750 research outputs found
La traduzione specializzata allâopera per una piccola impresa in espansione: la mia esperienza di internazionalizzazione in cinese di Bioretics© S.r.l.
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 âLanguage Toolkit â Le lingue straniere al servizio dellâinternazionalizzazione dellâimpresaâ 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
This ïŹfth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different ïŹelds 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 modiïŹed Proportional ConïŹict 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 classiïŹers, 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, identiïŹcation of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classiïŹcation.
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 classiïŹcation, and hybrid techniques mixing deep learning with belief functions as well
On the Utility of Representation Learning Algorithms for Myoelectric Interfacing
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 steerâa 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
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Neural Decoding Leveraging Motor-Cortex Population Geometry
Intracortical brain-computer interfaces (BCIs) provide the means to do something extraordinary: restore movement to patients with paralysis or amputated limbs. Realizing this potential requires the development of decode algorithms capable of accurately translating measurements of neural activity, in real time, into appropriate time-varying commands for an external device (e.g. prosthetic limb).
This problem is fundamentally interdisciplinary, drawing on tools and insights from engineering, neuroscience, statistics, and computer science, among others. Decode algorithms that have been favored historically tend to be computationally efficient, but perform suboptimally, likely because their assumptions fail to fully and accurately capture the complexity in neural population responses. Recent work harnessing the power of contemporary machine learning methods has raised the performance bar, yet these methods can be computationally demanding and it is unclear what properties of neural and/or behavioral data they exploit. In this dissertation, we characterize properties of motor-cortex population geometry and let these properties dictate decoder design, resulting in methods that perform very well, yet retain the benefits of simpler methods.
We use this approach to develop a closed-loop navigation BCI, and to design a highly accurate, general, and interpretable decoder. The properties described in this dissertation have implications for any BCI. By designing decoders to explicitly respect (and leverage) these properties, we can construct powerful yet practical BCIs that better meet the needs of patients
Forest productivity and stability under drought: the role of tree species richness, structural diversity and drought-tolerance trait diversity
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 2018â2019, 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 2â4, 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 speciesâ 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 2018â2019 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
SelbststÀndigkeitserklÀrun
Explainable Data Poison Attacks on Human Emotion Evaluation Systems based on EEG Signals
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
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 nodeâedgeâcloud 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
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 3Pâs (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 (3Pâs) 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 industryâs 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 firmsâ 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
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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