Proceedings of the 31st ACM International Conference on Information & Knowledge Management最新文献

ASDPred: An End-to-End Autism Screening Framework Using Few-Shot Learning ASDPred:使用少镜头学习的端到端自闭症筛查框架

Proceedings of the 31st ACM International Conference on Information & Knowledge Management Pub Date : 2022-11-07 DOI: 10.1145/3511808.3557210

Haishuai Wang, Lianhua Chi, Ziping Zhao

{"title":"ASDPred: An End-to-End Autism Screening Framework Using Few-Shot Learning","authors":"Haishuai Wang, Lianhua Chi, Ziping Zhao","doi":"10.1145/3511808.3557210","DOIUrl":"https://doi.org/10.1145/3511808.3557210","url":null,"abstract":"Autism spectrum disorder (ASD) is a neurodevelopmental condi-tion that affects social communication and behavior. Diagnosing ASD as early as possible is desirable because early detection enables timely access to interventions and support. This study aims to develop an innovative and interactive ASD diagnostic tool that incorporates artificial intelligence (AI) technology to empower parents and medical professionals to act on early concerns. Collecting and annotating large-scale ASD data is costly, time-consuming, and labor-intensive. Moreover, significant domain knowledge is required to annotate the collected data. Consequently, there are only a few samples available to train AI models to learn the memorization and generalization from the data. Therefore, we designed a few-shot learning framework that combines a Siamese network with a Wide & Deep network to learn both linear and non-linear relationships from small ASD datasets. The experiment results show that it is effective to apply both Siamese networks and Wide & Deep models to achieve ASD diagnostics using a limited number of samples. Based on the proposed model, we developed a diagnostic tool - ASDPred - embedded within a web-based platform to facilitate ASD diagnosis using the designed model.","PeriodicalId":389624,"journal":{"name":"Proceedings of the 31st ACM International Conference on Information & Knowledge Management","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125244040","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

ASDFace: Face-based Autism Diagnosis via Heterogeneous Domain Adaptation ASDFace:基于面孔的异质域适应自闭症诊断

Proceedings of the 31st ACM International Conference on Information & Knowledge Management Pub Date : 2022-11-07 DOI: 10.1145/3511808.3557170

Haishuai Wang, Lianhua Chi, Chanfei Su, Ziping Zhao

{"title":"ASDFace: Face-based Autism Diagnosis via Heterogeneous Domain Adaptation","authors":"Haishuai Wang, Lianhua Chi, Chanfei Su, Ziping Zhao","doi":"10.1145/3511808.3557170","DOIUrl":"https://doi.org/10.1145/3511808.3557170","url":null,"abstract":"While the prevalence of children with autism spectrum disorder (ASD) has emerged as a major public health concern, approximately 25% of children with ASD are not being diagnosed. The standard instruments to diagnose ASD are time-consuming and labor expensive, resulting in long wait times for a diagnosis. There is strong evidence that facial morphology is associated with autism phenotype expression. We hypothesize that the use of deep learning on facial images can speed the diagnosis without compromising accuracy. However, collecting and labeling large-scale facial images of autistic is a complicated and expensive process, which makes it inapplicable to train accurate deep learning-based diagnostic tools. To address this problem, we present a heterogeneous domain adaptation model that adopts sufficient individuals’ labeled characteristic and behavioural data as source domain to execute the facial classification task in the target domain. We also deploy this model to a web-based platform named ASDFace. ASDFace aims to provide a free preliminary ASD screening tool that can aid diagnosis and help parents decide whether they should take their children to an ASD specialist for further consultation.","PeriodicalId":389624,"journal":{"name":"Proceedings of the 31st ACM International Conference on Information & Knowledge Management","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133014816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Rank-Aware Gain-Based Evaluation of Extractive Summarization 基于秩感知增益的抽取摘要评价

Proceedings of the 31st ACM International Conference on Information & Knowledge Management Pub Date : 2022-10-17 DOI: 10.1145/3511808.3557821

Mousumi Akter

引用次数: 0

A Prerequisite Attention Model for Knowledge Proficiency Diagnosis of Students 学生知识熟练度诊断的前提注意模型

Proceedings of the 31st ACM International Conference on Information & Knowledge Management Pub Date : 2022-10-17 DOI: 10.1145/3511808.3557539

Haiping Ma, Jinwei Zhu, Shangshang Yang, Qi Liu, Haifeng Zhang, Xingyi Zhang, Yunbo Cao, Xuemin Zhao

{"title":"A Prerequisite Attention Model for Knowledge Proficiency Diagnosis of Students","authors":"Haiping Ma, Jinwei Zhu, Shangshang Yang, Qi Liu, Haifeng Zhang, Xingyi Zhang, Yunbo Cao, Xuemin Zhao","doi":"10.1145/3511808.3557539","DOIUrl":"https://doi.org/10.1145/3511808.3557539","url":null,"abstract":"With the rapid development of intelligent education platforms, how to enhance the performance of diagnosing students' knowledge proficiency has become an important issue, e.g., by incorporating the prerequisite relation of knowledge concepts. Unfortunately, the differentiated influence from different predecessor concepts to successor concepts is still underexplored in existing approaches. To this end, we propose a Prerequisite Attention model for Knowledge Proficiency diagnosis of students (PAKP) to learn the attentive weights of precursor concepts on successor concepts and model it for inferring the knowledge proficiency. Specifically, given the student response records and knowledge prerequisite graph, we design an embedding layer to output the representations of students, exercises, and concepts. Influence coefficient among concepts is calculated via an efficient attention mechanism in a fusion layer. Finally, the performance of each student is predicted based on the mined student and exercise factors. Extensive experiments on real-data sets demonstrate that PAKP exhibits great efficiency and interpretability advantages without accuracy loss.","PeriodicalId":389624,"journal":{"name":"Proceedings of the 31st ACM International Conference on Information & Knowledge Management","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115658822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Unsupervised Representation Learning on Attributed Multiplex Network 属性多路网络的无监督表示学习

Proceedings of the 31st ACM International Conference on Information & Knowledge Management Pub Date : 2022-10-17 DOI: 10.1145/3511808.3557486

Rui Zhang, A. Zimek, Peter Schneider-Kamp

{"title":"Unsupervised Representation Learning on Attributed Multiplex Network","authors":"Rui Zhang, A. Zimek, Peter Schneider-Kamp","doi":"10.1145/3511808.3557486","DOIUrl":"https://doi.org/10.1145/3511808.3557486","url":null,"abstract":"Embedding learning in multiplex networks has drawn increasing attention in recent years and achieved outstanding performance in many downstream tasks. However, most existing network embedding methods either only focus on the structured information of graphs, rely on the human-annotated data, or mainly rely on multi-layer GCNs to encode graphs at the risk of learning ill-posed spectral filters. Moreover, it is also challenging in multiplex network embedding to learn consensus embeddings for nodes across the multiple views by the inter-relationship among graphs. In this study, we propose a novel and flexible unsupervised network embedding method for attributed multiplex networks to generate more precise node embeddings by simplified Bernstein encoders and alternate contrastive learning between local and global. Specifically, we design a graph encoder based on simplified Bernstein polynomials to learn node embeddings of a specific graph view. During the learning of each specific view, local and global contrastive learning are alternately applied to update the view-specific embedding and the consensus embedding simultaneously. Furthermore, the proposed model can be easily extended as a semi-supervised model by adding additional semi-supervised cost or as an attention-based model to attentively integrate embeddings from multiple graphs. Experiments on three publicly available real-world datasets show that the proposed method achieves significant improvements on downstream tasks over state-of-the-art baselines, while being faster or competitive in terms of runtime compared to the previous studies.","PeriodicalId":389624,"journal":{"name":"Proceedings of the 31st ACM International Conference on Information & Knowledge Management","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124893130","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

SCC - A Test Collection for Search in Chat Conversations 一个在聊天对话中搜索的测试集合

Proceedings of the 31st ACM International Conference on Information & Knowledge Management Pub Date : 2022-10-17 DOI: 10.1145/3511808.3557692

Ismail Sabei, Ahmed Mourad, G. Zuccon

{"title":"SCC - A Test Collection for Search in Chat Conversations","authors":"Ismail Sabei, Ahmed Mourad, G. Zuccon","doi":"10.1145/3511808.3557692","DOIUrl":"https://doi.org/10.1145/3511808.3557692","url":null,"abstract":"We present SCC, a test collection for evaluating search in chat conversations. Chat applications such as Slack, WhatsApp and Wechat have become popular communication methods. Typical search requirements in these applications revolve around the task of known item retrieval, i.e. find information that the user has previously experienced in their chats. However, the search capabilities of these chat applications are often very basic. Our collection aims to support new research into building effective methods for chat conversations search. We do so by building a collection with 114 known item retrieval topics for searching over 437,893 Slack chat messages. An important aspect when searching through conversations is the unit of indexing (indexing granularity), e.g., it being a single message vs. an entire conversation. To support researchers to investigate this aspect and its influence on retrieval effectiveness, the collection has been processed with conversation disentanglement methods: these mark cohesive segments in which each conversation consists of messages whose senders interact with each other regarding a specific event or topic. This results in a total of 38,955 multi-participant conversations being contained in the collection. Finally, we also provide a set of baselines with related empirical evaluation, including traditional bag-of-words methods and zero-shot neural methods, at both indexing granularity levels.","PeriodicalId":389624,"journal":{"name":"Proceedings of the 31st ACM International Conference on Information & Knowledge Management","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124985974","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Utilizing Contrastive Learning To Address Long Tail Issue in Product Categorization 利用对比学习解决产品分类中的长尾问题

Proceedings of the 31st ACM International Conference on Information & Knowledge Management Pub Date : 2022-10-17 DOI: 10.1145/3511808.3557522

L. Chen, Tianqi Wang

{"title":"Utilizing Contrastive Learning To Address Long Tail Issue in Product Categorization","authors":"L. Chen, Tianqi Wang","doi":"10.1145/3511808.3557522","DOIUrl":"https://doi.org/10.1145/3511808.3557522","url":null,"abstract":"Neural network models trained in a supervised learning way have become dominant. Although high performances can be achieved when training data is ample, the performance on labels with sparse training instances can be poor. This performance drift caused by imbalanced data is named as long tail issue and impacts many NN models used in reality. In this talk, we will firstly review machine learning approaches addressing the long-tail issue. Next, we will report on our effort on applying one recent LT-addressing method on the item categorization (IC) task that aims to classify product description texts into leaf nodes in a category taxonomy tree. In particular, we adopted a new method, which consists of decoupling the entire classification task into (a) learning representations using the K-positive contrastive loss (KCL) and (b) training a classifier on balanced data set, into IC tasks. Using SimCSE to be our self-learning backbone, we demonstrated that the proposed method works on the IC text classification task. In addition, we spotted a shortcoming in the KCL: false negative (FN) instances may harm the representation learning step. After eliminating FN instances, IC performance (measured by macro-F1) has been further improved.","PeriodicalId":389624,"journal":{"name":"Proceedings of the 31st ACM International Conference on Information & Knowledge Management","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125106491","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Disentangled Representation for Long-tail Senses of Word Sense Disambiguation 词义消歧的长尾义解纠缠表示

Proceedings of the 31st ACM International Conference on Information & Knowledge Management Pub Date : 2022-10-17 DOI: 10.1145/3511808.3557288

Junwei Zhang, Ruifang He, Fengyu Guo, Jinsong Ma, Mengnan Xiao

{"title":"Disentangled Representation for Long-tail Senses of Word Sense Disambiguation","authors":"Junwei Zhang, Ruifang He, Fengyu Guo, Jinsong Ma, Mengnan Xiao","doi":"10.1145/3511808.3557288","DOIUrl":"https://doi.org/10.1145/3511808.3557288","url":null,"abstract":"The long-tailed distribution, also called the heavy-tailed distribution, is common in nature. Since both words and their senses in natural language have long-tailed phenomenon in usage frequency, the Word Sense Disambiguation (WSD) task faces serious data imbalance. The existing learning strategies or data augmentation methods are difficult to deal with the lack of training samples caused by the single application scenario of long-tail senses, and the word sense representations caused by unique word sense definitions. Considering that the features extracted from the Disentangled Representation (DR) independently describe the essential properties of things, and DR does not require deep feature extraction and fusion processes, it alleviates the dependence of the representation learning on the training samples. We propose a novel DR by constraining the covariance matrix of a multivariate Gaussian distribution, which can enhance the strength of independence among features compared to β-VAE. The WSD model implemented by the reinforced DR outperforms the baselines on the English all-words WSD evaluation framework, the constructed long-tail word sense datasets, and the latest cross-lingual datasets.","PeriodicalId":389624,"journal":{"name":"Proceedings of the 31st ACM International Conference on Information & Knowledge Management","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126036734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

IEEE13-AdvAttack A Novel Dataset for Benchmarking the Power of Adversarial Attacks against Fault Prediction Systems in Smart Electrical Grid 针对智能电网故障预测系统的对抗性攻击能力测试新数据集

Proceedings of the 31st ACM International Conference on Information & Knowledge Management Pub Date : 2022-10-17 DOI: 10.1145/3511808.3557612

C. Ardito, Yashar Deldjoo, Tommaso Di Noia, Eugenio Di Sciascio, Fatemeh Nazary

{"title":"IEEE13-AdvAttack A Novel Dataset for Benchmarking the Power of Adversarial Attacks against Fault Prediction Systems in Smart Electrical Grid","authors":"C. Ardito, Yashar Deldjoo, Tommaso Di Noia, Eugenio Di Sciascio, Fatemeh Nazary","doi":"10.1145/3511808.3557612","DOIUrl":"https://doi.org/10.1145/3511808.3557612","url":null,"abstract":"Due to their economic and significant importance, fault detection tasks in intelligent electrical grids are vital. Although numerous smart grid (SG) applications, such as fault detection and load forecasting, have adopted data-driven approaches, the robustness and security of these data-driven algorithms have not been widely examined. One of the greatest obstacles in the research of the security of smart grids is the lack of publicly accessible datasets that permit testing the system's resilience against various types of assault. In this paper, we present IEEE13-AdvAttack, a large-scaled simulated dataset based on the IEEE-13 test node feeder suitable for supervised tasks under SG. The dataset includes both conventional and renewable energy resources. We examine the robustness of fault type classification and fault zone classification systems to adversarial attacks. Through the release of datasets, benchmarking, and assessment of smart grid failure prediction systems against adversarial assaults, we seek to encourage the implementation of machine-learned security models in the context of smart grids. The benchmarking data and code for fault prediction are made publicly available on https://bit.ly/3NT5jxG.","PeriodicalId":389624,"journal":{"name":"Proceedings of the 31st ACM International Conference on Information & Knowledge Management","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126054344","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Graph Based Long-Term And Short-Term Interest Model for Click-Through Rate Prediction 基于图的长期和短期利率模型的点击率预测

Proceedings of the 31st ACM International Conference on Information & Knowledge Management Pub Date : 2022-10-17 DOI: 10.1145/3511808.3557336

Huinan Sun, Guang-hong Yu, Pengye Zhang, Bo Zhang, Xingxing Wang, Dong Wang

{"title":"Graph Based Long-Term And Short-Term Interest Model for Click-Through Rate Prediction","authors":"Huinan Sun, Guang-hong Yu, Pengye Zhang, Bo Zhang, Xingxing Wang, Dong Wang","doi":"10.1145/3511808.3557336","DOIUrl":"https://doi.org/10.1145/3511808.3557336","url":null,"abstract":"Click-through rate (CTR) prediction aims to predict the probability that the user will click an item, which has been one of the key tasks in online recommender and advertising systems. In such systems, rich user behavior (viz. long- and short-term) has been proved to be of great value in capturing user interests. Both industry and academy have paid much attention to this topic and propose different approaches to modeling with long-term and short-term user behavior data. But there are still some unresolved issues. More specially, (1) rule and truncation based methods to extract information from long-term behavior are easy to cause information loss, and (2) single feedback behavior regardless of scenario to extract information from short-term behavior lead to information confusion and noise. To fill this gap, we propose a Graph based Long-term and Short-term interest Model, termed GLSM. It consists of a multi-interest graph structure for capturing long-term user behavior, a multi-scenario heterogeneous sequence model for modeling short-term information, then an adaptive fusion mechanism to fused information from long-term and short-term behaviors. Comprehensive experiments on real-world datasets, GLSM achieved SOTA score on offline metrics. At the same time, the GLSM algorithm has been deployed in our industrial application, bringing 4.9% CTR and 4.3% GMV lift, which is significant to the business","PeriodicalId":389624,"journal":{"name":"Proceedings of the 31st ACM International Conference on Information & Knowledge Management","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126812140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2