Singapore Management University
Recent publications
Reaching consensus—a macroscopic state where the system constituents display the same microscopic state—is a necessity in multiple complex socio-technical and techno-economic systems: their correct functioning ultimately depends on it. In many distributed systems—of which blockchain-based applications are a paradigmatic example—the process of consensus formation is crucial not only for the emergence of a leading majority but for the very functioning of the system. We build a minimalistic network model of consensus formation on blockchain systems for quantifying how central nodes—with respect to their average distance to others—can leverage on their position to obtain competitive advantage in the consensus process. We show that in a wide range of network topologies, the probability of forming a majority can significantly increase depending on the centrality of nodes that initiate the spreading. Further, we study the role that network topology plays on the consensus process: we show that central nodes in scale-free networks can win consensus in the network even if they broadcast states significantly later than peripheral ones.
Neural networks are getting increasingly popular thanks to their exceptional performance in solving many real-world problems. At the same time, they are shown to be vulnerable to attacks, difficult to debug and subject to fairness issues. To improve people’s trust in the technology, it is often necessary to provide some human-understandable explanation of neural networks’ decisions, e.g., why is that my loan application is rejected whereas hers is approved? That is, the stakeholder would be interested to minimize the chances of not being able to explain the decision consistently and would like to know how often and how easy it is to explain the decisions of a neural network before it is deployed. In this work, we provide two measurements on the decision explainability of neural networks. Afterwards, we develop algorithms for evaluating the measurements of user-provided neural networks automatically. We evaluate our approach on multiple neural network models trained on benchmark datasets. The results show that existing neural networks’ decisions often have low explainability according to our measurements. This is in line with the observation that adversarial samples can be easily generated through adversarial perturbation, which are often hard to explain. Our further experiments show that the decisions of the models trained with robust training are not necessarily easier to explain, whereas decisions of the models retrained with samples generated by our algorithms are easier to explain.
Software bug database and benchmark are the wheels of advancing automated software testing. In practice, real bugs often occur sparsely relative to the amount of software code, the extraction and curation of which are quite labor-intensive but can be essential to facilitate the innovation of testing techniques. Over the past decade, several milestones have been made to construct bug databases, pushing the progress of automated software testing research. However, up to the present, it still lacks a real bug database and benchmark for game software, making current game testing research mostly stagnant. The missing of bug database and framework greatly limits the development of automated game testing techniques. To bridge this gap, we first perform large-scale real bug collection and manual analysis from 5 large commercial games, with a total of more than 250,000 lines of code. Based on this, we propose GBGallery, a game bug database and an extensible framework, to enable automated game testing research. In its initial version, GBGallery contains 76 real bugs from 5 games and incorporates 5 state-of-the-art testing techniques for comparative study as a baseline for further research. With GBGallery, we perform large-scale empirical studies and find that the current automated game testing is still at an early stage, where new testing techniques for game software should be extensively investigated. We make GBGallery publicly available, hoping to facilitate the game testing research.
Spectrum Based Fault Localization (SBFL) is a statistical approach to identify faulty code within a program given a program spectra (i.e., records of program elements executed by passing and failing test cases). Several SBFL techniques have been proposed over the years, but most evaluations of those techniques were done only on Java and C programs, and frequently involve artificial faults. Considering the current popularity of Python, indicated by the results of the Stack Overflow survey among developers in 2020, it becomes increasingly important to understand how SBFL techniques perform on Python projects. However, this remains an understudied topic. In this work, our objective is to analyze the effectiveness of popular SBFL techniques in real-world Python projects. We also aim to compare our observed performance on Python to previously-reported performance on Java. Using the recently-built bug benchmark BugsInPy as our fault dataset, we apply five popular SBFL techniques (Tarantula, Ochiai, OP, Barinel, and DStar) and analyze their performances. We subsequently compare our results with results from Java and C projects reported in earlier related works. We find that 1) the real faults in BugsInPy are harder to identify using SBFL techniques compared to the real faults in Defects4J, indicated by the lower performance of the evaluated SBFL techniques on BugsInPy; 2) older techniques such as Tarantula, Barinel, and Ochiai consistently outperform newer techniques (i.e., OP and DStar) in a variety of metrics and debugging scenarios; 3) claims in preceding studies done on artificial faults in C and Java (such as “OP outperforms Tarantula”) do not hold on Python real faults; 4) lower-performing techniques can outperform higher-performing techniques in some cases, emphasizing the potential benefit of combining SBFL techniques. Our results yield insight into how popular SBFL techniques perform in real Python faults and emphasize the importance of conducting SBFL evaluations on real faults.
In recent years, urban planners and designers are paying greater attention to Outdoor Thermal Comfort (OTC) studies due to the imminent threat of the Urban Heat Island and climate change on human health. Historically, indoor thermal comfort research assumed steady-state conditions, centralizing on the concept of thermal neutrality to determine optimal environmental parameters. Such research pivoted to investigating how non-steady-state, transient environmental conditions influence comfort. Recent studies underscore the usefulness of positive alliesthesia in providing a productive framework for OTC evaluation. In this article we first clarify the concepts related to thermal comfort-related terms, scales, and models in the literature. Then, we propose four research questions that we believe are important for the research of thermal transient sensations. To answer them, we present the state of current research and gaps for the field and provide directions that could advance the knowledge on dynamic OTC.
What are the basic types of social network ties captured by name generators? While there have been several classifications proposed, and a large proliferation of name generators capturing various tie content has emerged, there is no clear way to map a given name generator to a particular tie type. Building on previous research, this paper proposes a framework for doing so in a principled way based on two studies. Study 1 is a dimension reduction of 24 common name generators. We find two dimensions (Valence and Social Distance), three positive tie types (Admiration, Closeness, Socialize), and three negative tie types (Active Conflict, Passive Conflict, Contempt) and use Youden's J statistic as a metric to identify the name generator that best maximizes sensitivity and specificity for detecting our tie types. We find that the most common name generators used by researchers fall within just one tie type (Closeness). Study 2 models these six tie types as predictors and outcomes of important sociological variables and finds that each tie type is associated with distinct patterns of emotions, social support, social status, and social roles. Our taxonomy makes a contribution to network theory as well as study design. In particular, it advances our understanding of the nature of signed ties. We find that negative ties are both bipolar and orthogonal, and distinguish between two types of ambivalence. Moreover, the findings contribute to the further refinement and elaboration of a comprehensive taxonomy of network ties.
Cross-dockingis a useful concept used by many companies to control the product flow. It enables the transshipment process of products from suppliers to customers. This research thus extends the benefit of cross-docking with reverse logistics, since return process management has become an important field in various businesses. The vehicle routing problem in a distribution network is considered to be an integrated model, namely the vehicle routing problem with reverse cross-docking (VRP-RCD). This study develops a mathematical model to minimize the costs of moving products in a four-level supply chain network that involves suppliers, cross-dock, customers, and outlets. A matheuristic based on an adaptive large neighborhood search (ALNS) algorithm and a set partitioning formulation is introduced to solve benchmark instances. We compare the results against those obtained by optimization software, as well as other algorithms such as ALNS, a hybrid algorithm based on large neighborhood search and simulated annealing (LNS-SA), and ALNS-SA. Experimental results show the competitiveness of the matheuristic that is able to obtain all optimal solutions for small instances within shorter computational times. For larger instances, the matheuristic outperforms the other algorithms using the same computational times. Finally, we analyze the importance of the set partitioning formulation and the different operators.
This paper explores the ways in which the religious subject can be a contingent position that is responsive to the broader socio-religious context within which it is expressed. These contingencies are acutely observed amongst short-term missionaries (STM), who seek out encounters with difference in pursuit of a more cosmopolitan subjectivity. Yet, whilst spaces of missionary encounter are inherently relational, the missions literature has tended to downplay the effects of relationality on the realisation of these subject positions. By focussing on the experiences of Singaporean missionaries working amongst Christian communities in Southeast Asia, I contribute a more nuanced and less predetermined understanding of the dynamics that underpin intra-Asian missionary encounters. Drawing on interviews conducted with Singapore’s STM community, I explore how materiality and new media can structure encounters and subject positions within relational missionary space. I also emphasise the limits of relational space by highlighting its untranslatability beyond the missionary terrain.
This paper concerns the staffing optimization problem in multi-skill call centers. The objective is to find a minimal cost staffing solution while meeting a target level for the quality of service (QoS) to customers. We consider a staffing problem in which joint chance constraints are imposed on the QoS of the day. Our joint chance-constrained formulation is more rational capturing the correlation between different call types, as compared to separate chance-constrained versions considered in previous studies. We show that, in general, the probability functions in the joint-chance constraints display S-shaped curves, and the optimal solutions should belong to the concave regions of the curves. Thus, we propose an approach combining a heuristic phase to identify solutions lying in the concave part and a simulation-based cut generation phase to create outer-approximations of the probability functions. This allows us to find good staffing solutions satisfying the joint-chance constraints by simulation and linear programming. We test our formulation and algorithm using call center examples of up to 65 call types and 89 agent groups, which shows the benefits of our joint-chance constrained formulation and the advantage of our algorithm over standard ones.
In this paper, we study an on-demand housekeeping platform in which suppliers have heterogeneous opportunity costs, and customers are sensitive to service quality, price, and waiting time. The platform charges fees from customers and divides revenue with service suppliers in a certain proportion. We analyze two types of market coverage, namely full market coverage and partial market coverage. We find that as the potential demand market capacity expands, the platform will choose to lower prices to attract more customers and service suppliers until it reaches the partial market, thereby obtaining higher revenue, and suppliers will provide lower quality services to serve more customers and thus obtain more wages. Moreover, we show that the partial market is more favorable to the platform than the full market. However, for service suppliers, the partial market is not always more favorable. Meanwhile, as customers are more sensitive to the service value, suppliers will tend to lower their service rates to improve service quality, and the platform will tend to set higher service prices. Interestingly, we observe that when the sensitivity of service value is relatively small, the sensitivity of service value has even the opposite effect on the platform’s revenue and service suppliers’ payoffs, as well as the equilibrium number of service suppliers in different market scenarios. In addition, different market scenarios also will lead to the opposite effect of the service cost on the optimal equilibrium price, arrival rate, and service rate. However, the increase in the service cost will lead to a decrease in platform revenue and service supplier payoffs and the number of service suppliers in both market scenarios.
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4,704 members
Hwajin Yang
  • Psychology
Filip Lievens
  • Lee Kong Chian School of Business
Ch-Ying Cheng
  • School of Social Sciences
Robert H. Deng
  • School of Information Systems
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