Improving Information Alignment and Distributed Coordination for Secure Information Supply Chains

Industries are constantly striving to incorporate the latest technology systems into their operations so that they can maintain a competitive edge in their respective markets. However, even when they are able to stay up to speed with technological advancement, there continues to be a gap between the workforce skill set and available technologies. Organizations may acquire advanced systems, yet end up spending extended periods of time in the implementation and deployment phases, resulting in lost resources and productivity. The primary focus of this research is on streamlining the implementation and integration of new information technology systems to avoid the dire consequences of the process being prolonged or inefficient. Specifically, the goal of this research is to mitigate business challenges in information sharing and availability for employees and managers interacting with business tools and each other. This was accomplished by first interviewing work professionals in order to identify gap parameters. Based on the interview findings, recommendations were made in order to enhance the usability of existing tools. At this point, the research setting was shifted from network operations to supply chain operations due to the restrictive nature of network operations. The research team succeeded in developing a user-centered methodology to implement and deploy new business systems to mitigate risk during integration of new systems as the transition is made from the classic way of performing tasks. While this methodology was studied in supply chain operations, it enabled the identification of a common trend of challenges in operations work settings, regardless of the business application. Hence the findings of this research can be extrapolated to any business setting, besides the ones actually studied by the team. In addition, this research ensures that operational teams are able to maximize their benefit out of the technology available, thus enabling them to keep up with the rapidly evolving world of technology while minimizing sacrifices in resources or productivity in the process

Data-Driven Near-Optimal Control of Nonlinear Systems Over Finite Horizon

We examine the problem of two-point boundary optimal control of nonlinear systems over finite-horizon time periods with unknown model dynamics by employing reinforcement learning. We use techniques from singular perturbation theory to decompose the control problem over the finite horizon into two sub-problems, each solved over an infinite horizon. In the process, we avoid the need to solve the time-varying Hamilton-Jacobi-Bellman equation. Using a policy iteration method, which is made feasible as a result of this decomposition, it is now possible to learn the controller gains of both sub-problems. The overall control is then formed by piecing together the solutions to the two sub-problems. We show that the performance of the proposed closed-loop system approaches that of the model-based optimal performance as the time horizon gets long. Finally, we provide three simulation scenarios to support the paper's claims

Knowledge-Enhanced Multi-Label Few-Shot Product Attribute-Value Extraction

Existing attribute-value extraction (AVE) models require large quantities of labeled data for training. However, new products with new attribute-value pairs enter the market every day in real-world e-Commerce. Thus, we formulate AVE in multi-label few-shot learning (FSL), aiming to extract unseen attribute value pairs based on a small number of training examples. We propose a Knowledge-Enhanced Attentive Framework (KEAF) based on prototypical networks, leveraging the generated label description and category information to learn more discriminative prototypes. Besides, KEAF integrates with hybrid attention to reduce noise and capture more informative semantics for each class by calculating the label-relevant and query-related weights. To achieve multi-label inference, KEAF further learns a dynamic threshold by integrating the semantic information from both the support set and the query set. Extensive experiments with ablation studies conducted on two datasets demonstrate that KEAF outperforms other SOTA models for information extraction in FSL. The code can be found at: https://github.com/gjiaying/KEAFComment: 6 pages, 2 figures, published in CIKM 202

Small-Scale Catchment Analysis of Water Stress in Wet Regions of the U.S.: An Example from Louisiana

Groundwater is increasingly being overdrafted in the Southeastern U.S., despite abundant rainfall and the apparent availability of surface water. Using the state of Louisiana as an example, the current study quantifies the stresses on water resources and investigates the potential for opportunities to use surface water in lieu of groundwater pumping. The assessment is based on a fine watershed scale (12-digit Hydrological Unit Code [HUC] boundaries) water balance between the availability of surface and groundwater and surface water and groundwater demand. Water demand includes environmental flows, as well as public supply, rural domestic, industrial, power generation, agricultural, and aquaculture sectors. The seasonality of water stress is also addressed by incorporating monthly variations in surface water supply and irrigation demands. We develop several new weighting schemes to disaggregate the water withdrawals, provided by the U.S. Geological Survey on a county scale, to the HUC12 scale. The analysis on the smaller HUC12 scale is important for identifying areas with high water stress that would otherwise be masked at a larger scale (e.g. the county or HUC8 watershed scales). The results indicate that the annual water stress in Louisiana is below one (i.e. there is more water available than is used) for most watersheds; however, some watersheds (15 of the HUC12 units) show stresses greater than one, indicating an insufficient water supply to meet existing demands. The areas of the highest water stress are largely attributable to water consumption for power generating plants or irrigation. Moreover, estimating the stresses on surface water and groundwater sources separately confirms our speculation of abundant surface water and demonstrates a significant over-drafting/deficit of groundwater in many of the states aquifer systems. These results have implications for identifying new opportunities for reallocation of surface water use to reduce groundwater pumping and improve water sustainability in the region. Seasonal fluctuations in surface water supply and water withdrawals for irrigation highlight the fact that the water system is under more stress during the summer season. This observation underscores the need for infrastructure for shortterm surface water storage in agricultural regions. The water budget analysis presented here can be useful for stakeholders in developing water management plans and can also help to inform the development of a water code that will enable Louisiana to successfully manage and conserve its water resources for the future