Distributed software systems are pervasive today and they are increasingly developed/deployed to meet the growing needs for scalable computing. Given their critical roles in modern information infrastructures, assuring the quality of distributed software is crucial. As a fundamental methodology for software quality assurance in general, program analysis underlies a range of techniques and tools for

With the development of the Large Language Models (LLMs), a large range of LLM-based Text-to-SQL(Text2SQL) methods have emerged. This survey provides a comprehensive review of LLM-based Text2SQL studies. We first enumerate classic benchmarks and evaluation metrics. For the two mainstream methods, prompt engineering and finetuning, we introduce a comprehensive taxonomy and offer practical insights into

Performing software engineering (SE) tasks requires the activation of software developers’ brain neural networks. Electroencephalography (EEG) microstate analysis emerges as a promising neurophysiological method to investigate the spatiotemporal dynamics of brain networks at high temporal resolution. An EEG microstate represents a unique topography of electric potentials over the multichannel EEG records

Graph deep learning methods have become popular tools to process collections of correlated time series. Unlike traditional multivariate forecasting methods, graph-based predictors leverage pairwise relationships by conditioning forecasts on graphs spanning the time series collection. The conditioning takes the form of architectural inductive biases on the forecasting architecture, resulting in a family

Differential privacy (DP), a rigorously quantifiable privacy preservation technique, has found widespread application within the domain of machine learning. As DP techniques are implemented in machine learning algorithms, a significant and intricate trade-off between privacy and utility emerges, garnering extensive attention from researchers. In the pursuit of striking a delicate equilibrium between

This survey addresses the crucial issue of factuality in Large Language Models (LLMs). As LLMs find applications across diverse domains, the reliability and accuracy of their outputs become vital. We define the “factuality issue” as the probability of LLMs to produce content inconsistent with established facts. We first delve into the implications of these inaccuracies. Subsequently, we analyze the

Remaining a dominant force in Internet traffic, video streaming captivates end users, service providers, and researchers. This paper takes a pragmatic approach to reviewing recent advances in the field by focusing on the prevalent streaming paradigm that involves delivering long-form two-dimensional videos over the best-effort Internet with client-side adaptive bitrate (ABR) algorithms and assistance

Over the past decade, the integration of electromyography (EMG) techniques with machine learning has significantly advanced prosthetic device control. Researchers have developed sophisticated deep learning classifiers for gesture recognition and created EMG controllers capable of simultaneous proportional control across multiple degrees of freedom. However, the increasing complexity of these machine

Recommender systems play a pivotal role in helping users navigate a vast selection of products and services. On online platforms, users have the opportunity to share feedback in various modes, such as numerical ratings, textual reviews, and likes/dislikes. Traditional recommendation systems rely on users’ explicit ratings or implicit interactions (e.g., likes, clicks, shares, and saves) to learn user

Unmanned Aerial Vehicles (UAVs) have gained significant attention in recent years for their potential applications in surveillance, monitoring, search and rescue, and mapping. However, efficient and optimal path planning remains a key challenge for UAV navigation. This survey paper reviews various UAV path planning algorithms, encompassing Sampling-Based techniques, Potential Field methods, Bio-Inspired

The literature on machine learning in the context of data streams is vast and growing. This indicates not only an ongoing interest, but also an ongoing need for a synthesis of new developments in this area. Here we reformulate the definitions of supervised data-stream learning, alongside consideration of contemporary concept drift and temporal dependence. Equipped with this, carry out a fresh discussion

With the development of mobile technologies, users can easily access Online social networks (OSNs), consequently, massive contents including personal experiences, observations, or opinions are generated online. These contents are being shared and exchanged in OSNs, which have a significant influence on the minds of people toward politics, societies, economics, etc. In this case, sentiment diffusion

Gaze input offers strong potential for creating intuitive and engaging user interfaces, but remains constrained by inherent limitations in accuracy and precision. Although extensive research has explored gaze-based interaction over the past three decades, a systematic framework that fully captures the diversity of gaze interaction techniques is still lacking. To address this gap, we present a novel

Pain assessment poses unique challenges due to its subjective and multifaceted nature, often requiring the integration of various sensor modalities. This review aims to provide a comprehensive overview of recent research focused specifically on acute pain assessment, with specific attention to: (a) identifying combinations of sensor modalities utilised for pain assessment, (b) exploring methods for

In the ongoing network cybersecurity arms race, the defenders face a significant disadvantage as they must detect and counteract every attack. Conversely, the attacker only needs to succeed once to achieve their goal. To balance the odds, Autonomous Cyber Network Defence (ACND) employs autonomous agents for proactive and intelligent cyber-attack response. This article surveys the state of the art of

Expert Team Formation is the search for gathering a team of experts who are expected to collaboratively work towards accomplishing a given project, a problem that has historically been solved in a variety of ways, including manually in a time-consuming and bias-filled manner, and algorithmically within disciplines like social sciences and management. In the present effort, while providing a taxonomy

Artificial Intelligence (AI) is transforming industries, offering new opportunities to manage and enhance innovation. However, these advancements bring significant challenges for scientists and businesses, with one of the most critical being the ‘trustworthiness” of AI systems. A key requirement of trustworthiness is transparency , closely linked to explicability . Consequently, the exponential growth

Facial expressions convey human emotions and can be categorized into macro-expressions (MaEs) and micro-expressions (MiEs) based on duration and intensity. While MaEs are voluntary and easily recognized, MiEs are involuntary, rapid, and can reveal concealed emotions. The integration of facial expression analysis with Internet-of-Thing (IoT) systems has significant potential across diverse scenarios

This is a comprehensive survey of the applications of causal inference in the Banking, Financial Services and Insurance (BFSI) domain based on 45 papers published from 1992 to 2023. It categorizes papers into (i) Banking and risk management (ii) Finance (covering investment, asset and portfolio management; behavioral finance and time series), (iii) Financial markets and (iv) Insurance. Exploring methods

Skiplists have become prevalent in systems. The main advantages of skiplists are their simplicity and ease of implementation, and the ability to support operations in the same asymptotic complexities as their tree-based counterparts. In this survey, we explore skiplists and their many variants. We highlight many scenarios about how skiplists are useful, and how they fit well in these usage scenarios

Face Recognition Systems (FRS) are critical and essential components for user authentication via biometrics. To name a few, baking, e-Commerce, and border control are entities propelling their progress. These are of immense importance due to their economic and social relevance. FRS widespread usage leads to security vulnerabilities that need to be identified and mitigated. This paper provides a comprehensive

Rapid advancements in Machine Learning (ML) introduce unique maintainability and scalability challenges. Our research addresses the evolving challenges and identifies ML maintainability and scalability solutions by conducting a thorough literature review of over 17,000 papers, ultimately refining our focus to 124 relevant sources that meet our stringent selection criteria. We present a catalogue of

Root cause localization is the process of monitoring system behavior and analyzing fault patterns from behavioral data. It is applicable in software development, network operations, and cloud computing. However, with the advent of microservice architectures and cloud-native technologies, root cause localization becomes an arduous task. Frequent updates in systems result in large-scale data and complex

Time-sensitive networking (TSN) has been recognized as one of the key enabling technologies for Industry 4.0 and has been deployed in many mission- and safety-critical applications e.g., industry automation, automotive and aerospace systems. Given the stringent real-time requirements of these applications, the Time-Aware Shaper (TAS) draws special attention among TSN’s many traffic shapers due to its

Conformal prediction (CP), a distribution-free uncertainty quantification (UQ) framework, reliably provides valid predictive inference for black-box models. CP constructs prediction sets or intervals that contain the true output with a specified probability. However, modern data science’s diverse modalities, along with increasing data and model complexity, challenge traditional CP methods. These developments

Electroencephalography (EEG) is a non-invasive technique to record bioelectrical signals. Integrating supervised deep learning techniques with EEG signals has recently facilitated automatic analysis across diverse EEG-based tasks. However, the label issues of EEG signals have constrained the development of EEG-based deep models. Obtaining EEG annotations is difficult and requires domain experts to

Machine Translation (MT) revolutionizes cross-lingual communication but is prone to errors, necessitating thorough evaluation for enhancement. Translation quality can be assessed by humans and automatic evaluation metrics. Human evaluation, though valuable, is costly and subject to limitations in scalability and consistency. While automated metrics supplement manual evaluations, this field still has

Large Language Models (LLMs) have demonstrated remarkable effectiveness across various domain-specific applications. However, which fundamental capabilities most contribute to their success in different domains remains unclear. This uncertainty complicates LLM evaluation, as existing benchmark-based assessments often fail to capture their real-world performance, where the required capabilities may

Security Application Programming Interfaces (APIs) are crucial for ensuring software security. However, their misuse introduces vulnerabilities, potentially leading to severe data breaches and substantial financial loss. Complex API design, inadequate documentation, and insufficient security training often lead to unintentional misuse by developers. The software security community has devised and evaluated

Causal inference is the process of drawing conclusions about causal relationships between variables using a combination of assumptions, study designs, and estimation strategies. In machine learning, causal inference is crucial for uncovering the mechanisms behind complex systems and making informed decisions. This paper provides a comprehensive overview of using Instrumental Variables (IVs) in causal

The challenge of effectively and efficiently adapting statically pre-trained Large Language Models (LLMs) to ever-evolving data distributions remains predominant. When tailored for specific needs, pre-trained LLMs often suffer from significant performance degradation in previous knowledge domains – a phenomenon known as “catastrophic forgetting” . While extensively studied in the Continual Learning

Audio-visual correlation learning aims to capture and understand natural phenomena between audio and visual data. The rapid growth of Deep Learning propelled the development of proposals that process audio-visual data and can be observed in the number of proposals in the past years. Thus encouraging the development of a comprehensive survey. Besides analyzing the models used in this context, we also

The growing adoption of intelligent Electric Vehicles (EVs) has also created an opportunity for malicious actors to initiate attacks on the EV infrastructure, which can include a number of data exchange protocols across the various entities that are part of the EV charging ecosystem. These protocols possess a range of underlying vulnerabilities that attackers can exploit to disrupt the regular flow

Symbolic regression (SR) is an optimization problem that identifies the most suitable mathematical expression or model to fit the observed dataset. Over the past decade, SR has experienced rapid development due to its interpretability and broad applicability, leading to numerous algorithms for addressing SR problems and a steady increase in practical applications. Given the lack of a comprehensive

Neonatal biometrics, especially those based on fingerprint traits, can potentially improve early childhood identification with decisive applications in healthcare, identity management, and other critical social domains. Although many biometric approaches to human recognition exist, most of them can not be directly applied to neonates. The main barrier is the reduced size of children’s biometric traits

To address the throughput and capacity limitations of single-node centralized deduplication, distributed data deduplication has become a popular technology in big data management to save more storage space, enhance I/O performance, and improve system scalability. It includes inter-node data assignment from clients to multiple deduplication nodes by a data routing scheme, and independent intra-node

In various scientific and engineering domains, optimization problems often feature multiple objectives and sparse optimal solutions, which are commonly known as sparse multi-objective optimization problems (SMOPs). Since many SMOPs are pursued based on large datasets, they involve a large number of decision variables, leading to a huge search space that is challenging to find sparse Pareto optimal

Federated Learning (FL) enables collaborative model training over multiple clients’ data, without sharing these data for better privacy. Addressing bias in FL remains a challenge. In this paper, we first present a taxonomy of FL bias, presenting the causes and the different types of FL bias, namely demographic bias, performance-related bias, and contribution-related bias. We then categorize FL bias

Off-chain networks that support transactions outside of the blockchain can handle large numbers of transactions and relieve the pressure on on-chain storage, showing great potential in mitigating scalability challenges of blockchain. However, since off-chain networks are still in the early stage of development, how to ensure off-chain data security, off-chain trust, off-chain transaction privacy and

Nowadays, cyberattacks are growing at an alarming rate, causing widespread havoc to the digital community. In particular, authentication attacks have become a dominant attack vector, allowing intruders to impersonate legitimate users and maliciously access resources. Traditional single-factor authentication (SFA) protocols, which rely on a single authentication factor are often insufficient to address

Intelligent transportation systems are vital for modern traffic management and optimization, greatly improving traffic efficiency and safety. With the rapid development of generative artificial intelligence (Generative AI) technologies in areas like image generation and natural language processing, generative AI has also played a crucial role in addressing key issues in intelligent transportation systems

Bug localization is a crucial aspect of software maintenance, running through the entire software lifecycle. Information retrieval-based bug localization (IRBL) identifies buggy code based on bug reports, expediting the bug resolution process for developers. Recent years have witnessed significant achievements in IRBL, propelled by the widespread adoption of deep learning (DL). To provide a comprehensive

The continuous growth and widespread use of digital technologies has made the protection of personal data and individual rights become one of the major concerns when developing software systems and applications. Furthermore, data protection regulations and privacy standards have imposed additional responsibilities on software engineers. Failure to achieve these privacy and regulatory requirements can

The CPU architecture landscape is constantly evolving to optimize performance. However, this has inadvertently exposed vulnerabilities such as microarchitectural traces that can be exploited in side-channel attacks. The Branch Prediction Unit (BPU) plays a critical role in improving processor performance, but also introduces vulnerabilities to microarchitectural side-channel attacks. Despite ongoing

Wearable technologies have become a significant part of the healthcare industry, collecting personal health data and extracting valuable information for real-time assistance. This review paper analyzes thirty-five scientific publications on driving healthcare monitoring with IoT and wearable device applications. These papers were considered in a quantitative and qualitative analysis using the Natural

Task planning and scheduling are crucial for construction or fabrication (CF) processes. Automating them is necessary for more efficient plans in terms of time and resources. However, most construction planning processes are still performed manually despite the existence of various AI methods. Symbolic AI automated task planning (ATP) techniques offer a variety of features to tackle task planning problems

Error-bounded lossy compression has been effective in significantly reducing the data storage/transfer burden while preserving the reconstructed data fidelity very well. Many error-bounded lossy compressors have been developed for a wide range of parallel and distributed use cases for years. They are designed with distinct compression models and principles, such that each of them features particular

Reinforcement Learning (RL) has experienced significant advancement over the past decade, prompting a growing interest in applications within finance. This survey critically evaluates 167 publications, exploring diverse RL applications and frameworks in finance. Financial markets, marked by their complexity, multi-agent nature, information asymmetry, and inherent randomness, serve as an intriguing

Contention resolution addresses the challenge of coordinating access by multiple processes to a shared resource such as memory, disk storage, or a communication channel. Originally spurred by challenges in database systems and bus networks, contention resolution has endured as an important abstraction for resource sharing, despite decades of technological change. Here, we survey the literature on resolving

Prerequisite relation (PR) learning is a fundamental task in educational technology that identifies dependencies between learning resources to facilitate personalized learning experiences and optimize educational content organization. This survey provides a systematic review of prerequisite relation learning, emphasizing both methodological advances and practical applications. We first explore two

Reliably identifying and verifying subjects remains integral to computer system security. Various novel authentication techniques, such as biometric authentication systems, have been developed in recent years. This paper provides a detailed review of keystroke-based authentication systems and their applications. Keystroke dynamics is a behavioral biometric that is emerging as an important tool for

The proliferation of the Internet of Things (IoT) has reshaped the way we interact with technology, propelling the Routing Protocol for Low-Power and Lossy Networks (RPL) into a critical role as a communication framework. Amid this transformative landscape, security vulnerabilities within RPL-based IoT networks emerge as a substantial concern. This survey delves into these vulnerabilities, offering

In recent years, graph representation learning has achieved remarkable success while suffering from low-quality data problems. As a mature technology to improve data quality in computer vision, data augmentation has also attracted increasing attention in graph domain. To advance research in this emerging direction, this survey provides a comprehensive review and summary of existing graph data augmentation

The evolution of cybersecurity is closelynked to the development and improvement of artificial intelligence (AI). As a key tool for realizing more cybersecure ecosystems, Intrusion Detection Systems (IDSs) have evolved tremendously in recent years by integrating machine learning (ML) techniques to detect increasingly sophisticated cybersecurity attacks hidden in big data. However, traditional approaches

Material defects (MD) represent a primary challenge affecting product performance and giving rise to safety issues in related products. The rapid and accurate identification and localization of MD constitute crucial research endeavors in addressing contemporary challenges associated with MD. In recent years, propelled by the swift advancement of machine learning (ML) technologies, particularly exemplified

Secure communication makes the widespread use of telecommunication networks and services possible. With the constant progress of computing and mathematics, new cryptographic methods are being diligently developed. Quantum Key Distribution (QKD) is a promising technology that provides an Information-Theoretically Secure (ITS) solution to the secret-key agreement problem between two remote parties. QKD

In recent years, deep learning has made significant strides, especially in computer vision applications and, more specifically, in information forensics. On the other hand, data-driven approaches have shown much promise in identifying manipulations in images and videos. However, most forensic tools ignore deep learning in favour of more traditional methodologies. This article thoroughly analyses the

The rising interest in creating versatile robots to handle multiple tasks in various environments, with humans interacting through immersive interfaces. This survey provides a comprehensive review of extended reality (XR) applications in remote human-robot interaction (HRI). We developed a systematic search strategy based on the PRISMA methodology, focusing on peer-reviewed publications that demonstrate

Text summarization research has undergone several significant transformations with the advent of deep neural networks, pre-trained language models (PLMs), and recent large language models (LLMs). This survey thus provides a comprehensive review of the research progress and evolution in text summarization through the lens of these paradigm shifts. It is organized into two main parts: (1) a detailed

Programming Language Virtual Machines (VMs) are composed of several components that together execute and manage languages efficiently. They are deployed in virtually all computing systems through modern web browsers. However, vulnerabilities in any VM component pose a significant threat to security and privacy. In this paper, we present a survey of software attacks on Just-In-Time (JIT) compilers,