这些全面、易读的调查和辅导论文提供了文献导览，并向那些寻求以一种易于理解的方式学习其专业以外领域基础知识的人解释主题。计算调查 (CSUR) 中精心策划和介绍的介绍也是研究人员和专业人士发展观点和识别复杂技术趋势的绝佳方式。以新颖有趣的方式将现有和新兴技术（如机器学习）与各种科学和工程领域联系起来的贡献也受到欢迎。

ACM Computing Surveys is a journal that offers comprehensive and accessible survey and tutorial papers. These papers provide literature overviews and explain foundational knowledge in various fields in an easy-to-understand manner, making them ideal for those looking to broaden their expertise. The carefully curated introductions in Computing Surveys (CSUR) help researchers and professionals develop insights and identify complex technological trends. Contributions that connect existing and emerging technologies, such as machine learning, with various scientific and engineering domains in novel and engaging ways are also welcomed.

《ACM Computing Surveys》是一本提供全面、易读的调查和指导论文的期刊。这些论文不仅为读者提供文献导览，还以易于理解的方式解释了各个领域的基础知识，特别适合那些希望拓宽专业知识的人士。计算调查 (CSUR) 中精心策划的介绍部分，帮助研究人员和专业人士发展观点，识别复杂的技术趋势。此外，期刊也欢迎以新颖有趣的方式将现有和新兴技术（如机器学习）与各种科学和工程领域相结合的贡献。

ACM Computing Surveys

As blockchain technology continues to advance, the secure deployment of smart contracts has become increasingly prevalent, underscoring the critical need for robust security measures. This surge in usage has led to a rise in security breaches, often resulting in substantial financial losses for users. This paper presents a comprehensive survey of smart contract quality assurance, from understanding vulnerabilities to evaluating the effectiveness of detection tools. Our work is notable for its innovative classification of forty smart contract vulnerabilities, mapping them to established attack patterns. We further examine nine defense mechanisms, assessing their efficacy in mitigating smart contract attacks. Furthermore, we develop a labeled dataset as a benchmark encompassing ten common vulnerability types, which serves as a critical resource for future research. We also conduct comprehensive experiments to evaluate fourteen vulnerability detection tools, providing a comparative analysis that highlights their strengths and limitations. In summary, this survey synthesizes state-of-the-art knowledge in smart contract security, offering practical recommendations to guide future research and foster the development of robust security practices in the field.

Zhiyuan Wei

Jing Sun

Zijian Zhang

Xianhao Zhang

Xiaoxuan Yang

Liehuang Zhu

Survey on Quality Assurance of Smart Contracts

智能合约质量保障调查

This work considers the question of how convenient access to copious data impacts our ability to learn causal effects and relations. In what ways is learning causality in the era of big data different from -- or the same as -- the traditional one? To answer this question, this survey provides a comprehensive and structured review of both traditional and frontier methods in learning causality and relations along with the connections between causality and machine learning. This work points out on a case-by-case basis how big data facilitates, complicates, or motivates each approach.

Ruocheng Guo

Lu Cheng

Jundong Li

P. Richard Hahn

Huan Liu

ACM Comput. Surv.

A Survey of Learning Causality with Data

数据驱动的因果关系学习综述

Large, pre-trained language models (PLMs) such as BERT and GPT have drastically changed the Natural Language Processing (NLP) field. For numerous NLP tasks, approaches leveraging PLMs have achieved state-of-the-art performance. The key idea is to learn a generic, latent representation of language from a generic task once, then share it across disparate NLP tasks. Language modeling serves as the generic task, one with abundant self-supervised text available for extensive training. This article presents the key fundamental concepts of PLM architectures and a comprehensive view of the shift to PLM-driven NLP techniques. It surveys work applying the pre-training then fine-tuning, prompting, and text generation approaches. In addition, it discusses PLM limitations and suggested directions for future research.

Bonan Min

Hayley Ross

Elior Sulem

Amir Pouran Ben Veyseh

Thien Huu Nguyen

Oscar Sainz

Eneko Agirre

Ilana Heintz

Dan Roth

Recent Advances in Natural Language Processing via Large Pre-trained Language Models: A Survey

基于大规模预训练语言模型的自然语言处理最新进展：综述

This article surveys and organizes research works in a new paradigm in natural language processing, which we dub “prompt-based learning.” Unlike traditional supervised learning, which trains a model to take in an inputxand predict an outputyasP(y|x), prompt-based learning is based on language models that model the probability of text directly. To use these models to perform prediction tasks, the original inputxis modified using atemplateinto a textual stringpromptx′that has some unfilled slots, and then the language model is used to probabilistically fill the unfilled information to obtain a final stringx̂, from which the final outputycan be derived. This framework is powerful and attractive for a number of reasons: It allows the language model to bepre-trainedon massive amounts of raw text, and by defining a new prompting function the model is able to performfew-shotor evenzero-shotlearning, adapting to new scenarios with few or no labeled data. In this article, we introduce the basics of this promising paradigm, describe a unified set of mathematical notations that can cover a wide variety of existing work, and organize existing work along several dimensions, e.g., the choice of pre-trained language models, prompts, and tuning strategies. To make the field more accessible to interested beginners, we not only make a systematic review of existing works and a highly structured typology of prompt-based concepts but also release other resources, e.g., a websiteNLPedia–Pretrainincluding constantly updated survey and paperlist.

Pengfei Liu

Weizhe Yuan

Jinlan Fu

Zhengbao Jiang

Hiroaki Hayashi

Graham Neubig

Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing

预训练、提示与预测：自然语言处理中提示方法的系统综述

Fine-tuning visual models has been widely shown promising performance on many downstream visual tasks. With the surprising development of pre-trained visual foundation models, visual tuning jumped out of the standard modus operandi that fine-tunes the whole pre-trained model or just the fully connected layer. Instead, recent advances can achieve superior performance than full-tuning the whole pre-trained parameters by updating far fewer parameters, enabling edge devices and downstream applications to reuse the increasingly large foundation models deployed on the cloud. With the aim of helping researchers get the full picture and future directions of visual tuning, this survey characterizes a large and thoughtful selection of recent works, providing a systematic and comprehensive overview of existing work and models. Specifically, it provides a detailed background of visual tuning and categorizes recent visual tuning techniques into five groups: fine-tuning, prompt tuning, adapter tuning, parameter tuning, and remapping tuning. Meanwhile, it offers some exciting research directions for prospective pre-training and various interactions in visual tuning.

Bruce X.B. Yu

Jianlong Chang

Haixin Wang

Lingbo Liu

Shijie Wang

Zhiyu Wang

Junfan Lin

Lingxi Xie

Haojie Li

Zhouchen Lin

Qi Tian

Chang Wen Chen

Visual Tuning

视觉调谐

The entire Image Signal Processor (ISP) of a camera relies on several processes to transform the data from the Color Filter Array (CFA) sensor, such as demosaicing, denoising, and enhancement. These processes can be executed either by some hardware or via software. In recent years, Deep Learning(DL) has emerged as one solution for some of them or even to replace the entire ISP using a single neural network for the task. In this work, we investigated several recent pieces of research in this area and provide deeper analysis and comparison among them, including results and possible points of improvement for future researchers.

Claudio Filipi Goncalves dos Santos

Rodrigo Reis Arrais

Jhessica Victoria Santos da Silva

Matheus Henrique Marques da Silva

Wladimir Barroso Guedes de Araujo Neto

Leonardo Tadeu Lopes

Guilherme Augusto Bileki

Iago Oliveira Lima

Lucas Borges Rondon

Bruno Melo de Souza

Mayara Costa Regazio

Rodolfo Coelho Dalapicola

Arthur Alves Tasca

ISP Meets Deep Learning: A Survey on Deep Learning Methods for Image Signal Processing

ISP与深度学习：图像信号处理深度学习方法综述

Recommender systems (RS) play an integral role in many online platforms. Exponential growth and potential commercial interests are raising significant concerns around privacy, security, fairness, and overall responsibility. The existing literature around responsible recommendation services is diverse and multi-disciplinary. Most literature reviews cover a specific aspect or a single technology for responsible behavior, such as federated learning or blockchain. This study integrates relevant concepts across disciplines to provide a broader representation of the landscape. We review the latest advancements toward building privacy-preserved and responsible recommendation services for the e-commerce industry. The survey summarizes recent, high-impact works on diverse aspects and technologies that ensure responsible behavior in RS through an interconnected taxonomy. We contextualize potential privacy threats, practical significance, industrial expectations, and research remedies. From the technical viewpoint, we analyze conventional privacy defenses and provide an overview of emerging technologies including differential privacy, federated learning, and blockchain. The methods and concepts across technologies are linked based on their objectives, challenges, and future directions. In addition, we also develop an open-source repository that summarizes a wide range of evaluation benchmarks, codebases, and toolkits to aid the further research. The survey offers a holistic perspective on this rapidly evolving landscape by synthesizing insights from both recommender systems and responsible AI literature.

Waqar Ali

Xiangmin Zhou

Jie Shao

Privacy-preserved and Responsible Recommenders: From Conventional Defense to Federated Learning and Blockchain

隐私保护与负责任的推荐系统：从传统防御到联邦学习与区块链

Artificial intelligence (AI), and especially its sub-field of Machine Learning (ML), are impacting the daily lives of everyone with their ubiquitous applications. In recent years, AI researchers and practitioners have introduced principles and guidelines to build systems that make reliable and trustworthy decisions. From a practical perspective, conventional ML systems process historical data to extract the features that are consequently used to train ML models that perform the desired task. However, in practice, a fundamental challenge arises when the system needs to be operationalized and deployed to evolve and operate in real-life environments continuously. To address this challenge, Machine Learning Operations (MLOps) have emerged as a potential recipe for standardizing ML solutions in deployment. Although MLOps demonstrated great success in streamlining ML processes, thoroughly defining the specifications of robust MLOps approaches remains of great interest to researchers and practitioners. In this paper, we provide a comprehensive overview of the trustworthiness property of MLOps systems. Specifically, we highlight technical practices to achieve robust MLOps systems. In addition, we survey the existing research approaches that address the robustness aspects of ML systems in production. We also review the tools and software available to build MLOps systems and summarize their support to handle the robustness aspects. Finally, we present the open challenges and propose possible future directions and opportunities within this emerging field. The aim of this paper is to provide researchers and practitioners working on practical AI applications with a comprehensive view to adopt robust ML solutions in production environments.

Firas Bayram

Bestoun S. Ahmed

Towards Trustworthy Machine Learning in Production: An Overview of the Robustness in MLOps Approach

面向生产环境可信机器学习的研究：MLOps方法中鲁棒性概述

Multi-Agent Reinforcement Learning (MARL) is susceptible to Adversarial Machine Learning (AML) attacks. Execution-time AML attacks against MARL are complex due to effects that propagate across time and between agents. To understand the interaction between AML and MARL, this survey covers attacks and defences for MARL, Multi-Agent Learning (MAL), and Deep Reinforcement Learning (DRL). This survey proposes a novel perspective on AML attacks based on attack vectors. This survey also proposes a framework that addresses gaps in current modelling frameworks and enables the comparison of different attacks against MARL. Lastly, the survey identifies knowledge gaps and future avenues of research.

Maxwell Standen

Junae Kim

Claudia Szabo

Adversarial Machine Learning Attacks and Defences in Multi-Agent Reinforcement Learning

多智能体强化学习中的对抗机器学习攻击与防御

Procedural content generation (PCG) can be applied to a wide variety of tasks in games, from narratives, levels and sounds, to trees and weapons. A large amount of game content is comprised of graphical assets , such as clouds, buildings or vegetation, that do not require gameplay function considerations. There is also a breadth of literature examining the procedural generation of such elements for purposes outside of games. The body of research, focused on specific methods for generating specific assets, provides a narrow view of the available possibilities. Hence, it is difficult to have a clear picture of all approaches and possibilities, with no guide for interested parties to discover possible methods and approaches for their needs, and no facility to guide them through each technique or approach to map out the process of using them. Therefore, a systematic literature review has been conducted, yielding 239 accepted papers. This paper explores state-of-the-art approaches to graphical asset generation, examining research from a wide range of applications, inside and outside of games. Informed by the literature, a conceptual framework has been derived to address the aforementioned gaps.

Kaisei Fukaya

Damon Daylamani-Zad

Harry Agius

Intelligent Generation of Graphical Game Assets: A Conceptual Framework and Systematic Review of the State of the Art

图形游戏资产的智能生成：概念框架与研究现状的系统综述

Abbreviation	ACM Comput. Surv.
Journal Impact	23.97
Quartiles(Global)	COMPUTER SCIENCE, THEORY & METHODS(Q1)

ISSN	0360-0300, 1557-7341
h-index	213
Top Journals	Yes

Publication Information	Publisher: Association for Computing Machinery (ACM)，Publishing cycle: Quarterly，Journal Type: journal，Open Access Journals: No
Basic data	Year of publication: 1969，Proportion of original research papers: 84.27%，Self Citation Rate:2.10%， Gold OA Rate: 3.00%
Average review cycle	网友分享经验：>12周，或约稿
Average recruitment ratio	网友分享经验：较难

ACM Computing Surveys

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