AI WALKUP: A Computer-Vision Approach to Quantifying MDS-UPDRS in Parkinson's Disease

Parkinson's Disease (PD) is the second most common neurodegenerative disorder. The existing assessment method for PD is usually the Movement Disorder Society - Unified Parkinson's Disease Rating Scale (MDS-UPDRS) to assess the severity of various types of motor symptoms and disease progression. However, manual assessment suffers from high subjectivity, lack of consistency, and high cost and low efficiency of manual communication. We want to use a computer vision based solution to capture human pose images based on a camera, reconstruct and perform motion analysis using algorithms, and extract the features of the amount of motion through feature engineering. The proposed approach can be deployed on different smartphones, and the video recording and artificial intelligence analysis can be done quickly and easily through our APP.

CUEING: A pioneer work of encoding human gaze for autonomous driving

Recent analysis of incidents involving Autonomous Driving Systems (ADS) has shown that the decision-making process of ADS can be significantly different from that of human drivers. To improve the performance of ADS, it may be helpful to incorporate the human decision-making process, particularly the signals provided by the human gaze. There are many existing works to create human gaze datasets and predict the human gaze using deep learning models. However, current datasets of human gaze are noisy and include irrelevant objects that can hinder model training. Additionally, existing CNN-based models for predicting human gaze lack generalizability across different datasets and driving conditions, and many models have a centre bias in their prediction such that the gaze tends to be generated in the centre of the gaze map. To address these gaps, we propose an adaptive method for cleansing existing human gaze datasets and a robust convolutional self-attention gaze prediction model. Our quantitative metrics show that our cleansing method improves models' performance by up to 7.38% and generalizability by up to 8.24% compared to those trained on the original datasets. Furthermore, our model demonstrates an improvement of up to 12.13% in terms of generalizability compared to the state-of-the-art (SOTA) models. Notably, it achieves these gains while conserving up to 98.12% of computation resources.

Deep Learning-Based Autonomous Driving Systems: A Survey of Attacks and Defenses

The rapid development of artificial intelligence, especially deep learning technology, has advanced autonomous driving systems (ADSs) by providing precise control decisions to counterpart almost any driving event, spanning from anti-fatigue safe driving to intelligent route planning. However, ADSs are still plagued by increasing threats from different attacks, which could be categorized into physical attacks, cyberattacks and learning-based adversarial attacks. Inevitably, the safety and security of deep learning-based autonomous driving are severely challenged by these attacks, from which the countermeasures should be analyzed and studied comprehensively to mitigate all potential risks. This survey provides a thorough analysis of different attacks that may jeopardize ADSs, as well as the corresponding state-of-the-art defense mechanisms. The analysis is unrolled by taking an in-depth overview of each step in the ADS workflow, covering adversarial attacks for various deep learning models and attacks in both physical and cyber context. Furthermore, some promising research directions are suggested in order to improve deep learning-based autonomous driving safety, including model robustness training, model testing and verification, and anomaly detection based on cloud/edge servers.

SCEI: A Smart-Contract Driven Edge Intelligence Framework for IoT Systems

Federated learning (FL) utilizes edge computing devices to collaboratively train a shared model while each device can fully control its local data access. Generally, FL techniques focus on learning model on independent and identically distributed (iid) dataset and cannot achieve satisfiable performance on non-iid datasets (e.g. learning a multi-class classifier but each client only has a single class dataset). Some personalized approaches have been proposed to mitigate non-iid issues. However, such approaches cannot handle underlying data distribution shift, namely data distribution skew, which is quite common in real scenarios (e.g. recommendation systems learn user behaviors which change over time). In this work, we provide a solution to the challenge by leveraging smart-contract with federated learning to build optimized, personalized deep learning models. Specifically, our approach utilizes smart contract to reach consensus among distributed trainers on the optimal weights of personalized models. We conduct experiments across multiple models (CNN and MLP) and multiple datasets (MNIST and CIFAR-10). The experimental results demonstrate that our personalized learning models can achieve better accuracy and faster convergence compared to classic federated and personalized learning. Compared with the model given by baseline FedAvg algorithm, the average accuracy of our personalized learning models is improved by 2% to 20%, and the convergence rate is about 2$\times$ faster. Moreover, we also illustrate that our approach is secure against recent attack on distributed learning.

An Analysis of Adversarial Attacks and Defenses on Autonomous Driving Models

Nowadays, autonomous driving has attracted much attention from both industry and academia. Convolutional neural network (CNN) is a key component in autonomous driving, which is also increasingly adopted in pervasive computing such as smartphones, wearable devices, and IoT networks. Prior work shows CNN-based classification models are vulnerable to adversarial attacks. However, it is uncertain to what extent regression models such as driving models are vulnerable to adversarial attacks, the effectiveness of existing defense techniques, and the defense implications for system and middleware builders. This paper presents an in-depth analysis of five adversarial attacks and four defense methods on three driving models. Experiments show that, similar to classification models, these models are still highly vulnerable to adversarial attacks. This poses a big security threat to autonomous driving and thus should be taken into account in practice. While these defense methods can effectively defend against different attacks, none of them are able to provide adequate protection against all five attacks. We derive several implications for system and middleware builders: (1) when adding a defense component against adversarial attacks, it is important to deploy multiple defense methods in tandem to achieve a good coverage of various attacks, (2) a blackbox attack is much less effective compared with a white-box attack, implying that it is important to keep model details (e.g., model architecture, hyperparameters) confidential via model obfuscation, and (3) driving models with a complex architecture are preferred if computing resources permit as they are more resilient to adversarial attacks than simple models.