Modeling of Performance Creative Evaluation Driven by Multimodal Affective Data

Performance creative evaluation can be achieved through affective data, and the use of affective featuresto evaluate performance creative is a new research trend. This paper proposes a “Performance Creative—Multimodal Affective (PC-MulAff)” model based on the multimodal affective features for performance creative evaluation. The multimedia data acquisition equipment is used to collect the physiological data of the audience, including the multimodal affective data such as the facial expression, heart rate and eye movement. Calculate affective features of multimodal data combined with director annotation, and defined “Performance Creative—Affective Acceptance (PC-Acc)” based on multimodal affective features to evaluate the quality of performance creative. This paper verifies the PC-MulAff model on different performance data sets. The experimental results show that the PC-MulAff model shows high evaluation quality in different performance forms. In the creative evaluation of dance performance, the accuracy of the model is 7.44% and 13.95% higher than that of the single textual and single video evaluation

Human Pose Transfer with Augmented Disentangled Feature Consistency

Deep generative models have made great progress in synthesizing images with arbitrary human poses and transferring poses of one person to others. Though many different methods have been proposed to generate images with high visual fidelity, the main challenge remains and comes from two fundamental issues: pose ambiguity and appearance inconsistency. To alleviate the current limitations and improve the quality of the synthesized images, we propose a pose transfer network with augmented Disentangled Feature Consistency (DFC-Net) to facilitate human pose transfer. Given a pair of images containing the source and target person, DFC-Net extracts pose and static information from the source and target respectively, then synthesizes an image of the target person with the desired pose from the source. Moreover, DFC-Net leverages disentangled feature consistency losses in the adversarial training to strengthen the transfer coherence and integrates a keypoint amplifier to enhance the pose feature extraction. With the help of the disentangled feature consistency losses, we further propose a novel data augmentation scheme that introduces unpaired support data with the augmented consistency constraints to improve the generality and robustness of DFC-Net. Extensive experimental results on Mixamo-Pose and EDN-10k have demonstrated DFC-Net achieves state-of-the-art performance on pose transfer.Comment: 22 pages, 6 figure

Key Technology of Real-Time Road Navigation Method Based on Intelligent Data Research

The effect of traffic flow prediction plays an important role in routing selection. Traditional traffic flow forecasting methods mainly include linear, nonlinear, neural network, and Time Series Analysis method. However, all of them have some shortcomings. This paper analyzes the existing algorithms on traffic flow prediction and characteristics of city traffic flow and proposes a road traffic flow prediction method based on transfer probability. This method first analyzes the transfer probability of upstream of the target road and then makes the prediction of the traffic flow at the next time by using the traffic flow equation. Newton Interior-Point Method is used to obtain the optimal value of parameters. Finally, it uses the proposed model to predict the traffic flow at the next time. By comparing the existing prediction methods, the proposed model has proven to have good performance. It can fast get the optimal value of parameters faster and has higher prediction accuracy, which can be used to make real-time traffic flow prediction

Adaptive Vague Preference Policy Learning for Multi-round Conversational Recommendation

Conversational recommendation systems (CRS) effectively address information asymmetry by dynamically eliciting user preferences through multi-turn interactions. Existing CRS widely assumes that users have clear preferences. Under this assumption, the agent will completely trust the user feedback and treat the accepted or rejected signals as strong indicators to filter items and reduce the candidate space, which may lead to the problem of over-filtering. However, in reality, users' preferences are often vague and volatile, with uncertainty about their desires and changing decisions during interactions. To address this issue, we introduce a novel scenario called Vague Preference Multi-round Conversational Recommendation (VPMCR), which considers users' vague and volatile preferences in CRS.VPMCR employs a soft estimation mechanism to assign a non-zero confidence score for all candidate items to be displayed, naturally avoiding the over-filtering problem. In the VPMCR setting, we introduce an solution called Adaptive Vague Preference Policy Learning (AVPPL), which consists of two main components: Uncertainty-aware Soft Estimation (USE) and Uncertainty-aware Policy Learning (UPL). USE estimates the uncertainty of users' vague feedback and captures their dynamic preferences using a choice-based preferences extraction module and a time-aware decaying strategy. UPL leverages the preference distribution estimated by USE to guide the conversation and adapt to changes in users' preferences to make recommendations or ask for attributes. Our extensive experiments demonstrate the effectiveness of our method in the VPMCR scenario, highlighting its potential for practical applications and improving the overall performance and applicability of CRS in real-world settings, particularly for users with vague or dynamic preferences

RCEA-360VR: Real-time, continuous emotion annotation in 360◦ VR videos for collecting precise viewport-dependent ground truth labels

Precise emotion ground truth labels for 360◦ virtual reality (VR) video watching are essential for fne-grained predictions under varying viewing behavior. However, current annotation techniques either rely on post-stimulus discrete self-reports, or real-time, con- tinuous emotion annotations (RCEA) but only for desktop/mobile settings. We present RCEA for 360◦ VR videos (RCEA-360VR), where we evaluate in a controlled study (N=32) the usability of two peripheral visualization techniques: HaloLight and DotSize. We furthermore develop a method that considers head movements when fusing labels. Using physiological, behavioral, and subjective measures, we show that (1) both techniques do not increase users’ workload, sickness, nor break presence (2) our continuous valence and arousal annotations are consistent with discrete within-VR and original stimuli ratings (3) users exhibit high similarity in viewing behavior, where fused ratings perfectly align with intended labels. Our work contributes usable and efective techniques for collecting fne-grained viewport-dependent emotion labels in 360◦ VR

Real-time fluorescence and colorimetric identification of bulbus fritillariae using recombinase assisted loop-mediated isothermal DNA amplification (RALA)

Bulbus Fritillariae (BF) is a kind of herbal medicine widely used in many countries including China, Japan, Korea, and so on. Among the known BF medicinal materials, Bulbus Fritillariae cirrhosae (BF cirrhosae) was reported to have the best curative effect. Due to the limited resources of BF cirrhosae, a lot of adulterants have emerged in the market, impairing the market order, resource development, and above all, clinical efficacy. Here, a novel nucleic acid amplification technique, Recombinase Assisted Loop-mediated isothermal DNA Amplification (RALA), was used to establish a real-time fluorescence isothermal molecular authentication method for five commonly used BF drugs. Moreover, this RALA-based assay can also be developed as a colorimetric detection method for on-site detection. Both real-time fluorescence and visual methods could detect as low as 0.1% genuine targets in the mixed samples. In summary, we report an isothermal detection system for five kinds of BF circulating in the market, providing a new choice for the molecular identification of BF drugs and showing promise in the laboratory testing as well as field identification of other herbal medicines

Autophagic dysfunction of β cell dysfunction in type 2 diabetes, a double-edged sword

Diabetes is an age-related disease, most of which is type 2 diabetes, and islet β cell dysfunction and insulin resistance are the main mechanisms of type 2 diabetes. Recent studies have revealed that autophagy plays an important role in maintaining the structure and function of islet beta cells and inhibiting insulin resistance and apoptosis induced by oxidative stress. In this review, we discussed the positive and negative effects of autophagy and its dysfunction on type 2 diabetes mellitus, which is the so-called double-edged sword, analysed its possible mechanism, and identified possible research hot spots

Research on a method of completeness index based on complex model

The completeness of the complex model is an important index to evaluate the pros and cons of the complex model. It is of great significance to study the objective evaluation under different indicators and construct an indicator system to ensure the integrity of the complex. The construction of complex system indicators requires instructors who can fully describe the functions of complex models. This study designs a complex index EX, proves its authenticity through comparison with the real world, illustrates its similarity through conflict and comparison with the coverage of the target problem, and describes its intelligence level through confrontation and comparison with other complex models. Research on complex model problems based on the derivation process of domain theory proves that the quantified complete distance EX can calculate the similarity between the model and the actual environment to evaluate ‘true and false’. The experiment proved to be a reliable and complex model verification indicator

TSN: Performance Creative Choreography Based on Twin Sensor Network

The purpose of this paper is to improve the efficiency of performance creative choreography (PCC). Our research work shows that we can realize the model integration and data optimization for PCC in complex environments based on the combined architecture of sensor network (SN) and machine-learning algorithm (MLA). In order to explain the process and content of this research better, this paper designs a specific problem description framework for PCC, which mainly includes the following content: (1) a twin sensor network (TSN) architecture based on digital twin information interaction is proposed, which defines and describes the acquisition method, classification (creative data, rehearsal data, and live data), and temporal and spatial features of performance data. (2) Proposed a mobile computing method based on director semantic annotation (DSA) as the core computing module of TSN. (3) A spatial dynamic line (SDL) model and a creative activation mechanism (CAM) based on DSA are proposed to realize fast and efficient PCC of dance with the TSN architecture. Experimental results show that the TSN architecture proposed in this article is reasonable and effective. The SDL model achieved significantly better performance with little time increase and improved the computability and aesthetics of PCC. New research ideas are proposed to solve the computational problem of PCC in complex environments