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Jennifer Moore, a former teacher’s aide, was elected to the school board in 2020.

Many industries now rely on drones to monitor infrastructures. In this respect, this article considers maximizing the revenue of an Industrial Internet of Things operator that provides two services: 1) data trading; and 2) drones rental. In service 1), the operator sells data of locations/points it acquired via drones. For service 2), it rents idle drones to users. The problem at hand is to determine the allocation of drones to services 1) and 2) that maximizes the operator's revenue over a...

A problem with machine learning (ML) techniques for detecting intrusions in the Internet of Things (IoT) is that they are ineffective in the detection of low-frequency intrusions. In addition, as ML models are trained using specific attack categories, they cannot recognize unknown attacks. This article integrates strategies of cost-sensitive learning and multitask learning into a hybrid ML model to address these two challenges. The hybrid model consists of an autoencoder for feature extraction a...

In future Internet of Thing (IoT) networks, devices can be leveraged to compute tasks or services. To this end, this paper addresses a novel problem that requires devices to collaboratively execute tasks with dependencies. A key consideration is that in order to conserve energy, devices may execute a task in approximate mode, which generate errors. To optimize their operation mode, we outline a novel chance constrained program that aims to execute as many tasks as possible in approximate mode su...

As a newly emerging task, audio-visual question answering (AVQA) has attracted research attention. Compared with traditional single-modality (e.g., audio or visual) QA tasks, it poses new challenges due to the higher complexity of feature extraction and fusion brought by the multimodal inputs. First, AVQA requires more comprehensive understanding of the scene which involves both audio and visual information; Second, in the presence of more information, feature extraction has to be better connect...

This paper reviews the history of these traditions, the primary reason why the US has, to date, preferred to maintain the proficiency tradition embodied in AQP, and more detailed guidance regarding the use of competencies within the AQP framework.

Few-shot learning, especially few-shot image classification, has received increasing attention and witnessed significant advances in recent years. Some recent studies implicitly show that many generic techniques or “tricks”, such as data augmentation, pre-training, knowledge distillation, and self-supervision, may greatly boost the performance of a few-shot learning method. Moreover, different works may employ different software platforms, backbone architectures and input image sizes, making...

Mobile crowdsensing (MCS) combined with federated learning, as an emerging data collection and intelligent process paradigm, has received lots of attention in social networks and mobile Internet-of-Things, etc. However, as the openness and transparent of mobile crowdsensing tasks, federated learning model and training samples for crowdsensing data still face enormous privacy revealing risks, and it will reduce the willingness of people or nodes to actively participate and provide data in MCS. In...

The U.S. Navy is proposing a challenge for AI Large Language solutions to streamline its Instructional Systems Design Analysis, used for training for military readiness and human performance. Registration for the challenge closes 30 October 2023.

This paper considers targets coverage in energy harvesting Internet of Things (IoT) networks. Specifically, solar-powered sensor devices employ network virtualization technology to partition their resources, such as energy, memory, and computation workload, in order to serve requests with different coverage requirements. Our objective is to maximize the revenue from completing requests. To this end, we outline a mixed integer linear program (MILP) to optimize the start time of each request and t...