Supervised machine learning is a common approach for automated harmful content detection to support content moderation. This approach relies on data annotated by humans to train models to recognize classes of harmful content. For detection tasks, researchers or content moderation communities typically either design their own annotation tasks to generate training data for new harmful content detection models, or use off-the-shelf (OTS) pre-trained harmful content detection models. OTS model reuse can enable detection tasks in resource-constrained contexts and can help to reduce the environmental impact of training new models -- an energy-intensive process. However, given the plethora of OTS models now available for reuse, determining which OTS model to reuse for a particular task and how to use it can be challenging, especially given that many of these models have been developed for specific contexts that are not always easily transferred onto others. This work aims to provide best practices for reusing OTS models for harmful content detection tasks. By using content analysis and statistical methods to evaluate assumptions about OTS model utility and reusability, we show that model reusers cannot assume that a model claimed to detect a particular concept, will actually detect that concept. Instead, based on our findings, we offer a decision tree for how to assess whether an OTS model would be appropriate for reuse for a new harmful content detection task. This decision tree directs model reusers to critically assess concept definitions, annotation task design, and additional features specified in our content analysis codebook to identify expected model output, and consequently evaluate whether that OTS model is appropriate for reuse for a new detection task.

Large language models (LLMs) have been shown to perform well at a variety of syntactic, discourse, and reasoning tasks.While LLMs are increasingly deployed in many forms including conversational agents that interact with humans, we lack a grounded benchmark to measure how well LLMs understand social language.Here, we introduce a new theorydriven benchmark, SOCKET, that contains 58 NLP tasks testing social knowledge which we group into five categories: humor & sarcasm, offensiveness, sentiment & emotion, trustworthiness, and other social factors.In tests on the benchmark, we demonstrate that current models attain only moderate performance but reveal significant potential for task transfer among different types and categories of tasks, which were predicted from theory.Through zero-shot evaluations, we show that pretrained models already possess some innate but limited capabilities of social language understanding and training on one category of tasks can improve zero-shot testing on others.Our benchmark provides a systematic way to analyze model performance on an important dimension of language and points to clear room for improvement to build more socially-aware LLMs.The resources are released at https://github. com/minjechoi/SOCKET.

A neoteric, compact, low-profile, wide-band elastomeric textile antenna for medical and IoT applications is presented. The design synthesis of flexible artificial magnetic conductor (AMC)-integrated antenna is done using stratum of textile and operates within 4.76–6.08 GHz covering the 5.8-GHz industrial, scientific, medical (ISM) and 5-GHz Wi-Fi band for IoT applications. The rectangular fed design operates in the high frequency band which is widened and optimized to operate in the lower 5-GHz band and the notch etched with the monopole’s edges, suppress the cross-polarization component. The suggested design features an impedance bandwidth of 24.4% with a high gain of 10.59 dBi and has a compact footprint of$1.44\lambda _{0}\times $0.46$\lambda _{0}\times $0.0512$\lambda _{0}$. Simulation and experimental studies provide evidence that in the case of structural deformities, such as bending and crumpling and also for human body loading, the suggested AMC-integrated antenna is stable and shows superior efficiency relative to the traditional monopole antenna. Thus, to a large extent, the antenna is stable to maintain its radiation properties. Furthermore, numerical analysis demonstrates maximum amount of the specific absorption rate (SAR) is 0.0832 W/Kg according to IEEE standard safety guidelines. The advantage of the suggested AMC-integrated antenna design is that it is resilient to moisture conditions and the enhancement in impedance bandwidth is achieved by simple cut edges in the radiator and the ground plane leading to a simple staircase design. In addition, the simulation and measurement results are consistent, thus confirming it to be a promising contender for sensing and applications in body-centric wireless communications and Internet of Things.