As Audio Large Language Models (ALLMs) emerge as powerful tools for speech processing, their safety implications demand urgent attention. While considerable research has explored textual and vision safety, audio’s distinct characteristics present significant challenges. This paper first investigates: Is ALLM vulnerable to backdoor attacks exploiting acoustic triggers? In response to this issue, we introduce Hidden in the Noise (HIN), a novel backdoor attack framework designed to exploit subtle, audio-specific features. HIN applies acoustic modifications to raw audio waveforms, such as alterations to temporal dynamics and strategic injection of spectrally tailored noise. These changes introduce consistent patterns that an ALLM’s acoustic feature encoder captures, embedding robust triggers within the audio stream. To evaluate ALLM robustness against audio-feature-based triggers, we develop the AudioSafe benchmark, assessing nine distinct risk types. Extensive experiments on AudioSafe and three established safety datasets reveal critical vulnerabilities in existing ALLMs: (I) audio features like environment noise and speech rate variations achieve over 90% average attack success rate, (II) ALLMs exhibit significant sensitivity differences across acoustic features, particularly showing minimal response to volume as a trigger, and (III) poisoned sample inclusion causes only marginal loss curve fluctuations, highlighting the attack’s stealth.

In the realm of data mining, outlier detection has emerged as a pivotal research focus, aimed at uncovering anomalies within datasets to extract meaningful and valuable insights. The objective is to leverage data mining methodologies to pinpoint anomalies within datasets, thereby revealing crucial and enlightening information. Herein, we introduce a groundbreaking outlier detection methodology, ODMGIS, that seamlessly integrates multigranularity representation and information set concepts to devise the multigranularity information set (MGIS) model. This model adeptly characterizes the distribution patterns of data points. First, we employ entropy function and their complementary function as measurement tools to accurately quantify the inherent uncertainty in data with different distributions, and considers the sum of the two as a comprehensive representation of the overall uncertainty of the information source. Subsequently, an outlier score model is constructed based on MGIS, which can deeply characterize the degree of outlierness of samples, thereby effectively identifying abnormal points in the dataset. During validation, ODMGIS was rigorously tested on practical datasets from medicine and bioinformatics, and its performance was benchmarked against both traditional and the state-of-the-art algorithms, showcasing substantial benefits. This research not only contributes a fresh perspective to outlier detection, but also sparks innovative avenues for exploring and advancing the granular computing theory.

Representation learning is an important topic in machine learning, pattern recognition, and data mining research. Among many representation learning approaches, semi-nonnegative matrix factorization (SNMF) is a frequently-used one. However, a typical problem of SNMF is that usually there is no learning rate guidance during the optimization process, which often leads to a poor representation ability. To overcome this limitation, we propose a very general representation learning framework (DNSRF) that is based on a deep neural net. Essentially, the parameters of the deep net used to construct the DNSRF algorithms are obtained by matrix element update. In combination with different activation functions, DNSRF can be implemented in various ways. In our experiments, we tested nine instances of our DNSRF framework on six benchmark datasets. In comparison with other state-of-the-art methods, the results demonstrate the superior performance of our framework, which is thus shown to have a great representation ability.

Feature selection, also known as attribute reduction, plays a crucial role in machine learning and data mining tasks. Rough set theory-based feature selection methods have gained popularity due to their ability to handle imprecise and inconsistent data, ease of implementation, and generation of highly interpretable results. However, these methods still suffer from high computational cost when dealing with large-scale datasets with high dimensions. To overcome this shortcoming, we propose a fast attribute reduction method based on inconsistent equivalence classes. The presented method can accelerate those attribute reduction algorithms whose importance measures used can be computed using only inconsistent equivalence classes. Our proposed method improves attribute reduction efficiency through three key aspects: 1) transforming the original dataset into an equivalently simplified version with fewer samples, 2) accelerating the computation of core attributes, and 3) expediting the forward selection process by removing redundant objects and attributes. Experimental results demonstrate the high computational efficiency of our proposed method.

Representing the structural relations between entities, i.e., knowledge graph embedding, which is a method to learn low-dimensional representations of knowledge, has become an increasingly prevalent research orientation in cognitive and human intelligence. It is significant to study how to interrelate, fuse and embed the knowledge graph data from different domains while considering data not shared. In this paper, we propose a model of cross-domain knowledge graph embedding in federated learning (FedCKE), in which entity/relation embedding between different domains can interact securely in the case that data is not shared. In advance of client model training, we present an inter-domain encrypted entity/relation alignment method using the encrypted sample alignment method in vertical federated learning, which can obtain entity/relation intersections between different domains without revealing any triples structure and additional entities/relations in the respective datasets. On the server, we aggregate the same entity/relation embeddings by the association in conjunction with the parameter-secure aggregation method in horizontal federated learning. Experimental results on three real datasets show that the proposed FedCKE model is able to enhance the embedding of different clients (domains).

Multi-view clustering research is a hot topic in the field of data mining, where complementary information between views can better describe data objects and improve the clustering performance. Non-negative matrix factorization (NMF) based multi-view clustering algorithm suffers from weak feature extraction, slow convergence speed and low accuracy. To solve these problems, this paper proposes a generalized deep learning multi-view clustering (GDLMC) algorithm based on NMF. Firstly, via decoupling the elements in the matrix, the matrix elements are non-negatively restricted using an activation function with a non-negative value domain, and the elements are updated employing stochastic gradient descent with learning rate guidance. Then, the corresponding gradients when the elements update are transformed into generalized weights and generalized biases, followed by combining the generalized weights and generalized biases with activation functions to construct generalized deep learning (GDL). Further, GDL is adopted to learn the corresponding low-dimensional matrix of each view and consensus matrix for obtaining the GDLMC algorithm. In addition, the detailed reasoning of the GDLMC algorithm are given. Finally, extensive experiments are conducted on four public datasets including regular and large-scale datasets, and the experimental results show that GDLMC has significant advantages.

The graph regularized nonnegative matrix factorization (GNMF) algorithms have received a lot of attention in the field of machine learning and data mining, as well as the square loss method is commonly used to measure the quality of reconstructed data. However, noise is introduced when data reconstruction is performed; and the square loss method is sensitive to noise, which leads to degradation in the performance of data analysis tasks. To solve this problem, a novel graph regularized sparse NMF (GSNMF) is proposed in this article. To obtain a cleaner data matrix to approximate the high-dimensional matrix, the$l_{1}$-norm to the low-dimensional matrix is added to achieve the adjustment of data eigenvalues in the matrix and sparsity constraint. In addition, the corresponding inference and alternating iterative update algorithm to solve the optimization problem are given. Then, an extension of GSNMF, namely, graph regularized sparse nonnegative matrix trifactorization (GSNMTF), is proposed, and the detailed inference procedure is also shown. Finally, the experimental results on eight different datasets demonstrate that the proposed model has a good performance.

The main goal of most information quality (IQ)-based measures is to combine data provided by multiple information sources to enhance the quality of information essential for decision makers to perform their tasks. However, there is few work to fuse multisource information from the perspective of possibility distribution (PD) and use IQ as the evaluation criteria for feature selection. The PD is one of important concepts in the possibility theory, which is a generally acknowledged method for describing a kind of uncertain knowledge. In this article, we propose a novel representation model of PDs based on FMs, namely, a possibility distribution information system (PDIS). Then, several IQ measures are defined in the PDIS, including Gini entropy, compatibility, conflict, credibility, and separability degrees. In view of this, a minimal-separability-minimal-uncertainty-based unsupervised feature selection algorithm (UmSMU) is designed. The proposed UmSMU can sufficiently fuse multiple possibilistic information. Meanwhile, the selected features maintain as much information as possible while minimizing the uncertainty of information. The experimental results show that the proposed algorithm performs well, especially when it comes to selecting fewer features and improving performance.

Clustering is a popular research topic in the field of data mining, in which the clustering method based on non-negative matrix factorization (NMF) has been widely employed. However, in the update process of NMF, there is no learning rate to guide the update as well as the update depends on the data itself, which leads to slow convergence and low clustering accuracy. To solve these problems, a generalized deep learning clustering (GDLC) algorithm based on NMF is proposed in this article. Firstly, a nonlinear constrained NMF (NNMF) algorithm is constructed to achieve sequential updates of the elements in the matrix guided by the learning rate. Then, the gradient values corresponding to the element update are transformed into generalized weights and generalized biases, by inputting the elements as well as their corresponding generalized weights and generalized biases into the nonlinear activation function to construct the GDLC algorithm. In addition, for improving the understanding of the GDLC algorithm, its detailed inference procedure and algorithm design are provided. Finally, the experimental results on eight datasets show that the GDLC algorithm has efficient performance.