This companion paper provides artifacts and instructions on replicating the experiments in the ACM Multimedia 2024 paper entitled ''Swarical: An Integrated Hierarchical Approach to Localizing Flying Light Specks.'' Swarm-based hierarchical, Swarical, is a localization technique that enables miniature drones, Flying Light Specks (FLSs), to accurately and efficiently localize and illuminate complex 2D and 3D shapes. It consists of two components, an offline planner and an online localization technique that executes on an FLS. The offline planner uses the FLS sensor specification for positioning to convert mesh files into swarms of FLSs. Some FLSs are dark and used only for localization. We reported the online localization technique to be fast and highly accurate. We describe how to reproduce this finding using our artifacts.

We introduce HARPER, a novel dataset for 3D body pose estimation and forecasting in dyadic interactions between users and Spot, the quadruped robot manufactured by Boston Dynamics. The key-novelty of HARPER is its focus on the robot’s perspective, i.e., on the data captured by the robot’s sensors. This makes 3D body pose analysis challenging, as being close to the ground results in only partial captures of humans. The scenario underlying HARPER includes 15 actions, of which 10 involve physical contact between the robot and users. The corpus contains recordings not only from Spot’s built-in stereo cameras but also from a 6-camera OptiTrack system, with all recordings synchronized. This setup leads to ground-truth skeletal representations with a precision of less than a millimeter. Additionally, the corpus includes reproducible benchmarks for 3D Human Pose Estimation, Human Pose Forecasting, and Collision Prediction, all based on publicly available baseline approaches. This enables future HARPER users to rigorously compare their results with those provided in this work.

COVID-19 had substantial effects on the IoT community which designs systems for safety: the urge to face masks worn by everyone, the analysis of crowds to avoid the spread of the disease, and the sanitization of public environments has led to exceptional research acceleration and fast engineering of the related solutions. Now that the pandemic is losing power, some applications are becoming less important, while others are proving to be useful regardless of the criticality of COVID-19. The Safe Place project is a prime example of this situation (DATE23 MPP category: final stage). Safe Place is an Italian 3M euro regional industrial/academic project, financed by European funds, created to ensure a multidisciplinary choral reaction to COVID-19 in critical environments such as rest homes and public places. Safe Place consortium was able to understand what is no longer useful in this post-pandemic period, and what instead is potentially attractive for the market. For example, the detection of face masks has little importance, while sanitization does have much. This paper shares such analysis, which emerged through a co-design process of three public Safe Place project demonstrators, involving heterogeneous figures spanning from scientists to lawyers.

Many recent pattern recognition applications rely on complex distributed architectures in which sensing and computational nodes interact together through a communication network. Deep neural networks (DNNs) play an important role in this scenario, furnishing powerful decision mechanisms, at the price of a high computational effort. Consequently, powerful state-of-the-art DNNs are frequently split over various computational nodes, e.g., a first part stays on an embedded device and the rest on a server. Deciding where to split a DNN is a challenge in itself, making the design of deep learning applications even more complicated. Therefore, we propose Split-Et-Impera, a novel and practical framework that i) determines the set of the best-split points of a neural network based on deep network interpretability principles without performing a tedious try-and-test approach, ii) performs a communication-aware simulation for the rapid evaluation of different neural network rearrangements, and iii) suggests the best match between the quality of service requirements of the application and the performance in terms of accuracy and latency time.

This work makes a substantial step in the field of split computing, i.e., how to split a deep neural network to host its early part on an embedded device and the rest on a server. So far, potential split locations have been identified exploiting uniquely architectural aspects, i.e., based on the layer sizes. Under this paradigm, the efficacy of the split in terms of accuracy can be evaluated only after having performed the split and retrained the entire pipeline, making an exhaustive evaluation of all the plausible splitting points prohibitive in terms of time. Here we show that not only the architecture of the layers does matter, but the importance of the neurons contained therein too. A neuron is important if its gradient with respect to the correct class decision is high. It follows that a split should be applied right after a layer with a high density of important neurons, in order to preserve the information flowing until then. Upon this idea, we propose Interpretable Split (I-SPLIT): a procedure that identifies the most suitable splitting points by providing a reliable prediction on how well this split will perform in terms of classification accuracy, beforehand of its effective implementation. As a further major contribution of I-SPLIT, we show that the best choice for the splitting point on a multiclass categorization problem depends also on which specific classes the network has to deal with. Exhaustive experiments have been carried out on two networks, VGG16 and ResNet-50, and three datasets, Tiny-Imagenet-200, notMNIST, and Chest X-Ray Pneumonia. The source code is available at https://github.com/vips4/I-Split.