On behalf of the technical program committee, we are delighted to invite you to participate in the 2024 IEEE 8th International Conference on Smart Internet of Things (IEEE SmartIoT 2024), to be held in Shenzhen, China on November 14-16, 2024.
With the rapid advancement of computer technol-ogy, an increasing number of intelligent devices are being widely applied across various societal domains. Human motion prediction, in particular, holds significant value in human-computer interaction and intelligent surveillance, effectively enhancing the intelligence of these devices. Diversity prediction, a type of human motion prediction, seeks to forecast various potential future movements of the human body. Given the randomness in generation associated with Variational Autoencoders (VAEs), we employed a Recursive Neural network (RNN)-based VAE for future predictions. Additionally, considering the lack of correlation between random Gaussian samples during resampling, we proposed a novel sampling method to generate more reliable and diverse future predictions. Extensive experiments were conducted on the Human 3.6M [30] and HumanEva-I [31] datasets, yielding quite impressive results.
Scarab beetles, with over 35,000 species, significantly impact agriculture globally. Accurate and timely identification is crucial to control their damage to crops and orchards. This study introduces a method for detecting and identifying scarab species, using a dataset of 588 images covering five common species: Blackgill beetle, Verdigris beetle, Proagopertha lucidula, Brown beetle, and Tawny beetle. We developed an automatic scarab detection method using the Mask-RCNN network with the CBAM module, improving feature extraction and reducing background noise interference. The model's accuracy is further enhanced by integrating multi-source IoT data fusion, leveraging diverse data from IoT devices to refine detection. These devices capture a wide array of environmental variables and scarab behaviors, enriching the model's dataset for improved predictive accuracy. The performance of the enhanced C_Resnet algorithm, which includes these integrations, was thoroughly evaluated. Results indicate a significant improvement in detection accuracy, with an average rate of 98.6%, marking a 14% increase over the standard Mask-RCNN algorithm. Comparative analysis with other prevalent models like Yolov3, Solov2, and GNNET shows mAP improvements of 11.7%, 44.8%, and 5.6%, respectively. These findings underscore C_Resnet's superior capability in distinguishing between common beetle species.
Human activity recognition is becoming a research hotspot in recent years due to its wide range of application prospects in surveillance, healthcare, human-computer interaction, etc. A wide variety of sensors has been applied in human activity recognition including cameras, Bluetooth/WiFi receivers, embedding sensors in smartphones/smartwatches, etc. Radar can transmit electromagnetic wave signals and receive target echoes; echo signals contain human motion characteristics, which can be used for human activity recognition. Compared to other sensors, radar has the characteristics of a wide detection range, strong penetration ability, non-contact detection ability, etc. It is not affected by light and is not privacy-intrusive to the observed person. Radar-based human activity recognition is gradually attracting the attention of academia and industry. In this paper, we reviewed current research on radar-based human activity recognition from the aspects of applications and challenges. This paper aims to provide comprehensive and detailed guidance for the foreseen research of radar-based activity recognition.
This study addresses a significant gap in the current literature regarding the impact of hyperparameters on the Mamba model's running time and accuracy. To bridge this gap, a series of comprehensive experiments were designed and conducted to assess the effects of various initial hyperparameter settings, specifically focusing on the learning rate, batch size, and SSM state expansion factor, on the Mamba model's performance. Three widely recognized datasets, SST2, IMDB, and AG_NEWS, were utilized for these experimental evaluations. The primary objective of this research is to provide an indepth analysis of how these hyperparameters influence the running time and accuracy of the model. By varying the initial values of the learning rate, the study explored its impact on both the convergence speed and ultimate accuracy of the model. Adjustments to the batch size were examined to understand their implications for computational efficiency and overall model performance. Furthermore, modifications to the SSM state expansion factor were investigated to determine their effects on model capacity and accuracy. Through these experiments, this research aims to provide valuable insights and guidelines for researchers and practitioners working with the Mamba model. Ultimately, this study seeks to achieve an optimal balance between computational efficiency and model accuracy by contributing to a more nuanced understanding of hyperparameter optimization.
