Pub Date : 2019-11-01DOI: 10.1109/ICTAI.2019.00121
Fangli Ren, Zhengwei Jiang, Jian Liu
Domain generation algorithms (DGA) are employed by malware to generate domain names as a common practice, with which to confirm rendezvous points to their command-and-control (C2) servers. The detection of DGA domain names is one of the important technologies for command and control communication detection. Considering the randomness of the DGA domain names, recent work in DGA detection employed machine learning methods based on features extracting and deep learning architectures to classify domain names. However, these methods perform poorly on wordlistbased DGA families, which generate domain names by randomly concatenating dictionary words. In this paper, we proposed the ATT-CNN-BiLSTM model to detect and classify DGA domain names. Firstly, the Convolutional Neural Network (CNN) and bidirectional Long Short-Term Memory (BiLSTM) neural network layer was used to extract the features of the domain sequences information; secondly, the attention layer was used to allocate the corresponding weight of the extracted domain deep information. Finally, the domain feature messages of different weights were put into the output layer to complete the tasks of detection and classification. The experiment results demonstrate the effectiveness of the proposed model both on regular DGA domain names and wordlist-based ones. To be precise, we got a F1 score of 98.92% for the detection and macro average F1 score of 81% for the classification task of DGA domain names.
{"title":"Integrating an Attention Mechanism and Deep Neural Network for Detection of DGA Domain Names","authors":"Fangli Ren, Zhengwei Jiang, Jian Liu","doi":"10.1109/ICTAI.2019.00121","DOIUrl":"https://doi.org/10.1109/ICTAI.2019.00121","url":null,"abstract":"Domain generation algorithms (DGA) are employed by malware to generate domain names as a common practice, with which to confirm rendezvous points to their command-and-control (C2) servers. The detection of DGA domain names is one of the important technologies for command and control communication detection. Considering the randomness of the DGA domain names, recent work in DGA detection employed machine learning methods based on features extracting and deep learning architectures to classify domain names. However, these methods perform poorly on wordlistbased DGA families, which generate domain names by randomly concatenating dictionary words. In this paper, we proposed the ATT-CNN-BiLSTM model to detect and classify DGA domain names. Firstly, the Convolutional Neural Network (CNN) and bidirectional Long Short-Term Memory (BiLSTM) neural network layer was used to extract the features of the domain sequences information; secondly, the attention layer was used to allocate the corresponding weight of the extracted domain deep information. Finally, the domain feature messages of different weights were put into the output layer to complete the tasks of detection and classification. The experiment results demonstrate the effectiveness of the proposed model both on regular DGA domain names and wordlist-based ones. To be precise, we got a F1 score of 98.92% for the detection and macro average F1 score of 81% for the classification task of DGA domain names.","PeriodicalId":346657,"journal":{"name":"2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129971873","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}
Pub Date : 2019-11-01DOI: 10.1109/ICTAI.2019.00080
Zhuo Wang, Haonan Qin, Yunsong Li, Jie Lei, Weiying Xie
With great significance in military and civilian applications, detecting indistinguishable small objects in wide-scale remote sensing images is still a challenging topic. In this work, we propose a specially optimized one-stage network (SOON) focusing on extracting spatial information of high-resolution images by understanding and analyzing the combination of feature and semantic information of small objects, which consists of feature enhancement, multi-scale detection, and feature fusion. The first part is implemented by constructing a receptive field enhancement (RFE) module and incorporating it into the specific parts of the network where the information of small objects mainly exists. The second part is achieved by four detectors with different sensitivities accessing to the fused and enhanced features, which enables the network to make full use of features in different scales. The third part consolidates the high-level and low-level features by adopting up-sampling, concatenation and convolution operations to build a feature pyramid structure, which explicitly yields strong feature representation and semantic information. In addition, we introduce the Soft-NMS to preserve accurate bounding boxes in the post-processing stage for densely arranged objects. Note that the split and merge strategy, as well as the multi-scale training strategy, are employed in this work. Extensive experiments and thorough analysis are performed on the NWPU VHR-10-v2 dataset and the ACS dataset as compared with several state-of-the-art methods, in which satisfactory performance verifies the effectiveness of the design and optimization. The code will be released for reproduction.
{"title":"SOON: Specifically Optimized One-Stage Network for Object Detection in Remote Sensing Imagery","authors":"Zhuo Wang, Haonan Qin, Yunsong Li, Jie Lei, Weiying Xie","doi":"10.1109/ICTAI.2019.00080","DOIUrl":"https://doi.org/10.1109/ICTAI.2019.00080","url":null,"abstract":"With great significance in military and civilian applications, detecting indistinguishable small objects in wide-scale remote sensing images is still a challenging topic. In this work, we propose a specially optimized one-stage network (SOON) focusing on extracting spatial information of high-resolution images by understanding and analyzing the combination of feature and semantic information of small objects, which consists of feature enhancement, multi-scale detection, and feature fusion. The first part is implemented by constructing a receptive field enhancement (RFE) module and incorporating it into the specific parts of the network where the information of small objects mainly exists. The second part is achieved by four detectors with different sensitivities accessing to the fused and enhanced features, which enables the network to make full use of features in different scales. The third part consolidates the high-level and low-level features by adopting up-sampling, concatenation and convolution operations to build a feature pyramid structure, which explicitly yields strong feature representation and semantic information. In addition, we introduce the Soft-NMS to preserve accurate bounding boxes in the post-processing stage for densely arranged objects. Note that the split and merge strategy, as well as the multi-scale training strategy, are employed in this work. Extensive experiments and thorough analysis are performed on the NWPU VHR-10-v2 dataset and the ACS dataset as compared with several state-of-the-art methods, in which satisfactory performance verifies the effectiveness of the design and optimization. The code will be released for reproduction.","PeriodicalId":346657,"journal":{"name":"2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130632872","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}
Pub Date : 2019-11-01DOI: 10.1109/ICTAI.2019.00185
Pavel Rytír, L. Chrpa, B. Bosanský
Many problems from classical planning are applied in the environment with other, possibly adversarial agents. However, plans found by classical planning algorithms lack the robustness against the actions of other agents - the quality of computed plans can be significantly worse compared to the model. To explicitly reason about other (adversarial) agents, the game-theoretic framework can be used. The scalability of game-theoretic algorithms, however, is limited and often insufficient for real-world problems. In this paper, we combine classical domain-independent planning algorithms and game-theoretic strategy-generation algorithm where plans form strategies in the game. Our contribution is threefold. First, we provide the methodology for using classical planning in this game-theoretic framework. Second, we analyze the trade-off between the quality of the planning algorithm and the robustness of final randomized plans and the computation time. Finally, we analyze different variants of integration of classical planning algorithms into the game-theoretic framework and show that at the cost a minor loss in the robustness of final plans, we can significantly reduce the computation time.
{"title":"Using Classical Planning in Adversarial Problems","authors":"Pavel Rytír, L. Chrpa, B. Bosanský","doi":"10.1109/ICTAI.2019.00185","DOIUrl":"https://doi.org/10.1109/ICTAI.2019.00185","url":null,"abstract":"Many problems from classical planning are applied in the environment with other, possibly adversarial agents. However, plans found by classical planning algorithms lack the robustness against the actions of other agents - the quality of computed plans can be significantly worse compared to the model. To explicitly reason about other (adversarial) agents, the game-theoretic framework can be used. The scalability of game-theoretic algorithms, however, is limited and often insufficient for real-world problems. In this paper, we combine classical domain-independent planning algorithms and game-theoretic strategy-generation algorithm where plans form strategies in the game. Our contribution is threefold. First, we provide the methodology for using classical planning in this game-theoretic framework. Second, we analyze the trade-off between the quality of the planning algorithm and the robustness of final randomized plans and the computation time. Finally, we analyze different variants of integration of classical planning algorithms into the game-theoretic framework and show that at the cost a minor loss in the robustness of final plans, we can significantly reduce the computation time.","PeriodicalId":346657,"journal":{"name":"2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130880561","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}
Pub Date : 2019-11-01DOI: 10.1109/ICTAI.2019.00207
Olfa Slama, A. Yazidi
In this paper, we propose an adaptive fuzzy user profiling method for SPARQL: an RDF query language [1]. This work extends the study [2] where we proposed a manner by which we enrich SPARQL with fuzzy user preferences expression. According to our approach, users issue generic fuzzy quantified queries that are further refined based on his/her past interactions with the system. Unlike [2], we avoid prompting the user for manual expression of his/her preferences. Online preference learning approaches are by definition adaptive to changes over time of the user preferences which make them more attractive than their static counter-part. In order to achieve online learning, we resort to stochastic search and propose to integrate two different types of user feedback, namely rank-based and score-based. The efficiency of this approach was validated by some experimental results.
{"title":"Learning Fuzzy SPARQL User Preferences","authors":"Olfa Slama, A. Yazidi","doi":"10.1109/ICTAI.2019.00207","DOIUrl":"https://doi.org/10.1109/ICTAI.2019.00207","url":null,"abstract":"In this paper, we propose an adaptive fuzzy user profiling method for SPARQL: an RDF query language [1]. This work extends the study [2] where we proposed a manner by which we enrich SPARQL with fuzzy user preferences expression. According to our approach, users issue generic fuzzy quantified queries that are further refined based on his/her past interactions with the system. Unlike [2], we avoid prompting the user for manual expression of his/her preferences. Online preference learning approaches are by definition adaptive to changes over time of the user preferences which make them more attractive than their static counter-part. In order to achieve online learning, we resort to stochastic search and propose to integrate two different types of user feedback, namely rank-based and score-based. The efficiency of this approach was validated by some experimental results.","PeriodicalId":346657,"journal":{"name":"2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"137 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124320159","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}
Pub Date : 2019-11-01DOI: 10.1109/ICTAI.2019.00051
Chunfeng Liu, Yan Zhang, Mei Yu, Xuewei Li, Mankun Zhao, Tianyi Xu, Jian Yu, Ruiguo Yu
Knowledge representation learning (KRL), which transforms both the entities and relations into continuous low dimensional continuous vector space, has attracted considerable research. Most of existing knowledge graph (KG) completion models only considers the structural representation of triples, but do not consider the important text information about entity descriptions in the knowledge base. We propose a text-enhanced KG model based on gated convolution network (GConvTE), which can learn entity descriptions and symbol triples jointly by feature fusion. Specifically, each triple (head entity, relation, tail entity) is represented as a 3-column structural embedding matrix, a 3-column textual embedding matrix and a 3-column joint embedding matrix where each column vector represents a triple element. Textual embeddings are obtained by bidirectional gated recurrent unit with attention (A-BGRU) encoding entity descriptions and joint embeddings are obtained by the combination of textual embeddings and structural embeddings. Extending feature dimension in embedding layer, these three matrixs are concatenated into 3-channel feature block to be fed into convolution layer, where the gated unit is added to selectively output the joint features maps. These feature maps are concatenated and then multiplied with a weight vector via a dot product to return a score. The experimental results show that our model GConvTE achieves better link performance than previous state-of-art embedding models on two benchmark datasets.
{"title":"Text-Enhanced Knowledge Representation Learning Based on Gated Convolutional Networks","authors":"Chunfeng Liu, Yan Zhang, Mei Yu, Xuewei Li, Mankun Zhao, Tianyi Xu, Jian Yu, Ruiguo Yu","doi":"10.1109/ICTAI.2019.00051","DOIUrl":"https://doi.org/10.1109/ICTAI.2019.00051","url":null,"abstract":"Knowledge representation learning (KRL), which transforms both the entities and relations into continuous low dimensional continuous vector space, has attracted considerable research. Most of existing knowledge graph (KG) completion models only considers the structural representation of triples, but do not consider the important text information about entity descriptions in the knowledge base. We propose a text-enhanced KG model based on gated convolution network (GConvTE), which can learn entity descriptions and symbol triples jointly by feature fusion. Specifically, each triple (head entity, relation, tail entity) is represented as a 3-column structural embedding matrix, a 3-column textual embedding matrix and a 3-column joint embedding matrix where each column vector represents a triple element. Textual embeddings are obtained by bidirectional gated recurrent unit with attention (A-BGRU) encoding entity descriptions and joint embeddings are obtained by the combination of textual embeddings and structural embeddings. Extending feature dimension in embedding layer, these three matrixs are concatenated into 3-channel feature block to be fed into convolution layer, where the gated unit is added to selectively output the joint features maps. These feature maps are concatenated and then multiplied with a weight vector via a dot product to return a score. The experimental results show that our model GConvTE achieves better link performance than previous state-of-art embedding models on two benchmark datasets.","PeriodicalId":346657,"journal":{"name":"2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124620379","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}
Pub Date : 2019-11-01DOI: 10.1109/ICTAI.2019.00265
Yingbin Xue, Xiaoye Wang, Zan Gao
The traditional methods of sentiment classification usually see the data has positive and negative two kinds of attitudes only. But actually, the real data has multi-category sentiment, is positive, negative, neutral and not mentioned four classes. Therefore, when using common classifying methods to analyzing the data sentiment, if the number of few class data is too scarce, it is difficult to learn useful information from them and the final classifying result will tend to most classes. In order to obtain accurate classification results, this paper proposes a multi-classification method based on the combination of Bert (Bidirectional Encoder Representation from Transformers) model and Liblinear (A Library for Large Linear Classification) model (It is abbreviated as B-Liblinear). Due to the Bert model's breakthrough in data preprocessing, this paper prepressed training data set, and obtained the word vector and sentence vectors from data. Next, combined with attribute label and sentiment tendency data, the unstructured data was converted into a structured training data set. It was as the standard input data of Liblinear model to construct a classification model. This model's classification mechanism is "one vs. rest", it can effectively solve the heavy class imbalance problem of massive data in multiple classification tasks. In this paper, the classification result of B-Liblinear model was compared with several classical multi-classification methods. And the experimental results show that the combination of Bert model and Liblinear of dealing with the text multi-classification problem is more accurate.
传统的情感分类方法通常只看到数据有积极和消极两种态度。但实际上，真实数据有多类情绪，有正面、负面、中性和未提四类。因此，在使用常用的分类方法进行数据情感分析时，如果少数类数据数量过少，很难从中学习到有用的信息，最终的分类结果会倾向于大多数类。为了获得准确的分类结果，本文提出了一种基于Bert (Bidirectional Encoder Representation from Transformers)模型和Liblinear (a Library for Large Linear classification)模型(简称B-Liblinear)相结合的多分类方法。由于Bert模型在数据预处理方面的突破，本文对训练数据集进行预压，从数据中得到词向量和句子向量。然后，结合属性标签和情绪倾向数据，将非结构化数据转换为结构化训练数据集。将其作为线性模型的标准输入数据，构建分类模型。该模型的分类机制是“一对余”，可以有效解决海量数据在多个分类任务中严重的类不平衡问题。本文将b -线性模型的分类结果与几种经典的多重分类方法进行了比较。实验结果表明，Bert模型与线性模型相结合处理文本多分类问题更为准确。
{"title":"Multi-classfication Sentiment Analysis Based on the Fused Model","authors":"Yingbin Xue, Xiaoye Wang, Zan Gao","doi":"10.1109/ICTAI.2019.00265","DOIUrl":"https://doi.org/10.1109/ICTAI.2019.00265","url":null,"abstract":"The traditional methods of sentiment classification usually see the data has positive and negative two kinds of attitudes only. But actually, the real data has multi-category sentiment, is positive, negative, neutral and not mentioned four classes. Therefore, when using common classifying methods to analyzing the data sentiment, if the number of few class data is too scarce, it is difficult to learn useful information from them and the final classifying result will tend to most classes. In order to obtain accurate classification results, this paper proposes a multi-classification method based on the combination of Bert (Bidirectional Encoder Representation from Transformers) model and Liblinear (A Library for Large Linear Classification) model (It is abbreviated as B-Liblinear). Due to the Bert model's breakthrough in data preprocessing, this paper prepressed training data set, and obtained the word vector and sentence vectors from data. Next, combined with attribute label and sentiment tendency data, the unstructured data was converted into a structured training data set. It was as the standard input data of Liblinear model to construct a classification model. This model's classification mechanism is \"one vs. rest\", it can effectively solve the heavy class imbalance problem of massive data in multiple classification tasks. In this paper, the classification result of B-Liblinear model was compared with several classical multi-classification methods. And the experimental results show that the combination of Bert model and Liblinear of dealing with the text multi-classification problem is more accurate.","PeriodicalId":346657,"journal":{"name":"2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128412457","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}
Pub Date : 2019-11-01DOI: 10.1109/ICTAI.2019.00187
Javier Maldonado, M. Riff
Detecting various types of attacks is a major problem in cybersecurity. In this paper, we show different configurations of an evolutionary wrapper algorithm for selecting features to classify attacks using a decision tree. We use two metrics for the evaluation function and evolutionary operator acceptance criteria. As part of our experiments, we interchange them and test the effect on the classification quality. Results show that the algorithm is able to guide the classification to accomplish different goals.
{"title":"Evaluating Different Metric Configurations of an Evolutionary Wrapper for Attack Detection","authors":"Javier Maldonado, M. Riff","doi":"10.1109/ICTAI.2019.00187","DOIUrl":"https://doi.org/10.1109/ICTAI.2019.00187","url":null,"abstract":"Detecting various types of attacks is a major problem in cybersecurity. In this paper, we show different configurations of an evolutionary wrapper algorithm for selecting features to classify attacks using a decision tree. We use two metrics for the evaluation function and evolutionary operator acceptance criteria. As part of our experiments, we interchange them and test the effect on the classification quality. Results show that the algorithm is able to guide the classification to accomplish different goals.","PeriodicalId":346657,"journal":{"name":"2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123168141","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}
Pub Date : 2019-11-01DOI: 10.1109/ICTAI.2019.00022
Keisuke Otaki, Satoshi Koide, K. Hayakawa, Ayano Okoso, Tomoki Nishi
Cooperation among different vehicles is a promising concept for Mobility as a Service (MaaS). A principal problem in MaaS is optimizing the vehicle routes to reduce the total travel cost with cooperation. For example, we know that platooning among large trucks could reduce the fuel cost because it decreases the air resistance. Traditional platoons, however, cannot model cooperation among different types of vehicles because the model assumes the homogeneity of vehicle types. We then propose a model that permits heterogeneous cooperation. Targets of our model include a logistic scenario, where a truck for the long-distance delivery also carries small self-driving vehicles for the last mile delivery. For those purposes, we formalize a new route optimization problem with heterogeneous cooperation, and provide its integer programming (IP) formulation as an exact solver. We evaluate our formulation through numerical experiments using synthetic and real graphs. We also validate our concept of heterogeneous cooperation for MaaS with examples.
{"title":"Multi-agent Path Planning with Heterogeneous Cooperation","authors":"Keisuke Otaki, Satoshi Koide, K. Hayakawa, Ayano Okoso, Tomoki Nishi","doi":"10.1109/ICTAI.2019.00022","DOIUrl":"https://doi.org/10.1109/ICTAI.2019.00022","url":null,"abstract":"Cooperation among different vehicles is a promising concept for Mobility as a Service (MaaS). A principal problem in MaaS is optimizing the vehicle routes to reduce the total travel cost with cooperation. For example, we know that platooning among large trucks could reduce the fuel cost because it decreases the air resistance. Traditional platoons, however, cannot model cooperation among different types of vehicles because the model assumes the homogeneity of vehicle types. We then propose a model that permits heterogeneous cooperation. Targets of our model include a logistic scenario, where a truck for the long-distance delivery also carries small self-driving vehicles for the last mile delivery. For those purposes, we formalize a new route optimization problem with heterogeneous cooperation, and provide its integer programming (IP) formulation as an exact solver. We evaluate our formulation through numerical experiments using synthetic and real graphs. We also validate our concept of heterogeneous cooperation for MaaS with examples.","PeriodicalId":346657,"journal":{"name":"2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127735118","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}
Pub Date : 2019-11-01DOI: 10.1109/ICTAI.2019.00191
Nicolas Gutowski, O. Camp, F. Chhel, Tassadit Amghar, Patrick Albers
The pervasive deployment of low cost WiFi access points has accelerated the development of mobile computing to provide ubiquitous computing. Herein, we focus first on the discovery of urban districts, in several french cities, using the connection history of mobile users to a city-wide free public Wi-Fi network. The goal of our approach is to infer relevant spatial context features that can be exploitable by bandit-based recommendation systems and improve their performances. For the unsupervised context reasoning step, we use spectral clustering to deduce areas by grouping Wi-Fi access points according to their users' visitations. We have published an anonymized sample of our dataset and our results on the web. Then, we have integrated the deduced spatial context into a mobile cultural events visualization and recommendation app in order to evaluate the global performance online. Thus, over a year we have observed how such spatial context improves bandit-based recommendations in this app by comparing two use cases of the LinUCB algorithm: the first using the original context without the deduced geo-context, and the second using context enriched by our computed spatial context. Finally, our online evaluation shows that better results are obtained when combining spatial context reasoning with the bandit-based recommendation system, both in terms of accuracy and user participation.
{"title":"Improving Bandit-Based Recommendations with Spatial Context Reasoning: An Online Evaluation","authors":"Nicolas Gutowski, O. Camp, F. Chhel, Tassadit Amghar, Patrick Albers","doi":"10.1109/ICTAI.2019.00191","DOIUrl":"https://doi.org/10.1109/ICTAI.2019.00191","url":null,"abstract":"The pervasive deployment of low cost WiFi access points has accelerated the development of mobile computing to provide ubiquitous computing. Herein, we focus first on the discovery of urban districts, in several french cities, using the connection history of mobile users to a city-wide free public Wi-Fi network. The goal of our approach is to infer relevant spatial context features that can be exploitable by bandit-based recommendation systems and improve their performances. For the unsupervised context reasoning step, we use spectral clustering to deduce areas by grouping Wi-Fi access points according to their users' visitations. We have published an anonymized sample of our dataset and our results on the web. Then, we have integrated the deduced spatial context into a mobile cultural events visualization and recommendation app in order to evaluate the global performance online. Thus, over a year we have observed how such spatial context improves bandit-based recommendations in this app by comparing two use cases of the LinUCB algorithm: the first using the original context without the deduced geo-context, and the second using context enriched by our computed spatial context. Finally, our online evaluation shows that better results are obtained when combining spatial context reasoning with the bandit-based recommendation system, both in terms of accuracy and user participation.","PeriodicalId":346657,"journal":{"name":"2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114071989","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}
Pub Date : 2019-11-01DOI: 10.1109/ICTAI.2019.00202
Mathilde Fekom, N. Vayatis, Argyris Kalogeratos
In this paper we present the Warm-starting Dynamic Thresholding algorithm, developed using dynamic programming, for a variant of the standard online selection problem. The problem allows job positions to be either free or already occupied at the beginning of the process. Throughout the selection process, the decision maker interviews one after the other the new candidates and reveals a quality score for each of them. Based on that information, she can (re) assign each job at most once by taking immediate and irrevocable decisions. We relax the hard requirement of the class of dynamic programming algorithms to perfectly know the distribution from which the scores of candidates are drawn, by presenting extensions for the partial and no-information cases, in which the decision maker can learn the underlying score distribution sequentially while interviewing candidates.
{"title":"Optimal Multiple Stopping Rule for Warm-Starting Sequential Selection","authors":"Mathilde Fekom, N. Vayatis, Argyris Kalogeratos","doi":"10.1109/ICTAI.2019.00202","DOIUrl":"https://doi.org/10.1109/ICTAI.2019.00202","url":null,"abstract":"In this paper we present the Warm-starting Dynamic Thresholding algorithm, developed using dynamic programming, for a variant of the standard online selection problem. The problem allows job positions to be either free or already occupied at the beginning of the process. Throughout the selection process, the decision maker interviews one after the other the new candidates and reveals a quality score for each of them. Based on that information, she can (re) assign each job at most once by taking immediate and irrevocable decisions. We relax the hard requirement of the class of dynamic programming algorithms to perfectly know the distribution from which the scores of candidates are drawn, by presenting extensions for the partial and no-information cases, in which the decision maker can learn the underlying score distribution sequentially while interviewing candidates.","PeriodicalId":346657,"journal":{"name":"2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114281590","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}
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