Digital Commons Network^™

Multi-Domain Dialogue State Tracking With Recursive Inference, Lizi Liao, Tongyao Zhu, Le Hong Long, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Multi-domain dialogue state tracking (DST) is a critical component for monitoring user goals during the course of an interaction. Existing approaches have relied on dialogue history indiscriminately or updated on the most recent turns incrementally. However, in spite of modeling it based on fixed ontology or open vocabulary, the former setting violates the interactive and progressing nature of dialogue, while the later easily gets affected by the error accumulation conundrum. Here, we propose a Recursive Inference mechanism (ReInf) to resolve DST in multi-domain scenarios that call for more robust and accurate tracking capability. Specifically, our agent reversely reviews the dialogue …

Looking Back! Using Early Versions Of Android Apps As Attack Vectors, Yue Zhang, Jian Weng, Jia-Si Wneg, Lin Hou, Anjia Yang, Ming Li, Yang Xiang, Deng, Robert H.

Research Collection School Of Computing and Information Systems

Android platform is gaining explosive popularity. This leads developers to invest resources to maintain the upward trajectory of the demand. Unfortunately, as the profit potential grows higher, the chances of these Apps getting attacked also get higher. Therefore, developers improved the security of their Apps, which limits attackers ability to compromise upgraded versions of the Apps. However, developers cannot enhance the security of earlier versions that have been released on the Play Store. The earlier versions of the App can be subject to reverse engineering and other attacks. In this paper, we find that attackers can use these earlier versions …

Do Users Care About Ad's Performance Costs? Exploring The Effects Of The Performance Costs Of In-App Ads On User Experience, Cuiyun Gao, Jichuan Zeng, Federica Sarro, David Lo, Irwin King, Michael R. Lyu

Research Collection School Of Computing and Information Systems

Context: In-app advertising is the primary source of revenue for many mobile apps. The cost of advertising (ad cost) is non-negligible for app developers to ensure a good user experience and continuous profits. Previous studies mainly focus on addressing the hidden performance costs generated by ads, including consumption of memory, CPU, data traffic, and battery. However, there is no research on analyzing users’ perceptions of ads’ performance costs to our knowledge.Objective: To fill this gap and better understand the effects of performance costs of in-app ads on user experience, we conduct a study on analyzing user concerns about ads’ performance …

Escape From An Echo Chamber, Kuan-Chieh Lo, Shih-Chieh Dai, Aiping Xiong, Jing Jiang, Lun-Wei Ku

Research Collection School Of Computing and Information Systems

An echo chamber effect refers to the phenomena that online users revealed selective exposure and ideological segregation on political issues. Prior studies indicate the connection between the spread of misinformation and online echo chambers. In this paper, to help users escape from an echo chamber, we propose a novel news-analysis platform that provides a panoramic view of stances towards a particular event from different news media sources. Moreover, to help users better recognize the stances of news sources which published these news articles, we adopt a news stance classification model to categorize their stances into “agree”, “disagree”, “discuss”, or “unrelated” …

Homophily Outlier Detection In Non-Iid Categorical Data, Guansong Pang, Longbing Cao, Ling Chen

Research Collection School Of Computing and Information Systems

Most of existing outlier detection methods assume that the outlier factors (i.e., outlierness scoring measures) of data entities (e.g., feature values and data objects) are Independent and Identically Distributed (IID). This assumption does not hold in real-world applications where the outlierness of different entities is dependent on each other and/or taken from different probability distributions (non-IID). This may lead to the failure of detecting important outliers that are too subtle to be identified without considering the non-IID nature. The issue is even intensified in more challenging contexts, e.g., high-dimensional data with many noisy features. This work introduces a novel outlier …

Breaking Neural Reasoning Architectures With Metamorphic Relation-Based Adversarial Examples, Alvin Chan, Lei Ma, Felix Juefei-Xu, Yew-Soon Ong, Xiaofei Xie, Minhui Xue, Yang Liu

Research Collection School Of Computing and Information Systems

The ability to read, reason, and infer lies at the heart of neural reasoning architectures. After all, the ability to perform logical reasoning over language remains a coveted goal of Artificial Intelligence. To this end, models such as the Turing-complete differentiable neural computer (DNC) boast of real logical reasoning capabilities, along with the ability to reason beyond simple surface-level matching. In this brief, we propose the first probe into DNC's logical reasoning capabilities with a focus on text-based question answering (QA). More concretely, we propose a conceptually simple but effective adversarial attack based on metamorphic relations. Our proposed adversarial attack …

Urban Perception: Sensing Cities Via A Deep Interactive Multi-Task Learning Framework, Weili Guan, Zhaozheng Chen, Fuli Feng, Weifeng Liu, Liqiang Nie

Research Collection School Of Computing and Information Systems

Social scientists have shown evidence that visual perceptions of urban attributes, such as safe, wealthy, and beautiful perspectives of the given cities, are highly correlated to the residents' behaviors and quality of life. Despite their significance, measuring visual perceptions of urban attributes is challenging due to the following facts: (1) Visual perceptions are subjectively contradistinctive rather than absolute. (2) Perception comparisons between image pairs are usually conducted region by region, and highly related to the specific urban attributes. And (3) the urban attributes have both the shared and specific information. To address these problems, in this article, we present a …

Weakly Supervised Segmentation Via Instance-Aware Propagation, Huang Xin, Qianshu Zhu, Yongtuo Liu, Shengfeng He

Research Collection School Of Computing and Information Systems

Peak Response Map (PRM) highlighting the discriminative regions can be extracted from a pre-trained classification network. We can accurately localize instances of each class with the help of these response maps. However, these maps cannot provide reliable information for segmentation even with off-the-shelf object proposals. This is because neither PRM nor the proposals know which regions can be regarded as a complete instance. In this paper, we tackle this problem by proposing an Instance-aware Cue propagation Network (ICN) with a new proposal-matching strategy. In particular, the ICN aims to filter out background distractions and cover the complete instance, while our …

How Successful Are Open Source Contributions From Countries With Different Levels Of Human Development?, Leonardo Furtado, Bruno Cartaxo, Christoph Treude, Gustavo Pinto

Research Collection School Of Computing and Information Systems

In this article we studied whether developers? locations relate to the outcome of a pull request (PR). Our results suggest that developers from countries with low human development indexes perform a small fraction of the overall PRs and are the ones that face rejection the most.

Robust And Universal Seamless Handover Authentication In 5g Hetnets, Yinghui Zhang, Robert H. Deng, Elisa Bertino, Dong Zheng

Research Collection School Of Computing and Information Systems

The evolving fifth generation (5G) cellular networks will be a collection of heterogeneous and backward-compatible networks. With the increased heterogeneity and densification of 5G heterogeneous networks (HetNets), it is important to ensure security and efficiency of frequent handovers in 5G wireless roaming environments. However, existing handover authentication mechanisms still have challenging issues, such as anonymity, robust traceability and universality. In this paper, we address these issues by introducing RUSH, a Robust and Universal Seamless Handover authentication protocol for 5G HetNets. In RUSH, anonymous mutual authentication with key agreement is enabled for handovers by exploiting the trapdoor collision property of chameleon …

Integration Of Professional Certifications With Information Systems Business Analytics Track Curriculum, Kyong Jin Shim, Gottipati Swapna, Yi Meng Lau

Research Collection School Of Computing and Information Systems

In this study, we showcase a design of an undergraduate Business Analytics track that integrates professional certifications from Amazon Web Services, Google, SAS, and Salesforce with core Business Analytics courses in an Information Systems undergraduate degree program. Certifications provide an excellent way for students to attain practical, experiential, and demonstrable skills which increasingly more employers look for in job candidates' portfolios. In close collaboration with industry partners, curriculum designers and faculty in institutions of higher learning can leverage high quality hands-on training materials provided by the certification vendors and align it with the core academic course content. Excellent teaching by …

Cross-Topic Rumor Detection Using Topic-Mixtures, Weijieying Ren, Jing Jiang, Ling Min Serena Khoo, Hai Leong Chieu

Research Collection School Of Computing and Information Systems

There has been much interest in rumor detection using deep learning models in recent years. A well-known limitation of deep learning models is that they tend to learn superficial patterns, which restricts their generalization ability. We find that this is also true for cross-topic rumor detection. In this paper, we propose a method inspired by the “mixture of experts” paradigm. We assume that the prediction of the rumor class label given an instance is dependent on the topic distribution of the instance. After deriving a vector representation for each topic, given an instance, we derive a “topic mixture” vector for …

Iotbox: Sandbox Mining To Prevent Interaction Threats In Iot Systems, Hong Jin Kang, Sheng Qin Sim, David Lo

Research Collection School Of Computing and Information Systems

Internet of Things (IoT) apps provide great convenience but exposes us to new safety threats. Unlike traditional software systems, threats may emerge from the joint behavior of multiple apps. While prior studies use handcrafted safety and security policies to detect these threats, these policies may not anticipate all usages of the devices and apps in a smart home, causing false alarms. In this study, we propose to use the technique of mining sandboxes for securing an IoT environment. After a set of behaviors are analyzed from a bundle of apps and devices, a sandbox is deployed, which enforces that previously …

Sentiment-Oriented Metric Learning For Text-To-Image Retrieval, Quoc Tuan Truong, Hady W. Lauw

Research Collection School Of Computing and Information Systems

In this era of multimedia Web, text-to-image retrieval is a critical function of search engines and visually-oriented online platforms. Traditionally, the task primarily deals with matching a text query with the most relevant images available in the corpus. To an increasing extent, the Web also features visual expressions of preferences, imbuing images with sentiments that express those preferences. Cases in point include photos in online reviews as well as social media. In this work, we study the effects of sentiment information on text-to-image retrieval. Particularly, we present two approaches for incorporating sentiment orientation into metric learning for cross-modal retrieval. Each …

Efficient Retrieval Of Matrix Factorization-Based Top-K Recommendations: A Survey Of Recent Approaches, Dung D. Le, Hady W. Lauw

Research Collection School Of Computing and Information Systems

Top-k recommendation seeks to deliver a personalized list of k items to each individual user. An established methodology in the literature based on matrix factorization (MF), which usually represents users and items as vectors in low-dimensional space, is an effective approach to recommender systems, thanks to its superior performance in terms of recommendation quality and scalability. A typical matrix factorization recommender system has two main phases: preference elicitation and recommendation retrieval. The former analyzes user-generated data to learn user preferences and item characteristics in the form of latent feature vectors, whereas the latter ranks the candidate items based on the …

Time Period-Based Top-K Semantic Trajectory Pattern Query, Munkh-Erdene Yadamjav, Farhana Murtaza Choudhury, Zhifeng Bao, Baihua Zheng

Research Collection School Of Computing and Information Systems

The sequences of user check-ins form semantic trajectories that represent the movement of users through time, along with the types of POIs visited. Extracting patterns in semantic trajectories can be widely used in applications such as route planning and trip recommendation. Existing studies focus on the entire time duration of the data, which may miss some temporally significant patterns. In addition, they require thresholds to define the interestingness of the patterns. Motivated by the above, we study a new problem of finding top-k semantic trajectory patterns w.r.t. a given time period and categories by considering the spatial closeness of POIs. …

Dismastd: An Efficient Distributed Multi-Aspect Streaming Tensor Decomposition, Keyu Yang, Yunjun Gao, Yifeng Shen, Baihua Zheng, Lu Chen

Research Collection School Of Computing and Information Systems

Tensor decomposition is a fundamental multidimensional data analysis tool for many data-driven applications, such as social computing, computer vision, and bioinformatics, to name but a few. However, the rapidly increasing streaming data nowadays introduces new challenges to traditional static tensor decomposition. It requires an efficient distributed dynamic tensor decomposition without re-computing the whole tensor from scratch. In this paper, we propose DisMASTD, an efficient distributed multi-aspect streaming tensor decomposition. First, we prove the optimal tensor partitioning problem is NP-hard. Second, we present two heuristic tensor partitioning approaches to ensure the load balancing. Third, we develop a distributed multi-aspect streaming tensor …

Newslink: Empowering Intuitive News Search With Knowledge Graphs, Yueji Yang, Yuchen Li, Anthony Tung

Research Collection School Of Computing and Information Systems

News search tools help end users to identify relevant news stories. However, existing search approaches often carry out in a "black-box" process. There is little intuition that helps users understand how the results are related to the query. In this paper, we propose a novel news search framework, called NEWSLINK, to empower intuitive news search by using relationship paths discovered from open Knowledge Graphs (KGs). Specifically, NEWSLINK embeds both a query and news documents to subgraphs, called subgraph embeddings, in the KG. Their embeddings' overlap induces relationship paths between the involving entities. Two major advantages are obtained by incorporating subgraph …

Spectral Tensor Train Parameterization Of Deep Learning Layers, A. Obukhov, M. Rakhuba, A. Liniger, Zhiwu Huang, S. Georgoulis, D. Dai, Van Gool L.

Research Collection School Of Computing and Information Systems

We study low-rank parameterizations of weight matrices with embedded spectral properties in the Deep Learning context. The low-rank property leads to parameter efficiency and permits taking computational shortcuts when computing mappings. Spectral properties are often subject to constraints in optimization problems, leading to better models and stability of optimization. We start by looking at the compact SVD parameterization of weight matrices and identifying redundancy sources in the parameterization. We further apply the Tensor Train (TT) decomposition to the compact SVD components, and propose a non-redundant differentiable parameterization of fixed TT-rank tensor manifolds, termed the Spectral Tensor Train Parameterization (STTP). We …

Interactional Motifs: Leveraging Risks In Large And Distributed Software Development Teams, Subhajit Datta, Amrita Bhattacharjee, Subhashis Majumder

Research Collection School Of Computing and Information Systems

DeMarco and Lister begin their classic Peopleware with an air of ominous inevitability “somewhere today, a project is failing” (DeMarco and Lister, 2013). They are talking about software projects, and as the book so brilliantly establishes, software is peopleware. A failed project is the dreaded culmination of all the perceptible and imperceptible risks that are associated with the project. For software projects, a large majority of such risks originate in the interactions of people who are involved in the project. People who build the software are the most valued and the most vulnerable asset of any software project, something that …

Deepis: Susceptibility Estimation On Social Networks, Wenwen Xia, Yuchen Li, Jun Wu, Shenghong Li

Research Collection School Of Computing and Information Systems

Influence diffusion estimation is a crucial problem in social network analysis. Most prior works mainly focus on predicting the total influence spread, i.e., the expected number of influenced nodes given an initial set of active nodes (aka. seeds). However, accurate estimation of susceptibility, i.e., the probability of being influenced for each individual, is more appealing and valuable in real-world applications. Previous methods generally adopt Monte Carlo simulation or heuristic rules to estimate the influence, resulting in high computational cost or unsatisfactory estimation error when these methods are used to estimate susceptibility. In this work, we propose to leverage graph neural …

How Do Users Answer Matlab Questions On Q&A Sites? A Case Study On Stack Overflow And Mathworks, Mahshid Naghashzadeh, Amir Hagshenas, Ashkan Sami, David Lo

Research Collection School Of Computing and Information Systems

MATLAB is an engineering programming language with various toolboxes that has a dedicated Question and Answer (Q&A) platform on the MathWorks website, which is similar to Stack Overflow (SO). Moreover, some MATLAB users ask their questions on SO. This paper aims to compare these two Q&A platforms to see what kind of questions are asked and how developers answer these questions in each platform. The result of our analysis on 80,382 MATLAB questions on SO and 266,367 questions on MathWorks show that MATLAB questions on topics ranging from the MATLAB software installation to questions related to programming received high votes …

Adaptive Simultaneous Pervasive Visible Light Communication And Sensing, Ila Nitin Gokarn, Archan Misra

Research Collection School Of Computing and Information Systems

Driven by the rapid growth in the proliferation of low-cost LED luminaries, visible light is being increasingly explored as both a high-speed communication and sensing channel for a variety of IoT applications. Visible Light Communication (VLC) exploits the high-frequency modulation of an optical source while ensuring imperceptibility to the human eye. In parallel, recent approaches in Visible Light Sensing (VLS) have demonstrated how high frequency optical strobing can be used to perform vision-based remote sensing of mechanical vibrations (e.g., of factory equipment). To date, exemplars of VLC and VLS have, however, been explored in isolation, without consideration of their mutual …

Recent Advances On Intelligent Mobility And Edge Computing, Xun Shao, Zhi Liu, Xianfu Chen, Seng W. Loke, Hwee-Pink Tan

Research Collection School Of Computing and Information Systems

In recent years, we have seen fast development of wireless communications, networking, and cloud computing: 4G, 5G and multiaccess networks greatly enhance the quality of service (QoS) of wireless access networks; software-defined networking, network function virtualization, and information-centric networking largely reduce the cost of network service providers and improve the quality of experience (QoE) of end-users; the development of mobile devices and mobile cloud computing lead to explosive deployment of mobile services and applications; the recent development of advanced algorithms such as Deep Learning has shown great potential in resource allocation and service orchestration. Deep integration of the above technologies …

Structurally Enriched Entity Mention Embedding From Semi-Structured Textual Content, Lee Hsun Hsieh, Yang Yin Lee, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

In this research, we propose a novel and effective entity mention embedding framework that learns from semi-structured text corpus with annotated entity mentions without the aid of well-constructed knowledge graph or external semantic information other than the corpus itself. Based on the co-occurrence of words and entity mentions, we enrich the co-occurrence matrix with entity-entity, entity-word, and word-entity relationships as well as the simple structures within the documents. Experimentally, we show that our proposed entity mention embedding benefits from the structural information in link prediction task measured by mean reciprocal rank (MRR) and mean precision@K (MP@K) on two datasets for …

Singapore Airlines: Profit Recovery And Aircraft Allocation Models During The Covid-19 Pandemic, Michelle L. F. Cheong, Ulysses M. Z. Chong, Anne N. T. A. Nguyen, Su Yiin Ang, Gabriella P. Djojosaputro, Gordy Adiprasetyo, Kendra L. B. Gadong

Research Collection School Of Computing and Information Systems

COVID-19 has severely impacted the global aviation industry, causing many airlines to downsize or exit the industry. For airlines which attempt to sustain their operations, they will need to respond to the increase in passenger and cargo demand, as countries recover slowly from the crisis due to the availability of vaccines. We built a series of spreadsheet models to first project the COVID-19 recovery rates by countries from 2021 to 2025, then forecast the passenger and cargo demand, using historical data as base figures. Using the financial and operation data, the revenue, expense, and profit can be projected, then an …

Waste Collection Routing Problem: A Mini-Review Of Recent Heuristic Approaches And Applications, Yun-Chia Liang, Vanny Minanda, Aldy Gunawan

Research Collection School Of Computing and Information Systems

The waste collection routing problem (WCRP) can be defined as a problem of designing a route to serve all of the customers (represented as nodes) with the least total traveling time or distance, served by the least number of vehicles under specific constraints, such as vehicle capacity. The relevance of WCRP is rising due to its increased waste generation and all the challenges involved in its efficient disposal. This research provides a mini-review of the latest approaches and its application in the collection and routing of waste. Several metaheuristic algorithms are reviewed, such as ant colony optimization, simulated annealing, genetic …

Improving Neural Network Verification Through Spurious Region Guided Refinement, Pengfei Yang, Renjue Li, Jianlin Li, Cheng Chao Huang, Jingyi Wang, Jun Sun, Bai Xue, Lijun Zhang

Research Collection School Of Computing and Information Systems

We propose a spurious region guided refinement approach for robustness verification of deep neural networks. Our method starts with applying the DeepPoly abstract domain to analyze the network. If the robustness property cannot be verified, the result is inconclusive. Due to the over-approximation, the computed region in the abstraction may be spurious in the sense that it does not contain any true counterexample. Our goal is to identify such spurious regions and use them to guide the abstraction refinement. The core idea is to make use of the obtained constraints of the abstraction to infer new bounds for the neurons. …

Outsourcing Life Cycle Model For Financial Services In The Fintech Era, Tristan Lim, Patrick Thng

Research Collection School Of Computing and Information Systems

In today’s financial services landscape, staying ahead of the innovation curve and being disciplined at enhancing core service offerings entail careful resource planning. A well-structured outsourcing arrangement can go a long way towards enhancing long term organizational strategic growth. In the post-2014 FinTech era, (i) strategic management with an innovation focus and (ii) financial technology-associated risks, have brought about changes to outsourcing in the financial services industry. Presently, most outsourcing life cycle models in existing literature seek to provide comprehensive, yet industry-neutral guidelines lacking industry context and depth of coverage. A newly licensed financial institution deciding to embark on outsourcing …

Explainable Recommendation With Comparative Constraints On Product Aspects, Trung-Hoang Le, Hady W. Lauw

Research Collection School Of Computing and Information Systems

To aid users in choice-making, explainable recommendation models seek to provide not only accurate recommendations but also accompanying explanations that help to make sense of those recommendations. Most of the previous approaches rely on evaluative explanations, assessing the quality of an individual item along some aspects of interest to the user. In this work, we are interested in comparative explanations, the less studied problem of assessing a recommended item in comparison to another reference item.

In particular, we propose to anchor reference items on the previously adopted items in a user's history. Not only do we aim at providing comparative …