Users' behavioral footprints online enable firms to discover behavior-based
user segments (or, segments) and deliver segment specific messages to users.
Following the discovery of segments, delivery of messages to users through
preferred media channels like Facebook and Google can be challenging, as only a
portion of users in a behavior segment find match in a medium, and only a
fraction of those matched actually see the message (exposure). Even high
quality discovery becomes futile when delivery fails. Many sophisticated
algorithms exist for discovering behavioral segments; however, these ignore the
delivery component. The problem is compounded because (i) the discovery is
performed on the behavior data space in firms' data (e.g., user clicks), while
the delivery is predicated on the static data space (e.g., geo, age) as defined
by media; and (ii) firms work under budget constraint. We introduce a
stochastic optimization based algorithm for delivery optimized discovery of
behavioral user segmentation and offer new metrics to address the joint
optimization. We leverage optimization under a budget constraint for delivery
combined with a learning-based component for discovery. Extensive experiments
on a public dataset from Google and a proprietary dataset show the
effectiveness of our approach by simultaneously improving delivery metrics,
reducing budget spend and achieving strong predictive performance in discovery.

基于随机优化的算法通过同时提高交付度量、降低预算开销并在发现中获得强大的预测性能来优化传递行为用户分割。

行为用户分割下面向预算限制的优化发现

Delivery Optimized Discovery in Behavioral User Segmentation under  Budget Constrain

Delivery of items from the producer to the consumer has experienced
significant growth over the past decade and has been greatly fueled by the
recent pandemic. Amazon Fresh, Shopify, UberEats, InstaCart, and DoorDash are
rapidly growing and are sharing the same business model of consumer items or
food delivery. Existing food delivery methods are sub-optimal because each
delivery is individually optimized to go directly from the producer to the
consumer via the shortest time path. We observe a significant scope for
reducing the costs associated with completing deliveries under the current
model. We model our food delivery problem as a multi-objective optimization,
where consumer satisfaction and delivery costs, both, need to be optimized.
Taking inspiration from the success of ride-sharing in the taxi industry, we
propose DeliverAI - a reinforcement learning-based path-sharing algorithm.
Unlike previous attempts for path-sharing, DeliverAI can provide real-time,
time-efficient decision-making using a Reinforcement learning-enabled agent
system. Our novel agent interaction scheme leverages path-sharing among
deliveries to reduce the total distance traveled while keeping the delivery
completion time under check. We generate and test our methodology vigorously on
a simulation setup using real data from the city of Chicago. Our results show
that DeliverAI can reduce the delivery fleet size by 12\%, the distance
traveled by 13%, and achieve 50% higher fleet utilization compared to the
baselines.

通过强化学习实现路径共享算法，优化多目标最优化问题，提高送餐效率并降低成本。

DeliverAI: 基于强化学习的分布式送餐路径共享网络

DeliverAI: Reinforcement Learning Based Distributed Path-Sharing Network  for Food Deliveries

This study explores the benefits and challenges of integrating Artificial
Intelligence with Agile software development methodologies, focusing on
improving continuous integration and delivery. A systematic literature review
and longitudinal meta-analysis of the retrieved studies was conducted to
analyse the role of Artificial Intelligence and it's future applications within
Agile software development. The review helped identify critical challenges,
such as the need for specialised socio-technical expertise. While Artificial
Intelligence holds promise for improved software development practices, further
research is needed to better understand its impact on processes and
practitioners, and to address the indirect challenges associated with its
implementation.

研究人工智能与敏捷软件开发方法的整合，重点是改善持续集成和交付，发现了人工智能未来应用的关键挑战，如需要专业的社会技术专业知识。尽管人工智能有助于提高软件开发实践，但需要进一步研究其对进程和实践者的影响，并解决与其实施相关的间接挑战。

敏捷开发中的人工智能：一项元分析

AI for Agile development: a Meta-Analysis

We present an approach named CurlingNet that can measure the semantic
distance of composition of image-text embedding. In order to learn an effective
image-text composition for the data in the fashion domain, our model proposes
two key components as follows. First, the Delivery makes the transition of a
source image in an embedding space. Second, the Sweeping emphasizes
query-related components of fashion images in the embedding space. We utilize a
channel-wise gating mechanism to make it possible. Our single model outperforms
previous state-of-the-art image-text composition models including TIRG and
FiLM. We participate in the first fashion-IQ challenge in ICCV 2019, for which
ensemble of our model achieves one of the best performances.

CurlingNet 是一种可以测量图像 - 文本嵌入的语义距离的方法，利用交付和扫描两个关键组件来学习对时尚领域的数据进行有效的图像 - 文本组合，并采用通道间的门控机制，其单个模型在 ICCV2019 的第一次 fashion-IQ 挑战中获得了最佳表现之一。