Traffic accidents result in millions of injuries and fatalities globally, with a significant number occurring at intersections each year. Traffic Signal Control (TSC) is an effective strategy for enhancing safety at these urban junctures. Despite the growing popularity of Reinforcement Learning (RL) methods in optimizing TSC, these methods often prioritize driving efficiency over safety, thus failing to address the critical balance between these two aspects. Additionally, these methods usually need more interpretability. CounterFactual (CF) learning is a promising approach for various causal analysis fields. In this study, we introduce a novel framework to improve RL for safety aspects in TSC. This framework introduces a novel method based on CF learning to address the question: ``What if, when an unsafe event occurs, we backtrack to perform alternative actions, and will this unsafe event still occur in the subsequent period?'' To answer this question, we propose a new structure causal model to predict the result after executing different actions, and we propose a new CF module that integrates with additional ``X'' modules to promote safe RL practices. Our new algorithm, CFLight, which is derived from this framework, effectively tackles challenging safety events and significantly improves safety at intersections through a near-zero collision control strategy. Through extensive numerical experiments on both real-world and synthetic datasets, we demonstrate that CFLight reduces collisions and improves overall traffic performance compared to conventional RL methods and the recent safe RL model. Moreover, our method represents a generalized and safe framework for RL methods, opening possibilities for applications in other domains. The data and code are available in the github https://github.com/AdvancedAI-ComplexSystem/SmartCity/tree/main/CFLight.

Single-expert fake news detectors, such as Graph Neural Networks (GNNs) and Large Language Models (LLMs), increasingly struggle to counter the diversifying camouflage tactics of modern adversaries, which range from semantic (e.g., mimicking writing styles) to structural (e.g., manipulating propagation paths). To address this, existing methods attempt to build a hybrid model by sequentially incorporating GNNs and LLMs; however, such hybridization blurs the distinction between experts and prevents critical cross-validation. In addition, existing methods rely heavily on vast labeled data, which is costly to acquire, particularly for fake news samples. In this paper, we propose a Semi-supervised Mixture of Experts framework for Fake news detection, namely S2MOE-F. The core idea of S2MOE-F is to establish a robust defense against multifaceted camouflage by cross-validating the complementary judgments of two independent experts, GNN and LLM. On the one hand, S2MOE-F drives experts' judgments by using a One-Class Classification (OCC) objective, which constrains true news within a compact hypersphere and identifies samples outside this boundary as fake, reducing reliance on scarce fake news labels. On the other hand, S2MOE-F generates high-confidence pseudo-labels based on consensus or divergence between experts to exploit abundant unlabeled data. In addition, we propose a novel reinforcement learning (RL)-based routing policy that dynamically determines the dominant expert for input samples without explicit supervision. Finally, we design a disentangled masked Transformer to ensure experts' specialization by reducing inter-expert redundancy. Extensive experiments on real-world datasets sourced from Web platforms and social media demonstrate the superior performance of S2MOE-F.

The applications of machine learning and deep learning in demand forecasting have attracted increasing attention, as they offer remarkable predictive capabilities that help automate forecasting processes and achieve higher accuracy. While numerous review studies have examined solutions within specific industries, there is a lack of comprehensive literature review investigating these solutions across different sectors. Therefore, this study overviews machine learning and deep learning applications in demand forecasting across time-critical industries, including power, tourism, water, transportation, and food. A two-tier classification framework is proposed to categorize demand forecasting studies by both application industry and methodological architecture. In addition, the most popular statistical metrics for evaluating demand forecasting are reviewed and summarized. This study reveals that while machine learning and deep learning are effective for demand forecasting, model selection highly depends on the target industry, data availability, and computational resources. Therefore, this study proposes a conceptual, generic framework that maps data characteristics to appropriate model architecture classes for demand forecasting and recommends adopting scale-independent evaluation metrics. The proposed framework offers a structured pipeline and practical guidance for practitioners and researchers to design forecasting systems across diverse industries, enabling consistent comparative analysis.

Effective traffic signal control (TSC) is crucial in mitigating urban congestion and reducing emissions. Recently, reinforcement learning (RL) has been the research trend for TSC. However, existing RL algorithms face several real-world challenges that hinder their practical deployment in TSC: (1) Sensor accuracy deteriorates with increased sensor detection range, and data transmission is prone to noise, potentially resulting in unsafe TSC decisions. (2) During the training of online RL, interactions with the environment could be unstable, potentially leading to inappropriate traffic signal phase (TSP) selection and traffic congestion. (3) Most current TSC algorithms focus only on TSP decisions, overlooking the critical aspect of phase duration, affecting safety and efficiency. To overcome these challenges, we propose a robust two-stage fuzzy approach called FuzzyLight, which integrates compressed sensing and RL for TSC deployment. FuzzyLight offers several key contributions: (1) It employs fuzzy logic and compressed sensing to address sensor noise and enhances the efficiency of TSP decisions. (2) It maintains stable performance during training and combines fuzzy logic with RL to generate precise phases. (3) It works in real cities across 22 intersections and demonstrates superior performance in both real-world and simulated environments. Experimental results indicate that FuzzyLight enhances traffic efficiency by 48% compared to expert-designed timings in the real world. Furthermore, it achieves state-of-the-art (SOTA) performance in simulated environments using six real-world datasets with transmission noise. The code and deployment video are available at the Github.

Federated learning (FL) aims to train models collaboratively across clients without sharing data for privacy-preserving.However, one major challenge is the data heterogeneity issue, which refers to the biased labeling preferences at multiple clients.A number of existing FL methods attempt to tackle data heterogeneity locally (e.g., regularizing local models) or globally (e.g., fine-tuning global model), often neglecting inherent semantic information contained in each client.To explore the possibility of using intra-client semantically meaningful knowledge in handling data heterogeneity, in this paper, we propose Federated Learning with Semantic-Aware Collaboration (FedSC) to capture client-specific and class-relevant knowledge across heterogeneous clients.The core idea of FedSC is to construct relational prototypes and consistent prototypes at semantic-level, aiming to provide fruitful class underlying knowledge and stable convergence signals in a prototype-wise collaborative way.On the one hand, FedSC introduces an inter-contrastive learning strategy to bring instance-level embeddings closer to relational prototypes with the same semantics and away from distinct classes.On the other hand, FedSC devises consistent prototypes via a discrepancy aggregation manner, as a regularization penalty to constrain the optimization region of the local model.Moreover, a theoretical analysis for FedSC is provided to ensure a convergence guarantee.Experimental results on various challenging scenarios demonstrate the effectiveness of FedSC and the efficiency of crucial components.Our code is at https://github.com/hwang52/FedSC.

Learning resources in online learning systems typically adhere to uniform formats and settings, lacking flexibility and personalization to meet diverse learning needs and preferences. This inability to meet individualized learning needs and preferences has spurred research interest in personalized learning path recommendations. Many researchers have explored recommending learning path by leveraging user historical learning resource sequence to model personalized characteristics. However, these methods overlook the time information in the learning process and fail to interpret the dynamic shifts in learning preferences during recommendation. Therefore, we propose a method, termed TA-RL, for learning path recommendation, based on time-aware attention mechanism and reinforcement learning. First, we propose a novel time-aware attention mechanism to trace the evolving learning preferences of user, in which attention weights are computed using a context-aware time distance measure and the similarity between history learning resources. Then, we employ a Monte Carlo policy gradient reinforcement learning method to generate learning path recommendation based on learning preferences. We validate the effectiveness of our proposed method by comprehensive experiments on two real-world datasets.

Adversarial generative models, such as Generative Adversarial Networks (GANs), are widely applied for generating various types of data, i.e., images, text, and audio. Accordingly, its promising performance has led to the GAN-based adversarial attack methods in the white-box and black-box attack scenarios. The importance of transferable black-box attacks lies in their ability to be effective across different models and settings, more closely aligning with real-world applications. However, it remains challenging to retain the performance in terms of transferable adversarial examples for such methods. Meanwhile, we observe that some enhanced gradient-based transferable adversarial attack algorithms require prolonged time for adversarial sample generation. Thus, in this work, we propose a novel algorithm named GE-AdvGAN to enhance the transferability of adversarial samples whilst improving the algorithm's efficiency. The main approach is via optimising the training process of the generator parameters. With the functional and characteristic similarity analysis, we introduce a novel gradient editing (GE) mechanism and verify its feasibility in generating transferable samples on various models. Moreover, by exploring the frequency domain information to determine the gradient editing direction, GE-AdvGAN can generate highly transferable adversarial samples while minimizing the execution time in comparison to the state-of-the-art transferable adversarial attack algorithms. The performance of GE-AdvGAN is comprehensively evaluated by large-scale experiments on different datasets, which results demonstrate the superiority of our algorithm. The code for our algorithm is available at: https://github.com/LMBTough/GE-advGAN.

The rapid development of deep learning has demonstrated its potential for deployment in many intelligent service systems. However, some issues such as optimisation (e.g., how to reduce the deployment resources costs and further improve the detection speed), especially in scenarios where limited resources are available, remain challenging to address. In this paper, we aim to delve into the principles of deep neural networks, focusing on the importance of network neurons. The goal is to identify the neurons that exert minimal impact on model performances, thereby aiding in the process of model pruning. In this work, we have thoroughly considered the deep learning model pruning process with and without fine-tuning step, ensuring the model performance consistency. To achieve our objectives, we propose a methodology that employs adversarial attack methods to explore deep neural network parameters. This approach is combined with an innovative attribution algorithm to analyse the level of network neurons involvement. In our experiments, our approach can effectively quantify the importance of network neuron. We extend the evaluation through comprehensive experiments conducted on a range of datasets, including CIFAR-10, CIFAR-100 and Caltech101. The results demonstrate that, our method have consistently achieved the state-of-the-art performance over many existing methods. We anticipate that this work will help to reduce the heavy training and inference cost of deep neural network models where a lightweight deep learning enhanced service and system is possible. The source code is open source at https://github.com/LMBTough/FVW.

Deep neural networks (DNNs) have been shown vulnerable to interference from adversarial samples, leading to erroneous predictions. Investigating adversarial attacks can effectively improve the reliability as well as the performance of deep neural models in real-world applications. Since it is generally challenging to infer the parameters in black-box models, high transferability becomes an important factor for the success rate of an attack method. Recently, the Spectrum Simulation Attack method exhibits promising results based on the frequency domain. In light of SSA, we propose a novel attack approach in this paper, which achieves the best results among diverse state-of-the-art transferable adversarial attack methods. Our method aims to find a stationary point, which extends the ability to find multiple local optima with the optimal local attack effect. After finding the stationary point, a frequency-based search is employed to explore the best adversarial samples in the neighbouring space, utilmately determining the final adversarial direction. We compare our method against a variety of cutting-edge transferable adversarial methods. Extensive experiments validate that our method improves the attack success rate by 4.7% for conventionally trained models and 53.1% for adversarially trained models. Our code is available at https://github.com/LMBTough/FSPS

Traffic signal control (TSC) is still one of the most significant and challenging research problems in the transportation field. Reinforcement learning (RL) has achieved great success in TSC but suffers from critically high learning costs in practical applications due to the excessive trial-and-error learning process. Offline RL is a promising method to reduce learning costs whereas the data distribution shift issue is still up in the air. To this end, in this paper, we formulate TSC as a sequence modeling problem with a sequence of Markov decision process described by states, actions, and rewards from the traffic environment. A novel framework, namely TransformerLight, is introduced, which does not aim to fit into value functions by averaging all possible returns, but produces the best possible actions using a gated Transformer. Additionally, the learning process of TransformerLight is much more stable by replacing the residual connections with gated transformer blocks due to a dynamic system perspective. Through numerical experiments on offline datasets, we demonstrate that the TransformerLight model: (1) can build a high-performance adaptive TSC model without dynamic programming; (2) achieves a new state-of-the-art compared to most published offline RL methods so far; and (3) shows a more stable learning process than offline RL and recent Transformer-based methods. The relevant dataset and code are available at Github.

In the realm of high‐dimensional data analysis, numerous fields stand to benefit from its applications, including the biological and medical sectors that are crucial for computer‐aided disease diagnosis and prediction systems. However, the presence of a significant number of redundant or irrelevant features can adversely affect system accuracy and real‐time diagnosis efficiency. To mitigate this issue, this paper proposes two innovative wrapper feature selection (FS) methods that integrate the ant colony optimization (ACO) algorithm and hybrid rice optimization (HRO). HRO is a recently developed metaheuristic that mimics the breeding process of the three‐line hybrid rice, which is yet to be thoroughly explored in the context of solving high‐dimensional FS problems. In the first hybridization, ACO is embedded as an evolutionary operator within HRO and updated alternately with it. In the second form of hybridization, two subpopulations evolve independently, sharing the local search results to assist individual updating. In the initial stage preceding hybridization, a problem‐oriented heuristic factor assignment strategy based on the importance of the knee point feature is introduced to enhance the global search capability of ACO in identifying the smallest and most representative features. The performance of the proposed algorithms is evaluated on fourteen high‐dimensional biomedical datasets and compared with other recently advanced FS methods. Experimental results suggest that the proposed methods are efficient and computationally robust, exhibiting superior performance compared to the other algorithms involved in this study.

The Corona Virus Disease 2019 has a great impact on public health and public psychology. People stay at home for a long time and rarely go out. With the improvement of the epidemic situation, people began to go to different places to check in. To maintain public mental health, it is necessary to propose a point-of-interest (POI) prediction model which can mine users' interests. However, the current techniques suffer from lower precision during prediction and the practical value is poor, which is due to the sparse data of users' check-in. Faced with this challenge, we propose an attention-based bidirectional gated recurrent unit (GRU) model for POI category prediction (ABG_poic). We regard the user's POI category as the user's interest preference because the fuzzy POI category is easier to reflect the user's interest than the POI. This method can alleviate the data sparsity, and protect users' location privacy. Since users' preferences are variable, we utilize a bidirectional GRU to capture the dynamic dependence of users' check-ins. Furthermore, since the neural network is similar to a “black box” in feature learning, the decision-making stage is opaque. Thus, we combine the attention mechanism with bidirectional GRU to selectively focus on historical check-in records, which can improve the interpretability of the model. Considering the time impact on users' check-in, we utilize the time sliding window in the ABG_poic model. Experiments on two data sets demonstrate that our ABG_poic outperforms the comparison models for POI category prediction on sparse check-in data.

An intelligent industrial system is demanded for the development of Industry 4.0, which aims at providing efficient and intelligent computing service to increase the productivity. In-ternet of things become critical to achieve this goal by employing the sensors and connecting the objects over internet. In this study, we firstly investigate how the intelligent industrial service will be realised by constructing a five-layer framework based on our comprehensive field experiences. In detail, how the IoT sensor data are connected with the system and how the computational model is designed to improve the efficiency of the manufacturing system are discussed. Particularly, in this paper, the task of the defect identification of the steel is selected as our application on field. Since the longitudinal surface crack on the steel slab is a crucial indication suggesting the quality of continuous casting slab, how to discover the longitudinal surface crack on the slab in an early stage is of great significance. Traditional methods to detect the longitudinal surface crack have different drawbacks. Given the benefit of numerous IoT sensor data, we have proposed a novel computational model to incorporate the multiple factors of steel manufacturing system to improve the detection. Experiment evaluation has shown the efficiency and effectiveness of the model. In summary, we anticipate this work will contribute to an intelligent steel manufacturing system based on industrial IoT in building viable solutions, which benefit from the early stage identification and prediction of poor quality productions.

Cloud Computing (CC) is an emerging technology that can potentially revolutionise the application and delivery of IT. There has been little research, however, into the adoption of CC in Small and Medium-Sized Enterprises (SMEs). The indicators show that CC has been adopted very slowly. There is also a significant research gap in the investigation of the adoption of this innovation in SMEs. This article explores how the adoption of CC in Australia is related to technological factors, risk factors, and environmental factors. The study provides useful insights that can be utilised practically by SMEs, policymakers, and cloud vendors.

For classification tasks, such as protein-protein interactions (PPI), support vector machines (SVMs) have been continually utilised as a standard machine learning model. However, most practices in PPIs classifications are limited to common circumstances with small datasets and low feature dimensions, due to the big computation burden of kernel functions and quadratic optimization of SVM. Alternatively, these practical experiences might tend to employ a linear model once the dataset becomes larger, which may have exclusively lost the kernel function's potential. Since there are different defined kernels and various groups of hyperparameter, the time costs in estimating a best set of hyperparameter by traditional grid search are subsequently tremendous for PPI classification. To address this challenge, in this paper, we present a more efficient solution of hyperparameter estimation by gaining acceleration with GPU, which trains SVM efficiently and accurately with kernel functions calculation accelerated on various PPI datasets. The experiments are firstly conducted on PPI classification task, and we have exclusively evaluated the effectiveness on five public classification datasets. Our solution demonstrates a faster and more accurate performance comparing with the state-of-the-art.

Multiple multi-dimensional data streams are ubiquitous in the modern world, such as IoT applications, GIS applications and social networks. Detecting anomalies in such data streams in real-time is an important and challenging task. It is able to provide valuable information from data and then assists decision-making. However, exiting approaches for anomaly detection in multi-dimensional data streams have not properly considered the correlations among multiple multi-dimensional streams. Moreover, for multi-dimensional streaming data, online detection speed is often an important concern. In this paper, we propose a fast yet effective anomaly detection approach in multiple multi-dimensional data streams. This is based on a combination of ideas, i.e., stream pre-processing, locality sensitive hashing and dynamic isolation forest. Experiments on real datasets demonstrate that our approach achieves a magnitude increase in its efficiency compared with state-of-the-art approaches while maintaining competitive detection accuracy.

Testable prediction outcomes generated by computational models based on available databases are the primary sources helping to design biological experiments. Although numerous databases have been designed by collecting data either only from literature manually or together with prediction outcomes from computational models, there is currently not a comprehensive data service framework delivering better insights for these results. In this paper, we introduce a biological sequence data service towards delivering deeper insights and helping better biological experiments design. The service includes following major components: a comprehensive database for storing biological data, data analytics tools for analysing biological data, and computational models for delivering testable prediction outcomes. Specifically, we present this service in a framework for studies on host-pathogen interactions. The design of this framework aims to improve the understanding of host-pathogen interactions. The relationships of hierarchical databases and their working mechanism, specifically between PPIs and DDIs, are also presented in this framework. Finally, the preliminary and practical experiences of building computational model for prediction is discussed.

Recent advances in the steel industry have encountered challenges in soliciting decision making solutions for quality control of products based on data mining techniques. In this paper, we present a steel quality control prediction system encompassing with real-world data as well as comprehensive data analysis results. The core process is cautiously designed as a regression problem, which is then best handled by grouping various learning algorithms with their massive resource of historical production datasets. The characteristics of the currently most popular learning models used in regression problem analysis are as well investigated and compared. The performance indicates our steel quality control prediction system based on ensemble machine learning model can offer promising result whilst delivering high usability for local manufacturers to address the production problem by aid of development of machine learning techniques. Furthermore, real-world deployment of this system is demonstrated and discussed. Finally, future directions and the performance expectation are pointed out.

Personalisation and learner modelling are becoming more important in the area of mobile learning applications, taking into consideration learners' interests, preferences and contextual information. Students nowadays are able to learn anywhere and at any time. Mobile learning application content is one of several factors within various contexts that play an important role in the success of the adaptation process. The vast amount of data involved in any successful adaptation process creates complexity and poses serious challenges. This paper focuses on how to model the learner and all possible contexts in an extensible way that can be used for personalisation in mobile learning. The enhanced learner modelling structure to be used in a mobile learning system is proposed. The proposed structure provides personalisation by adopting a hybrid approach combining two machine learning techniques.