Wavelet Dynamic Selection Network for Inertial Sensor Signal Enhancement

As attitude and motion sensing components, inertial sensors are widely used in various portable devices. But the severe errors of inertial sensors restrain their function, especially the trajectory recovery and semantic recognition. As a mainstream signal processing method, wavelet is hailed as the mathematical microscope of signal due to the plentiful and diverse wavelet basis functions. However, complicated noise types and application scenarios of inertial sensors make selecting wavelet basis perplexing. To this end, we propose a wavelet dynamic selection network (WDSNet), which intelligently selects the appropriate wavelet basis for variable inertial signals. In addition, existing deep learning architectures excel at extracting features from input data but neglect to learn the characteristics of target categories, which is essential to enhance the category awareness capability, thereby improving the selection of wavelet basis. Therefore, we propose a category representation mechanism (CRM), which enables the network to extract and represent category features without increasing trainable parameters. Furthermore, CRM transforms the common fully connected network into category representations, which provide closer supervision to the feature extractor than the far and trivial one-hot classification labels. We call this process of imposing interpretability on a network and using it to supervise the feature extractor the feature supervision mechanism, and its effectiveness is demonstrated experimentally and theoretically in this paper. The enhanced inertial signal can perform impracticable tasks with regard to the original signal, such as trajectory reconstruction. Both quantitative and visual results show that WDSNet outperforms the existing methods. Remarkably, WDSNet, as a weakly-supervised method, achieves the state-of-the-art performance of all the compared fully-supervised methods.Comment: Accepted by AAAI 2024 - Association for the Advancement of Artificial Intelligenc

A study of energy correction for the electron beam data in the BGO ECAL of the DAMPE

The DArk Matter Particle Explorer (DAMPE) is an orbital experiment aiming at searching for dark matter indirectly by measuring the spectra of photons, electrons and positrons originating from deep space. The BGO electromagnetic calorimeter is one of the key sub-detectors of the DAMPE, which is designed for high energy measurement with a large dynamic range from 5 GeV to 10 TeV. In this paper, some methods for energy correction are discussed and tried, in order to reconstruct the primary energy of the incident electrons. Different methods are chosen for the appropriate energy ranges. The results of Geant4 simulation and beam test data (at CERN) are presented

Detecting Textual Adversarial Examples through Randomized Substitution and Vote

A line of work has shown that natural text processing models are vulnerable to adversarial examples. Correspondingly, various defense methods are proposed to mitigate the threat of textual adversarial examples, eg, adversarial training, input transformations, detection, etc. In this work, we treat the optimization process for synonym substitution based textual adversarial attacks as a specific sequence of word replacement, in which each word mutually influences other words. We identify that we could destroy such mutual interaction and eliminate the adversarial perturbation by randomly substituting a word with its synonyms. Based on this observation, we propose a novel textual adversarial example detection method, termed Randomized Substitution and Vote (RS&V), which votes the prediction label by accumulating the logits of k samples generated by randomly substituting the words in the input text with synonyms. The proposed RS&V is generally applicable to any existing neural networks without modification on the architecture or extra training, and it is orthogonal to prior work on making the classification network itself more robust. Empirical evaluations on three benchmark datasets demonstrate that our RS&V could detect the textual adversarial examples more successfully than the existing detection methods while maintaining the high classification accuracy on benign samples.Comment: Accepted by UAI 2022, code is avaliable at https://github.com/JHL-HUST/RS

Intercultural Communication Competence Revisited: Reconciling Trait and Relational Perspectives Using Social Network Analysis

What it takes for communicators to interact competently with intercultural others remains a contested conceptual site. This dissertation addresses the debate between the individual and relational perspectives regarding the origins or location of intercultural communication competence (ICC). The individual approach to ICC equates this concept with a set of individual attributes located within the communicator. The relational perspective conceives of this competence as a social judgment that communicators can make about each other in relationships. The dissertation brings together these two seemingly competing paradigms regarding intercultural communication competence research by combining the individual perspective (based on predictions from the trait perspective) and the relational perspective (based on social network theory). Through a social network analysis design, living-abroad individuals’ ego networks and multicultural personality traits were examined in relation to ICC, independently and collectively. Results revealed that multicultural personality traits were strong predictors of ICC. Specifically, when the traits were evaluated separately from or together with network variables, open-mindedness, cultural empathy, and social initiative positively, and flexibility and emotional stability negatively related to ICC. Ego network characteristics also had relationships with ICC, but they were weaker predictors than personality traits. In particular, when the ego network variables were examined independently, separately from traits, strong intercultural network size negatively, whereas heterophily and diversity positively, related to ICC. When the network variables were examined together with traits, the effect of network diversity disappeared. In addition, the trait of open-mindedness was found to mediate the associations between network diversity and several competence dimensions. These results offer some support for a cross-paradigmatically theoretical framework explaining what it takes to become interculturally competent, with antecedents at the individual and relational levels. The findings also have theoretical implications for the study of ICC as well as practical implications for living-abroad individuals, intercultural educators, and trainers