The girth vertex (resp. edge) tenacity g τ v ( G ) (resp. g τ e ( G ) ) of a non-acyclic simple graph G is defined to be the maximum number k such that the removal of any k vertices (resp. edges) of G does not change its girth. In this paper, we mainly investigated the girth tenacity of the exchanged hypercube E H ( s , t ) with s , t ≥ 1 , the ternary n -cube Q n 3 and the exchanged ternary n -cube E 3 C ( r , s , t ) with n = r + s + t and r , s , t ≥ 0 . We proved that ( i ) g τ v ( E H ( 1 , 1 ) ) = 0 , g τ v ( E H ( s , t ) ) = 2 ⌊ 2 t + 1 3 ⌋ − 1 for s = 1 and t ≥ 2 , g τ v ( E H ( s , t ) ) = 2 ⌊ 2 s + 1 3 ⌋ − 1 for t = 1 and s ≥ 2 , g τ v ( E H ( s , t ) ) = ⌊ 2 t + 1 3 ⌋ 2 s + ⌊ 2 s + 1 3 ⌋ 2 t − 1 for min { s , t } ≥ 2 , and ( i i ) g τ v ( Q n 3 ) = 3 n − 1 − 1 , g τ e ( Q n 3 ) = n 3 n − 1 − 1 , and ( i i i ) g τ v ( E 3 C ( r , s , t ) ) = 3 n − 1 − 1 , g τ e ( E 3 C ( r , s , t ) ) = ( n + 2 ) 3 n − 2 − 1 . Some results on the girth tenacity of the hypercube were also listed.

With the rapid development of the industrial internet of things and automation control systems, supervisory control and data acquisition (SCADA) systems have been widely adopted in industrial manufacturing due to their flexibility and scalability. The cloud-fog automation (CFA) paradigm is emerging to address higher real-time and computing demands in complex industrial environments. To fully leverage the efficiency, flexibility, and scalability of Kubernetes, an open-source container orchestration platform Kubernetes in managing containerized applications, this article investigates methods for deploying SCADA systems on the Kubernetes platform. This approach aims to capitalize on Kubernetes’ benefits, such as automated deployment, elastic scaling, and high availability, to optimize resource management and enhance system performance. To validate the proposed solution, we employs testing tools such as wrk and tc, along with monitoring tools like Prometheus and Grafana, to conduct a comprehensive evaluation of Kubernetes’ advantages in various scenarios. We focus on three key aspects: reliability, scalability, and security. The results demonstrate that Kubernetes can significantly improve the scalability, fault recovery capabilities, and stability of SCADA systems.

For intelligent surveillance systems, abnormal event detection automatically analyses surveillance video sequences and detects abnormal objects or unusual human actions at the frame level. Due to the lack of labelled data, most approaches are semi-supervised based on reconstruction or prediction methods. However, these methods may not generalize well to unseen scene contexts. To address this issue, we present a novel self and mutual scene-adaptive matching method for abnormal event detection. In the framework, we propose synergistic pose estimation and object detection, which effectively integrates human pose and object detection information to improve pose estimation accuracy. Then, the poses are resized to reduce the spatial distance between the source and target domains. The improved pose sequences are further fed into a spatio-temporal graph convolutional network to extract the geometric features. Finally, the features are embedded in a clustering layer to classify action types and compute normality scores. The training data is taken from the training part of common video anomaly detection datasets: UCSD PED1 & PED2, CHUK Avenue, and ShanghaiTech Campus. The proposed framework is evaluated on video sequences with unseen scene contexts in the UCSD PED2 and ShanghaiTech Campus datasets. The detection accuracy and efficiency are also evaluated in detail, and the proposed method for abnormal event detection achieves the highest AUC performance, 84.6%, on the ShanghaiTech Campus dataset and relatively high AUC performance, 96.9% and 74.8%, on UCSD PED2 & PED1 datasets. Compared with other state-of-the-art works, the performance analysis and results confirm the robustness and effectiveness of our proposed framework for cross-scene abnormal event detection.

In this article, we propose a position and orientation-aware one-shot learning framework for medical action recognition from signal data. The proposed framework comprises two stages and each stage includes signal-level image generation (SIG), cross-attention (CsA), and dynamic time warping (DTW) modules and the information fusion between the proposed privacy-preserved position and orientation features. The proposed SIG method aims to transform the raw skeleton data into privacy-preserved features for training. The CsA module is developed to guide the network in reducing medical action recognition bias and more focusing on important human body parts for each specific action, aimed at addressing similar medical action related issues. Moreover, the DTW module is employed to minimize temporal mismatching between instances and further improve model performance. Furthermore, the proposed privacy-preserved orientation-level features are utilized to assist the position-level features in both of the two stages for enhancing medical action recognition performance. Extensive experimental results on the widely-used and well-known NTU RGB+D 60, NTU RGB+D 120, and PKU-MMD datasets all demonstrate the effectiveness of the proposed method, which outperforms the other state-of-the-art methods with general dataset partitioning by 2.7%, 6.2% and 4.1%, respectively.

Augmented Reality (AR) has been proven beneficial to External Ventricular Drain (EVD) surgery by providing in-situ visual guidance during operations. During this procedure, the key challenge is estimating the spatial relationship between pre-operative images and actual patient anatomy accurately and efficiently. Previous works have revealed conflicts between tracking accuracy, workflow efficiency, and non-invasiveness in tracking pipelines. This research fully utilizes the capabilities of Time of Flight (ToF) depth sensors, including retro-reflective tool tracking and dense surface information, to construct a convenient and accurate EVD guiding pipeline. As previous studies have proven significant depth errors in ToF depth sensors, we first evaluated the feasibility of using ToF sensors in surgical guidance by estimating its accuracy under different conditions and corrected this error in our pipeline. Our results show $ \text{7.580}\pm \text{1.488}\,\text{mm}$7.580±1.488mm depth value errors on human skin under HoloLens 2 depth camera, indicating the significance of depth correction. This error was reduced by over 85% using proposed depth correction method on head phantoms in different materials. The corrected depth information can then be utilized to reconstruct the head surface with sub-millimeter accuracy, validated on a series of 3D-printed models and a sheep head. To demonstrate the effectiveness of the proposed framework, we conducted a case study simulating EVD surgery. Five surgeons were involved in this study, each performing nine k-wire insertions on a head phantom under virtual guidance without tracking for surgical tools. The results revealed $ \text{2.09} \pm \text{1.00}\,\text{mm}$2.09±1.00mm translational and $\text{2.97}\pm \text{1.95}^\circ$2.97±1.95∘ orientational guidance accuracy, demonstrating competitive performance with previous research.

With the rapid development of intelligent surveillance, video anomaly detection has become a popular topic in related areas of artificial intelligence. In this work, the main focus is on those applications where the privacy of human targets is concerned, such as outdoor and indoor surveillance and smart living systems. Video frames are the most common recorded and processed information source for human anomaly detection. However, video frames also contain privacy-sensitive information such as facial information and identification of human targets. This paper provides a privacy-preserving anomaly detection framework that introduces image segmentation masks to protect the privacy of the human targets. Meanwhile, object detection is implemented to improve anomaly detection performance by incorporating contextual information. The proposed method uses the ST-AE and CONV-AE models, which were trained and tested on the popular anomaly detection datasets UCSD Ped1 and Ped2. Experiments confirm that when image segmentation masks are applied to preserve human targets' privacy information, the anomaly detection models still achieve good performances with the orientation of object detection.

Abstract Let $H$ be a cartesian product graph of even cycles and paths, where the first multiplier is an even cycle of length at least $4$ and the second multiplier is a path with at least two nodes or an even cycle. Then $H$ is an equitable bipartite graph, which takes the torus, the column-torus and the even $k$-ary $n$-cube as its special cases. For any node $w$ of $H$ and any two different nodes $u$ and $v$ in the partite set of $H$ not containing $w$, an algorithm was introduced to construct a hamiltonian path connecting $u$ and $v$ in $H-w$.

Attention Deficit Hyperactivity Disorder (ADHD) causes significant impairment in various domains.Early diagnosis of ADHD and treatment could significantly improve the quality of life and functioning.Recently, machine learning methods have improved the accuracy and efficiency of the ADHD diagnosis process.However, the cost of the equipment and trained staff required by the existing methods are generally huge.Therefore, we introduce the video-based frame-level action recognition network to ADHD diagnosis for the first time.We also record a real multi-modal ADHD dataset and extract three action classes from the video modality for ADHD diagnosis.The whole process data have been reported to CNTW-NHS Foundation Trust, which would be reviewed by medical consultants/professionals and will be made public in due course.

In this paper, we address the classification of medical actions with only one single sample by developing a novel one-shot learning framework which contains both cross-attention and dynamic time warping (DTW) modules. To be concrete, we firstly transform the raw skeleton sequence into the signal-level image representation. We exploit a metric learning approach, which is the prototypical network for the proposed one-shot learning framework and choose the residual network (ResNet18) as the backbone which is widely used in recent years. Cross-attention is applied for guiding the network to focus on the more important joints from each specific action. The cross-attention mechanism that applies between the support and query set will be adapted for mining and matching the relationships with the human body. Furthermore, a DTW module is introduced to mitigate the temporal information mismatching issue between the actions from the support and query sets. The experimental results on the NTU RGB+D 120 dataset demonstrate the effectiveness of our proposed approach and the improved performance compared to the baseline approach. The code of this work is available at1.

Abstract Human activity anomaly detection plays a crucial role in the next generation of surveillance and assisted living systems. Most anomaly detection algorithms are generative models and learn features from raw images. This work shows that popular state‐of‐the‐art autoencoder‐based anomaly detection systems are not capable of effectively detecting human‐posture and object‐positions related anomalies. Therefore, a human pose‐driven and object‐detector‐based deep learning architecture is proposed, which simultaneously leverages human poses and raw RGB data to perform human activity anomaly detection. It is demonstrated that pose‐driven learning overcomes the raw RGB based counterpart limitations in different human activities classification. Extensive validation is provided by using popular datasets. Then, it is demonstrated that with the aid of object detection, the human activities classification can be effectively used in human activity anomaly detection. Moreover, novel challenging datasets, that is, BMbD, M‐BMbD and JBMOPbD, are proposed for single and multi‐target human posture anomaly detection and joint human posture and object position anomaly detection evaluations.

Abnormal event detection is a critical component of intelligent surveillance systems, focusing on identifying abnormal objects or unusual human behaviours in video sequences. However, conventional methods struggle due to the scarcity of labelled data. Existing solutions typically train on normal data, establish boundaries for regular events, and identify outliers during testing. These approaches are often inadequate as they do not efficiently leverage the geometry and image texture information, and they lack a specific focus on different types of abnormal events. This paper introduces a novel two-stream fusion algorithm for abnormal event detection to address these diverse abnormal events better. We first extract the object, pose, and optical flow features. Then, the object and pose information is combined early on to eliminate occluded pose graphs. The trusted pose graphs are fed into a Spatio-Temporal Graph Convolutional Network (ST-GCN) to detect abnormal behaviours. Simultaneously, we propose a video prediction framework that identifies abnormal frames by measuring the difference between predicted and ground truth frames. Lastly, we execute a decision-level fusion between the classification and prediction streams to achieve the final results. Our results on the UCSD PED1 dataset indicate the enhanced performance of the fusion model for various abnormal events. Furthermore, experimental results on the UCSD PED2 dataset and the ShanghaiTech campus dataset underscore our approach’s effectiveness compared to other related works.

Human abnormal activity detection for automatic surveillance systems is to detect abnormal objects and human behaviours in videos. In this paper, we propose to explicitly address different kinds of abnormal events by developing a two-stream fusion approach that integrates both geometry and image texture information. To be concrete, we firstly propose to utilize an object detector to divide the abnormal events into two catalogues: abnormal human behaviors and abnormal objects. For the detection of abnormal human behaviours, we exploit a spatial-temporal graph convolutional network (ST-GCN) which considers both spatial and temporal domains to capture the geometrical features from human pose graphs. The extracted geometric feature embeddings are further adapted with a clustering step to cluster the temporal graphs and output normality scores. For the detection of abnormal objects, the obtained from the object detector are reused to assist with generating normality scores of possible anomalies. Finally, a late fusion is performed to integrate normality scores from both screams for final decision. The experimental results on the datasets of UCSD PED2 and ShanghaiTech Campus demonstrate the effectiveness of our proposed approach and the improved performance compared to other state-of-the-art approaches.

The$k$-ary$n$-cube$Q_n^k$is one of the most attractive interconnection networks for parallel and distributed systems. Let$F$be a set of faulty links in$Q_n^k$and let$L$be a linear forest in$Q_n^k-F$such that$|E(L)|+|F|\leq 2n-3$. For any two distinct nodes$u$and$v$of$Q_n^k$with$n\geq 2$and odd$k\geq 3$, we prove that$Q_n^k-F$admits a Hamiltonian path between$u$and$v$passing through$L$if and only if none of the paths in$L$has$u$or$v$as internal nodes or both of them as end-nodes. The upper bound$2n-3$on$|E(L)|+|F|$is optimal in the worst case. The main results in this paper generalized some known results.

JavaScript (JS) is a dominant programming language in web/mobile development, while it is also notoriously abused by attackers due to its powerful characteristics, e.g., dynamic, prototype-based and multi-paradigm, which foil most static and dynamic analysis approaches. To detect malicious JS instances, several machine learning-based methods have been developed recently. However, these methods took JS as a natural language instead of a programming one, which can not capture its syntactic and semantic features. In this paper, we present JSAC, a novel framework to detect JS malware. It combines deep learning and program analysis techniques to capture the syntactic and semantic features of JS programs. Specifically, to get a JS program's syntactic information, we build its abstract syntax tree and employ a tree-based convolutional neural network (CNN) to extract features from it. To get its semantic information, we construct its control flow graph and feed it to another graph-based CNN. Last, the features extracted from two CNNs are fused for final detection. Evaluation on a corpus of 69,523 JS files indicates that JSAC outperforms 4 other models with 98.73% F1-score in detecting JS malware.

Abstract For a graph G and a connected subgraph H of G, denote by ℋ(G;H) the set of subgraphs of G which are isomorphic to H and denote by ℋS(G;H) the union of sets of subgraphs of T, where T ranges over all the elements in ℋ(G;H). An H-structure-cut (respectively, H-substructure-cut) of G is a subset of ℋ(G;H) (respectively, ℋS(G;H)), if any, whose removal disconnects G. The H-structure connectivity (respectively, H-substructure connectivity) is the cardinality of a minimum H-structure-cut (respectively, H-substructure-cut) of G. The hypercube is one of the most attractive interconnection networks for large-scale multiprocessor computer systems. In this paper, we will characterize the minimum H-structure-cuts and the minimum H-substructure-cuts of hypercubes for H∈{K1,1,K1,2,K1,3,C4}.