Inspect: Iterated Local Search For Solving Path Conditions,
2019
Hong Kong University of Science and Technology
Inspect: Iterated Local Search For Solving Path Conditions, Fuxiang Chen, Aldy Gunawan, David Lo, Sunghun Kim
Research Collection School Of Computing and Information Systems
Automated test case generation is attractive as it can reduce developer workload. To generate test cases, many Symbolic Execution approaches first produce Path Conditions (PCs), a set of constraints, and pass them to a Satisfiability Modulo Theories (SMT) solver. Despite numerous prior studies, automated test case generation by Symbolic Execution is still slow, partly due to SMT solvers’ high computationally complexity. We introduce InSPeCT, a Path Condition solver, that leverages elements of ILS (Iterated Local Search) and Tabu List. ILS is not computational intensive and focuses on generating solutions in search spaces while Tabu List prevents the use of previously …
Kgat: Knowledge Graph Attention Network For Recommendation,
2019
Singapore Management University
Kgat: Knowledge Graph Attention Network For Recommendation, Xiang Wang, Xiangnan He, Yixin Cao, Meng Liu, Tat-Seng Chua
Research Collection School Of Computing and Information Systems
To provide more accurate, diverse, and explainable recommendation, it is compulsory to go beyond modeling user-item interactions and take side information into account. Traditional methods like factorization machine (FM) cast it as a supervised learning problem, which assumes each interaction as an independent instance with side information encoded. Due to the overlook of the relations among instances or items (e.g., the director of a movie is also an actor of another movie), these methods are insufficient to distill the collaborative signal from the collective behaviors of users. In this work, we investigate the utility of knowledge graph (KG), which breaks …
Fintech Empowerment: Data Science, Ai, And Machine Learning,
2019
Singapore Management University
Fintech Empowerment: Data Science, Ai, And Machine Learning, Keng Siau, Michael Hilgers, Langtao Chen, Steve Liu, Fiona Fui-Hoon Nah, Richard Hall, Barry Flachsbart
Research Collection School Of Computing and Information Systems
The article discusses how data science, artificial intelligence and machine learning are affecting the evolution of “fintech,” the technologies used to deliver financial services. After presenting fintech’s competitive advantages in combination with these other advanced technologies, the article posits that financial institutions that don’t move forward with the innovations will be eliminated from the marketplace.
Multimodal Transformer Networks For End-To-End Video-Grounded Dialogue Systems,
2019
Singapore Management University
Multimodal Transformer Networks For End-To-End Video-Grounded Dialogue Systems, Hung Le, Doyen Sahoo, Nancy F. Chen, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
Developing Video-Grounded Dialogue Systems (VGDS), where a dialogue is conducted based on visual and audio aspects of a given video, is significantly more challenging than traditional image or text-grounded dialogue systems because (1) feature space of videos span across multiple picture frames, making it difficult to obtain semantic information; and (2) a dialogue agent must perceive and process information from different modalities (audio, video, caption, etc.) to obtain a comprehensive understanding. Most existing work is based on RNNs and sequence-to-sequence architectures, which are not very effective for capturing complex long-term dependencies (like in videos). To overcome this, we propose Multimodal …
Correlation-Sensitive Next-Basket Recommendation,
2019
Singapore Management University
Correlation-Sensitive Next-Basket Recommendation, Duc Trong Le, Hady Wirawan Lauw, Yuan Fang
Research Collection School Of Computing and Information Systems
Items adopted by a user over time are indicative ofthe underlying preferences. We are concerned withlearning such preferences from observed sequencesof adoptions for recommendation. As multipleitems are commonly adopted concurrently, e.g., abasket of grocery items or a sitting of media consumption, we deal with a sequence of baskets asinput, and seek to recommend the next basket. Intuitively, a basket tends to contain groups of relateditems that support particular needs. Instead of recommending items independently for the next basket, we hypothesize that incorporating informationon pairwise correlations among items would help toarrive at more coherent basket recommendations.Towards this objective, we develop a …
Adapting Bert For Target-Oriented Multimodal Sentiment Classification,
2019
Singapore Management University
Adapting Bert For Target-Oriented Multimodal Sentiment Classification, Jianfei Yu, Jing Jiang
Research Collection School Of Computing and Information Systems
As an important task in Sentiment Analysis, Target-oriented Sentiment Classification (TSC) aims to identify sentiment polarities over each opinion target in a sentence. However, existing approaches to this task primarily rely on the textual content, but ignoring the other increasingly popular multimodal data sources (e.g., images), which can enhance the robustness of these text-based models. Motivated by this observation and inspired by the recently proposed BERT architecture, we study Target-oriented Multimodal Sentiment Classification (TMSC) and propose a multimodal BERT architecture. To model intra-modality dynamics, we first apply BERT to obtain target-sensitive textual representations. We then borrow the idea from self-attention …
Cold-Start Aware Deep Memory Networks For Multi-Entity Aspect-Based Sentiment Analysis,
2019
Singapore Management University
Cold-Start Aware Deep Memory Networks For Multi-Entity Aspect-Based Sentiment Analysis, Kaisong Song, Wei Gao, Lujun Zhao, Changlong Sun, Xiaozhong Liu
Research Collection School Of Computing and Information Systems
Various types of target information have been considered in aspect-based sentiment analysis, such as entities and aspects. Existing research has realized the importance of targets and developed methods with the goal of precisely modeling their contexts via generating target-specific representations. However, all these methods ignore that these representations cannot be learned well due to the lack of sufficient human-annotated target-related reviews, which leads to the data sparsity challenge, a.k.a. cold-start problem here. In this paper, we focus on a more general multiple entity aspect-based sentiment analysis (ME-ABSA) task which aims at identifying the sentiment polarity of different aspects of multiple …
Improving Urban Crowd Flow Prediction On Flexible Region Partition,
2019
Singapore Management University
Improving Urban Crowd Flow Prediction On Flexible Region Partition, Xu Wang, Zimu Zhou, Yi Zhao, Xinglin Zhang, Kai Xing, Fu Xiao, Zheng Yang, Yunhao Liu
Research Collection School Of Computing and Information Systems
Accurate forecast of citywide crowd flows on flexible region partition benefits urban planning, traffic management, and public safety. Previous research either fails to capture the complex spatiotemporal dependencies of crowd flows or is restricted on grid region partition that loses semantic context. In this paper, we propose DeepFlowFlex, a graph-based model to jointly predict inflows and outflows for each region of arbitrary shape and size in a city. Analysis on cellular datasets covering 2.4 million users in China reveals dependencies and distinctive patterns of crowd flows in not only the conventional space and time domains, but also the speed domain, …
Industry 4.0: Challenges And Opportunities In Different Countries,
2019
Singapore Management University
Industry 4.0: Challenges And Opportunities In Different Countries, Keng Siau, Yingrui Xi, Cui Zou
Research Collection School Of Computing and Information Systems
Along with the rapid development of artificial intelligence (AI), cyber-physical systems (CPSs), big data analytics, and cloud computing, Industry 4.0 — a subset of the fourth Industrial Revolution — has started to emerge and take root in many countries. Many expect that Industry 4.0 will be transformative and revolutionary for multiple industries and countries. Its impact will be much more significant than those of Industry 1.0, 2.0, and 3.0. Most studies and papers on Industry 4.0 have examined its impact on various industries, jobs, and organizations. In this article, we investigate the impact of Industry 4.0 on countries and groups …
Shared Dynamic Data Audit Supporting Anonymous User Revocation In Cloud Storage,
2019
Singapore Management University
Shared Dynamic Data Audit Supporting Anonymous User Revocation In Cloud Storage, Yinghui Zhang, Chen Chen, Dong Zheng, Rui Guo, Shengmin Xu
Research Collection School Of Computing and Information Systems
Collusion between revoked users and cloud service providers can pose a threat to the security of cloud storage data. If the original legitimate users cannot be revoked securely, it will lead to the leakage of shared data, thus affecting the security of cloud storage. In this paper, we combine vector commitment and anonymous revocation of group signature to propose an integrity audit scheme for cloud storage data that can support data modification. The anonymity of the group signature ensures that users’ privacy information will not be snooped by the server. The proposed scheme supports the dynamic operation of stored data …
Trust Architecture And Reputation Evaluation For Internet Of Things,
2019
Singapore Management University
Trust Architecture And Reputation Evaluation For Internet Of Things, Juan Chen, Zhihong Tian, Xiang Cui, Lihua Yin, Xianzhi Wang
Research Collection School Of Computing and Information Systems
Internet of Things (IoT) represents a fundamental infrastructure and set of techniques that support innovative services in various application domains. Trust management plays an important role in enabling the reliable data collection and mining, context-awareness, and enhanced user security in the IoT. The main tasks of trust management include trust architecture design and reputation evaluation. However, existing trust architectures and reputation evaluation solutions cannot be directly applied to the IoT, due to the large number of physical entities, the limited computation ability of physical entities, and the highly dynamic nature of the network. In comparison, it generally requires a general …
Industry 4.0: Ethical And Moral Predicaments,
2019
Singapore Management University
Industry 4.0: Ethical And Moral Predicaments, W. Wang, Keng Siau
Research Collection School Of Computing and Information Systems
The advancements in software technology and data science are enabling Industry 4.0, aka the Fourth Industrial Revolution or the Industrial Internet of Things (IIoT). While the first three industrial revolutions have brought about immense change, the impact of Industry 4.0 will be much wider and far greater, especially with regard to the easily overlooked ethical and moral aspects. Widening wealth gaps between countries and among classes of people within countries, a potential growing unemployment rate, data privacy and accessibility issues, and the treatment of intelligent agents (e.g., military robots) present new and complex ethical and moral dilemmas. In this article, …
Low-Rank Sparse Subspace For Spectral Clustering,
2019
Guangxi Normal University
Low-Rank Sparse Subspace For Spectral Clustering, Xiaofeng Zhu, Shichao Zhang, Yonggang Li, Jilian Zhang, Lifeng Yang, Yue Fang
Research Collection School Of Computing and Information Systems
The current two-step clustering methods separately learn the similarity matrix and conduct k means clustering. Moreover, the similarity matrix is learnt from the original data, which usually contain noise. As a consequence, these clustering methods cannot achieve good clustering results. To address these issues, this paper proposes a new graph clustering methods (namely Low-rank Sparse Subspace clustering (LSS)) to simultaneously learn the similarity matrix and conduct the clustering from the low-dimensional feature space of the original data. Specifically, the proposed LSS integrates the learning of similarity matrix of the original feature space, the learning of similarity matrix of the low-dimensional …
Adversarial Learning On Heterogeneous Information Networks,
2019
Beijing University of Posts and Telecommunications
Adversarial Learning On Heterogeneous Information Networks, Binbin Hu, Yuan Fang, Chuan Shi
Research Collection School Of Computing and Information Systems
Network embedding, which aims to represent network data in alow-dimensional space, has been commonly adopted for analyzingheterogeneous information networks (HIN). Although exiting HINembedding methods have achieved performance improvement tosome extent, they still face a few major weaknesses. Most importantly, they usually adopt negative sampling to randomly selectnodes from the network, and they do not learn the underlying distribution for more robust embedding. Inspired by generative adversarial networks (GAN), we develop a novel framework HeGAN forHIN embedding, which trains both a discriminator and a generatorin a minimax game. Compared to existing HIN embedding methods,our generator would learn the node distribution to …
Ezlog: Data Visualization For Logistics,
2019
Singapore Management University
Ezlog: Data Visualization For Logistics, Aldy Gunawan, Benjamin Gan, Jin An Tan, Sheena L.S.L Villanueva, Timothy K.J. Wen
Research Collection School Of Computing and Information Systems
With the increasing availability of data in the logistics industry due to the digitalization trend, interest and opportunities for leveraging analytics in supply chain management to make data-driven decisions is growing rapidly. In this paper, we introduce EzLog, an integrated visualization prototype platform for supply chain analytics. This web-based platform built by two undergraduate student teams for their capstone course can be used for data wrangling and rapid analysis of data from different business units of a major logistics company. Other functionalities of the system include standard processes to perform data analysis such as supervised extraction, transformation, loading (ETL), data …
Evaluating Vulnerability To Fake News In Social Networks: A Community Health Assessment Model,
2019
University of Minnesota - Twin Cities
Evaluating Vulnerability To Fake News In Social Networks: A Community Health Assessment Model, Bhavtosh Rath, Wei Gao, Jaideep Srivastava
Research Collection School Of Computing and Information Systems
Understanding the spread of false information in social networks has gained a lot of recent attention. In this paper, we explore the role community structures play in determining how people get exposed to fake news. Inspired by approaches in epidemiology, we propose a novel Community Health Assessment model, whose goal is to understand the vulnerability of communities to fake news spread. We define the concepts of neighbor, boundary and core nodes of a community and propose appropriate metrics to quantify the vulnerability of nodes (individual-level) and communities (group-level) to spreading fake news. We evaluate our model on communities identified using …
Learning Multiple Maps From Conditional Ordinal Triplets,
2019
Singapore Management University
Learning Multiple Maps From Conditional Ordinal Triplets, Duy Dung Le, Hady Wirawan Lauw
Research Collection School Of Computing and Information Systems
Ordinal embedding seeks a low-dimensional representation of objects based on relative comparisons of their similarities. This low-dimensional representation lends itself to visualization on a Euclidean map. Classical assumptions admit only one valid aspect of similarity. However, there are increasing scenarios involving ordinal comparisons that inherently reflect multiple aspects of similarity, which would be better represented by multiple maps. We formulate this problem as conditional ordinal embedding, which learns a distinct low-dimensional representation conditioned on each aspect, yet allows collaboration across aspects via a shared representation. Our geometric approach is novel in its use of a shared spherical representation and multiple …
Ai-Fashion: Collaborative Ai In The Fashion Industry,
2019
Singapore Management University
Ai-Fashion: Collaborative Ai In The Fashion Industry, Y. Luo, Keng Siau
Research Collection School Of Computing and Information Systems
Abstract The word vintage is generally accepted to mean clothing produced in the period between 1920s and 1980s (Cervellon et al., 2012). According to Fischer (2015), fashion usually means rapid changes and up-to-date trendiness. Vintage dressing, however, has been a fashionable trend for over 40 years. Can AI be used to predict the next fashion trend? Fashion industry is currently exploring the use of AI to analyze customer behavior and predict next year’s fashion trends. Predicting the correct next trend is vital to the competitiveness and survivability of fashion brands. Research in this area is not new. For example, research …
Gradient Boosting With Piece-Wise Linear Regression Trees,
2019
Singapore Management University
Gradient Boosting With Piece-Wise Linear Regression Trees, Yu Shi, Jian Li, Zhize Li
Research Collection School Of Computing and Information Systems
Gradient Boosted Decision Trees (GBDT) is a very successful ensemble learning algorithm widely used across a variety of applications. Recently, several variants of GBDT training algorithms and implementations have been designed and heavily optimized in some very popular open sourced toolkits including XGBoost, LightGBM and CatBoost. In this paper, we show that both the accuracy and efficiency of GBDT can be further enhanced by using more complex base learners. Specifically, we extend gradient boosting to use piecewise linear regression trees (PL Trees), instead of piecewise constant regression trees, as base learners. We show that PL Trees can accelerate convergence of …
Topic Enhanced Word Embedding For Toxic Content Detection In Q&A Sites,
2019
Singapore Management University
Topic Enhanced Word Embedding For Toxic Content Detection In Q&A Sites, Do Yeon Kim, Xiaohang Li, Sheng Wang, Yunying Zhuo, Ka Wei, Roy Lee
Research Collection School Of Computing and Information Systems
Increasingly, users are adopting community question-and-answer (Q&A) sites to exchange information. Detecting and eliminating toxic and divisive content in these Q&A sites are paramount tasks to ensure a safe and constructive environment for the users. Insincere question, which is founded upon false premises, is one type of toxic content in Q&A sites. In this paper, we proposed a novel deep learning framework enhanced pre-trained word embeddings with topical information for insincere question classification. We evaluated our proposed framework on a large real-world dataset from Quora Q&A site and showed that the topically enhanced word embedding is able to achieve better …
