Contrastive learning is a primary approach to mitigating data sparsity in sequential recommendation, but its improvements on the item side are limited due to data augmentation impairing user representations. To address these challenges, this paper introduces the Global Graph Attention for Contrastive Sequential Recommendation (GGACSR). GGACSR integrates graph neural networks and self-attention mechanisms, with an Attention Convolution Layer replacing nonlinear transformations in Graph Convolutional Networks (GCNs) with Q, K, and V vector operations, facilitating better handling of sequence dependencies. Leveraging a global user connection graph and projecting embeddings into a lower dimension effectively improves item and user representations. Overall, GGACSR outperforms existing baselines on three public datasets by more accurately capturing complex relationships and adapting user preferences.

Recently, Large Language Models (LLMs) have shown remarkable success across a variety of tasks, with rapid advancements in supporting multilingual capabilities. However, these models exhibit varying degrees of demographic biases in text classification tasks. Most existing research focuses on debiasing pre-trained models or addressing biases in monolingual text classification, resulting in limited exploration in multilingual contexts. To solve the above problems, this paper introduces a tWo-stAge in-conText learning dEbiasing fRamework (WATER). Our approach does not require updating the model's parameters and is adaptable to any language. It includes three key modules: sample selection, sample filtering, and template filling and prediction. In the first stage, we leverage a sample selection module to identify text that closely matches the model embeddings. In the second stage, we introduce an innovative Contextual Disparity Measure (CDM) in the sample filtering module to filter out samples that effectively address the bias associated with specific attributes. Finally, the template filling and prediction module is used to fill the selected samples into the template and input them into the model to complete the multilingual text classification task. Our experimental results verify the effectiveness of our method in mitigating biases related to four sensitive attributes of gender, age, race, and country, demonstrating its potential to improve the fairness and accuracy of LLMs in multilingual classification tasks.

This paper presents a novel approach to path planning for multi-platform bearings-only tracking of a target in the presence of epistemic detection uncertainty. Instead of using the traditional probabilistic framework, we propose a solution with a coordinated intelligent sensor platform motion control strategy in the possibilistic framework, offering a viable and robust alternative for improved tracking performance. We use track fusion of possibilistic Bernoulli filter implemented with Gaussian-max models to integrate information gathered by multiple platforms. The reward function for intelligent platform motion control is constructed using the concept of possibilistic entropy. The tracking performance of the proposed solution is evaluated using selected metrics via simulations.

Abstract Data augmentation-based contrastive learning has been successfully employed in Variational AutoEncoders sequence recommendation systems to tackle the issue of data sparsity. Nevertheless, this strategy is generally less advantageous for tail users. The prospective transmission of information from head-to-tail users to alleviate long-tail impact is encouraging. However, data augmentation distorts the original sequence and embeds stochastic noise into latent variables, impeding the decoder’s capacity to accurately identify the user’s true preferences. In addition, contrastive learning seeks to achieve consistency in the latent variables of both the original and augmented data. However, the presence of noise in the augmented data might hamper the encoding of latent variables from the original data, especially impacting head users. In order to address these challenges, this work introduces a new sequence recommendation model called the Filter-enhanced Contrastive Variational Autoencoder (FeCVAE). It employs Fourier filters and adversarial attack training to minimize the impact of stochastic noise, thereby improving the quality of latent variables and facilitating more accurate decoder outputs. Moreover, a user enhancer is introduced to leverage knowledge from head users to empower tail users, thereby alleviating the long-tail effect. The efficacy of FeCVAE is demonstrated through comprehensive experiments across four benchmark datasets.

The paper formulates the solution to area search for targets in the framework of possibility theory. The rationale is that the required measurement model parameters, such as the probability of detection and/or the probability of false alarm, are rarely known as precise values. Possibility theory was developed for quantitative modelling of and reasoning with epistemic uncertainty. It provides an elegant Bayesian like solution to target area search. A reward function is proposed as an uncertainty measure which takes into account the epistemic uncertainty. The robustness of the proposed search algorithm is demonstrated by numerical results.

Sequential recommendation plays a key role in recommender systems, where the goal is to predict a user’s future points of interest by analyzing his or her historical interactions. This process not only requires the system to be able to accurately identify and recommend items that are likely to be of interest to the user but also ensures that all items receive equal exposure to prevent over-concentration or marginalization of items due to algorithmic bias. To address these challenges, in this paper, we propose a novel Adversarial Debiasing framework based on Sinusoidal Embedding for sequential recommendation, ADSE. This framework employs sinusoidal position embeddings to extract positional information between sequences more precisely and utilizes a dropout strategy to optimize the handling of cold-start sequences, aiming to resolve the cold-start issue while maintaining the semantics of the original sequences. Additionally, adversarial training was incorporated to reduce implicit bias due to assuming interactions in the calculation of exposure.

An extended object in target tracking refers to the object which produces a time-varying number of noisy detections (measurements) from its scattering or feature points. The optimal sequential Bayesian state estimator for an appearing/disappearing extended object in the presence of false and missed detections is known as the Bernoulli Filter Ext (BF-X) [1]. Bayesian estimation methods rely on probabilistic models. When probabilistic models are known only partially or imprecisely, quantitative modeling of uncertainty can be carried out using possibility functions. This paper formulates the analog of the BF-X in the framework of possibility theory, where uncertainty is represented using possibility functions, rather than probability distributions. Possibility functions have the capacity to model with integrity the partial or imprecise probabilistic specifications and thus the proposed possibilistic BF-X is characterised by an enhanced robustness in the absence of precise measurement or dynamic models.