Recently, large language models (LLMs) and multimodal large language models (MLLMs) have demonstrated promising results on document visual question answering (VQA) task, particularly after training on document instruction datasets. An effective evaluation method for document instruction data is crucial in constructing instruction data with high efficacy, which, in turn, facilitates the training of LLMs and MLLMs for document VQA. However, most existing evaluation methods for instruction data are limited to the textual content of the instructions themselves, thereby hindering the effective assessment of document instruction datasets and constraining their construction. In this paper, we propose ProcTag, a data-oriented method that assesses the efficacy of document instruction data. ProcTag innovatively performs tagging on the execution process of instructions rather than the instruction text itself. By leveraging the diversity and complexity of these tags to assess the efficacy of the given dataset, ProcTag enables selective sampling or filtering of document instructions. Furthermore, DocLayPrompt, a novel semi-structured layout-aware document prompting strategy, is proposed for effectively representing documents. Experiments demonstrate that sampling existing open-sourced and generated document VQA/instruction datasets with ProcTag significantly outperforms current methods for evaluating instruction data. Impressively, with ProcTag-based sampling in the generated document datasets, only 30.5 percent of the document instructions are required to achieve 100 percent efficacy compared to the complete dataset.

As smart mobile applications increasingly demand timely situational awareness and energy efficiency, the Age of Information (AoI) metric plays a vital role in maintaining data freshness. This need is further supported by the End-Edge-Cloud Computing (EECC) paradigm, which enhances application performance by facilitating task offloading to the edge or the cloud. However, existing AoI optimization solutions focus solely on task offloading, often neglecting critical aspects such as system resource allocation and energy efficiency, which can lead to resource waste, increased energy consumption, compromised Quality of Service (QoS), and system performance degradation. Therefore, this paper investigates the joint optimization of task offloading, communication and computing resource allocation in EECC systems, aiming to minimize AoI and energy consumption under constraints of deadlines and capacity constraints. To address this problem, we divide the decision space into multiple non-intersecting decision areas based on the characteristics of the studied problem and design a task offloading and resource allocation algorithm based on slow-movement particle swarm optimization (SPSO) to handle each decision area individually. In the algorithm design, we customize the position, velocity, update rules, and fitness function for the optimization problem. Finally, extensive simulation-based and testbed experiment results show that the proposed algorithm can save up to 14.56% of energy consumption, shorten AoI by up to 27.80%, and improve utility (weighted sum of AoI and energy consumption) by up to 15.89% compared with existing algorithms.

Recently, leveraging large language models (LLMs) or multimodal large language models (MLLMs) for document understanding has been proven very promising. However, previous works that employ LLMs/MLLMs for document understanding have not fully explored and utilized the document layout information, which is vital for precise document understanding. In this paper, we propose LayoutLLM, an LLM/MLLM based method for document understanding. The core of LayoutLLM is a layout instruction tuning strategy, which is specially designed to enhance the comprehension and utilization of document layouts. The proposed layout instruction tuning strategy consists of two components: Layout-aware Pre-training and Layout-aware Supervised Fine-tuning. To capture the characteristics of document layout in Layout-aware Pre-training, three groups of pretraining tasks, corresponding to document-level, region-level and segment-level information, are introduced. Furthermore, a novel module called layout chain-of-thought (LayoutCoT) is devised to enable LayoutLLM to focus on regions relevant to the question and generate accurate answers. LayoutCoT is effective for boosting the performance of document understanding. Meanwhile, it brings a certain degree of interpretability, which could facilitate manual inspection and correction. Experiments on standard benchmarks show that the proposed LayoutLLM significantly outperforms existing methods that adopt open-source 7B LLMs/MLLMs for document understanding.

The market penetration of Infrastructure-as-a-Service (IaaS) in cloud computing is increasing benefiting from its flexibility and scalability. One of the most important issues for IaaS cloud service providers is to minimize the monetary cost while meeting cloud user experience requirements such as makespan and security. Prior works on cloud service cost minimization ignore either security or makespan which is very important for user experience. In this paper, we propose a two-stage algorithm to solve the cloud service cost minimization problem at the premise of satisfying the security and makespan requirements of cloud users. Specifically, in the first stage, we propose a novel security service selection scheme to ensure system security by judiciously selecting security services with low cost for tasks under the constraints of time and security. In the second stage, to further reduce the cloud service cost, we design a workflow scheduling method based on an improved firefly algorithm (IFA). The IFA-based method schedules cloud service workflows to virtual machines of small cost at the premise of guaranteeing security and makespan. It can quickly find the workflow scheduling solution with minimized cost using our designed updating scheme and mapping operator. Extensive simulations are conducted on real-world workflows to verify the efficacy of the proposed two-stage method. Simulation results show that the proposed two-stage method outperforms the baseline and two benchmarking methods in terms of cost minimization without violating security and time constraints. Compared to benchmarking methods, the cloud service cost can be reduced by up to 57.6% by using our proposed approach.

Due to the great advancement in computation, communication, and control technologies, the Internet of Things (IoT) can provide ubiquitous connectivity for anyone and anything at any time and any place, leading to a revolution in an information society. Protecting devices against various security threats is one of the most important challenges in IoT since IoT applications are generally security-critical systems while IoT devices are often poorly secured. For IoT devices, employing security services provided by smart gateways or edge/cloud servers to defend against various threats is an effective way to enhance their security. However, the finite battery energy of devices and the limited fund of device users hinder the wide application of security services in IoT. This necessitates the demand for designing new methodologies to tackle the tradeoff among security, energy, and fund of IoT devices. Therefore, this article attempts to optimize system security of IoT devices under energy and fund constraints. Specifically, to formulate the energy and fund constrained security optimization problem, we first propose a pricing model for the security services provided by the smart gateway. We then formulate the problem as a mixed-integer linear programming (MILP) problem. Since using a solver to address the MILP problem may be time consuming, we leverage the swarm intelligence technique to design a new task scheduling scheme that can efficiently solve the optimization problem. Extensive experiments are conducted to validate our proposed MILP and swarm intelligence-based task scheduling algorithms. Simulation results show that our scheme outperforms two state-of-the-art methods in improving system quality of security and guaranteeing schedule feasibility.