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The increasing environmental footprint of the information and communication technology sector calls for innovative strategies for assessing and improving its sustainability. Life Cycle Assessment (LCA) methods are suitable for estimating the negative environmental impacts of products or services. While some LCA methods start to be applied during the engineering of Cyber-Physical Systems (CPS), some challenges remain unresolved. For instance, the energy consumption, and therefore the greenhouse gas emissions, of CPS is influenced by the specific configuration of components in their architecture, as well as by the geographical location where the components are deployed. Also, the skills and efforts required by LCA projects, remain prohibitive to many CPS engineering projects. This paper presents an LCA-based method tailored for CPS that facilitates the analysis of the environmental impact and the comparison of architectural and location variants. Moreover, we have developed a supporting tool that guides the process and automates part of the data collection activity. Through an illustrative case and expert assessment, we have been able to assess the benefits and drawbacks of our proposal. With these contributions, we hope to lower the barrier to adopting LCA practices in CPS engineering projects, both in industry and academia.

Human activity recognition (HAR) has a great impact on human-robot collaboration (HRC), especially in industrial works. However, it is difficult to find industrial activity data. With a goal of making the interactions between humans and robots more straightforward, we organized Bento Packaging Activity Recognition Challenge as a part of The 3rd International Conference on Activity and Behavior Computing. Here, the term Bento refers to a single-serving lunch box originated in Japan. We provided ten Bento packing activities data. The activities are performed by four subjects with a moving conveyor belt. In this work, we analyze and summarize the approaches of submission of the challenge. The challenge started on June 1st, 2021, and continued until August 25th, 2021. The participant teams used the given dataset to predict the ten activities, and they were evaluated using accuracy. The winning team used an ensemble model and achieved around 64% accuracy on testing data. To further improve the accuracy of the testing data, models particularly designed for small data with larger intra-class similarity could help., 3rd International Conference on Activity and Behavior Computing, ABC 2021, 22 October 2021 through 23 October 2021, Online

Sensor-based activity recognition can recognize simple activities such as walking and running with high accuracy, but it is difficult to recognize complex activities such as nursing care activities and cooking activities. One solution is to use multiple sensors, which is unrealistic in real life. Recently, learning using privileged information (LUPI) has been proposed, which enables training using additional information only in the training phase. In this paper, we used LUPI for improving the accuracy of complex activity recognition. In short, training is performed with multiple sensors during the training phase, and a single sensor is used during testing. We used four published datasets for evaluating our proposed method. As a result, our proposed method improves by up to 16% in F1-Score to 67% compared to the baseline method when we used random-split cross-validation of each subject., 3rd International Conference on Activity and Behavior Computing, ABC 2021, 22 October 2021 through 23 October 2021, Online

Background: A treat-to-target strategy, in which strictly defined treatment targets facilitate decision making in clinical practice, is advocated as an optimised management approach for some chronic disorders. The aim of the STARDUST trial was to assess whether a treat-to-target strategy with early endoscopy, regular biomarker and clinical symptom monitoring, and dose intensification for persistent inflammatory activity, was more successful in achieving endoscopic improvement at week 48 than a clinically driven maintenance strategy in patients with moderate-to-severe active Crohn's disease receiving ustekinumab. Methods: This open-label, multicentre, randomised phase 3b trial included adults with active, moderate-to-severe Crohn's disease (Crohn's Disease Activity Index [CDAI] 220–450 and Simple Endoscopic Score in Crohn's Disease [SES-CD] ≥3) for whom conventional therapy or one biologic therapy, or both, had failed. Patients received intravenous ustekinumab approximating 6 mg/kg at baseline and subcutaneous ustekinumab 90 mg at week 8. At week 16, patients with a CDAI improvement of 70 or more points from baseline were randomly assigned (1:1) to receive standard-of-care or treat-to-target maintenance treatment through week 48. Randomisation was balanced by using randomly permuted blocks and was stratified by biologic history status and baseline SES-CD score. All patients who signed informed consent, who were not screening failures, and who received at least one dose of study treatment were included in week 16 analyses. All patients included in week 16 analyses and randomly assigned to one of the maintenance treatment regimens were included in the week 48 efficacy and safety analyses (ie, on an intention-to-treat basis). Patients assigned to the treat-to-target arm received ustekinumab every 12 weeks or every 8 weeks based on SES-CD improvement from baseline and could escalate to every 4 weeks through week 48 if prespecified targets were missed. Patients assigne

type:Journal Article, In this paper, we address Zero-shot learning for sensor activity recognition using word embeddings. The goal of Zero-shot learning is to estimate an unknown activity class (i.e., an activity that does not exist in a given training dataset) by learning to recognize components of activities expressed in semantic vectors. The existing zero-shot methods use mainly 2 kinds of representation as semantic vectors, attribute vector and embedding word vector. However, few zero-shot activity recognition methods based on embedding vector have been studied; especially for sensor-based activity recognition, no such studies exist, to the best of our knowledge. In this paper, we compare and thoroughly evaluate the Zero-shot method with different semantic vectors: (1) attribute vector, (2) embedding vector, and (3) expanded embedding vector and analyze their correlation to performance. Our results indicate that the performance of the three spaces is similar but the use of word embedding leads to a more efficient method, since this type of semantic vector can be generated automatically. Moreover, our suggested method achieved higher accuracy than attribute-vector methods, in cases when there exist similar information in both the given sensor data and in the semantic vector; the results of this study help select suitable classes and sensor data to build a training dataset., source:https://doi.org/10.3390/s19225043

Complex activity recognition can benefit from understanding the steps that compose them. Current datasets, however, are annotated with one label only, hindering research in this direction. In this paper, we describe a new dataset for sensor-based activity recognition featuring macro and micro activities in a cooking scenario. Three sensing systems measured simultaneously, namely a motion capture system, tracking 25 points on the body; two smartphone accelerometers, one on the hip and the other one on the forearm; and two smartwatches one on each wrist. The dataset is labeled for both the recipes (macro activities) and the steps (micro activities). We summarize the results of a baseline classification using traditional activity recognition pipelines. The dataset is designed to be easily used to test and develop activity recognition approaches.

Ludwig van Beethoven composed his symphonies between 1799 and 1825, when he was writing his Tenth symphony. As we dispose of a great amount of data belonging to his work, the purpose of this paper is to investigate the possibility of extracting patterns on his compositional model from symbolic data and generate what would have been his last symphony, the Tenth. A neural network model has been built based on the Long Short-Therm Memory (LSTM) neural networks. After training the model, the generated music has been analysed by comparing the input data with the results, and establishing differences between the generated outputs based on the training data used to obtain them. The structure of the outputs strongly depends on the symphonies used to train the network.

In this study, we propose a method for single sensor-based activity recognition, trained with data from multiple sensors. There is no doubt that the performance of complex activity recognition systems increases when we use enough sensors with sufficient quality, however using such rich sensors may not be feasible in real-life situations for various reasons such as user comfort, privacy, battery-preservation, and/or costs. In many cases, only one device such as a smartphone is available, and it is challenging to achieve high accuracy with a single sensor, more so for complex activities. Our method combines representation learning with feature mapping to leverage multiple sensor information made available during training while using a single sensor during testing or in real usage. Our results show that the proposed approach can improve the F1-score of the complex activity recognition by up to 17\% compared to that in training while utilizing the same sensor data in a new user scenario., Comment: 17 pages

Luidwig van Beethoven composed his symphonies between 1799 and 1825, when he was writing his Tenth symphony. As we dispose of a great amount of data belonging to his work, the purpose of this project is to work on the possibility of extracting patterns on his compositional model and generate what would have been his last symphony, the Tenth. Computational creativity is an Artificial Intelligence field which is still being developed. One of its subfields is music generation, to which this project belongs. Also, there is an open discussion about the belonging of the creativity, to the machine or the programmer. Firstly we have extracted all the symphonies' scores information, structuring them by instrument. Then we have used Deep Learning techniques to extract knowledge from the data and later generate new music. The neural network model is built based on the Long Short-Therm Memory (LSTM) neural networks, which are distinguished from others since these ones contain a memory module. After training the model and predict new scores, the generated music has been analyzed by comparing the input data with the results, and establishing differences between the generated outputs based on the training data used to obtain them. The result's structure depends on the symphonies used for training, so obtained music presents Beethoven's style characteristics., Luidwig van Beethoven compuso sus sinfonías entre 1799 y 1825, cuando estaba componiendo su décima sinfonía. Como disponemos de una gran cantidad de datos provenientes de su obra, el objetivo de este proyecto es investigar en la posibilidad de extraer patrones en su modelo composicional y generar lo que hubiese sido su última sinfonía, la Décima. La creatividad computacional es un campo de la Inteligencia Artificial que se encuentra todavía en desarrollo, uno de los campos que abarca la creatividad computacional es la generación de música, en el que se encuentra este proyecto. Además, existe un debate abierto sobre si las máquinas pueden llegar a ser creativas, o el mérito debe atribuirse al programador. En primer lugar hemos extraído la información de las partituras de todas sus sinfonías, estructurándolas por instrumento. A continuación hemos utilizado técnicas de aprendizaje automático para extraer conocimiento de los datos y posteriormente predecir música. El modelo neuronal se ha basado en las redes neuronales LSTM (Long Short-Therm Memory), que se diferencian del resto dado a que disponen de memoria. Tras entrenar el modelo y predecir nuevas partituras, la música generada ha sido estudiada comparando los datos de entrada con dichos resultados, y estableciendo diferencias entre los resultados obtenidos dependiendo de los datos usados para generarlos. La estructura de los resultados depende de las sinfonías que se han usado para aprender de ellas, por lo que la música obtenida presenta características reconocibles del estilo de Beethoven.

type:Conference Paper, We detail the solution to the UCAmI Cup Challenge to recognizing on going activities at home from sensor measurements. We use binary sensors and proximity sensor measurements for the recognition. We use an hybrid strategy, combining a probabilistic model and a definition-based model. The former consists of a Hidden Markov Model using the result of a neural network as emission probabilities. It is trained with the labelled data provided by the Cup. The latter approach takes advantage of the descriptions provided for each of the activities which are expressed in logical statements based on the sensors states. We then combine the results with a weighted average. We compare the performance of each individual strategy and of the combined strategy., 12th International Conference on Ubiquitous Computing and Ambient Intelligence (UCAmI 2018), 4–7 December 2018, Punta Cana, Dominican Republic, source:https://doi.org/10.3390/proceedings2191266

Luidwig van Beethoven composed his symphonies between 1799 and 1825, when he was writing his Tenth symphony. As we dispose of a great amount of data belonging to his work, the purpose of this project is to work on the possibility of extracting patterns on his compositional model and generate what would have been his last symphony, the Tenth. Computational creativity is an Artificial Intelligence field which is still being developed. One of its subfields is music generation, to which this project belongs. Also, there is an open discussion about the belonging of the creativity, to the machine or the programmer. Firstly we have extracted all the symphonies' scores information, structuring them by instrument. Then we have used Deep Learning techniques to extract knowledge from the data and later generate new music. The neural network model is built based on the Long Short-Therm Memory (LSTM) neural networks, which are distinguished from others since these ones contain a memory module. After training the model and predict new scores, the generated music has been analyzed by comparing the input data with the results, and establishing differences between the generated outputs based on the training data used to obtain them. The result's structure depends on the symphonies used for training, so obtained music presents Beethoven's style characteristics., Luidwig van Beethoven compuso sus sinfonías entre 1799 y 1825, cuando estaba componiendo su décima sinfonía. Como disponemos de una gran cantidad de datos provenientes de su obra, el objetivo de este proyecto es investigar en la posibilidad de extraer patrones en su modelo composicional y generar lo que hubiese sido su última sinfonía, la Décima. La creatividad computacional es un campo de la Inteligencia Artificial que se encuentra todavía en desarrollo, uno de los campos que abarca la creatividad computacional es la generación de música, en el que se encuentra este proyecto. Además, existe un debate abierto sobre si las máquinas pueden llegar a ser creativas, o el mérito debe atribuirse al programador. En primer lugar hemos extraído la información de las partituras de todas sus sinfonías, estructurándolas por instrumento. A continuación hemos utilizado técnicas de aprendizaje automático para extraer conocimiento de los datos y posteriormente predecir música. El modelo neuronal se ha basado en las redes neuronales LSTM (Long Short-Therm Memory), que se diferencian del resto dado a que disponen de memoria. Tras entrenar el modelo y predecir nuevas partituras, la música generada ha sido estudiada comparando los datos de entrada con dichos resultados, y estableciendo diferencias entre los resultados obtenidos dependiendo de los datos usados para generarlos. La estructura de los resultados depende de las sinfonías que se han usado para aprender de ellas, por lo que la música obtenida presenta características reconocibles del estilo de Beethoven.