Proceedings of The Web Conference 2020最新文献_第7页

A Generic Solver Combining Unsupervised Learning and Representation Learning for Breaking Text-Based Captchas 结合无监督学习和表示学习的破解文本验证码的通用求解器

Proceedings of The Web Conference 2020 Pub Date : 2020-04-20 DOI: 10.1145/3366423.3380166

Sheng Tian, T. Xiong

{"title":"A Generic Solver Combining Unsupervised Learning and Representation Learning for Breaking Text-Based Captchas","authors":"Sheng Tian, T. Xiong","doi":"10.1145/3366423.3380166","DOIUrl":"https://doi.org/10.1145/3366423.3380166","url":null,"abstract":"Although there are many alternative captcha schemes available, text-based captchas are still one of the most popular security mechanism to maintain Internet security and prevent malicious attacks, due to the user preferences and ease of design. Over the past decade, different methods of breaking captchas have been proposed, which helps captcha keep evolving and become more robust. However, these previous works generally require heavy expert involvement and gradually become ineffective with the introduction of new security features. This paper proposes a generic solver combining unsupervised learning and representation learning to automatically remove the noisy background of captchas and solve text-based captchas. We introduce a new training scheme for constructing mini-batches, which contain a large number of unlabeled hard examples, to improve the efficiency of representation learning. Unlike existing deep learning algorithms, our method requires significantly fewer labeled samples and surpasses the recognition performance of a fully-supervised model with the same network architecture. Moreover, extensive experiments show that the proposed method outperforms state-of-the-art by delivering a higher accuracy on various captcha schemes. We provide further discussions of potential applications of the proposed unified framework. We hope that our work can inspire the community to enhance the security of text-based captchas.","PeriodicalId":20754,"journal":{"name":"Proceedings of The Web Conference 2020","volume":"341 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76394348","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: 12

Multi-Context Attention for Entity Matching 实体匹配的多上下文关注

Proceedings of The Web Conference 2020 Pub Date : 2020-04-20 DOI: 10.1145/3366423.3380017

Dongxiang Zhang, Yuyang Nie, Sai Wu, Yanyan Shen, K. Tan

引用次数: 24

Multimodal Post Attentive Profiling for Influencer Marketing 影响者营销的多模式后关注分析

Proceedings of The Web Conference 2020 Pub Date : 2020-04-20 DOI: 10.1145/3366423.3380052

Seungbae Kim, Jyun-Yu Jiang, Masaki Nakada, Jinyoung Han, Wei Wang

{"title":"Multimodal Post Attentive Profiling for Influencer Marketing","authors":"Seungbae Kim, Jyun-Yu Jiang, Masaki Nakada, Jinyoung Han, Wei Wang","doi":"10.1145/3366423.3380052","DOIUrl":"https://doi.org/10.1145/3366423.3380052","url":null,"abstract":"Influencer marketing has become a key marketing method for brands in recent years. Hence, brands have been increasingly utilizing influencers’ social networks to reach niche markets, and researchers have been studying various aspects of influencer marketing. However, brands have often suffered from searching and hiring the right influencers with specific interests/topics for their marketing due to a lack of available influencer data and/or limited capacity of marketing agencies. This paper proposes a multimodal deep learning model that uses text and image information from social media posts (i) to classify influencers into specific interests/topics (e.g., fashion, beauty) and (ii) to classify their posts into certain categories. We use the attention mechanism to select the posts that are more relevant to the topics of influencers, thereby generating useful influencer representations. We conduct experiments on the dataset crawled from Instagram, which is the most popular social media for influencer marketing. The experimental results show that our proposed model significantly outperforms existing user profiling methods by achieving 98% and 96% accuracy in classifying influencers and their posts, respectively. We release our influencer dataset of 33,935 influencers labeled with specific topics based on 10,180,500 posts to facilitate future research.","PeriodicalId":20754,"journal":{"name":"Proceedings of The Web Conference 2020","volume":"51 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90042786","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: 20

Few-Sample and Adversarial Representation Learning for Continual Stream Mining 连续流挖掘的少样本和对抗表示学习

Proceedings of The Web Conference 2020 Pub Date : 2020-04-20 DOI: 10.1145/3366423.3380153

Zhuoyi Wang, Yigong Wang, Yu Lin, Evan Delord, L. Khan

{"title":"Few-Sample and Adversarial Representation Learning for Continual Stream Mining","authors":"Zhuoyi Wang, Yigong Wang, Yu Lin, Evan Delord, L. Khan","doi":"10.1145/3366423.3380153","DOIUrl":"https://doi.org/10.1145/3366423.3380153","url":null,"abstract":"Deep Neural Networks (DNNs) have primarily been demonstrated to be useful for closed-world classification problems where the number of categories is fixed. However, DNNs notoriously fail when tasked with label prediction in a non-stationary data stream scenario, which has the continuous emergence of the unknown or novel class (categories not in the training set). For example, new topics continually emerge in social media or e-commerce. To solve this challenge, a DNN should not only be able to detect the novel class effectively but also incrementally learn new concepts from limited samples over time. Literature that addresses both problems simultaneously is limited. In this paper, we focus on improving the generalization of the model on the novel classes, and making the model continually learn from only a few samples from the novel categories. Different from existing approaches that rely on abundant labeled instances to re-train/update the model, we propose a new approach based on Few Sample and Adversarial Representation Learning (FSAR). The key novelty is that we introduce the adversarial confusion term into both the representation learning and few-sample learning process, which reduces the over-confidence of the model on the seen classes, further enhance the generalization of the model to detect and learn new categories with only a few samples. We train the FSAR operated in two stages: first, FSAR learns an intra-class compacted and inter-class separated feature embedding to detect the novel classes; next, we collect a few labeled samples belong to the new categories, utilize episode-training to exploit the intrinsic features for few-sample learning. We evaluated FSAR on different datasets, using extensive experimental results from various simulated stream benchmarks to show that FSAR effectively outperforms current state-of-the-art approaches.","PeriodicalId":20754,"journal":{"name":"Proceedings of The Web Conference 2020","volume":"74 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88059010","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: 13

Deconstructing Google’s Web Light Service 解构b谷歌的Web Light Service

Proceedings of The Web Conference 2020 Pub Date : 2020-04-20 DOI: 10.1145/3366423.3380168

Ammar Tahir, Muhammad Tahir Munir, Shaiq Munir Malik, Z. Qazi, I. Qazi

{"title":"Deconstructing Google’s Web Light Service","authors":"Ammar Tahir, Muhammad Tahir Munir, Shaiq Munir Malik, Z. Qazi, I. Qazi","doi":"10.1145/3366423.3380168","DOIUrl":"https://doi.org/10.1145/3366423.3380168","url":null,"abstract":"Web Light is a transcoding service introduced by Google to show lighter and faster webpages to users searching on slow mobile clients. The service detects slow clients (e.g., users on 2G) and tries to convert webpages on the fly into a version optimized for these clients. Web Light claims to significantly reduce page load times, save user data, and substantially increase traffic to such webpages. However, there are several concerns around this service, including, its effectiveness in, preserving relevant content on a page, showing third-party advertisements, improving user performance as well as privacy concerns for users and publishers. In this paper, we perform the first independent, empirical analysis of Google’s Web Light service to shed light on these concerns. Through a combination of experiments with thousands of real Web Light pages as well as controlled experiments with synthetic Web Light pages, we (i) deconstruct how Web Light modifies webpages, (ii) investigate how ads are shown on Web Light and which ad networks are supported, (iii) measure and compare Web Light’s page load performance, (iv) discuss privacy concerns for users and publishers and (v) investigate the potential use of Web Light as a censorship circumvention tool.","PeriodicalId":20754,"journal":{"name":"Proceedings of The Web Conference 2020","volume":"72 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85962422","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: 8

Dynamic Composition for Conversational Domain Exploration 会话领域探索的动态组合

Proceedings of The Web Conference 2020 Pub Date : 2020-04-20 DOI: 10.1145/3366423.3380167

Idan Szpektor, Deborah Cohen, G. Elidan, Michael Fink, A. Hassidim, Orgad Keller, Sayalı, Kulkarni, E. Ofek, S. Pudinsky, Asaf Revach, Shimi Salant

{"title":"Dynamic Composition for Conversational Domain Exploration","authors":"Idan Szpektor, Deborah Cohen, G. Elidan, Michael Fink, A. Hassidim, Orgad Keller, Sayalı, Kulkarni, E. Ofek, S. Pudinsky, Asaf Revach, Shimi Salant","doi":"10.1145/3366423.3380167","DOIUrl":"https://doi.org/10.1145/3366423.3380167","url":null,"abstract":"We study conversational domain exploration (CODEX), where the user’s goal is to enrich her knowledge of a given domain by conversing with an informative bot. Such conversations should be well grounded in high-quality domain knowledge as well as engaging and open-ended. A CODEX bot should be proactive and introduce relevant information even if not directly asked for by the user. The bot should also appropriately pivot the conversation to undiscovered regions of the domain. To address these dialogue characteristics, we introduce a novel approach termed dynamic composition that decouples candidate content generation from the flexible composition of bot responses. This allows the bot to control the source, correctness and quality of the offered content, while achieving flexibility via a dialogue manager that selects the most appropriate contents in a compositional manner. We implemented a CODEX bot based on dynamic composition and integrated it into the Google Assistant . As an example domain, the bot conversed about the NBA basketball league in a seamless experience, such that users were not aware whether they were conversing with the vanilla system or the one augmented with our CODEX bot. Results are positive and offer insights into what makes for a good conversation. To the best of our knowledge, this is the first real user experiment of open-ended dialogues as part of a commercial assistant system.","PeriodicalId":20754,"journal":{"name":"Proceedings of The Web Conference 2020","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80124187","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: 10

Fast Computation of Explanations for Inconsistency in Large-Scale Knowledge Graphs 大规模知识图中不一致解释的快速计算

Proceedings of The Web Conference 2020 Pub Date : 2020-04-20 DOI: 10.1145/3366423.3380014

T. Tran, Mohamed H. Gad-Elrab, D. Stepanova, E. Kharlamov, Jannik Strotgen

引用次数: 9

A Data-Driven Metric of Incentive Compatibility 激励兼容性的数据驱动度量

Proceedings of The Web Conference 2020 Pub Date : 2020-04-20 DOI: 10.1145/3366423.3380249

Yuan Deng, Sébastien Lahaie, V. Mirrokni, Song Zuo

引用次数: 11

P-Simrank: Extending Simrank to Scale-Free Bipartite Networks p - simmrank:将simmrank扩展到无标度二部网络

Proceedings of The Web Conference 2020 Pub Date : 2020-04-20 DOI: 10.1145/3366423.3380081

Prasenjit Dey, Kunal Goel, Rahul Agrawal

{"title":"P-Simrank: Extending Simrank to Scale-Free Bipartite Networks","authors":"Prasenjit Dey, Kunal Goel, Rahul Agrawal","doi":"10.1145/3366423.3380081","DOIUrl":"https://doi.org/10.1145/3366423.3380081","url":null,"abstract":"The measure of similarity between nodes in a graph is a useful tool in many areas of computer science. SimRank, proposed by Jeh and Widom [7], is a classic measure of similarities of nodes in graph that has both theoretical and intuitive properties and has been extensively studied and used in many applications such as Query-Rewriting, link prediction, collaborative filtering and so on. Existing works based on Simrank primarily focus on preserving the microscopic structure, such as the second and third order proximity of the vertices, while the macroscopic scale-free property is largely ignored. Scale-free property is a critical property of any real-world web graphs where the vertex degrees follow a heavy-tailed distribution. In this paper, we introduce P-Simrank which extends the idea of Simrank to Scale-free bipartite networks. To study the efficacy of the proposed solution on a real world problem, we tested the same on the well known query-rewriting problem in sponsored search domain using bipartite click graph, similar to Simrank++ [1], which acts as our baseline. We show that Simrank++ produces sub-optimal similarity scores in case of bipartite graphs where degree distribution of vertices follow power-law. We also show how P-Simrank can be optimized for real-world large graphs. Finally, we experimentally evaluate P-Simrank algorithm against Simrank++, using actual click graphs obtained from Bing, and show that P-Simrank outperforms Simrank++ in variety of metrics.","PeriodicalId":20754,"journal":{"name":"Proceedings of The Web Conference 2020","volume":"84 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77021093","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: 5

RLPer: A Reinforcement Learning Model for Personalized Search RLPer:个性化搜索的强化学习模型

Proceedings of The Web Conference 2020 Pub Date : 2020-04-20 DOI: 10.1145/3366423.3380294

Jing Yao, Zhicheng Dou, Jun Xu, Ji-rong Wen

{"title":"RLPer: A Reinforcement Learning Model for Personalized Search","authors":"Jing Yao, Zhicheng Dou, Jun Xu, Ji-rong Wen","doi":"10.1145/3366423.3380294","DOIUrl":"https://doi.org/10.1145/3366423.3380294","url":null,"abstract":"Personalized search improves generic ranking models by taking user interests into consideration and returning more accurate search results to individual users. In recent years, machine learning and deep learning techniques have been successfully applied in personalized search. Most existing personalization models simply regard the search history as a static set of user behaviours and learn fixed ranking strategies based on the recorded data. Though improvements have been observed, it is obvious that these methods ignore the dynamic nature of the search process: search is a sequence of interactions between the search engine and the user. During the search process, the user interests may dynamically change. It would be more helpful if a personalized search model could track the whole interaction process and update its ranking strategy continuously. In this paper, we propose a reinforcement learning based personalization model, referred to as RLPer, to track the sequential interactions between the users and search engine with a hierarchical Markov Decision Process (MDP). In RLPer, the search engine interacts with the user to update the underlying ranking model continuously with real-time feedback. And we design a feedback-aware personalized ranking component to catch the user’s feedback which has impacts on the user interest profile for the next query. Experimental results on the publicly available AOL search log verify that our proposed model can significantly outperform state-of-the-art personalized search models.","PeriodicalId":20754,"journal":{"name":"Proceedings of The Web Conference 2020","volume":"87 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89915837","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: 20