Handling user natural language instructions and implementing system requests for specific tasks is crucial for large language models (LLMs). However, users often struggle to provide complete and accurate instructions, leading to potential challenges in task execution. To address this issue, we propose a Multi-turn Diverse Synthetic Dialog Generation (MDSD) method aimed at generating large-scale, diverse dialogue datasets spanning various task types, request modes, and guiding logics. By leveraging these datasets, we train an LLM to effectively translate natural language requests into precise system inputs, even in scenarios where users provide incomplete or ambiguous information. To further enhance user interaction, we design an agent-based workflow that actively guides users towards formulating clear and precise requests through a dynamic, chat-style interface. This approach not only improves the model's ability to comprehend and fulfill user intentions but also empowers users to iteratively refine their instructions. Evaluation across different test scenarios demonstrates the effectiveness of the MDSD method, achieving a system request success rate exceeding 98%. These results underscore the potential of our approach in improving the accuracy and reliability of LLMs for real-world applications.

Object tracking based on Mean Shift (MS) algorithm has been very successful and thus receives significant research interests. Unfortunately, traditional MS based tracking only utilizes the gradient of the similarity function (SF), neglecting completely higher-order information of SF. The paper regards MS based tracking as an optimization problem, and proposes to make use of both the Gradient and Hessian of SF. Specifically, we introduce Newton algorithm with constant, unit step and Newton with varying step lengths, and Trust region algorithm. The advantage of exploiting higher-order information is that higher convergence rate and better performance are achieved. Diverse experiments are made to compare traditional MS based tracking with the proposed algorithms, showing that the proposed algorithms have better performance at comparable computational cost.

A sustainable market-like computational grid has two characteristics: it must allow resource providers and resource consumers to make autonomous scheduling decisions, and both parties of providers and consumers must have sufficient incentives to stay and play in the market. In this paper, we formulate this intuition of optimizing incentives for both parties as a dual-objective scheduling problem. The two objectives identified are to maximize the success rate of job execution and to minimize fairness deviation among resources. The challenge is to develop a grid scheduling scheme that enables individual participants to make autonomous decisions while producing a desirable emergent property in the grid system; that is, the two systemwide objectives are achieved simultaneously. We present an incentive-based scheduling scheme, which utilizes a peer-to-peer decentralized scheduling framework, a set of local heuristic algorithms, and three market instruments of job announcement, price, and competition degree. The performance of this scheme is evaluated via extensive simulation using synthetic and real workloads. The results show that our approach outperforms other scheduling schemes in optimizing incentives for both consumers and providers, leading to highly successful job execution and fair profit allocation.

Abstract With the rapid development of high‐speed wide‐area networks and powerful yet low‐cost computational resources, Grid computing has emerged as an attractive computing paradigm. In typical Grid environments, there are two distinct parties, resource consumers and resource providers. Enabling an effective interaction between the two parties (i.e. scheduling jobs of consumers across the resources of providers) is particularly challenging due to the distributed ownership of Grid resources. In this paper, we propose an incentive‐based peer‐to‐peer (P2P) scheduling for Grid computing, with the goal of building a practical and robust computational economy. The goal is realized by building a computational market supporting fair and healthy competition among consumers and providers. Each participant in the market competes actively and behaves independently for its own benefit. A market is said to be healthy if every player in the market gets sufficient incentive for joining the market. To build the healthy computational market, we propose the P2P scheduling infrastructure, which takes the advantages of P2P networks to efficiently support the scheduling. The proposed incentive‐based algorithms are designed for consumers and providers, respectively, to ensure every participant gets sufficient incentive. Simulation results show that our approach is successful in building a healthy and scalable computational economy. Copyright © 2006 John Wiley & Sons, Ltd.