Privacy-Preserving Federated Learning (PPFL) is a decentralized machine learning approach where multiple clients train a model collaboratively. PPFL preserves the privacy and security of a client's data without exchanging it. However, ensuring that data at each client is of high quality and ready for federated learning (FL) is a challenge due to restricted data access. In this paper, we introduce CADRE (Customizable Assurance of Data Readiness) for federated learning (FL), a novel framework that allows users to define custom data readiness (DR) metrics, rules, and remedies tailored to specific FL tasks. CADRE generates comprehensive DR reports based on the user-defined metrics, rules, and remedies to ensure datasets are prepared for FL while preserving privacy. We demonstrate a practical application of CADRE by integrating it into an existing PPFL framework. We conducted experiments across six datasets and addressed seven different DR issues. The results illustrate the versatility and effectiveness of CADRE in ensuring DR across various dimensions, including data quality, privacy, and fairness. This approach enhances the performance and reliability of FL models as well as utilizes valuable resources.

NASA's Cooperative Autonomous Distributed Robotic Exploration (CADRE) mission, slated for flight to the Moon's Reiner Gamma region in 2025/2026, is designed to demonstrate multi-agent autonomous exploration of the Lunar surface and sub-surface. A team of three robots and a base station will autonomously explore a region near the lander, collecting the data required for 3D reconstruction of the surface with no human input; and then autonomously perform distributed sensing with multi-static ground penetrating radars (GPR), driving in formation while performing coordinated radar soundings to create a map of the subsurface. At the core of CADRE's software architecture is a novel autonomous, distributed planning, scheduling, and execution (PS&E) system. The system coordinates the robots' activities, planning and executing tasks that require multiple robots' participation while ensuring that each individual robot's thermal and power resources stay within prescribed bounds, and respecting ground-prescribed sleep-wake cycles. The system uses a centralized-planning, distributed-execution paradigm, and a leader election mechanism ensures robustness to failures of individual agents. In this paper, we describe the architecture of CADRE's PS&E system; discuss its design rationale; and report on verification and validation (V&V) testing of the system on CADRE's hardware in preparation for deployment on the Moon.

Simulation is an indispensable tool in the development and testing of autonomous vehicles (AVs), offering an efficient and safe alternative to road testing by allowing the exploration of a wide range of scenarios. Despite its advantages, a significant challenge within simulation-based testing is the generation of safety-critical scenarios, which are essential to ensure that AVs can handle rare but potentially fatal situations. This paper addresses this challenge by introducing a novel generative framework, CaDRE, which is specifically designed for generating diverse and controllable safety-critical scenarios using real-world trajectories. Our approach optimizes for both the quality and diversity of scenarios by employing a unique formulation and algorithm that integrates real-world data, domain knowledge, and black-box optimization techniques. We validate the effectiveness of our framework through extensive testing in three representative types of traffic scenarios. The results demonstrate superior performance in generating diverse and high-quality scenarios with greater sample efficiency than existing reinforcement learning and sampling-based methods.

Artificial Intelligence (AI) is the new black, the shiny new object, the answer to every marketer's prayers, and the end of creativity. The recent emergence of AI from the arcane halls of academia and the backrooms of data science has been prompted by stories of drones, robots and driverless cars undertaken by tech giants like Amazon. But the hype exceeds the day-to-day reality. AI has a fifty-year history of mathematical and computer science development, experimentation and thought. What makes it exciting is the confluence of large data sets, improved platforms and software, faster and more robust processing capabilities and a growing cadre of data scientists eager to exploit a wider range of applications.

Vision-based autonomous urban driving in dense traffic is quite challenging due to the complicated urban environment and the dynamics of the driving behaviors. Widely-applied methods either heavily rely on hand-crafted rules or learn from limited human experience, which makes them hard to generalize to rare but critical scenarios. In this paper, we present a novel CAscade Deep REinforcement learning framework, CADRE, to achieve model-free vision-based autonomous urban driving. In CADRE, to derive representative latent features from raw observations, we first offline train a Co-attention Perception Module (CoPM) that leverages the co-attention mechanism to learn the inter-relationships between the visual and control information from a pre-collected driving dataset. Cascaded by the frozen CoPM, we then present an efficient distributed proximal policy optimization framework to online learn the driving policy under the guidance of particularly designed reward functions. We perform a comprehensive empirical study with the CARLA NoCrash benchmark as well as specific obstacle avoidance scenarios in autonomous urban driving tasks. The experimental results well justify the effectiveness of CADRE and its superiority over the state-of-the-art by a wide margin.

NASA is exploring a concept for a new fleet of mini-rovers that can work together to solve problems and make decisions as a unit. If one fails or gets stuck somewhere, the others could carry on without it. As part of the Cooperative Autonomous Distributed Robotic Exploration (CADRE) project, NASA engineers are designing compact, mobile robots the size of a shoebox (for comparison, Perseverance is the size of a small SUV) to autonomously explore the moon and other planets. These rovers will operate as a group to collect data in hard-to-reach places like craters and caves. In a demonstration mission expected in the next few years, CADRE's mini-rovers will explore the moon's massive lava tubes--areas where the top layer of soil has solidified, but lava still flows beneath.

Crowdsourcing is defined as the outsourcing of tasks to a crowd of contributors. The crowd is very diverse on these platforms and includes malicious contributors attracted by the remuneration of tasks and not conscientiously performing them. It is essential to identify these contributors in order to avoid considering their responses. As not all contributors have the same aptitude for a task, it seems appropriate to give weight to their answers according to their qualifications. This paper, published at the ICTAI 2019 conference, proposes a method, MONITOR, for estimating the profile of the contributor and aggregating the responses using belief function theory.

Artificial Intelligence (AI) could be the black, " the glistening brand new thing, the response to each marketer's insecurities. The latest development of AI in the Mine halls of academia along with the back rooms of info science was motivated by reports of drones, robots along with driver-less cars under-taken by technology giants such as Amazon. However, the hoopla exceeds the daily actuality. AI features a fifty-year record of computer and mathematical science enhancement, thought and experimentation. Why is it enjoyable would be that the confluence of high data collections, advanced programs and applications, more rapidly and stronger processing capacities and an increasing cadre of information boffins ready to exploit a larger variety of software. The prosaic daily applications of artificial intelligence and machine understanding is likely to produce a bigger variation in the lifestyles of brands and consumers in relation to the brassy software touted from the media. We're creating linking and data huge data collections at exemplary prices, that can be multiplying annually. Meanwhile, the development of cellular networking, societal support systems, programs, automatic private urges, wearables, digital clinical documents, self-reporting appliances and cars and also the coming Net of Matters (IOT) build competitive chances and struggles. Generally in the majority of instances, there's long and consideration job to align, interrogate, filling and join compacted data before any diagnosis may be initiated. Gathering, preserving, filtering and linking these pieces and bytes to some individual is catchy and much more sensitive. Compiling a so called"Golden report" necessitates contemplating computing ability, a solid stage, fuzzy-logic or profound understanding how to connect disparate parts of information and also proper privacy protections. Additionally, it takes believe talent in modeling and also a cadre of information boffins effective of visiting that the woods in contrast to the timber. One tone Continues to Be Inspirational. The fantasy of one-way personalized communicating is really on the horizon however nevertheless invisibly. Even the gating facets would be the should come up with common protocols such as individuality resolution, and solitude protections, and an comprehension of human sensitivities along with permissions, the identification of inflection factors and also a in depth story line of individual users and sections proceed through space and time into their travel from desire certainly to new taste. Utilizing AI," we're in a early test and learn phase directed by organizations from the financial services, retail and telecom businesses.

The crowdsourcing consists in the externalisation of tasks to a crowd of people remunerated to execute this ones. The crowd, usually diversified, can include users without qualification and/or motivation for the tasks. In this paper we will introduce a new method of user expertise modelization in the crowdsourcing platforms based on the theory of belief functions in order to identify serious and qualificated users.

We consider the problem in regression analysis of identifying subpopulations that exhibit different patterns of response, where each subpopulation requires a different underlying model. Unlike statistical cohorts, these subpopulations are not known a priori; thus, we refer to them as cadres. When the cadres and their associated models are interpretable, modeling leads to insights about the subpopulations and their associations with the regression target. We introduce a discriminative model that simultaneously learns cadre assignment and target-prediction rules. Sparsity-inducing priors are placed on the model parameters, under which independent feature selection is performed for both the cadre assignment and target-prediction processes. We learn models using adaptive step size stochastic gradient descent, and we assess cadre quality with bootstrapped sample analysis. We present simulated results showing that, when the true clustering rule does not depend on the entire set of features, our method significantly outperforms methods that learn subpopulation-discovery and target-prediction rules separately. In a materials-by-design case study, our model provides state-of-the-art prediction of polymer glass transition temperature. Importantly, the method identifies cadres of polymers that respond differently to structural perturbations, thus providing design insight for targeting or avoiding specific transition temperature ranges. It identifies chemically meaningful cadres, each with interpretable models. Further experimental results show that cadre methods have generalization that is competitive with linear and nonlinear regression models and can identify robust subpopulations.