Contrastive learning has emerged as a promising paradigm by inherently generating self-supervised signals and uncovering latent patterns from interaction data to enhance recommendation performance. However, most current graph contrastive learning-based recommendation methods rely on random augmentation strategies,which may disrupt graph structural information and compromise model robustness. In addition, long-tail items suffer from insufficient exposure, making it difficult to learn high-quality feature rep- resentations, ultimately degrading recommendation effectiveness.To overcome these limitations, this paper presents DDGCL, a dual diffusion-based graph contrastive learning method. A contrastive view optimization module is designed, which employs singular value decomposition to perform low-rank approximation on the interaction graph, efficiently extracting global structural features while accelerating the diffusion process. The diffusion model then performs noise addition and denoising on this basis to generate contrastive views that preserve graph structural information. In addition, a method for embedding augmentation designed for long-tail items is proposed. This module utilizes a conditional diffusion model, where global graph information serves as conditional con- straints to guide the denoising process of long-tail items, thereby improving their representation learning. A comprehensive evaluation on multiple public benchmark datasets demonstrates that DDGCL significantly outperforms various baseline models, validating the effectiveness of the proposed approach.

Hypergraph-based distillation methods have been proposed to mitigate the high computational cost of Hypergraph Neural Networks (HGNNs) in modeling high-order relationships. However, most existing methods use static and uniform distillation strategies for all nodes and hyperedges, ignoring their individual characteristics. In addition, they neglect the student model's capability to independently extract useful internal features. As a result, they are not effective in transferring higher-order structural knowledge from the teacher. To overcome these limitations, we propose ARCHER, an Adaptive and Reinforcement-Guided Contrastive HypER graph Distillation framework that enables a lightweight MLP student model to outperform its HGNN teacher model. First, we design an adaptive strategy that leverages node- and hyperedge-level confidence to mediate error guidance from the teacher model. Second, we introduce a contrastive learning module that guides the student to learn from both the teacher's outputs and its own internal representations, producing more expressive embeddings. Finally, we propose a multi-armed bandit-based reinforcement learning module that dynamically balances multiple loss objectives during training. Experiments on six benchmark datasets demonstrate that our method outperforms existing hypergraph distillation methods.

With the rapid growth of multi-modal content on the Web, robust vision-language models are essential for semantic understanding and classification of web images under diverse and dynamic contexts, supporting Web applications such as multimedia search and recommendation. Prompt learning has proven effective for enhancing vision-language models in semantic image classification tasks. However, previous methods often suffer from poor generalization: the learned prompts tend to overfit the base classes seen during training, leading to poor performance on unseen classes and under distribution shifts. This issue is especially challenging in Web-scale data, where new classes emerge and distributions shift dynamically. To address these limitations, we propose PLIKD, a novel prompt learning method that integrates instance-aware knowledge distillation for robust Web-scale semantic image classification. Specifically, PLIKD introduces an instance-aware knowledge extraction module, which leverages multi-modal large language models through a step-by-step strategy to extract external knowledge for each image instance. To incorporate this extracted knowledge, PLIKD further introduces an instance-aware knowledge distillation module, which consists of two key steps: (1) a dual-teacher strategy for robust and informative knowledge distillation, and (2) fine-grained cross-modal alignment via Smooth and Sparse Optimal Transport. Extensive experiments demonstrate that PLIKD significantly improves generalization to both seen and unseen classes, and remains robust under distribution shifts, outperforming existing state-of-the-art methods on Web-scale semantic image classification.

Multi-modal recommendation has become essential with the rapid expansion of online platforms such as e-commerce and video-sharing applications. In this work, we focus on the Multi-Modal New Item Recommendation (MMNIR) problem, where items with multi-modal content but newly introduced items lack interaction history. The MMNIR problem is particularly challenging in two aspects: (1) a large number of new items are created rapidly over time without any interaction data, (2) not all existing interactions are equally useful, and it is non-trivial to identify informative behaviors from users with similar preferences. However, previous methods fail to identify users with similar preferences and to capture informative behaviors from historical data. Furthermore, conventional models primarily rely on simple co-occurring signals, leading to spurious neighbors and neglecting the informative behaviors of truly similar users with consistent preferences. To fill this gap, we propose Joint Similar User Exploration and Informative Behavior Guidance (SuperG) for solving the MMNIR problem. SuperG first proposes a similar user exploration module to identify users with similar preferences to the target user. Then it incorporates an informative behavior mining module to retrieve informative behaviors from both the target user and similar users' histories to support new item recommendation. Finally, SuperG proposes a behavior-guided hybrid recommendation module to incorporate the retrieved behavioral signals to guide the recommendation of new items. Our empirical study on three real datasets demonstrates that SuperG outperforms the state-of-the-art models under the MMNIR setting.

Recently, hypergraph knowledge distillation has been proposed to alleviate the high computational cost of Hypergraph Neural Networks (HGNNs) when modeling high-order relationships in Web-related graph tasks. Its effectiveness primarily depends on the quality of knowledge transferred from the teacher and the representation capability of the student. However, existing methods remain limited on both sides. On the teacher side, most methods typically rely on a single HGNN teacher, which provides limited structural and semantic knowledge, thereby constraining the upper bound of the student's performance. The potential of exploiting multiple teachers in HGNNs remains largely underexplored. On the student side, existing methods ignore the student's capability to capture high-order semantic and structural information beyond simply imitating teacher outputs, leading to limited representation learning. To address these limitations, we propose MARCH, a framework for Multi-TeAcheR Contrastive Hypergraph Distillation, which advances semantic modeling and distillation for Web-scale structured data. Specifically, MARCH proposes a multi-teacher distillation strategy that adaptively transfers complementary knowledge from multiple teachers at both node and hyperedge levels, empowering the student model to learn richer and more discriminative representations and even outperform its teachers. Extensive experiments on six benchmark datasets demonstrate the superior performance of MARCH.

Graph-level anomaly detection (GLAD) is a critical task to identify graphs with abnormal properties in various domains, ranging from fraudulent social networks to malicious botnets on online platforms. The dominant paradigm for existing GLAD detectors has been partially unsupervised, relying on training data composed exclusively of normal samples. However, this partially unsupervised paradigm inevitably requires a costly expert filtering process to ensure the training data is free of anomalies. This creates a significant gap between current approaches and the real-world necessity of a fully unsupervised paradigm, which involves training a model directly on real-world data ''as-is'', with its inherent mix of normal and anomalous samples. To bridge this gap, we incorporate uncertainty learning into GLAD to promote fully unsupervised learning. We propose two frameworks: Score Uncertainty Learning (SUL) and Graph-data Uncertainty Learning (GUL). Specifically, SUL enhances existing GLAD detectors by modeling uncertainty through Gaussian distributions over the detectors' predictions, adaptively attenuating the influence of potential anomalies. GUL is an end-to-end framework that iteratively optimizes anomaly detection and uncertainty modeling via an Expectation-Maximization algorithm. In addition, we develop a dedicated loss that utilizes potential anomalies to enhance the effectiveness and robustness of GUL. Empirical results on sixteen benchmark datasets, covering real-world graphs from social networks and online platforms, demonstrate the superiority of our methods and highlight the promise of incorporating uncertainty into fully unsupervised GLAD.

Web-scale platforms and online services rely on log-based anomaly detection to safeguard availability, latency SLOs, and user experience. In real-world web interactions, system logs often exhibit irregular temporal intervals, bursty densities, and heterogeneous semantics, which pose significant challenges for log anomaly detection. Existing methods such as LSTM and Transformer assume a fixed input window, which conflicts with the inherently irregular nature of system logs. Moreover, most prior works build a single-view representation, overlooking the multi-relational nature of logs. To overcome these challenges, we propose DyLogNet, a dynamic multi-relational graph framework for log anomaly detection. Specifically, this framework constructs a density-aware dynamic graph with variable-length windows, and represents logs from three relational perspectives: temporal co-occurrence, semantic similarity, and anomaly tendency. Next, we design a cross-layer attention mechanism that integrates heterogeneous structures to highlight the most relevant relations and enhance event representations. Furthermore, a cross-snapshot memory injection module updates global memory through a recurrent unit and injects it into current graph representations via an affine transformation, enabling temporal continuity. Experiments on three public log datasets demonstrate that DyLogNet outperforms state-of-the-art methods, especially in few-shot scenarios.

Object recognition is essential for autonomous cars, and the amalgamation of camera and light detection and ranging (LiDAR) sensor data has emerged as a pivotal method for accurate three-dimensional (3D) object recognition. Contemporary algorithms face challenges with fragmented data, high processing costs, insufficient resolution, and limited dynamic information. This study presents a novel approach utilising 3D U-Net deep learning for precise 3D object detection and localisation by integrating camera and LiDAR data. The process involves obtaining and preprocessing camera and LiDAR data, utilising a geometric 3D frustum method to extract 3D information from LiDAR based on 2D camera bounding boxes, and training a You Only Look Once version 4 (YOLO v4) network to recognise these boundaries in camera images. The detected images are combined with LiDAR data, and a deep U-Net network is utilised to define 3D bounding boxes. Performance is assessed at various noise levels (0 %, 1 %, 2 %, 5 %, and 10 %) in the composite images. This method leverages the benefits of both sensors to better object recognition across diverse shapes and sizes, even in challenging situations, signifying a significant progression towards safer and more reliable autonomous vehicles with improved situational awareness in intricate urban environments.

Dynamic pricing on two-sided platforms such as Airbnb presents complex challenges due to the heterogeneity of listings, user behaviours, and contextual variables. In this work, we propose a robust and interpretable pricing framework that leverages Large Language Models (LLMs) and prompt engineering to automate the generation of high-level meta-features from unstructured and structured listing data. These meta-features are designed to capture nuanced semantic features that are often overlooked by traditional feature engineering pipelines. We further integrate these representations into a Transformer-based Graph Neural Network (GNN), which models the relational and spatial dependencies between listings in a data-driven and several relation-construction manner. By combining prompt-driven embeddings with graph-aware contextual learning, our framework significantly enhances price recommendation accuracy while offering transparency through assortativity analysis. Extensive experiments on real-world Airbnb datasets demonstrate our approach's performance in both prediction and unseen data across neighbourhoods and output interpretability. This work highlights the potential of unifying LLMs, structured graph learning, and interpretable AI for next-generation dynamic pricing systems.

Spatio-temporal graphs encode dynamic interactions across space and time, but their size and complexity pose challenges for analysis and computation. Graph sparsification provides an effective solution to these issues by reducing the number of edges while preserving the essential structural and dynamic properties of the network. This reduction is crucial for enhancing the interpretability of complex graphs, revealing hidden patterns, and enabling more efficient computational analysis. However, real-world graphs often exhibit continuous spatial and temporal evolution, which most existing sparsification algorithms, primarily designed for static graphs, fail to address. We introduce STGS (Spatio-Temporal Graph Sparsification), a reinforcement learning-based framework for sparsifying spatio-temporal graphs. By learning to prune edges while preserving key spatio-temporal patterns, STGS enables efficient analysis of evolving systems. Experiments on real-world datasets demonstrate that STGS outperforms existing methods in both structural preservation and downstream forecasting tasks.

Graph-level anomaly detection (GLAD) identifies graphs exhibiting abnormal properties within a graph dataset.Despite promising results in this task, the state-of-the-art methods cannot be fully trusted and deployed in realistic scenarios due to their black-box nature.To alleviate this, existing methods try to explain predictions by extracting important subgraphs from each graph, as instancelevel explanations.However, instance-level explanations across all samples are costly to verify and insufficient to capture the model's general behaviors.Thus, we propose a global interpretable Graph-Level Anomaly Detection model via Prototype (GLADPro), which provides global-level explanations throughout the entire dataset, that is, the significant subgraph patterns that consistently influence the model's decisions.Specifically, GLADPro incorporates prototype learning with the information bottleneck principle, enabling prototypes to capture the most significant subgraph patterns as global-level explanations through persistent interactions with key subgraphs from input graphs.In addition, a regularization term is proposed to prevent the collapse traps with theoretical proof.Finally, we filter redundant prototypes using the maximum mean discrepancy metric.Extensive experiments demonstrate the superiority of GLADPro in anomaly detection and explainability; for instance, on the mutagen dataset, it reduces the number of explanations to verify from 1403 to only 6.

Next Point-of-Interest (POI) recommendation has become a crucial task in Location-Based Social Networks (LBSNs), which provide personalized recommendations by predicting the user's next check-in locations. Commonly used models including Recurrent Neural Networks (RNNs) and Graph Convolutional Networks (GCNs) have been widely explored. However, these models face significant challenges, including the difficulty of capturing the hierarchical and tree-like structure of POIs in Euclidean space and the sparsity problem inherent in POI recommendations. To address these challenges, we propose a Hyperbolic Variational Graph Auto-Encoder (HVGAE) for next POI recommendation. Specifically, we utilize a Hyperbolic Graph Convolutional Network (Hyperbolic GCN) to model hierarchical structures and tree-like relationships by converting node embeddings from euclidean space to hyperbolic space. Then we use Variational Graph Auto-Encoder (VGAE) to convert node embeddings to probabilistic distributions, enhancing the capture of deeper latent features and providing a more robust model structure. Furthermore, we combine the Mamba4Rec recommender and Rotary Position Embedding (RoPE) and propose Rotary Position Mamba (RPMamba) to effectively utilize POI embeddings rich in sequential information, which improves the accuracy of the next POI recommendation. Extensive experiments on three public datasets demonstrate the superior performance of the HVGAE model.

Deep learning is increasingly crucial in scientific discovery, accelerating research in various fields. Exploring brain science using deep learning has garnered significant interest, particularly in the recognition of brain disorders. However, existing methods face limitations in the discriminability of learned brain graph representations and the identification of neurological biomarkers associated with brain disorders. Moreover, the lack of model explainability leads to suboptimal brain graph analysis. To address these challenges, we propose a bi-level brain graph contrastive learning framework with an interpretable learning kernel for brain disorder analysis. Our framework diverges from traditional graph contrastive learning methods by augmenting meaningful brain graphs using the counterfactual thinking-based mechanism, ensuring reliable graph generation that preserves discriminative information. Secondly, we introduce bi-level contrastive loss with intra-and inter-class contrasts to enhance the brain graph representation learning. Most importantly, we design an interpretable brain graph learning kernel to highlight critical regions and connections, thereby facilitating the discovery of potential neurological biomarkers associated with brain disorders in subsequent analysis. The effectiveness of our method11https://githuh.com/JustinGie/Cf-BCL in brain graph representation learning and discriminative substructure detection is demonstrated through the evaluation of disorder prediction and pathogenic analysis on three real-world brain disorder datasets. Moreover, our framework may provide novel insights into brain science based on the results of comparison with medical research.

Explainable Artificial Intelligence (XAI) aims to make artificial intelligence (AI) systems transparent and understandable to humans, providing clear explanations for the decisions made by AI models. This paper presents a novel pipeline and a digital dashboard that provides a user-friendly platform for interpreting the results of machine learning algorithms using XAI technology. The dashboard utilizes evidence-based design principles to deliver information clearly and concisely, enabling users to better understand the decisions made by their algorithms. We integrate XAI services into the dashboard to explain the algorithm's predictions, allowing users to understand how their models function and make informed decisions. We demonstrate a motivating scenario in banking and present how the proposed system enhances transparency and accountability and improves trust in the technology.

In this paper, we propose an interpretable brain graph contrastive learning framework, which aims to learn brain graph representations by a unsupervised way for disorder prediction and pathogenic analysis. Our framework consists of two key designs: We first utilize the controllable data augmentation strategy to perturb unimportant structures and attribute features for the generation of brain graphs. Then, considering that the difference of healthy and patient brain graphs is small, we introduce hard negative sample evaluation to weight negative samples of the contrastive loss, which can learn more discriminative brain graph representations. More importantly, our method can observe salient brain regions and connections for pathogenic analysis. We conduct disorder prediction and interpretable analysis experiments on three real-world neuroimaging datasets to demonstrate the effectiveness of our framework.

Automatic detection of Cyberbullying is a challenging task due to the availability of limited trained data, which is usually noisy and inherently multimodal. Transfer learning over pre-trained BERT-based language models has succeeded in various complex use cases like sequence-to-sequence translation and text classification. These methods mainly utilize transformer models to learn the word and sentence-level relationships. While they have demonstrated promising results, they only focus on textual features without taking contextual and structural information into account. Moreover, due to the data-heavy nature of BERT-based models, they may fail to model all the desired relationships if not adequate training data is provided to them during the fine-tuning process. In this paper, we propose a novel Session-level Contextualized Transformer-based architecture for Cyberbullying Detection (SECTR-CD), which can leverage transfer learning for modeling word-level attention while also being able to model sentence-level relationships in large bodies of text. The model is also capable of utilizing other contextual features from various modalities like images and social information. Our experimental results indicate remarkable improvement in the Cyberbullying detection task even in the presence of limited training samples.

Text summarization aims to condense text documents into a concise textual summary, which improves the efficiency of people in comprehending information. While deep learning-based summarization methods for individual documents have achieved good performance, there is an increasing demand for summarizing multiple related documents of a topic or event can yield a more coherent and succinct summary of the document set. However, the characteristics of multiple documents with more information, longer texts, and different styles impose new challenges to existing methods in dealing with the multi-aspect of a topic or an event. Therefore, in this paper, we propose a novel multi-granularity model with entity recognition for better sentence ranking and capturing the key information of different documents with a comprehensive and accurate summary. Specifically, we use PRIMERA as a token encoder based on the encoder-decoder framework. Then, a named entity recognition model is trained to identify key elements in documents such as people, location, organization, etc. The proposed model will focus more on these key elements. Based on the named entity recognition results, we further devise a sentence ranking module that allows the model to assign different weights to different sentences based on the sum of the frequencies of the entities contained in the sentences. Finally, based on the results of the multi-granularity encoding vector, the decoder can generate a comprehensive and accurate summary. To evaluate the performance of our proposed model, we conducted experiments on CoNLL2003, DUC2003, and DUC2004, which demonstrated the performance improvement of our proposed method over four previous models.

Anomaly detection is one of the crucial research topics in artificial intelligence, encompassing various fields such as health monitoring, network intrusion detection, and fraud detection in financial transactions. Deep anomaly detection (DAD) methods are considered as the effective approaches for addressing complex anomaly detection problems. Among them, the deep isolation forest methods have gained rapid development recently due to their simplicity in parameter turning and efficiency in model training. The existing deep isolation forest approaches are all based on representation learning, while OptiForest theoretically proves the crucial role of the tree structure in isolation forest based methods. In this paper, we analyse the search space of isolation trees under specific data instances and address the challenges in finding optimal isolation forest. Based on the theoretical underpinning and genetic algorithm, we design a deep model DOIForest with two mutation schemes and solution selection, which learns the optimal isolation forest and optimises the parameters in data partitioning. Extensive experiments on both synthetic dataset and a series of real-world datasets demonstrate that our approach can achieve better detection accuracy and robustness than the state-of-the-arts.

Artificial intelligence (AI) enables machines to learn from human experience, adjust to new inputs, and perform intelligent tasks without human intervention. AI is progressing rapidly and is transforming the way businesses operate, from process automation to cognitive augmentation of tasks and intelligent process/data analytics. However, the main challenge for the AI system users is to comprehend and trust the result of AI algorithms and methods. To address this challenge, we first study the recent techniques in the area of eXplainable Artificial Intelligence (XAI). Then, we introduce a novel XAI process to facilitate producing explainable models while maintaining a high level of learning performance. We present an interactive evidence-based approach to assist the users in comprehending and trusting the results and outputs generated by AI-enabled algorithms, resulting in developing a digital dashboard to facilitate inter-acting with the algorithm. Lastly, we discuss how the proposed XAI method can significantly improve the confidence of data scientists in understanding the result of AI-enabled algorithms with an application in the banking domain for analyzing customer transactions.

Mental health disorders may cause severe consequences for countries’ economies and health. Identifying early signs of these disorders is vital. The state-of-the-art research in identifying mental health disorder patterns from textual data, uses hand-labeled training sets, especially when a domain expert’s knowledge is required to analyze various symptoms in a patient. This task could be time-consuming and expensive. To address this challenge, in this paper, we study and analyze the various clinical and non-clinical approaches to identifying mental health disorders. We leverage the domain knowledge and expertise in cognitive science to build a domain-specific Knowledge Base for the mental health disorder concepts and patterns. We present a weaker form of supervision by facilitating and generating training data from a domain-specific Knowledge Base. We adopt a typical scenario for analyzing social media to identify depression symptoms from the textual content generated by social users.

Graph anomaly detection in this paper aims to distinguish abnormal nodes that behave differently from the benign ones accounting for the majority of graph-structured instances. Receiving increasing attention from both academia and industry, yet existing research on this task still suffers from two critical issues when learning informative anomalous behavior from graph data. For one thing, anomalies are usually hard to capture because of their subtle abnormal behavior and the shortage of background knowledge about them, which causes severe anomalous sample scarcity. Meanwhile, the overwhelming majority of objects in real-world graphs are normal, bringing the class imbalance problem as well. To bridge the gaps, this paper devises a novel Data Augmentation-based Graph Anomaly Detection (DAGAD) framework for attributed graphs, equipped with three specially designed modules: 1) an information fusion module employing graph neural network encoders to learn representations, 2) a graph data augmentation module that fertilizes the training set with generated samples, and 3) an imbalance-tailored learning module to discriminate the distributions of the minority (anomalous) and majority (normal) classes. A series of experiments on three datasets prove that DAGAD outperforms ten state-of-the-art baseline detectors concerning various mostly-used metrics, together with an extensive ablation study validating the strength of our proposed modules.

With the ever-increasing popularity of microservice architecture, a considerable number of enterprises or organizations have encapsulated their complex business services into various lightweight functions as published them accessible APIs (Application Programming Interfaces). Through keyword search, a software developer could select a set of APIs from a massive number of candidates to implement the functions of a complex mashup, which reduces the development cost significantly. However, traditional keyword search methods for APIs often suffer from several critical issues such as functional compatibility and limited diversity in search results, which may lead to mashup creation failures and lower development productivity. To deal with these challenges, this paper designs DAWAR, a diversity-aware Web APIs recommendation approach that finds diversified and compatible APIs for mashup creation. Specifically, the APIs recommendation problem for mashup creating is modelled as a graph search problem that aims to find the minimal group Steiner trees in a correlation graph of APIs. DAWAR innovatively employs the determinantal point processes to diversify the recommended results. Empirical evaluation is performed on commonly-used real-world datasets, and the statistic results show that DAWAR is able to achieve significant improvements in terms of recommendation diversity, accuracy, and compatibility.

Graph anomaly detection, here, aims to find rare patterns that are significantly different from other nodes. Attributed graphs containing complex structure and attribute information are ubiquitous in our life scenarios such as bank account transaction graph and paper citation graph. Anomalous nodes on attributed graphs show great difference from others in the perspectives of structure and attributes, and give rise to various types of graph anomalies. In this paper, we investigate three types of graph anomalies: local, global, and structure anomalies. And, graph neural networks (GNNs) based anomaly detection methods attract considerable research interests due to the power of modeling attributed graphs. However, the convolution operation of GNNs aggregates neighbors information to represent nodes, which makes node representations more similar and cannot effectively distinguish between normal and anomalous nodes, thus result in sub-optimal results. To improve the performance of anomaly detection, we propose a novel community-aware attributed graph anomaly detection framework (ComGA). We design a tailored deep graph convolutional network (tGCN) to anomaly detection on attributed graphs. Extensive experiments on eight real-life graph datasets demonstrate the effectiveness of ComGA.

The objective of fraud detection is to distinguish fraudsters from normal users. In graph/network environments, both fraudsters and normal users are modeled as nodes, and the connections between those nodes are represented as edges. Fraudsters typically try to camouflage themselves with “normal” behaviors, say, by deliberately establishing many connections to normal users. Such camouflage inherently makes their appearance inconsistent with the essence of what it is to be normal, and gives rise to inconsistencies in the graph. In this paper, we investigate three aspects of these graph inconsistencies: features, topologies, and relations. To date, graph-based fraud detectors have shown a rather limited capability to fuse information about different types of inconsistencies. Apart from that, there is another problem of imbalance to overcome. This is because fraudsters usually only account for a very small percentage of all users. To achieve a promising capability, i.e., dual-resistant to graph inconsistency and imbalance, we present a new fraud detection model FRAUDRE based on Graph Neural Networks. Extensive experiments comparing eight up-to-date baselines on two real-world datasets, Amazon and YelpChi, demonstrate the superiority of FRAUDRE.

Digitization, i.e., the process of converting information into a digital format, may provide various opportunities and challenges for businesses. In this context, one of the main challenges would be to accurately classify numerous scanned documents uploaded every day by customers as usual business processes. The current study has proposed a robust fusion model to define the type of identity documents accurately. The proposed approach is based on two different methods in which images are classified based on their visual features and text features. A novel model based on statistics and regression has been proposed to calculate the confidence level for the feature-based classifier. A fuzzy-mean fusion model has been proposed to combine the classifier results based on their confidence score. The proposed approach has been implemented using Python and experimentally validated on synthetic and real-world datasets. The performance is evaluated using the Receiver Operating Characteristic (ROC) curve analysis.

Recently, financial institutes have been dealing with an increase in financial crimes. In this context, financial services firms started to improve their vigilance and use new technologies and approaches to identify and predict financial fraud and crime possibilities. This task is challenging as institutions need to upgrade their data and analytics capabilities to enable new technologies such as Artificial Intelligence (AI) to predict and detect financial crimes. In this paper, we put a step towards AI-enabled financial crime detection in general and money laundering detection in particular to address this challenge. We study and analyse the recent works done in financial crime detection and present a novel model to detect money laundering cases with minimum human intervention needs.

Enabling the analysis of behavioral disorders over time in social networks, can help in suicide prevention, (school) bullying detection and extremist/criminal activity prediction. In this paper, we present a novel data analytics pipeline to enable the analysis of patterns of behavioral disorders on social networks. We present a Social Behavior Graph (sbGraph) model, to enable the analysis of factors that are driving behavior disorders over time. We use the golden standards in personality, behavior and attitude to build a domain specific Knowledge Base (KB). We use this domain knowledge to design cognitive services to automatically contextualize the raw social data and to prepare them for behavioral analytics. Then we introduce a pattern-based word embedding technique, namely personality2vec, on each feature extracted to build the sbGraph. The goal is to use mathematical embedding from a space with a dimension per feature to a continuous vector space which can be mapped to classes of behavioral disorders (such as cyber-bullying and radicalization) in the domain specific KB. We implement an interactive dashboard to enable social network analysts to analyze and understand the patterns of behavioral disorders over time. We focus on a motivating scenario in Australian government's office of the e-Safety commissioner, where the goal is to empowering all citizens to have safer, more positive experiences online.

Abstract Rule-based systems have been used increasingly to augment learning algorithms for annotating data. Rules alleviate many of the shortcomings inherent in pure algorithmic approaches, in cases algorithms are not working well or lack from enough training data. However, in dynamic curation environments where data are constantly changing, there is a need to craft and adapt rules to keep them applicable and precise. Rule adaptation has been proven to be painstakingly difficult and error-prone, as an analyst is needed for examining the precision of rules and applying different modifications to adapt the imprecise ones. In this paper, we present an autonomic and conceptual approach to adapt data annotation rules. Our approach offloads analysts from adapting rules; it boosts rules to annotate a larger number of items using a set of high-level conceptual features, e.g. topic. We utilize a Bayesian multi-armed-bandit algorithm, an online learning algorithm that adapts rules based on the feedback collects from the curation environment over time. We propose a summarization technique, which offers a set of high-level conceptual features for annotating items by identifying the semantical relationships among them. We conduct experiments on different curation domains and compare the performance of our approach with systems relying on analysts for adapting rules. The experimental results show that our approach has a comparative performance to analysts in adapting rules.

With Data Science continuing to emerge as a powerful differentiator across industries, organisations are now focused on transforming their data into actionable insights. This task is challenging as in today's knowledge-, service-, and cloud-based economy, businesses accumulate massive amounts of raw data from a variety of sources. Data Lakes introduced as a storage repository to organize this raw data in its native format (supporting from relational to NoSQL DBs) until it is needed. The rationale behind a Data Lake is to store raw data and let the data analyst decide how to cook/curate them later. In this paper, we present the notion of Knowledge Lake, i.e. a contextualized Data Lake. The Knowledge Lake will provide the foundation for big data analytics by automatically curating the raw data in the Data Lake and to prepare them for deriving insights. We present CoreKG-an open source Data and Knowledge Lake service- which offers researchers and developers a single REST API to organize, curate, index and query their data and metadata in the Lake and over time. CoreKG manages multiple database technologies (from Relational to NoSQL) and offers a built-in design for data curation, security and provenance.

The continuous improvement in connectivity, storage and data processing capabilities allow access to a data deluge from sensors, social-media, news, user-generated, government and private data sources. Accordingly, in a modern data-oriented landscape, with the advent of various data capture and management technologies, organizations are rapidly shifting to datafication of their processes. In such an environment, analysts may need to deal with a collection of datasets, from relational to NoSQL, that holds a vast amount of data gathered from various private/open data islands, i.e. Data Lake. Organizing, indexing and querying the growing volume of internal data and metadata, in a data lake, is challenging and requires various skills and experiences to deal with dozens of new databases and indexing technologies: How to store information items? What technology to use for persisting the data? How to deal with the large volume of streaming data? How to trace and persist information about data? What technology to use for indexing the data? How to query the data lake? To address the above mentioned challenges, we present CoreDB - an open source data lake service - which offers researchers and developers a single REST API to organize, index and query their data and metadata. CoreDB manages multiple database technologies and offers a built-in design for security and tracing.

A large volume of Open Data is being generated on a continuous basis. Examples of this are the case of social, natural, and information systems such as World Wide Web and social networks. Most entities and objects in the Open Data are interconnected, forming a complex, semi-structured, and information-rich networks. In this sense, Linked Open Data has the potential to be similar to a federated database. Since Linked Open Data is based on W3C standards, it is possible to implement a federation infrastructure, however, the current SPARQL standard makes it challenging to analyze the Open Data in an explorative manner. Consequently, it will be hard to discover the hidden knowledge in the relationships among entities in Open Data sources. In this paper, we present Galaxy, a platform for explorative analysis of Open Data Sources. Galaxy facilitates the analysis of Open Data graphs based on simple abstractions, i.e. folders and paths, which enable an analyst to group related entities in the graph or nd paths among entities. Galaxy uses Hadoop data processing platforms to store and retrieve large numbers of RDF triples and to support cost-eective and Web-scale processing of Semantic Web data through a Folder-Path enabled extension of SPARQL.

The Resource Description Framework (RDF) and SPARQL query language are gaining wide popularity and acceptance. In this paper, we present DREAM, a distributed and adaptive RDF system. As opposed to existing RDF systems, DREAM avoids partitioning RDF datasets and partitions only SPARQL queries. By not partitioning datasets, DREAM offers a general paradigm for different types of pattern matching queries, and entirely averts intermediate data shuffling (only auxiliary data are shuffled). Besides, by partitioning queries, DREAM presents an adaptive scheme, which automatically runs queries on various numbers of machines depending on their complexities. Hence, in essence DREAM combines the advantages of the state-of-the-art centralized and distributed RDF systems, whereby data communication is avoided and cluster resources are aggregated. Likewise, it precludes their disadvantages, wherein system resources are limited and communication overhead is typically hindering. DREAM achieves all its goals via employing a novel graph-based, rule-oriented query planner and a new cost model. We implemented DREAM and conducted comprehensive experiments on a private cluster and on the Amazon EC2 platform. Results show that DREAM can significantly outperform three related popular RDF systems.