Computer Sciences Commons^™

I4s: Capturing Shopper’S In-Store Interactions, Sougata Sen, Archan Misra, Vigneshwaran Subbaraju, Karan Grover, Meeralakshmi Radhakrishnan, Rajesh K. Balan, Youngki Lee

Research Collection School Of Computing and Information Systems

In this paper, we present I4S, a system that identifies item interactions of customers in a retail store through sensor data fusion from smartwatches, smartphones and distributed BLE beacons. To identify these interactions, I4S builds a gesture-triggered pipeline that (a) detects the occurrence of “item picks”, and (b) performs fine-grained localization of such pickup gestures. By analyzing data collected from 31 shoppers visiting a mid-sized stationary store, we show that we can identify person-independent picking gestures with a precision of over 88%, and identify the rack from where the pick occurred with 91%+ precision (for popular racks).

Mini-Case Study Pedagogy: Experience From A Technical Course In An Information Systems Program, Venky Shankararaman, Swapna Gottipati, Alan Megargel

Research Collection School Of Computing and Information Systems

Case method is one approach that has been widely adopted in teaching many professions including law, medicine and business. The general practice is to ask students to read the case, usually four to eight pages long, prior to the session, and then spend the whole session discussing the case. However, for technology courses, the students are required to learn concepts and apply the musing hands-on software tools through lab sessions. Hence, rather than use long cases, that cover the entire session, we need to use mini-cases that are usually one to two pages long. This pedagogy approach allows the class …

Using Gamification In Outreach Camps: Experience From An Information Systems Program, Swapna Gottipati, Venky Shankararaman

Research Collection School Of Computing and Information Systems

Reaching to young generation and attracting them to computing programs such as Information Systems (IS) and Computer Science (CS) is a key challenge faced by universities in Singapore. During their application process, many high quality students from junior colleges (JC) either don't choose IS program or choose IS program as the last option. School of Information Systems (SIS), Singapore Management University, decided to implement an innovative outreach program to reach and attract high quality JC students. A team of 30 faculty and staff worked on an outreach project to study and analyse the rationale behind the smaller numbers of applicants. …

Traffic-Cascade: Mining And Visualizing Lifecycles Of Traffic Congestion Events Using Public Bus Trajectories, Agus Trisnajaya Kwee, Meng-Fen Chiang, Philips Kokoh Prasetyo, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

As road transportation supports both economic and social activities in developed cities, it is important to maintain smooth traffic on all highways and local roads. Whenever possible, traffic congestions should be detected early and resolved quickly. While existing traffic monitoring dashboard systems have been put in place in many cities, these systems require high-cost vehicle speed monitoring instruments and detect traffic congestion as independent events. There is a lack of low-cost dashboards to inspect and analyze the lifecycle of traffic congestion which is critical in assessing the overall impact of congestion, determining the possible the source(s) of congestion and its …

Revisiting Supervised And Unsupervised Models For Effort-Aware Just-In-Time Defect Prediction, Qiao Huang, Xin Xia, David Lo

Research Collection School Of Computing and Information Systems

Effort-aware just-in-time (JIT) defect prediction aims at finding more defective software changes with limited code inspection cost. Traditionally, supervised models have been used; however, they require sufficient labelled training data, which is difficult to obtain, especially for new projects. Recently, Yang et al. proposed an unsupervised model (i.e., LT) and applied it to projects with rich historical bug data. Interestingly, they reported that, under the same inspection cost (i.e., 20 percent of the total lines of code modified by all changes), it could find about 12% - 27% more defective changes than a state-of-the-art supervised model (i.e., EALR) when using …

Predicting Visual Context For Unsupervised Event Segmentation In Continuous Photo-Streams, Ana García Del Molino, Joo-Hwee Lim, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Segmenting video content into events provides semantic structures for indexing, retrieval, and summarization. Since motion cues are not available in continuous photo-streams, and annotations in lifelogging are scarce and costly, the frames are usually clustered into events by comparing the visual features between them in an unsupervised way. However, such methodologies are ineffective to deal with heterogeneous events, e.g. taking a walk, and temporary changes in the sight direction, e.g. at a meeting. To address these limitations, we propose Contextual Event Segmentation (CES), a novel segmentation paradigm that uses an LSTM-based generative network to model the photo-stream sequences, predict their …

Geometry-Aware Similarity Learning On Spd Manifolds For Visual Recognition, Zhiwu Huang, R. Wang, X. Li, W. Liu, S. Shan, Gool L. Van, X Chen

Research Collection School Of Computing and Information Systems

Symmetric positive definite (SPD) matrices have been employed for data representation in many visual recognition tasks. The success is mainly attributed to learning discriminative SPD matrices encoding the Riemannian geometry of the underlying SPD manifolds. In this paper, we propose a geometry-aware SPD similarity learning (SPDSL) framework to learn discriminative SPD features by directly pursuing a manifold-manifold transformation matrix of full column rank. Specifically, by exploiting the Riemannian geometry of the manifolds of fixed-rank positive semidefinite (PSD) matrices, we present a new solution to reduce optimization over the space of column full-rank transformation matrices to optimization on the PSD manifold, …

Automating Intention Mining, Qiao Huang, Xin Xia, David Lo, Gail C. Murphy

Research Collection School Of Computing and Information Systems

Developers frequently discuss aspects of the systems they are developing online. The comments they post to discussions form a rich information source about the system. Intention mining, a process introduced by Di Sorbo et al., classifies sentences in developer discussions to enable further analysis. As one example of use, intention mining has been used to help build various recommenders for software developers. The technique introduced by Di Sorbo et al. to categorize sentences is based on linguistic patterns derived from two projects. The limited number of data sources used in this earlier work introduces questions about the comprehensiveness of intention …

Knowledge-Aware Multimodal Fashion Chatbot, Lizi Liao, You Zhou, Yunshan Ma, Richang Hong, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Multimodal fashion chatbot provides a natural and informative way to fulfill customers’ fashion needs. However, making it ‘smart’ in generating substantive responses remains a challenging problem. In this paper, we present a multimodal domain knowledge enriched fashion chatbot. It forms a taxonomy-based learning module to capture the fine-grained semantics in images and leverages an endto-end neural conversational model to generate responses based on the conversation history, visual semantics, and domain knowledge. To avoid inconsistent dialogues, deep reinforcement learning method is used to further optimize the model.

Optimal In-Place Suffix Sorting, Zhize Li, Jian Li, Hongwei Huo

Research Collection School Of Computing and Information Systems

The suffix array is a fundamental data structure for many applications that involve string searching and data compression. Designing time/space-efficient suffix array construction algorithms has attracted significant attentions and considerable advances have been made for the past 20 years. We obtain the first in-place linear time suffix array construction algorithms that are optimal both in time and space for (read-only) integer alphabets. Our algorithm settles the open problem posed by Franceschini and Muthukrishnan in ICALP 2007. The open problem asked to design in-place algorithms in $o(n \log n)$ time and ultimately, in $O(n)$ time for (read-only) integer alphabets with $|\Sigma| …

A Learning And Masking Approach To Secure Learning, Linh Nguyen, Sky Wang, Arunesh Sinha

Research Collection School Of Computing and Information Systems

Deep Neural Networks (DNNs) have been shown to be vulnerable against adversarial examples, which are data points cleverly constructed to fool the classifier. Such attacks can be devastating in practice, especially as DNNs are being applied to ever increasing critical tasks like image recognition in autonomous driving. In this paper, we introduce a new perspective on the problem. We do so by first defining robustness of a classifier to adversarial exploitation. Next, we show that the problem of adversarial example generation can be posed as learning problem. We also categorize attacks in literature into high and low perturbation attacks; well-known …

Augmenting And Structuring User Queries To Support Efficient Free-Form Code Search, Raphael Sirres, Tegawendé F. Bissyande, Dongsun Kim, David Lo, Jacques Klein, Kisub Kim, Yves Le Traon

Research Collection School Of Computing and Information Systems

Source code terms such as method names and variable types are often different from conceptual words mentioned in a search query. This vocabulary mismatch problem can make code search inefficient. In this paper, we present COde voCABUlary (CoCaBu), an approach to resolving the vocabulary mismatch problem when dealing with free-form code search queries. Our approach leverages common developer questions and the associated expert answers to augment user queries with the relevant, but missing, structural code entities in order to improve the performance of matching relevant code examples within large code repositories. To instantiate this approach, we build GitSearch, a code …

Unearthing The X-Streams: Visualizing Water Contamination, Akangsha Bandalkul, Angad Srivastava, Kishan Bharadwaj Shridhar, Jason Guan Jie Ong, Yanrong Zhang

Research Collection School Of Computing and Information Systems

The datasets released for VAST 2018 Mini Challenge 2 pertain to sensor readings capturing chemical concentrations and physical properties from water bodies in the Boonsong Lekagul wildlife preserve. This challenge is in continuation to the VAST 2017 Challenge, where the company Kasios was identified as the culprit in dumping the chemical - Methylosmoline. In the absence of actual chemical measurements in the soil, challenge participants need to visualize chemical contamination based on the proximal water bodies to identify trends of interest. A horizon plot developed helps to narrow down the complete list of 106 chemicals provided to only 7, …

An Evaluation Of Geotagged Twitter Data During Hurricane Irma Using Sentiment Analysis And Topic Modeling For Disaster Resilience, Ike Robert Vayansky

Electronic Theses and Dissertations

Disasters require quick response times, thought-out preparations, overall community, and government support. These efforts will ensure prevention of loss of life and reduce possible damages. The United States has been battered by multiple major hurricanes in the recent years and multiple avenues of disaster response efforts were being tested. Hurricane Irma can be recognized as the most popular hurricane in terms of social media attention. Irma made landfall in Florida as a Category 4 storm and preparation measures taken were intensive thus providing a good measure to evaluate in terms of efficacy. The effectiveness of the response methods utilized are …

Algorithms For Robot Coverage Under Movement And Sensing Constraints, Jeremy S. Lewis

Theses and Dissertations

This thesis explores the problem of generating coverage paths—that is, paths that pass within some sensor footprint of every point in an environment—for mobile robots. It both considers models for which navigation is a solved problem but motions are constrained, as well for models in which navigation must be considered along with coverage planning because of the robot’s unreliable sensing and movements.

The motion constraint we adopt for the former is a common constraint, that of a Dubins vehicle. We extend previous work that solves this coverage problem as a traveling salesman problem (TSP) by introducing a practical heuristic algorithm …

Using Magic In Computing Education And Outreach, Ronald I. Greenberg, Dale F. Reed

Computer Science: Faculty Publications and Other Works

This special session explores the use of magic tricks based on computer science ideas; magic tricks help grab students' attention and can motivate them to invest more deeply in underlying CS concepts. Error detection ideas long used by computer scientists provide a particularly rich basis for working such "magic'', with a CS Unplugged parity check activity being a notable example. Prior work has shown that one can perform much more sophisticated tricks than the relatively well-known CS Unplugged activity, and these tricks can motivate analyses across a wide variety of computer science concepts and are relevant to learning objectives across …

Wireless Sensor Networks For Smart Communications, Mu Zhou, Qilian Liang, Hongyi Wu, Weixiao Meng, Kunjie Xu

Electrical & Computer Engineering Faculty Publications

(First paragraph) In the first edition of the special issue titled “Wireless Sensor Networks for Smart Communications”, a total of 22 manuscripts were received and 6 of these were accepted. This issue demonstrated that network congestion, user mobility, and adjacent spectrum interference are the main reasons for the degradation ofcommunication quality inWireless Sensor Networks (WSNs).

Cmp 464 Web Programming, Ross Dakin, Nyc Tech-In-Residence Corps

Open Educational Resources

No abstract provided.

Certificate Revocation Table: Leveraging Locality Of Reference In Web Requests To Improve Tls Certificate Revocation, Luke Austin Dickinson

Theses and Dissertations

X.509 certificate revocation defends against man-in-the-middle attacks involving a compromised certificate. Certificate revocation strategies face scalability, effectiveness, and deployment challenges as HTTPS adoption rates have soared. We propose Certificate Revocation Table (CRT), a new revocation strategy that is competitive with or exceeds alternative state-of-the-art solutions in effectiveness, efficiency, certificate growth scalability, mass revocation event scalability, revocation timeliness, privacy, and deployment requirements. The CRT periodically checks the revocation status of X.509 certificates recently used by an organization, such as clients on a university's private network. By prechecking the revocation status of each certificate the client is likely to use, the client …

Influence Maximization On Social Graphs: A Survey, Yuchen Li, Ju Fan, Yanhao Wang, Kian-Lee Tan

Research Collection School Of Computing and Information Systems

Influence Maximization (IM), which selects a set of k users (called seed set) from a social network to maximize the expected number of influenced users (called influence spread), is a key algorithmic problem in social influence analysis. Due to its immense application potential and enormous technical challenges, IM has been extensively studied in the past decade. In this paper, we survey and synthesize a wide spectrum of existing studies on IM from an algorithmic perspective, with a special focus on the following key aspects (1) a review of well-accepted diffusion models that capture information diffusion process and build the foundation …

Overfitting In Semantics-Based Automated Program Repair, Dinh Xuan Bach Le, Ferdian Thung, David Lo, Claire Le Goues

Research Collection School Of Computing and Information Systems

The primary goal of Automated Program Repair (APR) is to automatically fix buggy software, to reduce the manual bug-fix burden that presently rests on human developers. Existing APR techniques can be generally divided into two families: semantics- vs. heuristics-based. Semantics-based APR uses symbolic execution and test suites to extract semantic constraints, and uses program synthesis to synthesize repairs that satisfy the extracted constraints. Heuristic-based APR generates large populations of repair candidates via source manipulation, and searches for the best among them. Both families largely rely on a primary assumption that a program is correctly patched if the generated patch leads …

Simknn: A Scalable Method For In-Memory Knn Search Over Moving Objects In Road Networks, Bin Cao, Chenyu Hou, Suifei Li, Jing Fan, Jianwei Yin, Baihua Zheng, Jie Bao

Research Collection School Of Computing and Information Systems

Nowadays, many location-based applications require the ability of querying k-nearest neighbors over a very large scale of5 moving objects in road networks, e.g., taxi-calling and ride-sharing services. Traditional grid index with equal-sized cells can not adapt6 to the skewed distribution of moving objects in real scenarios. Thus, to obtain the fast querying response time, the grid needs to be split7 into more smaller cells which introduces the side-effect of higher memory cost, i.e., maintaining such a large volume of cells requires a8 much larger memory space at the server side. In this paper, we present SIMkNN, a scalable and in-memory …

Categorizing The Content Of Github Readme Files, Gede Artha Azriadi Prana, Christoph Treude, Ferdian Thung, Thushari Atapattu, David Lo

Research Collection School Of Computing and Information Systems

README files play an essential role in shaping a developer’s first impression of a software repository and in documenting the software project that the repository hosts. Yet, we lack a systematic understanding of the content of a typical README file as well as tools that can process these files automatically. To close this gap, we conduct a qualitative study involving the manual annotation of 4,226 README file sections from 393 randomly sampled GitHub repositories and we design and evaluate a classifier and a set of features that can categorize these sections automatically. We find that information discussing the ‘What’ and …

Function Risk Assessment Under Memory Leakage, Jianming Fu, Rui Jin, Yan Lin, Baihe Jiang, Zhengwei Guo

Research Collection School Of Computing and Information Systems

Code reuse attack (CRA), specifically reusing and then reconstructing the codes (gadgets) already existed in programs and libraries, is widely exploited in software attacks. Admittedly, determination of the location of the gadgets consisted of target instructions along with control flow transfer instructions, is of critical importance. Address Space Randomization (ASR), which serves as an effective technique to mitigate CRA, increases the entropy by randomizing the location of the code or data, and baffles adversaries from figuring out the memory layout. Currently, variable randomization methods of high granularity are proposed by scholars to prevent adversaries from deducting memory layout. However, their …

Scaling-Up Stackelberg Security Games Applications Using Approximations, Arunesh Sinha, Aaron Schlenker, Donnabell Dmello, Milind Tambe

Research Collection School Of Computing and Information Systems

Stackelberg Security Games (SSGs) have been adopted widely for modeling adversarial interactions, wherein scalability of equilibrium computation is an important research problem. While prior research has made progress with regards to scalability, many real world problems cannot be solved satisfactorily yet as per current requirements; these include the deployed federal air marshals (FAMS) application and the threat screening (TSG) problem at airports. We initiate a principled study of approximations in zero-sum SSGs. Our contribution includes the following: (1) a unified model of SSGs called adversarial randomized allocation (ARA) games, (2) hardness of approximation for zero-sum ARA, as well as for …

Hawkeye: Towards A Desired Directed Grey-Box Fuzzer, Hongxu Chen, Yinxing Xue, Yuekang Li, Bihuan Chen, Xiaofei Xie, Xiuheng Wu, Yang Liu

Research Collection School Of Computing and Information Systems

Grey-box fuzzing is a practically effective approach to test real-world programs. However, most existing grey-box fuzzers lack directedness, i.e. the capability of executing towards user-specified target sites in the program. To emphasize existing challenges in directed fuzzing, we propose Hawkeye to feature four desired properties of directed grey-box fuzzers. Owing to a novel static analysis on the program under test and the target sites, Hawkeye precisely collects the information such as the call graph, function and basic block level distances to the targets. During fuzzing, Hawkeye evaluates exercised seeds based on both static information and the execution traces to generate …

Interpretable Multimodal Retrieval For Fashion Products, Lizi Liao, Xiangnan He, Bo Zhao, Chong-Wah Ngo, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Deep learning methods have been successfully applied to fashion retrieval. However, the latent meaning of learned feature vectors hinders the explanation of retrieval results and integration of user feedback. Fortunately, there are many online shopping websites organizing fashion items into hierarchical structures based on product taxonomy and domain knowledge. Such structures help to reveal how human perceive the relatedness among fashion products. Nevertheless, incorporating structural knowledge for deep learning remains a challenging problem. This paper presents techniques for organizing and utilizing the fashion hierarchies in deep learning to facilitate the reasoning of search results and user intent. The novelty of …

Interpretable Multimodal Retrieval For Fashion Products, Lizi Liao, Xiangnan He, Bo Zhao, Chong-Wah Ngo, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Deep learning methods have been successfully applied to fashion retrieval. However, the latent meaning of learned feature vectors hinders the explanation of retrieval results and integration of user feedback. Fortunately, there are many online shopping websites organizing fashion items into hierarchical structures based on product taxonomy and domain knowledge. Such structures help to reveal how human perceive the relatedness among fashion products. Nevertheless, incorporating structural knowledge for deep learning remains a challenging problem. This paper presents techniques for organizing and utilizing the fashion hierarchies in deep learning to facilitate the reasoning of search results and user intent. The novelty of …

Knowledge-Aware Multimodal Dialogue Systems, Lizi Liao, Yunshan Ma, Xiangnan He, Richang Hong, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

By offering a natural way for information seeking, multimodal dialogue systems are attracting increasing attention in several domains such as retail, travel etc. However, most existing dialogue systems are limited to textual modality, which cannot be easily extended to capture the rich semantics in visual modality such as product images. For example, in fashion domain, the visual appearance of clothes and matching styles play a crucial role in understanding the user's intention. Without considering these, the dialogue agent may fail to generate desirable responses for users. In this paper, we present a Knowledge-aware Multimodal Dialogue (KMD) model to address the …

Deep Understanding Of Cooking Procedure For Cross-Modal Recipe Retrieval, Jingjing Chen, Chong-Wah Ngo, Fu-Li Feng, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Finding a right recipe that describes the cooking procedure for a dish from just one picture is inherently a difficult problem. Food preparation undergoes a complex process involving raw ingredients, utensils, cutting and cooking operations. This process gives clues to the multimedia presentation of a dish (e.g., taste, colour, shape). However, the description of the process is implicit, implying only the cause of dish presentation rather than the visual effect that can be vividly observed on a picture. Therefore, different from other cross-modal retrieval problems in the literature, recipe search requires the understanding of textually described procedure to predict its …