A Dynamic Network Measure of Knowledge Evolution: A Case Study of MIS Quarterly

Citation measures are the central metrics to assessing the impact of an article, the viability of research streams, the career success of scholars, as well as the quality and status of journals and academic units. While measuring the magnitude of the future usage, they cannot capture the substantial effects that an article may have on the subsequent use of its predecessors - whether it amplifies or disrupts the existing literature. We embrace that it is imperative to not only assess its impact but also assess how an article reinforces the existing research streams or breaks into a new stream to understand its true effect. Accordingly, we introduce a new, dynamic measure, and conduct a case study using all articles published between 1979-2016 at MIS Quarterly to illustrate the validity of the new measure, and conclude with some future research topics and implications

Spatial-temporal Transformers for EEG Emotion Recognition

Electroencephalography (EEG) is a popular and effective tool for emotion recognition. However, the propagation mechanisms of EEG in the human brain and its intrinsic correlation with emotions are still obscure to researchers. This work proposes four variant transformer frameworks~(spatial attention, temporal attention, sequential spatial-temporal attention and simultaneous spatial-temporal attention) for EEG emotion recognition to explore the relationship between emotion and spatial-temporal EEG features. Specifically, spatial attention and temporal attention are to learn the topological structure information and time-varying EEG characteristics for emotion recognition respectively. Sequential spatial-temporal attention does the spatial attention within a one-second segment and temporal attention within one sample sequentially to explore the influence degree of emotional stimulation on EEG signals of diverse EEG electrodes in the same temporal segment. The simultaneous spatial-temporal attention, whose spatial and temporal attention are performed simultaneously, is used to model the relationship between different spatial features in different time segments. The experimental results demonstrate that simultaneous spatial-temporal attention leads to the best emotion recognition accuracy among the design choices, indicating modeling the correlation of spatial and temporal features of EEG signals is significant to emotion recognition

AICAttack: Adversarial Image Captioning Attack with Attention-Based Optimization

Recent advances in deep learning research have shown remarkable achievements across many tasks in computer vision (CV) and natural language processing (NLP). At the intersection of CV and NLP is the problem of image captioning, where the related models' robustness against adversarial attacks has not been well studied. In this paper, we present a novel adversarial attack strategy, which we call AICAttack (Attention-based Image Captioning Attack), designed to attack image captioning models through subtle perturbations on images. Operating within a black-box attack scenario, our algorithm requires no access to the target model's architecture, parameters, or gradient information. We introduce an attention-based candidate selection mechanism that identifies the optimal pixels to attack, followed by Differential Evolution (DE) for perturbing pixels' RGB values. We demonstrate AICAttack's effectiveness through extensive experiments on benchmark datasets with multiple victim models. The experimental results demonstrate that our method surpasses current leading-edge techniques by effectively distributing the alignment and semantics of words in the output

Robust Nonfragile H

This paper investigates the problem of robust nonfragile fuzzy H∞ filtering for uncertain Takagi-Sugeno (T-S) fuzzy systems with interval time-varying delays. Attention is focused on the design of a filter such that the filtering error system preserves a prescribed H∞ performance, where the filter to be designed is assumed to have gain perturbations. By developing a delay decomposition approach, both lower and upper bound information of the delayed plant states can be taken into full consideration; the proposed delay-fractional-dependent stability condition for the filter error systems is obtained based on the direct Lyapunov method allied with an appropriate and variable Lyapunov-Krasovskii functional choice and with tighter upper bound of some integral terms in the derivation process. Then, a new robust nonfragile fuzzy H∞ filter scheme is proposed, and a sufficient condition for the existence of such a filter is established in terms of linear matrix inequalities (LMIs). Finally, some numerical examples are utilized to demonstrate the effectiveness and reduced conservatism of the proposed approach

High-resolution X-ray microdiffraction analysis of natural teeth

In situ microzone X-ray diffraction analysis of natural teeth is presented. From our experiment, layer orientation and continuous crystal variations in teeth could be conveniently studied using fast online measurements by high-resolution X-ray microdiffraction equipment