Outlier interpretation methods, based on outlying aspect mining, have been extensively utilized in diverse applications due to their effectiveness and interpretability. The primary objective of these methods is to identify an outlying feature subspace where a detected outlier deviates most significantly from the inliers. However, this subspace is typically not personalized and incomplete. In this article, we propose a novel convertible outlying aspect mining method named Mining convErtible ouTlying Aspect (META) to interpret the detected outlier. META not only identifies a personalized outlying feature subspace (i.e., where) that differentiates the detected outlier from inliers, but also quantifies the outlying direction and degree within this subspace (i.e., how), thereby offering actionable interpretative insights, rather than corrective actions, for understanding the outlier. Specifically, META defines a convertible outlying aspect with a convertible cost to convert the detected outlier into a converted instance, and employs a pretrained adversary to evaluate whether the instance is an inlier or not. Subsequently, we formulate an objective function that minimizes the convertible cost, ensures the successful conversion of the instance into an inlier, and minimizes the size of the outlying feature subspace. META leverages this objective function to learn an optimal convertible outlying aspect for the detected outlier. The optimal convertible outlying aspect provides the outlying feature subspace, outlying direction, outlying degree, and converted instance. Empirical results from experiments conducted on both real-world and synthetic datasets demonstrate that META outperforms state-of-the-art (SOTA) baselines.

Fine-grained IP geolocation plays a critical role in applications such as location-based services and cybersecurity. Most existing fine-grained IP geolocation methods treat the task as a coordinate regression problem. However, due to inherent noise in the IP features, they frequently produce kilometer-scale coordinate errors, which in turn lead to inaccuracies when the predicted coordinates are mapped to geographic regions. To alleviate this issue, this paper introduces a novel IP region prediction framework HMCGeo, framing IP region prediction as a hierarchical multi-label classification problem. City administrators divide urban areas into regions at multiple granularities. The proposed framework employs residual connection-based feature extraction units to obtain IP representations at each granularity and introduces class prototype attention to predict the IP’s belonging region at the current granularity. Additionally, it adopts an output fusion strategy combined with hierarchical focal loss to further enhance region prediction performance. We evaluate HMCGeo on real-world datasets from New York, Los Angeles, and Shanghai. It significantly outperforms existing methods in region prediction across all granularities and achieves lower coordinate errors on most samples by similarity-weighted averaging of candidate region centers.

Fine-grained IP geolocation plays a critical role in location-based services and cybersecurity. However, due to the impact of inherent noise on data features, most existing fine-grained IP geolocation methods fail to accurately predict the region of the target host. Although recent studies have focused on IP region prediction, they do not provide reliability assessments for the predictions, which limits the practical application of IP geolocation. To address these challenges, this paper proposes UncertaintyGeo, a fine-grained IP geolocation framework based on the Dirichlet network. We revisit IP geolocation from a classification perspective and introduce a “region-first, coordinate-second” paradigm rooted in the Dirichlet distribution. This framework first predicts the region of the target host, and uses the properties of the Dirichlet concentration parameters to evaluate the reliability of the predictions, then obtains coordinate predictions by weighting the centers of candidate regions based on prediction confidence. Experiments on real-world datasets from New York, Los Angeles, and Shanghai demonstrate that UncertaintyGeo significantly outperforms state-of-the-art methods in region-level prediction while achieving notable advantages in coordinate-level prediction and reliability estimation.

Modern energy-harvesting devices use on-device compute to discard data uninteresting to the application, improving energy availability. These devices capture data at fixed rate, and process captured data at a rate that varies with environmental factors like input power and event activity. If capture rate exceeds processing rate, new inputs are stored in a small on-device input buffer (hundreds of kBs). When the input buffer fills up, the device discards newer inputs, missing potentially interesting events. Energy-harvesting devices must avoid such input buffer overflows (IBO) to avoid missing interesting events. A static solution to IBOs is impossible given dynamic variations in processing rate, and prior research fails to provide a suitable dynamic solution. We propose Quetzal, a new hardware-software solution targeted at avoiding IBOs. Quetzal's software has two parts: a new energy-aware scheduler that selects jobs with the lowest end-to-end latency (including energy recharging), and a runtime which uses queueing-theory to predict if the selected job will cause IBOs. Quetzal reacts to imminent IBOs by degrading the scheduled job. Quetzal's scheduler and runtime use a simple, system-agnostic hardware circuit to measure power at runtime. Quetzal reduces events missed due to IBOs by up to 4.2× compared to several baselines.

Ensemble clustering learns a consensus result by integrating a set of base clustering results. Recently, anchor-based methods construct an anchor similarity matrix to represent the affinity relationships among samples, significantly improving computational efficiency. However, these methods struggle with fixed anchors generated by static anchor learning strategies, which lead to low-quality anchor similarity matrix and poor clustering accuracy. To address this issue, we propose a novel method named dynamic anchor-based ensemble clustering via hypergraph reconstruction (YACHT). Specifically, YACHT first transforms the base clustering results into a hypergraph and designs a novel hypergraph enhancement strategy to improve the reliability of the initial hypergraph. YACHT reconstructs the hypergraph through matrix factorization and introduces a mapping matrix to filter out redundant information, capturing a high-quality anchor similarity matrix. Then, YACHT attempts to incorporate the hypergraph into the optimization objective to achieve hypergraph updates. To ensure the accuracy of hypergraph updates, we impose a hypergraph regularizer and a local consensus information alignment term. The alignment term is implemented by minimizing the discrepancy between the label partition derived from the hypergraph regularizer and the local consensus information indicator matrix extracted from the base clustering results. Extensive experimental results demonstrate the outstanding performance of the proposed YACHT. The code is available at https://github.com/scu-kdde/YACHT.

In this article, we attempt to study a problem of identifying outliers from multiple dimensions, terming it as multidimensional anomaly detection. This task assumes that each sample corresponds to heterogeneous discriminative spaces, where each space characterizes distinct semantic information along one dimension. Consequently, the abnormalities exhibited by a sample will vary under these semantically different dimensions. In contrast to traditional anomaly detection, multidimensional anomaly detection offers a more holistic assessment of a sample's abnormalities. However, the heterogeneity of discriminative spaces leads to incomparability of the outputs from different dimensions which is the major difficulty in designing multidimensional anomaly detection methods. This article introduces a novel model, maximum margin multidimensional anomaly detection (ALOE), specifically tailored for multidimensional anomaly detection. ALOE constructs a convex optimization problem with nonlinear constraints. The primary objective is to simultaneously learn multiple decision boundaries, utilizing the maximum margin principle and covariance regularization, while distinguishing between outliers and normal samples under multiple dimensions by capturing the correlation among multiple dimensions. To obtain the optimal decision boundary under each dimension, we devise an alternating optimization method for this convex optimization problem. To validate the effectiveness of ALOE, we conduct extensive experiments on 12 real-world datasets, comparing its performance against 34 anomaly detection methods. The experimental results demonstrate the superior performance of ALOE.

Improving the ability to predict protein function can potentially facilitate research in the fields of drug discovery and precision medicine. Technically, the properties of proteins are directly or indirectly reflected in their sequence and structure information, especially as the protein function is largely determined by its spatial properties. Existing approaches mostly focus on protein sequences or topological structures, while rarely exploiting the spatial properties and ignoring the relevance between sequence and structure information. Moreover, obtaining annotated data to improve protein function prediction is often time-consuming and costly. To this end, this work proposes a novel contrast-aware pre-training framework, called SCOP, for protein function prediction. We first design a simple yet effective encoder to integrate the protein topological and spatial features under the structure view. Then a convolutional neural network is utilized to learn the protein features under the sequence view. Finally, we pretrain SCOP by leveraging two types of auxiliary supervision to explore the relevance between these two views and thus extract informative representations to better predict protein function. Experimental results on four benchmark datasets and one self-built dataset demonstrate that SCOP provides more specific results, while using less pre-training data.

Recent advances in multi-kernel-based methods for outlier detection have positioned them as an attractive way to detect instances that are markedly different from the remaining data in a dataset. Currently, most outlier detection approaches based on multi-kernel learning are simply a convex combination of various kernels with handcrafted weights, meaning that these weights may not be suitable. Meanwhile, this combination of weights does not sufficiently consider the intrinsic correlations of instances when fusing different kernels. Thus, a key challenge is how to adaptively learn an appropriate combination of weights for capturing a new feature space in which outliers can be better detected than the original space. Simultaneously, it is still a burning issue to get the optimal combination of weights due to considerable computational cost and memory usage when the feature or instance size is large. In this paper, we propose a novel method forefficientadaptivemulti-kernel foroutlierdetection (EAMOD), which automatically learns the optimal weight for each training instance under different kernels using a non-negative function. In addition, we design a safe screening rule (SSR) for EAMOD to improve its training efficiency without any loss of accuracy. To the best of our knowledge, it is the first attempt to develop SSR for multi-kernel-based outlier detection methods. Extensive experiments show that EAMOD is effective and efficient.

Outlier detection methods based on distance measure have been used in numerous applications due to their effectiveness and interpretability. However, distances among instances heavily depend on the feature space in which they reside. For an outlier, distances from it to the normal instances may be extremely close in one feature space, failing to separate them from each other, while this situation is reversed in another space. Meanwhile, the distance measure is sensitive to a few “marginal instances” (i.e., normal instances located very close to outliers in the feature space) during the estimation of whether a test instance is an outlier or not. In this paper, we propose a robust multi-kernel nearest neighborhood (RMKN) method for outlier detection. Specifically, in the training phase, we only consider normal instances and transform them into a Polynomial kernel function weighted digraph to capture their geometric relationships in the original feature space. Then, we develop an objective function based on the weighted digraph to find a latent feature space via multi-kernel learning such that distances among normal instances in this latent feature space are as close as possible while preserving their original distributions. In the detecting phase, we design an outlying score based on the two-stage multi-kernel k-nearest nearest neighbors to detect outliers. Extensive experiments with ten datasets show that RMKN is effective and robust

Fraud detection aims to identify fraudsters from normal users. In graph environments, both fraudsters and normal users are modeled as nodes, while edges represent the connections between them. However, fraudulent nodes in the real world often camouflage themselves by establishing numerous fake connections with normal nodes, making them challenging to be identified. Existing fraud detection methods struggle to address this issue, they utilize graph neural networks to aggregate normal informations from normal neighbors, which leads to the smoothing of the fraudulent information. Furthermore, these methods exhibit poor generalization performance as they are unable to detect new fraudsters which not present in the training process. To overcome these limitations, this paper proposes GFAN, a novel model based on Graph Feature enhAncement Network. Specifically, GFAN introduces a specific semantic extraction module to screen and delete fake connections by evaluating the confidence level of edge presence. Additionally, GFAN provides a representation enhanced co-training module that highlights camouflaged fraudulent representations by training the small sphere and large margin support vector data description. Experimental results show that GFAN outperforms other competitive graph-based fraud detectors on public datasets. The GFAN code is available at: https://github.com/scu-kdde/OAM-GFAN-2023.

Rank support vector machine (RSVM) is widely used in multilabel classification problems. However, as the number of labels and instances soars, the training efficiency of the model will be greatly reduced. Unfortunately, few effective methods can solve this problem. In this article, we propose a safe screening rule (SSR) for RSVM to improve its training speed. This is the first attempt to construct SSR for multilabel learning problems. SSR for RSVM can screen and delete most of the instances based on their relevant–irrelevant label pairs, which is the biggest difference in the existing SSR. After this process, the scale of RSVM can be substantially reduced before solving it. The sequential version of SSR for RSVM is further introduced to accelerate the whole parameter tuning process. One important advantage of SSR is that it is safe, which means we can obtain the same optimal solution as the original problem by utilizing it. Extensive experiments with five benchmark datasets, three large-scale datasets, and one type 2 diabetes dataset show that our approach is efficient and safe.

Predicting molecular properties has significant implications for the discovery and generation of drugs and further research in the domain of medicinal chemistry. Learning representations of molecules plays a central role in deep learning-driven property prediction. However, the diversity of molecular features (e.g., chemical system languages, structure notations) brings inconsistency in molecular representation. Moreover, the scarcity of labeled molecular data limits the accuracy of the molecular property prediction model. To address the above issues, we proposed a two-stage method, named MORN, for learning molecular representations for molecular property prediction from a multi-view perspective. In the first stage, textual-topological-spatial multi-views were proposed to learn the molecular representations, so as to capture both chemical system language and structure notation features simultaneously. In the second stage, an adaptive strategy was used to fuse molecular representations learned from multi-views to predict molecular properties. To alleviate the limitation of the scarcity of labeled molecular data, the label restriction was introduced in both multi-view representation learning and fusion stages. The performance of MORN was assessed by seven benchmark molecular datasets and one self-built molecular dataset. Experimental results demonstrated that MORN is effective in molecular property prediction.