Feng Feng

Haitian Hu, Wei Zhang, Feng Feng et al.

This article introduces an advanced space mapping (SM) technique that applies a shared electromagnetic (EM)-based coarse model for multistate tuning-driven multiphysics optimization of tunable filters. The SM method combines the computational efficiency of EM single-physics simulations with the precision of multiphysics simulations. The shared coarse model is based on EM single-physics responses corresponding to various nontunable design parameters values. Conversely, the fine model is implemented to delineate the behavior of multiphysics responses concerning both nontunable and tunable design parameter values. The proposed overall surrogate model comprises multiple subsurrogate models, each consisting of one shared coarse model and two distinct mapping neural networks. The responses from the shared coarse model in the EM single-physics filed offer a suitable approximation for the fine responses in the multiphysics filed, whereas the mapping neural networks facilitate transition from the EM single-physics field to the multiphysics field. Each subsurrogate model maintains consistent nontunable design parameter values but possesses unique tunable design parameter values. By developing multiple subsurrogate models, optimization can be simultaneously performed for each tuning state. Nontunable design parameter values are constrained by all tuning states, whereas tunable design parameter values are confined to their respective tuning states. This optimization technique simultaneously accounts for all the tuning states to fulfill the necessary multiple tuning state requirements. Multiple EM and multiphysics training samples are generated concurrently to develop the surrogate model. Compared with existing direct multiphysics parameterized modeling techniques, our proposed method achieves superior multiphysics modeling accuracy with fewer training samples and reduced computational costs.

Longwei Xu, Feng Feng, Shaojie Zhang et al.

Optical Character Recognition (OCR) is increasingly regarded as a foundational capability for modern vision-language models (VLMs), enabling them not only to read text in images but also to support downstream reasoning in real-world visual question answering (VQA). However, practical applications further require reliable text anchors, i.e., accurately grounding queried text to its corresponding spatial region. To systematically evaluate this capability, we introduce TextAnchor-Bench (TABench), a benchmark for fine-grained text-region grounding, which reveals that both general-purpose and OCR-specific VLMs still struggle to establish accurate and stable text anchors. To address this limitation, we propose Q-Mask, a precise OCR framework built upon a causal query-driven mask decoder (CQMD). Inspired by chain-of-thought reasoning, Q-Mask performs causal visual decoding that sequentially generates query-conditioned visual masks before producing the final OCR output. This visual CoT paradigm disentangles where the text is from what the text is, enforcing grounded evidence acquisition prior to recognition and enabling explicit text anchor construction during inference. To train CQMD, we construct TextAnchor-26M, a large-scale dataset of image-text pairs annotated with fine-grained masks corresponding to specific textual elements, encouraging stable text-region correspondences and injecting strong spatial priors into VLM training. Extensive experiments demonstrate that Q-Mask substantially improves text anchoring and understanding across diverse visual scenes.

Feng Feng, Puhong Li, Tony Stockman

Leveraging the perceptual phenomenon of crossmoal correspondence has been shown to facilitate peoples information processing and improves sensorimotor performance. However for goal-oriented interactive tasks, the question of how to enhance the perception of specific Crossmodal information, and how Crossmodal information integration takes place during interaction is still unclear. The present paper reports two experiments investigating these questions. In the first experiment, a cognitive priming technique was introduced as a way to enhance the perception of two Crossmodal stimuli, in two conditions respectively, and their effect on sensory-motor performance was observed. Based on the results, the second experiment combined the two Crossmodal stimuli in the same interfaces in a way that their correspondence congruency was mutually exclusive. The same priming techniques was applied as a manipulating factor to observe the Crossmodal integration process. Results showed that first, the Crossmodal integration during interaction can be enhanced by the priming technique, but the effect varies according to the combination of Crossmodal stimuli and the types of priming material. Moreover, peoples subjective evaluations towards priming types were in contradiction with their objective behavioural data. Second, when two Crossmodal sequences can be perceived simultaneously, results suggested different perceptual weights are possessed by different participants, and the perceptual enhancement effect was observed only on the dominant one, the pitch-elevation. Furthermore, the Crossmodal integration tended to be integrated in a selective manner without priming. These results contribute design implications for multisensory feedback and mindless computing.

Feng Feng, Tony Stockman

Humans sense of distance depends on the integration of multi sensory cues. The incoming visual luminance, auditory pitch and tactile vibration could all contribute to the ability of distance judgement. This ability can be enhanced if the multimodal cues are associated in a congruent manner, a phenomenon has been referred to as Crossmodal correspondences. In the context of multi-sensory interaction, whether and how such correspondences influence information processing with continuous motor engagement, particularly for target searching activities, has rarely been investigated. This paper presents an experimental user study to address this question. We built a target-searching application based on a Table-top, displayed the unimodal and Crossmodal distance cues concurrently responding to peoples searching movement, measured task performance through kinematic evaluation. We find that the Crossmodal display an audio display lead to improved searching efficiency and accuracy. More interestingly, this improvement is confirmed by kinematic analysis, which also unveiled the underlying movement features that could account for this improvement. We discussed how these findings could shed lights on the design of assistive technology and of other multi sensory interaction.

Feng Feng, Shang Kai, Tony Stockman

Although it has been shown that augmented multimodal feedback has a facilitatory effect on motor performance for motor learning and music training, the functionality of haptic feedback combined with other modalities in rhythmic movement tasks has rarely been explored and analysed. In this paper, we evaluate the functionality of visual-haptic feedback in a rhythmic sketch task by comparing it with other multimodal conditions. Further, we examine the possibility of accessing the quality of task execution through kinematic analysis. Based on participants' speed profiles, we investigate the quality of motor control and movement smoothness under different feedback conditions. Results revealed better motor control ability with auditory feedback and improved movement smoothness with haptic feedback. Finally, we propose that haptic feedback can be integrated with other modal stimuli for different interaction purposes, and that kinematic analysis can be a complementary approach to gesture analysis as well as providing subjective evaluation of interaction performance.

Xuanzhe Liu, Huoran Li, Xuan Lu et al.

The prevalence of smart mobile devices has promoted the popularity of mobile applications (a.k.a. apps). Supporting mobility has become a promising trend in software engineering research. This article presents an empirical study of behavioral service profiles collected from millions of users whose devices are deployed with Wandoujia, a leading Android app store service in China. The dataset of Wandoujia service profiles consists of two kinds of user behavioral data from using 0.28 million free Android apps, including (1) app management activities (i.e., downloading, updating, and uninstalling apps) from over 17 million unique users and (2) app network usage from over 6 million unique users. We explore multiple aspects of such behavioral data and present patterns of app usage. Based on the findings as well as derived knowledge, we also suggest some new open opportunities and challenges that can be explored by the research community, including app development, deployment, delivery, revenue, etc.