The lack of actionable guidance is a fundamental limitation in Action Quality Assessment (AQA), as traditional methods provide overall scores without offering specific insights for improvement. Moreover, existing interpretable approaches often rely on expensive supervised spatial annotations or yield noisy, unsigned saliency maps. To address these challenges, we propose Learning Interpretability Based Supervoxels (LIBS), a novel framework for generating instructional feedback. Distinguishing itself from fully supervised methods, LIBS employs an unsupervised soft-clustering mechanism to segment videos into coherent supervoxels without requiring pixel-level mask annotations. This allows for scalable, fine-grained spatio-temporal analysis while preserving action continuity. Furthermore, we introduce a sensitivity propensity analysis to quantify the contribution of each supervoxel. Unlike traditional attribution methods, this mechanism explicitly decomposes the quality score into positive (strengths) and negative (flaws) components, enabling the system to decode abstract scores into concrete, actionable instructions. Experimental validation across multiple datasets demonstrates that LIBS achieves superior interpretability and efficiency compared to state-of-the-art baselines, marking an improvement from diagnostic to instructional AQA applications.

Human-robot collaboration (HRC) assembly necessitates precise mutual cognition to guarantee safe and efficient execution. In this context, human assembly intention recognition (HAIR) serves as a critical approach to achieving this mutual understanding. However, most current HAIR approaches struggle to extract sufficient spatiotemporal information from limited industrial data, particularly under complex conditions like varying scales and visual occlusions. Thereby, this article proposes an ensemble encoder approach to extract and fuse spatial and temporal features from visual and skeleton streams of the HRC assembly process, thus significantly improving HAIR accuracy and efficiency. First, an RGB feature extraction encoder is designed to model spatiotemporal dependencies of the assembly process with different scales of features from flexible input RGB encoders (RGBEs). Distinctively, a cross-attention module is utilized to fuse information from different-scale RGBEs, ensuring comprehensive assembly action representation with different granularities. Second, to address the occlusion challenge, a mask-aware skeleton feature extraction encoder is devised. By utilizing frame and joint masking strategies, it robustly models the relationship between operator pose evolution and assembly actions, maintaining high performance even under occlusion. Third, a global feature fusion encoder integrates and aligns features from RGB and skeleton feature extraction encoders. Experimental results demonstrate the state-of-the-art performance of the proposed approach, which achieves the highest accuracy of 99.12%, 99.23%, and 84.59% on MCV-Intention, HA4M, and HA-VID datasets, respectively. Six ablation studies demonstrate the performance effects of fusion positions, the number of depth channels, cross-attention fusion module, occlusions, illuminations, and computational efficiency.

Proactive human-robot collaborative (HRC) assembly has caught great attention as emerging paradigm for flexible mass personalization in manufacturing with respect to Industry 5.0. To realize adaptive and ergonomic collaboration, it is essential to enable robots recognize human assembly intention based on context-aware information precisely at edge side with Industrial Internet of Thing, also known as human assembly intention recognition (HAIR). For this purpose, this paper systematically reviewed the most relevant papers from major digital databases, where 127 papers are investigated with designed search procedure that published until July 2024. And reviewed papers are summarized from the perspective of: (1) assembly scene perception based on multimodalities data; (2) understanding of HAIR based on machine learning; (3) HAIR application for HRC process. In addition, four current challenges and future research trends are also discussed to facilitate full-adaptive and mutual-cognitive HRC assembly environments.

A new method to construct q-ary complementary sequence sets (CSSs) and complete complementary codes (CCCs) of size N is proposed by using desired para-unitary (PU) matrices.The concept of seed PU matrices is introduced and a systematic approach on how to compute the explicit forms of the functions in constructed CSSs and CCCs from the seed PU matrices is given.A general form of these functions only depends on a basis of the functions from ZN to Zq and representatives in the equivalent class of Butson-type Hadamard (BH) matrices.Especially, the realization of Golay pairs from the our general form exactly coincides with the standard Golay pairs.The realization of ternary complementary sequences of size 3 is first reported here.For the realization of the quaternary complementary sequences of size 4, almost all the sequences derived here are never reported before.Generalized seed PU matrices and the recursive constructions of the desired PU matrices are also studied, and a large number of new constructions of CSSs and CCCs are given accordingly.From the perspective of this paper, all the known results of CSSs and CCCs with explicit GBF form in the literature (except non-standard Golay pairs) are constructed from the Walsh matrices of order 2.This suggests that the proposed method with the BH matrices of higher orders will yield a large number of new CSSs and CCCs with the exponentially increasing number of the sequences of low peak-to-mean envelope power ratio.

Complementary sequence sets (CSSs) and complete complementary codes (CCCs) have many applications in science and engineering, especially in wireless communications. A construction of CSS and CCC, having size M = 2mand length MK, where m and K are positive integers, is presented. The proposed construction is a generalized paraunitary (PU) algorithm that greatly increases the number of permutations from K! to (mK)! compared to those of previous PU constructions. Moreover, this new construction can be generalized to the case M = pm, where p is a positive integer. The increase in the number of permutations means that a wide range of CCCs and CSSs can be obtained.