750 research outputs found

    La traduzione specializzata all’opera per una piccola impresa in espansione: la mia esperienza di internazionalizzazione in cinese di Bioretics© S.r.l.

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    Global markets are currently immersed in two all-encompassing and unstoppable processes: internationalization and globalization. While the former pushes companies to look beyond the borders of their country of origin to forge relationships with foreign trading partners, the latter fosters the standardization in all countries, by reducing spatiotemporal distances and breaking down geographical, political, economic and socio-cultural barriers. In recent decades, another domain has appeared to propel these unifying drives: Artificial Intelligence, together with its high technologies aiming to implement human cognitive abilities in machinery. The “Language Toolkit – Le lingue straniere al servizio dell’internazionalizzazione dell’impresa” project, promoted by the Department of Interpreting and Translation (ForlĂŹ Campus) in collaboration with the Romagna Chamber of Commerce (ForlĂŹ-Cesena and Rimini), seeks to help Italian SMEs make their way into the global market. It is precisely within this project that this dissertation has been conceived. Indeed, its purpose is to present the translation and localization project from English into Chinese of a series of texts produced by Bioretics© S.r.l.: an investor deck, the company website and part of the installation and use manual of the Aliquis© framework software, its flagship product. This dissertation is structured as follows: Chapter 1 presents the project and the company in detail; Chapter 2 outlines the internationalization and globalization processes and the Artificial Intelligence market both in Italy and in China; Chapter 3 provides the theoretical foundations for every aspect related to Specialized Translation, including website localization; Chapter 4 describes the resources and tools used to perform the translations; Chapter 5 proposes an analysis of the source texts; Chapter 6 is a commentary on translation strategies and choices

    Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5

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    This ïŹfth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different ïŹelds of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book presents some theoretical advances on DSmT, dealing mainly with modiïŹed Proportional ConïŹ‚ict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classiïŹers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identiïŹcation of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classiïŹcation. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classiïŹcation, and hybrid techniques mixing deep learning with belief functions as well

    On the Utility of Representation Learning Algorithms for Myoelectric Interfacing

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    Electrical activity produced by muscles during voluntary movement is a reflection of the firing patterns of relevant motor neurons and, by extension, the latent motor intent driving the movement. Once transduced via electromyography (EMG) and converted into digital form, this activity can be processed to provide an estimate of the original motor intent and is as such a feasible basis for non-invasive efferent neural interfacing. EMG-based motor intent decoding has so far received the most attention in the field of upper-limb prosthetics, where alternative means of interfacing are scarce and the utility of better control apparent. Whereas myoelectric prostheses have been available since the 1960s, available EMG control interfaces still lag behind the mechanical capabilities of the artificial limbs they are intended to steer—a gap at least partially due to limitations in current methods for translating EMG into appropriate motion commands. As the relationship between EMG signals and concurrent effector kinematics is highly non-linear and apparently stochastic, finding ways to accurately extract and combine relevant information from across electrode sites is still an active area of inquiry.This dissertation comprises an introduction and eight papers that explore issues afflicting the status quo of myoelectric decoding and possible solutions, all related through their use of learning algorithms and deep Artificial Neural Network (ANN) models. Paper I presents a Convolutional Neural Network (CNN) for multi-label movement decoding of high-density surface EMG (HD-sEMG) signals. Inspired by the successful use of CNNs in Paper I and the work of others, Paper II presents a method for automatic design of CNN architectures for use in myocontrol. Paper III introduces an ANN architecture with an appertaining training framework from which simultaneous and proportional control emerges. Paper Iv introduce a dataset of HD-sEMG signals for use with learning algorithms. Paper v applies a Recurrent Neural Network (RNN) model to decode finger forces from intramuscular EMG. Paper vI introduces a Transformer model for myoelectric interfacing that do not need additional training data to function with previously unseen users. Paper vII compares the performance of a Long Short-Term Memory (LSTM) network to that of classical pattern recognition algorithms. Lastly, paper vIII describes a framework for synthesizing EMG from multi-articulate gestures intended to reduce training burden

    Forest productivity and stability under drought: the role of tree species richness, structural diversity and drought-tolerance trait diversity

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    The increasing frequency and intensity of droughts threaten forests and their climate change mitigation potential. Mixed-species forests are promoted to increase forest productivity and stability compared to monospecific forests, but we still lack a mechanistic understanding of the strength, nature and drivers of tree diversity effects on productivity and stability under drought. Here, I studied the stress hotter droughts inflict on trees and examined whether diversification in tree species, structures and drought-tolerance traits is a potential solution to this threat. In study 1, I found that the hotter drought years 2018–2019, the severest droughts since records, induced unprecedented tree productivity and physiological stress responses (reduced growth and increased ÎŽ13C) in a Central European floodplain forest. Hotter droughts thus constitute a novel threat. In studies 2–4, I examined diversity-productivity and diversity-stability relationships across spatiotemporal scales in a tropical (study 2) and a subtropical (studies 3, 4) tree diversity experiment specifically designed to examine biodiversity-ecosystem functioning relationships. Tree species richness consistently increased productivity and stability, and this effect was strongest at the highest levels of diversity. Structural diversity increased productivity but was unrelated to stability, while diversity in drought-tolerance traits increased stability but not productivity. Assessing drought-tolerance traits was essential for understanding the role of tree diversity during drought. Positive diversity effects on productivity scaled up from the tree neighbourhood to the community level, but effects on stability emerged only at the community level. Community stability increased with species richness due to asynchronous species responses to dry and wet years driven by species’ drought-tolerance traits. I showed that diversity but not identity in drought-tolerance traits increases community stability. Overall, promoting structurally and functionally diverse mixed-species forests may enable high productivity and stability under intensifying climate change.:1. General introduction 1.1. Mixed-species forests 1.2. Diversity-productivity relationships 1.3. Diversity-productivity relationships during drought 1.4. Diversity-stability relationships 1.5. Diversity facets 1.6. Drought-tolerance traits 1.7. Linkages between the four studies 2. Methodological features 2.1. Study sites and approaches 2.2. Productivity, stability and physiological water stress 2.3. The quantification of diversity 2.4. Spatiotemporal analyses 3. Original contributions Study 1: Cumulative growth and stress responses to the 2018–2019 drought in a European floodplain forest Study 2: Drivers of productivity and its temporal stability in a tropical tree diversity experiment Study 3: Neighbourhood species richness and drought-tolerance traits modulate tree growth and ÎŽ13C responses to drought Study 4: Species richness stabilizes productivity via asynchrony and drought- tolerance diversity in a large-scale tree biodiversity experiment 4. General discussion 4.1. Summary of main findings 4.2. Hotter droughts and forest functioning 4.3. Diversity signals across spatial scales 4.4. Diversity signals across temporal scales 4.5. Diversity facets 4.6. Context dependency and transferability 4.7. Implications for forest management in the 21st century 5. Outlook and future research 5.1. Observation and experimentation under hotter droughts 5.2. Response variables 5.3. Diversity facets 5.4. Drought-tolerance traits 5.5. Zooming in 5.6. Zooming out 5.7. From understanding to use of BEF relationships 6. Conclusion 7. Summary 8. Zusammenfassung 9. References Acknowledgements Author contribution statements Curriculum vitae List of publications SelbststĂ€ndigkeitserklĂ€run

    Explainable Data Poison Attacks on Human Emotion Evaluation Systems based on EEG Signals

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    The major aim of this paper is to explain the data poisoning attacks using label-flipping during the training stage of the electroencephalogram (EEG) signal-based human emotion evaluation systems deploying Machine Learning models from the attackers' perspective. Human emotion evaluation using EEG signals has consistently attracted a lot of research attention. The identification of human emotional states based on EEG signals is effective to detect potential internal threats caused by insider individuals. Nevertheless, EEG signal-based human emotion evaluation systems have shown several vulnerabilities to data poison attacks. The findings of the experiments demonstrate that the suggested data poison assaults are model-independently successful, although various models exhibit varying levels of resilience to the attacks. In addition, the data poison attacks on the EEG signal-based human emotion evaluation systems are explained with several Explainable Artificial Intelligence (XAI) methods, including Shapley Additive Explanation (SHAP) values, Local Interpretable Model-agnostic Explanations (LIME), and Generated Decision Trees. And the codes of this paper are publicly available on GitHub

    Security and Privacy for Modern Wireless Communication Systems

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    The aim of this reprint focuses on the latest protocol research, software/hardware development and implementation, and system architecture design in addressing emerging security and privacy issues for modern wireless communication networks. Relevant topics include, but are not limited to, the following: deep-learning-based security and privacy design; covert communications; information-theoretical foundations for advanced security and privacy techniques; lightweight cryptography for power constrained networks; physical layer key generation; prototypes and testbeds for security and privacy solutions; encryption and decryption algorithm for low-latency constrained networks; security protocols for modern wireless communication networks; network intrusion detection; physical layer design with security consideration; anonymity in data transmission; vulnerabilities in security and privacy in modern wireless communication networks; challenges of security and privacy in node–edge–cloud computation; security and privacy design for low-power wide-area IoT networks; security and privacy design for vehicle networks; security and privacy design for underwater communications networks

    The Impact of Adapting Fair Trade on Organisational Performance in Sialkot Sports Balls Industry, Pakistan

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    This study contributes to an understanding of how the adaptation of fair trade impacts the organisational performance in the Sialkot sports balls industry. Presently, minimal research is available investigating the fair trade practices in Sialkot and the impacts of such practices, and overall organisational performance. Therefore, South Asia shows a blurry picture of fair trade role in various industries, particularly sports balls. Sialkot is the only city in South Asia where six sports firms are registered under fair trade. The research investigates how the fair trade approach has impacted the organisational performance that includes the 3P’s (people, planet, and profit). A mixed methods approach was chosen, integrating qualitative and quantitative research components to assess the impact of adapting fair trade on a specific performance indicator to understand more about the progress of the Sialkot sports industry. The data was collected through fifteen semi-structured interviews from management, and five focus groups, with the intention of avoiding the limitations of small samples and gaining from the benefits of triangulation. The target was to interview three people in each firm's senior management positions. The total fair trade registered firms were six in the Sialkot sports balls industry. There was one focus group from each firm involving eight to ten workers from various Units. The focus group individuals were mainly based on workers from factory stitching units because of their proximity to fair trade practices and premium projects. The findings of semi-structured interviews of the management and focus group were analysed using NVivo software, and this was done using thematic analysis. The profitability of the firms was measured using the performance sales growth indicator. The study focused on the relationship between fair trade and conventional sports balls sales. The indicators covered 11 years of data from 2009 - 2019 to calculate the ten years of sales growth, including sales of fair trade and conventional sports balls. The statistical analysis was conducted through SPSS software. The findings showed a need to integrate contextual factors and fair trade practices to configure business operations aligned with the three dimensions (3P’s) of organisational performance. Further results revealed a significant impact of fair trade premium money on factory workers' life in various ways. The study also revealed one of the main aims of the sports industry was to adapt fair trade, which was fair trade as a PR gimmick tool. The statistical data showed modest sales of fair trade products. Also, the correlation and regression analysis found no relationship between fair trade and conventional product sales growth. The data showed the sports industry’s positive efforts to protect the environment by taking strict measures to dispose of chemical waste and converting the printing facility to water-based ink. The study indicates that by supporting business processes and operations with a practical strategic framework, the industry can successfully achieve the desired goals through fair trade. The study concludes that there is an immense potential for sports firms’ growth by adapting fair trade. However, fair trade and the Sialkot sports industry must work together to promote sports products and achieve ultimate goals

    Machine Learning Algorithm for the Scansion of Old Saxon Poetry

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    Several scholars designed tools to perform the automatic scansion of poetry in many languages, but none of these tools deal with Old Saxon or Old English. This project aims to be a first attempt to create a tool for these languages. We implemented a Bidirectional Long Short-Term Memory (BiLSTM) model to perform the automatic scansion of Old Saxon and Old English poems. Since this model uses supervised learning, we manually annotated the Heliand manuscript, and we used the resulting corpus as labeled dataset to train the model. The evaluation of the performance of the algorithm reached a 97% for the accuracy and a 99% of weighted average for precision, recall and F1 Score. In addition, we tested the model with some verses from the Old Saxon Genesis and some from The Battle of Brunanburh, and we observed that the model predicted almost all Old Saxon metrical patterns correctly misclassified the majority of the Old English input verses
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