Physical Sciences and Mathematics Commons^™

Strategic Planning For Setting Up Base Stations In Emergency Medical Systems, Supriyo Ghosh, Pradeep Varakantham

Research Collection School Of Computing and Information Systems

Emergency Medical Systems (EMSs) are an important component of public health-care services. Improving infrastructure for EMS and specifically the construction of base stations at the ”right” locations to reduce response times is the main focus of this paper. This is a computationally challenging task because of the: (a) exponentially large action space arising from having to consider combinations of potential base locations, which themselves can be significant; and (b) direct impact on the performance of the ambulance allocation problem, where we decide allocation of ambulances to bases. We present an incremental greedy approach to discover the placement of bases that …

Robust Partial Order Schedules For Rcpsp/Max With Durational Uncertainty, Na Fu, Pradeep Varakantham, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

In this work, we consider RCPSP/max with durational uncertainty. We focus on computing robust Partial Order Schedules (or, in short POS) which can be executed with risk controlled feasibility and optimality, i.e., there is stochastic posteriori quality guarantee that the derived POS can be executed with all constraints honored and completion before robust makespan. To address this problem, we propose BACCHUS: a solution method on Benders Accelerated Cut Creation for Handling Uncertainty in Scheduling. In our proposed approach, we first give an MILP formulation for the deterministic RCPSP/max and partition the model into POS generation process and start time schedule …

Deduplication On Encrypted Big Data In Cloud, Zheng Yan, Wenxiu Ding, Xixun Yu, Haiqi Zhu, Deng, Robert H.

Research Collection School Of Computing and Information Systems

Cloud computing offers a new way of service provision by re-arranging various resources over the Internet. The most important and popular cloud service is data storage. In order to preserve the privacy of data holders, data are often stored in cloud in an encrypted form. However, encrypted data introduce new challenges for cloud data deduplication, which becomes crucial for big data storage and processing in cloud. Traditional deduplication schemes cannot work on encrypted data. Existing solutions of encrypted data deduplication suffer from security weakness. They cannot flexibly support data access control and revocation. Therefore, few of them can be readily …

Poster: A Device-Free Evaluation System For Gymnastics Using Passive Rfid Tags, Binbin Xie, Jie Xiong, Dingyi Fang, Xiaojiang Chen, Anwen Wang, Zhanyong Tang

Research Collection School Of Computing and Information Systems

No abstract provided.

Poster: Sonicnect: Accurate Hands-Free Gesture Input System With Smart Acoustic Sensing, Maotian Chang, Ping Li, Panlong Yang, Jie Xiong, Chang Tian

Research Collection School Of Computing and Information Systems

This work presents Sonicnect, an acoustic sensing system with smartphone that enables accurate hands-free gesture input. Sonicnect leverages the embedded microphone in the smartphone to capture the subtle audio signals generated with fingers touching on the table. It supports 9 commonly used gestures (click, flip, scroll and zoom, etc) with above 92% recognition accuracy, and the minimum gesture movement could be 2cm. Distinguishable features are then extracted by exploiting spatio-temporal and frequency properties of the subtle audio signals. We conduct extensive real environment experiments to evaluate its performance. The results validate the effectiveness and robustness of Sonicnect.

Dual Formulations For Optimizing Dec-Pomdp Controllers, Akshat Kumar, Hala Mostafa, Shlomo Zilberstein

Research Collection School Of Computing and Information Systems

Decentralized POMDP is an expressive model for multi-agent planning. Finite-state controllers (FSCs)---often used to represent policies for infinite-horizon problems---offer a compact, simple-to-execute policy representation. We exploit novel connections between optimizing decentralized FSCs and the dual linear program for MDPs. Consequently, we describe a dual mixed integer linear program (MIP) for optimizing deterministic FSCs. We exploit the Dec-POMDP structure to devise a compact MIP and formulate constraints that result in policies executable in partially-observable decentralized settings. We show analytically that the dual formulation can also be exploited within the expectation maximization (EM) framework to optimize stochastic FSCs. The resulting EM algorithm …

Self-Organizing Neural Network For Adaptive Operator Selection In Evolutionary Search, Teck Hou Teng, Stephanus Daniel Handoko, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

Evolutionary Algorithm is a well-known meta-heuristics paradigm capable of providing high-quality solutions to computationally hard problems. As with the other meta-heuristics, its performance is often attributed to appropriate design choices such as the choice of crossover operators and some other parameters. In this chapter, we propose a continuous state Markov Decision Process model to select crossover operators based on the states during evolutionary search. We propose to find the operator selection policy efficiently using a self-organizing neural network, which is trained offline using randomly selected training samples. The trained neural network is then verified on test instances not used for …

Geometric Aspects And Auxiliary Features To Top-K Processing [Advanced Seminar], Kyriakos Mouratidis

Research Collection School Of Computing and Information Systems

Top-k processing is a well-studied problem with numerous applications that is becoming increasingly relevant with the growing availability of recommendation systems and decision making software on PCs, PDAs and smart-phones. The objective of this seminar is twofold. First, we will delve into the geometric aspects of top-k processing. Second, we will cover complementary features to top-k queries that have a strong geometric nature. The seminar will close with insights in the effect of dimensionality on the meaningfulness of top-k queries, and interesting similarities to nearest neighbor search.

An Experimental Investigation Of Product Competition And Marketing In Social Networks, Cen Chen, Zhiling Guo, Shih-Fen Cheng, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

We conduct computational experiment using Facebook data to evaluate competing firms’ initial market seeding and subsequent targeted marketing strategies that influence consumers’ new product adoption decisions. We find that firms generally overspend their advertising budget in the market seeding phase. In the subsequent market advertising phase, a coupon strategy (equivalent to price discount) generally yields higher market share than the strategy of distributing free product samples. The effect is more significant when both price and product quality are low. We offer managerial insights into firms’ effective competition strategies for new product introduction in the presence of consumers’ word of mouth …

An Economic Analysis Of Consumer Learning For Online Entertainment Shopping, Jin Li, Zhiling Guo, Geoffrey K.F. Tso

Research Collection School Of Computing and Information Systems

Entertainment shopping supported by pay-to-bid auction is an emerging online business model in recent years. Consumers expect both entertainment value and monetary return from their participation in entertainment shopping. We propose a dynamic structural model to study consumers’ online shopping behavior. We analyze the learning process of consumers from two perspectives based on the Bayesian updating framework: (1) consumers update their beliefs about the entertainment value through their repeated personal participation experiences, and (2) consumers infer the expected monetary payoffs on the website by observing the publically available auction ending price information. We estimate the model using a large dataset …

Learning Natural Language Inference With Lstm, Shuohang Wang, Jing Jiang

Research Collection School Of Computing and Information Systems

Natural language inference (NLI) is a fundamentally important task in natural language processing that has many applications. The recently released Stanford Natural Language Inference (SNLI) corpus has made it possible to develop and evaluate learning-centered methods such as deep neural networks for natural language inference (NLI). In this paper, we propose a special long short-term memory (LSTM) architecture for NLI. Our model builds on top of a recently proposed neural attention model for NLI but is based on a significantly different idea. Instead of deriving sentence embeddings for the premise and the hypothesis to be used for classification, our solution …

Efficient Multi-Class Selective Sampling On Graphs, Peng Yang, Peilin Zhao, Zhen Hai, Wei Liu, Hoi, Steven C. H., Xiao-Li Li

Research Collection School Of Computing and Information Systems

A graph-based multi-class classification problem is typically converted into a collection of binary classification tasks via the one-vs.-all strategy, and then tackled by applying proper binary classification algorithms. Unlike the one-vs.-all strategy, we suggest a unified framework which operates directly on the multi-class problem without reducing it to a collection of binary tasks. Moreover, this framework makes active learning practically feasible for multi-class problems, while the one-vs.-all strategy cannot. Specifically, we employ a novel randomized query technique to prioritize the informative instances. This query technique based on the hybrid criterion of "margin" and "uncertainty" can achieve a comparable mistake bound …

Demo: Wearable Application To Manage Problem Behavior In Children With Neurodevelopmental Disorders, Camellia Zakaria, Richard C. Davis

Research Collection School Of Computing and Information Systems

Managing problem behaviors in children with neurodevelopmental disorders can be challenging. Such behaviors may discourage social participation and learning. Many of these behaviors warrant intervention, however, are challenging for caregivers to constantly supervise. Previous work focused on developing recognition systems for stereotypical and aggressive behaviors. Researchers also developed visualization interface for caregivers to better understand their child’s needs. Our goal however, is to design an independent behavior management application to help children manage problem behaviors with minimal supervision.We conducted a field study at a school for children with special needs in Singapore, and interviewed ten teachers. This study helped us …

Demo: Ta$Ker: Campus-Scale Mobile Crowd-Tasking Platform, Nikita Jaiman, Thivya Kandappu, Randy Tandriansyah, Archan Misra

Research Collection School Of Computing and Information Systems

We design and develop TA$Ker, a real-world mobile crowd- sourcing platform to empirically study the worker responses to various task recommendation and selection strategies.

Automated Identification Of High Impact Bug Reports Leveraging Imbalanced Learning Strategies, Xinli Yang, David Lo, Qiao Huang, Xin Xia, Jianling Sun

Research Collection School Of Computing and Information Systems

In practice, some bugs have more impact than others and thus deserve more immediate attention. Due to tight schedule and limited human resource, developers may not have enough time to inspect all bugs. Thus, they often concentrate on bugs that are highly impactful. In the literature, high impact bugs are used to refer to the bugs which appear in unexpected time or locations and bring more unexpected effects, or break pre-existing functionalities and destroy the user experience. Unfortunately, identifying high impact bugs from the thousands of bug reports in a bug tracking system is not an easy feat. Thus, an …

Mobipot: Understanding Mobile Telephony Threats With Honeycards, Marco Balduzzi, Payas Gupta, Lion Gu, Debin Gao, Mustaque Ahamad

Research Collection School Of Computing and Information Systems

Over the past decade, the number of mobile phones has increased dramatically, overtaking the world population in October 2014. In developing countries like India and China, mobile subscribers outnumber traditional landline users and account for over 90% of the active population. At the same time, convergence of telephony with the Internet with technologies like VoIP makes it possible to reach a large number of telephone users at a low or no cost via voice calls or SMS (short message service) messages. As a consequence, cybercriminals are abusing the telephony channel to launch attacks, e.g., scams that offer fraudulent services and …

Cace: Exploiting Behavioral Interactions For Improved Activity Recognition In Multi-Inhabitant Smart Homes, Mohammad Arif Ul Alam, Nirmalya Roy, Archan Misra, Joseph Taylor

Research Collection School Of Computing and Information Systems

We propose CACE (Constraints And Correlations mining Engine) which investigates the challenges of improving the recognition of complex daily activities in multi-inhabitant smart homes, by better exploiting the spatiotemporal relationships across the activities of different individuals. We first propose and develop a loosely-coupled Hierarchical Dynamic Bayesian Network (HDBN), which both (a) captures the hierarchical inference of complex (macro-activity) contexts from lower-layer microactivity context (postural and improved oral gestural context), and (b) embeds the various types of behavioral correlations and constraints (at both micro-and macro-activity contexts) across the individuals. While this model is rich in terms of accuracy, it is computationally …

Poster: Improving Communication And Communicability With Smarter Use Of Text-Based Messages On Mobile And Wearable Devices, Kenny T. W. Choo

Research Collection School Of Computing and Information Systems

While smartphones have undoubtedly afforded many modern conveniences such as emails, instant messaging or web search, the notifications from smartphones conversely impact our lives through a deluge of information, or stress arising from expectations that we should turn our immediate attention to them (e.g., work emails). In my latest research, we find that the glanceability of smartwatches may provide an opportunity to reduce the perceived disruption from mobile notifications. Text is a common medium for communication in smart devices, the application of natural language processing on text, together with the physical affordances of smartwatches, present exciting opportunities for research to …

Poster: Air Quality Friendly Route Recommendation System, Savina Singla, Divya Bansal, Archan Misra

Research Collection School Of Computing and Information Systems

To model the overall personal inhalation of hazardous gases through the air (both indoor and outdoor) by an individual, provide air quality friendly route recommendations, thus raising the overall quality of urban movement and living healthy life.

Demo: Smartwatch Based Shopping Gesture Recognition, Meeralakshmi Radhakrishnan, Sharanya Eswaran, Sougata Sen, Vigneshwaran Subbaraju, Archan Misra, Rajesh Krishna Balan

Research Collection School Of Computing and Information Systems

In the current retail segment, the retail store owners are keen to understand the browsing behavior and purchase pattern of the shoppers inside the physical stores. Profiling the behavior of the shopper is key to success for any marketing strategies that can optimize or personalize shopping-related services in real-time. We envision that exploiting the knowledge of real-time behavior of shopper’s in-store activities enables novel applications such as: (a) targeted advertising or recommendations: based on longer term shopper profiles, (b) proactive retail help to assist the shoppers who are confused in choosing between two items, (c) smart reminders that can remind …

Adaptable Key-Policy Attribute-Based Encryption With Time Interval, Siqi Ma, Junzuo Lai, Deng, Robert H., Xuhua Ding

Research Collection School Of Computing and Information Systems

In this paper, we introduce a new cryptographic primitive: adaptable KP-ABE with time interval (KP-TIABE), which is an extension of key-policy attribute-based encryption (KP-ABE). Adaptable KP-TIABE specifies a decryption time interval for every ciphertext such that the ciphertext can only be decrypted within this time interval. To be more flexible, the decryption time interval associated with a ciphertext can be adjusted on demand by a semi-trusted server. We propose a formal model for adaptable KP-TIABE, present a concrete adaptable KP-TIABE scheme and prove its security under the security model.

Value-Inspired Elderly Care Service Design For Aging-In-Place, Na Liu, Sandeep Purao, Hwee-Pink Tan

Research Collection School Of Computing and Information Systems

Most current projects aimed at in-home monitoring for the elderly appear to focus on demonstrating technical feasibility and ensuring safety. In doing so, they often overlook the complexity of the interactions between the elderly and the caregivers. This study explores this complexity by adopting a value-inspired design perspective. Following an action design method, we describe the (re)design of the system and service protocol for an elderly-home monitoring effort. The work requires that we leverage the capabilities (of the technological infrastructure system as well as the service providers) to reconcile the values held by the participants (the elderly and their caregivers). …

Fusing Wifi And Video Sensing For Accurate Group Detection In Indoor Spaces, Kasthuri Jayarajah, Zaman Lantra, Archan Misra

Research Collection School Of Computing and Information Systems

Understanding one's group context in indoor spaces is useful for many reasons - e.g., at a shopping mall, knowing a customer's group context can help in offering context-specific incentives, or estimating taxi demand for customers exiting the mall. Group detection and monitoring using WiFi-based indoor location traces fails when users are invisible (either because they don't carry smartphones, or because their WiFi is turned OFF) or when location tracking is inaccurate. In this paper, we propose a multi-modal group detection system that fuses two independent modes: video and WiFi, for detecting groups with low latency and high accuracy. We present …

Indoor Location Error-Detection Via Crowdsourced Multi-Dimensional Mobile Data, Savina Singla, Archan Misra

Research Collection School Of Computing and Information Systems

We explore the use of multi-dimensional mobile sensing data as a means of identifying errors in one or more of those data streams. More specifically, we look at the possibility of identifying indoor locations with likely incorrect/stale Wi-Fi fingerprints, by using concurrent readings from Wi-Fi and barometer sensors from a collection of mobile devices. Our key contribution is a novel two-step process: (i) using longitudinal, crowd-sourced readings of (possibly incorrect) Wi-Fi location estimates to statistically estimate the barometer calibration offset of individual mobile devices, and (ii) then, using such offset-corrected barometer readings from devices (that are supposedly collocated) to identify …

Cdrep: Automatic Repair Of Cryptographic-Misuses In Android Applications, Siqi Ma, David Lo, Teng Li, Robert H. Deng

Research Collection School Of Computing and Information Systems

Cryptography is increasingly being used in mobile applications to provide various security services; from user authentication, data privacy, to secure communications. However, there are plenty of mistakes that developers could accidentally make when using cryptography in their mobile apps and such mistakes can lead to a false sense of security. Recent research efforts indeed show that a significant portion of mobile apps in both Android and iOS platforms misused cryptographic APIs. In this paper, we present CDRep, a tool for automatically repairing cryptographic misuse defects in Android apps. We classify such defects into seven types and manually assemble the corresponding …

Fixed Versus Random Sampling Designs In Small South Dakota Glacial Lakes, Bradley J. Smith, Nathan S. Kruger, Nicholas S. Voss, Brian G. Blackwell

The Prairie Naturalist

Choice of sampling design is fundamental when planning surveys to monitor fisheries resources. However, little is known about the impact that different sampling designs may have on commonly collected fish population metrics used to index relative abundance, size structure, and diversity in small (<200 ha) glacial lakes. To address this issue, we sampled three small glacial lakes in eastern South Dakota with modified fyke nets and gill nets at fixed sites used by South Dakota Game, Fish and Parks and a complement of nets at randomized sites. Catch per unit effort (CPUE), proportional size distribution (PSD), and PSD-preferred (PSD-P) were compared between fixed and random designs for each species and gear by lake while Bray-Curtis distances were calculated between sample designs for each gear type in each lake. Precision of CPUE estimates for routinely indexed species were calculated for both gears used by each sampling design. No consistent bias in calculated population metrics was detected between sampling designs for any species collected with either gear type in the three lakes. Sampling precision of CPUE estimates were low for both gears and study designs; though randomized sites tended to yield lower precision. Power analyses indicated that current levels of sampling effort are insufficient to detect differences in CPUE or PSD/PSD-P between fixed and random sampling designs. In addition to being small, study lakes had relatively homogenous habitat allowing for effective sampling resulting in similar values of CPUE and PSD/PSD-P for both fixed and random study designs, and high assemblage overlap. We conclude that fixed sampling sites provide adequate representation of fish communities in small glacial lakes and are sufficient for monitoring temporal changes in these small, but numerous, systems.

Distribution Of Short-Tailed Shrews (Mammalia: Soricidae: Blarina) In Missouri, Russell S. Pfau, Aaron P. Figueroa

The Prairie Naturalist

Three species of short-tailed shrews (genus Bla- rina) occur throughout the eastern one-half of the United States and southern Canada. B. brevicauda is the most widely distributed, ranging from the Atlantic ocean to the western Great Plains. B. carolinensis occurs throughout the southeastern U.S., and B. hylophaga is found in the southern plains states. The current understanding of the geographic distributions of these species is based primarily on morphol- ogy; size is the primary characteristic used for identifica- tion with B. brevicauda the largest and B. carolinensis the smallest.

The only states in which all three species have been reported …

Notes: Comparisons Of Catch Between Two Bait Types With An Emphasis On Pallid Stur- Geon, Thad W. Huenemann, Kirk D. Steffensen

The Prairie Naturalist

Trotline sampling has been a common practice used by commercial and recreational anglers to target specific fish species (e.g. Ictaluridae and Acipenseridae), while biologists have used them as a management tool to monitor and evaluate fish populations (Graham 1997, Vokoun and Rabeni 1999, Arterburn and Berry 2002, Steffensen et al. 2011). Different trotline configurations have been evaluated to determine which combination of hook type and bait produces higher catch rates for catfish species (Johnson 1987, Arterburn and Berry 2002) but this information is lacking for sturgeon species. For example, Arterburn and Berry (2002) concluded that channel catfish (Ictalurus punctatus) were …

Isotopes From Fecal Material Provides Evidence Of Recent Diet Of Prairie Deer Mice, John W. Doudna, Brent J. Danielson

The Prairie Naturalist

Prairie deer mice are important predators in many agricultural systems, and through their diet they can help to regulate pest insect and weed populations. Our objective was to test whether fecal material is an effective means of detailing the foraging ecology of small mammals. We conducted three studies to evaluate the efficacy of this technique: 1) field-collected fecal material from unknown deer mice from late winter to early spring, 2) fecal material collected in an enclosure with mice fed a mix of C3 and C4 plant seeds, and 3) fecal material from tagged female mice in the field. We detected …

Optimizing Sequence Coverage For A Moderate Mass Protein In Nano-Electrospray Ionization Quadrupole Time-Of-Flight Mass Spectrometry, Ryan E. Matsuda, Venkata Kolli, Megan Woods, Eric D. Dodds, David S. Hage

David Hage Publications

Sample pretreatment was optimized to obtain high sequence coverage for human serum albumin (HSA, 66.5 kDa) when using nano-electrospray ionization quadrupole time-of-flight mass spectrometry (nESI-Q-TOF-MS). Use of the final method with trypsin, Lys-C and Glu-C digests gave a combined coverage of 98.8%. The addition of peptide fractionation resulted in 99.7% coverage. These results were comparable to those obtained previously with matrixassisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS). The sample pretreatment/nESI-Q-TOF-MS method was also used with collision-induced dissociation to analyze HSA digests and to identify peptides that could be employed as internal mass calibrants in future studies of modifications to HSA.