ABSTRACT Nowadays, with the growing penetration of renewable generation, economic dispatch is increasingly important in short‐term power system operation. In this paper, a deep renewable scenario generation model combining Multi‐Scale Decomposition mixer and Wasserstein Generative Adversarial Network with Gradient Penalty is proposed to achieve novel decision‐oriented forecasting, thus realizing effective characterization of renewable temporal dynamics and economic performance. From the perspective of wind and solar generation, the validity of the proposed method is demonstrated on a real‐world dataset with power station at regional level. Experimental results confirm the superiority of model performance through statistical indicators and power system scheduling test, compared with a number of scenario generation and time series forecasting benchmarks.

Role-based collaboration (RBC) theory is a promising paradigm for problem-solving in complex systems. Multigroup role assignment (MGRA) specifically tackles the task of assigning roles for multigroup collaboration. However, due to the constraint that an agent can only play a role in one group, the current MGRA models are incapable of handling when required agents outnumber the available supply. Group multirole assignment (GMRA) resolves the problem by permitting an agent to be assigned multiple roles, but it cannot address the assignment involving multiple environments-classes, agents, roles, groups, objects (E-CARGO) groups. Therefore, this article presents a comprehensive overview of the GMRA problem in multiple E-CARGO groups under various conditions, generalized as the multigroup multirole assignment (MGMRA) problem. The MGMRA problem primarily revolves around two key factors: the maximum number of roles that an agent can undertake within an E-CARGO group, and the maximum number of different roles across all E-CARGO groups, which have a significant impact on the sufficiency or necessity conditions of the algorithm as well as its performance. Therefore, a unified model and its special cases are proposed to solve the concrete assignment problems under different conditions. The effectiveness of models is verified through comprehensive experiments.

Role-based collaboration (RBC) is an emerging and advanced methodology for problem-solving. A critical aspect of RBC theory is agent evaluation, which aims to assess agents’ abilities through a qualification value derived from a comprehensive analysis of their characteristics. This evaluation directly impacts the quality of role assignments. Existing research typically assumes that the qualification value is either predetermined, based on multiscale criteria, or following a predefined distribution. These assumptions, however, are overly idealistic and difficult to generalize, failing to capture the inherent volatility of the qualification value. To address this challenge, this article introduces a Wasserstein-based ambiguity set to model potential fluctuations in the qualification value, drawing on empirical distributions derived from historical sample data. Building upon the RBC framework and its abstract model environments, classes, agents, roles, groups, and objects (E-CARGO), we propose two data-driven models: distributionally robust group role assignment (DRGRA) and group multirole assignment (DRGMRA). These models aim to achieve more robust and optimal role assignments under uncertainty in agent evaluation. Leveraging strong duality, we reformulate DRGRA and DRGMRA as tractable finite mixed 0–1 convex problems, providing an approximation framework that reduces computational complexity. Notably, these models are adaptable to other problems with no uncertainty in agent evaluation, highlighting their modeling scalability. Experimental results demonstrate the effectiveness and robustness of the proposed models.

Based on role-based collaboration (RBC), group role assignment (GRA) optimizes a team’s overall performance by assigning the most appropriate individual agents from the team’s viewpoint based on agents’ role-playing abilities. As an extension of GRA, GRA with a training plan (GRATP) deals with the impact of training on team management. Considering the correlation between roles, the training of one agent on one role also affects the performance of the agent in other roles. Moreover, in the adaptive collaboration (AC) problem, the training time also affects significantly the agent’s ability, as an agent’s ability changes over time. However, the existing GRATP models fail to consider these factors in the collaboration process. Therefore, we aim to address the role-correlation-based adaptive GRATP (RCA-GRATP) in this article. This article contributes two aspects to the literature on AC. 1) RCA-GRATP problem is abstracted based on RBC and GRA. To the best of the authors’ knowledge, this is the first article that explicitly considers role correlation in the RBC problems. 2) A comprehensive formalization of RCA-GRATP and two solving algorithms for diverse situations are proposed to solve the formalized problems. Experiments are carried out to verify the effectiveness of the proposed algorithms in diverse scenarios.

Role-based collaboration (RBC) is a new problem-solving paradigm that uses model environments-classes, agents, roles, groups, and objects (E-CARGO) to facilitate modeling. Task decomposition is widely adopted to reduce the difficulty of execution, resulting in multigroup collaboration problems. Multigroup role assignment (MGRA) has been proposed to solve the assignment of multiple groups. In many actual scenarios, there are dependencies between the decomposed subtasks, which is neglected by existing MGRA methods. Moreover, they disregard the fact that subtasks have diverse significance in the development of a project, which is of paramount importance to ensure the proper allocation of resources. To solve the complicated problem, the structured subtasks are formalized based on the emerging and promising RBC theory and E-CARGO model. Then, the assignment is abstracted into a complicated single-objective multiconstraint problem, named adaptive-equalized MGRA (AE-MGRA). In the formulated AE-MGRA problem, to improve the utilization of limited resources, the performance of each E-CARGO group needs to be equalized according to the corresponding subtask’s weight. As the optimal solution is difficult and time consuming to obtain, a tolerable deviation is utilized to achieve a near-optimal solution. Extensive experiments are conducted to sufficiently demonstrate the efficiency and stability of the proposed practical solution. In addition, the experimental results on static assignment and dynamic assignment further prove the effectiveness of the solution.

The three-way decision (3WD) using probabilistic rough set (PRS) is constructed based on the probabilistic thresholds, and the 3WD using decision-theoretic rough set (DTRS) can utilize the decision cost information. They are both popular and effective decision-making methods. In the same problem, experts may provide different types of information about the decision rules. However, few works try to combine these two kinds of models and utilize both kinds of information. Therefore, in this paper, we propose a 3WD model combining PRS and DTRS, which integrates the probabilistic risk and the Bayesian risk in decision problems. To achieve that, based on the given probabilistic thresholds and decision rules, the 3WD with PRS is analyzed from the perspective of decision risk. Following that, the total risk of three decisions is computed. Then the best option for the instance is selected according to the Bayesian minimum risk rule. After the existence and uniqueness of the thresholds are analyzed, the explicit expressions are provided. Finally, illustration examples demonstrate the effectiveness of the proposed approach.

There are already some text-based visual question answering (TextVQA) benchmarks for developing machine's ability to answer questions based on texts in images in recent years. However, models developed on these benchmarks cannot work effectively in many real-life scenarios (e.g. traffic monitoring, shopping ads and e-learning videos) where temporal reasoning ability is required. To this end, we propose a new task named Video Text Visual Question Answering (ViteVQA in short) that aims at answering questions by reasoning texts and visual information spatiotemporally in a given video. In particular, on the one hand, we build the first ViteVQA benchmark dataset named M4-ViteVQA --- the abbreviation of Multi-category Multi-frame Multi-resolution Multi-modal benchmark for ViteVQA, which contains 7,620 video clips of 9 categories (i.e., shopping, traveling, driving, vlog, sport, advertisement, movie, game and talking) and 3 kinds of resolutions (i.e., 720p, 1080p and 1176x664), and 25,123 question-answer pairs. On the other hand, we develop a baseline method named T5-ViteVQA for the ViteVQA task. T5-ViteVQA consists of five transformers. It first extracts optical character recognition (OCR) tokens, question features, and video representations via two OCR transformers, one language transformer and one video-language transformer, respectively. Then, a multimodal fusion transformer and an answer generation module are applied to fuse multimodal information and generate the final prediction. Extensive experiments on M4-ViteVQA demonstrate the superiority of T5-ViteVQA to the existing approaches of TextVQA and VQA tasks. The ViteVQA benchmark is available in https://github.com/bytedance/VTVQA.

In many practical cases, the original task is divided into smaller, easier-to-complete tasks and assigned to different groups. Group role assignment (GRA) is dedicated to optimizing the performance of a group, which is not applicable to the multi-group role assignment (MGRA). Moreover, in dynamic scenes, the agents’ capabilities change over time, further complicating the problem. Based on the emerging and promising role-based collaboration (RBC) theory and its E-CARGO (Environments - Classes, Agents, Roles, Groups, and Objects) model, we formulate the adaptive MGRA problem, and propose a novel current state-based MGRA (CSB-MGRA) algorithm to keep the entire team productive. The constraints of the tasks are not the same due to their diverse characteristics and needs. Moreover, team members do not necessarily remain the same in the whole process, and staff transfers may occur between groups. A constant assignment scheme is not guaranteed to maximize team performance. Therefore, the constraints of different groups are set to be different, and re-assignments of the whole team are considered in the construction of CSB_MGRA. The experimental results prove the practicality of the solution proposed in this paper.

Ubiquitous computing involves large number of devices which are connected via networks. This requires packet processing service to guarantee privacy, security, and high quality. We study to provide ubiquitous computing with stable and satisfied services through improving packet processing performance. Since the applications become more and more complicated, the task allocation among multi-cores for pipelined architecture becomes important and difficult. In order to map tasks onto pipelined architecture and maximize the overall throughput, we propose a task allocation scheme incorporated with profiling and globally thread refinement. This scheme relies on a performance model which determines the system throughput considering multi-thread, memory access and the effect of communications between stages. We evaluate the technique by implementing representative network processing applications on the Intel IXP architecture. Experimental results show that our scheme is able to generate mapping of realistic applications to balance the stages and obtain high throughput. Furthermore, it outperforms other methods even when the PE number is reduced.

Ubiquitous computing disappears from people's consciousness. To achieve the transparence, tremendous computing power is in need not only in WLAN where users access directly but also in the Internet where information is retrieved and sent. Bandwidth is crucial to improve userpsilas transparent experience when providing services such as video on demand. Network processors (NPs) are specialized design for fast packet processing to achieve a broad bandwidth. The banked register file on NPs is to provide operands parallel fetching and support a large number of registers to reduce memory access, whereas it may also impose some restrictions and problems. This paper discusses the dual-bank register allocation problem for Intelpsilas network processor IXP. Its register file is physically partitioned into two banks. If two source operands are specified in an instruction, they must come from different banks. This makes the IXP register allocator, which allocates registers and assigns bank, tricky and different from conventional ones. We first present an algorithm that provides optimal solution to the graph partition problem. Then a framework for IXP register allocation is established with it. Experimental result shows the framework is effective in practice.