Causal Learning for Trustworthy Recommender Systems: A Survey

Recommender Systems (RS) have significantly advanced online content discovery and personalized decision-making. However, emerging vulnerabilities in RS have catalyzed a paradigm shift towards Trustworthy RS (TRS). Despite numerous progress on TRS, most of them focus on data correlations while overlooking the fundamental causal nature in recommendation. This drawback hinders TRS from identifying the cause in addressing trustworthiness issues, leading to limited fairness, robustness, and explainability. To bridge this gap, causal learning emerges as a class of promising methods to augment TRS. These methods, grounded in reliable causality, excel in mitigating various biases and noises while offering insightful explanations for TRS. However, there lacks a timely survey in this vibrant area. This paper creates an overview of TRS from the perspective of causal learning. We begin by presenting the advantages and common procedures of Causality-oriented TRS (CTRS). Then, we identify potential trustworthiness challenges at each stage and link them to viable causal solutions, followed by a classification of CTRS methods. Finally, we discuss several future directions for advancing this field.

Semantic-Preserving Feature Partitioning for Multi-View Ensemble Learning

In machine learning, the exponential growth of data and the associated ``curse of dimensionality'' pose significant challenges, particularly with expansive yet sparse datasets. Addressing these challenges, multi-view ensemble learning (MEL) has emerged as a transformative approach, with feature partitioning (FP) playing a pivotal role in constructing artificial views for MEL. Our study introduces the Semantic-Preserving Feature Partitioning (SPFP) algorithm, a novel method grounded in information theory. The SPFP algorithm effectively partitions datasets into multiple semantically consistent views, enhancing the MEL process. Through extensive experiments on eight real-world datasets, ranging from high-dimensional with limited instances to low-dimensional with high instances, our method demonstrates notable efficacy. It maintains model accuracy while significantly improving uncertainty measures in scenarios where high generalization performance is achievable. Conversely, it retains uncertainty metrics while enhancing accuracy where high generalization accuracy is less attainable. An effect size analysis further reveals that the SPFP algorithm outperforms benchmark models by large effect size and reduces computational demands through effective dimensionality reduction. The substantial effect sizes observed in most experiments underscore the algorithm's significant improvements in model performance.

SimQ-NAS: Simultaneous Quantization Policy and Neural Architecture Search

Recent one-shot Neural Architecture Search algorithms rely on training a hardware-agnostic super-network tailored to a specific task and then extracting efficient sub-networks for different hardware platforms. Popular approaches separate the training of super-networks from the search for sub-networks, often employing predictors to alleviate the computational overhead associated with search. Additionally, certain methods also incorporate the quantization policy within the search space. However, while the quantization policy search for convolutional neural networks is well studied, the extension of these methods to transformers and especially foundation models remains under-explored. In this paper, we demonstrate that by using multi-objective search algorithms paired with lightly trained predictors, we can efficiently search for both the sub-network architecture and the corresponding quantization policy and outperform their respective baselines across different performance objectives such as accuracy, model size, and latency. Specifically, we demonstrate that our approach performs well across both uni-modal (ViT and BERT) and multi-modal (BEiT-3) transformer-based architectures as well as convolutional architectures (ResNet). For certain networks, we demonstrate an improvement of up to $4.80x$ and $3.44x$ for latency and model size respectively, without degradation in accuracy compared to the fully quantized INT8 baselines.

The Innovation-to-Occupations Ontology: Linking Business Transformation Initiatives to Occupations and Skills

The fast adoption of new technologies forces companies to continuously adapt their operations making it harder to predict workforce requirements. Several recent studies have attempted to predict the emergence of new roles and skills in the labour market from online job ads. This paper aims to present a novel ontology linking business transformation initiatives to occupations and an approach to automatically populating it by leveraging embeddings extracted from job ads and Wikipedia pages on business transformation and emerging technologies topics. To our knowledge, no previous research explicitly links business transformation initiatives, like the adoption of new technologies or the entry into new markets, to the roles needed. Our approach successfully matches occupations to transformation initiatives under ten different scenarios, five linked to technology adoption and five related to business. This framework presents an innovative approach to guide enterprises and educational institutions on the workforce requirements for specific business transformation initiatives.

ACGAN-GNNExplainer: Auxiliary Conditional Generative Explainer for Graph Neural Networks

Graph neural networks (GNNs) have proven their efficacy in a variety of real-world applications, but their underlying mechanisms remain a mystery. To address this challenge and enable reliable decision-making, many GNN explainers have been proposed in recent years. However, these methods often encounter limitations, including their dependence on specific instances, lack of generalizability to unseen graphs, producing potentially invalid explanations, and yielding inadequate fidelity. To overcome these limitations, we, in this paper, introduce the Auxiliary Classifier Generative Adversarial Network (ACGAN) into the field of GNN explanation and propose a new GNN explainer dubbed~\emph{ACGAN-GNNExplainer}. Our approach leverages a generator to produce explanations for the original input graphs while incorporating a discriminator to oversee the generation process, ensuring explanation fidelity and improving accuracy. Experimental evaluations conducted on both synthetic and real-world graph datasets demonstrate the superiority of our proposed method compared to other existing GNN explainers.

Full-resolution Lung Nodule Segmentation from Chest X-ray Images using Residual Encoder-Decoder Networks

Lung cancer is the leading cause of cancer death and early diagnosis is associated with a positive prognosis. Chest X-ray (CXR) provides an inexpensive imaging mode for lung cancer diagnosis. Suspicious nodules are difficult to distinguish from vascular and bone structures using CXR. Computer vision has previously been proposed to assist human radiologists in this task, however, leading studies use down-sampled images and computationally expensive methods with unproven generalization. Instead, this study localizes lung nodules using efficient encoder-decoder neural networks that process full resolution images to avoid any signal loss resulting from down-sampling. Encoder-decoder networks are trained and tested using the JSRT lung nodule dataset. The networks are used to localize lung nodules from an independent external CXR dataset. Sensitivity and false positive rates are measured using an automated framework to eliminate any observer subjectivity. These experiments allow for the determination of the optimal network depth, image resolution and pre-processing pipeline for generalized lung nodule localization. We find that nodule localization is influenced by subtlety, with more subtle nodules being detected in earlier training epochs. Therefore, we propose a novel self-ensemble model from three consecutive epochs centered on the validation optimum. This ensemble achieved a sensitivity of 85% in 10-fold internal testing with false positives of 8 per image. A sensitivity of 81% is achieved at a false positive rate of 6 following morphological false positive reduction. This result is comparable to more computationally complex systems based on linear and spatial filtering, but with a sub-second inference time that is faster than other methods. The proposed algorithm achieved excellent generalization results against an external dataset with sensitivity of 77% at a false positive rate of 7.6.

Ethical ChatGPT: Concerns, Challenges, and Commandments

Large language models, e.g. ChatGPT are currently contributing enormously to make artificial intelligence even more popular, especially among the general population. However, such chatbot models were developed as tools to support natural language communication between humans. Problematically, it is very much a ``statistical correlation machine" (correlation instead of causality) and there are indeed ethical concerns associated with the use of AI language models such as ChatGPT, such as Bias, Privacy, and Abuse. This paper highlights specific ethical concerns on ChatGPT and articulates key challenges when ChatGPT is used in various applications. Practical commandments for different stakeholders of ChatGPT are also proposed that can serve as checklist guidelines for those applying ChatGPT in their applications. These commandment examples are expected to motivate the ethical use of ChatGPT.

SRCNet: Seminal Representation Collaborative Network for Marine Oil Spill Segmentation

Effective oil spill segmentation in Synthetic Aperture Radar (SAR) images is critical for marine oil pollution cleanup, and proper image representation is helpful for accurate image segmentation. In this paper, we propose an effective oil spill image segmentation network named SRCNet by leveraging SAR image representation and the training for oil spill segmentation simultaneously. Specifically, our proposed segmentation network is constructed with a pair of deep neural nets with the collaboration of the seminal representation that describes SAR images, where one deep neural net is the generative net which strives to produce oil spill segmentation maps, and the other is the discriminative net which trys its best to distinguish between the produced and the true segmentations, and they thus built a two-player game. Particularly, the seminal representation exploited in our proposed SRCNet originates from SAR imagery, modelling with the internal characteristics of SAR images. Thus, in the training process, the collaborated seminal representation empowers the mapped generative net to produce accurate oil spill segmentation maps efficiently with small amount of training data, promoting the discriminative net reaching its optimal solution at a fast speed. Therefore, our proposed SRCNet operates effective oil spill segmentation in an economical and efficient manner. Additionally, to increase the segmentation capability of the proposed segmentation network in terms of accurately delineating oil spill details in SAR images, a regularisation term that penalises the segmentation loss is devised. This encourages our proposed SRCNet for accurately segmenting oil spill areas from SAR images. Empirical experimental evaluations from different metrics validate the effectiveness of our proposed SRCNet for oil spill image segmentation.

Meta-learning approaches for few-shot learning: A survey of recent advances

Despite its astounding success in learning deeper multi-dimensional data, the performance of deep learning declines on new unseen tasks mainly due to its focus on same-distribution prediction. Moreover, deep learning is notorious for poor generalization from few samples. Meta-learning is a promising approach that addresses these issues by adapting to new tasks with few-shot datasets. This survey first briefly introduces meta-learning and then investigates state-of-the-art meta-learning methods and recent advances in: (I) metric-based, (II) memory-based, (III), and learning-based methods. Finally, current challenges and insights for future researches are discussed.