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The demand for creating increasingly dynamic, autonomous and proactive software systems is challenging for the traditional Multi-agent Systems (MASs) approaches. Such requirement has given rise to adaptive software agents approaches. At the same time, norm is an essential and challenging feature that still tends to be addressed in adaptive MAS. In fact, norms to regulate agent behavior is still a vague concept that has not been properly investigated in terms of modeling and implementation. Even though many researchers have proposed modeling languages to deal with different abstractions, these languages fail to support the modeling of abstractions, such as adaptation and norms. Even more severe is the fact that little has been done to support the systematic design of Adaptive Normative Multi-Agent Systems (ANMASs). To facilitate the design and development of ANMASs, this paper presents a new metamodel, as well as language support, as means to provide tools to enable software developers. The proposed metamodel fosters a better understanding of the way agents are able to change their behaviors to deal with norms and captures interactions between agent’s norms and adaptation. To this end, our research is organized into five steps: (i) a literature review to identify the limitations of existing approaches related to ANMAS modeling; (ii) propose a new metamodel to support adaptative and normative concepts; (iii) propose a new language for modeling ANMASs; (iv) perform a qualitative and quantitative evaluation of the proposed language using a real case scenario, and (v) an empirical evaluation. The proposed metamodel and its associated modeling language advances the state of the art in modeling MASs and the approach is assessed in terms of correctness, time and difficulty. Our initial results revealed that our approach can be feasibly applied in a real world application, and is less difficult to apply and requires less time in comparison with a traditional approach. As software applications become more dynamic and adaptive, we believe it is essential to support developers to model MASs with abstractions such as adaptive agents, norms and their relationships. Such information can be foundational to steer future research on modeling adaptive agents capable of understanding and dealing with norms and adaptation.

This paper summarizes the results of the first Monocular Depth Estimation Challenge (MDEC) organized at WACV2023. This challenge evaluated the progress of self-supervised monocular depth estimation on the challenging SYNS-Patches dataset. The challenge was organized on CodaLab and received submissions from 4 valid teams. Participants were provided a devkit containing updated reference implementations for 16 State-of-the-Art algorithms and 4 novel techniques. The threshold for acceptance for novel techniques was to outperform every one of the 16 SotA baselines. All participants outperformed the baseline in traditional metrics such as MAE or AbsRel. However, pointcloud reconstruction metrics were challenging to improve upon. We found predictions were characterized by interpolation artefacts at object boundaries and errors in relative object positioning. We hope this challenge is a valuable contribution to the community and encourage authors to participate in future editions., Comment: WACV-Workshops 2023

A transparent Optical-subTHz-Optical link providing record-high single line rates of 240 Gbit/s and 192 Gbit/s on a single optical carrier over distances from 5 to 115 m is demonstrated. Besides a direct mapping of the optical to a 230 GHz subTHz-carrier frequency by means of a uni-traveling carrier (UTC) photodiode, we demonstrate direct conversion of data from the subTHz domain back to the optical domain by a plasmonic modulator. It is shown that the subTHz-to-optical upconversion can even be performed at good quality without any electrical amplifiers. Finally, at the receiver, the local oscillator is employed to directly map the optical signal back to the electrical baseband within a coherent receiver., Journal of Lightwave Technology, 40 (6), ISSN:0733-8724, ISSN:1558-2213

In recent years, some studies brought the complex networks theory into neuroscience. Connectivity patterns are widely studied and vary from the data acquisition method: fMRI, EEG, and so on. Those connectivity data can be classified into three main types: anatomical, functional, and effective. The first one relies only on anatomical connections, the second relies on the correlations of signals recorded from different parts of the brain, and the latter on the direction of the information flow. In this work, it is proposed to study the variability of the parameters of complex networks, for two methods of effective connectivity (namely: Transfer Entropy and Phase Transfer Entropy) and EEG data, of 10 patients with absence epilepsy. It is known that the topology of the complex network of the epilepsy crisis varies substantially between patients. However, for the same patient, effective connectivity topologies may vary, but they should not. The variation of network parameters, for the same complex network (the same patient), can lead to misinterpretations of the analyzed phenomena. In this work, preliminary results indicate that such variability indeed occurs, for the same patient, even in strictly similar measures, such as Transfer Entropy and Phase Transfer Entropy.

As part of the NASA SnowEx 2020 campaign, we performed a time series experiment with NASA's UAVSAR, an airborne L-band InSAR, over 13 sites across 5 states. Six flights were performed (December-March), capturing a wide range of snow conditions. On Grand Mesa, Colorado, two of the InSAR overflights were well aligned with two airborne lidar surveys. We show that in a 4 km2 wind exposed area on the west end of Grand Mesa, the measured change in phase can be used to estimate change in snow depth. In this region we observed both scouring and drifting, with a dynamic range of ± 20 cm. We use the measured surface density, change in phase, and local incidence angle to estimate change in snow depth over approximately a 2 week period, with a RMSD of 4.7cm depth, and 0.9cm SWE. This technique shows promise in open regions in dry snow conditions, and may be a useful component of a global snow satellite concept.

We address the problem of unsupervised classification of players in a team sport according to their team affiliation, when jersey colours and design are not known a priori. We adopt a contrastive learning approach in which an embedding network learns to maximize the distance between representations of players on different teams relative to players on the same team, in a purely unsupervised fashion, without any labelled data. We evaluate the approach using a new hockey dataset and find that it outperforms prior unsupervised approaches by a substantial margin, particularly for real-time application when only a small number of frames are available for unsupervised learning before team assignments must be made. Remarkably, we show that our contrastive method achieves 94% accuracy after unsupervised training on only a single frame, with accuracy rising to 97% within 500 frames (17 seconds of game time). We further demonstrate how accurate team classification allows accurate team-conditional heat maps of player positioning to be computed.

Most team sports such as hockey involve periods of active play interleaved with breaks in play. When watching a game remotely, many fans would prefer an abbreviated game showing only periods of active play. Here we address the problem of identifying these periods in order to produce a time-compressed viewing experience. Our approach is based on a hidden Markov model of play state driven by deep visual and optional auditory cues. We find that our deep visual cues generalize well across different cameras and that auditory cues can improve performance but only if unsupervised methods are used to adapt emission distributions to domain shift across games. Our system achieves temporal compression rates of 20-50% at a recall of 96%.

This research is focused on the development of a one-dimensional mathematical model (with respect to the height of the reactor) of fluidized bed gasifier in a bubbling regime at atmospheric pressure, by means of simulation by numerical methods with the use of MATLAB, based on the Theory of the Two Phases (emulsion and bubble). The model developed through empirical equations proposed in several publications, allows the prediction of the energy quality (LHV) and the composition of the synthesis gas throughout the height of the reactor during the gasification process, by estimating the molar flows of the main volatile species produced (CO, CO 2 , H 2 O, H 2 , O 2 , CH 4 , N 2 and char). In addition, the model has been validated with research using the values of the molar fractions of hydrogen (H 2 ), carbon monoxide (CO) and methane (CH 4 ) obtained experimentally.

We demonstrate a fibre-coupled, fully monolithic electro-optic coherent interface on the silicon-photonics platform that senses terahertz electric fields with sub-cycle resolution by means of nonlinear coupling with photons at telecom frequencies. A strong confinement of the terahertz field to a cavity mode enables a coupling strength to sense fields as low as 20 V/m. We discuss the opportunities such platforms provide for sensing of cavity-confined terahertz states at the quantum limit.

Software Process Improvement (SPI) consists of a set of changes in a software development company, introducing new and improved methods, techniques, and tools. We call SPI Diagnostic, the process to know the organization current status. Typically, these SPI Diagnostic processes are manually performed, thus demanding consultant practitioner and a high effort from the organization under analysis. Based on this we propose the following question: “What solutions have been proposed to SPI Diagnostic?” We looked for other Systematic Literature Reviews with the same goal and the found studies do not answer our question. Hence, we performed a Systematic Literature Review (SLR) to investigate solutions to the SPI Diagnostic process. We executed an SLR, based on snowballing technique. This SLR characterizes 14 solutions aiming at systematizing the SPI Diagnostic process through a method, model, or framework. As a result, we advocate that Artificial Intelligence (AI) based solutions should be more explored in research to deal with SPI Diagnostic complexity and, therefore helping the SPI Diagnostic.

The last five years have seen dramatic growth in the use of Visible Shortwave Infrared (VSWIR) and Thermal Infrared (TIR) imaging spectrometers to detect and characterize greenhouse methane sources. Targets include: dairy and animal husbandry emissions; landfills; fossil fuel extraction, storage, and transport infrastructure; geologic sources; natural emissions associated with sensitive arctic ecosystems; and more. These campaigns have resulted in significant new discoveries and advances in our understanding of the North American CH4 budget. Recent algorithm improvements have been critical for these campaigns, enabling robust statistical CH₄ measurement, fully-automated image-space source identification, and quantification of flux. Here we survey recent campaigns by NASA's Next Generation Airborne Visible Infrared Imaging Spectrometer (AVIRIS-NG) and NASA's Hyperspectral Thermal Emission Spectrometer (HyTES). We describe their algorithmic advances and major findings.

The last five years have seen dramatic growth in the use of Visible Shortwave Infrared (VSWIR) and Thermal Infrared (TIR) imaging spectrometers to detect and characterize greenhouse methane sources. Targets include: dairy and animal husbandry emissions; landfills; fossil fuel extraction, storage, and transport infrastructure; geologic sources; natural emissions associated with sensitive arctic ecosystems; and more. These campaigns have resulted in significant new discoveries and advances in our understanding of the North American CH4 budget. Recent algorithm improvements have been critical for these campaigns, enabling robust statistical CH 4 measurement, fully-automated image-space source identification, and quantification of flux. Here we survey recent campaigns by NASA's Next Generation Airborne Visible Infrared Imaging Spectrometer (AVIRIS-NG) and NASA's Hyperspectral Thermal Emission Spectrometer (HyTES). We describe their algorithmic advances and major findings.

In this work we resort to the Bluetooth Low Energy (BLE) beaconing mechanism to propose an hybrid indoor positioning system (H-IPS) that fuses both multilateration (MLT) and fingerprinting (FP) RSSI-based approaches. The aim is to estimate the localization of an indoor target node, which is assumed to follow a uniform motion model. We adopt Kalman Filtering (KF) to diminish the MLT and FP errors while performing a track-to-track fusion (TTF) of the two KF outputs to further improve the performance. Our results indicate that the proposed H-IPS improves the estimation accuracy when individually compared to the standalone FP scheme in up to 46% in the considered scenarios, while the standalone MLT is outperformed in approximately 54%. Moreover, we also provide some insights on the influence of parameters such as the FP grid size, number of access points (APs) and number of samples on the accuracy of the proposed scheme. Finally, we show that the probability that the distance error of the proposed H-IPS is lower than 2 m is 92%, while for the FP and MLT the same probability is 43% and 47%, respectively.