Current service robots suffer from limited natural language communication abilities, heavy reliance on predefined commands, ongoing human intervention, and, most notably, a lack of proactive collaboration awareness in human-populated environments. This results in narrow applicability and low utility. In this paper, we introduce AssistantX, an LLM-powered proactive assistant designed for autonomous operation in real-world scenarios with high accuracy. AssistantX employs a multi-agent framework consisting of 4 specialized LLM agents, each dedicated to perception, planning, decision-making, and reflective review, facilitating advanced inference capabilities and comprehensive collaboration awareness, much like a human assistant by your side. We built a dataset of 210 real-world tasks to validate AssistantX, which includes instruction content and status information on whether relevant personnel are available. Extensive experiments were conducted in both text-based simulations and a real office environment over the course of a month and a half. Our experiments demonstrate the effectiveness of the proposed framework, showing that AssistantX can reactively respond to user instructions, actively adjust strategies to adapt to contingencies, and proactively seek assistance from humans to ensure successful task completion. More details and videos can be found at https://assistantx-agent.github.io/AssistantX/.

When visiting an online platform, a user generates various actions, such as clicks, long views, likes, comments, etc.To capture user preferences in these aspects, we learn these objectives and return multiple rankings of candidate items for each user.We need to aggregate them into one to truncate the candidate set, and ranking ensemble model is proposed for this task.However, there is a critical issue: though we input abundant information, what model learns depends on the supervision.Unfortunately, the existing supervision is poorly designed, leading to serious information loss issue.To address this issue, we designed an unsupervised loss to compel the ranking ensemble model to learn all information of input rankings, including sequential and numerical information.(1) For sequential information, we design a distance measure between two rankings, and train the ensemble ranking to have similar order with all input rankings by minimizing the distance.(2) For numerical information, we design a decoder to reconstruct values of original rankings from the hidden layer of the model, to guarantee that the model captures as much input information as possible.Our unsupervised loss is compatible with all ranking ensemble models.We optimize several widely-used structures to propose unsupervised ranking ensemble models.We devise comprehensive experiments on two real-world datasets to demonstrate the effectiveness of the proposed models.We also apply our model in a short video platform with billions of users, and achieve significant improvement.

Tiny machine learning (TinyML) has emerged recently for resource constrained Internet of Things (IoT) devices. However, the deployed TinyML model cannot handle outof-distribution (OOD) inputs appropriately. While many high-accuracy OOD detection methods have emerged, they often ignore the limitations of the deployment environment. In this paper, we propose a novel effective out-of-distribution detection method for TinyML (TinyOOD), which exploits cascading early exit and channel-attention-based neural mean discrepancy (CA-NMD) for dynamic and efficient OOD detection on microcontroller units (MCUs). To demonstrate its effectiveness, we extensively evaluate TinyOOD using four public datasets, one as the in-distribution (ID) dataset and the others as the OOD datasets. Experiments demonstrate that TinyOOD significantly reduces the computations by up to 38.23% in inference while maintaining the performance of OOD detection.

Abstract To improve the uniformity of one‐pixel width and continuity of the thinned edges, this paper proposes an edge thinning algorithm acting on grey‐scale edge images based on 24 self‐defined single‐pixel connection patterns. First, for binary or blurred grey‐scale gradient edge images, a distance–greyscale coupling algorithm is proposed to achieve gradient enhancement in the edge width direction. Then the elimination rules of noise points and gradient calculation method are given. Secondly, the marking rules of the first three pixels of each edge are given. The next pixel to be marked must meet that the new last three pixels belong to the 24 connection modes. Whether the qualified pixels are retained depends on its grey value and the local edge gradient. The algorithm is tested on four types of images. The results show that the proposed method can guarantee uniform, smooth, and connected one‐pixel‐wide lines that lie at the centre of the initial edges. The algorithm and the existing algorithms are performed on portrait image and four scenarios of the indoor datasets. Five evaluation indicators are statistically analyzed to prove the feasibility and effectiveness of the proposed algorithm.

Complex systems consist of a large number of entities with their independent local rules and goals, along with their interactions. In the operations of complex system, such as naval ship, proper decisions and controls are required to keep the system working functionally and effectively. An Integrated Reconfigurable Intelligent System (IRIS) framework is proposed for facilitating the design and operation of such naval complex systems through increased automation and reconfigurability. With the reconfigurable systems, the IRIS designed ship will assess the incoming information and then configure itself into the mode most adequate to deal with the situation under consideration. The study in this paper presents a hierarchical control architecture to deal with ever-evolving real time information and making autonomous control for achieving the reconfigurability of naval ship. The control architecture consists of three levels working together to achieve the overall operational goal. It is implemented on a resource allocation problem for a chilled water system. The successful resource allocation leads to a reconfiguration of the system which is the most suitable to handle the situation at hand.