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

Without an integrated model of how the human brain works and processes information, artificial intelligence (AI) will remain a mysterious black box that can misfire as circumstances change. An integrated study of the three cognitive computing components (AI, cognitive psychology, and neurobiology) is necessary to create explainable AI findings. This paper introduces cognitive computing systems (CCS)

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

Immersive platforms, accessed via VR and AR interfaces, offer a profound digital experience but raise significant privacy and ethical concerns. Current systems collect extensive user data; this enables manipulative advertising and behavior control, fosters self-censorship, and diminishes authentic self-expression – especially among marginalized communities. The unchecked development of immersive platforms

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

In this theoretical review, we analyse the IS literature on the rapidly evolving strategic phenomenon of sports digitalisation. We provide insights on how sports digitalisation is currently understood in the IS literature when examined from a stakeholder perspective. Our analysis identifies the key stakeholders involved in sports, the competition stages (i.e., before, during, or after), the nature

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

Adaptive learning systems support students in acquiring complex skills, provided they deliver appropriate instructional support, such as scaffolding. Few studies have examined whether the optimal level of scaffolding depends on the system's support for strategies familiar to the learner—a situation that often arises when students use software developed abroad or aligned with a different curriculum

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

Digital reading with multiple-text comprehension is essential in daily life, especially with the rise of AI-generated contents, making source evaluation crucial for authenticity of online information, particularly in academic contexts. Despite its importance, there is limited moment-to-moment evidence on reading processes in multiple-text use. To bridge this gap, we used eye-tracking technology to

Geospatial data enhances traditional datasets by integrating spatial and temporal dimensions, facilitating advanced visualizations and comprehensive analytical insights. As a fundamental aspect of geospatial analytics, geospatial data clustering (GDC) has become a prominent area of academic research, playing a critical role in theoretical exploration and applied domains. GDC seeks to group geospatial

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

Developing reflective thinking skills (RTS) in pre-service teachers remains a challenge in teacher education, particularly in the context of integrating emerging technologies. While digital storytelling (DST) has shown promise in fostering reflective practice, traditional methods often present technical barriers that hinder deeper reflection. Few studies have explored how generative artificial intelligence

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