Machine learning systems are known to be sensitive In recent years, spurious correlations have been studied under to spurious correlations between nonessential various names, such as shortcuts, dataset biases, group features of the inputs (e.g., background, robustness, simplicity bias, and so on. We have seen significant texture, and secondary objects) and the corresponding progress in analyzing and mitigating spurious correlations labels. These features and their correlations in various areas such as computer vision (Wang et al., with the labels are known as "spurious" 2021), natural language processing (Du et al., 2022b), and because they tend to change with shifts in realworld healthcare (Huang et al., 2022). Despite the progress, there data distributions, which can negatively impact lacks a survey in this area that formally defines spurious correlations the model's generalization and robustness.

Deep learning, a branch of artificial intelligence, is a computational model that uses multiple layers of interconnected units (neurons) to learn intricate patterns and representations directly from raw input data. Empowered by this learning capability, it has become a powerful tool for solving complex problems and is the core driver of many groundbreaking technologies and innovations. Building a deep learning model is a challenging task due to the algorithm`s complexity and the dynamic nature of real-world problems. Several studies have reviewed deep learning concepts and applications. However, the studies mostly focused on the types of deep learning models and convolutional neural network architectures, offering limited coverage of the state-of-the-art of deep learning models and their applications in solving complex problems across different domains. Therefore, motivated by the limitations, this study aims to comprehensively review the state-of-the-art deep learning models in computer vision, natural language processing, time series analysis and pervasive computing. We highlight the key features of the models and their effectiveness in solving the problems within each domain. Furthermore, this study presents the fundamentals of deep learning, various deep learning model types and prominent convolutional neural network architectures. Finally, challenges and future directions in deep learning research are discussed to offer a broader perspective for future researchers.

We propose a new paradigm for machine translation that is particularly useful for no-resource languages (those without any publicly available bilingual or monolingual corpora): LLM-RBMT (LLM-Assisted Rule Based Machine Translation). Using the LLM-RBMT paradigm, we design the first language education/revitalization-oriented machine translator for Owens Valley Paiute (OVP), a critically endangered Indigenous American language for which there is virtually no publicly available data. We present a detailed evaluation of the translator's components: a rule-based sentence builder, an OVP to English translator, and an English to OVP translator. We also discuss the potential of the paradigm, its limitations, and the many avenues for future research that it opens up.

The advent of transformers with attention mechanisms and associated pre-trained models have revolutionized the field of Natural Language Processing (NLP). However, such models are resource-intensive due to highly complex architecture. This limits their application to resource-constrained environments. While choosing an appropriate NLP model, a major trade-off exists over choosing accuracy over efficiency and vice versa. This paper presents a commentary on the evolution of NLP and its applications with emphasis on their accuracy as-well-as efficiency. Following this, a survey of research contributions towards enhancing the efficiency of transformer-based models at various stages of model development along with hardware considerations has been conducted. The goal of this survey is to determine how current NLP techniques contribute towards a sustainable society and to establish a foundation for future research.

Automatic programming has seen increasing popularity due to the emergence of tools like GitHub Copilot which rely on Large Language Models (LLMs). At the same time, automatically generated code faces challenges during deployment due to concerns around quality and trust. In this article, we study automated coding in a general sense and study the concerns around code quality, security and related issues of programmer responsibility. These are key issues for organizations while deciding on the usage of automatically generated code. We discuss how advances in software engineering such as program repair and analysis can enable automatic programming. We conclude with a forward looking view, focusing on the programming environment of the near future, where programmers may need to switch to different roles to fully utilize the power of automatic programming. Automated repair of automatically generated programs from LLMs, can help produce higher assurance code from LLMs, along with evidence of assurance

With the widespread use of surveillance cameras in smart cities [104] and the boom of online video applications powered by 4/5G communication technologies, traditional human inspection is no longer able to accurately monitor the video data generated around the clock, which is not only time-consuming and labor-intensive but also poses the risk of leaking important information (e.g., biometrics and sensitive speech). In contrast, VAD-empowered IoVT applications [54], such as Intelligent Surveillance Systems (IVSS) and automated content analysis platforms, can process massive video streams online and detect events of interest in real-time, sending only noteworthy anomaly parts for human review, significantly reducing data storage and communication costs, and helping to eliminate public concerns about data security and privacy protection. As a result, VAD has gained widespread attention in academia and industry over the last decade and has been used in emerging fields such as information forensics [154], industrial manufacturing [71] in smart cities as well as online content analysis in mobile video applications [153]. VAD extends the data scope of conventional Anomaly Detection (AD) from time series, images, and graphs to video, which not only needs to cope with the endogenous data complexity, but also needs to take into account the computational and communication costs in resource-limited devices [55]. Specifically, the inherent high-dimensional structure of video data, high information density and redundancy, heterogeneity of temporal and spatial patterns, and feature entanglement between foreground targets and background scenes make VAD more challenging than traditional AD tasks at the levels of representation learning and anomaly discrimination [89]. Existing studies [4, 60, 69, 76] have shown that high-performance VAD models need to target the modeling of appearance and motion information, i.e., the difference between regular events and anomalous examples in both spatial and temporal dimensions. In contrast to time series AD that mainly measures periodic temporal patterns of variables, and image AD which only focusing on spatial contextual deviations, VAD needs to extract both discriminative spatial and temporal features from a large amount of redundant information (e.g., repetitive temporal contexts and label-independent data distributions), as well as to learn the differences between normal and anomalous events in terms of their local appearances and global motions [100]. However, video anomalies are ambiguous and subjective [48].

Information Retrieval (IR) systems are crucial tools for users to access information, widely applied in scenarios like search engines, question answering, and recommendation systems. Traditional IR methods, based on similarity matching to return ranked lists of documents, have been reliable means of information acquisition, dominating the IR field for years. With the advancement of pre-trained language models, generative information retrieval (GenIR) has emerged as a novel paradigm, gaining increasing attention in recent years. Currently, research in GenIR can be categorized into two aspects: generative document retrieval (GR) and reliable response generation. GR leverages the generative model's parameters for memorizing documents, enabling retrieval by directly generating relevant document identifiers without explicit indexing. Reliable response generation, on the other hand, employs language models to directly generate the information users seek, breaking the limitations of traditional IR in terms of document granularity and relevance matching, offering more flexibility, efficiency, and creativity, thus better meeting practical needs. This paper aims to systematically review the latest research progress in GenIR. We will summarize the advancements in GR regarding model training, document identifier, incremental learning, downstream tasks adaptation, multi-modal GR and generative recommendation, as well as progress in reliable response generation in aspects of internal knowledge memorization, external knowledge augmentation, generating response with citations and personal information assistant. We also review the evaluation, challenges and future prospects in GenIR systems. This review aims to offer a comprehensive reference for researchers in the GenIR field, encouraging further development in this area.

Federated learning (FL) enables collaborative model training while preserving data privacy, making it suitable for decentralized human-centered AI applications. However, a significant research gap remains in ensuring fairness in these systems. Current fairness strategies in FL require knowledge of bias-creating/sensitive attributes, clashing with FL's privacy principles. Moreover, in human-centered datasets, sensitive attributes may remain latent. To tackle these challenges, we present a novel bias mitigation approach inspired by "Fairness without Demographics" in machine learning. The presented approach achieves fairness without needing knowledge of sensitive attributes by minimizing the top eigenvalue of the Hessian matrix during training, ensuring equitable loss landscapes across FL participants. Notably, we introduce a novel FL aggregation scheme that promotes participating models based on error rates and loss landscape curvature attributes, fostering fairness across the FL system. This work represents the first approach to attaining "Fairness without Demographics" in human-centered FL. Through comprehensive evaluation, our approach demonstrates effectiveness in balancing fairness and efficacy across various real-world applications, FL setups, and scenarios involving single and multiple bias-inducing factors, representing a significant advancement in human-centered FL.

Generative artificial intelligence (AI) technologies and large models are producing realistic outputs across various domains, such as images, text, speech, and music. Creating these advanced generative models requires significant resources, particularly large and high-quality datasets. To minimize training expenses, many algorithm developers use data created by the models themselves as a cost-effective training solution. However, not all synthetic data effectively improve model performance, necessitating a strategic balance in the use of real versus synthetic data to optimize outcomes. Currently, the previously well-controlled integration of real and synthetic data is becoming uncontrollable. The widespread and unregulated dissemination of synthetic data online leads to the contamination of datasets traditionally compiled through web scraping, now mixed with unlabeled synthetic data. This trend portends a future where generative AI systems may increasingly rely blindly on consuming self-generated data, raising concerns about model performance and ethical issues. What will happen if generative AI continuously consumes itself without discernment? What measures can we take to mitigate the potential adverse effects? There is a significant gap in the scientific literature regarding the impact of synthetic data use in generative AI, particularly in terms of the fusion of multimodal information. To address this research gap, this review investigates the consequences of integrating synthetic data blindly on training generative AI on both image and text modalities and explores strategies to mitigate these effects. The goal is to offer a comprehensive view of synthetic data's role, advocating for a balanced approach to its use and exploring practices that promote the sustainable development of generative AI technologies in the era of large models.

This paper focuses on the integration of generative techniques into spatial-temporal data mining, considering the significant growth and diverse nature of spatial-temporal data. With the advancements in RNNs, CNNs, and other non-generative techniques, researchers have explored their application in capturing temporal and spatial dependencies within spatial-temporal data. However, the emergence of generative techniques such as LLMs, SSL, Seq2Seq and diffusion models has opened up new possibilities for enhancing spatial-temporal data mining further. The paper provides a comprehensive analysis of generative technique-based spatial-temporal methods and introduces a standardized framework specifically designed for the spatial-temporal data mining pipeline. By offering a detailed review and a novel taxonomy of spatial-temporal methodology utilizing generative techniques, the paper enables a deeper understanding of the various techniques employed in this field. Furthermore, the paper highlights promising future research directions, urging researchers to delve deeper into spatial-temporal data mining. It emphasizes the need to explore untapped opportunities and push the boundaries of knowledge to unlock new insights and improve the effectiveness and efficiency of spatial-temporal data mining. By integrating generative techniques and providing a standardized framework, the paper contributes to advancing the field and encourages researchers to explore the vast potential of generative techniques in spatial-temporal data mining.