Rakuten (Japan)

Abstract The advent of generative artificial intelligence (AI) represents a transformative leap in project management, offering significant capabilities in creativity and problem‐solving. This essay explores the impact of generative AI, highlighting its potential to support project planning and execution through dynamic resource management, improved stakeholder interaction, and predictive analytics. By understanding the differences between traditional AI and generative AI, project managers can harness these advanced tools to address a variety of challenges, from simple scheduling tasks to complex strategic planning. Generative AI enables project managers to interact with large language models (LLMs) through precise prompt engineering, ensuring tailored and actionable outputs. Practical examples illustrate how generative AI can provide innovative solutions to project delays and other common issues, showcasing its ability to transform traditional practices. Despite its advantages, the integration of generative AI in project management comes with significant risks, including job displacement, ethical concerns, data privacy, and the potential for overreliance on AI systems. Effective change management, robust ethics guidelines, and continuous learning are essential to mitigate these risks and maximize the benefits of AI. As project managers navigate this evolving landscape, they must remain proactive, balancing innovation with ethical considerations to lead their teams toward a future where AI‐driven project management is both effective and responsible.

ABSTRACT Long-term public issues face the intergenerational problem: current policy decisions place a disproportionate burden on future generations while primarily benefitting those in the present. The interests of present generations trump those of future generations, as the latter play no explicit part as stakeholders in policy making processes. How can the interests of future generations be voiced in the present? In this paper, we explore an innovative method to incorporate the interests of future generations in the process of policymaking: future design. First, we situate future design in the policy process and relate it to other intergenerational policymaking initiatives that aim to redeem the intergenerational problem. Second, we show how we applied future design and provide insights into three pilots that we organized on two long-term public issues in the Netherlands: housing shortages and water management. We conclude that future design can effectively contribute to representing the interests of future generations, but that adoption of future design in different contexts also requires adaptation of the method. The findings increase our understanding of the value of future design as an innovative policymaking practice to strengthen intergenerational policymaking. As such, it provides policymakers with insights into how to use this method.

Optimizing synchronizability in multilayer complex networks is a challenging problem due to the absence of a unique set of weights on the Pareto frontier. Addressing this problem, a novel model for multilayer networks has been proposed that leverages the underlying graphs derived from edge-transitive association schemes. Then, based on the stability interval originating from the master stability function (MSF) approach for the Rössler system, an optimal point with the most negative MSF value is selected as the most stable point on the Pareto Frontier. Two distinct solution methods for optimizing the synchronizability measure, based on linear programming (LP) and the direct solution, have been provided. The effectiveness of these methods is demonstrated on cycle and hypercube multilayer networks through both analytical results and simulations.

Collaborative machine learning (CML) techniques, such as federated learning, have been proposed to train deep learning models across multiple mobile devices and a server. CML techniques are privacy-preserving as a local model that is trained on each device instead of the raw data from the device is shared with the server. However, CML training is inefficient due to low resource utilization. We identify idling resources on the server and devices due to sequential computation and communication as the principal cause of low resource utilization. A novel framework PiPar that leverages pipeline parallelism for CML techniques is developed to substantially improve resource utilization. A new training pipeline is designed to parallelize the computations on different hardware resources and communication on different bandwidth resources, thereby accelerating the training process in CML. A low overhead automated parameter selection method is proposed to optimize the pipeline, maximizing the utilization of available resources. The experimental results confirm the validity of the underlying approach of PiPar and highlight that when compared to federated learning: (i) the idle time of the server can be reduced by up to 64.1×, and (ii) the overall training time can be accelerated by up to 34.6× under varying network conditions for a collection of six small and large popular deep neural networks and four datasets without sacrificing accuracy. It is also experimentally demonstrated that PiPar achieves performance benefits when incorporating differential privacy methods and operating in environments with heterogeneous devices and changing bandwidths.

Blur artifacts can seriously degrade the visual quality of images, and numerous deblurring methods have been proposed for specific scenarios. However, in most real-world images, blur is caused by different factors, e.g., motion, and defocus. In this paper, we address how other deblurring methods perform in the case of multiple types of blur. For in-depth performance evaluation, we construct a new large-scale multi-cause image deblurring dataset (MC-Blur), including real-world and synthesized blurry images with different blur factors. The images in the proposed MC-Blur dataset are collected using other techniques: averaging sharp images captured by a 1000-fps high-speed camera, convolving Ultra-High-Definition (UHD) sharp images with large-size kernels, adding defocus to images, and real-world blurry images captured by various camera models. Based on the MC-Blur dataset, we conduct extensive benchmarking studies to compare SOTA methods in different scenarios, analyze their efficiency, and investigate the buildataset’s capacity. These benchmarking results provide a comprehensive overview of the advantages and limitations of current deblurring methods, revealing our dataset’s advances. The dataset is available to the public at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/HDCVLab/MC-Blur-Dataset</uri> .

The authors have no conflicts of interest to declare. Not applicable: there are no original data shown provided in this paper and all references are provided. Table S1. Table S2. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

Recommender systems are powerful tools for enhancing customer engagement and driving sales for Rakuten businesses. However, to achieve their full potential, these systems must possess a profound understanding of customer behaviors. This understanding can be gained from a variety of sources, including customer purchase history, customer feedback, and customer behavioral patterns. One of the most important aspects of customer understanding is the ability to identify lookalike customers, understand their behavioral patterns, and predict lifestyles, for example, whether a customer is married or unmarried, owns a car or plays golf, etc. Rakuten provides more than 70 different services and heavily relies on recommendations for many of its products. In our platforms, we can observe groups of customers who share similar interests, needs, or behaviors often end up being attracted to similar products or services. Customer preferences can change over time, so it is important for recommender systems to adapt those changes. This can be achieved by tracking customer behavior, static or dynamic environment changes around targeted customers, and their feedback. We utilize various graph and deep learning based models to address the customer understanding problem.

This work presents a solution to the challenges of real-time access and processing of High-Definition maps (HD-maps) in vehicular networks with limited bandwidth and high latency. Leveraging the Deep Deterministic Policy Gradient (DDPG) algorithm, the proposed approach dynamically caches relevant map tiles in Roadside Units (RSUs). By intelligently determining which map tiles to cache, considering factors like popularity, recency, and anticipated demand, RSUs can serve a significantly portion of map requests from vehicles. Empirical results demonstrate the superiority of proposed DDPG-based HD map caching over traditional methods leading to improved map retrieval times and alleviating network congestion.