Towards a Multimodal Time-Based Empathy Prediction System

We describe our system for empathic emotion recognition. It is based on deep learning on multiple modalities in a late fusion architecture. We describe the modules of our system and discuss the evaluation results. Our code is also available for the research community

Artifact for Enhancing Genetic Improvement of Software with Regression Test Selection

We present in this document the basic information needed to download, unpack, and then interpret the instructions we provide as requested in the ICSE 2021 Artifact Submission Guidelines. The artifact contains all the subject programs, scripts, tools, results, and a series of guidelines on how to use them. We aim at obtaining the badges of Available and Reusable. In order to do so, we have added all the components needed for the full execution of the experiments and analyses as we originally did during the writing of our paper, making it readily available. Furthermore, we have included instructions to the INSTALL file on how to add new programs to the experiments and analyses, making it reusable for the next researchers that intend to replicate or extend our experiments

Enhancing Genetic Improvement of Software with Regression Test Selection

Genetic improvement uses artificial intelligence to automatically improve software with respect to non-functional properties (AI for SE). In this paper, we propose the use of existing software engineering best practice to enhance Genetic Improvement (SE for AI). We conjecture that existing Regression Test Selection (RTS) techniques (which have been proven to be efficient and effective) can and should be used as a core component of the GI search process for maximising its effectiveness. To assess our idea, we have carried out a thorough empirical study assessing the use of both dynamic and static RTS techniques with GI to improve seven real-world software programs. The results of our empirical evaluation show that incorporation of RTS within GI significantly speeds up the whole GI process, making it up to 78% faster on our benchmark set, being still able to produce valid software improvements. Our findings are significant in that they can save hours to days of computational time, and can facilitate the uptake of GI in an industrial setting, by significantly reducing the time for the developer to receive feedback from such an automated technique. Therefore, we recommend the use of RTS in future test-based automated software improvement work. Finally, we hope this successful application of SE for AI will encourage other researchers to investigate further applications in this area

Refining Fitness Functions for Search-Based Automated Program Repair: A Case Study with ARJA and ARJA-e

Several tools support code templates as a means to specify searches within a program’s source code. Despite their ubiquity, code templates can often prove difficult to specify, and may produce too many or too few match results. In this paper, we present a search-based approach to support developers in specifying templates. This approach uses a suite of mutation operators to recommend changes to a given template, such that it matches with a desired set of code snippets. We evaluate our approach on the problem of inferring a code template that matches all instances of a design pattern, given one instance as a starting template

Simplicial temporal networks from Wi-Fi data in a university campus: The effects of restrictions on epidemic spreading

Wireless networks are commonly used in public spaces, universities, and public institutions and provide accurate and easily accessible information to monitor the mobility and behavior of users. Following the application of containment measures during the recent pandemic, we analyzed extensive data from the Wi-Fi network in a university campus in Italy during three periods, corresponding to partial lockdown, partial opening, and almost complete opening. We measured the probability distributions of groups and link activations at Wi-Fi access points, investigating how different areas are used in the presence of restrictions. We ranked the hotspots and the area they cover according to their crowding and to the probability of link formation, which is the relevant variable in determining potential outbreaks. We considered a recently proposed epidemic model on simplicial temporal networks, and we used the measured distributions to infer the change in the reproduction number in the three phases. Our data show that additional measures are necessary to limit the spread of epidemic in the total opening phase due to the dramatic increase in the number of contacts

GAP WORK project report: training for youth practitioners on tackling gender-related violence

This project sought to challenge gender-related violence against (and by) children and young people by developing training for practitioners who have everyday contact with general populations of children and young people (‘youth practitioners’). Through improved knowledge and understanding practitioners can better identify and challenge sexist, sexualising, homophobic or controlling language and behaviour, and know when and how to refer children and young people to the most appropriate support services. This summary outlines the Project and our initial findings about the success of the four training programmes developed and piloted.Co-funded by the DAPHNE III programme of the EU

TokaMaker: An open-source time-dependent Grad-Shafranov tool for the design and modeling of axisymmetric fusion devices

In this paper, we present a new static and time-dependent MagnetoHydroDynamic (MHD) equilibrium code, TokaMaker, for axisymmetric configurations of magnetized plasmas, based on the well-known Grad-Shafranov equation. This code utilizes finite element methods on an unstructured triangular grid to enable capturing accurate machine geometry and simple mesh generation from engineering-like descriptions of present and future devices. The new code is designed for ease of use without sacrificing capability and speed through a combination of Python, Fortran, and C/C++ components. A detailed description of the numerical methods of the code, including a novel formulation of the boundary conditions for free-boundary equilibria, and validation of the implementation of those methods using both analytic test cases and cross-code validation is shown. Results show expected convergence across tested polynomial orders for analytic and cross-code test cases

L3DAS21 Challenge: Machine Learning for 3D Audio Signal Processing

The L3DAS21 Challenge is aimed at encouraging and fostering collaborative research on machine learning for 3D audio signal processing, with particular focus on 3D speech enhancement (SE) and 3D sound localization and detection (SELD). Alongside with the challenge, we release the L3DAS21 dataset, a 65 hours 3D audio corpus, accompanied with a Python API that facilitates the data usage and results submission stage. Usually, machine learning approaches to 3D audio tasks are based on single-perspective Ambisonics recordings or on arrays of single-capsule microphones. We propose, instead, a novel multichannel audio configuration based multiple-source and multiple-perspective Ambisonics recordings, performed with an array of two first-order Ambisonics microphones. To the best of our knowledge, it is the first time that a dual-mic Ambisonics configuration is used for these tasks. We provide baseline models and results for both tasks, obtained with state-of-the-art architectures: FaSNet for SE and SELDNet for SELD. This report is aimed at providing all needed information to participate in the L3DAS21 Challenge, illustrating the details of the L3DAS21 dataset, the challenge tasks and the baseline models.Comment: Documentation paper for the L3DAS21 Challenge for IEEE MLSP 2021. Further information on www.l3das.com/mlsp202

Types and characteristics of urban and peri-urban green spaces having an impact on human mental health and wellbeing: a systematic review

Green spaces have been put forward as contributing to good mental health. In an urban context, space is a scarce resource while urbanisation and climate change are increasingly putting pressure on existing urban green space infrastructures and increasing morbidity caused by mental health disorders. Policy makers, designers, planners and other practitioners face the challenge of designing public open spaces as well as preserving and improving natural resources that are important for maintaining and optimizing human wellbeing. Knowing which types of blue and green spaces, with which characteristics, are most beneficial for mental health and wellbeing is critical. EKLIPSE received a request from the Ministry in charge of the Environment of France (MTES) to review: “Which types of urban and peri‐urban green and blue spaces, and which characteristics of such spaces, have a significant impact on human mental health and wellbeing?”. After a preliminary scoping, a decision was made to perform two systematic reviews (SR) assessing the specific types and characteristics of blue space (SR1) and green space (SR2) with respect to mental health and wellbeing. This report presents the systematic review for green space (SR2)

Ovário-histerectomia laparoscópica com três portais em cães

O artigo não apresenta resumo