LoRA-like Calibration for Multimodal Deception Detection using ATSFace Data

Recently, deception detection on human videos is an eye-catching techniques and can serve lots applications. AI model in this domain demonstrates the high accuracy, but AI tends to be a non-interpretable black box. We introduce an attention-aware neural network addressing challenges inherent in video data and deception dynamics. This model, through its continuous assessment of visual, audio, and text features, pinpoints deceptive cues. We employ a multimodal fusion strategy that enhances accuracy; our approach yields a 92\% accuracy rate on a real-life trial dataset. Most important of all, the model indicates the attention focus in the videos, providing valuable insights on deception cues. Hence, our method adeptly detects deceit and elucidates the underlying process. We further enriched our study with an experiment involving students answering questions either truthfully or deceitfully, resulting in a new dataset of 309 video clips, named ATSFace. Using this, we also introduced a calibration method, which is inspired by Low-Rank Adaptation (LoRA), to refine individual-based deception detection accuracy.Comment: 10 pages, 9 figure

Learning satisfaction of undergraduates in single-sex-dominated academic fields in Taiwan

AbstractThe present study investigated relationships between undergraduates’ learning satisfaction, academic identity, self-esteem and feeling of depression and loneliness in Taiwan. Data were from a national survey in Taiwan. Participants were 15,706 third-year undergraduates (8719 female, 6987 male). The results showed that, after controlling for undergraduates’ academic performance and attitudes toward university and department, (1) learning satisfaction of females in male-dominant fields was negatively correlated with their feeling of depression, (2) learning satisfaction of males in female-dominant fields was positively correlated with their academic identity and self-esteem, and (3) learning satisfaction of undergraduates in non-dominated fields was positively correlated with their academic identity and self-esteem but also negatively correlated with their feelings of depression

Reinforcement Learning-based Livestreaming E-commerce Recommendation System

Unlike conventional commerce, livestreaming e-commerce continuously introduces new products, resulting in a dynamic and complex context. To address the trade-off between exploration and exploitation in such a rapidly evolving recommendation context, we propose a reinforcement learning-based solution focusing on the relationships between customers, streamers, and products. We apply RNN to model the context changes in users’ preferences for streamers and products while maintaining long-term engagement. The proposed livestreaming e-commerce recommendation system (LERS) enhances the exploration of new items by incorporating uncertainty into neural networks through VAE for user modeling and BNN for product recommendation. We conducted comparisons between LERS and multi-armed bandit algorithms using real-world business data. Our findings support the proposed theoretical framework and highlight the potential practical applications of our algorithm

Aberrant KDM5B expression promotes aggressive breast cancer through MALAT1 overexpression and downregulation of hsa-miR-448

Relative expression of KDM5B, MALAT1, SNAIL, Vimentin and miR 448 normalized against GAPDH in MCF10A WT, MCF10A OE, MDA-MB-231 WT and MDA-MB-231 KD cells. Data are representative of 3 independent experiments and analyzed by student’s t-test. All data are shown as mean ± SEM. WT, wild type; OE, KDM5B overexpressed; KD, knockdown using shKDM5B clone II. (DOCX 519 kb

Clinical Study of Uric Acid Urolithiasis

Uric acid urolithiasis develops from various causes. To investigate the clinical and biochemical presentation of patients with uric acid urolithiasis, a retrospective study was designed. A total of 46 cases were enrolled between January 2004 and December 2005. The compositions of the stones were analyzed by infrared spectrophotometry. There were 39 males (84.8%) and seven females (15.2%), with a mean age of 61.5 ± 10.6 years and mean body mass index (BMI) of 26.7 ± 3.1 kg/m2. The stone location was kidney in 10 (21.7%), ureter in 22 (41.8%), and bladder in 14 (30.5%). Multiple stones were diagnosed in 36 patients (78.3%). Pre-existing comorbidities included diabetes mellitus in 11 patients (23.9%), hypertension in 23 (50%), gout in 13 (28.2%), and benign prostatic hyperplasia in 14 (30.4%). Mean serum creatinine and uric acid was 1.6 ± 0.6 mg/dL and 7.6 ± 1.8 mg/dL, respectively. There were 27 patients (58%) with creatinine > 1.4 mg/dL. The mean urinary pH was 5.42 ± 0.46. Patients with uric acid urolithiasis were predominantly male, older, with higher BMI, multiple stone presentation, with lower urinary pH, and hyperuricemia. Exacerbation of the renal function should also be of concern because of the high proportion of patients with renal insufficiency diagnosed in this study

Benchmarking of eight recurrent neural network variants for breath phase and adventitious sound detection on a self-developed open-access lung sound database-HF_Lung_V1

A reliable, remote, and continuous real-time respiratory sound monitor with automated respiratory sound analysis ability is urgently required in many clinical scenarios-such as in monitoring disease progression of coronavirus disease 2019-to replace conventional auscultation with a handheld stethoscope. However, a robust computerized respiratory sound analysis algorithm has not yet been validated in practical applications. In this study, we developed a lung sound database (HF_Lung_V1) comprising 9,765 audio files of lung sounds (duration of 15 s each), 34,095 inhalation labels, 18,349 exhalation labels, 13,883 continuous adventitious sound (CAS) labels (comprising 8,457 wheeze labels, 686 stridor labels, and 4,740 rhonchi labels), and 15,606 discontinuous adventitious sound labels (all crackles). We conducted benchmark tests for long short-term memory (LSTM), gated recurrent unit (GRU), bidirectional LSTM (BiLSTM), bidirectional GRU (BiGRU), convolutional neural network (CNN)-LSTM, CNN-GRU, CNN-BiLSTM, and CNN-BiGRU models for breath phase detection and adventitious sound detection. We also conducted a performance comparison between the LSTM-based and GRU-based models, between unidirectional and bidirectional models, and between models with and without a CNN. The results revealed that these models exhibited adequate performance in lung sound analysis. The GRU-based models outperformed, in terms of F1 scores and areas under the receiver operating characteristic curves, the LSTM-based models in most of the defined tasks. Furthermore, all bidirectional models outperformed their unidirectional counterparts. Finally, the addition of a CNN improved the accuracy of lung sound analysis, especially in the CAS detection tasks.Comment: 48 pages, 8 figures. To be submitte