Computational Language Assessment in patients with speech, language, and communication impairments

Speech, language, and communication symptoms enable the early detection, diagnosis, treatment planning, and monitoring of neurocognitive disease progression. Nevertheless, traditional manual neurologic assessment, the speech and language evaluation standard, is time-consuming and resource-intensive for clinicians. We argue that Computational Language Assessment (C.L.A.) is an improvement over conventional manual neurological assessment. Using machine learning, natural language processing, and signal processing, C.L.A. provides a neuro-cognitive evaluation of speech, language, and communication in elderly and high-risk individuals for dementia. ii. facilitates the diagnosis, prognosis, and therapy efficacy in at-risk and language-impaired populations; and iii. allows easier extensibility to assess patients from a wide range of languages. Also, C.L.A. employs Artificial Intelligence models to inform theory on the relationship between language symptoms and their neural bases. It significantly advances our ability to optimize the prevention and treatment of elderly individuals with communication disorders, allowing them to age gracefully with social engagement.Comment: 36 pages, 2 figures, to be submite

Identification of Mild Cognitive Impairment From Speech in Swedish Using Deep Sequential Neural Networks

While people with mild cognitive impairment (MCI) portray noticeably incipient memory difficulty in remembering events and situations along with problems in decision making, planning, and finding their way in familiar environments, detailed neuropsychological assessments also indicate deficits in language performance. To this day, there is no cure for dementia but early-stage treatment can delay the progression of MCI; thus, the development of valid tools for identifying early cognitive changes is of great importance. In this study, we provide an automated machine learning method, using Deep Neural Network Architectures, that aims to identify MCI. Speech materials were obtained using a reading task during evaluation sessions, as part of the Gothenburg MCI research study. Measures of vowel duration, vowel formants (F1 to F5), and fundamental frequency were calculated from speech signals. To learn the acoustic characteristics associated with MCI vs. healthy controls, we have trained and evaluated ten Deep Neural Network Architectures and measured how accurately they can diagnose participants that are unknown to the model. We evaluated the models using two evaluation tasks: a 5-fold crossvalidation and by splitting the data into 90% training and 10% evaluation set. The findings suggest first, that the acoustic features provide significant information for the identification of MCI; second, the best Deep Neural Network Architectures can classify MCI and healthy controls with high classification accuracy (M = 83%); and third, the model has the potential to offer higher accuracy than 84% if trained with more data (cf., SD≈15%). The Deep Neural Network Architecture proposed here constitutes a method that contributes to the early diagnosis of cognitive decline, quantify the progression of the condition, and enable suitable therapeutics

D7.1 Report on the ECoE research clusters and research groups: management, function and technical capacity

This deliverable focuses on the formation of the Eratosthenes Centre of Excellence thematic research clusters of Environment & Climate, the Resilient Society and Big Earth Data Analytics in terms of the operations, research collaborations, tools to facilitate research, agreeing internal structures and allocating staff responsibilities. This deliverable will focus on the integration of recruited research personnel, research equipment and the Strategic Partners’ expertise to meet the needs of the research groups

D6.2 Workplan for transfer of knowledge and experience

This document represents the ‘Workplan for transfer of knowledge and experience’ (deliverable D.6.2) for the EXCELSIOR project. It focuses on the scope and activities of WP6 ”Knowledge Transfer and Capacity Building”. The main objective of WP6 is to coordinate and manage the knowledge transfer and capacity building that will take place during the EXCELSIOR project with Strategic Partners. The document will provide a workplan of how knowledge transfer and capacity building will take place between the Strategic Partners via workshops, seminars and secondments. This plan relies heavily on the extensive work done at the preparation of the project in defining the seminars, workshops and secondments that will take place between the Strategic Partners. This deliverable focuses on the initial workplan developed for Capacity Building Scheme A, which runs from M26 to M44. The deliverable includes the capacity building and knowledge transfer activities that will be conducted by the Strategic Partners DLR, NOA and TROPOS. The course description and program for selected trainings can be found in the appendices. The present document constitutes the ‘Workplan for transfer of knowledge and experience’ for Capacity Building Scheme period ‘A’ in the framework of the EXCELSIOR project, dedicated to Task T6.1 ‘Personnel Mobility Scheme’ under work package WP6 ‘Knowledge Transfer and Capacity Building’. D6.2 focuses on the trainings that will take place during the Capacity Building Scheme A of the project. This document provides a guideline of the knowledge transfer activities, but it is not limited to the activities that will take place during Capacity Building Scheme A. The Strategic Partners suggested that a flexible workplan is needed in order to identify the gaps and needs of the researchers of the ECoE, especially during the first Capacity Building Scheme and adjust the workplan as needed in order to facilitate more effective knowledge transfer and capacity building. The secondments will be selected by the Strategic Partners as needed, during the knowledge transfer activities, parallel to the demonstration projects in WP7. Selected descriptions of knowledge transfer activities are featured in Appendix A and Appendix B

D1.15 Impact Assessment Report for RP 2

This deliverable provides the impact assessment report for RP2 (M16-M30). It provides an update on the overall and specific objectives of the EXCELSIOR project that have been achieved within RP2. This task undertakes the establishment of a methodology for the yearly monitoring of the impact of the different activities carried out by Eratosthenes Centre of Excellence (ECoE) and its partners through EXCELSIOR against a set of quantified targets. The list of Key Performance Indicators established in D1.12 has been revised based on the comments received by the EXCELSIOR project reviewers on 23 June 2021 following the first project review. This list is hereby updated to reflect the activities of RP2. By monitoring the impact for the RP2, it will provide direction of the activities needed to fulfil the KPIs for the following reporting periods. The impact assessment report will be used to assess the implementation of the work plan and adjust the activities in agreement with WP and task Leaders to ensure the achievement of the Project’s strategic objectives. WP1 provides the KPI monitoring framework and general quality processes, while the WP3 defines concrete actions affecting all other WPs for meeting the Impact KPIs. This task’s activities will be coordinated with WP3 activities on strategy definition as a continuous process, in order to update the human resources, infrastructure acquisition and overall work plan and to meet new priorities identified. The analysis outputs will update the Project Action Plan of Task 1.1. The following activities were examined and assessed according to the KPIs. These activities include proposals, dissemination events, publications, academia, networks, etc. The impact for each activity was also included

The "Excelsior" H2020 Widespread Teaming Phase 2 Project: ERATOSTHENES: EXcellence Research Centre for Earth SurveiLlance and Space-Based MonItoring Of the EnviRonment

The EXCELSIOR project aims to upgrade the existing ERATOSTHENES Research Centre established within the Cyprus University of Technology into a sustainable, viable and autonomous ERATOSTHENES Centre of Excellence (ECoE) for Earth Surveillance and Space-Based Monitoring of the Environment. The ECoE for Earth Surveillance and Space-Based Monitoring of the Environment will provide the highest quality of related services both on the National, European and International levels through the ‘EXCELSIOR’ Project under H2020 WIDESPREAD TEAMING. The vision of the ECoE is to become a world-class Digital Innovation Hub (DIH) for Earth observation and Geospatial Information becoming the reference Centre in the Eastern Mediterranean, Middle East and North Africa (EMMENA) within the next 7 years. The ECoE will lead multidisciplinary Earth observation research towards a better understanding, monitoring and sustainable exploitation and protection of the physical, built and human environment, in line with International policy frameworks. Indeed, the scientific potential of the new upgraded ECoE focusing on the integration of novel Earth observation, space and ground based integrated technologies for the efficient systematic monitoring of the environment. Furthermore, ECoE aims to excel in five domains: i) Access to energy; ii) Disaster Risk Reduction; iii) Water Resource Management; iv) Climate Change Monitoring and v) Big Earth observation Data Analytics. This will be achieved through research and innovation excellence in the respective scientific and technological disciplines and working together with other Earth observation industries, whereby the ECoE will develop a pool of scientific expertise and engineering capability as well as technical facilities. The partners of the EXCELSIOR consortium include the Cyprus University of Technology as the Coordinator, the German Airspace Center (DLR), the Leibniz Institute for Tropospheric Research (TROPOS), the National Observatory of Athens (NOA) and the Department of Electronic Communications, of the Ministry of Transport, Communications and Works of Cyprus

Differential cross section measurements for the production of a W boson in association with jets in proton–proton collisions at √s = 7 TeV

Measurements are reported of differential cross sections for the production of a W boson, which decays into a muon and a neutrino, in association with jets, as a function of several variables, including the transverse momenta (pT) and pseudorapidities of the four leading jets, the scalar sum of jet transverse momenta (HT), and the difference in azimuthal angle between the directions of each jet and the muon. The data sample of pp collisions at a centre-of-mass energy of 7 TeV was collected with the CMS detector at the LHC and corresponds to an integrated luminosity of 5.0 fb[superscript −1]. The measured cross sections are compared to predictions from Monte Carlo generators, MadGraph + pythia and sherpa, and to next-to-leading-order calculations from BlackHat + sherpa. The differential cross sections are found to be in agreement with the predictions, apart from the pT distributions of the leading jets at high pT values, the distributions of the HT at high-HT and low jet multiplicity, and the distribution of the difference in azimuthal angle between the leading jet and the muon at low values.United States. Dept. of EnergyNational Science Foundation (U.S.)Alfred P. Sloan Foundatio

Optimasi Portofolio Resiko Menggunakan Model Markowitz MVO Dikaitkan dengan Keterbatasan Manusia dalam Memprediksi Masa Depan dalam Perspektif Al-Qur`an

Risk portfolio on modern finance has become increasingly technical, requiring the use of sophisticated mathematical tools in both research and practice. Since companies cannot insure themselves completely against risk, as human incompetence in predicting the future precisely that written in Al-Quran surah Luqman verse 34, they have to manage it to yield an optimal portfolio. The objective here is to minimize the variance among all portfolios, or alternatively, to maximize expected return among all portfolios that has at least a certain expected return. Furthermore, this study focuses on optimizing risk portfolio so called Markowitz MVO (Mean-Variance Optimization). Some theoretical frameworks for analysis are arithmetic mean, geometric mean, variance, covariance, linear programming, and quadratic programming. Moreover, finding a minimum variance portfolio produces a convex quadratic programming, that is minimizing the objective function ðð¥with constraintsð ð 𥠥 ðandð´ð¥ = ð. The outcome of this research is the solution of optimal risk portofolio in some investments that could be finished smoothly using MATLAB R2007b software together with its graphic analysis