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Verifying Monadic Second-Order Properties Of Graph Programs, Christopher M. Poskitt, Detlef Plump
Verifying Monadic Second-Order Properties Of Graph Programs, Christopher M. Poskitt, Detlef Plump
Research Collection School Of Computing and Information Systems
The core challenge in a Hoare- or Dijkstra-style proof system for graph programs is in defining a weakest liberal precondition construction with respect to a rule and a postcondition. Previous work addressing this has focused on assertion languages for first-order properties, which are unable to express important global properties of graphs such as acyclicity, connectedness, or existence of paths. In this paper, we extend the nested graph conditions of Habel, Pennemann, and Rensink to make them equivalently expressive to monadic second-order logic on graphs. We present a weakest liberal precondition construction for these assertions, and demonstrate its use in verifying …
Reinforcement Learning For Adaptive Operator Selection In Memetic Search Applied To Quadratic Assignment Problem, Stephanus Daniel Handoko, Duc Thien Nguyen, Zhi Yuan, Hoong Chuin Lau
Reinforcement Learning For Adaptive Operator Selection In Memetic Search Applied To Quadratic Assignment Problem, Stephanus Daniel Handoko, Duc Thien Nguyen, Zhi Yuan, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
Memetic search is well known as one of the state-of-the-art metaheuristics for finding high-quality solutions to NP-hard problems. Its performance is often attributable to appropriate design, including the choice of its operators. In this paper, we propose a Markov Decision Process model for the selection of crossover operators in the course of the evolutionary search. We solve the proposed model by a Q-learning method. We experimentally verify the efficacy of our proposed approach on the benchmark instances of Quadratic Assignment Problem.
Facilitating Crowd Sourced Software Engineering Via Stack Overflow, Ohad Barzilay, Christoph Treude, Alexey Zagalsky
Facilitating Crowd Sourced Software Engineering Via Stack Overflow, Ohad Barzilay, Christoph Treude, Alexey Zagalsky
Research Collection School Of Computing and Information Systems
The open source community, as well as numerous technical blogs and community web sites, put online vast quantities of free source code, ranging from snippets to full-blown products. This code embodies the software development community’s domain knowledge, and mirrors the structure of the Internet: it is distributed rather than hierarchical; it is chaotic, incomplete, and inconsistent. StackOverflow.com is a Question and Answer (Q&A) website which uses social media to facilitate knowledge exchange between programmers by mitigating the pitfalls involved in using code from the Internet. Its design nurtures a community of developers, and enables crowd sourced software engineering activities ranging …
Click-Through-Based Cross-View Learning For Image Search, Yingwei Pan, Ting Yao, Tao Mei, Houqiang Li, Chong-Wah Ngo, Yong Rui
Click-Through-Based Cross-View Learning For Image Search, Yingwei Pan, Ting Yao, Tao Mei, Houqiang Li, Chong-Wah Ngo, Yong Rui
Research Collection School Of Computing and Information Systems
One of the fundamental problems in image search is to rank image documents according to a given textual query. Existing search engines highly depend on surrounding texts for ranking images, or leverage the query-image pairs annotated by human labelers to train a series of ranking functions. However, there are two major limitations: 1) the surrounding texts are often noisy or too few to accurately describe the image content, and 2) the human annotations are resourcefully expensive and thus cannot be scaled up. We demonstrate in this paper that the above two fundamental challenges can be mitigated by jointly exploring the …
User Daily Activity Pattern Learning: A Multi-Memory Modeling Approach, Shan Gao, Ah-Hwee Tan
User Daily Activity Pattern Learning: A Multi-Memory Modeling Approach, Shan Gao, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
In this paper, we propose a multi-memory model, ADLART model, to discover the daily activity pattern of a sensor monitored user from his/her activities of daily living (ADL). The proposed model mimics the human multiple memory system which comprises a working memory, an episodic memory, and a semantic memory. Through encoding user's daily activities patterns in episodic memory and extracting the regularities of activity routines in semantic memory, the ADLART system is able to learn, recognize, compare, and retrieve daily ADL patterns of the user. Experiments are presented to show the performance of the ADLART model using different parameter settings …
Lifetime Lexical Variation In Social Media, Lizi Liao, Jing Jiang, Ying Ding, Heyan Huang, Ee-Peng Lim
Lifetime Lexical Variation In Social Media, Lizi Liao, Jing Jiang, Ying Ding, Heyan Huang, Ee-Peng Lim
Research Collection School Of Computing and Information Systems
As the rapid growth of online social media attracts a large number of Internet users, the large volume of content generated by these users also provides us with an opportunity to study the lexical variation of people of different ages. In this paper, we present a latent variable model that jointly models the lexical content of tweets and Twitter users' ages. Our model inherently assumes that a topic has not only a word distribution but also an age distribution. We propose a Gibbs-EM algorithm to perform inference on our model. Empirical evaluation shows that our model can learn meaningful age-specific …
Fsph: Fitted Spectral Hashing For Efficient Similarity Search, Yong-Dong Zhang, Yu Wang, Sheng Tang, Steven C. H. Hoi, Jin-Tao Li
Fsph: Fitted Spectral Hashing For Efficient Similarity Search, Yong-Dong Zhang, Yu Wang, Sheng Tang, Steven C. H. Hoi, Jin-Tao Li
Research Collection School Of Computing and Information Systems
Spectral hashing (SpH) is an efficient and simple binary hashing method, which assumes that data are sampled from a multidimensional uniform distribution. However, this assumption is too restrictive in practice. In this paper we propose an improved method, fitted spectral hashing (FSpH), to relax this distribution assumption. Our work is based on the fact that one-dimensional data of any distribution could be mapped to a uniform distribution without changing the local neighbor relations among data items. We have found that this mapping on each PCA direction has certain regular pattern, and could be fitted well by S-curve function (Sigmoid function). …
A Retail Bank's Bpm Experience, Shankararaman, Venky, Gottipati Swapna, Randall E. Duran
A Retail Bank's Bpm Experience, Shankararaman, Venky, Gottipati Swapna, Randall E. Duran
Research Collection School Of Computing and Information Systems
This real-life case study, which was undertaken by a leading financial services group in the Asia-Pacific region, is used to demonstrate the innovative use of BPM (Business Process Management) technology in a competitive business area. It describes how a BPM project, within the Application Verification and Capture (AVC), was conceived, designed and implemented in order to deliver strategic value to the organization. Hereafter, the financial services group will be referred to as “the bank”. The AVC project was targeted at one of the bank's processes called the Application Verification and Capture (AVC) process for unit trust products. This process involved …
Synchronicity: Pushing The Envelope Of Fine-Grained Localization With Distributed Mimo, Jie Xiong, Kyle Jamieson, Karthikeyan Sundaresan
Synchronicity: Pushing The Envelope Of Fine-Grained Localization With Distributed Mimo, Jie Xiong, Kyle Jamieson, Karthikeyan Sundaresan
Research Collection School Of Computing and Information Systems
Indoor localization of mobile devices and tags has received much attention recently, with encouraging fine-grained localization results available with enough line-of-sight coverage and enough hardware infrastructure. Synchronicity is a location system that aims to push the envelope of highly-accurate localization systems further in both dimensions, requiring less line-of-sight and less infrastructure. With Distributed MIMO network of wireless LAN access points (APs) as a starting point, we leverage the time synchronization that such a network affords to localize with time-difference-of-arrival information at the APs. We contribute novel super-resolution signal processing algorithms and reflection path elimination schemes, yielding superior results even in …
Near-Optimal Nonmyopic Contact Center Planning Using Dual Decomposition, Akshat Kumar, Sudhanshu Singh, Pranav Gupta, Gyana Parija
Near-Optimal Nonmyopic Contact Center Planning Using Dual Decomposition, Akshat Kumar, Sudhanshu Singh, Pranav Gupta, Gyana Parija
Research Collection School Of Computing and Information Systems
We address the problem of minimizing staffing cost in a contact center subject to service level requirements over multiple weeks. We handle both the capacity planning and agent schedule generation aspect of this problem. Our work incorporates two unique business requirements. First, we develop techniques that can provide near-optimal staffing for 247 contact centers over long term, upto eight weeks, rather than planning myopically on a week-on-week basis. Second, our approach is usable in an online interactive setting in which staffing managers using our system expect high quality plans within a short time period. Results on large real world and …
Streets: Game-Theoretic Traffic Patrolling With Exploration And Exploitation, Matthew Brown, Sandhya Saisubramanian, Pradeep Varakantham, Milind Tambe
Streets: Game-Theoretic Traffic Patrolling With Exploration And Exploitation, Matthew Brown, Sandhya Saisubramanian, Pradeep Varakantham, Milind Tambe
Research Collection School Of Computing and Information Systems
To dissuade reckless driving and mitigate accidents, cities deploy resources to patrol roads. In this paper, we present STREETS, an application developed for the city of Singapore, which models the problem of computing randomized traffic patrol strategies as a defenderattacker Stackelberg game. Previous work on Stackelberg security games has focused extensively on counterterrorism settings. STREETS moves beyond counterterrorism and represents the first use of Stackelberg games for traffic patrolling, in the process providing a novel algorithm for solving such games that addresses three major challenges in modeling and scale-up. First, there exists a high degree of unpredictability in travel times …
Lifetime Lexical Variation In Social Media, Lizi Liao, Jing Jiang, Ying Ding, Heyan Huang, Ee Peng Lim
Lifetime Lexical Variation In Social Media, Lizi Liao, Jing Jiang, Ying Ding, Heyan Huang, Ee Peng Lim
Research Collection School Of Computing and Information Systems
As the rapid growth of online social media attracts a large number of Internet users, the large volume of content generated by these users also provides us with an opportunity to study the lexical variation of people of different ages. In this paper, we present a latent variable model that jointly models the lexical content of tweets and Twitter users’ ages. Our model inherently assumes that a topic has not only a word distribution but also an age distribution. We propose a Gibbs-EM algorithm to perform inference on our model. Empirical evaluation shows that our model can learn meaningful age-specific …
Placing Videos On A Semantic Hierarchy For Search Result Navigation, Song Tan, Yu-Gang Jiang, Chong-Wah Ngo
Placing Videos On A Semantic Hierarchy For Search Result Navigation, Song Tan, Yu-Gang Jiang, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
Organizing video search results in a list view is widely adopted by current commercial search engines, which cannot support efficient browsing for complex search topics that have multiple semantic facets. In this article, we propose to organize video search results in a highly structured way. Specifically, videos are placed on a semantic hierarchy that accurately organizes various facets of a given search topic. To pick the most suitable videos for each node of the hierarchy, we define and utilize three important criteria: relevance, uniqueness, and diversity. Extensive evaluations on a large YouTube video dataset demonstrate the effectiveness of our approach.
Online Community Transition Detection, Biying Tan, Feida Zhu, Qiang Qu, Siyuan Liu
Online Community Transition Detection, Biying Tan, Feida Zhu, Qiang Qu, Siyuan Liu
Research Collection School Of Computing and Information Systems
Mining user behavior patterns in social networks is of great importance in user behavior analysis, targeted marketing, churn prediction and other applications. However, less effort has been made to study the evolution of user behavior in social communities. In particular, users join and leave communities over time. How to automatically detect the online community transitions of individual users is a research problem of immense practical value yet with great technical challenges. In this paper, we propose an algorithm based on the Minimum Description Length (MDL) principle to trace the evolution of community transition of individual users, adaptive to the noisy …
Ar-Miner: Mining Informative Reviews For Developers From Mobile App Marketplace, Ning Chen, Jialiu Lin, Steven C. H. Hoi, Xiaokui Xiao, Boshen Zhang
Ar-Miner: Mining Informative Reviews For Developers From Mobile App Marketplace, Ning Chen, Jialiu Lin, Steven C. H. Hoi, Xiaokui Xiao, Boshen Zhang
Research Collection School Of Computing and Information Systems
With the popularity of smartphones and mobile devices, mobile application (a.k.a. “app”) markets have been growing exponentially in terms of number of users and downloads. App developers spend considerable effort on collecting and exploiting user feedback to improve user satisfaction, but suffer from the absence of effective user review analytics tools. To facilitate mobile app developers discover the most “informative” user reviews from a large and rapidly increasing pool of user reviews, we present “AR-Miner” — a novel computational framework for App Review Mining, which performs comprehensive analytics from raw user reviews by (i) first extracting informative user reviews by …
Institutional Boundaries And Trust Of Virtual Teams In Collaborative Design: An Experimental Study In A Virtual World Environment, Shu Z. Schiller, Brian Mennecke, Fiona Fui-Hoon Nah, Andy Luse
Institutional Boundaries And Trust Of Virtual Teams In Collaborative Design: An Experimental Study In A Virtual World Environment, Shu Z. Schiller, Brian Mennecke, Fiona Fui-Hoon Nah, Andy Luse
Research Collection School Of Computing and Information Systems
Members of virtual teams often collaborate within and across institutional boundaries. This research investigates the effects of boundary spanning conditions on the development of team trust and team satisfaction. Two hundred and eighty-two participants carried out a collaborative design task over several weeks in a virtual world, Second Life. Multigroup structural equation modeling was used to examine our research model, which compares individual level measurement between two boundary spanning team conditions. The results indicate that trusting beliefs have a positive impact on team trust, which in turn, influences team satisfaction. Further, we found that, compared to cross-boundary teams, within-boundary teams …
Global Immutable Region Computation, Jilian Zhang, Kyriakos Mouratidis, Hwee Hwa Pang
Global Immutable Region Computation, Jilian Zhang, Kyriakos Mouratidis, Hwee Hwa Pang
Research Collection School Of Computing and Information Systems
A top-k query shortlists the k records in a dataset that best match the user's preferences. To indicate her preferences, the user typically determines a numeric weight for each data dimension (i.e., attribute). We refer to these weights collectively as the query vector. Based on this vector, each data record is implicitly mapped to a score value (via a weighted sum function). The records with the k largest scores are reported as the result. In this paper we propose an auxiliary feature to standard top-k query processing. Specifically, we compute the maximal locus within which the query vector incurs no …
Air Indexing For On-Demand Xml Data Broadcast, Weiwei Sun, Rongrui Qin, Jinjin Wu, Baihua Zheng
Air Indexing For On-Demand Xml Data Broadcast, Weiwei Sun, Rongrui Qin, Jinjin Wu, Baihua Zheng
Research Collection School Of Computing and Information Systems
XML data broadcast is an efficient way to disseminate semi-structured information in wireless mobile environments. In this paper, we propose a novel two-tier index structure to facilitate the access of XML document in an on-demand broadcast system. It provides the clients with an overall image of all the XML documents available at the server side and hence enables the clients to locate complete result sets accordingly. A pruning strategy is developed to cut down the index size and a two-tier structure is proposed to further remove any redundant information. In addition, two index distribution strategies, namely naive distribution and partial …
Version History, Similar Report, And Structure: Putting Them Together For Improved Bug Localization, Shaowei Wang, David Lo
Version History, Similar Report, And Structure: Putting Them Together For Improved Bug Localization, Shaowei Wang, David Lo
Research Collection School Of Computing and Information Systems
During the evolution of a software system, a large number of bug reports are submitted. Locating the source code files that need to be fixed to resolve the bugs is a challenging problem. Thus, there is a need for a technique that can automatically figure out these buggy files. A number of bug localization solutions that take in a bug report and output a ranked list of files sorted based on their likelihood to be buggy have been proposed in the literature. However, the accuracy of these tools still need to be improved. In this paper, to address this need, …
Towards More Accurate Content Categorization Of Api Discussions, Bo Zhou, Xin Xia, David Lo, Cong Tian, Xinyu Wang
Towards More Accurate Content Categorization Of Api Discussions, Bo Zhou, Xin Xia, David Lo, Cong Tian, Xinyu Wang
Research Collection School Of Computing and Information Systems
Nowadays, software developers often discuss the usage of various APIs in online forums. Automatically assigning pre-defined semantic categorizes to API discussions in these forums could help manage the data in online forums, and assist developers to search for useful information. We refer to this process as content categorization of API discussions. To solve this problem, Hou and Mo proposed the usage of naive Bayes multinomial, which is an effective classification algorithm. In this paper, we propose a Cache-bAsed compoSitE algorithm, short formed as CASE, to automatically categorize API discussions. Considering that the content of an API discussion contains both textual …
Condensing Class Diagrams By Analyzing Design And Network Metrics Using Optimistic Classification, Ferdian Thung, David Lo, Mohd Hafeez Osman, Michel R.V. Chaudron
Condensing Class Diagrams By Analyzing Design And Network Metrics Using Optimistic Classification, Ferdian Thung, David Lo, Mohd Hafeez Osman, Michel R.V. Chaudron
Research Collection School Of Computing and Information Systems
A class diagram of a software system enhances our ability to understand software design. However, this diagram is often unavailable. Developers usually reconstruct the diagram by reverse engineering it from source code. Unfortunately, the resultant diagram is often very cluttered; making it difficult to learn anything valuable from it. Thus, it would be very beneficial if we are able to condense the reverse- engineered class diagram to contain only the important classes depicting the overall design of a software system. Such diagram would make program understanding much easier. A class can be important, for example, if its removal would break …
An Air Index For Spatial Query Processing In Road Networks, Weiwei Sun, Chunan Chen, Baihua Zheng, Chong Chen, Peng Liu
An Air Index For Spatial Query Processing In Road Networks, Weiwei Sun, Chunan Chen, Baihua Zheng, Chong Chen, Peng Liu
Research Collection School Of Computing and Information Systems
Spatial queries such as range query and kNN query in road networks have received a growing number of attention in real life. Considering the large population of the users and the high overhead of network distance computation, it is extremely important to guarantee the efficiency and scalability of query processing. Motivated by the scalable and secure properties of wireless broadcast model, this paper presents an air index called Network Partition Index (NPI) to support efficient spatial query processing in road networks via wireless broadcast. The main idea is to partition the road network into a number of regions and then …
Optimal Performance Trade-Offs In Mac For Wireless Sensor Networks Powered By Heterogeneous Ambient Energy Harvesting, Jin Yunye, Hwee-Pink Tan
Optimal Performance Trade-Offs In Mac For Wireless Sensor Networks Powered By Heterogeneous Ambient Energy Harvesting, Jin Yunye, Hwee-Pink Tan
Research Collection School Of Computing and Information Systems
In wireless sensor networks powered by ambient energy harvesting (WSNs-HEAP), sensor nodes' energy harvesting rates are spatially heterogeneous and temporally variant, which impose difficulties for medium access control (MAC). In this paper, we first derive the necessary conditions under which channel utilization and fairness are optimal in a WSN-HEAP, respectively. Based on the analysis, we propose an earliest deadline first (EDF) polling MAC protocol, which regulates transmission sequence of the sensor nodes based on the spatially heterogeneous energy harvesting rates. It also mitigates temporal variations in energy harvesting rates by a prediction and update mechanism. Simulation results verify the performance …
Socio-Physical Analytics: Challenges & Opportunities, Archan Misra, Kasthuri Jayarajah, Shriguru Nayak, Philips Kokoh Prasetyo, Ee-Peng Lim
Socio-Physical Analytics: Challenges & Opportunities, Archan Misra, Kasthuri Jayarajah, Shriguru Nayak, Philips Kokoh Prasetyo, Ee-Peng Lim
Research Collection School Of Computing and Information Systems
In this paper, we argue for expanded research into an area called Socio-Physical Analytics, that focuses on combining the behavioral insight gained from mobile-sensing based monitoring of physical behavior with the inter-personal relationships and preferences deduced from online social networks. We highlight some of the research challenges in combining these heterogeneous data sources and then describe some examples of our ongoing work (based on real-world data being collected at SMU) that illustrate two aspects of socio-physical analytics: (a) how additional demographic and online analytics based attributes can potentially provide better insights into the preferences and behaviors of individuals or groups …
Does Latitude Hurt While Longitude Kills? Geographical And Temporal Separation In A Large Scale Software Development Project, Patrick Wagstrom, Subhajit Datta
Does Latitude Hurt While Longitude Kills? Geographical And Temporal Separation In A Large Scale Software Development Project, Patrick Wagstrom, Subhajit Datta
Research Collection School Of Computing and Information Systems
Distributed software development allows firms to leverage cost advantages and place work near centers of competency. This distribution comes at a cost -- distributed teams face challenges from differing cultures, skill levels, and a lack of shared working hours. In this paper we examine whether and how geographic and temporal separation in a large scale distributed software development influences developer interactions. We mine the work item trackers for a large commercial software project with a globally distributed development team. We examine both the time to respond and the propensity of individuals to respond and find that when taken together, geographic …
Revisiting Risk-Sensitive Mdps: New Algorithms And Results, Ping Hou, William Yeoh, Pradeep Reddy Varakantham
Revisiting Risk-Sensitive Mdps: New Algorithms And Results, Ping Hou, William Yeoh, Pradeep Reddy Varakantham
Research Collection School Of Computing and Information Systems
While Markov Decision Processes (MDPs) have been shown to be effective models for planning under uncertainty, theobjective to minimize the expected cumulative cost is inappropriate for high-stake planning problems. As such, Yu, Lin, and Yan (1998) introduced the Risk-Sensitive MDP (RSMDP) model, where the objective is to find a policy that maximizes the probability that the cumulative cost is within some user-defined cost threshold. In this paper, we revisit this problem and introduce new algorithms that are based on classical techniques, such as depth-first search and dynamic programming, and a recently introduced technique called Topological Value Iteration (TVI). We demonstrate …
Evolving An Information Systems Capstone Course To Align With The Fast Changing Singapore Marketplace, Chris Boesch, Benjamin Kok Siew Gan
Evolving An Information Systems Capstone Course To Align With The Fast Changing Singapore Marketplace, Chris Boesch, Benjamin Kok Siew Gan
Research Collection School Of Computing and Information Systems
Every year, around fifty-five undergraduate teams of four to six students are required to complete a capstone course for the School of Information Systems at Singapore Management University. Each team spends approximately five months working with an industry sponsor using the latest tools and techniques. Students actively learn by implementing the system to solve a real world problem. In addition to delivering value to the local sponsor, our students learn specialized skills currently needed in the marketplace, which might not yet be incorporated into electives and core courses. In this paper, we discuss the tradeoffs of providing students and project …
Fault Attacks On Hyperelliptic Curve Discrete Logarithm Problem Over Binary Field, Mingqiang Wang, Haiyang Xue, Tao Zhan
Fault Attacks On Hyperelliptic Curve Discrete Logarithm Problem Over Binary Field, Mingqiang Wang, Haiyang Xue, Tao Zhan
Research Collection School Of Computing and Information Systems
In this paper, we present invalid-curve attacks that apply to the hyperelliptic curve scalar multiplication (HECSM) algorithm proposed by Avanzi et al. on the genus 2 hyperelliptic curve over binary field. We observe some new properties of the HECSM. Our attacks are based on these new properties and the observation that the parameters f 0 and f 1 of the hyperelliptic curve equation are not utilized for the HECSM. We show that with different “values” for curve parameters f 0, f 1, there exsit cryptographically weak groups in the Koblitz hyperelliptic curve. Also, we compute the theoretical probability of getting …
Joint Virtual Machine And Bandwidth Allocation In Software Defined Network (Sdn) And Cloud Computing Environments, Jonathan David Chase, Rakpong Kaewpuang, Wen Yonggang, Dusit Niyato
Joint Virtual Machine And Bandwidth Allocation In Software Defined Network (Sdn) And Cloud Computing Environments, Jonathan David Chase, Rakpong Kaewpuang, Wen Yonggang, Dusit Niyato
Research Collection School Of Computing and Information Systems
Cloud computing provides users with great flexibility when provisioning resources, with cloud providers offering a choice of reservation and on-demand purchasing options. Reservation plans offer cheaper prices, but must be chosen in advance, and therefore must be appropriate to users' requirements. If demand is uncertain, the reservation plan may not be sufficient and on-demand resources have to be provisioned. Previous work focused on optimally placing virtual machines with cloud providers to minimize total cost. However, many applications require large amounts of network bandwidth. Therefore, considering only virtual machines offers an incomplete view of the system. Exploiting recent developments in software …
Technology Investment Decision-Making Under Uncertainty In Mobile Payment Systems, Robert J. Kauffman, Jun Liu, Dan Ma
Technology Investment Decision-Making Under Uncertainty In Mobile Payment Systems, Robert J. Kauffman, Jun Liu, Dan Ma
Research Collection School Of Computing and Information Systems
Innovations in the mobile payments industry provide potentially profitable investment opportunities for banks. Nonetheless, significant uncertainties are associated with decision-making for this IT investment context, regarding future market conditions, technology standards, and consumer and merchant responses, especially their willingness to adopt. As a result, traditional capital budgeting approach and experienced intuition have not been effective. We develop a model to support a bank’s mobile payment systems adoption decision-making at the firm level when it faces endogenous technological risks and exogenous market conditions. This study applies theory and modeling from financial economics for decision-making under uncertainty to investments in m-payment systems …