In order for autonomous mobile robots to play an active role in our daily lives, the robots must be able to recognize an unknown 3D environment in real time. To address this challenge, we introduce an innovative method for object tracking using 3D point clouds as input. Our approach leverages the Growing Neural Gas (GNG) algorithm for real-time environment recognition in unfamiliar settings, coupled with object detection using Axis-Aligned Bounding Box (AABB), and Intersection over Union (IoU) for precise tracking. Our method achieves real-time object tracking in an unknown environment, processing in approximately 33 milliseconds. The proposed method showcases performance with an average IoU value of around 0.77, which was sufficient for object tracking. Finally, we validate the efficacy of this method by conducting two experiments in both a simulated environment using a depth camera and a real environment using a 3D-Lidar.
Meta-heuristic algorithms require a large amount of computational memory. The use of probabilistic models has been suggested as an alternative to the compact method of particles. In this study, a novel lightweight multi-strategy particle swarm algorithm is proposed. The use of a new probabilistic model is proposed to replace the commonly used normal distribution. The proposed lightweight algorithm is evaluated on the CEC2014 test set with excellent results.
To detect the suspect poisoned data in the training phase, most backdoor defenses rely on a prevalent assumption, i.e., the feature separability between poisoned and benign samples. However, this assumption can be bypassed by novel adaptive attacks, which merge the features of poisoned and benign samples. In this paper, we contrast these adaptive attacks and propose a so-called Local-Feature-Powered Defense (LFPD), which leverages a local feature algorithm to measure samples' similarity in the image space and uses it to guide the training process to increase the feature sepa-rability between poisoned and benign samples. Then, our LFPD detects the outliers in the training dataset as poisoned samples and removes the backdoor by unlearning them. Finally, we compare our LFPD with five existing defenses, and our experimental results demonstrate that LFPD outperforms them in defending against adaptive attacks.
In-silico toxicity prediction plays a key role in the health industry and research. Machine learning has been widely rewarded for its high efficiency in this field, but the methods are mostly expertise driven and with limited growth. After mitigating many practical problems smartly, deep learning has also been entrusted with high expectations in toxicity prediction. In this work, we attempted to predict compound toxicity by leveraging a graph representation of the compounds and a neat graph-learning framework. Key atomic and bond features were captured by the graph representations, and different graph-learning techniques in the framework were investigated. As evaluated on the Tox21 data, the graph-learning framework possesses a good potential in attaining state-of-the-art performances and handling imbalanced data in toxicity prediction tasks.
The security of academic credentials is increasingly at risk due to cyberattacks and credential fraud. Traditional verification systems rely on centralised databases, creating single points of failure and privacy concerns. This paper explores Zero-Knowledge Proofs (ZKPs) with blockchain to enhance educational data security. We propose an optimised ZKP protocol for education, improving lightweight infrastructure, scalability, credential revocation, and ease of use. By refining existing techniques, our approach enhances secure, privacy-preserving credential verification, creating a resilient educational data system.
