Many people browse reviews online before making purchasing decisions. It is essential to identify... more Many people browse reviews online before making purchasing decisions. It is essential to identify the subset of helpful reviews from the large number of reviews of varying quality. This paper aims to build a model to predict review helpfulness automatically. Our work is inspired by the observation that a customer's expectation of a review can be greatly affected by review sentiment and the degree to which the customer is aware of pertinent product information. Consequently, a customer may pay more attention to that specific content of a review which contributes more to its helpfulness from their perspective. To model such customer expectations and capture important information from a review text, we propose a novel neural network which leverages review sentiment and product information. Specifically, we encode the sentiment of a review through an attention module, to get sentiment-driven information from review text. We also introduce a product attention layer that fuses information from both the target product and related products, in order to capture the product related information from review text. Our experimental results show an AUC improvement of 5.4% and 1.5% over the previous state of the art model on Amazon and Yelp data sets, respectively.
With the fast growth of e-commerce, more people choose to purchase products online and browse rev... more With the fast growth of e-commerce, more people choose to purchase products online and browse reviews before making decisions. It is essential to identify helpful reviews, given the typical large number of reviews and the various range of quality. In this paper, we aim to build a model to predict review helpfulness automatically. Our work is inspired by the observation that a customer’s expectation of a review can be greatly affected by review sentiment and the degree to which the customer is aware of pertinent product information. Consequently, a customer may pay more attention to that specific content of a review which contributes more to its helpfulness from their perspective. To model such customer expectations and capture important information from a review text, we propose a novel neural network which leverages review sentiment and product information. Specifically, we encode the sentiment of a review through an attention module, to get sentiment-driven information from review...
The contextual information (i.e., the time and location) in which a photo is taken can be easily ... more The contextual information (i.e., the time and location) in which a photo is taken can be easily tampered with or falsely claimed by forgers to achieve malicious purposes, e.g., creating fear among the general public. A rich body of work has focused on detecting photo tampering and manipulation by verifying the integrity of image content. Instead, we aim to detect photo misuse by verifying the capture time and location of photos. This paper is motivated by the law of nature that sun position varies with the time and location, which can be used to determine whether the claimed contextual information corresponds with the sun position that the image content actually indicates. Prior approaches to inferring sun position from images mainly rely on vanishing points associated with at least two shadows, while we propose novel algorithms which utilize only one shadow in the image to infer the sun position. Meanwhile, we compute the sun position by applying astronomical algorithms which take...
In this paper we describe the implementation of a convolutional neural network (CNN) used to asse... more In this paper we describe the implementation of a convolutional neural network (CNN) used to assess online review helpfulness. To our knowledge, this is the first use of this architecture to address this problem. We explore the impact of two related factors impacting CNN performance: different word embedding initializations and different input review lengths. We also propose an approach to combining rating star information with review text to further improve prediction accuracy. We demonstrate that this can improve the overall accuracy by 2%. Finally, we evaluate the method on a benchmark dataset and show an improvement in accuracy relative to published results for traditional methods of 2.5% for a model trained using only review text and 4.24% for a model trained on a combination of rating star information and review text.
With the emerging of touch-less human-computer interaction techniques and gadgets, mid-air hand g... more With the emerging of touch-less human-computer interaction techniques and gadgets, mid-air hand gestures have been widely used for authentication. Much literature examined either the usability or security of a handful of gestures. This paper aims at quantifying usability and security of gestures as well as understanding their relationship across multiple gestures. To study gesture-based authentication, we design an authentication method that combines Dynamic Time Warping (DTW) and Support Vector Machine (SVM), and conducted a user study with 42 participants over a period of 6 weeks. We objectively quantify the usability of a gesture by the number of corners and the frame length of all gesture samples, quantify the security using the equal error rate (EER), and the consistency by EER over a period of time. Meanwhile, we obtain subjective evaluation of usability and security by conducting a survey. By examining the responses, we found that the subjective evaluation confirms with the o...
2019 IEEE International Conference on Big Data (Big Data), 2019
Real-time bidding (RTB) that features perimpression-level real-time ad auctions has become a popu... more Real-time bidding (RTB) that features perimpression-level real-time ad auctions has become a popular practice in today’s digital advertising industry. In RTB, click-through rate (CTR) prediction is a fundamental problem to ensure the success of an ad campaign and boost revenue. In this paper, we present a dynamic CTR prediction model designed for the Samsung demand-side platform (DSP). From our production data, we identify two key technical challenges that have not been fully addressed by the existing solutions: the dynamic nature of RTB and user information scarcity. To address both challenges, we develop a Dynamic Neural Network model. Our model effectively captures the dynamic evolutions of both users and ads and integrates auxiliary data sources (e.g., installed apps) to better model users’ preferences. We put forward a novel interaction layer that fuses both explicit user responses (e.g., clicks on ads) and auxiliary data sources to generate consolidated user preference representations. We evaluate our model using a large amount of data collected from the Samsung advertising platform and compare our method against several state-of-the-art methods that are likely suitable for real-world deployment. The evaluation results demonstrate the effectiveness of our method and the potential for production. In addition, we discuss how to address a few practical engineering challenges caused by big data toward making our model in readiness for deployment.
Photos have been commonly used in our society to convey information, and the associated contextua... more Photos have been commonly used in our society to convey information, and the associated contextual information (i.e., the capture time and location) is a key part of what a photo conveys. However, the contextual information can be easily tampered or falsely claimed by forgers to achieve malicious goals, e.g., creating fear among the general public or distorting public opinions. Thus, this paper aims at verifying the capture time and location using the content of the photos only. Motivated by how the ancients estimate the time of the day by shadows, we designed algorithms based on projective geometry to estimate the sun position by leveraging shadows in the image. Meanwhile, we compute the sun position by applying astronomical algorithms according to the claimed capture time and location. By comparing the two estimations of the sun position, we are able to validate the consistency of the capture time and location, and hence the time-location of the photos. Experimental results show that our algorithms can estimate sun position and detect the inconsistency caused by falsified time, date, and latitude of location. By choosing the thresholds to be 9.2 ∘ and 4.8 ∘ for the sun position distance and altitude angle distance respectively, our framework can correctly identify. % of the positive samples, with. % error in identifying the negative samples. Note that we assume that the photos contain at least one vertical object and its shadow. Nevertheless, we believe this work serves as the first and important attempt in verifying the consistency of the contextual information only using the content of the photos.
Proceedings of the 19th International Workshop on Mobile Computing Systems & Applications, 2018
With the proliferation of smartphones, children often use the same smartphones of their parents t... more With the proliferation of smartphones, children often use the same smartphones of their parents to play games or surf Internet, and can potentially access kid-unfriendly content from the Internet jungle. It is critical to employ parent patrol mechanisms such that children are limited to child-friendly contents only. A successful parent patrol strategy has to be user-friendly and privacy-aware. The apps that require explicit actions from parents may not be effective when parents forget to enable them, and the ones that use built-in cameras to detect children may impose privacy violations. In this paper, we propose iCare, which can identify child users automatically and seamlessly as users operate smartphones. In particular, iCare investigates the intrinsic differences of screen-touch patterns between child and adult users. We discover that users' touch behaviors depend on a user's age. Thus, iCare records the touch behaviors and extracts hand-geometry and finger dexterity features that capture the age information. We conducted experiments on 31 people including 17 elementary school kids (3 to 11 years old) and 14 adults (22 to 60). Results show that iCare can achieve 84% accuracy for child identification using only a single swipe on the screen, and the accuracy becomes 97% with 8 consecutive swipes.
With the proliferation of smart devices, children can be easily exposed to violent or adult-only ... more With the proliferation of smart devices, children can be easily exposed to violent or adult-only content on the Internet. Without any precaution, the premature and unsupervised use of smart devices can be harmful to both children and their parents. Thus, it is critical to employ parent patrol mechanisms such that children are restricted to child-friendly content only. A successful parent patrol strategy has to be user friendly and privacy aware. The apps that require explicit actions from parents are not effective because a parent may forget to enable them, and the ones that use built-in cameras or microphones to detect child users may impose privacy violations. In this article, we propose iCare, a system that can identify child users automatically and seamlessly when users operate smartphones. In particular, iCare investigates the intrinsic differences of screen-touch patterns between child and adult users from the aspect of physiological maturity. We discover that one’s touch beha...
Proceedings of the 6th ACM SIGPLAN International Symposium on Machine Programming
Today's programmers, especially data science practitioners, make heavy use of data-processing lib... more Today's programmers, especially data science practitioners, make heavy use of data-processing libraries (APIs) such as PyTorch, Tensorflow, NumPy, Pandas, and the like. Program synthesizers can provide significant coding assistance to this community of users; however program synthesis also can be slow due to enormous search spaces. In this work, we examine ways in which machine learning can be used to accelerate enumerative program synthesis. We present a deep-learning-based model to predict the sequence of API functions that would be needed to go from a given input to a desired output, both being numeric vectors. Our work is based on two insights. First, it is possible to learn, based on a large number of input-output examples, to predict the likely API function needed in a given situation. Second, and crucially, it is also possible to learn to compose API functions into a sequence, given an input and the desired final output, without explicitly knowing the intermediate values. We show that we can speed up an enumerative program synthesizer by using predictions from our model variants. These speedups significantly outperform previous ways (e.g. DeepCoder [1]) in which researchers have used ML models in enumerative synthesis.
Many people browse reviews online before making purchasing decisions. It is essential to identify... more Many people browse reviews online before making purchasing decisions. It is essential to identify the subset of helpful reviews from the large number of reviews of varying quality. This paper aims to build a model to predict review helpfulness automatically. Our work is inspired by the observation that a customer's expectation of a review can be greatly affected by review sentiment and the degree to which the customer is aware of pertinent product information. Consequently, a customer may pay more attention to that specific content of a review which contributes more to its helpfulness from their perspective. To model such customer expectations and capture important information from a review text, we propose a novel neural network which leverages review sentiment and product information. Specifically, we encode the sentiment of a review through an attention module, to get sentiment-driven information from review text. We also introduce a product attention layer that fuses information from both the target product and related products, in order to capture the product related information from review text. Our experimental results show an AUC improvement of 5.4% and 1.5% over the previous state of the art model on Amazon and Yelp data sets, respectively.
With the fast growth of e-commerce, more people choose to purchase products online and browse rev... more With the fast growth of e-commerce, more people choose to purchase products online and browse reviews before making decisions. It is essential to identify helpful reviews, given the typical large number of reviews and the various range of quality. In this paper, we aim to build a model to predict review helpfulness automatically. Our work is inspired by the observation that a customer’s expectation of a review can be greatly affected by review sentiment and the degree to which the customer is aware of pertinent product information. Consequently, a customer may pay more attention to that specific content of a review which contributes more to its helpfulness from their perspective. To model such customer expectations and capture important information from a review text, we propose a novel neural network which leverages review sentiment and product information. Specifically, we encode the sentiment of a review through an attention module, to get sentiment-driven information from review...
The contextual information (i.e., the time and location) in which a photo is taken can be easily ... more The contextual information (i.e., the time and location) in which a photo is taken can be easily tampered with or falsely claimed by forgers to achieve malicious purposes, e.g., creating fear among the general public. A rich body of work has focused on detecting photo tampering and manipulation by verifying the integrity of image content. Instead, we aim to detect photo misuse by verifying the capture time and location of photos. This paper is motivated by the law of nature that sun position varies with the time and location, which can be used to determine whether the claimed contextual information corresponds with the sun position that the image content actually indicates. Prior approaches to inferring sun position from images mainly rely on vanishing points associated with at least two shadows, while we propose novel algorithms which utilize only one shadow in the image to infer the sun position. Meanwhile, we compute the sun position by applying astronomical algorithms which take...
In this paper we describe the implementation of a convolutional neural network (CNN) used to asse... more In this paper we describe the implementation of a convolutional neural network (CNN) used to assess online review helpfulness. To our knowledge, this is the first use of this architecture to address this problem. We explore the impact of two related factors impacting CNN performance: different word embedding initializations and different input review lengths. We also propose an approach to combining rating star information with review text to further improve prediction accuracy. We demonstrate that this can improve the overall accuracy by 2%. Finally, we evaluate the method on a benchmark dataset and show an improvement in accuracy relative to published results for traditional methods of 2.5% for a model trained using only review text and 4.24% for a model trained on a combination of rating star information and review text.
With the emerging of touch-less human-computer interaction techniques and gadgets, mid-air hand g... more With the emerging of touch-less human-computer interaction techniques and gadgets, mid-air hand gestures have been widely used for authentication. Much literature examined either the usability or security of a handful of gestures. This paper aims at quantifying usability and security of gestures as well as understanding their relationship across multiple gestures. To study gesture-based authentication, we design an authentication method that combines Dynamic Time Warping (DTW) and Support Vector Machine (SVM), and conducted a user study with 42 participants over a period of 6 weeks. We objectively quantify the usability of a gesture by the number of corners and the frame length of all gesture samples, quantify the security using the equal error rate (EER), and the consistency by EER over a period of time. Meanwhile, we obtain subjective evaluation of usability and security by conducting a survey. By examining the responses, we found that the subjective evaluation confirms with the o...
2019 IEEE International Conference on Big Data (Big Data), 2019
Real-time bidding (RTB) that features perimpression-level real-time ad auctions has become a popu... more Real-time bidding (RTB) that features perimpression-level real-time ad auctions has become a popular practice in today’s digital advertising industry. In RTB, click-through rate (CTR) prediction is a fundamental problem to ensure the success of an ad campaign and boost revenue. In this paper, we present a dynamic CTR prediction model designed for the Samsung demand-side platform (DSP). From our production data, we identify two key technical challenges that have not been fully addressed by the existing solutions: the dynamic nature of RTB and user information scarcity. To address both challenges, we develop a Dynamic Neural Network model. Our model effectively captures the dynamic evolutions of both users and ads and integrates auxiliary data sources (e.g., installed apps) to better model users’ preferences. We put forward a novel interaction layer that fuses both explicit user responses (e.g., clicks on ads) and auxiliary data sources to generate consolidated user preference representations. We evaluate our model using a large amount of data collected from the Samsung advertising platform and compare our method against several state-of-the-art methods that are likely suitable for real-world deployment. The evaluation results demonstrate the effectiveness of our method and the potential for production. In addition, we discuss how to address a few practical engineering challenges caused by big data toward making our model in readiness for deployment.
Photos have been commonly used in our society to convey information, and the associated contextua... more Photos have been commonly used in our society to convey information, and the associated contextual information (i.e., the capture time and location) is a key part of what a photo conveys. However, the contextual information can be easily tampered or falsely claimed by forgers to achieve malicious goals, e.g., creating fear among the general public or distorting public opinions. Thus, this paper aims at verifying the capture time and location using the content of the photos only. Motivated by how the ancients estimate the time of the day by shadows, we designed algorithms based on projective geometry to estimate the sun position by leveraging shadows in the image. Meanwhile, we compute the sun position by applying astronomical algorithms according to the claimed capture time and location. By comparing the two estimations of the sun position, we are able to validate the consistency of the capture time and location, and hence the time-location of the photos. Experimental results show that our algorithms can estimate sun position and detect the inconsistency caused by falsified time, date, and latitude of location. By choosing the thresholds to be 9.2 ∘ and 4.8 ∘ for the sun position distance and altitude angle distance respectively, our framework can correctly identify. % of the positive samples, with. % error in identifying the negative samples. Note that we assume that the photos contain at least one vertical object and its shadow. Nevertheless, we believe this work serves as the first and important attempt in verifying the consistency of the contextual information only using the content of the photos.
Proceedings of the 19th International Workshop on Mobile Computing Systems & Applications, 2018
With the proliferation of smartphones, children often use the same smartphones of their parents t... more With the proliferation of smartphones, children often use the same smartphones of their parents to play games or surf Internet, and can potentially access kid-unfriendly content from the Internet jungle. It is critical to employ parent patrol mechanisms such that children are limited to child-friendly contents only. A successful parent patrol strategy has to be user-friendly and privacy-aware. The apps that require explicit actions from parents may not be effective when parents forget to enable them, and the ones that use built-in cameras to detect children may impose privacy violations. In this paper, we propose iCare, which can identify child users automatically and seamlessly as users operate smartphones. In particular, iCare investigates the intrinsic differences of screen-touch patterns between child and adult users. We discover that users' touch behaviors depend on a user's age. Thus, iCare records the touch behaviors and extracts hand-geometry and finger dexterity features that capture the age information. We conducted experiments on 31 people including 17 elementary school kids (3 to 11 years old) and 14 adults (22 to 60). Results show that iCare can achieve 84% accuracy for child identification using only a single swipe on the screen, and the accuracy becomes 97% with 8 consecutive swipes.
With the proliferation of smart devices, children can be easily exposed to violent or adult-only ... more With the proliferation of smart devices, children can be easily exposed to violent or adult-only content on the Internet. Without any precaution, the premature and unsupervised use of smart devices can be harmful to both children and their parents. Thus, it is critical to employ parent patrol mechanisms such that children are restricted to child-friendly content only. A successful parent patrol strategy has to be user friendly and privacy aware. The apps that require explicit actions from parents are not effective because a parent may forget to enable them, and the ones that use built-in cameras or microphones to detect child users may impose privacy violations. In this article, we propose iCare, a system that can identify child users automatically and seamlessly when users operate smartphones. In particular, iCare investigates the intrinsic differences of screen-touch patterns between child and adult users from the aspect of physiological maturity. We discover that one’s touch beha...
Proceedings of the 6th ACM SIGPLAN International Symposium on Machine Programming
Today's programmers, especially data science practitioners, make heavy use of data-processing lib... more Today's programmers, especially data science practitioners, make heavy use of data-processing libraries (APIs) such as PyTorch, Tensorflow, NumPy, Pandas, and the like. Program synthesizers can provide significant coding assistance to this community of users; however program synthesis also can be slow due to enormous search spaces. In this work, we examine ways in which machine learning can be used to accelerate enumerative program synthesis. We present a deep-learning-based model to predict the sequence of API functions that would be needed to go from a given input to a desired output, both being numeric vectors. Our work is based on two insights. First, it is possible to learn, based on a large number of input-output examples, to predict the likely API function needed in a given situation. Second, and crucially, it is also possible to learn to compose API functions into a sequence, given an input and the desired final output, without explicitly knowing the intermediate values. We show that we can speed up an enumerative program synthesizer by using predictions from our model variants. These speedups significantly outperform previous ways (e.g. DeepCoder [1]) in which researchers have used ML models in enumerative synthesis.
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