To address issues such as data sparsity, overfitting, and the inability to fully extract latent information in traditional recommendation methods, a new insurance product recommendation algorithm is proposed that combines knowledge graphs improved by matrix factorization with deep neural networks (DNN). First, to tackle the issue of sparse data in existing insurance products, an improved knowledge graph recommendation algorithm based on matrix factorization (FunkSVD) is proposed. By training the data through matrix factorization the problem of data sparsity is mitigated. After refining the customer-product matrix, knowledge graph triples are constructed based on customer characteristics and insurance product features such as insured age and coverage. Feature extraction and customer preference prediction are carried out using an alternating learning approach within a multi-task knowledge graph framework. Then, to alleviate issues like local optima and vanishing gradients during recommendation, DNN is applied for further recommendation. A fully connected layer is constructed, and forward propagation and backpropagation algorithms are used to train customer features and product matrices, predicting customer purchasing behavior and generating recommendations. Finally, comparative experimental results show that, compared to other recommendation algorithms such as collaborative filtering and DNN, the proposed algorithm improves accuracy, recall, F1 score, and other metrics. This algorithm not only speeds up recommendations but also improves recommendation quality.

Systemic risks do not arise only as a result of a crisis event, and it is important to understand the ex-ante risk contagion mechanisms. There has been no research on ex-ante contagion valuation and contagion modeling of multilayer networks. This study proposes the ex-ante-contagion mechanism of a two-layer network financial system with interbank lending connections and cross-holding connections, constructs a general valuation model of the financial system based on the Eisenberg and Noe clearing framework, and develops a model of ex-ante risk contagion and valuation functions. Stress tests further verify that bankruptcy is not a necessary condition for loss generation. By simulating different shock scenarios, we obtain the systemic risk and systemically important banks in China. Our models and analyses provide new research perspectives for studying risk contagion mechanisms in financial networks and offer empirical corroboration for regulators and policy makers.

Currently, renewable energy sources (RES) have been widely deployed in the smart grid, especially in the active distribution network (ADN). However, the inherent uncertainty of the renewable energy output significantly impacts the economy of the ADN operation. It is suggested that the utilization of flexible resources (FR) can effectively even out the uncertainty of RES. Nevertheless, the non-marketization of FR may prevent the emerging park-level integrated energy systems (PIES), important entities in ADN, from sufficiently offering their potential flexibilities. Therefore, this paper presents a two-stage local flexibility trading mechanism for motivating multi-PIESs to provide their flexible resources (PFR) to improve the operational flexibility of ADN. The first stage determines the dispatching plan of ADN according to the day-ahead forecasted values of RES. The second stage enables the multi-PIESs to trade their PFR with ADN to adjust the day-ahead dispatching plan in real-time to correct the forecasted errors of RES. In terms of implementing the two stages, firstly, the capacity of PFR with involving the adjustable tie line power of PIES is quantified using an optimization-based as sessment model. Secondly, based on the change of the operation cost before and after the sale of PFR, a pricing model of PFR is established. And then, to determine the trading amount of PFR, a real-time ADN economic dispatching model with the network constraints is further constructed, which aims at minimizing the comprehensive operation cost. Afterwards, a marginal-based method is employed to obtain the clearing price of PFR. Finally, to ensure the feasibility of the trading results and to provide the accurate dispatching strategies for the PFR trading in next time interval, a rolling dispatch for the multi-PIESs is carried out. Case studies demonstrate that the presented flexibility trading mechanism significantly reduces the power curtailment of ADN, the operation costs of ADN and the multi-PIESs.

Uncertainty estimation is a critical component of building safe and reliable machine learning models. Accurate estimation of uncertainties is essential for identifying and mitigating potential risks and ensuring that machine learning systems operate reliably in real-world scenarios. Various approaches, such as ensemble and Bayesian neural networks have been developed by sampling probability predictions from submodels, which is computatinally expensive. Currently, these methods are unable to clearly define the boundary between in distribution (ID) and out-of-distribution (OOD) data. To fill up this research gap, this paper presents a normalizing flow based framework to directly predict parameters of prior distributions over the probability with a neural network, which is capable of effectively distinguishing ID and OOD data for regression problems. The posterior distributions learned by the proposed model accurately model uncertainties for OOD data from ID data without requiring OOD data at training time. This approach has shown promising results in a number of applications, including image depth estimation and image adversarial attacks.

Corporate fraud risk detection is a branch of fraud. It may exist in various industries and cause economic problems. Effective identification of corporate fraud can protect the safety of funds for investors in some sense. This paper proposes a classifier model of a fractional -order immune BP neural network based on the self -attention mechanism to improve efficiency. The improved artificial immune algorithm with dynamic region contraction strategy is used to optimize the initialization process of the BP neural network. Furthermore, it combines the self -attention mechanism to design the input layer. Finally, Caputo fractional noncausal calculus is used to optimize the parameter updating process in BP neural network. The experiment results indicate that our model has fast convergence rate and powerful capacity of detection, and performs efficiently in detecting fraud behaviors.

B cells possess anti-tumor functions mediated by granzyme B, in addition to their role in antigen presentation and antibody production. However, the variations in granzyme B+ B cells between tumor and non-tumor tissues have been largely unexplored. Therefore, we integrated 25 samples from the Gene Expression Omnibus database and analyzed the tumor immune microenvironment. The findings uncovered significant inter- and intra-tumoral heterogeneity. Notably, single-cell data showed higher proportions of granzyme B+ B cells in tumor samples compared to control samples, and these levels were positively associated with disease-free survival. The elevated levels of granzyme B+ B cells in tumor samples resulted from tumor cell chemotaxis through the MIF- (CD74 + CXCR4) signaling pathway. Furthermore, the anti-tumor function of granzyme B+ B cells in tumor samples was adversely affected, potentially providing an explanation for tumor progression. These findings regarding granzyme B+ B cells were further validated in an independent clinic cohort of 40 liver transplant recipients with intrahepatic cholangiocarcinoma. Our study unveils an interaction between granzyme B+ B cells and intrahepatic cholangiocarcinoma, opening up potential avenues for the development of novel therapeutic strategies against this disease.

Process mining has received much attention in the field of business process management. Event logs that are generated from information systems can be correlated with the process models for conformance checking. The process models describe event activities at an abstraction level. However, hierarchical business processes, as a kind of typical complex process scenario, describe sub -processes invocation and multi -instantiation patterns. As existing conformance, checking approaches cannot identify sub -processes within hierarchical process models, they cannot be used for conformance checking of hierarchical process models. To handle this limitation, a definition of hierarchically alignment sequences is presented in this paper. Meanwhile, a novel conformance checking approach for hierarchical process models and event logs is proposed. The proposed method has been implemented within the ProM toolkit, which is an open -source process mining software. To evaluate the effectiveness of the proposed approach, both artificial and real -world event logs are utilized in a comparative analysis against existing state-of-the-art approaches.

The Internet's rapid growth has led to a surge in social network users, resulting in an increase in extreme emotional and hate speech online. This study focuses on the security of public opinion in cyber security by analyzing Twitter data. The goal is to develop a model that can detect both sentiment and hate speech in user texts, aiding in the identification of content that may violate laws and regulations. The study involves pre-processing the acquired forensic data, including tasks like lowercasing, stop word removal, and stemming, to obtain clear and effective data. This paper contributes to the field of public opinion security by linking forensic data with machine learning techniques, showcasing the potential for detecting and analyzing Twitter text data.

Internet of Things (IoT) is a growing computing trend that encompasses every connected thing. Over the recent years, IoT has recorded an exponential growth, leading to billions of smart devices, and still increasing. In contrast to other computing devices, some IoTs generate large amount of data, however, this has become a source of concern as data could contain users’ privacy which should be protected at all costs against any potential security breach incident. Securing IoT is very significant with its continuous adoption and use, hence, researchers have proposed several security mechanisms and techniques to safeguard and protect IoT systems and devices. Notwithstanding, there are some research gaps that are yet to be addressed irrespective of the relevant contributions made in protection of users’ privacy and confidentiality using IoTs. In this paper, the researcher solely focused on a review of AI approaches leveraged by researchers in protecting the device and data security aspects of privacy specifically for de-centralised architecture based industrial IoT systems (IIOTs) as they are generating large amount of data and are safety critical. The results achieved, unresolved issues and recommendations for future research are contained in this review.

Survey/review study A Review of Techniques on Gait-Based Person Re-Identification Babak Rahi 1,*, Maozhen Li 1, and Man Qi 2 1 Department of Electronics and Computer Engineering, Brunel University London, Uxbridge, Middlesex, UB8 3PH, United Kingdom 2 The School of Engineering, University of Warwick, Coventry CV4 7AL, United Kingdom * Correspondence: babak.h.rahi@hotmail.com Received: 16 October 2022 Accepted: 14 December 2022 Published: 27 March 2023 Abstract: Person re-identification at a distance across multiple non-overlapping cameras has been an active research area for years. In the past ten years, short-term Person re-identification techniques have made great strides in accuracy using only appearance features in limited environments. However, massive intra-class variations and inter-class confusion limit their ability to be used in practical applications. Moreover, appearance consistency can only be assumed in a short time span from one camera to the other. Since the holistic appearance will change drastically over days and weeks, the technique, as mentioned above, will be ineffective. Practical applications usually require a long-term solution in which the subject's appearance and clothing might have changed after the elapse of a significant period. Facing these problems, soft biometric features such as Gait has stirred much interest in the past years. Nevertheless, even Gait can vary with illness, ageing and emotional states, walking surfaces, shoe types, clothes types, carried objects (by the subject) and even environment clutters. Therefore, Gait is considered as a temporal cue that could provide biometric motion information. On the other hand, the shape of the human body could be viewed as a spatial signal which can produce valuable information. So extracting discriminative features from both spatial and temporal domains would benefit this research. This article examines the main approaches used in gait analysis for re-identification over the past decade. We identify several relevant dimensions of the problem and provide a taxonomic analysis of current research. We conclude by reviewing the performance levels achievable with current technology and providing a perspective on the most challenging and promising research directions.

Several unexpected behaviors may occur during actual treatment of clinical pathways, which will have negative impact on the implementation and the future work. To increase the performance of current deviation detection algorithms, a method is presented according to business alignment, which can effectively detect the anomaly in the implementation of the clinical pathways, provide judgment basis for the intervention in the process of the clinical pathway implementation, and play a crucial role in improving the clinical pathways. Firstly, the noise in diagnosis and treatment logs of clinical pathways will be removed. Then, the synchronous composition model is constructed to embody the deviations between the actual process and the theoretical model. Finally, A ∗ algorithm is selected to search for optimal alignment. A clinical pathway for ST-Elevation Myocardial Infarction (STEMI) under COVID-19 is used as a case study, and the superiority and effectiveness of this method in deviation detection are illustrated in the result of experiments.

A method of repairing process models with non-free-choice constructs is proposed based on logical Petri nets, aiming at the problem of low precision in the existing repair methods. An extended successor matrix of transitions is determined according to the distance between any two transitions. There are two types of choice-construct transitions. One is a non-free-choice construct transition, and the other is a general choice construct transition. The type of choice-construct transitions can be determined based on the extended successor matrix and the relationship between the front and back sets of transitions. The location of the deviations is calculated by an improved replaying method. Finally, a model can be repaired according to remaining-token places and missing-token places. Based on the experiments on real event logs, the method proposed in this paper has a better performance in fitness, precision, and simplicity compared with its peers.

This paper proposes a novel architecture that utilises an attention mech-anism in conjunction with multi-stream convolutional neural networks (CNN) to obtain high accuracy in human re-identification (Reid). The proposed architecture consists of four blocks. First, the pre-pro cessing block prepares the input data and feeds it into a spatial-temporal two-stream CNN (STC) with two fusion points that extract the spatial-temporal features. Next, the spatial-temporal attentional LSTM block (STA) automatically fine-tunes the extracted features and assigns weight to the more critical frames in the video sequence by using an attention mechanism. Extensive experiments on four of the most popular datasets support our architec-ture. Finally, the results are compared with the state of the art, which shows the superiority of this approach.

In this article, a novel real-time object detector called Transformers Only Look Once (TOLO) is proposed to resolve two problems. The first problem is the inefficiency of building long-distance dependencies among local features for amounts of modern real-time object detectors. The second one is the lack of inductive biases for vision Transformer networks with heavily computational cost. TOLO is composed of Convolutional Neural Network (CNN) backbone, Feature Fusion Neck (FFN), and different Lite Transformer Heads (LTHs), which are used to transfer the inductive biases, supply the extracted features with high-resolution and high-semantic properties, and efficiently mine multiple long-distance dependencies with less memory overhead for detection, respectively. Moreover, to find the massive potential correct boxes during prediction, we propose a simple and efficient nonlinear combination method between the object confidence and the classification score. Experiments on the PASCAL VOC 2007, 2012, and the MS COCO 2017 datasets demonstrate that TOLO significantly outperforms other state-of-the-art methods with a small input size. Besides, the proposed nonlinear combination method can further elevate the detection performance of TOLO by boosting the results of potential correct predicted boxes without increasing the training process and model parameters.

This paper proposes a method based on LDA model and Word2Vec for analyzing Microblog users' insurance demands. First of all, we use LDA model to analyze the text data of Microblog user to get their candidate topic. Secondly, we use CBOW model to implement topic word vectorization and use word similarity calculation to expand it. Then we use K-means model to cluster the expanded words and redefine the topic category. Then we use the LDA model to extract the keywords of various insurance information on the "Pingan Insurance" website and analyze the possibility of users with different demands to purchase various types of insurance with the help of word vector similarity. Finally, the validity of the method in this paper is verified against Microblog user information. The experimental results show that the accuracy, recall rate and F1 value of the LDACBOWextending method have been proposed compared with that of the traditional LDA model, respectively, which proves the feasibility of this method. The results of this paper will help insurance companies to accurately grasp the preferences of Microblog users, understand the potential insurance needs of users timely, and lay a foundation for personalized recommendation of insurance products.

目的 探讨改良手工免疫组化染色和原位杂交方法的实用价值和推广意义.方法 免疫组化:选取本院近期病例80例,每例分别进行常规手工和改良手工染色及全自动免疫组化仪染色,进行结果对比;原位杂交:选取本院近期原位杂交EBER阳性病例15例和HPV阳性病例18例,每例分别进行传统和改良手工原位杂交染色方法,并比较染色结果.改良手工染色方法为:省去一抗后的清洗步骤,直至显色前充分清洗.结果 三种免疫组化染色方法均定位准确,无非特异性染色,待测目的组织与内对照的表达情况一致,复染清晰,染色评分无差异(P=0.985),但全自动免疫组化仪的染色背景更加干净.三种方法的脱片率分别为18.1％、17.8％和10.0％,传统手工和改良手工免疫组化方法之间脱片率差异无统计学意义(P = 0.911),传统手工与全自动免疫组化仪染色方法之间、改良手工与全自动免疫组化仪染色方法之间脱片率差异有统计学意义(P = 0.009,P= 0.012).传统手工方法和改良手工方法原位杂交染色的结果完全一致.结论 免疫组化和原位杂交的改良手工染色法可以代替传统手工染色法.

目的 探讨胃腺癌伴肠母细胞分化(gastric adenocarcinoma with enteroblastic differentiation,GAED)的临床病理学特征及FAT1表达的意义.方法 采用免疫组化EnVision两步法检测306例胃腺癌中SALL4、Glypican-3、AFP的表达,共诊断42例GAED,分析其临床病理学特征.免疫组化EnVision两步法检测GAED中CD10、CDX2、MUC-2和FAT1的表达,分析各标志物与GAED临床病理学特征的关系.结果 GAED与同期普通型胃癌相比,具有更高的淋巴管血管侵犯和转移率,具有多种组织学形态.GAED不同程度表达SALL4、Glypican-3和AFP,三种标志物表达与肿瘤大小、T分期及血管、淋巴管侵犯具有相关性(P<0.05).GAED中CDX-2高表达,FAT1表达明显降低(P<0.05).结论 GAED是一类侵袭性高、预后差的肿瘤,SALL4和Glypican-3联合检测可以提高诊断率.FAT1在GAED中发挥抑癌基因作用.GAED和肝样腺癌在形态学、免疫表型上有较大重叠性,应规范此类肿瘤的名称.

目的 探讨伴有浆膜腔累及的浆细胞瘤的临床病理特征、免疫表型、诊断和鉴别诊断.方法 收集首都医科大学附属北京朝阳医院病理科2010-01-2021-01间诊断的伴有浆膜腔累及的浆细胞骨髓瘤6例,制作细胞学涂片及细胞蜡块,进行HE染色和免疫组化检测.结果 6例患者平均年龄69岁;均为浆细胞骨髓瘤累及浆膜腔,样本来源:胸腔积液4例,腹水和心包积液各1例.镜下特征:2例成熟浆细胞样、1例免疫母细胞样、1例浆母细胞样、1例腺癌细胞样、1例小淋巴细胞样,部分呈浆细胞分化;4例进行了细胞蜡块的免疫组化染色,4例CD138均(+),3例 CD38、CD138及 CD56同时(+),2例 Kappa+Lambda(-),2例 Lambda+Kappa(-),1 例 Mum-1(+).结论 伴有浆膜腔累及的浆细胞骨髓瘤较为罕见且预后不良,其诊断需要结合临床病史、细胞形态和免疫组化,病理医生应提高对该病的认识,防止漏诊和误诊.

Smart homes, which incorporate IoT technologies to provide home security, efficient environmental services, conveniences, and improved living standards, are becoming the centre of smart urban developments. With the increased inter-connectivity of smart objects and sensors, there is now, also, an increased level of cyber threats, which can compromise privacy and security. These threats either modify packets of information or inject modified packets into the networks. This chapter examines current intrusion detection systems (IDSs) and presents a unique solution to overcome intrusion detection challenges. It discusses the implementation of smart home IDS (SHIDS), using a machine learning based signature and anomaly intrusion detection scheme to detect network intrusions in the smart home. Suggested mechanism is based on naïve Bayes technique to improve the detection performance. The performance of SHIDS has been tested with network intrusions resulting from DoS, probe, remote-to-local (R2L), and user-to-root (U2R) attacks.

目的 分析胃癌伴肠母细胞分化(GAED)的临床病理学及分子特征及C-MYC在GAED中的表达意义.方法 免疫组化方法检测42例GAED中SALL4、Glypican-3、AFP、CD10,CDX2、MUC-2和c-myc表达,分析其临床病理学特征.下一代测序方法检测34例GAED的分子特征.结果 GAED与同期普通型胃癌相比具有更高的淋巴管血管侵犯和转移率,GAED具有多种组织学形态,组织学生长方式与生存无相关性.胚胎蛋白SALL4、Glypican-3和AFP表达与肿瘤大小、T分期及血管、淋巴管侵犯具有相关性.GAED高表达肠分化标志物,C-MYC在癌组织中表达升高.GAED中TP53是突变发生频率最高的基因,其次依次是MCL1、CCNE1、MYC等,与普通型胃癌突变基因存在差异.MYC基因在GAED中表现为基因扩增.结论 与普通型胃腺癌相比,GAED是一类侵袭性高、预后差的肿瘤.SALL4和Glypican-3联合可以提高本肿瘤诊断率.TP53高突变是GAED的一个分子特点.MYC基因在GAED中表现为基因扩增,在癌组织中表达增强.