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Compiler optimization presents a formidable challenge, seeking to enhance code quality through various techniques. One significant obstacle is the phase-ordering problem, where the sequence of optimization algorithms can impact code quality and potentially lead to performance deterioration. While machine learning has shown promise in addressing this issue, it still encounters limitations in certain scenarios. Instruction Sink is a machine-independent optimization that segregates instructions like modulo and division into separate basic blocks. This separation prevents the application of the machine-dependent optimization called Division-Modulo Combine at the backend, which could otherwise lead to a decline in program performance. However, reordering these optimizations to avoid conflicts can prove to be challenging. In this paper, we propose an algorithm to enhance Instruction Sink, resolving the blocking problem, and implements it within the LLVM optimizer. Leveraging the modularity of LLVM enables us to tackle all affected back-end issues simultaneously. To assess the effectiveness of our algorithm, we conduct a comprehensive evaluation focusing on feasibility, compatibility, and targeted improvements. The results demonstrate that our approach effectively addresses the blocking problem without conflicting with other optimization algorithms, solely impacting problematic instructions while leaving normal ones unaffected. [ABSTRACT FROM AUTHOR]

Contingency analysis (CA) is one of the essential tools for the optimal design and security assessment of a reliable power system. However, its computational requirements rise with the growth of distributed generations in the interconnected power system. As CA is a complex and computationally intensive problem, it requires a fast and accurate calculation to ensure the secure operation. Therefore, efficient mathematical modelling and parallel programming are key to efficient static security analysis. This paper proposes a parallel algorithm for static CA that uses both central processing units (CPUs) and graphical processing units (GPUs). To enhance the accuracy, AC load flow is used, and parallel computation of load flow is done simultaneously, with efficient screening and ranking of the critical contingencies. We perform extensive experiments to evaluate the efficacy of the proposed algorithm. As a result, we establish that the proposed parallel algorithm with high-performance computing (HPC) computing is much faster than the traditional algorithms. Furthermore, the HPC experiments were conducted using the national supercomputing facility, which demonstrates the proposed algorithm in the context of N−1 and N−2 static CA with immense power systems, such as the Indian northern regional power grid (NRPG) 246-bus and the polish 2383-bus networks. [ABSTRACT FROM AUTHOR]

As a foundation task of natural language processing, text classification is widely used in information retrieval, public opinion analysis, and other related tasks. Facing the problem of sparse features of Chinese short texts, which affects the classification accuracy of Chinese short texts, this paper proposes a Chinese short text classification method based on the Character Frequency Sub-word Enhancement (CFSE), which can effectively improve the classification accuracy of Chinese short texts. First, the initial Chinese-character sequence is mapped to the corresponding Character Frequency Sub-word (CFS) sequence based on the global character1 frequency information. Second, the relationship features among data are extracted based on BiLSTM-Att processing CFS sequence, and the semantic features of the initial Chinese-character sequence are obtained through ERNIE. Finally, these two kinds of features are fused and input into the text classifier to obtain the classification results. Experimental results show that the proposed method can improve the classification accuracy of Chinese short texts. [ABSTRACT FROM AUTHOR]

Reliable evaluation of damage in vehicles is a primary concern in the insurance industry. Consequently, solutions enhanced with Artificial Intelligence (AI) have become the norm. During the assessment, precise damage segmentation plays a crucial role. Dent is a type of damage that can commonly occur in vehicles. It is difficult to pinpoint and tends to blend in with the background. This paper proposes a novel loss function to improve dent segmentation accuracy in vehicle insurance claims. Centre and Pixel-based Weighted DICE (CPW-DICE) is a loss function that performs pixel-based weighting. The CPW-DICE aims to concentrate on the centre of the dent damage to lessen faulty segmentations. CPW-DICE generates a weight mask during training by employing ground truth (GT) and prediction masks. Simultaneously, the weight mask is incorporated into DICE loss. Experiments conducted on our comprehensive internal dataset show a 3% improvement in Intersection over Union (IoU) score for three state-of-the-art (SOTA) approaches compared to DICE loss. Finally, CPW-DICE is evaluated in similar tasks to demonstrate its benefits beyond car damage segmentation. [ABSTRACT FROM AUTHOR]

Due to the advantages of all-weather capability and high resolution, synthetic aperture radar (SAR) image ship detection has been widely applied in the military, civilian, and other domains. However, SAR-based ship detection suffers from limitations such as strong scattering of targets, multiple scales, and background interference, leading to low detection accuracy. To address these limitations, this paper presents a novel SAR ship detection method, NAS-YOLOX, which leverages the efficient feature fusion of the neural architecture search feature pyramid network (NAS-FPN) and the effective feature extraction of the multi-scale attention mechanism. Specifically, NAS-FPN replaces the PAFPN in the baseline YOLOX, greatly enhances the fusion performance of the model's multi-scale feature information, and a dilated convolution feature enhancement module (DFEM) is designed and integrated into the backbone network to improve the network's receptive field and target information extraction capabilities. Furthermore, a multi-scale channel-spatial attention (MCSA) mechanism is conceptualised to enhance focus on target regions, improve small-scale target detection, and adapt to multi-scale targets. Additionally, extensive experiments conducted on benchmark datasets, HRSID and SSDD, demonstrate that NAS-YOLOX achieves comparable or superior performance compared to other state-of-the-art ship detection models and reaches best accuracies of 91.1% and 97.2% on AP0.5, respectively. [ABSTRACT FROM AUTHOR]

Online reviews contain comparative opinions that reveal the competitive relationships of related products, help identify the competitiveness of products in the marketplace, and influence consumers' purchasing choices. The Class Sequence Rule (CSR) method, which is previously commonly used to identify the comparative relations of reviews, suffers from low recognition efficiency and inaccurate generation of rules. In this paper, we improve on the CSR method by proposing a hybrid CSR method, which utilises dependency relations and the part-of-speech to identify frequent sequence patterns in customer reviews, which can reduce manual intervention and reinforce sequence rules in the relation mining process. Such a method outperforms CSR and other CSR-based models with an F-value of 84.67%. In different experiments, we find that the method is characterised by less time-consuming and efficient in generating sequence patterns, as the dependency direction helps to reduce the sequence length. In addition, this method also performs well in implicit relation mining for extracting comparative information that lacks obvious rules. In this study, the optimal CSR method is applied to automatically capture the deeper features of comparative relations, thus improving the process of recognising explicit and implicit comparative relations. [ABSTRACT FROM AUTHOR]

The Dual-motor multi-gear coupling powertrain (DMCP) has the potential to improve transmission system efficiency and driving comfort, but its complex structure and multiple working modes present challenges. The switching between different modes is easy to cause longitudinal biggish vehicle jerk. To address these issues,this paper introduces the Deep Deterministic Policy Gradient (DDPG) algorithm in the design of an Energy Management Strategy (EMS) that minimises total drive power consumption. And the number of working modes is divided and simplified. The process of switching dual motor and single motor to single motor is introduced in detail. The simulation results using AMESim and MATLAB show that the energy management strategy can effectively improve the economy, achieve no power interruption during mode switching, shift impact is less than 8m/s3, and output torque is remains stable. [ABSTRACT FROM AUTHOR]

Third-party APIs have been widely used to develop various applications. As the number of third-party APIs grows, it becomes increasingly challenging to quickly find suitable APIs that meet users' requirements. Inspired by recommender systems, API recommendation methods have been proposed to address this issue. However, previous API recommendation methods are insufficient in utilising the high-order interactions between users and APIs, and thus have limited performance. Based on the model of lightweight graph convolutional neural network, this paper proposes an effective API recommendation method by exploiting both low-order and high-order interactions between users and APIs. It first learns the embedding of users and APIs from the user-API interaction graph, and then adopts a weighted summation operator to aggregate the embeddings learned from different propagation layers for API recommendation. Extensive experiments are conducted on a real dataset with 160,309 API users and 21,031 Web APIs, and the results show that our method has significantly better precision and recall than other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

With the rapid growth of Internet data traffic, the means of malicious attack become more diversified. The single modal intrusion detection model cannot fully exploit the rich feature information in the massive network traffic data, resulting in unsatisfactory detection results. To address this issue, this paper proposes a multimodal hybrid parallel network intrusion detection model (MHPN). The proposed model extracts network traffic features from two modalities: the statistical information of network traffic and the original load of traffic, and constructs appropriate neural network models for each modal information. Firstly, a two-branch convolutional neural network is combined with Long Short-Term Memory (LSTM) network to extract the spatio-temporal feature information of network traffic from the original load mode of traffic, and a convolutional neural network is used to extract the feature information of traffic statistics. Then, the feature information extracted from the two modalities is fused and fed to the CosMargin classifier for network traffic classification. The experimental results on the ISCX-IDS 2012 and CIC-IDS-2017 datasets show that the MHPN model outperforms the single-modal models and achieves an average accuracy of 99.98 $ \% $ %. The model also demonstrates strong robustness and a positive sample recognition rate. [ABSTRACT FROM AUTHOR]

The proliferation of location-based service applications has led to a substantial surge in the amount of life trajectory data produced by mobile devices. And these data frequently contain confidential personal details. Simultaneously, the corresponding relatively lagging privacy protection technology and the improper trajectory data handling method will make tremendous problems with privacy breach. Therefore, this paper presents a trajectory privacy data publishing scheme, denoted as LORDP, which is based on local optimisation and R-tree. The proposed scheme aims to handle sensitive data while improves trajectory protection effectiveness. Firstly, the scheme combines the LKC-privacy model requirement to filter out the minimum violating sequences set, to reduce data sensitivity and the amount of injected noise. Secondly, R-tree is constructed based on trajectory similarity. Finally, Laplacian noise is added to the R-tree's leaf nodes constrained by differential privacy. The experiments show that the proposed LORDP algorithm significantly enhances the utility of data compared to other algorithms, and reduces the loss rate of about approximately 2% for per trajectory data, which shows that the present algorithm is extremely effective. [ABSTRACT FROM AUTHOR]

Morphological segmentation is a basic task in agglutinative language information processing, dividing words into the smallest semantic unit morphemes. There are two types of morphological segmentation: canonical segmentation and surface segmentation. As a typical agglutinative language, Uyghur usually uses statistical-based methods in canonical segmentation, which relies on the artificial extraction of features. In surface segmentation, the artificial feature extraction process is avoided by using the neural network. However, to date, no model can provide both segmentation results in Uyghur without adding features. In addition, morphological segmentation is usually regarded as a sequence annotation task, so label imbalance easily occurs in datasets. Given the above situation, this paper proposes an improved labelling scheme that joins morphological boundary labels and voice harmony labels for the two kinds of segmentation simultaneously. Then, a convolution network and attention mechanism are added to capture local and global features, respectively. Finally, morphological segmentation is regarded as a sequence labeling task of character sequences. Due to the problem of label proportion imbalance and noise in the dataset, a focal loss function with label smoothing is used. The experimental results show that the F1 values of canonical segmentation and surface segmentation achieve the best results. [ABSTRACT FROM AUTHOR]

Model incremental updating enhances the initial model by analysing discrepancy parts of the system to improve the model's adaptability for new scenarios. These discrepancy components originate from the deviation between the increasing business process operational state and the outdated original planning model. Considerable domain-specific knowledge is required to determine the threshold points for selecting appropriate activities, but process analysts rarely know each scenario's domain knowledge. Moreover, most analytical processes only focussed on the control flow level. Aiming at these issues, this paper proposes a Hybrid Behavioural and Resource Trusted intervals Updating algorithm (BI&RI Updating) for model enhancement based on control level and resource level of online event streams. First, analyse the reference model to construct multi-perspective trusted interval constraints in the offline stage. From a control-flow perspective, the behavioural relationships between activities are researched using a deep clustering approach. Moreover, from a data-flow standpoint, resource co-occurrence relationships are analysed based on association rules. Next, the incremental update algorithm in online scenarios is proposed to update the model by filtering the event streams and iteratively optimising the trusted intervals. Finally, the proposed algorithm is implemented based on the PM4PY framework and evaluated using real logs with both outcome model quality and execution efficiency. The results show that the algorithm can execute quickly and improve the quality of the model even in a non-ideal state where the logs contain noise. [ABSTRACT FROM AUTHOR]

With the development of IoT technology, more and more IoT devices are connected to the network. Due to the hardware constraints of IoT devices themselves, it is difficult for developers to embed security software into them. Therefore, it is better to protect IoT devices at the traffic level. The effect of malicious traffic detection based on neural networks is promising. Still, the slow computation brings some difficulties to deploying AI-based detection systems on edge servers. Time Convolutional Network (TCN) is a high-speed neural network suitable for massively parallel computation. In this paper, we propose Multi-class S-TCN, an improved network supporting multiple classifications based on TCN for the practical needs of IoT scenarios. Besides, we implement a complete IoT traffic security detection procedure based on deep packet inspection and protocol analysis. The proposed Multi-class S-TCN significantly improves the detection speed without degrading the detection effect. Experiments show that this work has better detection performance and faster detection speed compared to existing approaches, proving the effectiveness of the proposed detection flow and Multi-class S-TCN in IoT scenarios. [ABSTRACT FROM AUTHOR]

Site-specific recombination systems are widely used as bioengineering tools. However, the traditional site-specific recombination system requires a consensus sequence for the specific site. Such sequence-level constraints limit effective recombination between sites. Therefore, in order to develop an efficient site-specific recombination system, we investigated the attC site of the bacterial integration subsystem and built a predictive model to infer the important features that contribute to recombination. Here, we design an attC site prediction algorithm based on a combination optimisation strategy. Based on the structural features of attC sites, the prediction algorithm realises the high-precision prediction of the recombination frequencies between sites and the screening of the top 20 important features that play a role in recombination, which are effective for improving the design method of attC sites. The algorithm has better portability and higher prediction accuracy compared with the existing advanced algorithms, among which the Pearson correlation coefficient is 0.87, explained variance score is 0.73, root mean square error is 0.006 and mean absolute error is 0.041. This can not only provide ideas for the research of efficient recombination systems but also provide a theoretical basis for developing genetic engineering further. [ABSTRACT FROM AUTHOR]

In the field of image segmentation in industrial production. When using depth neural network to extract features from image information. Deep neural network can not effectively use the feature information between different levels. The accuracy of image semantic segmentation is damaged. To solve this problem, we present an image semantic segmentation with hierarchical feature fusion based on deep neural network (ISHF). It can be widely used in image perception and recognition of various living and industrial backgrounds. This algorithm uses convolution structure to extract the shallow low-level features with pixel level and the deep semantic features with image level, fully obtaining the hidden feature information in the shallow low-level features and deep semantic features. Then, after thinning the shallow low-level feature information by up-sampling operation, all feature information is merged and fused. Finally, the image semantic segmentation of hierarchical feature fusion is realised. Experimental results shows that our proposed method not only get better performance of image semantic segmentation, but also achieve faster running speed than SegNet, PSPNet, and DeepLabV3. [ABSTRACT FROM AUTHOR]

Nowadays, Blockchain-based rating/review systems are gaining popularity as a backbone for recommender systems due to the inherent cryptographically secured decentralised architecture, immutability, user anonymity, and inclusion of smart contracts. However, the existing Blockchain-based rating/review systems address resistance to the standard attacks, i.e. collusion attack, user threatening, and unfair rating. Still, they do not present security analyses of smart contracts that may result in substantial threats to the users of the systems. This manuscript presents an in-depth study of twelve publicly available security analysis tools and standard vulnerabilities in smart contracts and reviews. The experimental setup uses a two-step approach for selecting the security analysis tool. The first step identifies the seven tools their proposers or independent researchers have compared, and the second step proposes a new method for selecting tools based on continuous improvement. Our experimental results show security issues in 51.72% of the analysed smart contracts of four Blockchain-based rating/review systems. 6.67% of vulnerable smart contracts exhibit high-level severity threats that raise an alarming condition for the current state of system developments. [ABSTRACT FROM AUTHOR]

An ontology provides a formal description on the domain concepts and their relationships. Due to the subjectivity of ontology engineers, one concept might be expressed in various ways, yielding the so-called ontology heterogeneity problem, and ontology matching is a ground method to address this problem. Ontology matching technique uses the similarity measure to determine the correspondences between two heterogeneous ontology entities. In order to improve the quality of ontology alignment, it is necessary to combine different kinds of similarity measures, and how to optimize the aggregating weights is called the ontology meta-matching problem. Tin this work, a heuristic evaluating metric on the ontology alignment is presented to evaluate the ontology alignment's quality, and a mathematical model on ontology meta-matching problem is constructed. Then, an Adaptive Evolutionary Algorithm (AEA) is proposed to effectively solve this problem. In particular, when the elite solution remains unchanged, AEA adaptively activates three independent exploring strategies, which, respectively use the adaptive selection, crossover and mutation operators based on the population diversity metric. In the experiment, we compare AEA among EA based matching technique and the state-of-the-art ontology matching technique, and the experimental results show its effectiveness. [ABSTRACT FROM AUTHOR]

In recent years, one of the prominent research areas in the complex network field has been the Influence Maximization Problem. This problem focuses on selecting seed sets to achieve optimal information propagation across networks. Practical networks often encounter challenges like node or link failures due to internal issues or external disturbances. Addressing this, researchers emphasize seed robustness against potential interferences, framing it as the robust influence maximisation problem. However, current approaches to this problem are incomplete, leaving several challenges unaddressed. On one hand, existing methods primarily handle seed selection under isolated disruptions, neglecting the simultaneous threats posed by both node-based and link-based attacks. On the other hand, prevailing algorithms fail to capture information synergy from multiple scenarios during the solution process. To bridge these gaps, this study integrates the multi-tasking optimisation theory into robust influence maximisation, introducing an evolutionary algorithm called DMFEA. DMFEA concurrently addresses multiple optimization scenarios, leveraging synergy between tasks while emphasizing information diversity. Experimental results demonstrate DMFEA's competitive edge over existing methods. This research significantly advances collaborative optimization for robust influence maximisation under multi-scenario disruptions, offering a reliable solution for robust information diffusion in complex environments. [ABSTRACT FROM AUTHOR]

We have witnessed substantial growth in super-resolution research within the computer vision community. Unlike previous works that mainly focus on the super-resolution synthesis of images, videos, or single volumes, our research is dedicated to the super-resolution synthesis of time-varying volumetric data, which are generated from scientific simulations and are crucial for domain scientists to understand and analyse complex scientific phenomena. Compared to previous works, our research presents a greater challenge: the time-varying volumetric data have higher dimensions, making it more difficult to synthesise super-resolution that maintains good spatio-temporal consistency while achieving high visual quality. To tackle this challenge, we introduce a new GAN-based network called SSR-DoubleUNetGAN, which includes novel network architecture and loss functions, allowing for accurate synthesis of spatial super-resolution for time-varying volumetric data with relatively fast training time. Our method can be applied in the context of in-situ visualisation to aid domain scientists in analysing more time-varying volumetric data more efficiently. In addition, it can be used in the compression-decompression pipeline to recover the super-resolution time-varying volumetric data from their low-resolution counterpart. To demonstrate its effectiveness, we applied various time-varying volumetric datasets from different scientific simulations to it. To demonstrate its advantages, we compared it qualitatively and quantitatively with five state-of-the-art super-resolution techniques, namely SSR-TVD, Tricubic, SRResNet, Cubic, and Linear. Furthermore, we conducted an ablation study to validate its important modules. The experimental results show that our method outperforms the compared state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

This thesis deals with the problem of negative behaviour due to changes in mental and physical stress in adolescence. In particular, it is a study to solve the health care problem of students exposed to violence. Among the problematic behaviours, students exposed to violence, especially, have many problems with healthcare. A projective test using pictures can elicit information from adolescents through direct experiences represented by pictures to which the subject unconsciously reacts. Few methods analyse images drawn by adolescents as image data. This study analyses data from 134 violent students who received fifth-degree punishment for violent behaviour and 134 nonviolent students. We use the convolutional neural network (CNN)(softmax), CNN (support vector machine (SVM)), with the style transfer generative adversarial network, and ensemble techniques to analyse drawn images using WZT and predict violence through deep learning. We predict violence from pictures with an accuracy of 93%–98%. This study is the first to automatically analyze and predict violence with a deep learning model in images drawn by adolescents on WZT. It also features WZT to proactively conduct violence investigations to improve health care for students. Advances in deep learning for image feature extraction are expected to provide more research opportunities. [ABSTRACT FROM AUTHOR]

With the popularity of Non-Fungible Tokens (NFTs), which has now become a financial market that has attracted extensive attention worldwide. A large number of investors and creators are flocking to this emerging market in search of investment opportunities. Nowadays, many studies have analysed this phenomenon from an economic perspective. However, we know little about the players and ecosystem characteristics of this market. To fill this knowledge gap, we first provide a processed large-scale dataset of the Ethereum blockchain-based NFT market, containing more than 80 million NFT transaction records from January 2018 to April 2022. Second, we constructed the NFT creator graph (NCG) and NFT holder graph (THG) to delve into the characteristics of the NFT market. Further, we analyse the market preferences and trends using statistical methods to reveal the development trends of the NFT market. Finally, we focus on predicting the transaction volume of the NFT market and analyse the influence factors. This study provides data support for participants and researchers to explore the NFT market, while our analysis promotes a deeper understanding of the NFT market among the public. [ABSTRACT FROM AUTHOR]

Recent progress integrates security requirements into BPMN, enhancing its framework. Extensions aim to seamlessly embed security concepts, yet the inherent ambiguity of security terms may lead to misinterpretations and vulnerabilities. Unfortunately, many business process experts lack the expertise to accurately interpret and integrate vital security concepts. In this study, we present an innovative automated methodology tailored to assist business process experts in identifying security threats and conducting risk assessments, particularly in the context of e-Government processes. Our approach streamlines the process, requiring only a business specialist to annotate BPMN entities with high-level, non-security-related information. Based on these annotations, potential threats to the system can be automatically identified. To develop our methodology, we leverage the standard BPMN annotation mechanism. From the annotated BPMN, the methodology utilises the ENISA Threat Landscape knowledge base for threat identification and employs the OWASP Risk Rating Methodology for risk assessment. To demonstrate the effectiveness of our approach, we applied it to a straightforward case study within the e-Government domain. Through this example, we illustrate how our methodology can be employed to ensure compliance with the General Data Protection Regulation and meet the mandatory Data Protection Impact Assessment requirements. [ABSTRACT FROM AUTHOR]

To properly predict the security posture of these networks, we provide a method based on machine learning algorithms to detect the security condition of power information networks. A perception model outlines the consequences of the abstracted perception problem. Sample data is initially pre-processed using linear discriminant analysis methods to optimise the data, get integrated features, and ascertain the best projection. To assess system safety posture and find mapping relationships with network posture values, the cleaned data is subsequently input into an RBF neural network as training data. The reliability of the suggested technique for network security posture analysis is finally shown by simulations using the KDD Cup99 dataset and attack data from power information networks, with detection rates frequently surpassing 90%. [ABSTRACT FROM AUTHOR]

Dynamic measurement via deep learning can be applied in many industrial fields significantly (e.g. electrical power load and fault diagnosis acquisition). Nowadays, accurate and continuous loading measurement is essential in coal mine production. The existing methods are weak in loading measurement because they ignore the symbol characteristics of loading and adjusting features. To address the problem, we propose a hybrid learning method (called ScTCN-LightGBM) to realize the loading measurement of industrial material effectively. First, we provide an abnormal data processing method to guarantee raw data accuracy. Second, we design a sided-composited temporal convolutional network that combines a novel transposed dimensionality-reduction convolution residual block with the conventional residual block. This module can extract symbol characteristics and values of loading and adjusting features well. Finally, we utilize the light-gradient boosting machine to measure loading capacity. Experimental results show that the ScTCN-LightGBM outperforms existing measurement models with high metrics, especially the stability coefficient R2 is 0.923. Compared to the conventional loading measurement method, the measurement performance via ScTCN-LigthGBM improves by 40.2% and the continuous measurement time is 11.28s. This study indicates that the proposed model can achieve the loading measurement of industrial material effectively. [ABSTRACT FROM AUTHOR]

Internet of Vehicles (IoV) provides an overview of the Internet of Things (IoT) and the Internet of Everything (IoE). Generally, it connects the different items, Vehicles, and Environments, which transfers data between the network. The various challenges associated with IoV are routing security and data transmission. It is a different form of the traditional intelligent transportation system, and thus, many researchers have studied routing protocols and their simulation tools. Finding all Such methodologies and their development are unavailable at a single source. Focusing on this objective, our research explores routing protocols within the Internet of Vehicles (IoV) context. Our study comprehensively reviews diverse routing algorithms and their associated assessment methodologies. To systematically categorise these protocols, we employ a multi-tiered taxonomy. Firstly, we classify them into three main groups based on their transmission strategies: unicast, geo-cast, and broadcast. Secondly, we categorise them into four classes: topology-based, position-based, map-based, and path-based. Thirdly, we organise them according to their dimensions, differentiating between 1-D, 2-D, and 3-D approaches. Finally, we classify these protocols based on their applicability to homogeneous or heterogeneous network environments. The combination of classical routing protocols with the emerging heterogeneous network paradigm is of particular interest in our research, representing a compelling area for future exploration. By presenting this extensive framework, we aim to inspire researchers in the IoV field to develop innovative and efficient routing protocols and technologies. [ABSTRACT FROM AUTHOR]

The penaeid shrimp farming industry is experiencing rapid growth. To reduce costs and labour, automation techniques such as counting and size estimation are increasingly being adopted. Feeding based on the degree of stomach fullness can significantly reduce food waste and water contamination. Therefore, we propose an intelligent shrimp farming system that includes shrimp detection, measurement of approximated shrimp length, shrimp quantity, and two methods for determining the degree of digestive tract fullness. We introduce AR-YOLOv5 (Angular Rotation YOLOv5) in the system to enhance both shrimp growth and the environmental sustainability of shrimp farming. Our experiments were conducted in a real shrimp farming environment. The length and quantity are estimated based on the bounding box, and the level of stomach fullness is approximated using the ratio of the shrimp´s digestive tract to its body size. In terms of detection performance, our proposed method achieves a precision rate of 97.70%, a recall rate of 91.42%, a mean average precision of 94.46%, and an F1-score of 95.42% using AR-YOLOv5. Furthermore, our stomach fullness determined method achieves an accuracy of 88.8%, a precision rate of 91.7%, a recall rate of 90.9%, and an F1-score of 91.3% in real shrimp farming environments. [ABSTRACT FROM AUTHOR]

Although the existing semantic image synthesis methods based on generative adversarial networks (GANs) have achieved great success, the quality of the generated images still cannot achieve satisfactory results. This is mainly caused by two reasons. One reason is that the information in the semantic layout is sparse. Another reason is that a single constraint cannot effectively control the position relationship between objects in the generated image. To address the above problems, we propose a dual-conditional GAN with based on an external attention for semantic image synthesis (DCSIS). In DCSIS, the adaptive normalization method uses the one-hot encoded semantic layout to generate the first latent space and the external attention uses the RGB encoded semantic layout to generate the second latent space. Two latent spaces control the shape of objects and the positional relationship between objects in the generated image. The graph attention (GAT) is added to the generator to strengthen the relationship between different categories in the generated image. A graph convolutional segmentation network (GSeg) is designed to learn information for each category. Experiments on several challenging datasets demonstrate the advantages of our method over existing approaches, regarding both visual quality and the representative evaluating criteria. [ABSTRACT FROM AUTHOR]

Despite the extensive deployment of digital instruments in modern times, their stability is challenging to maintain in adverse environmental conditions such as extreme temperatures, pressure, or powerful electromagnetic radiation. Analog meters, owing to their mechanical resilience and electromagnetic impedance, persistently find usage across nuclear power plants, petroleum, and chemical industries. However, under these harsh conditions, manual reading of the instruments may prove to be difficult and dangerous while failing to meet the requirements of real-time monitoring. In recent years, several machine vision-based meter reading systems have been proposed, however, achieving high accuracy through camera-based methods under varying angles and lighting conditions poses significant challenges. Cloud deployment may compromise plant privacy, while edge computing faces limitations in real-time meter reading due to limited computing power. To address these issues, we propose a real-time reading system based on the YolactEdge instance segmentation framework for single-point analog meters. Our system is more accurate than previous studies and is implemented and deployed on the Jetson Xavier NX edge computing device. Our performance evaluation shows that our model outperforms other baselines, with low reference values and relative errors of 0.0237% and 0.0300%, respectively, and an average inference speed of 10.26 FPS with INIT 8 linear acceleration on Nvidia Jetson NX. [ABSTRACT FROM AUTHOR]

The healthcare industry is collecting considerable patient and medical data by using Internet of Things (IoT) devices. Consequently, ransomware attacks to encrypt healthcare systems or leak such data have increased recently. Many studies are aiming to predict ransomware behaviours early to protect the healthcare IoT environment from such attacks. However, previous studies analysed ransomware behaviours for long periods of time, and systems would already get infected and encrypted meanwhile. To avoid this problem, this study proposes an early prediction scheme of ransomware behaviour (EPS-Ran) to reduce the likelihood of systems being infected during behavioural analysis. EPS-Ran analyses behaviours for 30 s to extract the opcode and API calls sequence. The extracted behaviour features are entered into a hybrid deep learning model that combines the bidirectional gated recurrent unit (Bi-GRU) model and the temporal convolutional network (TCN) model to predict a future 90 s API calls sequence. The MAE, MSE, and RMSE of the prediction performance of EPS-Ran were measured to be 0.3438, 0.5648, and 0.6342, respectively. EPS-Ran predicted ransomware behaviours early with a low error rate even when the analysis time was reduced from 120 s to 30 s. [ABSTRACT FROM AUTHOR]

Through social media platforms and the internet, the world is becoming more and more connected, and producing enormous amounts of data. Also, the texts are collected from social media, newspapers, user reviews of products, company press releases, etc. The correctness of the classification is mainly dependent on the kind of words utilised in the corpus and the features utilised for classification. Hence, due to the increasing growth of text data on the Internet, the accurate organisation and management of text data has become a great challenge. Hence, in this research, an effective Invasive Weed Tunicate Swarm Optimization-based Hierarchical Attention Network (IWTSO-based HAN) is implemented for achieving categorisation of text. Here, the features are mined from the text and thereby the optimal features are acquired to perform the classification strategy. The incorporation of parametric features of each optimisation ensures the proposed method to increase the convergence of global solutions by improving the categorisation effectiveness. The proposed method obtained better performance for text classification with measures, like accuracy, True Positive Rate (TRP), True Negative Rate (TNR), precision, and False Negative Rate (FNR) with values of 92.4%, 92.4%, 94.1%, 95.4%, and 0.0758. [ABSTRACT FROM AUTHOR]

Although machine learning classifiers have been successfully used in the medical and engineering fields, there is still room for improving the predictive accuracy of model classification. The higher the accuracy of the classifier, the better suggestions can be provided for the decision makers. Therefore, in this study, we propose an ensemble machine learning approach, called Feature generation-based Ensemble Support Vector Machine (FESVM), for classification tasks. We first apply the feature selection technique to select the most related features. Next, we introduce an ensemble strategy to aggregate multiple base estimators for the final prediction using the meta-classifier SVM. During this stage, we use the classification probabilities obtained from the base classifier to generate new features. After that, the generated features are added to the original data set to form a new data set. Finally, this new data set is utilised to train the meta-classifier SVM to obtain the final classification results. For example, for a binary classification task, each base classifier has two probabilities (p for one class and 1−p for the other class). In this case, two new features are generated from the combination of probabilities based on these base classifiers. One is the sum of p as new feature 1, and the other is the sum of 1−p as new feature 2. These two new features are then added to the original data set to form the new data set. In the same way, our feature generation method can be easily extended for a multi-class task for generating new features, where the number of features depends on the number of classes. Those generated features from the base estimators (first layer) are added to the original data set to form a new data set. This new data set is used as the input to the second layer (meta-classifier) to obtain the final model. Experiments based on the 20 data sets show that our proposed model FESVM has the best performance compared to the other machine learning classifiers under comparison. In addition, our FESVM has better performance than the original stacking method in the multi-class classification tasks. Statistical results based on the Wilcoxon–Holm method also confirms that our FESVM can significantly outperform the other models. These indicate that our FESVM can be a useful tool for classification tasks, especially multi-classification tasks. [ABSTRACT FROM AUTHOR]

Asset price jumps are very common in financial markets, and they are essential to accurately predict volatility. This article focuses on 50 randomly selected stocks from the Chinese stock market, utilising high-frequency data to construct two jump models, the heterogeneous autoregressive quarticity jump model (HARQ-J) and the full heterogeneous autoregressive quarticity jump model (HARQ-F-J), which take into account jump variables based on existing models (HARQ and HARQ-F). To further enhance the accuracy of our volatility forecasts, the study combines the newly constructed models with the machine learning (ML) to form a hybrid model. Finally, the empirical research shows that the new hybrid model performs better than existing traditional prediction methods. In particular, the long- and short-term memory (LSTM) function is significantly better than other machine learning functions. Among all the LSTM models tested by the model confidence set (MCS), the HARQ-F-J-LSTM model has the highest prediction accuracy, followed by the HARQ-J-LSTM model. [ABSTRACT FROM AUTHOR]

Aiming at the problem of low detection accuracy of network traffic data types by traditional intrusion detection methods, we propose an improved Harris Hawk hybrid intrusion detection method to enhance the detection capability. The improved Harris Hawk optimization algorithm is used as a feature selection scheme to reduce the impact of redundant and noisy features on the performance of the classification model. The algorithm introduces the singer map to initialise the population, uses multi-information fusion to obtain the best prey position, and applies the sine function-based escape energy to execute a prey search strategy to obtain the optimal subset of features. In addition, the original data is preprocessed by the k-nearest neighbour and deep denoising autoencoder (KNN-DDAE) to relieve the imbalance problem of the network traffic data. Finally, a deep neural network (DNN) is used to complete the classification. Simulation experiments are conducted on the dataset NSL-KDD, KDD CUP99, and UNSW-NB15. The results show that our feature selection and data balancing scheme greatly improves the detection accuracy. In addition, the detection performance of this method is better than the current popular intrusion detection schemes. [ABSTRACT FROM AUTHOR]

Process variants usually exhibit a high degree of internal heterogeneity, in the sense that the executions of the process differ widely from each other due to contextual factors, human factors, or deliberate business decisions. Understanding differences among process variants helps analysts and managers to make informed decisions as to how to standardise or otherwise improve a business process. Existing process variant mining approaches typically fall short in full supporting semantic process variability mining, especially rarely taking activity behaviour relationships and trace context semantic into consideration. Here, we propose a semantic process variant discovery method, aimed at solving the difficulty of distinguishing similar-but-different behaviours directly from event logs. More specifically, we adapt concepts of benchmark logs and trace context tree to formalise context semantic of event log, to classify benchmark logs into several parts, thereby the clustered trace cohorts are mapped to discover the configurable process variants. In the experimental part, some performance metrics of the proposed method are evaluated and calculated by real-world event logs, supporting the usefulness of the proposed method. The experimental results show that the proposed method is able to distinguish similar-but-different behaviours and is superior to the characteristic trace clustering method using conventional neural networks. [ABSTRACT FROM AUTHOR]

Safety-critical systems play a significant role in industrial domains, and their complexity is increasing with advanced technologies such as Artificial Intelligence (AI). To provide efficient services, safety-critical systems that integrate AI applications are always built based on Linux, where Linux offers massive amounts of features and an incredibly perfect software ecosystem for AI applications. Since Linux is a pre-existing complex software system, different research programmes aim to pave the way for developing Linux-based safety-critical systems. Still, only some focus on the system calls for file operations. However, the execution path of a system call is effectively non-deterministic in Linux kernel space, which challenges the test coverage-based verification recommended by the functional safety standards. This research analyzes the influence of system state on Linux kernel path variability from two perspectives: file system type and system load. Therefore, an online data collection system for system call execution paths was constructed based on Ftrace, network file system (NFS), and MD5 hash function, uniquely identifying the system call execution path. The collected data were processed and analysed in this study. Evaluations show that the number of function execution paths of the system calls relevant to file systems increased with the increase in system load but would eventually be stable. Additionally, the function execution paths of the system call varied in different file systems. Based on the evaluations, the results of this work can provide advice for analyzing Linux-based safety-critical systems. In addition, the method introduced in this research can also provide support for the verification of Linux-based safety-critical systems. [ABSTRACT FROM AUTHOR]

Aspect-based sentiment analysis (ABSA) aims to automatically identify the sentiment polarity of specific aspect words in a given sentence or document. Existing studies have recognised the value of interactive learning in ABSA and have developed various methods to precisely model aspect words and their contexts through interactive learning. However, these methods mostly take a shallow interactive way to model aspect words and their contexts, which may lead to the lack of complex sentiment information. To solve this issue, we propose a Lightweight Multilayer Interactive Attention Network (LMIAN) for ABSA. Specifically, we first employ a pre-trained language model to initialise word embedding vectors. Second, an interactive computational layer is designed to build correlations between aspect words and their contexts. Such correlation degree is calculated by multiple computational layers with neural attention models. Third, we use a parameter-sharing strategy among the computational layers. This allows the model to learn complex sentiment features with lower memory costs. Finally, LMIAN conducts instance validation on six publicly available sentiment analysis datasets. Extensive experiments show that LMIAN performs better than other advanced methods with relatively low memory consumption. [ABSTRACT FROM AUTHOR]

With the development of Medical Internet of Things (MIoT) technology and the global COVID-19 pandemic, hospitals gain access to patients' health data from remote wearable medical equipment. Federated learning (FL) addresses the difficulty of sharing data in remote medical systems. However, some key issues and challenges persist, such as heterogeneous health data stored in hospitals, which leads to high communication cost and low model accuracy. There are many approaches of federated distillation (FD) methods used to solve these problems, but FD is very vulnerable to poisoning attacks and requires a centralised server for aggregation, which is prone to single-node failure. To tackle this issue, we combine FD and blockchain to solve data sharing in remote medical system called FedRMD. FedRMD use reputation incentive to defend against poisoning attacks and store reputation values and soft labels of FD in Hyperledger Fabric. Experimenting on COVID-19 radiography and COVID-Chestxray datasets shows our method can reduce communication cost, and the performance is higher than FedAvg, FedDF, and FedGen. In addition, the reputation incentive can reduce the impact of poisoning attacks. [ABSTRACT FROM AUTHOR]

With the rapid development of machine learning in the field of classification, the classification fairness has become the research emphasis second to prediction accuracy. However, the data bias and algorithmic discrimination that affect the fair classification of models have not been well resolved, which may damage or benefit the specific groups related to the sensitive attributes (e.g. age, race, and gender). To alleviate the unfairness of the classification model, this study proposes a novel bias-alleviated hybrid ensemble model (BAHEM) based on over-sampling and post-processing. First, a new clustering-based over-sampling method is proposed to reduce the data bias caused by the imbalance in label and sensitive attribute. Then, a stacking-based ensemble learning method is employed to obtain the higher performance and robustness of the BAHEM. Finally, a new classification with alternating normalisation (CAN)-based post-processing method is proposed to further improve the fairness and maintain the accuracy of the BAHEM. Three datasets with different sensitive attributes and four evaluation metrics were used to evaluate the prediction accuracy and fairness of the BAHEM. The experimental results verify the superior fairness of the BAHEM with little accuracy reduction. [ABSTRACT FROM AUTHOR]

Span classification is a popular method for nested named entity recognition. To recognise full-span named entities, span-based models should enumerate and verify all possible entity spans in a sentence, which leads to serious problems regarding computational complexity and data imbalance. In this study, we propose a boundary regression model to support full-span named entity recognition, where a regression operation is adopted to refine spatial locations of entity spans in a sentence. Therefore, instead of exhaustively enumerating all possible spans, we need only verify a small number of them. Span boundaries are regressed to find all possible named entities in a sentence. Furthermore, for a better representation of long-named entities, a multi-granule sentence representation is adopted to encode semantic features with different semantic granularities. In our experiments, even enumerating a small number of entity spans, our model still has competitive performance, achieving 87.35% and 80.85% F1 scores on the ACE2005 and GENIA datasets. Analytical experiments show that our model is able to find all named entities in a sentence without exhaustively verifying all possible entity spans. It is effective in mitigating the computational complexity and data imbalance problems in full-span named entity recognition. [ABSTRACT FROM AUTHOR]

The development of 5G communication, big data technology and intelligent transportation system has driven the rapid development of vehicle network (VANET). However, with the rapid development of vehicle networks, it will be invaded by various security problems, such as privacy disclosure, session key security, forward security, etc. To eliminate the security threats faced by the vehicle network and ensure privacy and secure communication between vehicles and roadside units(RSU), it is necessary to introduce a secure, reliable, and efficient authentication key agreement scheme. Therefore, based on the principle of challenge authentication handshake protocol, we propose a novel security authentication and key agreement scheme for the vehicle networks. The scheme meets mutual authentication, and is the session key of communication protocol between vehicle and roadside unit to resist common attacks. In addition, compared with other schemes, our scheme has the advantage that we can flexibly set time keys to constrain vehicle behaviour and achieve dynamic vehicle management. We also give a formal security proof of the scheme under the random oracle model (ROM). Finally, the efficiency of the protocol is evaluated, and the computing and communication overhead of our scheme is about 35% lower than that of the existing scheme. So, our scheme is more practical. [ABSTRACT FROM AUTHOR]

The work presented here is a high dimensional color image encryption architecture (HDIEA) founded on the Lorenz-Gauss-Logistic (LGL) encryption algorithm. The primary objective is to demonstrate that both the proposed novel five-dimensional (5D) Gauss-Logistic and four-dimensional (4D) Lorenz system are operating in a hyper-chaotic condition. The visual study of their most important characteristics, such as the sensitivity of the starting value of both maps and the Lyapunov exponent of the 5D Gauss Logistic map, is carried out. The Runge–Kutta technique is used to discretise the Lorenz system in order to construct a pseudo-random sequence generation for the control parameter that has a greater degree of randomness. The 5D Gauss-Logistic system is then selected to serve as the principal hyper-chaotic mapping scheme. The simulation results demonstrate that the suggested image encryption method is successful according to the NIST test and has powerful anti-attack, a larger key space as large as 2847, which is prone to multiple attacks, and key sensitivity capabilities. Also, the pixel correlation reached −0.0019, −0.0016, and −0.0069, while the information entropy was at 7.9996. This demonstrates the excellent scrambling effect of the proposed approach, which is capable of greatly improving the color image security performance. [ABSTRACT FROM AUTHOR]

In the game of a two-dimensional plane with a circular obstacle, a strategy of multi-pursuers and a single evader is studied. All the agents are Unmanned Aerial Vehicle and the evader is faster than the pursuers. The collaborative pursuit-evasion mission is divided into two stages: the encirclement stage and pursuit-evasion stage. When pursuers fly to the mission area from a distance, the formation and obstacle avoidance method based on leader-follower mode is designed for the encirclement process. In the pursuit-evasion stage, considering obstacle avoidance and manoeuver constraint, a collaborative strategy combining Apollonius circle algorithm and geometric algorithm is proposed, and the condition for the successful capture of a single superior evader is analysed. In the simulation, the influence of obstacle size and manoeuver constraint is compared and analysed in detail, which indicates that they will increase the capture time even changes the result of the game in the limited map. In addition, the complexity of the algorithm is $ \boldsymbol{M} $ M times higher than that of the obstacle-free case ( $ \boldsymbol{M} $ M is the number of obstacles in the map). [ABSTRACT FROM AUTHOR]

Cancer is a disease with a complex genome of altered functions. However, most existing driver gene identification approaches rarely consider driver genes may have the same functional properties. To overcome this issue, we propose the gamma distribution test for the driver gene identification based on similarity networks, termed GASN, which identifies driver genes by combining machine learning and distributional statistics methods. Similarity networks are able to learn gene similarities and key features that represent the functional impact of genes. In addition, we classify genes into different cellular compartments and use the gamma distribution test within cellular compartments to identify significant driver genes. The experimental results show that our method outperforms the other 17 comparative methods. [ABSTRACT FROM AUTHOR]

In this study, we propose an image encryption algorithm based on ZigZag transform with Durstenfeld Shuffling Algorithm (DSA), where we adopt confusion–diffusion architecture. Our encryption method is simpler to implement than high-dimensional chaos-based approaches, and its computational complexity is very low, which make it very fast and suitable for real-world applications. Additionally, its dependence on plaintext image makes it adaptive and hence very robust against brute force and differential attacks. Experimental analyses using histogram, correlation, NIST, NPCR, UACI and PSNR values suggest that our proposed algorithm is strong and secure against statistical and differential attacks. [ABSTRACT FROM AUTHOR]

Financial enterprises generate profits based on economic development. More importantly, a healthy market is difficult to achieve due to their susceptibility to the parasitic credit card fraud transactions that accompany economic growth, unless an effective anti-counterfeiting technology is developed to alleviate the issue. To solve the problem, we propose a gradient-boosting decision tree based anti-fraud protection with blockchain Technology, referred to as GBDT-APBT, which treats anti-fraud transaction model as the accumulation of the classfiers' weakness and builds up a classifiers' to judge whether the transaction is fraudulent. Each user's private data is trained offline at the local blockchain node, then the trained model is directly uploaded to the cloud, and the final consensus model is obtained by voting. Due to incorporating blockchain technology, GBDT-APBT demonstrates decentralisation, openness, autonomy, anonymity, and immutability, showing its ability to satisfying the demand for an effective and beneficial anti-counterfeiting system, with high performance and effectiveness in detecting fraud information. Experiments show that compared with other methods, GBDT-APBT offers a promising approach to the security of credit card transactions with reference to the detection accuracy. [ABSTRACT FROM AUTHOR]

Prompt-tuning models output relation types as verbalised-type tokens instead of predicting the confidence scores for each relation type. However, existing prompt-tuning models cannot perceive named entities of a relation instance because they are normally implemented on raw input that is too weak to encode the contextual features and semantic dependencies of a relation instance. This study proposes a cue prompt adapting (CPA) model for relation extraction (RE) that encodes contextual features and semantic dependencies by implanting task-relevant cues in a sentence. Additionally, a new transformer architecture is proposed to adapt pre-trained language models (PLMs) to perceive named entities in a relation instance. Finally, in the decoding process, a goal-oriented prompt template is designed to take advantage of the potential semantic features of a PLM. The proposed model is evaluated using three public corpora: ACE, ReTACRED, and Semeval. The performance achieves an impressive improvement, outperforming existing state-of-the-art models. Experiments indicate that the proposed model is effective for learning task-specific contextual features and semantic dependencies in a relation instance. [ABSTRACT FROM AUTHOR]

Natural language processing text similarity calculation is a crucial and difficult problem that enables matching between various messages. This approach is the foundation of many applications. The word representation features and contextual relationships extracted by current text similarity computation methods are insufficient, and too many factors increase the computational complexity. Re-LSTM, a weighted word embedding long and short-term memory network, has therefore been proposed as a text similarity computing model. The two-gate mechanism of Re-LSTM neurons is built on the foundation of the conventional LSTM model and is intended to minimise the parameters and computation to some level. The hidden features and state information of the layer above each gate are considered for extracting more implicit features. By fully utilising the feature word and its domain association, the feature word's position, and the word frequency information, the TF-IDF method and the χ²-C algorithm may effectively improve the representation of the weights on the words. The Attention mechanism is used in Re-LSTM to combine dependencies and feature word weights for deeper text semantic mining. The experimental results demonstrate that the Re-LSTM model outperforms baselines in terms of precision, recall, accuracy, and F1 values, all of which reach above 85% when applied to the QQPC and ATEC datasets. [ABSTRACT FROM AUTHOR]

Anomaly detection in the scope of network security aims to identify network instances for the unexpected and unique, with various security operations employing such techniques to facilitate effective threat detection. However, many systems have been designed based on the absolute mapping of attacks to one of three anomaly types (i.e. point, collective, or contextual), a strategy not supported by the recent findings of hybrid anomaly classifications. Given the growing usage of network anomaly detection and the implications of hybrid anomalies, we propose several heterogeneous anomaly types and provide an unsupervised approach for the automated mapping of network threats. Initial findings on publicly available intrusion datasets support the existence of four unique heterogeneous anomaly types, providing unique insight regarding the next generation of network anomaly detection systems. [ABSTRACT FROM AUTHOR]

Most of the recent research has focused on the Internet of Things (IoT) and its applications. The open interface and network connectivity of the interconnected systems under the IoT network make them vulnerable to hackers. A model has been proposed to identify and classify IoT routing attacks. To generate IoT routing datasets, the Cooja simulator is used at first. The IoT routing dataset is then augmented into larger volumes using ADASYN, which is also used to solve the class imbalance problems. A deep learning hybrid model based on a Long-Short-Term Memory (LSTM) network and adaptive Mayfly Optimization Algorithm (LAMOA) was presented for the classification of IoT attacks. The adaptive MOA adjusts the weights in the various layers of the LSTM network and the Fully Connected Layer with SoftMax Classification. As part of the validation process, the proposed model was also compared with benchmark datasets NSL-KDD, BoT-IoT, and IoT-23. Using benchmark datasets, LAMOA achieved 99.94% accuracy for multiclassifications, 99.92% accuracy for binary classifications, and 98.42% accuracy for real-time datasets. Compared to other models, our proposed model significantly improves the accuracy of each attack's classification by 5–10%. [ABSTRACT FROM AUTHOR]

The cross-domain sequential recommendation aims to predict the next item based on a sequence of recorded user behaviours in multiple domains. We propose a novel Cross-domain Sequential Recommendation approach with Graph-Collaborative Filtering (CsrGCF) to alleviate the sparsity issue of user-interaction data. Specifically, we design time-aware and relation-aware graph attention mechanisms with collaborative filtering to exploit high-order behaviour patterns of users for promising results in both domains. Time-aware Graph Attention mechanism (TGAT) is designed to learn the inter-domain sequence-level representation of items. Relationship-aware Graph Attention mechanism (RGAT) is proposed to learn collaborative items' and users' feature representations. Moreover, to simultaneously improve the recommendation performance in the two domains, a Cross-domain Feature Bidirectional Transfer module (CFBT) is proposed, transferring user's common sharing features in both domains and retaining user's domain-specific features in a specific domain. Finally, cross-domain and sequential information jointly recommend the next items that users like. We conduct extensive experiments on two real-world datasets that show that CsrGCF outperforms several state-of-the-art baselines in terms of Recall and MRR. These demonstrate the necessity of exploiting high-order behaviour patterns of users for a cross-domain sequential recommendation. Meanwhile, retaining domain-specific features is an important step in the process of cross-domain feature bidirectional transferring. [ABSTRACT FROM AUTHOR]