zhao said in his welcoming remarks. "Already, AI solutions are being developed to help increase crop yields, manage natural disasters, reduce road congestion, or diagnose heart, eye, and blood disorders." The summit gathered AI innovators with public and private-sector decision-makers, creating collaboration opportunities to execute the AI for Good project proposals in the near and medium terms.

Like any class project, it must address a topic related to machine learning and you must have started the project while taking this class (can't be something you did last semester). You will need to submit a project proposal with everyone else, and present a poster with everyone. You don't need to submit a milestone or final paper. You must get at least 80% on the poster presentation part of the project. Like any class project, it must address a topic related to machine learning and you must have started the project while taking this class (can't be something you did last semester).