Content-based image retrieval (CBIR) systems have emerged as crucial tools in the field of computer vision, allowing for image search based on visual content rather than relying solely on metadata. This survey paper presents a comprehensive overview of CBIR, emphasizing its role in object detection and its potential to identify and retrieve visually similar images based on content features. Challenges faced by CBIR systems, including the semantic gap and scalability, are discussed, along with potential solutions. It elaborates on the semantic gap, which arises from the disparity between low-level features and high-level semantic concepts, and explores approaches to bridge this gap. One notable solution is the integration of relevance feedback (RF), empowering users to provide feedback on retrieved images and refine search results iteratively. The survey encompasses long-term and short-term learning approaches that leverage RF for enhanced CBIR accuracy and relevance. These methods focus on weight optimization and the utilization of active learning algorithms to select samples for training classifiers. Furthermore, the paper investigates machine learning techniques and the utilization of deep learning and convolutional neural networks to enhance CBIR performance. This survey paper plays a significant role in advancing the understanding of CBIR and RF techniques. It guides researchers and practitioners in comprehending existing methodologies, challenges, and potential solutions while fostering knowledge dissemination and identifying research gaps. By addressing future research directions, it sets the stage for advancements in CBIR that will enhance retrieval accuracy, usability, and effectiveness in various application domains.

We address the Continual Learning (CL) problem, where a model has to learn a sequence of tasks from non-stationary distributions while preserving prior knowledge as it encounters new experiences. With the advancement of foundation models, CL research has shifted focus from the initial learning-from-scratch paradigm to the use of generic features from large-scale pre-training. However, existing approaches to CL with pre-trained models only focus on separating the class-specific features from the final representation layer and neglect the power of intermediate representations that capture low- and mid-level features naturally more invariant to domain shifts. In this work, we propose LayUP, a new class-prototype-based approach to continual learning that leverages second-order feature statistics from multiple intermediate layers of a pre-trained network. Our method is conceptually simple, does not require any replay buffer, and works out of the box with any foundation model. LayUP improves over the state-of-the-art on four of the seven class-incremental learning settings at a considerably reduced memory and computational footprint compared with the next best baseline. Our results demonstrate that fully exhausting the representational capacities of pre-trained models in CL goes far beyond their final embeddings.

Mosquito-borne diseases present considerable risks to the health of both animals and humans. Aedes aegypti mosquitoes are the primary vectors for numerous medically important viruses such as dengue, Zika, yellow fever, and chikungunya. To characterize this mosquito neural activity, it is essential to classify the generated electrical spikes. However, no open-source neural spike classification method is currently available for mosquitoes. Our work presented in this paper provides an innovative artificial intelligence-based method to classify the neural spikes in uninfected, dengue-infected, and Zika-infected mosquitoes. Aiming for outstanding performance, the method employs a fusion of normalization, feature importance, and dimension reduction for the preprocessing and combines convolutional neural network and extra gradient boosting (XGBoost) for classification. The method uses the electrical spiking activity data of mosquito neurons recorded by microelectrode array technology. We used data from 0, 1, 2, 3, and 7 days post-infection, containing over 15 million samples, to analyze the method's performance. The performance of the proposed method was evaluated using accuracy, precision, recall, and the F1 scores. The results obtained from the method highlight its remarkable performance in differentiating infected vs uninfected mosquito samples, achieving an average of 98.1%. The performance was also compared with 6 other machine learning algorithms to further assess the method's capability. The method outperformed all other machine learning algorithms' performance. Overall, this research serves as an efficient method to classify the neural spikes of Aedes aegypti mosquitoes and can assist in unraveling the complex interactions between pathogens and mosquitoes.

Most of the current speech data augmentation methods operate on either the raw waveform or the amplitude spectrum of speech. In this paper, we propose a novel speech data augmentation method called PhasePerturbation that operates dynamically on the phase spectrum of speech. Instead of statically rotating a phase by a constant degree, PhasePerturbation utilizes three dynamic phase spectrum operations, i.e., a randomization operation, a frequency masking operation, and a temporal masking operation, to enhance the diversity of speech data. We conduct experiments on wav2vec2.0 pre-trained ASR models by fine-tuning them with the PhasePerturbation augmented TIMIT corpus. The experimental results demonstrate 10.9\% relative reduction in the word error rate (WER) compared with the baseline model fine-tuned without any augmentation operation. Furthermore, the proposed method achieves additional improvements (12.9\% and 15.9\%) in WER by complementing the Vocal Tract Length Perturbation (VTLP) and the SpecAug, which are both amplitude spectrum-based augmentation methods. The results highlight the capability of PhasePerturbation to improve the current amplitude spectrum-based augmentation methods.

Inspired by the success of contrastive learning, we systematically examine recommendation losses, including listwise (softmax), pairwise (BPR), and pointwise (MSE and CCL) losses. In this endeavor, we introduce InfoNCE+, an optimized generalization of InfoNCE with balance coefficients, and highlight its performance advantages, particularly when aligned with our new decoupled contrastive loss, MINE+. We also leverage debiased InfoNCE to debias pointwise recommendation loss (CCL) as Debiased CCL. Interestingly, our analysis reveals that linear models like iALS and EASE are inherently debiased. Empirical results demonstrates the effectiveness of MINE+ and Debiased-CCL.

In this paper, we perform a systemic examination of the recommendation losses, including listwise (softmax), pairwise(BPR), and pointwise (mean-squared error, MSE, and Cosine Contrastive Loss, CCL) losses through the lens of contrastive learning. We introduce and study both debiased InfoNCE and mutual information neural estimator (MINE), for the first time, under the recommendation setting. We also relate and differentiate these two losses with the BPR loss through the lower bound analysis. Furthermore, we present the debiased pointwise loss (for both MSE and CCL) and theoretically certify both iALS and EASE, two of the most popular linear models, are inherently debiased. The empirical experimental results demonstrate the effectiveness of the debiased losses and newly introduced mutual-information losses outperform the existing (biased) ones.

The protection of sensitive data becomes more vital, as data increases in value and potency. Furthermore, the pressure increases from regulators and society on model developers to make their Artificial Intelligence (AI) models non-discriminatory. To boot, there is a need for interpretable, transparent AI models for high-stakes tasks. In general, measuring the fairness of any AI model requires the sensitive attributes of the individuals in the dataset, thus raising privacy concerns. In this work, the trade-offs between fairness, privacy and interpretability are further explored. We specifically examine the Statistical Parity (SP) of Decision Trees (DTs) with Differential Privacy (DP), that are each popular methods in their respective subfield. We propose a novel method, dubbed Privacy-Aware Fairness Estimation of Rules (PAFER), that can estimate SP in a DP-aware manner for DTs. DP, making use of a third-party legal entity that securely holds this sensitive data, guarantees privacy by adding noise to the sensitive data. We experimentally compare several DP mechanisms. We show that using the Laplacian mechanism, the method is able to estimate SP with low error while guaranteeing the privacy of the individuals in the dataset with high certainty. We further show experimentally and theoretically that the method performs better for DTs that humans generally find easier to interpret.

Optical coherence tomography (OCT) is a non-invasive imaging technique with extensive clinical applications in ophthalmology. OCT enables the visualization of the retinal layers, playing a vital role in the early detection and monitoring of retinal diseases. OCT uses the principle of light wave interference to create detailed images of the retinal microstructures, making it a valuable tool for diagnosing ocular conditions. This work presents an open-access OCT dataset (OCTDL) comprising over 1600 high-resolution OCT images labeled according to disease group and retinal pathology. The dataset consists of OCT records of patients with Age-related Macular Degeneration (AMD), Diabetic Macular Edema (DME), Epiretinal Membrane (ERM), Retinal Artery Occlusion (RAO), Retinal Vein Occlusion (RVO), and Vitreomacular Interface Disease (VID). The images were acquired with an Optovue Avanti RTVue XR using raster scanning protocols with dynamic scan length and image resolution. Each retinal b-scan was acquired by centering on the fovea and interpreted and cataloged by an experienced retinal specialist. In this work, we applied Deep Learning classification techniques to this new open-access dataset.

In today's digital landscape, journalists urgently require tools to verify the authenticity of facial images and videos depicting specific public figures before incorporating them into news stories. Existing deepfake detectors are not optimized for this detection task when an image is associated with a specific and identifiable individual. This study focuses on the deepfake detection of facial images of individual public figures. We propose to condition the proposed detector on the identity of the identified individual given the advantages revealed by our theory-driven simulations. While most detectors in the literature rely on perceptible or imperceptible artifacts present in deepfake facial images, we demonstrate that the detection performance can be improved by exploiting the idempotency property of neural networks. In our approach, the training process involves double neural-network operations where we pass an authentic image through a deepfake simulating network twice. Experimental results show that the proposed method improves the area under the curve (AUC) from 0.92 to 0.94 and reduces its standard deviation by 17\%. For evaluating the detection performance of individual public figures, a facial image dataset with individuals' names is required, a criterion not met by the current deepfake datasets. To address this, we curated a dataset comprising 32k images featuring 45 public figures, which we intend to release to the public after the paper is published.

This paper proposes a blockchain-secured deep reinforcement learning (BC-DRL) optimization framework for {data management and} resource allocation in decentralized {wireless mobile edge computing (MEC)} networks. In our framework, {we design a low-latency reputation-based proof-of-stake (RPoS) consensus protocol to select highly reliable blockchain-enabled BSs to securely store MEC user requests and prevent data tampering attacks.} {We formulate the MEC resource allocation optimization as a constrained Markov decision process that balances minimum processing latency and denial-of-service (DoS) probability}. {We use the MEC aggregated features as the DRL input to significantly reduce the high-dimensionality input of the remaining service processing time for individual MEC requests. Our designed constrained DRL effectively attains the optimal resource allocations that are adapted to the dynamic DoS requirements. We provide extensive simulation results and analysis to} validate that our BC-DRL framework achieves higher security, reliability, and resource utilization efficiency than benchmark blockchain consensus protocols and {MEC} resource allocation algorithms.