Mobile crowdsourcing refers to systems where the completion of tasks
necessarily requires physical movement of crowdworkers in an on-demand
workforce. Evidence suggests that in such systems, tasks often get assigned to
crowdworkers who struggle to complete those tasks successfully, resulting in
high failure rates and low service quality. A promising solution to ensure
higher quality of service is to continuously adapt the assignment and respond
to failure-causing events by transferring tasks to better-suited workers who
use different routes or vehicles. However, implementing task transfers in
mobile crowdsourcing is difficult because workers are autonomous and may reject
transfer requests. Moreover, task outcomes are uncertain and need to be
predicted. In this paper, we propose different mechanisms to achieve outcome
prediction and task coordination in mobile crowdsourcing. First, we analyze
different data stream learning approaches for the prediction of task outcomes.
Second, based on the suggested prediction model, we propose and evaluate two
different approaches for task coordination with different degrees of autonomy:
an opportunistic approach for crowdshipping with collaborative, but
non-autonomous workers, and a market-based model with autonomous workers for
crowdsensing.

移动众包是指任务的完成必须要求众包人员在按需工作力量中进行物理移动的系统。本文提出了在移动众包中实现任务转移、结果预测和任务协调的不同机制，包括数据流学习方法用于任务结果预测，以及具有不同自治程度的两种不同方法实现任务协调，分别为具有协作但非自治的众包运输机制和具有自治众包人员的市场模式。

数据流支持下的移动众包协同评估

Evaluating Collaborative and Autonomous Agents in Data-Stream-Supported  Coordination of Mobile Crowdsourcing

Many applications such as autonomous driving and augmented reality, require
the concurrent running of multiple deep neural networks (DNN) that poses
different levels of real-time performance requirements. However, coordinating
multiple DNN tasks with varying levels of criticality on edge GPUs remains an
area of limited study. Unlike server-level GPUs, edge GPUs are resource-limited
and lack hardware-level resource management mechanisms for avoiding resource
contention. Therefore, we propose Miriam, a contention-aware task coordination
framework for multi-DNN inference on edge GPU. Miriam consolidates two main
components, an elastic-kernel generator, and a runtime dynamic kernel
coordinator, to support mixed critical DNN inference. To evaluate Miriam, we
build a new DNN inference benchmark based on CUDA with diverse representative
DNN workloads. Experiments on two edge GPU platforms show that Miriam can
increase system throughput by 92% while only incurring less than 10\% latency
overhead for critical tasks, compared to state of art baselines.

提出了 Miriam，一种用于多个边缘 GPU 上的深度神经网络任务协调的计划，通过弹性内核生成器和运行时动态内核协调器，可以实现混合关键性 DNN 推理，并且在关键任务上只产生不到 10％的延迟开销，相比现有技术基准，系统吞吐量可提高 92％。

Miriam：在边缘 GPU 上利用弹性核心进行实时多 DNN 推理

Miriam: Exploiting Elastic Kernels for Real-time Multi-DNN Inference on  Edge GPU

Human collaborators coordinate effectively their actions through both verbal
and non-verbal communication. We believe that the the same should hold for
human-robot teams. We propose a formalism that enables a robot to decide
optimally between doing a task and issuing an utterance. We focus on two types
of utterances: verbal commands, where the robot expresses how it wants its
human teammate to behave, and state-conveying actions, where the robot explains
why it is behaving this way. Human subject experiments show that enabling the
robot to issue verbal commands is the most effective form of communicating
objectives, while retaining user trust in the robot. Communicating why
information should be done judiciously, since many participants questioned the
truthfulness of the robot statements.

研究了人类与机器人进行任务协作时的语言交流方式问题，提出了一种方法，使机器人能够通过口头命令或状态传递行为进行最佳决策，并通过实验验证了使用口头命令是在保持用户信任的同时提高有效沟通的最佳方式。