Zhiping Xiao | University of California, Los Angeles (original) (raw)

Papers by Zhiping Xiao

The base station (BS) in a multi-channel cognitive radio (CR) network has to broadcast to seconda... more The base station (BS) in a multi-channel cognitive radio (CR) network has to broadcast to secondary (or unlicensed) receivers/users on more than one broadcast channels via channel hopping (CH), because a single broadcast channel can be reclaimed by the primary (or licensed) user, leading to broadcast failures. Meanwhile, a secondary receiver needs to synchronize its clock with the BS's clock to avoid broadcast failures caused by the possible clock drift between the CH sequences of the secondary receiver and the BS. In this paper, we propose a CH-based broadcast protocol called SASS, which enables a BS to successfully broadcast to secondary receivers over multiple broadcast channels via channel hopping. Specifically, the CH sequences are constructed on basis of a mathematical construct-the Self-Adaptive Skolem sequence. Moreover, each secondary receiver under SASS is able to adaptively synchronize its clock with that of the BS without any information exchanges, regardless of any amount of clock drift.

Proceedings of the 49th ACM Technical Symposium on Computer Science Education, 2018

2016 IEEE 83rd Vehicular Technology Conference (VTC Spring), 2016

The base station (BS) in a multi-channel cognitive radio (CR) network has to broadcast to seconda... more The base station (BS) in a multi-channel cognitive radio (CR) network has to broadcast to secondary (or unlicensed) receivers/users on more than one broadcast channels via channel hopping (CH), because a single broadcast channel can be reclaimed by the primary (or licensed) user, leading to broadcast failures. Meanwhile, a secondary receiver needs to synchronize its clock with the BS's clock to avoid broadcast failures caused by the possible clock drift between the CH sequences of the secondary receiver and the BS. In this paper, we propose a CH-based broadcast protocol called SASS, which enables a BS to successfully broadcast to secondary receivers over multiple broadcast channels via channel hopping. Specifically, the CH sequences are constructed on basis of a mathematical construct-the Self-Adaptive Skolem sequence. Moreover, each secondary receiver under SASS is able to adaptively synchronize its clock with that of the BS without any information exchanges, regardless of any amount of clock drift.

arXiv (Cornell University), Sep 16, 2022

Ideological divisions in the United States have become increasingly prominent in daily communicat... more Ideological divisions in the United States have become increasingly prominent in daily communication. Accordingly, there has been much research on political polarization, including many recent efforts that take a computational perspective. By detecting political biases in a corpus of text, one can attempt to describe and discern the polarity of that text. Intuitively, the named entities (i.e., the nouns and the phrases that act as nouns) and hashtags in text often carry information about political views. For example, people who use the term "pro-choice" are likely to be liberal, whereas people who use the term "pro-life" are likely to be conservative. In this paper, we seek to reveal political polarities in social-media text data and to quantify these polarities by explicitly assigning a polarity score to entities and hashtags. Although this idea is straightforward, it is difficult to perform such inference in a trustworthy quantitative way. Key challenges include the small number of known labels, the continuous spectrum of political views, and the preservation of both a polarity score and a polarity-neutral semantic meaning in an embedding vector of words. To attempt to overcome these challenges, we propose the Polarity-aware Embedding Multi-task learning (PEM) model. This model consists of (1) a self-supervised context-preservation task, (2) an attention-based tweet-level polarity-inference task, and (3) an adversarial learning task that promotes independence between an embedding's polarity dimension and its semantic dimensions. Our experimental results demonstrate that our PEM model can successfully learn polarity-aware embeddings that perform well at tweet-level and account-level classification tasks. We examine a variety of applications-including spatial and temporal distributions of polarities and a comparison between tweets from Twitter and posts from Parler-and we thereby demonstrate the effectiveness of our PEM model. We also discuss important limitations of our work and encourage caution when applying the PEM model to real-world scenarios.

In this project, we study the problem of political polarity detection, which aims to predict a pe... more In this project, we study the problem of political polarity detection, which aims to predict a person’s political leaning according to his/her behaviors on social network. We designed a conditional random field model that can classify a person into two partisans: democratic and republican, conducted experiment on a politicians’ twitter dataset released in 2016 and compared with existing baselines, and also collected a new twitter dataset with more complete information. Our experiment results indicate our model is effective.

Proceedings of the 28th ACM International Conference on Information and Knowledge Management, 2019

Click-through rate (CTR) prediction, which aims to predict the probability of a user clicking on ... more Click-through rate (CTR) prediction, which aims to predict the probability of a user clicking on an ad or an item, is critical to many online applications such as online advertising and recommender systems. The problem is very challenging since (1) the input features (e.g., the user id, user age, item id, item category) are usually sparse and high-dimensional, and (2) an effective prediction relies on highorder combinatorial features (a.k.a. cross features), which are very time-consuming to hand-craft by domain experts and are impossible to be enumerated. Therefore, there have been efforts in finding lowdimensional representations of the sparse and high-dimensional raw features and their meaningful combinations. In this paper, we propose an effective and efficient method called the AutoInt to automatically learn the high-order feature interactions of input features. Our proposed algorithm is very general, which can be applied to both numerical and categorical input features. Specifically, we map both the numerical and categorical features into the same low-dimensional space. Afterwards, a multihead self-attentive neural network with residual connections is proposed to explicitly model the feature interactions in the lowdimensional space. With different layers of the multi-head selfattentive neural networks, different orders of feature combinations of input features can be modeled. The whole model can be efficiently fit on large-scale raw data in an end-to-end fashion. Experimental results on four real-world datasets show that our proposed approach not only outperforms existing state-of-the-art approaches for prediction but also offers good explainability. Code is available at: https://github.com/DeepGraphLearning/RecommenderSystems.

Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, 2019

Online communities such as Facebook and Twitter are enormously popular and have become an essenti... more Online communities such as Facebook and Twitter are enormously popular and have become an essential part of the daily life of many of their users. Through these platforms, users can discover and create information that others will then consume. In that context, recommending relevant information to users becomes critical for viability. However, recommendation in online communities is a challenging problem: 1) users' interests are dynamic, and 2) users are influenced by their friends. Moreover, the influencers may be context-dependent. That is, different friends may be relied upon for different topics. Modeling both signals is therefore essential for recommendations. We propose a recommender system for online communities based on a dynamic-graph-attention neural network. We model dynamic user behaviors with a recurrent neural network, and contextdependent social influence with a graph-attention neural network, which dynamically infers the influencers based on users' current interests. The whole model can be efficiently fit on large-scale data. Experimental results on several real-world data sets demonstrate the effectiveness of our proposed approach over several competitive baselines including state-of-the-art models. The source code and data are available at https://github.com/DeepGraphLearning/ RecommenderSystems.

Proceedings of the 2016 ITiCSE Working Group Reports, 2016

Games can be a valuable tool for enriching computer science education, since they can facilitate ... more Games can be a valuable tool for enriching computer science education, since they can facilitate a number of conditions that promote learning: student motivation, active learning, adaptivity, collaboration, and simulation. Additionally, they provide the instructor the ability to collect learning metrics with relative ease. As part of 21st Annual Conference on Innovation and Technology in Computer Science Education (ITiCSE 2016), the Game Development for Computer Science Education working group convened to examine the current role games play in computer science (CS) education, including where and how they fit into CS education. Based on reviews of literature, academic research, professional practice, and a comprehensive list of games for computing education, we present this working group report. This report provides a summary of existing digital games designed to enrich computing education, an index of where these games may fit into a teaching paradigm using the ACM/IEEE Computer Science Curricula 2013 [13], and a guide to developing digital games designed to teach knowledge, skills, and attitudes related to computer science.

Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

We aim at solving the problem of predicting people's ideology, or political tendency. We estimate... more We aim at solving the problem of predicting people's ideology, or political tendency. We estimate it by using Twitter data, and formalize it as a classification problem. Ideology-detection has long been a challenging yet important problem. Certain groups, such as the policy makers, rely on it to make wise decisions. Back in the old days when labor-intensive survey-studies were needed to collect public opinions, analyzing ordinary citizens' political tendencies was uneasy. The rise of social medias, such as Twitter, has enabled us to gather ordinary citizen's data easily. However, the incompleteness of the labels and the features in social network datasets is tricky, not to mention the enormous data size and the heterogeneousity. The data differ dramatically from many commonly-used datasets, thus brings unique challenges. In our work, first we built our own datasets from Twitter. Next, we proposed TIMME, a multitask multi-relational embedding model, that works efficiently on sparsely-labeled heterogeneous real-world dataset. It could also handle the incompleteness of the input features. Experimental results showed that TIMME is overall better than the state-of-the-art models for ideology detection on Twitter. Our findings include: links can lead to good classification outcomes without text; conservative voice is under-represented on Twitter; follow is the most important relation to predict ideology; retweet and mention enhance a higher chance of like, etc. Last but not least, TIMME could be extended to other datasets and tasks in theory. CCS CONCEPTS • Computing methodologies → Multi-task learning; Neural networks.

The base station (BS) in a multi-channel cognitive radio (CR) network has to broadcast to seconda... more The base station (BS) in a multi-channel cognitive radio (CR) network has to broadcast to secondary (or unlicensed) receivers/users on more than one broadcast channels via channel hopping (CH), because a single broadcast channel can be reclaimed by the primary (or licensed) user, leading to broadcast failures. Meanwhile, a secondary receiver needs to synchronize its clock with the BS's clock to avoid broadcast failures caused by the possible clock drift between the CH sequences of the secondary receiver and the BS. In this paper, we propose a CH-based broadcast protocol called SASS, which enables a BS to successfully broadcast to secondary receivers over multiple broadcast channels via channel hopping. Specifically, the CH sequences are constructed on basis of a mathematical construct-the Self-Adaptive Skolem sequence. Moreover, each secondary receiver under SASS is able to adaptively synchronize its clock with that of the BS without any information exchanges, regardless of any amount of clock drift.

Proceedings of the 49th ACM Technical Symposium on Computer Science Education, 2018

2016 IEEE 83rd Vehicular Technology Conference (VTC Spring), 2016

The base station (BS) in a multi-channel cognitive radio (CR) network has to broadcast to seconda... more The base station (BS) in a multi-channel cognitive radio (CR) network has to broadcast to secondary (or unlicensed) receivers/users on more than one broadcast channels via channel hopping (CH), because a single broadcast channel can be reclaimed by the primary (or licensed) user, leading to broadcast failures. Meanwhile, a secondary receiver needs to synchronize its clock with the BS's clock to avoid broadcast failures caused by the possible clock drift between the CH sequences of the secondary receiver and the BS. In this paper, we propose a CH-based broadcast protocol called SASS, which enables a BS to successfully broadcast to secondary receivers over multiple broadcast channels via channel hopping. Specifically, the CH sequences are constructed on basis of a mathematical construct-the Self-Adaptive Skolem sequence. Moreover, each secondary receiver under SASS is able to adaptively synchronize its clock with that of the BS without any information exchanges, regardless of any amount of clock drift.

arXiv (Cornell University), Sep 16, 2022

Ideological divisions in the United States have become increasingly prominent in daily communicat... more Ideological divisions in the United States have become increasingly prominent in daily communication. Accordingly, there has been much research on political polarization, including many recent efforts that take a computational perspective. By detecting political biases in a corpus of text, one can attempt to describe and discern the polarity of that text. Intuitively, the named entities (i.e., the nouns and the phrases that act as nouns) and hashtags in text often carry information about political views. For example, people who use the term "pro-choice" are likely to be liberal, whereas people who use the term "pro-life" are likely to be conservative. In this paper, we seek to reveal political polarities in social-media text data and to quantify these polarities by explicitly assigning a polarity score to entities and hashtags. Although this idea is straightforward, it is difficult to perform such inference in a trustworthy quantitative way. Key challenges include the small number of known labels, the continuous spectrum of political views, and the preservation of both a polarity score and a polarity-neutral semantic meaning in an embedding vector of words. To attempt to overcome these challenges, we propose the Polarity-aware Embedding Multi-task learning (PEM) model. This model consists of (1) a self-supervised context-preservation task, (2) an attention-based tweet-level polarity-inference task, and (3) an adversarial learning task that promotes independence between an embedding's polarity dimension and its semantic dimensions. Our experimental results demonstrate that our PEM model can successfully learn polarity-aware embeddings that perform well at tweet-level and account-level classification tasks. We examine a variety of applications-including spatial and temporal distributions of polarities and a comparison between tweets from Twitter and posts from Parler-and we thereby demonstrate the effectiveness of our PEM model. We also discuss important limitations of our work and encourage caution when applying the PEM model to real-world scenarios.

In this project, we study the problem of political polarity detection, which aims to predict a pe... more In this project, we study the problem of political polarity detection, which aims to predict a person’s political leaning according to his/her behaviors on social network. We designed a conditional random field model that can classify a person into two partisans: democratic and republican, conducted experiment on a politicians’ twitter dataset released in 2016 and compared with existing baselines, and also collected a new twitter dataset with more complete information. Our experiment results indicate our model is effective.

Proceedings of the 28th ACM International Conference on Information and Knowledge Management, 2019

Click-through rate (CTR) prediction, which aims to predict the probability of a user clicking on ... more Click-through rate (CTR) prediction, which aims to predict the probability of a user clicking on an ad or an item, is critical to many online applications such as online advertising and recommender systems. The problem is very challenging since (1) the input features (e.g., the user id, user age, item id, item category) are usually sparse and high-dimensional, and (2) an effective prediction relies on highorder combinatorial features (a.k.a. cross features), which are very time-consuming to hand-craft by domain experts and are impossible to be enumerated. Therefore, there have been efforts in finding lowdimensional representations of the sparse and high-dimensional raw features and their meaningful combinations. In this paper, we propose an effective and efficient method called the AutoInt to automatically learn the high-order feature interactions of input features. Our proposed algorithm is very general, which can be applied to both numerical and categorical input features. Specifically, we map both the numerical and categorical features into the same low-dimensional space. Afterwards, a multihead self-attentive neural network with residual connections is proposed to explicitly model the feature interactions in the lowdimensional space. With different layers of the multi-head selfattentive neural networks, different orders of feature combinations of input features can be modeled. The whole model can be efficiently fit on large-scale raw data in an end-to-end fashion. Experimental results on four real-world datasets show that our proposed approach not only outperforms existing state-of-the-art approaches for prediction but also offers good explainability. Code is available at: https://github.com/DeepGraphLearning/RecommenderSystems.

Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, 2019

Online communities such as Facebook and Twitter are enormously popular and have become an essenti... more Online communities such as Facebook and Twitter are enormously popular and have become an essential part of the daily life of many of their users. Through these platforms, users can discover and create information that others will then consume. In that context, recommending relevant information to users becomes critical for viability. However, recommendation in online communities is a challenging problem: 1) users' interests are dynamic, and 2) users are influenced by their friends. Moreover, the influencers may be context-dependent. That is, different friends may be relied upon for different topics. Modeling both signals is therefore essential for recommendations. We propose a recommender system for online communities based on a dynamic-graph-attention neural network. We model dynamic user behaviors with a recurrent neural network, and contextdependent social influence with a graph-attention neural network, which dynamically infers the influencers based on users' current interests. The whole model can be efficiently fit on large-scale data. Experimental results on several real-world data sets demonstrate the effectiveness of our proposed approach over several competitive baselines including state-of-the-art models. The source code and data are available at https://github.com/DeepGraphLearning/ RecommenderSystems.

Proceedings of the 2016 ITiCSE Working Group Reports, 2016

Games can be a valuable tool for enriching computer science education, since they can facilitate ... more Games can be a valuable tool for enriching computer science education, since they can facilitate a number of conditions that promote learning: student motivation, active learning, adaptivity, collaboration, and simulation. Additionally, they provide the instructor the ability to collect learning metrics with relative ease. As part of 21st Annual Conference on Innovation and Technology in Computer Science Education (ITiCSE 2016), the Game Development for Computer Science Education working group convened to examine the current role games play in computer science (CS) education, including where and how they fit into CS education. Based on reviews of literature, academic research, professional practice, and a comprehensive list of games for computing education, we present this working group report. This report provides a summary of existing digital games designed to enrich computing education, an index of where these games may fit into a teaching paradigm using the ACM/IEEE Computer Science Curricula 2013 [13], and a guide to developing digital games designed to teach knowledge, skills, and attitudes related to computer science.

Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

We aim at solving the problem of predicting people's ideology, or political tendency. We estimate... more We aim at solving the problem of predicting people's ideology, or political tendency. We estimate it by using Twitter data, and formalize it as a classification problem. Ideology-detection has long been a challenging yet important problem. Certain groups, such as the policy makers, rely on it to make wise decisions. Back in the old days when labor-intensive survey-studies were needed to collect public opinions, analyzing ordinary citizens' political tendencies was uneasy. The rise of social medias, such as Twitter, has enabled us to gather ordinary citizen's data easily. However, the incompleteness of the labels and the features in social network datasets is tricky, not to mention the enormous data size and the heterogeneousity. The data differ dramatically from many commonly-used datasets, thus brings unique challenges. In our work, first we built our own datasets from Twitter. Next, we proposed TIMME, a multitask multi-relational embedding model, that works efficiently on sparsely-labeled heterogeneous real-world dataset. It could also handle the incompleteness of the input features. Experimental results showed that TIMME is overall better than the state-of-the-art models for ideology detection on Twitter. Our findings include: links can lead to good classification outcomes without text; conservative voice is under-represented on Twitter; follow is the most important relation to predict ideology; retweet and mention enhance a higher chance of like, etc. Last but not least, TIMME could be extended to other datasets and tasks in theory. CCS CONCEPTS • Computing methodologies → Multi-task learning; Neural networks.