Co-clustering enables the simultaneous clustering of features and samples by exploiting their associations. Based on the commonly used matrix tri-factorization objective function in co-clustering, we propose an adaptive feature-weighted co clustering with local coordinate coding (AFC-LCC) model in this paper, by making two improvements to enhance the data clustering performance. On one hand, a local coordinate constraint is introduced to enforce the smoothness of the sample cluster indicators along the scaled feature cluster spaces; on the other hand, a quantitative measurement is incorporated to adaptively learn the feature contributions in co-clustering for model discriminative ability enhancement. By co-optimizing the involved variables in the AFC-LCC model objective function, the experimental results not only show competitive clustering performance in comparison with related clustering models, but also depict the rationality and effectiveness of the local coordinate constraint and the feature importance descriptor.

Clustering has long been a fundamental problem in machine learning and data mining, with the aim of grouping data samples on the basis of their intrinsic similarity. However, the consensus that different features often exhibit varying levels of discriminative power in clustering model learning is under explored sufficiently in collaboration with the pseudo-label guided unsupervised discriminative analysis. To this end, we propose an Adaptive Feature-Weighted Local-global data Clustering (AFW-LGC) model which is featured by two improvements. First, AFW-LGC takes into account both global separability (between-cluster scatter) and local compactness (within-cluster scatter) whose impacts are mediated by a learnable parameter. Second, the different contributions of features are adaptively learned in AFW-LGC for further discriminative ability enhancement. Both improvements are seamlessly integrated for feature-weighted unsupervised discriminative subspace clustering nature of AFW-LGC. Extensive experiments on eight data sets demonstrate the superior clustering performance of AFW-LGC over some SOTA methods as well as the rationality of our proposed feature importance exploration strategy.

Garment manipulation (e.g., unfolding, folding and hanging clothes) is essential for future robots to accomplish home-assistant tasks, while highly challenging due to the diversity of garment configurations, geometries and deformations. Although able to manipulate similar shaped garments in a certain task, previous works mostly have to design different policies for different tasks, could not generalize to garments with diverse geometries, and often rely heavily on human-annotated data. In this paper, we leverage the property that, garments in a certain category have similar structures, and then learn the topological dense (point-level) visual correspondence among garments in the category level with different deformations in the self-supervised manner. The topological correspondence can be easily adapted to the functional correspondence to guide the manipulation policies for various downstream tasks, within only one or few-shot demonstrations. Experiments over garments in 3 different categories on 3 representative tasks in diverse scenarios, using one or two arms, taking one or more steps, inputting flat or messy garments, demonstrate the effectiveness of our proposed method. Project page: https://warshallrho.github.io/unigarmentmanip.

A control method based on disturbance observer is proposed to address the positioning drift issue of the gantry crane under unmatched disturbances, aiming at precise positioning of the trolley and effective payload swing angle attenuation in a variable-length two-dimensional lifting system. The control scheme comprises a combination of a proportional-derivative controller and a disturbance observer. Firstly, the design of the coupling function in this paper is based on the observation of the natural characteristics of the dynamical model. It cleverly designs the coupling function, which not only helps to effectively reduce the swing angle but also prevents the problem of positioning drift under non-matching disturbances, subsequently, design a controller by integrating intelligent computing with control theory. Secondly, the designed disturbance observer observes and compensates for the system output to achieve disturbance suppression. Thirdly, a thorough stability analysis has been conducted to demonstrate that the proposed controller satisfies the desired conditions. Finally, simulation results illustrate that the pro-posed method success-fully addresses the limitations of existing methods and exhibits superior performance and robustness.

Intelligent education stands as a prominent application of machine learning. Within this domain, cognitive diagnosis (CD) is a key research focus that aims to diagnose students' proficiency levels in specific knowledge concepts. As a crucial task within the field of education, cognitive diagnosis encompasses two fundamental requirements: accuracy and fairness. Existing studies have achieved significant success by primarily utilizing observed historical logs of student-exercise interactions. However, real-world scenarios often present a challenge, where a substantial number of students engage with a limited number of exercises. This data sparsity issue can lead to both inaccurate and unfair diagnoses. To this end, we introduce a monotonic data augmentation framework, CMCD, to tackle the data sparsity issue and thereby achieve accurate and fair CD results. Specifically, CMCD integrates the monotonicity assumption, a fundamental educational principle in CD, to establish two constraints for data augmentation. These constraints are general and can be applied to the majority of CD backbones. Furthermore, we provide theoretical analysis to guarantee the accuracy and convergence speed of CMCD. Finally, extensive experiments on real-world datasets showcase the efficacy of our framework in addressing the data sparsity issue with accurate and fair CD results.

Traffic fingerprinting allows making inferences about encrypted traffic flows through passive observation. They have been used for tasks such as network performance management and analytics and in attacker settings such as censorship and surveillance. A key challenge when implementing traffic fingerprinting in real-time settings is how the state-of-the-art traffic fingerprint models can be ported into programmable in-network computing devices with limited computing resources. Towards this, in this work, we characterize the performance of binarized traffic fingerprinting neural networks that are efficient and well-suited for in-network computing devices and propose a new data encoding method that is better suited for network traffic. Overall, we show that the proposed binary neural network with first-layer binarization and last-layer quantization reduces the performance requirement of hardware equipment while retaining the accuracies of those models of binary datasets over 70%. Furthermore, when combined with our proposed encoding algorithm, accuracies of binarized models of numeric datasets show further improvements to achieve over 65% accuracy.

This study examines the impact of audible alarm types and presentation rates on pilot performance in beyond visual range combat scenarios. Three types of alarms and presentation rates were investigated for their effectiveness in completing an absolute recognition task with accuracy. Additionally, the study explores the physiological and psychological load on participants using the NASA-TLX questionnaire. The results indicate that higher alarm presentation rates and voice alarms improve operational performance, and voice alarms outperformed other alarm types significantly. These findings have practical implications for designing more effective and efficient alarm systems in safety-critical domains, particularly for pilots.

In order to improve robustness of permanent magnet synchronous motor (PMSM) servo system, a nonlinear generalized predictive control (NGPC) strategy based on extended state observer (ESO) is proposed. According to the NGPC theory, a NGPC controller is designed by using the continuous-time model of PMSM. In the case of the parameter variations and external load disturbance in the actual motor system, an ESO based on inverse hyperbolic sine function is introduced to estimate the total disturbance of the system and compensate dynamically in real time. The simulation results show that the proposed control strategy has good dynamic performance and strong robustness, providing an effective approach for engineering implementation.

In rail transit application, the performance of the permanent magnet synchronous motor (PMSM) sensorless control system is limited by the low switching frequency. In this case, the time-delay of system is becoming more significant and degrades the control performance. To solve the problem, a time-delay compensation method based on high frequency (HF) square-wave voltage injection strategy is proposed in this paper. Firstly, a series time-delay filter is used to extract the HF response current, which can improve the signal-to-noise ratio. Then, a zero-order holder is used to obtain the HF current envelope containing rotor position information. The rotor speed and position are estimated by a Luenberger observer. For the time-delay compensation, the feedback current in a-β coordinates is utilized to calculate the estimated delay angle error. It serves as the input of a PI controller, through which the estimated delay angle can be acquired. And then, the estimated delay angle is compensated to the actual system. Simulations verified the effectiveness of the proposed method.

State-of-the-art i-vector based speaker verification relies on variants of Probabilistic Linear Discriminant Analysis (PLDA) for discriminant analysis. We are mainly motivated by the recent work of the joint Bayesian (JB) method, which is originally proposed for discriminant analysis in face verification. We apply JB to speaker verification and make three contributions beyond the original JB. 1) In contrast to the EM iterations with approximated statistics in the original JB, the EM iterations with exact statistics are employed and give better performance. 2) We propose to do simultaneous diagonalization (SD) of the within-class and between-class covariance matrices to achieve efficient testing, which has broader application scope than the SVD-based efficient testing method in the original JB. 3) We scrutinize similarities and differences between various Gaussian PLDAs and JB, complementing the previous analysis of comparing JB only with Prince-Elder PLDA. Extensive experiments are conducted on NIST SRE10 core condition 5, empirically validating the superiority of JB with faster convergence rate and 9 - 13% EER reduction compared with state-of-the-art PLDA.