Transformer-based Self-supervised Multimodal Representation Learning for Wearable Emotion Recognition

Recently, wearable emotion recognition based on peripheral physiological signals has drawn massive attention due to its less invasive nature and its applicability in real-life scenarios. However, how to effectively fuse multimodal data remains a challenging problem. Moreover, traditional fully-supervised based approaches suffer from overfitting given limited labeled data. To address the above issues, we propose a novel self-supervised learning (SSL) framework for wearable emotion recognition, where efficient multimodal fusion is realized with temporal convolution-based modality-specific encoders and a transformer-based shared encoder, capturing both intra-modal and inter-modal correlations. Extensive unlabeled data is automatically assigned labels by five signal transforms, and the proposed SSL model is pre-trained with signal transformation recognition as a pretext task, allowing the extraction of generalized multimodal representations for emotion-related downstream tasks. For evaluation, the proposed SSL model was first pre-trained on a large-scale self-collected physiological dataset and the resulting encoder was subsequently frozen or fine-tuned on three public supervised emotion recognition datasets. Ultimately, our SSL-based method achieved state-of-the-art results in various emotion classification tasks. Meanwhile, the proposed model proved to be more accurate and robust compared to fully-supervised methods on low data regimes.Comment: Accepted IEEE Transactions On Affective Computin

Cross-relation Cross-bag Attention for Distantly-supervised Relation Extraction

Distant supervision leverages knowledge bases to automatically label instances, thus allowing us to train relation extractor without human annotations. However, the generated training data typically contain massive noise, and may result in poor performances with the vanilla supervised learning. In this paper, we propose to conduct multi-instance learning with a novel Cross-relation Cross-bag Selective Attention (C$^2$SA), which leads to noise-robust training for distant supervised relation extractor. Specifically, we employ the sentence-level selective attention to reduce the effect of noisy or mismatched sentences, while the correlation among relations were captured to improve the quality of attention weights. Moreover, instead of treating all entity-pairs equally, we try to pay more attention to entity-pairs with a higher quality. Similarly, we adopt the selective attention mechanism to achieve this goal. Experiments with two types of relation extractor demonstrate the superiority of the proposed approach over the state-of-the-art, while further ablation studies verify our intuitions and demonstrate the effectiveness of our proposed two techniques.Comment: AAAI 201

Exploration on the Construction of Digital Content Security Course under the Background of "New Engineering Disciplines"

According to the development and construction of the "new engineering disciplines", the training requirements for talents and the construction of digital content security course are discussed in this paper. Based on the current development situation, this paper clarifies the tightness of digital content security and the development of "new engineering disciplines". The digital content security course has both a complete frontier theoretical system and close correlation with various new engineering disciplines. Combining these two characteristics, this paper proposes three aspects of construction: comprehensive social resources, the formation of a new curriculum teaching system, and the creation of a digital content security gold course; further introduction of school-enterprise cooperation, promotion of the combination of production and education, practical and targeted activities; training of students’ ability to master and apply digital content security and promotion of the construction of applied undergraduate programs

Fusion of Physiological and Behavioural Signals on SPD Manifolds with Application to Stress and Pain Detection

Existing multimodal stress/pain recognition approaches generally extract features from different modalities independently and thus ignore cross-modality correlations. This paper proposes a novel geometric framework for multimodal stress/pain detection utilizing Symmetric Positive Definite (SPD) matrices as a representation that incorporates the correlation relationship of physiological and behavioural signals from covariance and cross-covariance. Considering the non-linearity of the Riemannian manifold of SPD matrices, well-known machine learning techniques are not suited to classify these matrices. Therefore, a tangent space mapping method is adopted to map the derived SPD matrix sequences to the vector sequences in the tangent space where the LSTM-based network can be applied for classification. The proposed framework has been evaluated on two public multimodal datasets, achieving both the state-of-the-art results for stress and pain detection tasks.Comment: International Conference on Systems, Man, and Cybernetics, IEEE SMC 2022, October 9-12, 202

Systemic-Lupus-Erythematosus-Related Acute Pancreatitis: A Cohort from South China

Acute pancreatitis (AP) is a rare but life-threatening complication of SLE. The current study evaluated the clinical characteristics and risk factors for the mortality of patients with SLE-related AP in a cohort of South China. Methods. Inpatient medical records of SLE-related AP were retrospectively reviewed. Results. 27 out of 4053 SLE patients were diagnosed as SLE-related AP, with an overall prevalence of 0.67%, annual incidence of 0.56‰ and mortality of 37.04%. SLE patients with AP presented with higher SLEDAI score (21.70 ± 10.32 versus 16.17 ± 7.51, P = 0.03), more organ systems involvement (5.70 ± 1.56 versus 3.96 ± 1.15, P = 0.001), and higher mortality (37.04% versus 0, P = 0.001), compared to patients without AP. Severe AP (SAP) patients had a significant higher mortality rate compared to mild AP (MAP) (75% versus 21.05%, P = 0.014). 16 SLE-related AP patients received intensive GC treatment, 75% of them exhibited favorable prognosis. Conclusion. SLE-related AP is rare but concomitant with high mortality in South Chinese people, especially in those SAP patients. Activity of SLE, multiple-organ systems involvement may attribute to the severity and mortality of AP. Appropriate glucocorticosteroid (GC) treatment leads to better prognosis in majority of SLE patients with AP

Mixed-Variable PSO with Fairness on Multi-Objective Field Data Replication in Wireless Networks

Digital twins have shown a great potential in supporting the development of wireless networks. They are virtual representations of 5G/6G systems enabling the design of machine learning and optimization-based techniques. Field data replication is one of the critical aspects of building a simulation-based twin, where the objective is to calibrate the simulation to match field performance measurements. Since wireless networks involve a variety of key performance indicators (KPIs), the replication process becomes a multi-objective optimization problem in which the purpose is to minimize the error between the simulated and field data KPIs. Unlike previous works, we focus on designing a data-driven search method to calibrate the simulator and achieve accurate and reliable reproduction of field performance. This work proposes a search-based algorithm based on mixedvariable particle swarm optimization (PSO) to find the optimal simulation parameters. Furthermore, we extend this solution to account for potential conflicts between the KPIs using {\alpha}-fairness concept to adjust the importance attributed to each KPI during the search. Experiments on field data showcase the effectiveness of our approach to (i) improve the accuracy of the replication, (ii) enhance the fairness between the different KPIs, and (iii) guarantee faster convergence compared to other methods.Comment: Accepted in International Conference on Communications (ICC) 202

Effects of wave parameters on load reduction performance for amphibious aircraft with V-hydrofoil

An investigation of the influence of the hydrofoil on load reduction performance during an amphibious aircraft landing on still and wavy water is conducted by solving the Unsteady Reynolds-Averaged Navier-Stokes equations coupled with the standard $k-\omega$ turbulence model in this paper. During the simulations, the numerical wave tank is realized by using the velocity-inlet boundary wave maker coupled with damping wave elimination technique on the outlet, while the volume of fluid model is employed to track the water-air interface. Subsequently, the effects of geometric parameters of hydrofoil have been first discussed on still water, which indicates the primary factor influencing the load reduction is the static load coefficient of hydrofoil. Furthermore, the effects of descent velocity, wave length and wave height on load reduction are comprehensively investigated. The results show that the vertical load reduces more than 55$\%$ at the early stage of landing on the still water through assembling the hydrofoil for different descent velocity cases. Meanwhile, for the amphibious aircraft with high forward velocity, the bottom of the fuselage will come into close contact with the first wave when landing on crest position, and then the forebody will impact the next wave surface with extreme force. In this circumstance, the load reduction rate decreases to around 30$\%$, which will entail a further decline with the increase of wave length or wave height

Intraoperative method of femoral head central measurement to prevent leg length discrepancy following hemiarthroplasty

PurposeThis study aimed to introduce and investigate the safety and efficiency of the intraoperative central measurement method of the femoral head (IM-CMFH) to prevent leg length discrepancies (LLD) after hemiarthroplasty.MethodsOverall, 79 patients aged 75 to 85 years with femoral neck fractures who underwent hemiarthroplasty were divided into two groups: the Control group (n = 46) and the IM-CMFH group (n = 33). The two groups were compared for postoperative LLD and the proportions of patients with greater than 10 mm, 6–10 mm, and within 5 mm, postoperative femoral offset (FO) difference and the proportions of patients within 5 mm, incremental greater than 5 mm and reduction greater than 5 mm. Next, the vertical distance from the center of the femoral head to the tip of the greater trochanter on the anatomical axis of the femur (VD-CFH-TGTAAF), leg length, and FO on the operative and non-operative sides within the IM-CMFH group. Finally, operative time, hemoglobin loss, Harris scores 3 months after surgery, and postoperative complications were analyzed.ResultsCompared with the control group, the postoperative LLD and FO differences were significantly lower in the IM-CMFH group (P = 0.031; P = 0.012), and the proportion of patients with postoperative LLD greater than 10 mm decreased significantly (P = 0.041), while the proportion of patients with FO difference of within 5 mm increased (P = 0.009). In addition, there was no significant difference in the operative time, hemoglobin loss, and Harris score at 3 months postoperatively and postoperative complications between the two groups (P > 0.05). There was no significant difference in FO, leg-length, and VD-CFH-TGTAAF between the operative and non-operative sides within the IM-CMFH group (P > 0.05).ConclusionSatisfactory results can be achieved by using the IM-CMFH to prevent LLD following hemiarthroplasty, and there is no increase in operative time, hemoglobin loss, or postoperative complications. This technique is efficient for hemiarthroplasties and is both simple and convenient