Nowadays, machine learning is widely used in various applications. Training a model requires huge amounts of data, but it can pose a threat to user privacy. With the growing concern for privacy, the “Right to be Forgotten” has been proposed, which means that users have the right to request that their personal information be removed from machine learning models. The emergence of machine unlearning is a response to this need. Implementing machine unlearning is not easy because simply deleting samples from a database does not allow the model to “forget” the data. Therefore, this paper summarises the definition of the machine unlearning formulation, process, deletion requests, design requirements and validation, algorithms, applications, and future perspectives, in the hope that it will help future researchers in machine unlearning.

Heuristic algorithms have been developed to find approximate solutions for high-utility itemset mining (HUIM) problems that compensate for the performance bottlenecks of exact algorithms. However, heuristic algorithms still face the problem of long runtime and insufficient mining quality, especially for large transaction datasets with thousands to tens of thousands of items and up to millions of transactions. To solve these problems, a novel GPU-based efficient parallel heuristic algorithm for HUIM (PHA-HUIM) is proposed in this paper. The iterative process of PHA-HUIM consists of three main steps: the search strategy, fitness evaluation, and ring topology communication. The search strategy and ring topology communication are designed to run in constant time on GPU. The parallelism of fitness evolution helps to substantially accelerate the algorithm. To improve the mining quality, a multi-start strategy with an unbalanced allocation strategy is employed in the search process. Ring topology communication is adopted to maintain population diversity. A load balancing strategy is introduced to reduce the thread divergence to improve the parallel efficiency. The experimental results on nine large datasets show that PHA-HUIM outperforms state-of-the-art HUIM algorithms in terms of speedup performance, runtime, and mining quality.

Learning a decent representation from unlabeled time series is a challenging task, especially when the time series data is derived from diverse channels at different sampling rates. Our motivation stems from the financial domain, where sparsely labeled covariates are commonly collected at different frequencies, *e.g.*, daily stock market index, monthly unemployment rate and quarterly net revenue of a certain listed corporation. This paper presents **M**ulti-**F**requency **C**ontrastive **L**earning **R**epresentation (MF-CLR), aimed at learning a good representation of multi-frequency time series in a self-supervised paradigm by leveraging the ability of contrastive learning. MF-CLR introduces a hierarchical mechanism that spans across different frequencies along the feature dimension. Within each contrastive block, two groups of subseries with adjacent frequencies are embedded based on our proposed cross-frequency consistency. To validate the effectiveness of MF-CLR, we conduct extensive experiments on five downstream tasks, including long-term and short-term forecasting, classification, anomaly detection and imputation. Experimental evidence shows that MF-CLR delivers a leading performance in all the downstream tasks and keeps consistent performance across different target dataset scales in the transfer learning scenario.

With the rapid advancement of hardware and software technologies, research in autonomous driving has seen significant growth. The prevailing framework for multi-sensor autonomous driving encompasses sensor installation, perception, path planning, decision-making, and motion control. At the perception phase, a common approach involves utilizing neural networks to infer 3D bounding box (Bbox) attributes from raw sensor data, including classification, size, and orientation.In this paper, we present a novel attribute and its corresponding algorithm: 3D object visibility. By incorporating multi-task learning, the introduction of this attribute, visibility, negligibly affects the model's effectiveness and efficiency. Our proposal of this attribute and its computational strategy aims to expand the capabilities for downstream tasks, thereby enhancing the safety and reliability of real-time autonomous driving in real-world scenarios.

The BERT-style (Bidirectional Encoder Representations from Transformers) pre-training paradigm has achieved remarkable success in both NLP (Natural Language Processing) and CV (Computer Vision). However, due to the sparsity and lack of semantic information in point cloud data, directly applying this paradigm faces significant obstacles. To address these issues, we propose LSV-MAE, a masked autoencoding pre-training scheme designed for voxel representations. We pre-train the backbone to reconstruct the masked voxels features extracted by PointNN. To enhance the feature extraction capability of the encoder, the point cloud is voxelized with different voxel sizes at different pre-training stages. Meanwhile, to minimize the effect of masking key points during the masking stage, the masked voxel features is re-integrated during the decoder processing. To test the proposed approach, experiments are conducted on well-known datasets. It is shown that the proposed method can improve detection accuracy by 1% to 18%, compared to the model without pre-training, across datasets of different sizes.

Federated Learning (FL) transforms collaborative machine learning by enabling data privacy-preserving model training across dispersed devices. Unfortunately, several challenges need to be concerned, such as the non-ID features of cross-client data and the possibility of backdoor attacks, which can result in inconsistent models, inefficiency, and security issues. To address these issues, we propose hypernetwork-based personalization and poisoning-free federated learning (HBIpFL), a unique architecture to improve security and personalization in FL. HBIpFL dramatically reduces communication overhead without sacrificing performance by using a hypernetwork-based parameter classifier to dynamically analyze and only upload the most important model parameters. Furthermore, it utilizes Local Gradient Ascent (LGA) methods to track training loss trends and identify possible backdoor intrusions, guaranteeing the resilience and dependability of the global model. We then compare HBIpFL to the state-of-the-art methods in the context of accuracy, communication efficiency, and defense against adversarial attacks. The results demonstrate that our HBIpFL offers a secure and effective FL environment for practical scenarios with a wide range of data distributions and strict privacy specifications.

Heuristic algorithms have been developed to find approximate solutions for high-utility itemset mining (HUIM) problems that compensate for the performance bottlenecks of exact algorithms. However, heuristic algorithms still face the problem of long runtime and insufficient mining quality, especially for large transaction datasets with thousands to tens of thousands of items and up to millions of transactions. To solve these problems, a novel GPU-based efficient parallel heuristic algorithm for HUIM (PHA-HUIM) is proposed in this paper. The iterative process of PHA-HUIM consists of three main steps: the search strategy, fitness evaluation, and ring topology communication. The search strategy and ring topology communication are designed to run in constant time on GPU. The parallelism of fitness evolution helps to substantially accelerate the algorithm. A new data structure with a sort-mapping strategy is proposed to enhance the search ability and reduce memory usage. To improve the mining quality, a multi-start strategy with an unbalanced allocation strategy is employed in the search process. Ring topology communication is adopted to maintain population diversity. A load balancing strategy is introduced to reduce the thread divergence to improve the parallel efficiency. The experimental results on nine large datasets show that PHA-HUIM outperforms state-of-the-art HUIM algorithms in terms of speedup performance, runtime, and mining quality.