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1. TIME-VARYING ESTIMATION AND DYNAMIC MODEL SELECTION WITH AN APPLICATION OF NETWORK DATA

   Lan Xue, Xinxin Shu (January 2020, Statistica sinica)

   In many biomedical and social science studies, it is important to identify and predict the dynamic changes of associations among network data over time. We propose a varying-coefficient model to incorporate time-varying network data, and impose a piecewise penalty function to capture local features of the network associations. The proposed approach is semi-parametric, and therefore flexible in modeling dynamic changes of association in network data problems. Furthermore, the approach can identify the time regions when dynamic changes of associations occur. To achieve a sparse network estimation at local time intervals, we implement a group penalization strategy involving parameters that overlap between groups. However, this makes the optimization process challenging for large-dimensional network data observed at many time points. We develop a fast algorithm, based on the smoothing proximal-gradient method, that is computationally efficient and accurate. We illustrate the proposed method through simulation studies and children's attention deficit hyperactivity disorder fMRI data, showing that the proposed method and algorithm recover dynamic network changes over time efficiently. 

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2. Topic-aware Incentive Mechanism for Task Diffusion in Mobile Crowdsourcing through Social Network

   https://doi.org/10.1145/3487580  

   Xu, Jia; Zhou, Yuanhang; Chen, Gongyu; Ding, Yuqing; Yang, Dejun; Liu, Linfeng (February 2022, ACM Transactions on Internet Technology)

   Crowdsourcing has become an efficient paradigm to utilize human intelligence to perform tasks that are challenging for machines. Many incentive mechanisms for crowdsourcing systems have been proposed. However, most of existing incentive mechanisms assume that there are sufficient participants to perform crowdsourcing tasks. In large-scale crowdsourcing scenarios, this assumption may be not applicable. To address this issue, we diffuse the crowdsourcing tasks in social network to increase the number of participants. To make the task diffusion more applicable to crowdsourcing system, we enhance the classic Independent Cascade model so the influence is strongly connected with both the types and topics of tasks. Based on the tailored task diffusion model, we formulate the Budget Feasible Task Diffusion ( BFTD ) problem for maximizing the value function of platform with constrained budget. We design a parameter estimation algorithm based on Expectation Maximization algorithm to estimate the parameters in proposed task diffusion model. Benefitting from the submodular property of the objective function, we apply the budget-feasible incentive mechanism, which satisfies desirable properties of computational efficiency, individual rationality, budget-feasible, truthfulness, and guaranteed approximation, to stimulate the task diffusers. The simulation results based on two real-world datasets show that our incentive mechanism can improve the number of active users and the task completion rate by 9.8% and 11%, on average. 

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3. Solving Markov decision processes for network-level post-hazard recovery via simulation optimization and rollout

   Sarkale, Y.; Nozhati, S.; Chong, E. K.; Ellingwood, B. R.; Mahmoud, H. (August 2019, 2018 IEEE 14th International Conference on Automation Science and Engineering (CASE))

   Computation of optimal recovery decisions for community resilience assurance post-hazard is a combinatorial decision-making problem under uncertainty. It involves solving a large-scale optimization problem, which is significantly aggravated by the introduction of uncertainty. In this paper, we draw upon established tools from multiple research communities to provide an effective solution to this challenging problem. We provide a stochastic model of damage to the water network (WN) within a testbed community following a severe earthquake and compute near-optimal recovery actions for restoration of the water network. We formulate this stochastic decision-making problem as a Markov Decision Process (MDP), and solve it using a popular class of heuristic algorithms known as rollout. A simulation-based representation of MDPs is utilized in conjunction with rollout and the Optimal Computing Budget Allocation (OCBA) algorithm to address the resulting stochastic simulation optimization problem. Our method employs non-myopic planning with efficient use of simulation budget. We show, through simulation results, that rollout fused with OCBA performs competitively with respect to rollout with total equal allocation (TEA) at a meagre simulation budget of 5-10% of rollout with TEA, which is a crucial step towards addressing large-scale community recovery problems following natural disasters. 

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4. Neurodiverse Programmers and the Accessibility of Parsons Problems: An Exploratory Multiple-Case Study

   https://doi.org/10.1145/3626252.3630898  

   Haynes-Magyar, Carl (March 2024, Proceedings of the 55th ACM Technical Symposium on Computer Science Education)

   Parsons problems are drag-and-drop computer programming puzzles that require learners to place code blocks in the correct order and sometimes indentation. Introductory computer programming instructors use them to teach novice programmers how to code while optimizing problem-solving efficiency and cognitive load. While there is research on the design of Parsons problems for programmers without disabilities and programmers with visual or motor impairments, research regarding their accessibility for programmers with cognitive disabilities is scant. To identify the accessibility barriers and benefits of Parsons problems for neurodiverse programmers, an exploratory multiple-case study was conducted. Participants were asked to read eight chapters of an interactive eBook on Python and to solve Parsons problems. Within-case analyses of 15 retrospective think-aloud interviews with five novice programmers with disabilities led to four recommendations for improving the cognitive accessibility of Parsons problems. For example, programmers with seizure disorders may experience seizures when solving programming problems that require numeric calculations. Hence, creating a range of Parsons problems that do not require mental arithmetic could improve the learning experience for programmers with seizure disorders and those who struggle with mental calculations by lowering their cognitive load. Given this study's qualitative and exploratory approach, it does not offer conclusive, broadly generalizable results. Yet, it reveals detailed and promising avenues for exploration in computing education research that might elude many quantitative techniques. 

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5. Population-Aware Relay Placement for Wireless Multi-Hop Based Network Disaster Recovery

   https://doi.org/10.1109/GLOCOM.2017.8253922  

   Zhong, Lei; Ji, Yusheng; Wang, Xiaoyan; Yamada, Shigeki; Takano, Kiyoshi; Xue, Guoliang (December 2017, GLOBECOM 2017 - 2017 IEEE Global Communications Conference)

   Network disaster recovery is one of the greatest concerns for Mobile Network Operators (MNOs) and first responders during large-scale natural disasters such as earth- quakes. In many recent studies, wireless multi-hop networking has been demonstrated as an effective technique to quickly and efficiently extend the network coverage during disasters. In this paper, we specifically address the network deployment problem by proposing the Population-Aware Relay Placement (PARP) solution, which seeks the efficient deployment of a limited number of relays such that population coverage is maximized in the scenario of network disaster recovery. We provide a graph-based modeling and prove its NP-hardness accordingly. In order to efficiently solve this problem, we propose a heuristic solution, which is constructed in two steps. We first design a simple algorithm based on a disk graph to determine the Steiner locations, which is the biggest challenge in this problem. Then, we formulate the problem as an integer programming problem, which is inspired by the formulation of Prize-Collecting Steiner Tree (PCST). Thus, the integer problem is solved by exploring the similarity of the existing algorithm for PCST. To evaluate the proposed solution extensively, we present numerical results on both real-world and random scenarios, which validate the effectiveness of the proposed solution and show substantial improvement by comparing to the previous one. 

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