Anti-money laundering (AML) is a classical data mining problem in finance applications. As well known, money laundering (ML) is critical to the effective operation of transnational and organized crime, which affects a country's economy, government, and social wellbeings. Financial services organizations facilitate the movement of money and have been enlisted by governments to assist with the detection and prevention of money laundering, which is a key tool in the fight to reduce crime and create sustainable economic development. In the application of AML, user identity and financial behavior data are widely used to detect laundering transactions. In recent years, an increasing number of money laundering activities have been conducted by organized criminal gangs while most existing works still treat the actions of each account as independent identity behavior without considering the group-level conspired interactions. Therefore, in this paper, we propose a group-aware deep graph learning-based approach for organized money-laundering detection. In particular, we design a community-centric encoder to represent the nodes and attributes in user transaction graphs and derive the adjacent gang behaviors. Then, we devise a scheme of local enhancement to accommodate nodes with similar transaction features, which are aggregated into gangs for downstream detection. Extensive experiments on the real-world dataset from one of the largest bank card alliances worldwide show that our proposed method outperforms state-of-the-art methods in both offline and online modes, showing the effectiveness of money laundering detection with group-aware deep graph learning.

Modern crowd counting methods in natural scenes, even when video datasets are available, are mostly based on images. Because of background interference or occlusion in the scene, these methods can easily lead to mutations and instability in density prediction. There has been minimal research on how to exploit the inherent consistency among adjacent frames to achieve high estimation accuracy of video sequences. In this study, we explore the long-term global temporal consistency in the video sequence and propose a novel Global Representation Guided Adaptive Fusion Network (GRGAF) for video crowd counting. The primary aim is to establish a long-term temporal representation among consecutive frames to guide the density estimation of local frames, which can alleviate the prediction instability caused by background noise and occlusions in crowd scenes. Moreover, in order to further enforce the temporal consistency, we apply the generative adversarial learning scheme and design a global-local joint loss, which can make the estimated density maps more temporally coherent. Extensive experiments on four challenging video-based crowd counting datasets (FDST, DroneCrowd, MALL and UCSD) demonstrate that our method makes effective use of spatio-temporal information of video and outperforms the other state-of-the-art approach.

Graph generation is beneficial to comprehend the creation of meaningful structures of networks in a broad spec-trum of applications such as social networks and biological net-works. Recent studies tend to leverage deep learning techniques to learn the topology structures in graphs. However, we notice that the community structure, which is one of the most unique and prominent features of the graph, cannot be well captured by the existing graph generators. Moreover, the existing advanced deep learning-based graph generators are not efficient and scalable, which can only handle small graphs. In this paper, we propose a novel community-preserving generative adversarial network (CPGAN) for effective and efficient (scalable) graph simulation. We employ graph convolution networks in the encoder and share parameters in the generation process to transmit information about community structures and preserve the permutation-invariance in CPGAN. We conducted extensive experiments on benchmark datasets, including six sets of real-life graphs. The results demonstrate that CPGAN can achieve a good trade-off between efficiency (scalability) and graph simulation quality for real-life graph simulation compared with state-of-the-art baselines.

Unsupervised cross-domain counting research using synthetic datasets becomes imminent when considering the laborious labeling for supervised methods. However, the existing methods only focus on learning domain shared knowledge to narrow the gap between the source domain and target domain (inter-domain gap). Nevertheless, these methods do not consider the enormous distribution gap among the target domain data itself (intra-domain gap). In this paper, we propose a two-step domain adaptation method with multi-level feature response branches, which further uses the intra-domain knowledge to strengthen the target domain’s adaptability. Specifically, we first use different feature response branches to learn inter-domain knowledge more robustly, reducing the prediction inconsistency of different scenarios. Subsequently, the trained model is used to generate pseudo-labels for the target domain. The entire model was retrained by using pseudo-labels. Various experiments on synthetic dataset GCC and three real public datasets validate our proposed method’s availability with higher accuracy.

Accurate pedestrian counting algorithm is critical to eliminate insecurity in the congested public scenes. However, counting pedestrians in crowded scenes often suffer from severe perspective distortion. In this paper, basing on the straightline double region pedestrian counting method, we propose a dynamic region division algorithm to keep the completeness of counting objects. Utilizing the object bounding boxes obtained by YoloV3 and expectation division line of the scene, the boundary for nearby region and distant one is generated under the premise of retaining whole head. Ulteriorly, appropriate learning models are applied to count pedestrians in each obtained region. In the distant region, a novel inception dilated convolutional neural network is proposed to solve the problem of choosing dilation rate. In the nearby region, YoloV3 is used for detecting the pedestrian in multi-scale. Accordingly, the total number of pedestrians in each frame is obtained by fusing the result in nearby and distant regions. A typical subway pedestrian video dataset is chosen to conduct experiment in this paper. The result demonstrate that proposed algorithm is superior to existing machine learning based methods in general performance.

Improving the medical quality and guarantee the safety is the eternal theme of medical industry, which emphasizes the patient-centered and happiness-oriented. The healthcare service is not only a technological but also a high-risk industry. Since that risk exists throughout the medical work, risk prediction and management have become an important part of hospital management. The comprehensive risk management of hospital has been a focus at presence and could be a popular in the future. However, the study of comprehensive risk management for hospitals of Chinese Medicine is still theoretical and lack of summary and communication of practical experience. To improve the quality of medical service and the management level of medical safety, this article preliminarily summarized the innovation theory and practice on construction of comprehensive risk management of hospitals of Chinese Medicine.

Objective: To offer reference data for standardized study the clinical syndrome differentiation for the senile patients of post lumbar surgery, we study on the feature of Traditional Chinese Medicine(TCM) syndrome differentiation for the senile patients of post lumbar surgery. Methods: Adopting clinical epidemiological method, clinical investigation on 359 senile patients of post lumbar surgery was carried out. We designed the clinical syndrome questionnaire and filled in, acquired the data, then analyzed the data with descriptive statistics. Results: On the first day of post lumbar surgery, according to constituent ratio, the syndromes were mainly: syndrome of spleen deficiency and blood stasis(20.7%), syndrome of qi stagnation and blood stasis(19.8%), syndrome of qi deficiency with blood stasis, syndrome of spleen-stomach weakness, syndrome of damp abundance due to spleen asthenia, syndrome of dual deficiency of qi and blood. On the third day of post lumbar surgery, according to constituent ratio, the syndromes were mainly: syndrome of qi deficiency with blood stasis(35.6%), syndrome of spleen deficiency and blood stasis(21.9%), syndrome of damp abundance due to spleen asthenia, syndrome of qi stagnation and blood stasis, syndrome of spleenstomach weakness, syndrome of kidney deficiency and blood stasis. On the seventh day of post lumbar surgery, according to constituent ratio, the syndromes were mainly: syndrome of qi deficiency with blood stasis(36.6%), syndrome of qi stagnation and blood stasis(18.8%), syndrome of damp abundance due to spleen asthenia, syndrome of blood deficiency, syndrome of kidney deficiency and blood stasis, syndrome of spleen deficiency and blood stasis. On the fourteen day of post lumbar surgery, according to constituent ratio, the syndromes were mainly: syndrome of qi stagnation and blood stasis(29.2%), syndrome of dual deficiency of qi and blood(28.2%), syndrome of qi deficiency with blood stasis, syndrome of kidney deficiency and blood stasis, syndrome of spleen deficiency and blood stasis, syndrome of liver-kidney yin deficiency. Conclusions: The dynamic change law of early syndrome for the senile patients of post lumbar surgery was from deficiency syndrome to deficiency with excess complication syndrome, then to the excess syndrome.

Objective: To analyze the component law of Chinese medicines for knee osteoarthritis, and develop new prescriptions for knee osteoarthritis through unsupervised data mining methods. Methods: Chinese medicine recipes for knee osteoarthritis were collected and recorded in the database, and then the correlation coefficient between herbs, core combinations of herbs and new prescriptions were analyzed by using modified mutual information, complex system entropy cluster and unsupervised hierarchical clustering, respectively. Results: Based on the analysis of 64 Chinese medicine recipes for knee osteoarthritis, 23 drugs with high-frequency occurrence in these recipes, 24 frequently-used herb pairs and 8 core combinations were founded, and 3 new recipes for knee osteoarthritis were developed. Conclusion: Chinese medicines for knee osteoarthritis were mainly consist of blood-activating and stasis-resolving medicinal, blood-activating and qi-moving medicinal, kidney-enriching and liver-nourishing medicinal. The treatment principle for knee osteoarthritis is to eliminate the pathogenic factors and reinforce the healthy qi. Radix Saposhnikoviae, Radix Salviae Miltiorrhizae, Fructus Chaenomelis, Caulis Spatholobi, Poria, Herba Asari, Radix Angelicae Pubescentis, Corthex Eucommiae, Radix Angelicae Sinensis, Radix Paeoniae Alba, Radix Gentianae Macrophyllae, Radix Glycyrrhiz ae, Radix Achyranthis Bidentatae, Radix Clematidis, Radix Rehmanniae Preparata, Herba Taxilli, Radix Astragall, Rhizoma Chuanxiong, Radix Dipsaci and Rhizoma Drynariae are the main herbal drugs used in the treatment on knee osteoarthritis.

To analyze the component law of Chinese medicines for rheumatoid arthritis, and develop new prescriptions for rheumatoid arthritis through mutual Information mining methods. Chinese medicine recipes for rheumatoid arthritis were collected and recorded in the database, and then the correlation coefficient between herbs, core combinations of herbs and new prescriptions were analyzed by using modified mutual information, complex system entropy cluster and unsupervised hierarchical clustering, respectively. Based on the analysis of 63 Chinese medicine recipes for rheumatoid arthritis, 30 drugs with high-frequency and the distribution of Four Qi and Five Flavors of Chinese medicines occurrence in these recipes,34 frequently-used herb pairs and 9 core combinations were founded, and 3 new recipes for rheumatoid arthritis were developed. Chinese medicines for rheumatoid arthritis were mainly consist of blood-activating and stasis-dispelling edicinal, wind-dampnessdispelling and heatclearing medicinal, heat-clearing and detoxicating medicinal, tonifying and replenishing medicinal. So, the therapeutic principle for rheumatoid arthritisis to reinforce the healthy qi and eliminate the pathogenic factors. Radix Gentianae Macrophyllae, Rhizoma Chuanxiong, Radix Glycyrrhizae,Radix Angelicae Pubescentis,Radix Paeoniae Alba,Radix Angelicae Sinensis,Radix Achyranthis Bidentatae,Ramulus Cinnamomi,Rhizoma Et Radix Notopterygii and Radix Astragali are the main herbal drugs used in the treatment of rheumatoid arthritis.