Multimodal intent recognition poses significant challenges, requiring the incorporation of non-verbal modalities from real-world contexts to enhance the comprehension of human intentions. However, most existing multimodal intent benchmark datasets are limited in scale and suffer from difficulties in handling out-of-scope samples that arise in multi-turn conversational interactions. In this paper, we introduce MIntRec2.0, a large-scale benchmark dataset for multimodal intent recognition in multi-party conversations. It contains 1,245 high-quality dialogues with 15,040 samples, each annotated within a new intent taxonomy of 30 fine-grained classes, across text, video, and audio modalities. In addition to more than 9,300 in-scope samples, it also includes over 5,700 out-of-scope samples appearing in multi-turn contexts, which naturally occur in real-world open scenarios, enhancing its practical applicability. Furthermore, we provide comprehensive information on the speakers in each utterance, enriching its utility for multi-party conversational research. We establish a general framework supporting the organization of single-turn and multi-turn dialogue data, modality feature extraction, multimodal fusion, as well as in-scope classification and out-of-scope detection. Evaluation benchmarks are built using classic multimodal fusion methods, ChatGPT, and human evaluators. While existing methods incorporating nonverbal information yield improvements, effectively leveraging context information and detecting out-of-scope samples remains a substantial challenge. Notably, powerful large language models exhibit a significant performance gap compared to humans, highlighting the limitations of machine learning methods in the advanced cognitive intent understanding task. We believe that MIntRec2.0 will serve as a valuable resource, providing a pioneering foundation for research in human-machine conversational interactions, and significantly facilitating related applications. The full dataset and codes are available for use at https://github.com/thuiar/MIntRec2.0.

Tracking-and-inserting a moving peg using a robot manipulator is a challenging task in manufacturing. In the past decades, various visual-based methods have been proposed for robotic manipulating static targets, which usually ignore the time delay in robot command transmission and image processing. However, for tracking and inserting a moving peg, time delays cannot be overlooked because they can reduce the tracking performance and even cause manipulations to fail. In this article, a robot visual-based control with a time-delay compensator is presented to solve the problem of inserting a moving peg. The time-delay compensator was designed using radial basis function neural network, a feedback compensator aimed at eliminating the tracking errors caused by the time delays. Thus, we could manipulate a moving object using a commercial industry robot, even with the time-variant delays in the control loop. Furthermore, the visual-based controller with the pseudoinverse image Jacobian matrix was designed using a linearization model. Thus, the matrix could be efficiently updated using the model. In the experiment, we inserted a peg into a moving hole using an eye-in-hand robot with precision.

In the cloud computing environment, the release and dynamic deployment of microservices face many challenges, and the services' dependency is an important consideration for service deployment. The service call chain logs record rich information, such as the time and delay of each service call in the business tracking process, and present the logs in the form of a call chain. Existing research does not fully consider the local composite service dependencies and their discontinuous dependencies in the service chain. In order to obtain these complex dependencies and provide basic supporting data for the dynamic deployment and adjustment of microservices, a dependency model of microservices is proposed, and a dependency mining method based on call chain logs is designed to extract local dependencies and the discontinuous dependency relationship. The effectiveness of the algorithm is verified by testing on Alibaba Cloud Computing's public data set.

Business process model and notation BPMN2.0 is an industry standard in the field of business process management. In order not to destroy the association rules between the various tasks of the business process, and to schedule them in the cloud edge environment, improve the service quality of the business process, this paper proposes a method of segmentation and reconstruction of the BPMN2.0 model, based on the BMPN2.0 model of the original business process and its constraints of the cloud-edge environment, the original business process is divided and reconstructed into multiple sub-business processes, so that the reconstructed sub-business process conforms to the BPMN2.0 modeling standard and is actually usable, providing basis for the scheduling of business processes in the cloud-edge environment. Through example verification and experimental analysis, the effectiveness of the segmentation reconstruction method in this paper is verified.

Development of a flexible low-cost robotic system to grasp various 3D workpieces is of practical important. Traditional approaches on 3D grasping usually need to compute the effective contact positions based on sufficient conditions, such as “force-closure” or “form-closure,” to prevent all the motions of the grasped objects. Compared with the previous work motivated by high-precision applications, caging provides a way to manipulate an object without needing to immobilize it. However, most caging conditions consider only 2D motions of the object in grasping. In some cases, other motions in 3D space, e.g., the pitch and roll rotations of the objects, would also possibly change a caging configuration to an uncaging configuration. This paper aims to discuss caging with frictionless contact, by taking into consideration of all the motions of the object. We first discuss the relationship between the gripper configuration and the state of the grasped object in grasping, where all the motions of the object are taken into account. We then establish the sufficient conditions to find a set of 3D caging configurations in terms of the width of the object's projection and the gap of the gripper, such that we can utilize the projection to find the feasible placements of the pins to determine the caging configuration. Furthermore, we discuss how to find the caging configuration that is capable of leading to a form-closure based on attractive region formed in the configuration space.

Object grasping or localization is an essential stage in automatic manufacturing processes. In general, stable grasping of 3-D objects is achieved by a multifinger hand. Different from the existing approaches, in this paper, we propose a novel vision-based method for grasping 3-D objects with a simple 2-D gripper. We describe the grasping strategy, the range of 3-D objects which can be grasped, and the region of grasping orientations which can guarantee successful actions. The proposed method facilitates the freedom of grasping orientation, which endows the strategy with a larger application range. Furthermore, we also explain the potential applications of the proposed method in grasping 3-D objects with other 2-D grippers. Although the proposed method uses a 2-D gripper and 2-D vision information, it can be regarded as a 3-D grasping approach due to the following facts. 1) The objects to be grasped are 3-D. The grasping process and the grasping orientations are analyzed and given in a 3-D space. 2) The contact points between the object and the gripper are not always in a 2-D plane. In the proposed method, the concept attractive region in configuration space, which was proposed in the previous work, is introduced to analyze the whole grasping process. Compared with other traditional methods, the proposed method directly gives the range of objects which can be grasped as well as the region of grasping orientations. By establishing the relationship between our method and traditional ones, it is proved that the bottom of the attractive region corresponds to a stable grasp.