561 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 (Forlì Campus) in collaboration with the Romagna Chamber of Commerce (Forlì-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

    A Comprehensive Survey of Artificial Intelligence Techniques for Talent Analytics

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    In today's competitive and fast-evolving business environment, it is a critical time for organizations to rethink how to make talent-related decisions in a quantitative manner. Indeed, the recent development of Big Data and Artificial Intelligence (AI) techniques have revolutionized human resource management. The availability of large-scale talent and management-related data provides unparalleled opportunities for business leaders to comprehend organizational behaviors and gain tangible knowledge from a data science perspective, which in turn delivers intelligence for real-time decision-making and effective talent management at work for their organizations. In the last decade, talent analytics has emerged as a promising field in applied data science for human resource management, garnering significant attention from AI communities and inspiring numerous research efforts. To this end, we present an up-to-date and comprehensive survey on AI technologies used for talent analytics in the field of human resource management. Specifically, we first provide the background knowledge of talent analytics and categorize various pertinent data. Subsequently, we offer a comprehensive taxonomy of relevant research efforts, categorized based on three distinct application-driven scenarios: talent management, organization management, and labor market analysis. In conclusion, we summarize the open challenges and potential prospects for future research directions in the domain of AI-driven talent analytics.Comment: 30 pages, 15 figure

    Stress detection in lifelog data for improved personalized lifelog retrieval system

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    Stress can be categorized into acute and chronic types, with acute stress having short-term positive effects in managing hazardous situations, while chronic stress can adversely impact mental health. In a biological context, stress elicits a physiological response indicative of the fight-or-flight mechanism, accompanied by measurable changes in physiological signals such as blood volume pulse (BVP), galvanic skin response (GSR), and skin temperature (TEMP). While clinical-grade devices have traditionally been used to measure these signals, recent advancements in sensor technology enable their capture using consumer-grade wearable devices, providing opportunities for research in acute stress detection. Despite these advancements, there has been limited focus on utilizing low-resolution data obtained from sensor technology for early stress detection and evaluating stress detection models under real-world conditions. Moreover, the potential of physiological signals to infer mental stress information remains largely unexplored in lifelog retrieval systems. This thesis addresses these gaps through empirical investigations and explores the potential of utilizing physiological signals for stress detection and their integration within the state-of-the-art (SOTA) lifelog retrieval system. The main contributions of this thesis are as follows. Firstly, statistical analyses are conducted to investigate the feasibility of using low-resolution data for stress detection and emphasize the superiority of subject-dependent models over subject-independent models, thereby proposing the optimal approach to training stress detection models with low-resolution data. Secondly, longitudinal stress lifelog data is collected to evaluate stress detection models in real-world settings. It is proposed that training lifelog models on physiological signals in real-world settings is crucial to avoid detection inaccuracies caused by differences between laboratory and free-living conditions. Finally, a state-of-the-art lifelog interactive retrieval system called \lifeseeker is developed, incorporating the stress-moment filter function. Experimental results demonstrate that integrating this function improves the overall performance of the system in both interactive and non-interactive modes. In summary, this thesis contributes to the understanding of stress detection applied in real-world settings and showcases the potential of integrating stress information for enhancing personalized lifelog retrieval system performance

    Seamless Multimodal Biometrics for Continuous Personalised Wellbeing Monitoring

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    Artificially intelligent perception is increasingly present in the lives of every one of us. Vehicles are no exception, (...) In the near future, pattern recognition will have an even stronger role in vehicles, as self-driving cars will require automated ways to understand what is happening around (and within) them and act accordingly. (...) This doctoral work focused on advancing in-vehicle sensing through the research of novel computer vision and pattern recognition methodologies for both biometrics and wellbeing monitoring. The main focus has been on electrocardiogram (ECG) biometrics, a trait well-known for its potential for seamless driver monitoring. Major efforts were devoted to achieving improved performance in identification and identity verification in off-the-person scenarios, well-known for increased noise and variability. Here, end-to-end deep learning ECG biometric solutions were proposed and important topics were addressed such as cross-database and long-term performance, waveform relevance through explainability, and interlead conversion. Face biometrics, a natural complement to the ECG in seamless unconstrained scenarios, was also studied in this work. The open challenges of masked face recognition and interpretability in biometrics were tackled in an effort to evolve towards algorithms that are more transparent, trustworthy, and robust to significant occlusions. Within the topic of wellbeing monitoring, improved solutions to multimodal emotion recognition in groups of people and activity/violence recognition in in-vehicle scenarios were proposed. At last, we also proposed a novel way to learn template security within end-to-end models, dismissing additional separate encryption processes, and a self-supervised learning approach tailored to sequential data, in order to ensure data security and optimal performance. (...)Comment: Doctoral thesis presented and approved on the 21st of December 2022 to the University of Port

    Tradition and Innovation in Construction Project Management

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    This book is a reprint of the Special Issue 'Tradition and Innovation in Construction Project Management' that was published in the journal Buildings

    Спосіб розпізнавання емоційних станів у зображеннях людини

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    У магістерській дисертації описується розробка способу розпізнавання емоційних станів людини у її зображеннях. В основі способу лежить використання нейронної мережі зі спеціальним модулем уваги. Розроблений спосіб дозволяє класифікувати вхідне зображення людини за одним із класів емоцій. Як практична сторона, реалізований програмний прототип, який моделює його роботу. Прототип створений за допомогою мови програмування Python та відповідних бібліотек до неї.The master's thesis describes the development of a method for emotional states recognition in human images. The method is based on the use of neural network with a special attention module. The developed method allows to classify the input image of a person according to one of the emotion classes. As a practical side, a software prototype was implemented that simulates its operation. The prototype is created using the Python programming language and its corresponding libraries

    Music and musicality in brain surgery:The effect on delirium and language

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    Delirium is a neuropsychiatric clinical syndrome with overlapping symptoms withthe neurologic primary disease. This is why delirium is such a difficult and underexposedtopic in neurosurgical literature. Delirium is a complication which mightaffect recovery after brain surgery, hence we describe in Chapter 2 a systematicreview which focuses on how delirium is defined in the neurosurgical literature.We included twenty-four studies (5589 patients) and found no validation studiesof screening instruments in neurosurgical papers. Delirium screening instruments,validated in other cohorts, were used in 70% of the studies, consisting of theConfusion Assessment Method (- Intensive Care Unit) (45%), Delirium ObservationScreening Scale (5%), Intensive Care Delirium Screening Checklist (10%), Neelonand Champagne Confusion Scale (5%), and Nursing Delirium Screening Scale (5%).Incidence of post-operative delirium after intracranial surgery was 19%, ranging from12 – 26% caused by variation in clinical features and delirium assessment methods.Our review highlighted the need of future research on delirium in neurosurgery,which should focus on optimizing diagnosis, and assessing prognostic significanceand management.It is unclear what the impact of delirium is on the recovery after brain surgery,as delirium is often a self-limiting and temporary complication. In Chapter 3 wetherefore investigated the impact of delirium, by means of incidence and healthoutcomes, and identified independent risk factors by including 2901 intracranialsurgical procedures. We found that delirium was present in 19.4% with an averageonset (mean/SD) within 2.62/1.22 days and associated with more Intensive CareUnit (ICU) admissions and more discharge towards residential care. These numbersconfirm the impact of delirium with its incidence rates, which were in line with ourprevious systematic review, and significant health-related outcomes. We identifiedseveral independent non-modifiable risk factors such as age, pre-existing memoryproblems, emergency operations, and modifiable risk factors such as low preoperativepotassium and opioid and dexamethasone administration, which shed lighton the pathophysiologic mechanisms of POD in this cohort and could be targetedfor future intervention studies.10As listening to recorded music has been proven to lower delirium-eliciting factors inthe surgical population, such as pain, we were interested in the size of analgesic effectand its underlying mechanism before applying this into our clinical setting. In Chapter4 we describe the results of a two-armed experimental randomized controlled trial inwhich 70 participants received increasing electric stimuli through their non-dominantindex finger. This study was conducted within a unique pain model as participantswere blinded for the outcome. Participants in the music group received a 20-minutemusic intervention and participants in the control group a 20-minute resting period.Although the effect of the music intervention on pain endurance was not statisticallysignificant in our intention-to-treat analysis (p = 0.482, CI -0.85; 1.79), the subgroupanalyses revealed an increase in pain endurance in the music group after correcting fortechnical uncertainties (p = 0.013, CI 0.35; 2.85). This effect on pain endurance couldbe attributed to increased parasympathetic activation, as an increased Heart RateVariability (HRV) was observed in the music vs. the control group (p=0.008;0.032).As our prior chapters increased our knowledge on the significance of delirium on thepost-operative recovery after brain surgery and the possible beneficial effects of music,we decided to design a randomized controlled trial. In Chapter 5 we describe theprotocol and in Chapter 6 we describe the results of this single-centered randomizedcontrolled trial. In this trial we included 189 patients undergoing craniotomy andcompared the effects of music administered before, during and after craniotomy withstandard of clinical care. The primary endpoint delirium was assessed by the deliriumobservation screening scale (DOSS) and confirmed by a psychiatrist accordingto DSM-5 criteria. A variety of secondary outcomes were assessed to substantiatethe effects of music on delirium and its clinical implications. Our results supportthe efficacy of music in preventing delirium after craniotomy, as found with DOSS(OR:0.49, p=0.048) but not after DSM-5 confirmation (OR:0.47, p=0.342). Thispossible beneficial effect is substantiated by the effect of music on pre-operativeautonomic tone, measured with HRV (p=0.021;0.025), and depth of anesthesia(p=&lt;0.001;0.022). Our results fit well within the current literature and support theimplementation of music for the prevention of delirium within the neurosurgicalpopulation. However, delirium screening tools should be validated and the long-termimplications should be evaluated after craniotomy to assess the true impact of musicafter brain surgery.Musicality and language in awake brain surgeryIn the second part of this thesis, the focus swifts towards maintaining musicality andlanguage functions around awake craniotomy. Intra-operative mapping of languagedoes not ensure complete maintenance which mostly deteriorates after tumor resection.Most patients recover to their baseline whereas other remain to suffer from aphasiaaffecting their quality of life. The level of musical training might affect the speed andextend of postoperative language recovery, as increased white matter connectivity inthe corpus callosum is described in musicians compared to non-musicians. Hence,in Chapter 7 we evaluate the effect of musicality on language recovery after awakeglioma surgery in a cohort study of forty-six patients. We divided the patients intothree groups based on the musicality and compared the language scores between thesegroups. With the first study on this topic, we support that musicality protects againstlanguage decline after awake glioma surgery, as a trend towards less deterioration oflanguage was observed within the first three months on the phonological domain (p= 0.04). This seemed plausible as phonology shares a common hierarchical structurebetween language and singing. Moreover, our results support the hypothesis ofmusicality induced contralateral compensation in the (sub-) acute phase through thecorpus callosum as the largest difference of size was found in the anterior corpuscallosum in non- musicians compared to trained musicians (p = 0.02).In Chapter 8 we addressed musicality as a sole brain function and whether it canbe protected during awake craniotomy in a systematic review consisting of tenstudies and fourteen patients. Isolated music disruption, defined as disruption duringmusic tasks with intact language/speech and/or motor functions, was identified intwo patients in the right superior temporal gyrus, one patient in the right and onepatient in the left middle frontal gyrus and one patient in the left medial temporalgyrus. Pre-operative functional MRI confirmed these localizations in three patients.Assessment of post-operative musical function, only conducted in seven patients bymeans of standardized (57%) and non-standardized (43%) tools, report no loss ofmusical function. With these results we concluded that mapping music is feasibleduring awake craniotomy. Moreover, we identified certain brain regions relevant formusic production and detected no decline during follow-up, suggesting an addedvalue of mapping musicality during awake craniotomy. A systematic approach to mapmusicality should be implemented, to improve current knowledge on the added valueof mapping musicality during awake craniotomy.<br/

    Evaluating EEG–EMG Fusion-Based Classification as a Method for Improving Control of Wearable Robotic Devices for Upper-Limb Rehabilitation

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    Musculoskeletal disorders are the biggest cause of disability worldwide, and wearable mechatronic rehabilitation devices have been proposed for treatment. However, before widespread adoption, improvements in user control and system adaptability are required. User intention should be detected intuitively, and user-induced changes in system dynamics should be unobtrusively identified and corrected. Developments often focus on model-dependent nonlinear control theory, which is challenging to implement for wearable devices. One alternative is to incorporate bioelectrical signal-based machine learning into the system, allowing for simpler controller designs to be augmented by supplemental brain (electroencephalography/EEG) and muscle (electromyography/EMG) information. To extract user intention better, sensor fusion techniques have been proposed to combine EEG and EMG; however, further development is required to enhance the capabilities of EEG–EMG fusion beyond basic motion classification. To this end, the goals of this thesis were to investigate expanded methods of EEG–EMG fusion and to develop a novel control system based on the incorporation of EEG–EMG fusion classifiers. A dataset of EEG and EMG signals were collected during dynamic elbow flexion–extension motions and used to develop EEG–EMG fusion models to classify task weight, as well as motion intention. A variety of fusion methods were investigated, such as a Weighted Average decision-level fusion (83.01 ± 6.04% accuracy) and Convolutional Neural Network-based input-level fusion (81.57 ± 7.11% accuracy), demonstrating that EEG–EMG fusion can classify more indirect tasks. A novel control system, referred to as a Task Weight Selective Controller (TWSC), was implemented using a Gain Scheduling-based approach, dictated by external load estimations from an EEG–EMG fusion classifier. To improve system stability, classifier prediction debouncing was also proposed to reduce misclassifications through filtering. Performance of the TWSC was evaluated using a developed upper-limb brace simulator. Due to simulator limitations, no significant difference in error was observed between the TWSC and PID control. However, results did demonstrate the feasibility of prediction debouncing, showing it provided smoother device motion. Continued development of the TWSC, and EEG–EMG fusion techniques will ultimately result in wearable devices that are able to adapt to changing loads more effectively, serving to improve the user experience during operation

    A review of natural language processing in contact centre automation

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    Contact centres have been highly valued by organizations for a long time. However, the COVID-19 pandemic has highlighted their critical importance in ensuring business continuity, economic activity, and quality customer support. The pandemic has led to an increase in customer inquiries related to payment extensions, cancellations, and stock inquiries, each with varying degrees of urgency. To address this challenge, organizations have taken the opportunity to re-evaluate the function of contact centres and explore innovative solutions. Next-generation platforms that incorporate machine learning techniques and natural language processing, such as self-service voice portals and chatbots, are being implemented to enhance customer service. These platforms offer robust features that equip customer agents with the necessary tools to provide exceptional customer support. Through an extensive review of existing literature, this paper aims to uncover research gaps and explore the advantages of transitioning to a contact centre that utilizes natural language solutions as the norm. Additionally, we will examine the major challenges faced by contact centre organizations and offer reco
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