Liqiang Nie
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- IEEE Transactions on Multimedia (29)
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- MM '23: Proceedings of the 31st ACM International Conference on Multimedia (15)
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- SIGIR '18: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval (5)
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- SIGIR '21: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (4)
- IJCAI'18: Proceedings of the 27th International Joint Conference on Artificial Intelligence (3)
- MM '18: Proceedings of the 26th ACM international conference on Multimedia (3)
- SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (3)
- SIGIR '24: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (3)
- ICMR '15: Proceedings of the 5th ACM on International Conference on Multimedia Retrieval (2)
- MM '15: Proceedings of the 23rd ACM international conference on Multimedia (2)
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- research-articlefreePublished By ACMPublished By ACM
Efficient and Effective Role Player: A Compact Knowledge-grounded Persona-based Dialogue Model Enhanced by LLM Distillation
- Linmei Hu
Beijing Institute of Technology, China
, - Xinyu Zhang
Beijing Institute of Technology, China
, - Dandan Song
Beijing Institute of Technology, China
, - Changzhi Zhou
Beijing Institute of Technology, China
, - Hongyu He
Beijing University of Posts and Telecommunications, China
, - Liqiang Nie
Harbin Institute of Technology (Shenzhen), China
ACM Transactions on Information Systems, Volume 0, Issue ja • https://doi.org/10.1145/3711857Incorporating explicit personas into dialogue models is critical for generating responses that fulfill specific user needs and preferences, creating a more personalized and engaging interaction. Early works on persona-based dialogue generation directly ...
- 0Citation
- 88
- Downloads
MetricsTotal Citations0Total Downloads88Last 12 Months88Last 6 weeks88
- Linmei Hu
- research-article
Breaking barriers of system heterogeneity: straggler-tolerant multimodal federated learning via knowledge distillation
- Jinqian Chen
School of Software, Shandong University and School of Software Engineering, Xi'an Jiaotong University
, - Haoyu Tang
School of Software, Shandong University
, - Junhao Cheng
School of Software, Shandong University
, - Ming Yan
Alibaba Group
, - Ji Zhang
Alibaba Group
, - Mingzhu Xu
School of Software, Shandong University
, - Yupeng Hu
School of Software, Shandong University
, - Liqiang Nie
Harbin Institute of Technology (Shenzhen)
IJCAI '24: Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence•August 2024, Article No.: 419, pp 3789-3797• https://doi.org/10.24963/ijcai.2024/419Internet of Things (IoT) devices possess valuable yet private multimodal data, calling for a decentralized machine learning scheme. Though several multimodal federated learning (MFL) methods have been proposed, most of them merely overlook the system ...
- 0Citation
MetricsTotal Citations0
- Jinqian Chen
- research-article
Exploiting the social-like prior in transformer for visual reasoning
- Yudong Han
School of Software, Shandong University
, - Yupeng Hu
School of Software, Shandong University
, - Xuemeng Song
School of Computer Science and Technology, Shandong University
, - Haoyu Tang
School of Software, Shandong University
, - Mingzhu Xu
School of Software, Shandong University
, - Liqiang Nie
School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen)
AAAI'24/IAAI'24/EAAI'24: Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence and Thirty-Sixth Conference on Innovative Applications of Artificial Intelligence and Fourteenth Symposium on Educational Advances in Artificial Intelligence•February 2024, Article No.: 229, pp 2058-2066• https://doi.org/10.1609/aaai.v38i3.27977Benefiting from instrumental global dependency modeling of self-attention (SA), transformer-based approaches have become the pivotal choices for numerous downstream visual reasoning tasks, such as visual question answering (VQA) and referring expression ...
- 0Citation
MetricsTotal Citations0
- Yudong Han
- research-article
LLM vs small model? large language model based text augmentation enhanced personality detection model
- Linmei Hu
School of Computer Science and Technology, Beijing Institute of Technology
, - Hongyu He
School of Computer Science, Beijing University of Posts and Telecommunications
, - Duokang Wang
School of Computer Science, Beijing University of Posts and Telecommunications
, - Ziwang Zhao
School of Computer Science, Beijing University of Posts and Telecommunications
, - Yingxia Shao
School of Computer Science, Beijing University of Posts and Telecommunications
, - Liqiang Nie
School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen)
AAAI'24/IAAI'24/EAAI'24: Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence and Thirty-Sixth Conference on Innovative Applications of Artificial Intelligence and Fourteenth Symposium on Educational Advances in Artificial Intelligence•February 2024, Article No.: 2034, pp 18234-18242• https://doi.org/10.1609/aaai.v38i16.29782Personality detection aims to detect one's personality traits underlying in social media posts. One challenge of this task is the scarcity of ground-truth personality traits which are collected from self-report questionnaires. Most existing methods learn ...
- 0Citation
MetricsTotal Citations0
- Linmei Hu
- research-article
Multi-factor adaptive vision selection for egocentric video question answering
- Haoyu Zhang
School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, China and Peng Cheng Laboratory, Shenzhen, China
, - Meng Liu
School of Computer Science and Technology, Shandong Jianzhu University, Jinan, China
, - Zixin Liu
Peng Cheng Laboratory, Shenzhen, China
, - Xuemeng Song
School of Computer Science and Technology, Shandong University, Qingdao, China
, - Yaowei Wang
Peng Cheng Laboratory, Shenzhen, China and School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, China
, - Liqiang Nie
School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, China
ICML'24: Proceedings of the 41st International Conference on Machine Learning•July 2024, Article No.: 2450, pp 59310-59328The challenge of interpreting the world from a human perspective in Artificial Intelligence (AI) is particularly evident in egocentric video question answering, which grapples with issues like small object recognition, noise suppression, and spatial-...
- 0Citation
MetricsTotal Citations0
- Haoyu Zhang
- research-article
RoboMP2: a robotic multimodal perception-planning framework with multimodal large language models
- Qi Lv
School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen) and School of Engineering and School of Computing and Information Technology, Great Bay University
, - Hao Li
School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen)
, - Xiang Deng
School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen)
, - Rui Shao
School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen)
, - Michael Yu Wang
School of Engineering, Great Bay University
, - Liqiang Nie
School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen)
ICML'24: Proceedings of the 41st International Conference on Machine Learning•July 2024, Article No.: 1363, pp 33558-33574Multimodal Large Language Models (MLLMs) have shown impressive reasoning abilities and general intelligence in various domains. It inspires researchers to train end-to-end MLLMs or utilize large models to generate policies with human-selected prompts for ...
- 0Citation
MetricsTotal Citations0
- Qi Lv
- research-articlePublished By ACMPublished By ACM
Domain-aware Multimodal Dialog Systems with Distribution-based User Characteristic Modeling
- Xiaolin Chen
School of Software, Joint SDU-NTU Centre for Artificial Intelligence Research, Shandong University, Jinan, China
, - Xuemeng Song
School of Computer Science and Technology, Shandong University, Jinan, China
, - Jianhui Zuo
School of Computer Science and Technology, Shandong University, Jinan, China
, - Yinwei Wei
Department of Human Centred Computing, Monash University, Melbourne, Australia
, - Liqiang Nie
School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, China
, - Tat-Seng Chua
School of Computing, National University of Singapore, Singapore, Singapore
ACM Transactions on Multimedia Computing, Communications, and Applications, Volume 21, Issue 2•February 2025, Article No.: 66, pp 1-22 • https://doi.org/10.1145/3704811Textual response generation is a pivotal yet challenging task for multimodal task-oriented dialog systems, which targets at generating the appropriate textual response given the multimodal context. Although existing efforts have obtained remarkable ...
- 0Citation
- 124
- Downloads
MetricsTotal Citations0Total Downloads124Last 12 Months124Last 6 weeks36
- Xiaolin Chen
- research-articleOpen AccessPublished By ACMPublished By ACM
Interactive Garment Recommendation with User in the Loop
- Federico Becattini
University of Siena, Siena, Italy
, - Xiaolin Chen
School of Software, Shandong University, Jinan, China
, - Andrea Puccia
University of Florence, Firenze, Italy
, - Haokun Wen
School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, China
, - Xuemeng Song
School of Computer Science and Technology, Shandong University, Jinan, China
, - Liqiang Nie
School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, China
, - Alberto Del Bimbo
University of Florence, Firenze, Italy
ACM Transactions on Multimedia Computing, Communications, and Applications, Volume 21, Issue 1•January 2025, Article No.: 37, pp 1-21 • https://doi.org/10.1145/3702327Recommending fashion items often leverages rich user profiles and makes targeted suggestions based on past history and previous purchases. In this paper, we work under the assumption that no prior knowledge is given about a user. We propose to build a ...
- 0Citation
- 134
- Downloads
MetricsTotal Citations0Total Downloads134Last 12 Months134Last 6 weeks78
- Federico Becattini
- research-articlefreePublished By ACMPublished By ACM
Understanding Before Recommendation: Semantic Aspect-Aware Review Exploitation via Large Language Models
- Fan Liu
National University of Singapore, Singapore
, - Yaqi Liu
National University of Singapore, Singapore
, - Huilin Chen
Hefei University of Technology, China
, - Zhiyong Cheng
Hefei University of Technology, China
, - Liqiang Nie
Harbin Institute of Technology, Shenzhen, China
, - Mohan Kankanhalli
National University of Singapore, Singapore
ACM Transactions on Information Systems, Volume 0, Issue ja • https://doi.org/10.1145/3704999Recommendation systems harness user-item interactions like clicks and reviews to learn their representations. Previous studies improve recommendation accuracy and interpretability by modeling user preferences across various aspects and intents. However, ...
- 0Citation
- 177
- Downloads
MetricsTotal Citations0Total Downloads177Last 12 Months177Last 6 weeks111
- Fan Liu
- research-articlePublished By ACMPublished By ACM
Harnessing Representative Spatial-Temporal Information for Video Question Answering
- Yuanyuan Wang
Shandong University, Qingdao, China
, - Meng Liu
Shandong Jianzhu University, Jinan, China
, - Xuemeng Song
Shandong University, Qingdao, China
, - Liqiang Nie
Harbin Institute of Technology, Shenzhen, China
ACM Transactions on Multimedia Computing, Communications, and Applications, Volume 20, Issue 10•October 2024, Article No.: 306, pp 1-20 • https://doi.org/10.1145/3675399Video question answering, aiming to answer a natural language question related to the given video, has become prevalent in the past few years. Although remarkable improvements have been obtained, it is still exposed to the challenge of insufficient ...
- 8Citation
- 186
- Downloads
MetricsTotal Citations8Total Downloads186Last 12 Months186Last 6 weeks28
- Yuanyuan Wang
- short-paperPublished By ACMPublished By ACM
MIS '24: 1st ACM Multimedia Workshop on Multi-modal Misinformation Governance in the Era of Foundation Models
- Shaojing Fan
National University of Singapore, Singapore, Singapore
, - Zheng Wang
Wuhan University, Wuhan, China
, - Rui Shao
Harbin Institute of Technology (Shenzhen), Shenzhen, China
, - Song Bai
ByteDance, Singapore, Singapore
, - Hongyuan Zhu
Institute for Infocomm Research, A*STAR, Singapore, Singapore
, - Liqiang Nie
Harbin Institute of Technology (Shenzhen), Shenzhen, China
, - Shin'ichi Satoh
National Institute of Informatics, Tokyo, Japan
MIS '24: Proceedings of the 1st ACM Multimedia Workshop on Multi-modal Misinformation Governance in the Era of Foundation Models•October 2024, pp 1-2• https://doi.org/10.1145/3689090.3696058The rise of foundation models like GPT and CLIP has transformed artificial intelligence, driving significant advancements in natural language processing and computer vision. However, these large-scale models also present challenges in misinformation ...
- 0Citation
- 46
- Downloads
MetricsTotal Citations0Total Downloads46Last 12 Months46Last 6 weeks11
- Shaojing Fan
- research-articlePublished By ACMPublished By ACM
Differential-Perceptive and Retrieval-Augmented MLLM for Change Captioning
- Xian Zhang
Harbin Institute of Technology (Shenzhen), Shenzhen, China
, - Haokun Wen
Harbin Institute of Technology (Shenzhen), Shenzhen, China
, - Jianlong Wu
Harbin Institute of Technology (Shenzhen), Shenzhen, China
, - Pengda Qin
Alibaba Group, Beijing, China
, - Hui Xue'
Alibaba Group, Hangzhou, China
, - Liqiang Nie
Harbin Institute of Technology (Shenzhen), Shenzhen, China
MM '24: Proceedings of the 32nd ACM International Conference on Multimedia•October 2024, pp 4148-4157• https://doi.org/10.1145/3664647.3681453Change captioning involves describing the subtle changes between a pair of similar images. Although existing efforts have achieved compelling success, they overlook the potential of multimodal large language models (MLLMs) in tackling this challenging ...
- 0Citation
- 130
- Downloads
MetricsTotal Citations0Total Downloads130Last 12 Months130Last 6 weeks42
- Xian Zhang
- research-articlePublished By ACMPublished By ACM
Revisiting Unsupervised Temporal Action Localization: The Primacy of High-Quality Actionness and Pseudolabels
- Han Jiang
School of Software, Shandong University & School of Software Engineering, Xi'an Jiaotong University, Jinan, China
, - Haoyu Tang
School of Software, Shandong University, Jinan, China
, - Ming Yan
Alibaba Group, Hangzhou, China
, - Ji Zhang
Alibaba Group, Hangzhou, China
, - Mingzhu Xu
School of Software, Shandong University, Jinan, China
, - Yupeng Hu
School of Software, Shandong University, Jinan, China
, - Jihua Zhu
School of Software Engineering, Xi'an Jiaotong University, Xi'an, China
, - Liqiang Nie
Department of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, China
MM '24: Proceedings of the 32nd ACM International Conference on Multimedia•October 2024, pp 5643-5652• https://doi.org/10.1145/3664647.3681197Recently, temporal action localization (TAL) methods, especially the weakly-supervised and unsupervised ones, have become a hot research topic. Existing unsupervised methods follow an iterative ''clustering and training'' strategy with diverse model ...
- 0Citation
- 59
- Downloads
MetricsTotal Citations0Total Downloads59Last 12 Months59Last 6 weeks21
- Han Jiang
- research-articlePublished By ACMPublished By ACM
Attribute-driven Disentangled Representation Learning for Multimodal Recommendation
- Zhenyang Li
Shandong University, Qingdao, China
, - Fan Liu
National University of Singapore, Singapore, Singapore
, - Yinwei Wei
Monash University, Melbourne, Australia
, - Zhiyong Cheng
Hefei University of Technology, Hefei, China
, - Liqiang Nie
Harbin Institute of Technology, Shenzhen, Shenzhen, China
, - Mohan Kankanhalli
National University of Singapore, Singapore, Singapore
MM '24: Proceedings of the 32nd ACM International Conference on Multimedia•October 2024, pp 9660-9669• https://doi.org/10.1145/3664647.3681148Recommendation algorithms predict user preferences by correlating user and item representations derived from historical interaction patterns. In pursuit of enhanced performance, many methods focus on learning robust and independent representations by ...
- 0Citation
- 167
- Downloads
MetricsTotal Citations0Total Downloads167Last 12 Months167Last 6 weeks55
- Zhenyang Li
- research-articlePublished By ACMPublished By ACM
Diffusion Facial Forgery Detection
- Harry Cheng
Shandong University, Qingdao, China
, - Yangyang Guo
National University of Singapore, Singapore, Singapore
, - Tianyi Wang
Nanyang Technological University, Singapore, Singapore
, - Liqiang Nie
Harbin Institute of Technology (Shenzhen), Shenzhen, China
, - Mohan Kankanhalli
National University of Singapore, Singapore, Singapore
MM '24: Proceedings of the 32nd ACM International Conference on Multimedia•October 2024, pp 5939-5948• https://doi.org/10.1145/3664647.3680797Detecting diffusion-generated images has recently developed as an emerging research area. Existing diffusion-based datasets predominantly focus on general image generation. However, facial forgeries, which pose severe social risks, have remained less ...
- 1Citation
- 131
- Downloads
MetricsTotal Citations1Total Downloads131Last 12 Months131Last 6 weeks43
- Harry Cheng
- research-articlePublished By ACMPublished By ACM
NovaChart: A Large-scale Dataset towards Chart Understanding and Generation of Multimodal Large Language Models
- Linmei Hu
SCST, Beijing Institute of Technology, Beijing, China
, - Duokang Wang
SCS, Beijing University of Posts and Telecommunications, Beijing, China
, - Yiming Pan
SCST, Beijing Institute of Technology, Beijing, China
, - Jifan Yu
DCST, Tsinghua University, Beijing, China
, - Yingxia Shao
SCS, Beijing University of Posts and Telecommunications, Beijing, China
, - Chong Feng
SCST, Beijing Institute of Technology, Beijing, China
, - Liqiang Nie
SCST, Harbin Institute of Technology (Shenzhen), Shenzhen, China
MM '24: Proceedings of the 32nd ACM International Conference on Multimedia•October 2024, pp 3917-3925• https://doi.org/10.1145/3664647.3680790Multimodal Large Language Models (MLLMs) have shown significant potential for chart understanding and generation. However, they are still far from achieving the desired effectiveness in practical applications. This could be due to the limitations of the ...
- 0Citation
- 130
- Downloads
MetricsTotal Citations0Total Downloads130Last 12 Months130Last 6 weeks48
- Linmei Hu
- research-articlePublished By ACMPublished By ACM
Explicit Granularity and Implicit Scale Correspondence Learning for Point-Supervised Video Moment Localization
- Kun Wang
Shandong University, Jinan, China
, - Hao Liu
Shandong University, Jinan, China
, - Lirong Jie
Shandong University, Jinan, China
, - Zixu Li
Shandong University, Jinan, China
, - Yupeng Hu
Shandong University, Jinan, China
, - Liqiang Nie
Harbin Institute of Technology, Shenzhen, China
MM '24: Proceedings of the 32nd ACM International Conference on Multimedia•October 2024, pp 9214-9223• https://doi.org/10.1145/3664647.3680774Video moment localization (VML) aims to identify the temporal boundary semantically matching the given query. Point-supervised VML balances localization accuracy and annotation cost but is still immature due to granularity alignment and scale perception ...
- 0Citation
- 80
- Downloads
MetricsTotal Citations0Total Downloads80Last 12 Months80Last 6 weeks36
- Kun Wang
- research-articlePublished By ACMPublished By ACM
Cluster-Based Graph Collaborative Filtering
- Fan Liu
National University of Singapore, Singapore, Singapore
, - Shuai Zhao
Qilu University of Technology (Shandong Academy of Sciences), Shandong Artificial Intelligence Institute, Jinan, China
, - Zhiyong Cheng
Hefei University of Technology, Hefei, China
, - Liqiang Nie
Harbin Institute of Technology, School of Computer Science and Technology, Shenzhen, China
, - Mohan Kankanhalli
National University of Singapore, School of Computing, Singapore, Singapore
ACM Transactions on Information Systems, Volume 42, Issue 6•November 2024, Article No.: 167, pp 1-24 • https://doi.org/10.1145/3687481Graph Convolution Networks (GCNs) have significantly succeeded in learning user and item representations for recommendation systems. The core of their efficacy is the ability to explicitly exploit the collaborative signals from both the first- and high-...
- 2Citation
- 286
- Downloads
MetricsTotal Citations2Total Downloads286Last 12 Months286Last 6 weeks47
- Fan Liu
- research-articlePublished By ACMPublished By ACM
Breaking Through the Noisy Correspondence: A Robust Model for Image-Text Matching
- Haitao Shi
Shandong University, Jinan, China
, - Meng Liu
Shandong Jianzhu University, Jinan, China
, - Xiaoxuan Mu
Shandong University, Jinan, China
, - Xuemeng Song
Shandong University, Qingdao, China
, - Yupeng Hu
Shandong University, Jinan, China
, - Liqiang Nie
Harbin Institute of Technology (Shenzhen), Shenzhen, China
ACM Transactions on Information Systems, Volume 42, Issue 6•November 2024, Article No.: 149, pp 1-26 • https://doi.org/10.1145/3662732Unleashing the power of image-text matching in real-world applications is hampered by noisy correspondence. Manually curating high-quality datasets is expensive and time-consuming, and datasets generated using diffusion models are not adequately well-...
- 1Citation
- 652
- Downloads
MetricsTotal Citations1Total Downloads652Last 12 Months652Last 6 weeks78
- Haitao Shi
- research-article
UNK-VQA: A Dataset and a Probe Into the Abstention Ability of Multi-Modal Large Models
- Yangyang Guo
National University of Singapore, Singapore
, - Fangkai Jiao
Nanyang Technological University, IR, A*STAR, Singapore
, - Zhiqi Shen
National University of Singapore, Singapore
, - Liqiang Nie
Harbin Institute of Technology (Shenzhen), Shenzhen, China
, - Mohan Kankanhalli
National University of Singapore, Singapore
IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 46, Issue 12•Dec. 2024, pp 10284-10296 • https://doi.org/10.1109/TPAMI.2024.3437288Teaching Visual Question Answering (VQA) models to refrain from answering unanswerable questions is necessary for building a trustworthy AI system. Existing studies, though have explored various aspects of VQA but somewhat ignored this particular ...
- 0Citation
MetricsTotal Citations0
- Yangyang Guo
Author Profile Pages
- Description: The Author Profile Page initially collects all the professional information known about authors from the publications record as known by the ACM bibliographic database, the Guide. Coverage of ACM publications is comprehensive from the 1950's. Coverage of other publishers generally starts in the mid 1980's. The Author Profile Page supplies a quick snapshot of an author's contribution to the field and some rudimentary measures of influence upon it. Over time, the contents of the Author Profile page may expand at the direction of the community.
Please see the following 2007 Turing Award winners' profiles as examples: - History: Disambiguation of author names is of course required for precise identification of all the works, and only those works, by a unique individual. Of equal importance to ACM, author name normalization is also one critical prerequisite to building accurate citation and download statistics. For the past several years, ACM has worked to normalize author names, expand reference capture, and gather detailed usage statistics, all intended to provide the community with a robust set of publication metrics. The Author Profile Pages reveal the first result of these efforts.
- Normalization: ACM uses normalization algorithms to weigh several types of evidence for merging and splitting names.
These include:- co-authors: if we have two names and cannot disambiguate them based on name alone, then we see if they have a co-author in common. If so, this weighs towards the two names being the same person.
- affiliations: names in common with same affiliation weighs toward the two names being the same person.
- publication title: names in common whose works are published in same journal weighs toward the two names being the same person.
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The more conservative the merging algorithms, the more bits of evidence are required before a merge is made, resulting in greater precision but lower recall of works for a given Author Profile. Many bibliographic records have only author initials. Many names lack affiliations. With very common family names, typical in Asia, more liberal algorithms result in mistaken merges.
Automatic normalization of author names is not exact. Hence it is clear that manual intervention based on human knowledge is required to perfect algorithmic results. ACM is meeting this challenge, continuing to work to improve the automated merges by tweaking the weighting of the evidence in light of experience.
- Bibliometrics: In 1926, Alfred Lotka formulated his power law (known as Lotka's Law) describing the frequency of publication by authors in a given field. According to this bibliometric law of scientific productivity, only a very small percentage (~6%) of authors in a field will produce more than 10 articles while the majority (perhaps 60%) will have but a single article published. With ACM's first cut at author name normalization in place, the distribution of our authors with 1, 2, 3..n publications does not match Lotka's Law precisely, but neither is the distribution curve far off. For a definition of ACM's first set of publication statistics, see Bibliometrics
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The initial release of the Author Edit Screen is open to anyone in the community with an ACM account, but it is limited to personal information. An author's photograph, a Home Page URL, and an email may be added, deleted or edited. Changes are reviewed before they are made available on the live site.
ACM will expand this edit facility to accommodate more types of data and facilitate ease of community participation with appropriate safeguards. In particular, authors or members of the community will be able to indicate works in their profile that do not belong there and merge others that do belong but are currently missing.
A direct search interface for Author Profiles will be built.
An institutional view of works emerging from their faculty and researchers will be provided along with a relevant set of metrics.
It is possible, too, that the Author Profile page may evolve to allow interested authors to upload unpublished professional materials to an area available for search and free educational use, but distinct from the ACM Digital Library proper. It is hard to predict what shape such an area for user-generated content may take, but it carries interesting potential for input from the community.
Bibliometrics
The ACM DL is a comprehensive repository of publications from the entire field of computing.
It is ACM's intention to make the derivation of any publication statistics it generates clear to the user.
- Average citations per article = The total Citation Count divided by the total Publication Count.
- Citation Count = cumulative total number of times all authored works by this author were cited by other works within ACM's bibliographic database. Almost all reference lists in articles published by ACM have been captured. References lists from other publishers are less well-represented in the database. Unresolved references are not included in the Citation Count. The Citation Count is citations TO any type of work, but the references counted are only FROM journal and proceedings articles. Reference lists from books, dissertations, and technical reports have not generally been captured in the database. (Citation Counts for individual works are displayed with the individual record listed on the Author Page.)
- Publication Count = all works of any genre within the universe of ACM's bibliographic database of computing literature of which this person was an author. Works where the person has role as editor, advisor, chair, etc. are listed on the page but are not part of the Publication Count.
- Publication Years = the span from the earliest year of publication on a work by this author to the most recent year of publication of a work by this author captured within the ACM bibliographic database of computing literature (The ACM Guide to Computing Literature, also known as "the Guide".
- Available for download = the total number of works by this author whose full texts may be downloaded from an ACM full-text article server. Downloads from external full-text sources linked to from within the ACM bibliographic space are not counted as 'available for download'.
- Average downloads per article = The total number of cumulative downloads divided by the number of articles (including multimedia objects) available for download from ACM's servers.
- Downloads (cumulative) = The cumulative number of times all works by this author have been downloaded from an ACM full-text article server since the downloads were first counted in May 2003. The counts displayed are updated monthly and are therefore 0-31 days behind the current date. Robotic activity is scrubbed from the download statistics.
- Downloads (12 months) = The cumulative number of times all works by this author have been downloaded from an ACM full-text article server over the last 12-month period for which statistics are available. The counts displayed are usually 1-2 weeks behind the current date. (12-month download counts for individual works are displayed with the individual record.)
- Downloads (6 weeks) = The cumulative number of times all works by this author have been downloaded from an ACM full-text article server over the last 6-week period for which statistics are available. The counts displayed are usually 1-2 weeks behind the current date. (6-week download counts for individual works are displayed with the individual record.)
ACM Author-Izer Service
Summary Description
ACM Author-Izer is a unique service that enables ACM authors to generate and post links on both their homepage and institutional repository for visitors to download the definitive version of their articles from the ACM Digital Library at no charge.
Downloads from these sites are captured in official ACM statistics, improving the accuracy of usage and impact measurements. Consistently linking to definitive version of ACM articles should reduce user confusion over article versioning.
ACM Author-Izer also extends ACM’s reputation as an innovative “Green Path” publisher, making ACM one of the first publishers of scholarly works to offer this model to its authors.
To access ACM Author-Izer, authors need to establish a free ACM web account. Should authors change institutions or sites, they can utilize the new ACM service to disable old links and re-authorize new links for free downloads from a different site.
How ACM Author-Izer Works
Authors may post ACM Author-Izer links in their own bibliographies maintained on their website and their own institution’s repository. The links take visitors to your page directly to the definitive version of individual articles inside the ACM Digital Library to download these articles for free.
The Service can be applied to all the articles you have ever published with ACM.
Depending on your previous activities within the ACM DL, you may need to take up to three steps to use ACM Author-Izer.
For authors who do not have a free ACM Web Account:
- Go to the ACM DL http://dl.acm.org/ and click SIGN UP. Once your account is established, proceed to next step.
For authors who have an ACM web account, but have not edited their ACM Author Profile page:
- Sign in to your ACM web account and go to your Author Profile page. Click "Add personal information" and add photograph, homepage address, etc. Click ADD AUTHOR INFORMATION to submit change. Once you receive email notification that your changes were accepted, you may utilize ACM Author-izer.
For authors who have an account and have already edited their Profile Page:
- Sign in to your ACM web account, go to your Author Profile page in the Digital Library, look for the ACM Author-izer link below each ACM published article, and begin the authorization process. If you have published many ACM articles, you may find a batch Authorization process useful. It is labeled: "Export as: ACM Author-Izer Service"
ACM Author-Izer also provides code snippets for authors to display download and citation statistics for each “authorized” article on their personal pages. Downloads from these pages are captured in official ACM statistics, improving the accuracy of usage and impact measurements. Consistently linking to the definitive version of ACM articles should reduce user confusion over article versioning.
Note: You still retain the right to post your author-prepared preprint versions on your home pages and in your institutional repositories with DOI pointers to the definitive version permanently maintained in the ACM Digital Library. But any download of your preprint versions will not be counted in ACM usage statistics. If you use these AUTHOR-IZER links instead, usage by visitors to your page will be recorded in the ACM Digital Library and displayed on your page.
FAQ
- Q. What is ACM Author-Izer?
A. ACM Author-Izer is a unique, link-based, self-archiving service that enables ACM authors to generate and post links on either their home page or institutional repository for visitors to download the definitive version of their articles for free.
- Q. What articles are eligible for ACM Author-Izer?
- A. ACM Author-Izer can be applied to all the articles authors have ever published with ACM. It is also available to authors who will have articles published in ACM publications in the future.
- Q. Are there any restrictions on authors to use this service?
- A. No. An author does not need to subscribe to the ACM Digital Library nor even be a member of ACM.
- Q. What are the requirements to use this service?
- A. To access ACM Author-Izer, authors need to have a free ACM web account, must have an ACM Author Profile page in the Digital Library, and must take ownership of their Author Profile page.
- Q. What is an ACM Author Profile Page?
- A. The Author Profile Page initially collects all the professional information known about authors from the publications record as known by the ACM Digital Library. The Author Profile Page supplies a quick snapshot of an author's contribution to the field and some rudimentary measures of influence upon it. Over time, the contents of the Author Profile page may expand at the direction of the community. Please visit the ACM Author Profile documentation page for more background information on these pages.
- Q. How do I find my Author Profile page and take ownership?
- A. You will need to take the following steps:
- Create a free ACM Web Account
- Sign-In to the ACM Digital Library
- Find your Author Profile Page by searching the ACM Digital Library for your name
- Find the result you authored (where your author name is a clickable link)
- Click on your name to go to the Author Profile Page
- Click the "Add Personal Information" link on the Author Profile Page
- Wait for ACM review and approval; generally less than 24 hours
- Q. Why does my photo not appear?
- A. Make sure that the image you submit is in .jpg or .gif format and that the file name does not contain special characters
- Q. What if I cannot find the Add Personal Information function on my author page?
- A. The ACM account linked to your profile page is different than the one you are logged into. Please logout and login to the account associated with your Author Profile Page.
- Q. What happens if an author changes the location of his bibliography or moves to a new institution?
- A. Should authors change institutions or sites, they can utilize ACM Author-Izer to disable old links and re-authorize new links for free downloads from a new location.
- Q. What happens if an author provides a URL that redirects to the author’s personal bibliography page?
- A. The service will not provide a free download from the ACM Digital Library. Instead the person who uses that link will simply go to the Citation Page for that article in the ACM Digital Library where the article may be accessed under the usual subscription rules.
However, if the author provides the target page URL, any link that redirects to that target page will enable a free download from the Service.
- Q. What happens if the author’s bibliography lives on a page with several aliases?
- A. Only one alias will work, whichever one is registered as the page containing the author’s bibliography. ACM has no technical solution to this problem at this time.
- Q. Why should authors use ACM Author-Izer?
- A. ACM Author-Izer lets visitors to authors’ personal home pages download articles for no charge from the ACM Digital Library. It allows authors to dynamically display real-time download and citation statistics for each “authorized” article on their personal site.
- Q. Does ACM Author-Izer provide benefits for authors?
- A. Downloads of definitive articles via Author-Izer links on the authors’ personal web page are captured in official ACM statistics to more accurately reflect usage and impact measurements.
Authors who do not use ACM Author-Izer links will not have downloads from their local, personal bibliographies counted. They do, however, retain the existing right to post author-prepared preprint versions on their home pages or institutional repositories with DOI pointers to the definitive version permanently maintained in the ACM Digital Library.
- Q. How does ACM Author-Izer benefit the computing community?
- A. ACM Author-Izer expands the visibility and dissemination of the definitive version of ACM articles. It is based on ACM’s strong belief that the computing community should have the widest possible access to the definitive versions of scholarly literature. By linking authors’ personal bibliography with the ACM Digital Library, user confusion over article versioning should be reduced over time.
In making ACM Author-Izer a free service to both authors and visitors to their websites, ACM is emphasizing its continuing commitment to the interests of its authors and to the computing community in ways that are consistent with its existing subscription-based access model.
- Q. Why can’t I find my most recent publication in my ACM Author Profile Page?
- A. There is a time delay between publication and the process which associates that publication with an Author Profile Page. Right now, that process usually takes 4-8 weeks.
- Q. How does ACM Author-Izer expand ACM’s “Green Path” Access Policies?
- A. ACM Author-Izer extends the rights and permissions that authors retain even after copyright transfer to ACM, which has been among the “greenest” publishers. ACM enables its author community to retain a wide range of rights related to copyright and reuse of materials. They include:
- Posting rights that ensure free access to their work outside the ACM Digital Library and print publications
- Rights to reuse any portion of their work in new works that they may create
- Copyright to artistic images in ACM’s graphics-oriented publications that authors may want to exploit in commercial contexts
- All patent rights, which remain with the original owner