Proceedings of the 10th International Conference on Web Intelligence, Mining and Semantics最新文献_第3页

3D Face Recognition using Photometric Stereo and Deep Learning 使用光度立体和深度学习的3D人脸识别

Proceedings of the 10th International Conference on Web Intelligence, Mining and Semantics Pub Date : 2020-06-30 DOI: 10.1145/3405962.3405995

Bryan Kneis, Wenhao Zhang

{"title":"3D Face Recognition using Photometric Stereo and Deep Learning","authors":"Bryan Kneis, Wenhao Zhang","doi":"10.1145/3405962.3405995","DOIUrl":"https://doi.org/10.1145/3405962.3405995","url":null,"abstract":"Illumination variance is one of the largest real-world problems when deploying face recognition systems. Over the last few years much work has gone into the development of novel 3D face recognition methods to overcome this issue. Photometric stereo is a well-established 3D reconstruction technique capable of recovering the normals and albedo of a surface. Although it provides a way to obtain 3D data, the amount of training data available captured using photometric stereo often does not provide sufficient modelling capacity for training state-of-the-art feature extractors, such as deep convolutional neural networks, from scratch. In this work we present a novel approach to utilising the lighting apparatus commonly used for photometric stereo to synthesise data that can act as a biometric. Combining this with deep learning techniques not only did we achieve near state-of-the-art results, but it gave insight into the possibility of using photometric stereo without the need of reconstruction. This could not only simplify the face recognition process but avoid unnecessary error that may arise from reconstruction. Additionally, we utilise the active lighting from photometric stereo to evaluate the effect of illumination on face recognition. We compare our method to the state-of-the-art 3D methods and discuss potential use cases for our system.","PeriodicalId":247414,"journal":{"name":"Proceedings of the 10th International Conference on Web Intelligence, Mining and Semantics","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128771278","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

Lyrics or Audio for Music Recommendation? 歌词或音频音乐推荐?

Proceedings of the 10th International Conference on Web Intelligence, Mining and Semantics Pub Date : 2020-06-30 DOI: 10.1145/3405962.3405963

M. Vystrcilová, Ladislav Peška

引用次数: 10

Incremental Information Retrieval: Finding obscured information in Internet search 增量信息检索:查找互联网搜索中的模糊信息

Proceedings of the 10th International Conference on Web Intelligence, Mining and Semantics Pub Date : 2020-06-30 DOI: 10.1145/3405962.3405969

Erlend Johannessen, Randi Karlsen

{"title":"Incremental Information Retrieval: Finding obscured information in Internet search","authors":"Erlend Johannessen, Randi Karlsen","doi":"10.1145/3405962.3405969","DOIUrl":"https://doi.org/10.1145/3405962.3405969","url":null,"abstract":"When searching the internet e.g. for a person, solution to a problem, or some topic of interest, the wanted outcome is usually specific answers. The result quality for this kind of search is reasonably precise, most of the time we get the answers we need. However, searching a second or third time with the same query, the outcome seems to be minor variations on the same results. So what if the search for information is of a different nature, more like exploring. A typical case would be when a person has a hobby, and time after time wants to search for information about it. Very soon all the quickly accessed information has already been seen, and is not that interesting in the context of new information. This paper presents an approach to Incremental Information Retrieval, where each repeated search with a given query, will provide the user with previously obscured (i.e. unseen) results. We have implemented a prototype system, called IIR, where we demonstrate and test our approach. The system targets situations where users have a continuous information need, that cannot be satisfied through a single search on the Internet, but where the user may want to see new results on the same subject over a period of days, months, or even years. A detailed description of the IIR system and results of our tests are presented.","PeriodicalId":247414,"journal":{"name":"Proceedings of the 10th International Conference on Web Intelligence, Mining and Semantics","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132171724","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

BREXIT BREXIT

Proceedings of the 10th International Conference on Web Intelligence, Mining and Semantics Pub Date : 2020-06-30 DOI: 10.1145/3405962.3405981

J. Usher, Pierpaolo Dondio

{"title":"BREXIT","authors":"J. Usher, Pierpaolo Dondio","doi":"10.1145/3405962.3405981","DOIUrl":"https://doi.org/10.1145/3405962.3405981","url":null,"abstract":"Whilst the CIA have been using psychometric profiling for decades, Cambridge Analytica showed that peoples psychological characteristics can be accurately predicted from their digital footprints, such as their Facebook or Twitter accounts. To exploit this form of psychological assessment from digital footprints, we propose machine learning methods for assessing political personality from Twitter. We have extracted the tweet content of Prime Minster Boris Johnsons Twitter account and built three predictive personality models based on his Twitter political content. We use a Multi-Layer Perceptron Neural network, a Naive Bayes multinomial model and a Support Machine Vector model to predict the OCEAN model which consists of the Big Five personality factors from a sample of 3355 political tweets. The approach vectorizes political tweets, then it learns word vector representations as embeddings from spaCy that are then used to feed a supervised learner classifier. We demonstrate the effectiveness of the approach by measuring the quality of the predictions for each trait per model from a classification algorithm. Our findings show that all three models compute the personality trait \"Openness\" with the Support Machine Vector model achieving the highest accuracy. \"Extraversion\" achieved the second highest accuracy personality score by the Multi-Layer Perceptron neural network and Support Machine Vector model.","PeriodicalId":247414,"journal":{"name":"Proceedings of the 10th International Conference on Web Intelligence, Mining and Semantics","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125268646","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

Tumbling to Succeed: A Predictive Analysis of User Success in Interactive Ontology Visualization 走向成功:交互式本体可视化中用户成功的预测分析

Proceedings of the 10th International Conference on Web Intelligence, Mining and Semantics Pub Date : 2020-06-30 DOI: 10.1145/3405962.3405966

Bo Fu, B. Steichen, Alexandra McBride

{"title":"Tumbling to Succeed: A Predictive Analysis of User Success in Interactive Ontology Visualization","authors":"Bo Fu, B. Steichen, Alexandra McBride","doi":"10.1145/3405962.3405966","DOIUrl":"https://doi.org/10.1145/3405962.3405966","url":null,"abstract":"Ontology visualization is an important component in the support of human-ontology interaction, as it amplifies cognition and offloads cognitive efforts to the human perceptual system. While a significant amount of research efforts has focused on designing and developing various visual layouts and improve performance of large-scale visualizations, the differences in user preferences and cognitive abilities have been largely overlooked. This provides an opportunity to investigate ways to potentially provide more personalized visual support in human-ontology interaction. To this end, this paper demonstrates successful predictions on an individual user's likelihood to succeed in a given task, based on this person's gaze data collected during interaction. Specifically, we show several statistically significant predictions against a baseline classifier when inferring users' success before a given task is actually completed. Moreover, we present results showing that accurate predictions of user success can be achieved early on during user interaction, such as after just a few minutes in some cases. These findings suggest there are ample opportunities throughout various stages of human-ontology interaction where the underlying visual system may adapt in real time to the user's visual needs to provide the most appropriate visualization with the overall goal of possibly increasing user success in a given task.","PeriodicalId":247414,"journal":{"name":"Proceedings of the 10th International Conference on Web Intelligence, Mining and Semantics","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125910053","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

Automatic detection method of tourist spots using SNS 基于SNS的旅游景点自动检测方法

Proceedings of the 10th International Conference on Web Intelligence, Mining and Semantics Pub Date : 2020-06-30 DOI: 10.1145/3405962.3405993

Munenori Takahashi, Masaki Endo, Shigeyoshi Ohno, Masaharu Hirota, H. Ishikawa

{"title":"Automatic detection method of tourist spots using SNS","authors":"Munenori Takahashi, Masaki Endo, Shigeyoshi Ohno, Masaharu Hirota, H. Ishikawa","doi":"10.1145/3405962.3405993","DOIUrl":"https://doi.org/10.1145/3405962.3405993","url":null,"abstract":"Tourism information collection using the web has become popular in recent years. Moreover, tourists are increasingly using the web to obtain tourist information. Particularly because of the spread of social network services (SNSs), various tourism information is available. Various studies are being conducted using Twitter, which is one of SNS. A low-cost moving average method using geotagged tweets posted location information has been proposed to estimate the best time (peak period) for phenological observation. Geotagged tweets are also useful for estimating and acquiring local tourist information in real time, as a social sensor, because the information can reflect real-world situations. We have been working on, we are pursuing an estimation of the best time to view cherry blossoms. Our earlier studies have improved methods of estimating cherry blossom viewing times. The research so far can estimate the spot that the user knows. However, we cannot estimate the cherry blossoms that the users do not know. Therefore, a user requires a system that is independent of the amount of knowledge. It is possible to provide useful information to all users. We propose a prototype system that estimates the best time without prior knowledge of tourist destinations. In the early stages, the purpose is to use tweets to find spots already featured in magazines and the web. As described herein, we detected spots automatically using a geotagged tweet by visualization with a heat map and setting conditions. The proposed method achieved it in about 80%.","PeriodicalId":247414,"journal":{"name":"Proceedings of the 10th International Conference on Web Intelligence, Mining and Semantics","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130714113","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

A Hybrid Approach to Interpretable Analysis of Research Paper Collections 研究论文集合可解释性分析的混合方法

Proceedings of the 10th International Conference on Web Intelligence, Mining and Semantics Pub Date : 2020-06-30 DOI: 10.1145/3405962.3405976

B. Mirkin, Dmitry Frolov, Alex Vlasov, Susana Nascimento, T. Fenner

{"title":"A Hybrid Approach to Interpretable Analysis of Research Paper Collections","authors":"B. Mirkin, Dmitry Frolov, Alex Vlasov, Susana Nascimento, T. Fenner","doi":"10.1145/3405962.3405976","DOIUrl":"https://doi.org/10.1145/3405962.3405976","url":null,"abstract":"We define and find a most specific generalization of a fuzzy set of topics assigned to leaves of the rooted tree of a taxonomy. This generalization lifts the set to a \"head subject\" in the higher ranks of the taxonomy, that is supposed to \"tightly\" cover the query set, possibly bringing in some errors, both \"gaps\" and \"offshoots\". Our method involves two more automated analysis techniques: a fuzzy clustering method, FADDIS, involving both additive and spectral properties, and a purely structural string-to-text relevance measure based on suffix trees annotated by frequencies. We apply this to extract research tendencies from two collections of research papers: (a) about 18000 research papers published in Springer journals on data science for 20 years, and (b) about 27000 research papers retrieved from Springer and Elsevier journals in response to data science related queries. We consider a taxonomy of Data Science based on the Association for Computing Machinery Classification of Computing System (ACM-CCS 2012). Our findings allow us to make some comments on the tendencies of research that cannot be derived by using more conventional techniques.","PeriodicalId":247414,"journal":{"name":"Proceedings of the 10th International Conference on Web Intelligence, Mining and Semantics","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131890515","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

Cooperative Multi-Agent Reinforcement Learning for Spectrum Management in IoT Cognitive Networks 协同多智能体强化学习在物联网认知网络中的频谱管理

Proceedings of the 10th International Conference on Web Intelligence, Mining and Semantics Pub Date : 2020-06-30 DOI: 10.1145/3405962.3405996

Dejan Dašić, Miljan Vucetic, M. Perić, M. Beko, M. Stanković

{"title":"Cooperative Multi-Agent Reinforcement Learning for Spectrum Management in IoT Cognitive Networks","authors":"Dejan Dašić, Miljan Vucetic, M. Perić, M. Beko, M. Stanković","doi":"10.1145/3405962.3405996","DOIUrl":"https://doi.org/10.1145/3405962.3405996","url":null,"abstract":"The paper investigates the applications of cooperative Multi-Agent Reinforcement Learning (MARL) schemes to Cognitive Radio Networking (CRN), which in turn can facilitate spectrum utilization for wireless (ad hoc) networks within the Internet of Things (IoT). These schemes provide the ability of wireless transceivers to learn the optimal control and configuration in unknown environmental and application conditions, exploiting potential for cooperation among spectrum secondary users. An overview of the existing MARL approaches to the CRN is provided, with an analysis of their advantages and weaknesses compared to the rest of CRN approaches. We argue that in typical CRN practical scenarios including IoT systems, it is of essential importance that the cooperative algorithms are completely decentralized and distributed, having also a capability that the agents/nodes together can successfully calculate the optimal strategy even if the individual agents cannot. Hence, we propose a new scheme for cooperative spectrum sensing and selection within CRN, based on an adaptation of a recently proposed cooperative MARL scheme, provide detailed analysis of its properties and potential performance, indicating its superiority compared to the existing schemes.","PeriodicalId":247414,"journal":{"name":"Proceedings of the 10th International Conference on Web Intelligence, Mining and Semantics","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130155170","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

Towards Semantic Detection of Smells in Cloud Infrastructure Code 云基础架构代码中气味的语义检测

Proceedings of the 10th International Conference on Web Intelligence, Mining and Semantics Pub Date : 2020-06-30 DOI: 10.1145/3405962.3405979

I. Kumara, Zoe Vasileiou, G. Meditskos, D. Tamburri, W. Heuvel, A. Karakostas, S. Vrochidis, Y. Kompatsiaris

引用次数: 17

TRIAGE 伤检分类

Proceedings of the 10th International Conference on Web Intelligence, Mining and Semantics Pub Date : 2020-06-30 DOI: 10.1145/3405962.3405998

Ilias Dimitriadis, Marinos Poiitis, Christos Faloutsos, A. Vakali

{"title":"TRIAGE","authors":"Ilias Dimitriadis, Marinos Poiitis, Christos Faloutsos, A. Vakali","doi":"10.1145/3405962.3405998","DOIUrl":"https://doi.org/10.1145/3405962.3405998","url":null,"abstract":"Given a node-attributed network of Twitter users, can we capture their posting behavior over time and identify patterns that could probably describe, model or predict their activity? Based on the assumption that the posts of these users are topic-specific, can we identify temporal connectivity patterns that emerge from the use of specific attributes? More challengingly, are there any particular attribute usage patterns which indicate an inherent anomaly either for users or attributes? Our study attempts to provide solid answers to all the above questions, extending previous work on other social networks and attribute types. We propose TRIAGE, a pipeline of methods which: (a) identify temporal behavioral patterns in individual attribute distributions, (b) model the temporal evolution of attribute induced graphs and (c) detect irregular attributes and users based on the patterns identified earlier; More specifically, we model the attribute distributions using the log-Odds ratio, we provide explanations with respect to the attribute induced subgraph patterns and we observe the structural differences of attribute induced subgraphs based on these patterns. Experimental results show that: most of the individual attribute distributions remain stable over time following mostly power laws norm; the temporal evolution of attribute induced graphs obey certain laws and deviations are outliers; finally, we discover that we can indeed identify the structure of each subgraph, based on the emerging patterns. Real dataset experiments on 50K Twitter users activities and attributes has successfully proven that TRIAGE has effectively identified Twitter user and attribute behavioral patterns and can identify irregular activities for users and anomalous graph structures for attribute induced subgraphs.","PeriodicalId":247414,"journal":{"name":"Proceedings of the 10th International Conference on Web Intelligence, Mining and Semantics","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127331298","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