Search results for: Deep Neural Network

Authors: Shaode Yu, Jiajian Meng, Bing Zhu, Hang Yu, Qiurui Sun

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Speech emotion recognition (SER) is for recognizing human subjective emotions through audio data in-depth analysis. From speech audios, how to comprehensively extract emotional information and how to effectively fuse extracted features remain challenging. This paper presents a dual-stream SER framework that embraces both full training and transfer learning of different networks for thorough feature encoding. Besides, a plug-and-play cross-attention fusion (CAF) module is implemented for the valid integration of the dual-stream encoder output. The effectiveness of the proposed CAF module is compared to the other three fusion modules (feature summation, feature concatenation, and feature-wise linear modulation) on two databases (RAVDESS and IEMO-CAP) using different dual-stream encoders (full training network, DPCNN or TextRCNN; transfer learning network, HuBERT or Wav2Vec2). Experimental results suggest that the CAF module can effectively reconcile conflicts between features from different encoders and outperform the other three feature fusion modules on the SER task. In the future, the plug-and-play CAF module can be extended for multi-branch feature fusion, and the dual-stream SER framework can be widened for multi-stream data representation to improve the recognition performance and generalization capacity.

Keywords: speech emotion recognition, cross-attention fusion, dual-stream, pre-trained

Procedia PDF Downloads 76

Authors: Nigar Kantarci Carsibasi

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Coarse-grained elastic network models, namely Gaussian network model (GNM) and Anisotropic network model (ANM), are utilized in order to investigate the fluctuation dynamics of Murine Double Minute 2 (MDM2), which is the native inhibitor of p53. Conformational dynamics of MDM2 are elucidated in unbound, p53 bound, and non-peptide small molecule inhibitor bound forms. With this, it is aimed to gain insights about the alterations brought to global dynamics of MDM2 by native peptide inhibitor p53, and two small molecule inhibitors (HDM201 and NVP-CGM097) that are undergoing clinical stages in cancer studies. MDM2 undergoes significant conformational changes upon inhibitor binding, carrying pieces of evidence of induced-fit mechanism. Small molecule inhibitors examined in this work exhibit similar fluctuation dynamics and characteristic mode shapes with p53 when complexed with MDM2, which would shed light on the design of novel small molecule inhibitors for cancer therapy. The results showed that residues Phe 19, Trp 23, Leu 26 reside in the minima of slowest modes of p53, pointing to the accepted three-finger binding model. Pro 27 displays the most significant hinge present in p53 and comes out to be another functionally important residue. Three distinct regions are identified in MDM2, for which significant conformational changes are observed upon binding. Regions I (residues 50-77) and III (residues 90-105) correspond to the binding interface of MDM2, including (α2, L2, and α4), which are stabilized during complex formation. Region II (residues 77-90) exhibits a large amplitude motion, being highly flexible, both in the absence and presence of p53 or other inhibitors. MDM2 exhibits a scattered profile in the fastest modes of motion, while binding of p53 and inhibitors puts restraints on MDM2 domains, clearly distinguishing the kinetically hot regions. Mode shape analysis revealed that the α4 domain controls the size of the cleft by keeping the cleft narrow in unbound MDM2; and open in the bound states for proper penetration and binding of p53 and inhibitors, which points to the induced-fit mechanism of p53 binding. P53 interacts with α2 and α4 in a synchronized manner. Collective modes are shifted upon inhibitor binding, i.e., second mode characteristic motion in MDM2-p53 complex is observed in the first mode of apo MDM2; however, apo and bound MDM2 exhibits similar features in the softest modes pointing to pre-existing modes facilitating the ligand binding. Although much higher amplitude motions are attained in the presence of non-peptide small molecule inhibitor molecules as compared to p53, they demonstrate close similarity. Hence, NVP-CGM097 and HDM201 succeed in mimicking the p53 behavior well. Elucidating how drug candidates alter the MDM2 global and conformational dynamics would shed light on the rational design of novel anticancer drugs.

Keywords: cancer, drug design, elastic network model, MDM2

Procedia PDF Downloads 130

Authors: Shaukat Ali Shahee, Usha Ananthakumar

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A data set exhibits class imbalance problem when one class has very few examples compared to the other class, and this is also referred to as between class imbalance. The traditional classifiers fail to classify the minority class examples correctly due to its bias towards the majority class. Apart from between-class imbalance, imbalance within classes where classes are composed of a different number of sub-clusters with these sub-clusters containing different number of examples also deteriorates the performance of the classifier. Previously, many methods have been proposed for handling imbalanced dataset problem. These methods can be classified into four categories: data preprocessing, algorithmic based, cost-based methods and ensemble of classifier. Data preprocessing techniques have shown great potential as they attempt to improve data distribution rather than the classifier. Data preprocessing technique handles class imbalance either by increasing the minority class examples or by decreasing the majority class examples. Decreasing the majority class examples lead to loss of information and also when minority class has an absolute rarity, removing the majority class examples is generally not recommended. Existing methods available for handling class imbalance do not address both between-class imbalance and within-class imbalance simultaneously. In this paper, we propose a method that handles between class imbalance and within class imbalance simultaneously for binary classification problem. Removing between class imbalance and within class imbalance simultaneously eliminates the biases of the classifier towards bigger sub-clusters by minimizing the error domination of bigger sub-clusters in total error. The proposed method uses model-based clustering to find the presence of sub-clusters or sub-concepts in the dataset. The number of examples oversampled among the sub-clusters is determined based on the complexity of sub-clusters. The method also takes into consideration the scatter of the data in the feature space and also adaptively copes up with unseen test data using Lowner-John ellipsoid for increasing the accuracy of the classifier. In this study, neural network is being used as this is one such classifier where the total error is minimized and removing the between-class imbalance and within class imbalance simultaneously help the classifier in giving equal weight to all the sub-clusters irrespective of the classes. The proposed method is validated on 9 publicly available data sets and compared with three existing oversampling techniques that rely on the spatial location of minority class examples in the euclidean feature space. The experimental results show the proposed method to be statistically significantly superior to other methods in terms of various accuracy measures. Thus the proposed method can serve as a good alternative to handle various problem domains like credit scoring, customer churn prediction, financial distress, etc., that typically involve imbalanced data sets.

Keywords: classification, imbalanced dataset, Lowner-John ellipsoid, model based clustering, oversampling

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Authors: Meital Amzalag, Sharon Hardof-Jaffe

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In the last few decades, children and youth have been immersed in digital technologies. Indeed, recent studies explored the implication of technology use in their leisure and learning activities. Educators face an essential need to utilize technology and implement them into the curriculum. To do that, educators need to understand how young people use digital technology. This study aims to explore K12 students' digital lives from their point of view, to reveal their digital activities, age and gender differences with respect to digital activities, and to present the students' attitudes towards technologies in learning. The study approach is quantitative and includes354 students ages 6-16 from three schools in Israel. The online questionnaire was based on self-reports and consists of four parts: Digital activities: leisure time activities (such as social networks, gaming types), search activities (information types and platforms), and digital application use (e.g., calendar, notes); Digital skills (requisite digital platform skills such as evaluation and creativity); Social and emotional aspects of digital use (conducting digital activities alone and with friends, feelings, and emotions during digital use such as happiness, bullying); Digital attitudes towards digital integration in learning. An academic ethics board approved the study. The main findings reveal the most popular K12digital activities: Navigating social network sites, watching TV, playing mobile games, seeking information on the internet, and playing computer games. In addition, the findings reveal age differences in digital activities, such as significant differences in the use of social network sites. Moreover, the finding raises gender differences as girls use more social network sites and boys use more digital games, which are characterized by high complexity and challenges. Additionally, we found positive attitudes towards technology integration in school. Students perceive technology as enhancing creativity, promoting active learning, encouraging self-learning, and helping students with learning difficulties. The presentation will provide an up-to-date, accurate picture of the use of various digital technologies by k12 students. In addition, it will discuss the learning potentials of such use and how to implement digital technologies in the curriculum. Acknowledgments: This study is a part of a broader study about K-12 digital life in Israel and is supported by Mofet-the Israel Institute for Teachers'Development.

Keywords: technology and learning, K-12, digital life, gender differences

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Authors: G. S. Thyagaraju, U. P. Kulkarni

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Context-aware computing refers to a general class of computing systems that can sense their physical environment, and adapt their behaviour accordingly. Context aware computing makes systems aware of situations of interest, enhances services to users, automates systems and personalizes applications. Context-aware services have been introduced into mobile devices, such as PDA and mobile phones. In this paper we are presenting a novel approaches used to realize the context aware mobile. The context aware mobile phone (CAMP) proposed in this paper senses the users situation automatically and provides user context required services. The proposed system is developed by using artificial intelligence techniques like Bayesian Network, fuzzy logic and rough sets theory based decision table. Bayesian Network to classify the incoming call (high priority call, low priority call and unknown calls), fuzzy linguistic variables and membership degrees to define the context situations, the decision table based rules for service recommendation. To exemplify and demonstrate the effectiveness of the proposed methods, the context aware mobile phone is tested for college campus scenario including different locations like library, class room, meeting room, administrative building and college canteen.

Keywords: context aware mobile, fuzzy logic, decision table, Bayesian probability

Procedia PDF Downloads 366

Authors: M. Zafar, S. Rasheed, Imran Hashmi

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Very little is concerned about the bacterial contamination in drinking water biofilm which provide a potential source for bacteria to grow and increase rapidly. So as to understand the microbial density in DWDs, a three-month study was carried out. The aim of this study was to examine biofilm in three different pipe materials including PVC, PPR and GI. A set of all these pipe materials was installed in DWDs at nine different locations and assessed on monthly basis. Drinking water quality was evaluated by different parameters and characterization of biofilm. Among various parameters are Temperature, pH, turbidity, TDS, electrical conductivity, BOD, COD, total phosphates, total nitrates, total organic carbon (TOC) free chlorine and total chlorine, coliforms and spread plate counts (SPC) according to standard methods. Predominant species were Bacillus thuringiensis, Pseudomonas fluorescens , Staphylococcus haemolyticus, Bacillus safensis and significant increase in bacterial population was observed in PVC pipes while least in cement pipes. The quantity of DWDs bacteria was directly depended on biofilm bacteria and its increase was correlated with growth and detachment of bacteria from biofilms. Pipe material also affected the microbial community in drinking water distribution network biofilm while Similarity in bacterial species was observed between systems due to same disinfectant dose, time period and plumbing pipes.

Keywords: biofilm, DWDs, pipe material, bacterial population

Procedia PDF Downloads 347

Authors: Vedant Vardhan Rathour, Anant Agrawal

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The project aims to provide a touchless, intuitive interface for human-computer interaction, enabling users to control their computer using hand gestures and voice commands. The system leverages advanced computer vision techniques using the MediaPipe framework and OpenCV to detect and interpret real time hand gestures, transforming them into mouse actions such as clicking, dragging, and scrolling. Additionally, the integration of a voice assistant powered by the Speech Recognition library allows for seamless execution of tasks like web searches, location navigation and gesture control on the system through voice commands.

Keywords: gesture recognition, hand tracking, machine learning, convolutional neural networks

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Authors: Boris M. Fedorov, Liudmila P. Goncharenko, Sergey A. Sybachin, Natalia A. Mamedova, Ekaterina V. Makarenkova, Saule Rakhimova

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In modern economic conditions, the question of the possibility of correct recognition of a signature on digital documents in order to verify the expression of will or confirm a certain operation is relevant. The additional complexity of processing lies in the dynamic variability of the signature for each individual, as well as in the way information is processed because the signature refers to biometric data. The article discusses the issues of using artificial intelligence models in order to improve the quality of signature confirmation in document recognition. The analysis of several possible options for using the model is carried out. The results of the study are given, in which it is possible to correctly determine the authenticity of the signature on small samples.

Keywords: signature recognition, biometric data, artificial intelligence, neural networks

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Authors: Bianca Maria Rulli, Lenny Valentino Schiaretti

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The considerable recent urbanization has increasingly sharpened environmental and social problems all over the world. During the recent years, many answers to the alarming attitudes in modern cities have emerged: a drastic reduction in the rate of growth is becoming essential for future generations and small scale economies are considered more adaptive and sustainable. According to the concept of degrowth, cities should consider surpassing the centralization of urban living by redefining the relationship between rural and urban knowledge; growing food in cities fundamentally contributes to the increase of social and ecological resilience. Through an innovative approach, this research combines the benefits of urban agriculture (increase of biological diversity, shorter and thus more efficient supply chains, food security) and temporary land use. They stimulate collaborative practices to satisfy the changing needs of communities and stakeholders. The concept proposes a coherent strategy to create a sustainable development of urban spaces, introducing a productive green-network to link specific areas in the city. By shifting the current relationship between architecture and landscape, the former process of ground consumption is deeply revised. Temporary modules can be used as concrete tools to create temporal areas of innovation, transforming vacant or marginal spaces into potential laboratories for the development of the city. The only permanent ground traces, such as foundations, are minimized in order to allow future land re-use. The aim is to describe a new mindset regarding the quality of space in the metropolis which allows, in a completely flexible way, to bring back the green and the urban farming into the cities. The wide possibilities of the research are analyzed in two different case-studies. The first is a regeneration/connection project designated for social housing, the second concerns the use of temporary modules to answer to the potential needs of social structures. The intention of the productive green-network is to link the different vacant spaces to each other as well as to the entire urban fabric. This also generates a potential improvement of the current situation of underprivileged and disadvantaged persons.

Keywords: degrowth, green network, land use, temporary building, urban farming

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Authors: Pavneet Kaur, Palak Sharma

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Virtualization is a term that basically describe creation of virtual version of something like operating system, network, etc. Virtualization is a technology which is in use from 1970, but with new developments and inventions, it is now useful in education, software development etc. This paper will describe basic introduction of virtualization, along with its various categories. It will also describe use of virtualization in software engineering, its various benefits and shortcomings.

Keywords: virtualization, hardware, software, os

Procedia PDF Downloads 369

Authors: B. D. Patni, Ashwani Jain, Arindom Chakraborty

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The world’s highest mountain range has been forming since the collision of Indian Plate with Asian Plate 40-50 million years ago. The Indian subcontinent has been deeper and deeper in to the rest of Asia resulting upliftment of Himalaya & Tibetan Plateau. The complex domain has become a major challenge for construction of hydro electric projects. The Himalayas are geologically complex & seismically active. Shifting of Indian Plate northwardly and increasing the amount of stresses in the fragile domain which leads to deformation in the form of several fold, faults and upliftment. It is difficult to undergo extensive geological investigation to ascertain the geological problems to be encountered during construction. Inaccessibility of the terrain, high rock cover, unpredictable ground water condition etc. are the main constraints. The hydroelectric projects located in Himalayas have faced many geological and geo-hydrological problems while construction of surface and subsurface works. Based on the experience, efforts have been made to identify the expected geological problems during and after construction of the projects. These have been classified into surface and subsurface problems which include existence of inhomogeneous deep overburden in the river bed or buried valley, abrupt change in bed rock profile, Occurrences of fault zones/shear zones/fractured rock in dam foundation and slope instability in the abutments. The tunneling difficulties are many such as squeezing ground condition, popping, rock bursting, high temperature gradient, heavy ingress of water, existence of shear seams/shear zones and emission of obnoxious gases. However, these problems were mitigated by adopting suitable remedial measures as per site requirement. The support system includes shotcrete, wire mesh, rock bolts, steel ribs, fore-poling, pre-grouting, pipe-roofing, MAI anchors, toe wall, retaining walls, reinforced concrete dowels, drainage drifts, anchorage cum drainage shafts, soil nails, concrete cladding and shear keys. Controlled drilling & blasting, heading & benching, proper drainage network and ventilation system are other remedial measures adopted to overcome such adverse situations. The paper highlights the geological uncertainties and its remedial measures in Himalaya, based on the analysis and evaluation of 20 hydroelectric projects during construction.

Keywords: geological problems, shear seams, slope, drilling & blasting, shear zones

Procedia PDF Downloads 402

Authors: M. Abou-Amouna, A. Radwan, L. Al-kuwari, A. Hammuda, K. Al-Khalifa

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There is growing concern over increasing incidences of road accidents and consequent loss of human life in Qatar. In light to the future planned event in Qatar, World Cup 2022; Qatar should put into consideration the future deaths caused by road accidents, and past trends should be considered to give a reasonable picture of what may happen in the future. Qatar roads should be arranged and paved in a way that accommodate high capacity of the population in that time, since then there will be a huge number of visitors from the world. Qatar should also consider the risk issues of road accidents raised in that period, and plan to maintain high level to safety strategies. According to the increase in the number of road accidents in Qatar from 1995 until 2012, an analysis of elements affecting and causing road accidents will be effectively studied. This paper aims to identify and criticize the factors that have high effect on causing road accidents in the state of Qatar, and predict the total number of road accidents in Qatar 2022. Alternative methods are discussed and the most applicable ones according to the previous researches are selected for further studies. The methods that satisfy the existing case in Qatar were the multiple linear regression model (MLR) and artificial neutral network (ANN). Those methods are analyzed and their findings are compared. We conclude that by using MLR the number of accidents in 2022 will become 355,226 accidents, and by using ANN 216,264 accidents. We conclude that MLR gave better results than ANN because the artificial neutral network doesn’t fit data with large range varieties.

Keywords: road safety, prediction, accident, model, Qatar

Procedia PDF Downloads 258

Authors: Artion Kashuri, Rozana Liko

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In this paper, some generalization integral inequalities of Hermite–Hadamard type for functions whose derivatives are s–convex in modulus are given by using k–fractional integrals. Some applications to special means are obtained as well. Some known versions are recovered as special cases from our results. We note that our inequalities can be viewed as new refinements of the previous results. Finally, our results have a deep connection with various fractional integral operators and interested readers can find new interesting results using our idea and technique as well.

Keywords: Hermite-Hadamard's inequalities, Hölder's inequality, k-Riemann-Liouville fractional integral, special means

Procedia PDF Downloads 128

Authors: Mahmoud Ahmed Farrag, Ali Zain Elabdeen Heikal, Mohamed Shawky Ahmed, Ahmed Osama Amer

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Crowd-sourced data refers to data that is collected and shared by a large number of individuals or organizations, often through the use of digital technologies such as mobile devices and social media. The shortage in crash data collection in developing countries makes it difficult to fully understand and address road safety issues in these regions. In developing countries, crowd-sourced data can be a valuable tool for improving road safety, particularly in urban areas where the majority of road crashes occur. This study is -to our best knowledge- the first to develop safety performance functions using crowd-sourced data by adopting a negative binomial structure model and the Full Bayes model to investigate traffic safety for urban road networks and provide insights into the impact of roadway characteristics. Furthermore, as a part of the safety management process, network screening has been undergone through applying two different methods to rank the most hazardous road segments: PCR method (adopted in the Highway Capacity Manual HCM) as well as a graphical method using GIS tools to compare and validate. Lastly, recommendations were suggested for policymakers to ensure safer roads.

Keywords: crowdsourced data, road crashes, safety performance functions, Full Bayes models, network screening

Procedia PDF Downloads 52

Authors: Shayla He

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Background and Purpose: According to Chamie (2017), it’s estimated that no less than 150 million people, or about 2 percent of the world’s population, are homeless. The homeless population in the United States has grown rapidly in the past four decades. In New York City, the sheltered homeless population has increased from 12,830 in 1983 to 62,679 in 2020. Knowing the trend on the homeless population is crucial at helping the states and the cities make affordable housing plans, and other community service plans ahead of time to better prepare for the situation. This study utilized the data from New York City, examined the key factors associated with the homelessness, and developed systematic modeling to predict homeless populations of the future. Using the best model developed, named HP-RNN, an analysis on the homeless population change during the months of 2020 and 2021, which were impacted by the COVID-19 pandemic, was conducted. Moreover, HP-RNN was tested on the data from Seattle. Methods: The methodology involves four phases in developing robust prediction methods. Phase 1 gathered and analyzed raw data of homeless population and demographic conditions from five urban centers. Phase 2 identified the key factors that contribute to the rate of homelessness. In Phase 3, three models were built using Linear Regression, Random Forest, and Recurrent Neural Network (RNN), respectively, to predict the future trend of society's homeless population. Each model was trained and tuned based on the dataset from New York City for its accuracy measured by Mean Squared Error (MSE). In Phase 4, the final phase, the best model from Phase 3 was evaluated using the data from Seattle that was not part of the model training and tuning process in Phase 3. Results: Compared to the Linear Regression based model used by HUD et al (2019), HP-RNN significantly improved the prediction metrics of Coefficient of Determination (R2) from -11.73 to 0.88 and MSE by 99%. HP-RNN was then validated on the data from Seattle, WA, which showed a peak %error of 14.5% between the actual and the predicted count. Finally, the modeling results were collected to predict the trend during the COVID-19 pandemic. It shows a good correlation between the actual and the predicted homeless population, with the peak %error less than 8.6%. Conclusions and Implications: This work is the first work to apply RNN to model the time series of the homeless related data. The Model shows a close correlation between the actual and the predicted homeless population. There are two major implications of this result. First, the model can be used to predict the homeless population for the next several years, and the prediction can help the states and the cities plan ahead on affordable housing allocation and other community service to better prepare for the future. Moreover, this prediction can serve as a reference to policy makers and legislators as they seek to make changes that may impact the factors closely associated with the future homeless population trend.

Keywords: homeless, prediction, model, RNN

Procedia PDF Downloads 121

Authors: Lee Yan Nian

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Safety investigation is different from an administrative investigation in that the former is conducted by an independent agency and the purpose of such investigation is to prevent accidents in the future and not to apportion blame or determine liability. Before October 2018, Taiwan railway occurrences were investigated by local supervisory authority. Characteristics of this kind of investigation are that enforcement actions, such as administrative penalty, are usually imposed on those persons or units involved in occurrence. On October 21, 2018, due to a Taiwan Railway accident, which caused 18 fatalities and injured another 267, establishing an agency to independently investigate this catastrophic railway accident was quickly decided. The Taiwan Transportation Safety Board (TTSB) was then established on August 1, 2019 to take charge of investigating major aviation, marine, railway and highway occurrences. The objective of this study is to assess the effectiveness of safety investigations conducted by the TTSB. In this study, the major railway occurrence investigation reports published by the TTSB are used for modeling and analysis. According to the classification of railway occurrences investigated by the TTSB, accident types of Taiwan railway occurrences can be categorized into: derailment, fire, Signal Passed at Danger and others. A Causal Factor Analysis System (CFAS) developed by the TTSB is used to identify the influencing causal factors and their causal relationships in the investigation reports. All terminologies used in the CFAS are equivalent to the Human Factors Analysis and Classification System (HFACS) terminologies, except for “Technical Events” which was added to classify causal factors resulting from mechanical failure. Accordingly, the Bayesian network structure of each occurrence category is established based on the identified causal factors in the CFAS. In the Bayesian networks, the prior probabilities of identified causal factors are obtained from the number of times in the investigation reports. Conditional Probability Table of each parent node is determined from domain experts’ experience and judgement. The resulting networks are quantitatively assessed under different scenarios to evaluate their forward predictions and backward diagnostic capabilities. Finally, the established Bayesian network of derailment is assessed using investigation reports of the same accident which was investigated by the TTSB and the local supervisory authority respectively. Based on the assessment results, findings of the administrative investigation is more closely tied to errors of front line personnel than to organizational related factors. Safety investigation can identify not only unsafe acts of individual but also in-depth causal factors of organizational influences. The results show that the proposed methodology can identify differences between safety investigation and administrative investigation. Therefore, effective intervention strategies in associated areas can be better addressed for safety improvement and future accident prevention through safety investigation.

Keywords: administrative investigation, bayesian network, causal factor analysis system, safety investigation

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Authors: Harvey Sanchez-Restrepo, Jorge Louca

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Equity and education are major focus of government policies around the world due to its relevance for addressing the sustainable development goals launched by Unesco. In this research, we developed a primary analysis of a data set of more than one hundred educational and non-educational factors associated with learning, coming from a census-based large-scale assessment carried on in Ecuador for 1.038.328 students, their families, teachers, and school directors, throughout 2014-2018. Each participating student was assessed by a standardized computer-based test. Learning outcomes were calibrated through item response theory with two-parameters logistic model for getting raw scores that were re-scaled and synthetized by a learning index (LI). Our objective was to develop a network for modelling educational deprivation and analyze the structure of inequality gaps, as well as their relationship with socioeconomic status, school financing, and student's ethnicity. Results from the model show that 348 270 students did not develop the minimum skills (prevalence rate=0.215) and that Afro-Ecuadorian, Montuvios and Indigenous students exhibited the highest prevalence with 0.312, 0.278 and 0.226, respectively. Regarding the socioeconomic status of students (SES), modularity class shows clearly that the system is out of equilibrium: the first decile (the poorest) exhibits a prevalence rate of 0.386 while rate for decile ten (the richest) is 0.080, showing an intense negative relationship between learning and SES given by R= –0.58 (p < 0.001). Another interesting and unexpected result is the average-weighted degree (426.9) for both private and public schools attending Afro-Ecuadorian students, groups that got the highest PageRank (0.426) and pointing out that they suffer the highest educational deprivation due to discrimination, even belonging to the richest decile. The model also found the factors which explain deprivation through the highest PageRank and the greatest degree of connectivity for the first decile, they are: financial bonus for attending school, computer access, internet access, number of children, living with at least one parent, books access, read books, phone access, time for homework, teachers arriving late, paid work, positive expectations about schooling, and mother education. These results provide very accurate and clear knowledge about the variables affecting poorest students and the inequalities that it produces, from which it might be defined needs profiles, as well as actions on the factors in which it is possible to influence. Finally, these results confirm that network analysis is fundamental for educational policy, especially linking reliable microdata with social macro-parameters because it allows us to infer how gaps in educational achievements are driven by students’ context at the time of assigning resources.

Keywords: complex network, educational deprivation, evidence-based policy, large-scale assessments, policy informatics

Procedia PDF Downloads 124

Authors: Wei Wang, Yannick Liu, Lu Zhen, H. Wang

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Maritime transport plays an important role in global economic development but also inevitably faces increasing pressures from all sides, such as ship operating cost reduction and environmental protection. An ideal innovation to address these pressures is electric ships. The electric ship is in the early stage. Considering the special characteristics of electric ships, i.e., travel range limit, to guarantee the efficient operation of electric ships, the service network needs to be re-designed carefully. This research designs a cost-efficient and environmentally friendly service network for electric ships, including the location of charging stations, charging plan, route planning, ship scheduling, and ship deployment. The problem is formulated as a mixed-integer linear programming model with the objective of minimizing total cost comprised of charging cost, the construction cost of charging stations, and fixed cost of ships. A case study using data of the shipping network along the Yangtze River is conducted to evaluate the performance of the model. Two operating scenarios are used: an electric ship scenario where all the transportation tasks are fulfilled by electric ships and a conventional ship scenario where all the transportation tasks are fulfilled by fuel oil ships. Results unveil that the total cost of using electric ships is only 42.8% of using conventional ships. Using electric ships can reduce 80% SOx, 93.47% NOx, 89.47% PM, and 42.62% CO2, but will consume 2.78% more time to fulfill all the transportation tasks. Extensive sensitivity analyses are also conducted for key operating factors, including battery capacity, charging speed, volume capacity, and a service time limit of transportation task. Implications from the results are as follows: 1) it is necessary to equip the ship with a large capacity battery when the number of charging stations is low; 2) battery capacity will influence the number of ships deployed on each route; 3) increasing battery capacity will make the electric ship more cost-effective; 4) charging speed does not affect charging amount and location of charging station, but will influence the schedule of ships on each route; 5) there exists an optimal volume capacity, at which all costs and total delivery time are lowest; 6) service time limit will influence ship schedule and ship cost.

Keywords: cost reduction, electric ship, environmental protection, sustainable shipping

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Authors: Tugbanur Ozen Balaban, Gultekin Tarcan, Unsal Gemici, Mumtaz Colak, I. Hakki Karamanderesi

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Buyuk Menderes Graben is located in the Western Anatolia (Turkey). The graben has become the largest industrial and agricultural area with a total population exceeding 3.000.000. There are two big cities within the study areas from west to east as Aydın and Denizli. The study area is very rich with regard to cold ground waters and thermal waters. Electrical production using geothermal potential has become very popular in the last decades in this area. Buyuk Menderes Graben is a tectonically active extensional region and is undergoing a north–south extensional tectonic regime which commenced at the latest during Early Middle Miocene period. The basement of the study area consists of Menderes massif rocks that are made up of high-to low-grade metamorphics and they are aquifer for both cold ground waters and thermal waters depending on the location. Neogene terrestrial sediments, which are mainly composed by alluvium fan deposits unconformably cover the basement rocks in different facies have very low permeability and locally may act as cap rocks for the geothermal systems. The youngest unit is Quaternary alluvium which is the shallow regional aquifer consists of Holocene alluvial deposits in the study area. All the waters are of meteoric origin and reflect shallow or deep circulation according to the 8O, 2H and 3H contents. Meteoric waters move to deep zones by fractured system and rise to the surface along the faults. Water samples (drilling well, spring and surface waters) and local seawater were collected between 2010 and 2012 years. Geochemical modeling was calculated distribution of the aqueous species and exchange processes by using PHREEQCi speciation code. Geochemical analyses show that cold ground water types are evolving from Ca–Mg–HCO3 to Na–Cl–SO4 and geothermal aquifer waters reflect the water types of Na-Cl-HCO3 in Aydın. Water types of Denizli are Ca-Mg-HCO3 and Ca-Mg-HCO3-SO4. Thermal water types reflect generally Na-HCO3-SO4. The B versus Cl rates increase from east to west with the proportion of seawater introduced into the fresh water aquifers and geothermal reservoirs. Concentrations of some elements (As, B, Fe and Ni) are higher than the tolerance limit of the drinking water standard of Turkey (TS 266) and international drinking water standards (WHO, FAO etc).

Keywords: Buyuk Menderes, isotope chemistry, geochemical modelling, water quality

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Authors: Qihang Jiang

Abstract:

Fansubbing, the combination of fan and subtitling, is one of the main branches of Audiovisual Translation (AVT) having kindled more and more interest of researchers into the AVT field in recent decades. In particular, the quality of so-called non-professional translation seems questionable due to the non-transparent qualification of subtitlers in a huge community network. This paper attempts to figure out how YYeTs aka 'ZiMuZu', the largest fansubbing group in China, translates swearwords from English to Chinese for its fans of the prevalent American sitcom The Big Bang Theory, taking cultural, social and political elements into account in the context of China. By building a bilingual corpus containing both the source and target texts, this paper found that most of the original swearwords were translated in a toned-down manner, probably due to Chinese audiences’ cultural and social network features as well as the strict censorship under the Chinese government. Additionally, House (2015)’s newly revised model of Translation Quality Assessment (TQA) was applied and examined. Results revealed that most of the subtitled swearwords achieved their pragmatic functions and exerted a communicative effect for audiences. In conclusion, this paper enriches the empirical research concerning House’s new TQA model, gives a full picture of the subtitling of swearwords in AVT field and provides a practical guide for the practitioners in their career of subtitling.

Keywords: corpus-based approach, fansubbing, pragmatic functions, swearwords, translation quality assessment

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Authors: Panupong Makvichian

Abstract:

Global Navigation Satellite System (GNSS) is nowadays a common technology that improves navigation functions in our life. Additionally, GNSS is also being employed on behalf of an accurate atmospheric sensor these times. Meteorology is a practical application of GNSS, which is unnoticeable in the background of people’s life. GNSS Precise Point Positioning (PPP) is a positioning method that requires data from a single dual-frequency receiver and precise information about satellite positions and satellite clocks. In addition, careful attention to mitigate various error sources is required. All the above data are combined in a sophisticated mathematical algorithm. At this point, the research is going to demonstrate how GNSS and PPP method is capable to provide high-precision estimates, such as 3D positions or Zenith tropospheric delays (ZTDs). ZTDs combined with pressure and temperature information allows us to estimate the water vapor in the atmosphere as precipitable water vapor (PWV). If the process is replicated for a network of GNSS sensors, we can create thematic maps that allow extract water content information in any location within the network area. All of the above are possible thanks to the advances in GNSS data processing. Therefore, we are able to use GNSS data for climatic trend analysis and acquisition of the further knowledge about the atmospheric water content.

Keywords: GNSS, precise point positioning, Zenith tropospheric delays, precipitable water vapor

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Authors: Pratibha Srivastava, Chithra V. J., Sudhakar S., Nitin K. D.

Abstract:

A closed-loop system comprises of a controller, a response system, and an actuating system. The controller, which is the system under test for us, excites the actuators based on feedback from the sensors in a periodic manner. The sensors should provide the feedback to the System Under Test (SUT) within a deterministic time post excitation of the actuators. Any delay or miss in the generation of response or acquisition of excitation pulses may lead to control loop controller computation errors, which can be catastrophic in certain cases. Such systems categorised as hard real-time systems that need special strategies. The real-time operating systems available in the market may be the best solutions for such kind of simulations, but they pose limitations like the availability of the X Windows system, graphical interfaces, other user tools. In this paper, we present strategies that can be used on a general purpose operating system (Bare Linux Kernel) to achieve a deterministic deadline and hence have the added advantages of a GPOS with real-time features. Techniques shall be discussed how to make the time-critical application run with the highest priority in an uninterrupted manner, reduced network latency for distributed architecture, real-time data acquisition, data storage, and retrieval, user interactions, etc.

Keywords: real time data acquisition, real time kernel preemption, scheduling, network latency

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Authors: Tahar Alimi, Rahma Boujebane, Wiem Derouich, Lamia Hadrich Belguith

Abstract:

Machine translation task of low-resourced languages such as Arabic is a challenging task. Despite the appearance of sophisticated models based on the latest deep learning techniques, namely the transfer learning and transformers, all models prove incapable of carrying out an acceptable translation, which includes Arabic Dialects (AD), because they do not have official status. In this paper, we present a machine translation model designed to translate Arabic multidialectal content into Modern Standard Arabic (MSA), leveraging both new and existing parallel resources. The latter achieved the best results for both Levantine and Maghrebi dialects with a BLEU score of 64.99.

Keywords: Arabic translation, dialect translation, fine-tune, MSA translation, transformer, translation

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Authors: Yeong-Gyeong Choi, Kyoung-Seok Kim

Abstract:

This study, literature review research, intends to deal with the problem of conceptual ambiguity among research on emotional labor, and to look into the evolutionary trends and changing aspects of defining the concept of emotional labor. For this, it gropes for methods for reducing conceptual ambiguity. Further, it arranges the concept of emotional labor; and examines and reviews comparatively the currents of the existing studies and looks for the characteristics and correlations of their classification criteria. That is, this study intends to arrange systematically and examine theories on emotional labor suggested hitherto, and suggest a future direction of research on emotional labor on the basis thereof. In addition, it attempts to look for positive aspects of the results of emotional labor.

Keywords: emotion labor, dimensions of emotional labor, surface acting, deep acting

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Authors: C. Jameke

Abstract:

During the cognitivist era in cognitive psychology, a theory of internal rules and symbolic representations was posited as an account of human cognition. This type of cognitive architecture had its heyday during the 1970s and 80s, but it has now been largely abandoned in favour of subsymbolic architectures (e.g. connectionism), non-representational frameworks (e.g. dynamical systems theory), and statistical approaches such as Bayesian theory. In this presentation I describe this changing landscape of research, and comment on the increasing influence of neuroscience on cognitive psychology. I then briefly review a few recent developments in connectionism, and neurocomputation relevant to cognitive psychology, and critically discuss the assumption made by some researchers in these frameworks that higher-level aspects of human cognition are simply emergent properties of massively large distributed neural networks

Keywords: connectionism, emergentism, postocgnitivist, representations, subsymbolic archiitecture

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Authors: Fernando Ojedo, Alejandro Alvarez, Pedro Macizo

Abstract:

In the present study, we explored electrophysiological correlates of Arabic digits naming to determine semantic processing of numbers. Participants named Arabic digits grouped by category or intermixed with exemplars of other semantic categories while the N400 event-related potential was examined. Around 350-450 ms after the presentation of Arabic digits, brain waves were more positive in anterior regions and more negative in posterior regions when stimuli were grouped by category relative to the mixed condition. Contrary to what was found in other studies, electrophysiological results suggested that the production of numerals involved semantic mediation.

Keywords: Arabic digit naming, event-related potentials, semantic processing, number production

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Authors: Julio Vega

Abstract:

Expression recognition is a field of Artificial Intelligence whose main objectives are to recognize basic forms of affective expression that appear on people’s faces and contributing to behavioral studies. In this work, a ROS node has been developed that, based on Deep Learning techniques, is capable of detecting the facial expressions of the people that appear in the image. These algorithms were optimized so that they can be executed in real time on an embedded platform. The experiments were carried out in a PC with a USB camera and in a Raspberry Pi 4 with a PiCamera. The final results shows a plausible system, which is capable to work in real time even in an embedded platform.

Keywords: python, low-cost, raspberry pi, emotion detection, human-robot interaction, ROS node

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Authors: Charles Lee

Abstract:

The Principal Orthogonal Decomposition (POD) technique has been used as a model reduction tool for many applications in engineering and science. In principle, one begins with an ensemble of data, called snapshots, collected from an experiment or laboratory results. The beauty of the POD technique is that when applied, the entire data set can be represented by the smallest number of orthogonal basis elements. It is the such capability that allows us to reduce the complexity and dimensions of many physical applications. Mathematical formulations and numerical schemes for the POD method will be discussed along with applications in NASA’s Deep Space Large Antenna Arrays, Satellite Image Reconstruction, Cancer Detection with DNA Microarray Data, Maximizing Stock Return, and Medical Imaging.

Keywords: reduced-order methods, principal component analysis, cancer detection, image reconstruction, stock portfolios

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Authors: Elena Carcano, James Ball

Abstract:

This work details the strategies adopted by the Italian Water Utilities during the distribution of water in emergency conditions which glide from earthquakes and droughts to floods and fires. Several water bureaus located over the national territory have been interviewed, and the collected information has been used in a database of potential interventions to be taken. The work discusses the actions adopted by water utilities. These are generally prioritized in order to minimize the social, temporal, and economic burden that the damaged and nearby areas need to support. Actions are defined relying on the Bayesian Network Approach, which constitutes the hard core of any decision support system. The Bayesian Networks give answers to interventions to real and most likely risky cases. The added value of this research consists in supplying the National Bureau, namely Protezione Civile, in charge of managing havoc and catastrophic situations with a univocal plot outline so as to be able to handle actions uniformly at the expense of different local laws or contradictory customs which squander any recovery conditions, proper technical service, and economic aids. The paper is organized as follows: in section 1, the introduction is stated; section 2 provides a brief discussion of BNNs (Bayesian Networks), section 3 introduces the adopted methodology; and in the last sections, results are presented, and conclusions are drawn.

Keywords: hierarchical process, strategic plan, water emergency conditions, water supply

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Authors: Charbel Aoun, Loic Lagadec

Abstract:

A Sensor Network (SN) is considered as an operation of two phases: (1) the observation/measuring, which means the accumulation of the gathered data at each sensor node; (2) transferring the collected data to some processing center (e.g., Fusion Servers) within the SN. Therefore, an underwater sensor network can be defined as a sensor network deployed underwater that monitors underwater activity. The deployed sensors, such as Hydrophones, are responsible for registering underwater activity and transferring it to more advanced components. The process of data exchange between the aforementioned components perfectly defines the Marine Observatory (MO) concept which provides information on ocean state, phenomena and processes. The first step towards the implementation of this concept is defining the environmental constraints and the required tools and components (Marine Cables, Smart Sensors, Data Fusion Server, etc). The logical and physical components that are used in these observatories perform some critical functions such as the localization of underwater moving objects. These functions can be orchestrated with other services (e.g. military or civilian reaction). In this paper, we present an extension to our MO meta-model that is used to generate a design tool (ArchiMO). We propose new constraints to be taken into consideration at design time. We illustrate our proposal with an example from the MO domain. Additionally, we generate the corresponding simulation code using our self-developed domain-specific model compiler. On the one hand, this illustrates our approach in relying on Enterprise Architecture (EA) framework that respects: multiple views, perspectives of stakeholders, and domain specificity. On the other hand, it helps reducing both complexity and time spent in design activity, while preventing from design modeling errors during porting this activity in the MO domain. As conclusion, this work aims to demonstrate that we can improve the design activity of complex system based on the use of MDE technologies and a domain-specific modeling language with the associated tooling. The major improvement is to provide an early validation step via models and simulation approach to consolidate the system design.

Keywords: smart sensors, data fusion, distributed fusion architecture, sensor networks, domain specific modeling language, enterprise architecture, underwater moving object, localization, marine observatory, NS-3, IMS

Procedia PDF Downloads 177