Reviewerly/exHarmony huggingface dataset:
Defending the Graph Reconstruction Attacks for Simplicial Neural Networks

Releasing the representations of nodes in real-world graphs associated with people or human-related activities, such as social and economic networks, gives adversaries a potential way to infer the sensitive information of edges. For example, graph convolutional layers initially aggregate node representations with their neighbors before passing them through non-linear activation functions. Hence, the released node representations may potentially breach edge privacy of the node neighbors. Thus, in this work, we study whether representations can be inverted to recover the graph used to generate them. We study three types of outputs that are trained on the graph, i.e., representations output from graph convolutional networks (GCNs), representations output from graph attention networks (GATs), and representations output from our proposed simplicial neural networks (SNNs). Unlike the first two types of representations that only encode pairwise relationships, the third type of representation, i.e., SNN outputs, encodes higher-order interactions (e.g., homological features) between nodes. We propose two graph reconstruction attacks (GRAs), i.e., Type-1 and Type-2 attacks, to recover a graph’s adjacency matrix from the three types of outputs trained on the graph. Specifically, our GRAs utilize a graph-decoder to minimize the reconstruction loss for the generated adjacency matrix via back-propagation. Our conclusions are two folds. First, our Type-2 attack achieves the best performance among all current GRAs. Second, we find that GCN outputs obtain the least precision and AUC on five datasets, followed by the GAT outputs, followed by the SNN outputs. Therefore, the SNN outputs reveal the lowest privacy-preserving ability to defend the GRAs. We further propose an unbiased multi-bit rectifier, by which the server can communicate with the nodes to privately collect their representations to defend the GRAs from potential adversaries.
Link Prediction on Graphs Using NLP Embedding

This paper summarizes our approach (team Math-Land) to the DSAA 2023 Data Science Competition, which focuses on link prediction. Our proposed model is based on embedding techniques commonly used for natural language processing, and the embedding is constructed as part of the neural network training, eliminating the need for a separate step. We train the model using the binary cross entropy loss and the Adam optimizer. The approach achieves high F1-scores on validation and test sets.
CRIMEO: Criminal Behavioral Patterns Mining and Extraction from Video Contents

The security and well-being of a nation’s citizens, as well as the protection of their lives and properties, are fundamental for prosperity. Unfortunately, in recent years, we have witnessed a surge in various types of crimes such as murder, robbery, terrorism, and kidnapping. This has placed significant pressure on Law Enforcement Agencies (LEAs) to effectively prevent and detect crimes, driving them to adopt various technologies in the criminal investigation process. Among these technologies, surveillance systems have emerged as a valuable tool for monitoring human behaviors and activities, particularly in public and densely populated areas of large cities. However, video analysis in crime investigation poses significant challenges for LEAs, requiring accurate and timely detection, recognition, and tracking of objects and individuals. Addressing this issue, we present CRIMEO, a smart system designed for mining and extracting behavioral patterns from video content. CRIMEO operates in real-time and leverages an ontology-based approach to represent complex semantic events and employ video analytics. This enables LEAs to automatically detect and identify different types of crimes. CRIMEO encompasses four key phases: data collection, analysis, storage, and visualization. In the data collection phase, video data from surveillance systems is gathered and transmitted, via Apache Kafka, to the subsequent phase after the video is split into frames. The data analysis phase applies a range of video analytics techniques, including face detection and recognition, object detection, action recognition, and more, to extract behavioral patterns from the video content. These extracted patterns are then stored in the Neo4j graph database during the data storage phase. Finally, in the visualization phase, inference rules defined by LEA experts are applied to detect and visualize criminal activities. To demonstrate the effectiveness of CRIMEO, we implemented the system across multiple scenarios, showcasing its relevance in aiding LEAs in the detection of various crime types. By utilizing CRIMEO, LEAs can benefit from advanced video analysis capabilities and real-time crime detection, ultimately enhancing their ability to maintain safety and security within their jurisdictions.
Graph Disentangled Collaborative Filtering based on Multi-order Similarity Constraint

Disentangled collaborative filtering can explicitly generate embeddings based on users’ interests and help improve the interpretability and robustness of recommendations. However, the existing disentangled graph collaborative filtering methods rely solely on direct interaction constraints between nodes to learn node embeddings, which cannot represent higher-order constraints between nodes and node-type differences, resulting in suboptimal node representations and negatively affecting recommendation performance. To address this problem, we propose a Multi-order Similarity Constraint Disentangled Graph Collaborative Filtering (DGCF-MSC) method, which considers not only direct interaction constraints between nodes but also designs a neighborhood enhancement mechanism based on high-order relationships between homogeneous nodes. We realize the disentanglement of heterogeneous type nodes in different feature spaces in a graph convolutional neural network to make the generated embedding more interpretable and improve the performance of graph collaborative filtering. We conduct extensive experiments with three recommendation system datasets and the results demonstrate that DGCF-MSC outperforms the existing disentangled graph collaborative filtering methods in all performance metrics. Our code is released on https://github.com/lustrelake/DGCF_MSC.
Stochastic Perturbation Averaging Boosts Transferability of Adversarial Examples

In image, video and even real physics domains, adversarial examples can mislead deep models to produce wrong predictions. Transfer-based attacks against black-box models are more in line with realistic scenarios, but adversarial examples made on surrogate model have a low success rate when transferred to the target model due to overfitting the source model. We study the Stochastic Weight Averaging strategy in the domain generalization process and propose a Stochastic Perturbation Averaging method (SPA). Specifically, we add stochastic perturbations to the examples during the gradient descent attack, and we design a Central Amplification method (CAM) to enhance this random variation, then SPA stabilizes the iteration direction by computing the gradient average of the perturbed examples to find a relatively flat local minimum of the loss function. SPA is an efficient and general strategy which can significantly improve the transferability of the gradient-based attack methods. For instance, the average attack success rate of the adversarial examples produced based on four single models against seven pre-trained models reached 90.10%, which is the best result so far. Code is available at https://github.con yangrongbo/SPA.
A Multimodal Adversarial Database: Towards A Comprehensive Assessment of Adversarial Attacks and Defenses on Medical Images

Deep learning models have been widely applied in many fields, including medical image analysis and computer-aided disease diagnosis. However, these models are easily fooled by adversarial attacks from some created adversarial examples which are hardly distinguished by humans. In this paper, we implement a comprehensive assessment of six popular adversarial attacks on four multimodal medical image datasets using two main deep learning-based target models. Moreover, in order to evaluate the capability of defense, two new defense methods are leveraged to cope with medical adversarial attacks. More importantly, we also build and release a big multimodal medical adversarial database (including four medical adversarial datasets) with 712,596 examples to facilitate future research of adversarial attacks and defenses in the multimodal medical image field. Extensive experiments indicate that all-sided adversarial attacks like BIM are still scarce under different evaluation metrics and defenses are not universally successful.
Causal Feature Selection: Methods and a Novel Causal Metric Evaluation Framework

The proliferation of high-dimensional data in the era of big data has presented significant challenges for machine learning models. Feature selection methods have emerged as essential preprocessing techniques to address these challenges. However, most existing feature selection techniques primarily rely on correlations or associations between features and the target variable, overlooking the consideration of causal relationships. This study introduces a novel causal feature selection (CFS) algorithm that leverages causal structure learning to identify a subset of causal features. Our approach involves employing a causal graph discovery method to represent the causal relationships among variables and the causal effects of features on the target variable. To evaluate the effectiveness of our proposed CFS algorithm, we introduce a new evaluation criterion based on causal metrics, offering a principled and rigorous approach to assess the performance of causal feature selection methods. We empirically evaluate our algorithm using synthetic and real-world datasets, demonstrating that the truncated subsets of features selected by the CFS algorithm exhibit comparable or improved performance compared to baseline methods while utilizing fewer causal features.
Towards Contiguous Sequences in Uncertain Data

In data mining, high-utility sequential pattern mining (HUSPM) focuses more on the specific values of items than on their frequency, making it more practical in real-life scenarios. HUSPM with the contiguous constraint can be used to solve some applications requiring the sequence elements to occur consecutively. Due to device, environment, privacy issues, and other factors, the data is often not accurate, and traditional algorithms for mining high utility continuous sequence patterns (HUCSPs) do not perform well in handling uncertain data. To address this challenge, this paper presents a new algorithm named uncertain utility-driven contiguous pattern mining (UUCPM), which can discover HUCSPs efficiently and correctly. The algorithm is designed to obtain results from sequence data with uncertain probabilities set on the item level. Two tighter upper bounds on utility and corresponding pruning strategies are also proposed, which can effectively process and reduce the number of candidate patterns generated during pattern mining, thereby improving the performance of the mining process. Through extensive experiments, the proposed UUCPM algorithm has been verified for accuracy and performance, demonstrating its advanced properties.
Multi-Granularity Entity Recognition based Sentence Ranking for Multi-Document Summarization

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.
ORANGE: Opposite-label soRting for tANGent Explanations in heterogeneous spaces

Most real-world datasets have a heterogeneous feature space composed of binary, categorical, ordinal, and continuous features. However, the currently available local surrogate explainability algorithms do not consider this aspect, generating infeasible neighborhood centers which may provide erroneous explanations. To overcome this issue, we propose ORANGE, a local surrogate explainability algorithm that generates highaccuracy and high-fidelity explanations in heterogeneous spaces. ORANGE has three main components: (1) it searches for the closest feasible counterfactual point to a given instance of interest by considering feasible values in the features to ensure that the explanation is built around the closest feasible instance and not any, potentially non-existent instance in space; (2) it generates a set of neighboring points around this close feasible point based on the correlations among features to ensure that the relationship among features is preserved inside the neighborhood; and (3) the generated instances are weighted, firstly based on their distance to the decision boundary, and secondly based on the disagreement between the predicted labels of the global model and a surrogate model trained on the neighborhood. Our extensive experiments on synthetic and public datasets show that the performance achieved by ORANGE is best-in-class in both explanation accuracy and fidelity.
Hybrid Approaches to Optimization and Machine Learning Methods

This paper conducts a comprehensive literature review concerning hybrid techniques that combine optimization and machine learning approaches for clustering and classification problems. The aim is to identify the potential benefits of integrating these methods to address challenges in both fields. The paper outlines optimization and machine learning methods and provides a quantitative overview of publications since 1970. Additionally, it offers a detailed review of recent advancements in the last three years. The study includes a SWOT analysis of the top ten most cited algorithms from the collected database, examining their strengths and weaknesses as well as uncovering opportunities and threats explored through hybrid approaches. Through this research, the study highlights significant findings in the realm of hybrid methods for clustering and classification, showcasing how such integrations can enhance the shortcomings of individual techniques.
Online Explainable Model Selection for Time Series Forecasting

Several machine learning models have been used to tackle time series forecasting. However, it is generally accepted that none of them is universally valid for every application and over time. Therefore, adequate and adaptive real-time model selection is often required to cope with the time-evolving nature of time series and the fact that models have specific Regions of Competence (RoCs) across the time series. In this paper, we perform an online single model selection for time series forecasting by using an adaptive clustering method to compute the RoCs of candidate models. This method can be extended to ensemble base models selection by combining clustering with a rank-based approach. In this framework, the appropriate model(s) is selected online, and the RoCs responsible for model selection update is done adaptively in an informed manner following concept drift detection in the RoCs’ structure. Moreover, the computed RoCs can be used to provide suitable explanations for the reason for selecting certain model(s) in a certain time interval or instant. Since the RoCs are computed independently of the family of forecasting models in question, the explanations we provide are model-agnostic. An extensive empirical study on various real-world datasets shows that our method achieves excellent or on-par results compared to state-of-the-art approaches and various baselines.
HyperTab: Hypernetwork Approach for Deep Learning on Small Tabular Datasets

Deep learning has achieved impressive performance in many domains, such as computer vision and natural language processing, but its advantage over classical shallow methods on tabular datasets remains questionable. It is especially challenging to surpass the performance of tree-like ensembles, such as XGBoost or Random Forests, on small-sized datasets (less than 1k samples). To tackle this challenge, we introduce HyperTab, a hypernetwork-based approach to solving small sample problems on tabular datasets. By combining the advantages of Random Forests and neural networks, HyperTab generates an ensemble of neural networks, where each target model is specialized to process a specific lower-dimensional view of the data. Since each view plays the role of data augmentation, we virtually increase the number of training samples while keeping the number of trainable parameters unchanged, which prevents model overfitting. We evaluated HyperTab on more than 40 tabular datasets of a varying number of samples and domains of origin and compared its performance with shallow and deep learning models representing the current state-of-the-art. We show that HyperTab consistently outranks other methods on small data (with statistically significant differences) and scores comparable to them on larger datasets.
Knowledge Graph-based Embedding for Connecting Scholars in Academic Social Networks

In recent years, research tasks have increasingly involved using multi-disciplinary knowledge through collaborations of scholars from multiple fields. However, identifying a team of suitable collaborators from diverse fields for a given research task is a challenging and time-consuming process. In this paper, we propose a novel “ScholarTeamFinder” model that uses knowledge graph based link prediction to identify collaborators within an academic social network (ASN) to form a research team to address a multi-disciplinary research problem. Our approach involves building a heterogeneous knowledge graph within an ASN using entities such as scholars, publications, research grants, and the relationship among these entities. Following this, we use graph-based deep learning to learn the node embedding from the knowledge graph that can be used for scholar team recommendation. More specifically, we used the classical meth-path2vec as our base graph learning algorithm and improved its performance by considering semantic meaning of entities and encoding edge embeddings in the graph. Finally, we propose a beam-search algorithm for scholar team prediction based on our model embeddings. Our evaluation of ScholarTeamFinder is performed using large ASN datasets including a unique dataset (i.e., NSF award dataset) of federal grant awards collected over the last ten years and the scholars’ publication data, as well as three other widely used datasets (i.e., APS, SCHOLAT and Gowalla). Experiment results show that our model outperforms the state-of-the-art models across the different datasets.
Incremental Targeted Mining in Sequences

High utility sequential pattern mining (HUSPM) is a critical research topic in data analytics (e.g., smart-city technologies), which takes into consideration three pivotal factors of data: timestamp, internal quantization, and external utility. Recently, a query-enabled HUSPM approach has been proposed, which aims to discover patterns based on a query sequence. However, this approach only works on static data and does not solve the tasks well under dynamic data. When the data is updated, it needs to restart the mining process, which leads to a lot of duplicate calculations and resource consumption. In the paper, to address the mining task of increasing sequence data over time, we develop an Incremental Targeted HUSPM algorithm called ITUS. A tighter upper bound called tight extension sequence utility (TESU) is proposed to determine key candidates, which can avoid the generation of unpromising patterns. By using TESU, a target candidate pattern tree (TCP-tree) is utilized to record the sequence information, and several efficient strategies are implemented to incrementally update the tree. Finally, we extensively evaluate our proposed algorithm on both real-world and synthetic datasets. The experimental results clearly demonstrate that not only does the novel algorithm guarantee the accuracy of the results after multiple database updates, but it also achieves higher efficiency than the baseline approach.
Sample Topology Exploration for Label Distribution Learning

Label distribution learning (LDL) employs probabilistic labels to capture the varying degrees of relevance among decision attributes. Existing LDL algorithms usually employ sample correlations to improve their predictive validity. However, they merely employ the superficial features of the sample for correlation analysis, seldom delving into its latent deep features. In this paper, we propose an algorithm to explore the sample topology (ST-LDL) to address this problem. First, we construct a locally weighted directed graph for each target sample. The target sample and its k neighbors are regarded as nodes within the graph. The asymmetrical correlation among these $k+1$ samples is computed separately to determine the weight value of the graph. Then, we utilise the local topology between samples to mine latent information and reconstruct the samples. The local graph is fed into the graph convolutional network to nonlinearly reconstruct the features by mining the latent information. Finally, we designed a new optimization objective function for the reconstructed samples. Experiments are carried out on elven real-world datasets in comparison with seven state-of-the-art algorithms. The results show that our algorithm outperforms several other algorithms, proving the effectiveness of our algorithm.