Advanced Gesture Recognition in Autism: Integrating YOLOv7, Video Augmentation and VideoMAE for Video Analysis

Deep learning and advancements in contactless sensors have significantly enhanced our ability to understand complex human activities in healthcare settings. In particular, deep learning models utilizing computer vision have been developed to enable detailed analysis of human gesture recognition, especially repetitive gestures which are commonly observed behaviors in children with autism. This research work aims to identify repetitive behaviors indicative of autism by analyzing videos captured in natural settings as children engage in daily activities. The focus is on accurately categorizing real-time repetitive gestures such as spinning, head banging, and arm flapping. To this end, we utilize the publicly accessible Self-Stimulatory Behavior Dataset (SSBD) to classify these stereotypical movements. A key component of the proposed methodology is the use of \textbf{VideoMAE}, a model designed to improve both spatial and temporal analysis of video data through a masking and reconstruction mechanism. This model significantly outperformed traditional methods, achieving an accuracy of 97.7\%, a 14.7\% improvement over the previous state-of-the-art.

Fluid Dynamic DNNs for Reliable and Adaptive Distributed Inference on Edge Devices

Distributed inference is a popular approach for efficient DNN inference at the edge. However, traditional Static and Dynamic DNNs are not distribution-friendly, causing system reliability and adaptability issues. In this paper, we introduce Fluid Dynamic DNNs (Fluid DyDNNs), tailored for distributed inference. Distinct from Static and Dynamic DNNs, Fluid DyDNNs utilize a novel nested incremental training algorithm to enable independent and combined operation of its sub-networks, enhancing system reliability and adaptability. Evaluation on embedded Arm CPUs with a DNN model and the MNIST dataset, shows that in scenarios of single device failure, Fluid DyDNNs ensure continued inference, whereas Static and Dynamic DNNs fail. When devices are fully operational, Fluid DyDNNs can operate in either a High-Accuracy mode and achieve comparable accuracy with Static DNNs, or in a High-Throughput mode and achieve 2.5x and 2x throughput compared with Static and Dynamic DNNs, respectively.

Aesthetic Attribute Assessment of Images Numerically on Mixed Multi-attribute Datasets

With the continuous development of social software and multimedia technology, images have become a kind of important carrier for spreading information and socializing. How to evaluate an image comprehensively has become the focus of recent researches. The traditional image aesthetic assessment methods often adopt single numerical overall assessment scores, which has certain subjectivity and can no longer meet the higher aesthetic requirements. In this paper, we construct an new image attribute dataset called aesthetic mixed dataset with attributes(AMD-A) and design external attribute features for fusion. Besides, we propose a efficient method for image aesthetic attribute assessment on mixed multi-attribute dataset and construct a multitasking network architecture by using the EfficientNet-B0 as the backbone network. Our model can achieve aesthetic classification, overall scoring and attribute scoring. In each sub-network, we improve the feature extraction through ECA channel attention module. As for the final overall scoring, we adopt the idea of the teacher-student network and use the classification sub-network to guide the aesthetic overall fine-grain regression. Experimental results, using the MindSpore, show that our proposed method can effectively improve the performance of the aesthetic overall and attribute assessment.

Confused Modulo Projection based Somewhat Homomorphic Encryption -- Cryptosystem, Library and Applications on Secure Smart Cities

With the development of cloud computing, the storage and processing of massive visual media data has gradually transferred to the cloud server. For example, if the intelligent video monitoring system cannot process a large amount of data locally, the data will be uploaded to the cloud. Therefore, how to process data in the cloud without exposing the original data has become an important research topic. We propose a single-server version of somewhat homomorphic encryption cryptosystem based on confused modulo projection theorem named CMP-SWHE, which allows the server to complete blind data processing without \emph{seeing} the effective information of user data. On the client side, the original data is encrypted by amplification, randomization, and setting confusing redundancy. Operating on the encrypted data on the server side is equivalent to operating on the original data. As an extension, we designed and implemented a blind computing scheme of accelerated version based on batch processing technology to improve efficiency. To make this algorithm easy to use, we also designed and implemented an efficient general blind computing library based on CMP-SWHE. We have applied this library to foreground extraction, optical flow tracking and object detection with satisfactory results, which are helpful for building smart cities. We also discuss how to extend the algorithm to deep learning applications. Compared with other homomorphic encryption cryptosystems and libraries, the results show that our method has obvious advantages in computing efficiency. Although our algorithm has some tiny errors ($10^{-6}$) when the data is too large, it is very efficient and practical, especially suitable for blind image and video processing.

DATE: Defense Against TEmperature Side-Channel Attacks in DVFS Enabled MPSoCs

Given the constant rise in utilizing embedded devices in daily life, side channels remain a challenge to information flow control and security in such systems. One such important security flaw could be exploited through temperature side-channel attacks, where heat dissipation and propagation from the processing elements are observed over time in order to deduce security flaws. In our proposed methodology, DATE: Defense Against TEmperature side-channel attacks, we propose a novel approach of reducing spatial and temporal thermal gradient, which makes the system more secure against temperature side-channel attacks, and at the same time increases the reliability of the device in terms of lifespan. In this paper, we have also introduced a new metric, Thermal-Security-in-Multi-Processors (TSMP), which is capable of quantifying the security against temperature side-channel attacks on computing systems, and DATE is evaluated to be 139.24% more secure at the most for certain applications than the state-of-the-art, while reducing thermal cycle by 67.42% at the most.

MAT-CNN-SOPC: Motionless Analysis of Traffic Using Convolutional Neural Networks on System-On-a-Programmable-Chip

Intelligent Transportation Systems (ITS) have become an important pillar in modern "smart city" framework which demands intelligent involvement of machines. Traffic load recognition can be categorized as an important and challenging issue for such systems. Recently, Convolutional Neural Network (CNN) models have drawn considerable amount of interest in many areas such as weather classification, human rights violation detection through images, due to its accurate prediction capabilities. This work tackles real-life traffic load recognition problem on System-On-a-Programmable-Chip (SOPC) platform and coin it as MAT-CNN- SOPC, which uses an intelligent re-training mechanism of the CNN with known environments. The proposed methodology is capable of enhancing the efficacy of the approach by 2.44x in comparison to the state-of-art and proven through experimental analysis. We have also introduced a mathematical equation, which is capable of quantifying the suitability of using different CNN models over the other for a particular application based implementation.