Human voice is a rich source of information that can reveal a range of sensitive personal attributes, such as age, gender, and country of origin. With advances in Artificial Intelligence (AI), especially in speech processing, these personal attributes can now be inferred on a scale with high accuracy, raising serious privacy concerns. In fact, the ability to extract demographic or identification information from voice data poses risks related to surveillance, profiling, and misuse of personal data, highlighting the urgent need for privacy-preserving solutions in voice-based AI systems. Therefore, this paper proposes Selective Attribute Masking (SAM) , a new model-agnostic framework that uses gradient-based adversarial perturbations to suppress the inference of specific speaker attributes from voice recordings, while preserving the accuracy of non-target attributes and maintaining the utility of Automatic Speech Recognition (ASR). Experimental results using CommonVoice dataset demonstrate that SAM achieves selective masking success rates of up to 74.5 % for age, 59.4 % for gender, and 54.6 % for accent–substantially outperforming baseline methods. At the same time, voice utility (that is, ASR) remains largely unaffected, with the word error rate increasing by less than 3 % absolute under moderate perturbations. These findings demonstrate the effectiveness of our proposed framework (SAM) in balancing privacy and utility in voice-based systems.

Recent deep learning techniques have significantly improved voice authentication systems. However, they remain vulnerable to threats, including data poisoning and Man-in-the-Middle (MitM) attacks. This paper reevaluates the attack and defense mechanisms of “The Guardian”, examining their assumptions and modifying the proposal. We conduct experiments with real-world datasets to validate their effectiveness under realistic conditions and assess the feasibility of executing such attacks. Additionally, we introduce a defense mechanism that improves resilience after redefining a threat model grounded in operational feasibility, specifically by isolating the enrollment phase from training phase assumptions. By analyzing the current literature, we identify open challenges and suggest directions for further improving the security of voice authentication systems.

Diffusion models have recently emerged as powerful tools for missing data imputation by modeling the joint distribution of observed and unobserved variables. However, existing methods, typically based on stochastic denoising diffusion probabilistic models (DDPMs), suffer from high inference latency and variable outputs, limiting their applicability in real-world tabular settings. To address these deficiencies, we present in this paper MissDDIM, a conditional diffusion framework that adapts Denoising Diffusion Implicit Models (DDIM) for tabular imputation. While stochastic sampling enables diverse completions, it also introduces output variability that complicates downstream processing. MissDDIM replaces this with a deterministic, non-Markovian sampling path, yielding faster and more consistent imputations. To better leverage incomplete inputs during training, we introduce a self-masking strategy that dynamically constructs imputation targets from observed features-enabling robust conditioning without requiring fully observed data. Experiments on five benchmark datasets demonstrate that MissDDIM matches or exceeds the accuracy of state-of-the-art diffusion models, while significantly improving inference speed and stability. These results highlight the practical value of deterministic diffusion for real-world imputation tasks.

Handling incomplete and heterogeneous data remains a central challenge in real-world machine learning, where missing values may follow complex mechanisms (MCAR, MAR, MNAR) and features can be of mixed types (numerical and categorical). Existing methods often rely on imputation, which may introduce bias or privacy risks, or fail to jointly address data heterogeneity and structured missingness. We propose the Heterogeneous Incomplete Probability Mass Kernel (HI-PMK), a novel data-dependent representation learning approach that eliminates the need for imputation. HI-PMK introduces two key innovations: (1) a probability mass-based dissimilarity measure that adapts to local data distributions across heterogeneous features (numerical, ordinal, nominal), and (2) a missingness-aware uncertainty strategy (MaxU) that conservatively handles all three missingness mechanisms by assigning maximal plausible dissimilarity to unobserved entries. Our approach is privacy-preserving, scalable, and readily applicable to downstream tasks such as classification and clustering. Extensive experiments on over 15 benchmark datasets demonstrate that HI-PMK consistently outperforms traditional imputation-based pipelines and kernel methods across a wide range of missing data settings. Code is available at: github.com/echoid/Incomplete-Heter-Kernel

Recommendation systems are now an integral part of our daily lives. We rely on them for tasks such as discovering new movies, finding friends on social media, and connecting job seekers with relevant opportunities. Given their vital role, we must ensure these recommendations are free from societal stereotypes. Therefore, evaluating and addressing such biases in recommendation systems is crucial. Previous work evaluating the fairness of recommended items fails to capture certain nuances as they mainly focus on comparing performance metrics for different sensitive groups. In this paper, we introduce a set of comprehensive metrics for quantifying gender bias in recommendations. Specifically, we show the importance of evaluating fairness on a more granular level, which can be achieved using our metrics to capture gender bias using categories of recommended items like genres for movies. Furthermore, we show that employing a category-aware fairness metric as a regularization term along with the main recommendation loss during training can help effectively minimize bias in the models' output. We experiment on three real-world datasets, using five baseline models alongside two popular fairness-aware models, to show the effectiveness of our metrics in evaluating gender bias. Our metrics help provide an enhanced insight into bias in recommended items compared to previous metrics. Additionally, our results demonstrate how incorporating our regularization term significantly improves the fairness in recommendations for different categories without substantial degradation in overall recommendation performance.

The growing adoption of network technologies, particularly the Internet of Things (IoT), has led to the emergence of new and increasingly complex cyberattacks. To protect critical infrastructure from these evolving threats, it is essential to implement Intrusion Detection Systems (IDS) capable of accurately detecting a wide range of attacks while minimizing false alarms. While machine learning has been widely applied in IDS, most approaches rely on flat multi-class classification to distinguish between normal traffic and various attack types. However, cyberattacks often exhibit a hierarchical structure, where granular attack subtypes can be grouped under broader high-level categories—an aspect largely underexplored in IDS research. In this paper, we investigate the effectiveness of hierarchical classification in the context of IDS. We propose a three-level hierarchical classification model: the first level distinguishes between benign and attack traffic; the second level categorizes coarse-grained attack types; and the third level identifies specific, fine-grained attack subtypes. Our experimental evaluation, conducted using 10 different machine learning classifiers across 10 contemporary IDS datasets, reveals that hierarchical and flat classification approaches achieve comparable performance in terms of overall accuracy, precision, recall, and F1-score. However, flat classifiers are more likely to misclassify attack traffic as normal, whereas the hierarchical approach tends to misclassify one attack type as another. This distinction is critical, as failing to identify an attack altogether poses a greater risk to cybersecurity than incorrectly labeling its type. Thus, our findings highlight the value of hierarchical classification in enhancing the robustness of IDS, especially in environments where minimizing false negatives is paramount.

In today’s digital age, our dependence on IoT (Internet of Things) and IIoT (Industrial IoT) systems has grown immensely, which facilitates sensitive activities such as banking transactions and personal, enterprise data, and legal document exchanges. Cyberattackers consistently exploit weak security measures and tools. The Network Intrusion Detection System (IDS) acts as a primary tool against such cyber threats. However, machine learning-based IDSs, when trained on specific attack patterns, often misclassify new emerging cyberattacks. Further, the limited availability of attack instances for training a supervised learner and the ever-evolving nature of cyber threats further complicate the matter. This emphasizes the need for an adaptable IDS framework capable of recognizing and learning from unfamiliar/unseen attacks over time. In this research, we propose a one-class classification-driven IDS system structured on two tiers. The first tier distinguishes between normal activities and attacks/threats, while the second tier determines if the detected attack is known or unknown. Within this second tier, we also embed a multi-classification mechanism coupled with a clustering algorithm. This model not only identifies unseen attacks but also uses them for retraining them by clustering unseen attacks. This enables our model to be future-proofed, capable of evolving with emerging threat patterns. Leveraging one-class classifiers (OCC) at the first level, our approach bypasses the need for attack samples, addressing data imbalance and zero-day attack concerns and OCC at the second level can effectively separate unknown attacks from the known attacks. Our methodology and evaluations indicate that the presented framework exhibits promising potential for real-world deployments.

In this era of advanced manufacturing , it is now more crucial than ever to diagnose machine faults as early as possible to guarantee their safe and efficient operation. With the increasing complexity of modern industrial processes, traditional machine health monitoring approaches cannot provide efficient performance. With the massive surge in industrial big data and the advancement in sensing and computational technologies, data-driven machinery fault diagnosis solutions based on machine/deep learning approaches have been used ubiquitously in manufacturing applications. Timely and accurately identifying faulty machine signals is vital in industrial applications for which many relevant solutions have been proposed and are reviewed in many earlier articles. Despite the availability of numerous solutions and reviews on machinery fault diagnosis, existing works often lack several aspects. Most of the available literature has limited applicability in a wide range of manufacturing settings due to their concentration on a particular type of equipment or method of analysis. Additionally, discussions regarding the challenges associated with implementing data-driven approaches, such as dealing with noisy data, selecting appropriate features, and adapting models to accommodate new or unforeseen faults, are often superficial or completely overlooked. Thus, this survey provides a comprehensive review of the articles using different types of machine learning approaches for the detection and diagnosis of various types of machinery faults, highlights their strengths and limitations, provides a review of the methods used for predictive analyses, comprehensively discusses the available machinery fault datasets, introduces future researchers to the possible challenges they have to encounter while using these approaches for fault diagnosis and recommends the probable solutions to mitigate those problems. The future research prospects are also pointed out for a better understanding of the field. We believe that this article will help researchers and contribute to the further development of the field.

Audio profiling is at the forefront of a technological breakthrough, offering rich insights into human behavior, emotions, physical attributes, and environmental contexts through detailed analysis of voice data. As we embrace an era where the integration of smart technologies equipped with the ability to capture sound is becoming ubiquitous, the capacity to accurately infer personal traits such as age, gender, height, weight, emotional state , personality, and even environmental contexts through voice analysis opens up vast opportunities across law enforcement, healthcare, social and commercial services, and entertainment. This emerging field promises to enhance our interaction with technology by not only understanding who we are but also by interpreting the world around us. However, the remarkable landscape is fraught with challenges, including data imbalances, the complexity of predictive models , and significant privacy concerns regarding the handling of sensitive paralinguistic information. This survey explores deep into the current landscape of audio profiling, examining the techniques and datasets in use, and showcasing its diverse applications while highlighting the need for advanced methodologies, enriched dataset development, and robust privacy preservation techniques.

Abstract In many critical machine learning applications, such as autonomous driving and medical image diagnosis, the detection of out-of-distribution (OOD) samples is as crucial as accurately classifying in-distribution (ID) inputs. Recently, outlier exposure (OE)-based methods have shown promising results in detecting OOD inputs via model fine-tuning with auxiliary outlier data. However, most of the previous OE-based approaches emphasize more on synthesizing extra outlier samples or introducing regularization to diversify OOD sample space, which is rather unquantifiable in practice. In this work, we propose a novel and straightforward method called Margin-bounded Confidence Scores (MaCS) to address the nontrivial OOD detection problem by enlarging the disparity between ID and OOD scores, which in turn makes the decision boundary more compact facilitating effective segregation with a simple threshold. Specifically, we augment the learning objective of an OE regularized classifier with a supplementary constraint, which penalizes high confidence scores for OOD inputs compared to that of ID and significantly enhances the OOD detection performance while maintaining the ID classification accuracy. Extensive experiments on various benchmark datasets for image classification tasks demonstrate the effectiveness of the proposed method by significantly outperforming state-of-the-art methods on various benchmarking metrics. The code is publicly available at https://github.com/lakpa-tamang9/margin_ood/tree/kais

In the field of Machine Learning (ML) and data-driven applications, one of the significant challenge is the change in data distribution between the training and deployment stages, commonly known as distribution shift. This paper outlines different mechanisms for handling two main types of distribution shifts: (i)Covariate shift:where the value of features or covariates change between train and test data, and (ii)Concept/Semantic-shift:where model experiences shift in the concept learned during training due to emergence of novel classes in the test phase. We sum up our contributions in three folds. First, we formalize distribution shifts, recite on how the conventional method fails to handle them adequately and urge for a model that can simultaneously perform better in all types of distribution shifts. Second, we discuss why handling distribution shifts is important and provide an extensive review of the methods and techniques that have been developed to detect, measure, and mitigate the effects of these shifts. Third, we discuss the current state of distribution shift handling mechanisms and propose future research directions in this area. Overall, we provide a retrospective synopsis of the literature in the distribution shift, focusing on OOD data that had been overlooked in the existing surveys.

In many critical Machine Learning applications, such as autonomous driving and medical image diagnosis, the detection of out-of-distribution (OOD) samples is as crucial as accurately classifying in-distribution (ID) inputs. Recently Outlier Exposure (OE) based methods have shown promising results in detecting OOD inputs via model fine-tuning with auxiliary outlier data. However, most of the previous OE-based approaches emphasize more on synthesizing extra outlier samples or introducing regularization to diversify OOD sample space, which is rather unquantifiable in practice. In this work, we propose a novel and straightforward method called Margin bounded Confidence Scores (MaCS) to address the nontrivial OOD detection problem by enlarging the disparity between ID and OOD scores, which in turn makes the decision boundary more compact facilitating effective segregation with a simple threshold. Specifically, we augment the learning objective of an OE regularized classifier with a supplementary constraint, which penalizes high confidence scores for OOD inputs compared to that of ID and significantly enhances the OOD detection performance while maintaining the ID classification accuracy. Extensive experiments on various benchmark datasets for image classification tasks demonstrate the effectiveness of the proposed method by significantly outperforming state-of-the-art (S.O.T.A) methods on various benchmarking metrics. The code is publicly available at https://github.com/lakpa-tamang9/margin_ood

Nowadays, to accommodate the swift expansion of cloud computing, big data, and other emerging technologies, integrating data center networks and SDN (Software Defined Network) is suggested, which also improves the network management’s convenience and flexibility. As network devices in these data centers continue to increase at a high rate, efficient route management to handle these devices’ vast amounts of data has emerged as a significant concern. This work suggests a novel admission and routing scheme considering the importance or priorities of the network flows, path energy, and routing path load. Using the SDN paradigm in DCN (Data Center Network), we first develop a Mixed Integer Linear Programming (MILP) formulation by jointly considering flow priority, path energy, and path load. The MILP formulation can maximize the number of flows as well as minimize the energy consumption and load variance in the network, or it can make a trade-off among all three. However, due to the potentially long running time, later, we introduce two greedy methods, Priority Based Energy Minimization Algorithm (PEMA) and Priority Based Evenly Load Distribution Algorithm (PEDL), where PEMA aims to maximize the flow with less energy, and PEDL focuses on maximizing the flow with reduced load variation. Finally, the developed routing strategies are implemented, and the simulation results demonstrate the out-performance of our work compared to the existing works in terms of successful flow ratio, energy savings, and load balancing.

The ongoing expansion of urban areas facilitated by advancements in science and technology has resulted in a considerable increase in the number of privately owned vehicles worldwide, including in South Korea. However, this gradual increment in the number of vehicles has inevitably led to parking-related issues, including the abuse of disabled parking spaces (hereafter referred to as accessible parking spaces) designated for individuals with disabilities. Traditional license plate recognition (LPR) systems have proven inefficient in addressing such a problem in real-time due to the high frame rate of surveillance cameras, the presence of natural and artificial noise, and variations in lighting and weather conditions that impede detection and recognition by these systems. With the growing concept of parking 4.0, many sensors, IoT and deep learning-based approaches have been applied to automatic LPR and parking management systems. Nonetheless, the studies show a need for a robust and efficient model for managing accessible parking spaces in South Korea. To address this, we have proposed a novel system called, ‘Shine’, which uses the deep learning-based object detection algorithm for detecting the vehicle, license plate, and disability badges (referred to as cards, badges, or access badges hereafter) and verifies the rights of the driver to use accessible parking spaces by coordinating with the central server. Our model, which achieves a mean average precision of 92.16%, is expected to address the issue of accessible parking space abuse and contributes significantly towards efficient and effective parking management in urban environments.

Deep Q-Networks algorithm (DQN) was the first reinforcement learning algorithm using deep neural network to successfully surpass human level performance in a number of Atari learning environments. However, divergent and unstable behaviour have been long standing issues in DQNs. The unstable behaviour is often characterised by overestimation in the Q-values, commonly referred to as the overestimation bias. To address the overestimation bias and the divergent behaviour, a number of heuristic extensions have been proposed. Notably, multi-step updates have been shown to drastically reduce unstable behaviour while improving agent’s training performance. However, agents are often highly sensitive to the selection of the multi-step update horizon (n), and our empirical experiments show that a poorly chosen static value for n can in many cases lead to worse performance than single-step DQN. Inspired by the success of n-step DQN and the effects that multi-step updates have on overestimation bias, this paper proposes a new algorithm that we call ‘Elastic Step DQN’ (ES-DQN) to alleviate overestimation bias in DQNs. ES-DQN dynamically varies the step size horizon in multi-step updates based on the similarity between states visited. Our empirical evaluation shows that ES-DQN out-performs n-step with fixed n updates, Double DQN and Average DQN in several OpenAI Gym environments while at the same time alleviating the overestimation bias.

Omnidirectional Videos (or 360° videos) are widely used in Virtual Reality (VR) to facilitate immersive and interactive viewing experiences. However, the limited spatial resolution in 360° videos does not allow for each degree of view to be represented with adequate pixels, limiting the visual quality offered in the immersive experience. Deep learning Video Super-Resolution (VSR) techniques used for conventional videos could provide a promising software-based solution; however, these techniques do not tackle the distortion present in equirectangular projections of 360° video signals. An additional obstacle is the limited 360° video datasets to study. To address these issues, this paper creates a novel 360° Video Dataset (360VDS) with a study of the extensibility of conventional VSR models to 360° videos. This paper further proposes a novel deep learning model for 360° Video Super-Resolution (360° VSR), called Spherical Signal Super-resolution with a Proportioned Optimisation (S3PO). S3PO adopts recurrent modelling with an attention mechanism, unbound from conventional VSR techniques like alignment. With a purpose-built feature extractor and a novel loss-function addressing spherical distortion, S3PO outperforms most state-of-the-art conventional VSR models and 360° specific super-resolution models on 360° video datasets. A step-wise ablation study is presented to understand and demonstrate the impact of the chosen architectural sub-components, targeted training and optimisation.

A major challenge in deep reinforcement learning is that it requires more data to converge to an policy for complex problems. One way to improve sample efficiency is to use n-step updates to reduce the number of samples required to converge to a good policy. However n-step updates are known to be brittle and difficult to tune. Elastic Step DQN has shown that it is possible to automate the value of$n$in DQN to solve problems involving discrete action spaces, however the efficacy of the technique when applied on more complex problems and against problems with continuous action spaces is yet to be shown. In this paper we adapt the innovations proposed by Elastic Step DQN onto the DDPG algorithm and show empirically that Elastic Step DDPG is able to achieve a much stronger final training policy and is more sample efficient than DDPG.

Brain tumors are among the most fatal and devastating diseases, often resulting in significantly reduced life expectancy. An accurate diagnosis of brain tumors is crucial to devise treatment plans that can extend the lives of affected individuals. Manually identifying and analyzing large volumes of MRI data is both challenging and time-consuming. Consequently, there is a pressing need for a reliable deep learning (DL) model to accurately diagnose brain tumors. In this study, we propose a novel DL approach based on transfer learning to effectively classify brain tumors. Our novel method incorporates extensive pre-processing, transfer learning architecture reconstruction, and fine-tuning. We employ several transfer learning algorithms, including Xception, ResNet50V2, InceptionResNetV2, and DenseNet201. Our experiments used the Figshare MRI brain tumor dataset, comprising 3,064 images, and achieved accuracy scores of 99.40%, 99.68%, 99.36%, and 98.72% for Xception, ResNet50V2, InceptionResNetV2, and DenseNet201, respectively. Our findings reveal that ResNet50V2 achieves the highest accuracy rate of 99.68% on the Figshare MRI brain tumor dataset, outperforming existing models. Therefore, our proposed model’s ability to accurately classify brain tumors in a short timeframe can aid neurologists and clinicians in making prompt and precise diagnostic decisions for brain tumor patients.

Abstract Alzheimer's disease, often known as dementia, is a severe neurodegenerative disorder that causes irreversible memory loss by destroying brain cells. People die because there is no specific treatment for this disease. Alzheimer's is most common among seniors 65 years and older. However, the progress of this disease can be reduced if it can be diagnosed earlier. Recently, artificial intelligence has instilled hope in the diagnosis of Alzheimer's disease by performing sophisticated analyses on extensive patient datasets, enabling the identification of subtle patterns that may elude human experts. Researchers have investigated various deep learning and machine learning models to diagnose this disease at an early stage using image datasets. In this paper, a new Deep learning (DL) methodology is proposed, where MRI images are fed into the model after applying various pre‐processing techniques. The proposed Alzheimer's disease detection approach adopts transfer learning for multi‐class classification using brain MRIs. The MRI Images are classified into four categories: mild dementia (MD), moderate dementia (MOD), very mild dementia (VMD), and non‐dementia (ND). The model is implemented and extensive performance analysis is performed. The finding shows that the model obtains 97.31% accuracy. The model outperforms the state‐of‐the‐art models in terms of accuracy, precision, recall, and F‐score.

Recurrent Neural Networks (RNN) are widespread for Video Super-Resolution (VSR) because of their proven ability to learn spatiotemporal inter-dependencies across the temporal dimension. Despite RNN’s ability to propagate memory across longer sequences of frames, vanishing gradient and error accumulation remain major obstacles to unidirectional RNNs in VSR. Several bi-directional recurrent models are suggested in the literature to alleviate this issue; however, these models are only applicable to offline use cases due to heavy demands for computational resources and the number of frames required per input. This paper proposes a novel unidirectional recurrent model for VSR, namely “Replenished Recurrency with Dual-Duct” (R2D2), that can be used in an online application setting. R2D2 incorporates a recurrent architecture with a sliding-window-based local alignment resulting in a recurrent hybrid architecture. It also uses a dual-duct residual network for concurrent and mutual refinement of local features along with global memory for full utilisation of the information available at each timestamp. With novel modelling and sophisticated optimisation, R2D2 demonstrates competitive performance and efficiency despite the lack of information available at each time-stamp compared to its offline (bi-directional) counterparts. Ablation analysis confirms the additive benefits of the proposed sub-components of R2D2 over baseline RNN models.The PyTorch-based code for the R2D2 model will be released at R2D2 GitRepo.

Electronic payment methods have become increasingly popular for business transactions, both online and in-person, across the globe. Anomalies like online fraud and default payments, which can result in substantial financial losses, have become more common as the usage of credit cards in online purchases has increased. To address this issue, researchers have explored various machine learning models and their ensemble techniques for detecting anomalies in credit card transaction data. However, detecting anomalies in this data can be challenging due to overlapping class samples and an imbalanced class distribution. Therefore, the detection rate of anomalies from minority class samples is relatively low, and general learning algorithms can be biased towards the majority class samples. In this paper, we propose a model called Credit Card Anomaly Detection (CCAD) that leverages the base learners paradigm and meta-learning ensemble techniques to improve the detection rate of credit card anomalies. We utilize four outlier detection algorithms as base learners and XGBoost algorithm as meta learner in the proposed stacked ensemble approach to detect anomaly in credit card transactions. We apply stratified sampling technique and k-fold cross-validation process to address the issues of data imbalance and overfitting. In addition, the discordance rate is calculated to enhance the accuracy of ensemble learning performances. The proposed model is trained and tested using two datasets: CCF (Credit Card Fraud) and CCDP (Credit Card Default Payment). Experimental results demonstrate that our approach outperforms existing approaches, particularly in detecting anomalies from the minority class instances of these datasets.

Anomaly detection in hyperspectral image (HSI) is affected by redundant bands and the limited utilization capacity of spectral–spatial information. In this article, we propose a novel improved Isolation Forest (IIF) algorithm based on the assumption that anomaly pixels are more susceptible to isolation than background pixels. The proposed IIF is a modified version of the Isolation Forest (iForest) algorithm, which addresses the poor performance of iForest in detecting local anomalies and anomaly detection in high-dimensional data. Furthermore, we propose a spectral–spatial anomaly detector based on IIF (SSIIFD) to make full use of global and local information, as well as spectral and spatial information. To be specific, first, we apply the Gabor filter to extract spatial features, which are then employed as input to the relative mass isolation forest (ReMass-iForest) detector to obtain the spatial anomaly score. Next, original images are divided into several homogeneous regions via the entropy rate segmentation (ERS) algorithm, and the preprocessed images are then employed as input to the proposed IIF detector to obtain the spectral anomaly score. Finally, we fuse the spatial and spectral anomaly scores by combining them linearly to predict anomaly pixels. The experimental results on four real hyperspectral datasets demonstrate that the proposed detector outperforms other state-of-the-art methods.

In today's world, databases are growing rapidly. Fast automatic detection of anomalous records in these massive databases is a challenging task. Traditional distance-based anomaly detectors are limited to small datasets because of their high time complexities. The univariate histogram-based method is arguably the fastest anomaly detection method. The anomaly score of a data instance is computed as the product of the probability mass of histograms in each dimension. Recent studies proved that such a simple method is comparable with many state-of-the-art methods on several datasets. However, as data features are assumed to be independent, it results in poor performance when features are correlated. Such an issue can be taken care of by using Principal Component (PC) features, which is the primary element of this paper. Our results show that integrating PCs with the original input features improves the performance of histogram-based anomaly detector with no real compromise in computational complexity.

Nowadays it is prevalent to take features extracted from pre-trained deep learning models as image representations which have achieved promising classification performance. Existing methods usually consider either object-based features or scene-based features only. However, both types of features are important for complex images like scene images, as they can complement each other. In this paper, we propose a novel type of features - hybrid deep features, for scene images. Specifically, we exploit both object-based and scene-based features at two levels: part image level (i.e., parts of an image) and whole image level (i.e., a whole image), which produces a total number of four types of deep features. Regarding the part image level, we also propose two new slicing techniques to extract part based features. Finally, we aggregate these four types of deep features via the concatenation operator. We demonstrate the effectiveness of our hybrid deep features on three commonly used scene datasets (MIT-67, Scene-15, and Event-8), in terms of the scene image classification task. Extensive comparisons show that our introduced features can produce state-of-the-art classification accuracies which are more consistent and stable than the results of existing features across all datasets.

This tutorial has four aims: (1) Providing the current comparative works on different outlier detectors, and analysing the strengths and weaknesses of these works and their recommendations. (2) Presenting non-obvious applications of outlier detectors. This provides examples of how outlier detectors are used in areas which are not normally considered to be the domains of outlier detection. (3) Inviting the research community to explore future research directions, in terms of both comparative study and outlier detection in general. (4) Giving an advice on the factors to consider when choosing an outlier detector, and strengths and weaknesses of some "top" recommended algorithms based on the current understanding in the literature.

In Bayesian classifier learning, estimating the joint probability distributionp(x,y) or the likelihoodp(x|y) directly from training data is considered to be difficult, especially in large multidimensional data sets. To circumvent this difficulty, existing Bayesian classifiers such as Naive Bayes, BayesNet, and AηDE have focused on estimating simplified surrogates ofp(x,y) from different forms of one‐dimensional likelihoods. Contrary to the perceived difficulty in multidimensional likelihood estimation, we present a simple generic ensemble approach to estimate multidimensional likelihood directly from data. The idea is to aggregatepi(x|y) estimated from a random subsample of data . This article presents two ways to estimate multidimensional likelihoods using the proposed generic approach and introduces two new Bayesian classifiers calledENNBayesandMassBayesthat estimatepi(x|y) using a nearest‐neighbor density estimation and a probability estimation through feature space partitioning, respectively. Unlike the existing Bayesian classifiers, ENNBayes and MassBayes have constant training time and space complexities and they scale better than existing Bayesian classifiers in very large data sets. Our empirical evaluation shows that ENNBayes and MassBayes yield better predictive accuracy than the existing Bayesian classifiers in benchmark data sets.

Nearest neighbour search is a core process in many data mining algorithms. Finding reliable closest matches of a query in a high dimensional space is still a challenging task. This is because the effectiveness of many dissimilarity measures, that are based on a geometric model, such as lp-norm, decreases as the number of dimensions increases. In this paper, we examine how the data distribution can be exploited to measure dissimilarity between two instances and propose a new data dependent dissimilarity measure called 'mp-dissimilarity'. Rather than relying on geometric distance, it measures the dissimilarity between two instances in each dimension as a probability mass in a region that encloses the two instances. It deems the two instances in a sparse region to be more similar than two instances in a dense region, though these two pairs of instances have the same geometric distance. Our empirical results show that the proposed dissimilarity measure indeed provides a reliable nearest neighbour search in high dimensional spaces, particularly in sparse data. Mp-dissimilarity produced better task specific performance than lp-norm and cosine distance in classification and information retrieval tasks.