Miracle-dz/UniFAR-FTUs huggingface dataset:
ANN-based model to identify noticed sound events. A tool for inclusion in action plans against environmental noise in urban environments

Over recent years, acoustic pollution has become a common problem for urban environments and its treatment one of the new challenges in environmental policy. In this framework, the enactment of the European Directive on Environmental Noise Management (Directive 2002/49/EC) set forth the need to undertake strategic action plans to improve the sound conditions of cities. According to this point, based on the noise mapping results, competent authorities are required to develop action plans to reduce noise where necessary and maintain environmental noise quality where it is good. In developing plans of action, the competent authorities are faced with the problem of determining which specific measures can be addressed from a technical and economical standpoint. An economically and technically feasible action to undertake to improve problems identified in terms of noise pollution in urban agglomerations could be the identification and elimination of noticed sound events present in these environments. Noticed sound events are loaded with large amounts of sound energy, creating a significant increase above the sound climate where they appear and, therefore, behaving within the sound space as highly disruptive elements. For this reason, in this paper a tool based on artificial neural networks for the automatic identification of noticed sound events in monitored records of sound pressure level has been developed. To build the ANN-based model several types, structures and configurations of neural networks have been analyzed. The final goal is that the developed tool provides the information required for the implementation of a noticed sound event management plan.
Performance Analysis of Glioma Brain Tumor Segmentation using Ridgelet Transform and CANFES Methodology

Objective:The Glioma brain tumor detection and segmentation methods are proposed in this paper using machine learning approaches. Methods:The boundary edge pixels are detected using Kirsch’s edge detectors and then contrast adaptive histogram equalization method is applied on the edge detected pixels. Then, Ridgelet transform is applied on this enhanced brain image in order to obtain the Ridgelet multi resolution coefficients. Further, features are derived from the Ridgelet transformed coefficients and the features are optimized using Principal Component Analysis (PCA) method and these optimized features are classified into Glioma or non-Glioma brain images using Co-Active Adaptive Neuro Fuzzy Expert System (CANFES) classifier.Results:The proposed method with PCA and CANFES classification approach obtains 97.6% of se, 98.56% of sp, 98.73% of Acc, 98.85% of Pr, 98.11% of FPR and 98.185 of FNR, then the proposed Glioma brain tumor detection method using CANFES classification approach only.
Stochastic finite element model updating and its application in aeroelasticity

Knowledge in the field of modelling and predicting the dynamic responses of structures is constantly developing. Modelling of uncertainty is considered as one of the tools that increases confidence by providing extra information. This information may then be useful in planning physical tests. However, the complexity of structures together with uncertainty-based methods leads inevitably to increased computation; therefore deterministic approaches are preferred by industry and a safety factor is incorporated to account for uncertainties. However, the selection of a proper safety factor relies on engineering insight. Hence, there has been much interest in developing efficient uncertainty-based methods with a good degree of accuracy. This thesis focuses on the uncertainty propagation methods; namely Monte Carlo Simulation, first-order and second-order perturbation, asymptotic integral, interval analysis, fuzzy-logic analysis and meta-models. The feasibility of using these methods (in terms of computational time) to propagate structural model variability to linear and Computational Fluid Dynamic (CFD) based aeroelastic stability is investigated. In this work only the uncertainty associated with the structural model is addressed, but the approaches developed can be also used for other types of non-structural uncertainties. Whichever propagation method is used, an issue of very practical significance is the initial estimation of the parameter uncertainty to be propagated particularly when the uncertain parameters cannot be measured, such as damping and stiffness terms in mechanical joints or material-property variability. What can be measured is the variability in dynamic behaviour as represented by natural frequencies, mode shapes, or frequency response functions. The inverse problem then becomes one of inferring the parameter uncertainty from statistical mea-
NOMA-Assisted Machine-Type Communications in UDN: State-of-the-Art and Challenges

The inevitable next era of smart living requires unprecedented advances in enabling technologies. The building blocks of this era are devices such as sensors, actuators, and Internet of Things (IoT) devices. Machine-Type Communication (MTC), also known as Machine-to-Machine (M2M) communication, constitutes the main enabling technology to support communications among such devices. The massive number of these devices and the immense amount of traffic generated by them require a paramount shift in cellular and non-cellular wireless technologies to achieve the required connectivity. Ultra-Dense Network (UDN) is intuitively one of the most convenient approaches to tackle such requirements of massive connectivity and high throughput found in MTC. In UDNs, small cells are deployed in large densities compared to Human-Type Communication Users (HTCUs) like smartphones and tablets. In a different scope, multiple access techniques also require a paradigm shift to cope with the increasing density of required connections while confined by limited resources. Recently, Non-Orthogonal Multiple Access (NOMA) has received a great attention as an efficacious candidate to enhance the network’s performance compared to traditional Orthogonal Multiple Access (OMA) techniques. For this sake, in this paper, we provide a comprehensive survey and illustrative simulation results on the application of NOMA to support MTC in a UDN environment. First, we give a brief discussion on MTC and its different applications and supporting technologies. Then, we focus our effort on investigating the main challenges facing MTC along with the existing opportunities found in both NOMA and UDN, respectively. Finally, we show via simulations the possible gains of both technologies and conclude by discussing the open problems for future research directions.
Extraction of Public Transportation Networks from Openstreetmap Data and Estimation of OD Patterns Through a Graph Convolutional Approach

OpenStreetMap (OSM) is the prime crowed-sourced open access platform used by a range of applications. Though quality of data varies, it can provide information on transportation networks in most parts of the world. Recently, also the quantity and quality of uploaded information regarding the public transport networks has significantly improved though issues remain. In this research we first describe the development of an algorithm that allows simple extraction of Points of interests, road networks and public transportation networks in terms of stops and routes. This allows us to easily compare and analyse network quality in different cities. With a few examples we discuss first potential problems of the OSM data. We then propose that in particular the extracted PT network can be used to understand passenger origin and destination patterns of the network due to the endogeneous relationship between demand and supply.
Style‐Controllable Speech‐Driven Gesture Synthesis Using Normalising Flows

Automatic synthesis of realistic gestures promises to transform the fields of animation, avatars and communicative agents. In off‐line applications, novel tools can alter the role of an animator to that of a director, who provides only high‐level input for the desired animation; a learned network then translates these instructions into an appropriate sequence of body poses. In interactive scenarios, systems for generating natural animations on the fly are key to achieving believable and relatable characters. In this paper we address some of the core issues towards these ends. By adapting a deep learning‐based motion synthesis method called MoGlow, we propose a new generative model for generating state‐of‐the‐art realistic speech‐driven gesticulation. Owing to the probabilistic nature of the approach, our model can produce a battery of different, yet plausible, gestures given the same input speech signal. Just like humans, this gives a rich natural variation of motion. We additionally demonstrate the ability to exert directorial control over the output style, such as gesture level, speed, symmetry and spacial extent. Such control can be leveraged to convey a desired character personality or mood. We achieve all this without any manual annotation of the data. User studies evaluating upper‐body gesticulation confirm that the generated motions are natural and well match the input speech. Our method scores above all prior systems and baselines on these measures, and comes close to the ratings of the original recorded motions. We furthermore find that we can accurately control gesticulation styles without unnecessarily compromising perceived naturalness. Finally, we also demonstrate an application of the same method to full‐body gesticulation, including the synthesis of stepping motion and stance.
Virtual resource-sharing mechanisms in software-defined and virtualized wireless network

Zhang, Di Virtual Resource-Sharing Mechanisms in Software-Defined and Virtualized Wireless Network Jyväskylä: University of Jyväskylä, 2018, 62 p.(+included articles) (Jyväskylä Studies in Computing ISSN 1456-5390; ) ISBN 978-951-39-7469-5 (nid.) ISBN 978-951-39-7470-1 (PDF) Finnish summary Diss. On the way towards providing scenario-based transmission (enhanced mobile broadband (eMBB), ultra-reliable and low-latency communications (uRLLC), and massive machine type communications (mMTC)) in 5G, the incorporation of software-defined networking (SDN) and wireless network virtualization (WNV) is foreseen to offer such softwarization and service-oriented architecture. This research concentrates on reducing the capital expenses (CapEx) and operation expenses (OpEx) significantly from a radio resource management(RRM) perspective. With this objective, one software-defined and virtualized (SDV) architecture for enabling different-level virtualization is first proposed. The designed wireless virtualization scheme offers the ability to abstract and slice the wireless network by separating the control and data planes in a flexible m anner. Second, multiple virtual resource-sharing (VRS) mechanisms for ensuring fairness and competitiveness among mobile operators and providers using auction and contract theories are proposed. With such VRS mechanisms, diverse quality-of-service (QoS) requirements of users (data rate, delay, priority, etc.), profits of conflicting operators and providers, and different system-level objectives (system throughput and energy efficiency) are e nsured. To validate our proposed VRS mechanisms, a variety of mathematical demonstrations are conducted with different theoretic approaches (auction and contract theories). The performance of each mechanism is separately evaluated with system level simulations.
Υπολογιστική όραση και μηχανική μάθηση για αναγνώριση συμβόλων νοηματικής γλώσσας

This master thesis presents the computer vision in combination with machine learning techniques, in order to develop applications that recognize patterns, such as hand gestures. Initially, there is a throwback to the disciplines of computer vision and machine learning, in addition to the recognition of hand gestures. Then, there is a deeper analysis in computer vision, by studying contour finding and convex hull algorithms, and the way they are improved through image processing. After that, there is a reference in machine learning techniques, focusing on classification algorithms, especially the Random Forest classifier. Finally, an application is developed and analyzed, which recognizes seventeen hand gestures, through their contour and the convex hull, and by generating and training a dataset with the defects. Additionally, the application is capable of arithmetic calculations, of constructing matrices and applying basic algebra on them as well as of creating functions and applying analysis, through hand gestures and a helper panel.
Data Analytics for Advanced Energy Management in Real-Time based on Open Source Technologies

Due to the climate change, the need of sustainable technological progress is growing. Renewable energies undergo a significant upturn, but they come with the price of nearly uncontrollable and hard-to-predict fluctuating energy generation. This forces a paradigm change in handling the energy resources in the society. Especially in the industry, there will appear a need to manage energy in the sense of planning and controlling power consumption. If a company wants to make use of its flexibility in energy consumption - for example by managing a local virtual power plant - there is a need to make decisions in the required pace. Thus, a real-time decision-making process needs an adequate IT-infrastructure and algorithms tailored to the specific use case. This paper gives an overview of the necessary components to realize real-time analytics for industrial energy management. The concepts are expatiated on a scalable real-time computing architecture and a mathematical approach for short term prediction of energy consumption and pattern recognition that are included in a Business Intelligence Tool for decision support using only open source components.
Nutritional Status and Associated Factors among Adult Psychiatric Patients in Dessie Referral Hospital, Northeast Ethiopia

Background Psychiatric illnesses involve alterations in the brain or nervous system function and result in altered perception, responses to the environment, and daily functioning, which ultimately diminish the capacity to cope with the regular demands of life, including nutritional intake. Little is known about nutritional status in psychiatric patients in our setup, particularly in northeast Ethiopia. Thus, this study is aimed at assessing the nutritional status and associated factors among adult psychiatric patients in Dessie referral hospital, northeast Ethiopia. Methods A facility-based cross-sectional study was employed among 530 psychiatric patients using an interviewer-administered structured questionnaire, and anthropometric measurements were taken from April 10 to June 20, 2018. Multinomial logistic regression analysis was used to identify the associated factors with the nutritional status of adult psychiatric patients. Results The study revealed that the proportion of undernutrition and overnutrition was 20.0% (95% CI: 16.80-23.60) and 23.4% (95% CI: 19.80-27.00), respectively. Being male (AOR: 2.39, 95% CI: 1.28-4.47), private employed (AOR: 0.08, 95% CI: 0.02-0.31), and not consuming alcohol (AOR: 0.20, 95% CI: 0.56-0.74) were factors associated with undernutrition. Whereas not involved in physical activity (AOR: 2.98, 95% CI: 1.37-6.49), being newly diagnosed patient (AOR: 1.86, 95% CI: 1.01-3.42), and not chewing Khat (AOR: 3.92, 95% CI: 1.63-9.42) were factors associated with overnutrition of adult psychiatric patients. Conclusion The proportion of both undernutrition and overnutrition was above the national average. Notably, nutrition significantly affects the production and use of neurotransmitters, and this may result in significant effects on physical, mental, or emotional processes. This undoubtedly affects the stabilization processes of neuropsychiatric patients. Therefore, healthcare managers, supervisors, and policymakers identify those vulnerable groups early and thereby design effective nutritional strategies to intervene in malnutrition among adult patients with a psychiatric disorder.
New advances in mechanomyography sensor technology and signal processing: Validity and intrarater reliability of recordings from muscle

Introduction The Mechanical Muscle Activity with Real-time Kinematics project aims to develop a device incorporating wearable sensors for arm rehabilitation following stroke. These will record kinematic activity using inertial measurement units and mechanical muscle activity. The gold standard for measuring muscle activity is electromyography; however, mechanomyography offers an appropriate alterative for our home-based rehabilitation device. We have patent filed a new laboratory-tested device that combines an inertial measurement unit with mechanomyography. We report on the validity and reliability of the mechanomyography against electromyography sensors. Methods In 18 healthy adults (27–82 years), mechanomyography and electromyography recordings were taken from the forearm flexor and extensor muscles during voluntary contractions. Isometric contractions were performed at different percentages of maximal force to examine the validity of mechanomyography. Root-mean-square of mechanomyography and electromyography was measured during 1 s epocs of isometric flexion and extension. Dynamic contractions were recorded during a tracking task on two days, one week apart, to examine reliability of muscle onset timing. Results Reliability of mechanomyography onset was high (intraclass correlation coefficient = 0.78) and was comparable with electromyography (intraclass correlation coefficient = 0.79). The correlation between force and mechanomyography was high (R2 = 0.94). Conclusion The mechanomyography device records valid and reliable signals of mechanical muscle activity on different days.
Supporting Deaf Students in Undergraduate Research Experiences: Perspectives of American Sign Language Interpreters

Deaf undergraduates are eager to engage in research but often feel marginalized due to lack of appropriate accommodations to allow for effective communication within heterogeneous research teams consisting of hearing peers and/or mentors. In this case study, we interviewed four American Sign Language (ASL) interpreters who provided full-time accommodations to teams consisting of one deaf student and two hearing peers during a six-week internship. We queried the interpreters on their role and experiences in supporting the research teams. Our findings indicate that the interpreters can be a valuable asset to heterogeneous teams by supporting both deaf and hearing individuals and advocating for the deaf student. That said, interpreters also had to overcome challenges unique to interpreting in the research environment, such as deciding when and how to interpret. The insights provided by the interpreters interviewed here are valuable as undergraduate research programs evaluate how to provide appropriate accommodations to deaf students engaged in research. In addition, they also highlight the need for research experience coordinators and mentors to consider supporting diverse teams in developing effective communication strategies and applying universal design for learning to the research environment.
Survivorship of geographic Pomacea canaliculata populations in responses to cold acclimation

Abstract Pomacea canaliculata, a freshwater snail from South America, has rapidly established natural populations from south to north subtropical region in China, since its original introductions in the 1980s. Low temperature in winter is a limiting factor in the geographic expansion and successfully establishment for apple snail populations. There have been some studies on population level of low temperature tolerance for P. canaliculata, yet little is quantified about its life‐history traits in responses to cold temperatures. Whether these responses vary with the acclimation location is also unclear. We investigated the survivorship and longevity of P. canaliculata in responses to cold temperatures and examine whether these responses vary with the location and snail size. We hypothesized that survival of the snails depends on their shell height and the level of low temperature, and P. canaliculata population from the mid-subtropical zone may exhibit the highest viability over the cold thermal range. We sampled P. canaliculata populations from five latitude and longitude ranges of subtropical China: Guangzhou population in southernmost (SM‐GZ), three populations of Yingtan (MR‐YT), Ningbo (MR‐NB), Ya'an (MR‐YA) in midrange, and Huanggang population in northernmost (NM‐HG) subtropical zone. For each P. canaliculata population, survival and longevity at six cold acclimation temperature levels (12, 9, 6, 3, 0, and −3°C) were quantified, and the effects of location and shell height were examined. The MR‐YA population from mid-subtropical zone of China exhibited the highest survival rate and prolonged survival time regardless of the temperature acclimation treatments, whereas the SM‐GZ population from southern subtropical was the most sensitive to cold temperatures, particular temperatures below 9°C. No individuals of the SM‐GZ population could survive after stressed for 30 days (3°C), 5 days (0°C) and 2 days (−3°C), respectively. For each experimental P. canaliculata population held at 3, 0, and −3°C, individuals with intermediate shell height of 15.0–25.0 mm had significantly higher survivals. The results highlight a request of a more thorough investigation on acclimation responses in each of the life table demographic parameters for P. canaliculata, and pose the question of whether natural selection or some genetic changes may have facilitated adaptation in invasive locations.
Weighing the odds: Assessing underdiagnosis of adult obesity via electronic medical record problem list omissions

Background Obesity is a continuing national epidemic, and the condition can have a physical, psychological, as well as social impact on one’s well-being. Consequently, it is critical to diagnose and document obesity accurately in the patient’s electronic medical record (EMR), so that the information can be used and shared to improve clinical decision making and health communication and, in turn, the patient’s prognosis. It is therefore worthwhile identifying the various factors that play a role in documenting obesity diagnosis and the methods to improve current documentation practices. Method We used a retrospective cross-sectional design to analyze outpatient EMRs of patients at an academic outpatient clinic. Obese patients were identified using the measured body mass index (BMI; ≥30 kg/m2) entry in the EMR, recorded at each visit, and checked for documentation of obesity in the EMR problem list. Patients were categorized into two groups (diagnosed or undiagnosed) based on a documented diagnosis (or omission) of obesity in the EMR problem list and compared. Results A total of 10,208 unique patient records of obese patients were included for analysis, of which 4119 (40%) did not have any documentation of obesity in their problem list. Chi-square analysis between the diagnosed and undiagnosed groups revealed significant associations between documentation of obesity in the EMR and patient characteristics. Conclusion EMR designers and developers must consider employing automated decision support techniques to populate and update problem lists based on the existing recorded BMI in the EMR in order to prevent omissions occurring from manual entry.