Search results for: support vector machine

Authors: Panagiota Katsikouli, William Hamilton Byrne, Thomas Gammeltoft-Hansen, Tijs Slaats

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An individual who demonstrates a well-founded fear of persecution or faces real risk of being subjected to torture is eligible for asylum. In Danish law, the exact legal thresholds reflect those established by international conventions, notably the 1951 Refugee Convention and the 1950 European Convention for Human Rights. These international treaties, however, remain largely silent when it comes to how states should assess asylum claims. As a result, national authorities are typically left to determine an individual’s legal eligibility on a narrow basis consisting of an oral testimony, which may itself be hampered by several factors, including imprecise language interpretation, insecurity or lacking trust towards the authorities among applicants. The leaky ground, on which authorities must assess their subjective perceptions of asylum applicants' credibility, questions whether, in all cases, adjudicators make the correct decision. Moreover, the subjective element in these assessments raises questions on whether individual asylum cases could be afflicted by implicit biases or stereotyping amongst adjudicators. In fact, recent studies have uncovered significant correlations between decision outcomes and the experience and gender of the assigned judge, as well as correlations between asylum outcomes and entirely external events such as weather and political elections. In this study, we analyze a publicly available dataset containing approximately 8,000 summaries of asylum cases, initially rejected, and re-tried by the Refugee Appeals Board (RAB) in Denmark. First, we look for variations in the recognition rates, with regards to a number of applicants’ features: their country of origin/nationality, their identified gender, their identified religion, their ethnicity, whether torture was mentioned in their case and if so, whether it was supported or not, and the year the applicant entered Denmark. In order to extract those features from the text summaries, as well as the final decision of the RAB, we applied natural language processing and regular expressions, adjusting for the Danish language. We observed interesting variations in recognition rates related to the applicants’ country of origin, ethnicity, year of entry and the support or not of torture claims, whenever those were made in the case. The appearance (or not) of significant variations in the recognition rates, does not necessarily imply (or not) bias in the decision-making progress. None of the considered features, with the exception maybe of the torture claims, should be decisive factors for an asylum seeker’s fate. We therefore investigate whether the decision can be predicted on the basis of these features, and consequently, whether biases are likely to exist in the decisionmaking progress. We employed a number of machine learning classifiers, and found that when using the applicant’s country of origin, religion, ethnicity and year of entry with a random forest classifier, or a decision tree, the prediction accuracy is as high as 82% and 85% respectively. tentially predictive properties with regards to the outcome of an asylum case. Our analysis and findings call for further investigation on the predictability of the outcome, on a larger dataset of 17,000 cases, which is undergoing.

Keywords: asylum adjudications, automated decision-making, machine learning, text mining

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Authors: Simone Leonardi, Luca Ardito

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Micro-influencers are effective elements in the marketing strategies of companies and institutions because of their capability to create an hyper-engaged audience around a specific topic of interest. In recent years, many scientific approaches and commercial tools have handled the task of detecting this type of social media users. These strategies adopt solutions ranging from rule based machine learning models to deep neural networks and graph analysis on text, images, and account information. This work compares the existing solutions and proposes an ensemble method to generalize them with different input data and social media platforms. The deployed solution combines deep learning models on unstructured data with statistical machine learning models on structured data. We retrieve both social media accounts information and multimedia posts on Twitter and Instagram. These data are mapped into feature vectors for an eXtreme Gradient Boosting (XGBoost) classifier. Sixty different topics have been analyzed to build a rule based gold standard dataset and to compare the performances of our approach against baseline classifiers. We prove the effectiveness of our work by comparing the accuracy, precision, recall, and f1 score of our model with different configurations and architectures. We obtained an accuracy of 0.91 with our best performing model.

Keywords: deep learning, gradient boosting, image processing, micro-influencers, NLP, social media

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Authors: Deeman Yousif Mahmood, Mohammed Abdullah Hussein

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The goal of a network-based intrusion detection system is to classify activities of network traffics into two major categories: normal and attack (intrusive) activities. Nowadays, data mining and machine learning plays an important role in many sciences; including intrusion detection system (IDS) using both supervised and unsupervised techniques. However, one of the essential steps of data mining is feature selection that helps in improving the efficiency, performance and prediction rate of proposed approach. This paper applies unsupervised K-means clustering algorithm with information gain (IG) for feature selection and reduction to build a network intrusion detection system. For our experimental analysis, we have used the new NSL-KDD dataset, which is a modified dataset for KDDCup 1999 intrusion detection benchmark dataset. With a split of 60.0% for the training set and the remainder for the testing set, a 2 class classifications have been implemented (Normal, Attack). Weka framework which is a java based open source software consists of a collection of machine learning algorithms for data mining tasks has been used in the testing process. The experimental results show that the proposed approach is very accurate with low false positive rate and high true positive rate and it takes less learning time in comparison with using the full features of the dataset with the same algorithm.

Keywords: information gain (IG), intrusion detection system (IDS), k-means clustering, Weka

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Authors: Riaan Kleyn

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Mass wine production companies across the globe are provided with grapes from winegrowers that predominantly utilize mechanical harvesting machines to harvest wine grapes. Mechanical harvesting accelerates the rate at which grapes are harvested, allowing grapes to be delivered faster to meet the demands of wine cellars. The disadvantage of the mechanical harvesting method is the inclusion of material-other-than-grapes (MOG) in the harvested wine grape loads arriving at the cellar which degrades the quality of wine that can be produced. Currently, wine cellars do not have a method to determine the amount of MOG present within wine grape loads. This paper seeks to find an optimal computer vision method capable of detecting the amount of MOG within a wine grape load. A MOG detection method will encourage winegrowers to deliver MOG-free wine grape loads to avoid penalties which will indirectly enhance the quality of the wine to be produced. Traditional image segmentation methods were compared to deep learning segmentation methods based on images of wine grape loads that were captured at a wine cellar. The Mask R-CNN model with a ResNet-50 convolutional neural network backbone emerged as the optimal method for this study to determine the amount of MOG in an image of a wine grape load. Furthermore, a statistical analysis was conducted to determine how the MOG on the surface of a grape load relates to the mass of MOG within the corresponding grape load.

Keywords: computer vision, wine grapes, machine learning, machine harvested grapes

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Authors: David Krejci, Tony Schönherr, Quirin Koch, Valentin Hugonnaud, Lou Grimaud, Alexander Reissner, Bernhard Seifert

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In 2018 the NANO thruster became the first Field Emission Electric Propulsion (FEEP) system ever to be verified in space in an In-Orbit Demonstration mission conducted together with Fotec. Since then, 160 additional ENPULSION NANO propulsion systems have been deployed in orbit on 73 different spacecraft across multiple customers and missions. These missions included a variety of different satellite bus sizes ranging from 3U Cubesats to >100kg buses, and different orbits in Low Earth Orbit and Geostationary Earth orbit, providing an abundance of on orbit data for statistical analysis. This large-scale industrialization and flight heritage allows for a holistic way of gathering data from testing, integration and operational phases, deriving lessons learnt over a variety of different mission types, operator approaches, use cases and environments. Based on these lessons learnt a new generation of propulsion systems is developed, addressing key findings from the large NANO heritage and adding new capabilities, including increased resilience, thrust vector steering and increased power and thrust level. Some of these successor products have already been validated in orbit, including the MICRO R3 and the NANO AR3. While the MICRO R3 features increased power and thrust level, the NANO AR3 is a successor of the heritage NANO thruster with added thrust vectoring capability. 5 NANO AR3 have been launched to date on two different spacecraft. This work presents flight telemetry data of ENPULSION NANO systems and onorbit statistical data of the ENPULSION NANO as well as lessons learnt during onorbit operations, customer assembly, integration and testing support and ground test campaigns conducted at different facilities. We discuss how transfer of lessons learnt and operational improvement across independent missions across customers has been accomplished. Building on these learnings and exhaustive heritage, we present the design of the new generation of propulsion systems that increase the power and thrust level of FEEP systems to address larger spacecraft buses.

Keywords: FEEP, field emission electric propulsion, electric propulsion, flight heritage

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Authors: Xiaoqing Wang, Junfeng Wang, Xiaolan Zhu

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Android operating system has been recognized by most application developers because of its good open-source and compatibility, which enriches the categories of applications greatly. However, it has become the target of malware attackers due to the lack of strict security supervision mechanisms, which leads to the rapid growth of malware, thus bringing serious safety hazards to users. Therefore, it is critical to detect Android malware effectively. Generally, the permissions declared in the AndroidManifest.xml can reflect the function and behavior of the application to a large extent. Since current Android system has not any restrictions to the number of permissions that an application can request, developers tend to apply more than actually needed permissions in order to ensure the successful running of the application, which results in the abuse of permissions. However, some traditional detection methods only consider the requested permissions and ignore whether it is actually used, which leads to incorrect identification of some malwares. Therefore, a machine learning detection method based on the actually used permissions combination and API calls was put forward in this paper. Meanwhile, several experiments are conducted to evaluate our methodology. The result shows that it can detect unknown malware effectively with higher true positive rate and accuracy while maintaining a low false positive rate. Consequently, the AdaboostM1 (J48) classification algorithm based on information gain feature selection algorithm has the best detection result, which can achieve an accuracy of 99.8%, a true positive rate of 99.6% and a lowest false positive rate of 0.

Keywords: android, API Calls, machine learning, permissions combination

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Authors: Mohamed Raouf Benmakrelouf, Joseph Rynkiewicz

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Targeted maximum likelihood estimation (TMLE) is well-established method for causal effect estimation with desirable statistical properties. TMLE is a doubly robust maximum likelihood based approach that includes a secondary targeting step that optimizes the target statistical parameter. A causal interpretation of the statistical parameter requires assumptions of the Rubin causal framework. The causal effect of binary variable, E, on outcomes, Y, is defined in terms of comparisons between two potential outcomes as E[YE=1 − YE=0]. Our aim in this paper is to present an adaptation of TMLE methodology to estimate the causal effect of a non-binary categorical variable, providing a large application. We propose coding on the initial data in order to operate a binarization of the interest variable. For each category, we get a transformation of the non-binary interest variable into a binary variable, taking value 1 to indicate the presence of category (or group of categories) for an individual, 0 otherwise. Such a dummy variable makes it possible to have a pair of potential outcomes and oppose a category (or a group of categories) to another category (or a group of categories). Let E be a non-binary interest variable. We propose a complete disjunctive coding of our variable E. We transform the initial variable to obtain a set of binary vectors (dummy variables), E = (Ee : e ∈ {1, ..., |E|}), where each vector (variable), Ee, takes the value of 0 when its category is not present, and the value of 1 when its category is present, which allows to compute a pairwise-TMLE comparing difference in the outcome between one category and all remaining categories. In order to illustrate the application of our strategy, first, we present the implementation of TMLE to estimate the causal effect of non-binary variable on outcome using simulated data. Secondly, we apply our TMLE adaptation to survey data from the French Political Barometer (CEVIPOF), to estimate the causal effect of education level (A five-level variable) on a potential vote in favor of the French extreme right candidate Jean-Marie Le Pen. Counterfactual reasoning requires us to consider some causal questions (additional causal assumptions). Leading to different coding of E, as a set of binary vectors, E = (Ee : e ∈ {2, ..., |E|}), where each vector (variable), Ee, takes the value of 0 when the first category (reference category) is present, and the value of 1 when its category is present, which allows to apply a pairwise-TMLE comparing difference in the outcome between the first level (fixed) and each remaining level. We confirmed that the increase in the level of education decreases the voting rate for the extreme right party.

Keywords: statistical inference, causal inference, super learning, targeted maximum likelihood estimation

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Authors: Lucy W. Mburu, Richard Karanja, Simon N. Mwendia

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Over recent years, learners have experienced a constant need to access on demand knowledge that is fully aligned with the paradigm of cloud computing. As motivated by the global sustainable development goal to ensure inclusive and equitable learning opportunities, this research has developed a framework hinged on the knowledge-as-a-service architecture that utilizes knowledge from ambient learning systems. Through statistical analysis and decision tree modeling, the study discovers influential variables for ambient learning among university students. The main aim is to generate a platform for disseminating and exploiting the available knowledge to aid the learning process and, thus, to improve educational support on the ambient learning system. The research further explores how collaborative effort can be used to form a knowledge network that allows access to heterogeneous sources of knowledge, which benefits knowledge consumers, such as the developers of ambient learning systems.

Keywords: actionable knowledge, ambient learning, cloud computing, decision trees, knowledge as a service

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Authors: C. B. Le, V. N. Pham

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In modern data analysis, multi-source data appears more and more in real applications. Multi-source data clustering has emerged as a important issue in the data mining and machine learning community. Different data sources provide information about different data. Therefore, multi-source data linking is essential to improve clustering performance. However, in practice multi-source data is often heterogeneous, uncertain, and large. This issue is considered a major challenge from multi-source data. Ensemble is a versatile machine learning model in which learning techniques can work in parallel, with big data. Clustering ensemble has been shown to outperform any standard clustering algorithm in terms of accuracy and robustness. However, most of the traditional clustering ensemble approaches are based on single-objective function and single-source data. This paper proposes a new clustering ensemble method for multi-source data analysis. The fuzzy optimized multi-objective clustering ensemble method is called FOMOCE. Firstly, a clustering ensemble mathematical model based on the structure of multi-objective clustering function, multi-source data, and dark knowledge is introduced. Then, rules for extracting dark knowledge from the input data, clustering algorithms, and base clusterings are designed and applied. Finally, a clustering ensemble algorithm is proposed for multi-source data analysis. The experiments were performed on the standard sample data set. The experimental results demonstrate the superior performance of the FOMOCE method compared to the existing clustering ensemble methods and multi-source clustering methods.

Keywords: clustering ensemble, multi-source, multi-objective, fuzzy clustering

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Authors: Toufic Sarieddine

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The current debate on the hegemonic impact of China’s Belt and Road Initiative (BRI) is of two opposing strands: Resilient and absolute US hegemony on the one hand and various models of multipolar hegemony such as bifurcation on the other. Bifurcation theories illustrate an unprecedented division of hegemonic functions between China and the US, whereby Beijing becomes the world’s economic hegemon, leaving Washington the world’s military hegemon and security guarantor. While consensus points to China being the main driver of unipolarity’s rupturing, the debate among bifurcationists is on the location of the first rupture. In this regard, the Middle East and North Africa (MENA) region has seen increasing Chinese foreign direct investment in recent years while that to other regions has declined, ranking it second in 2018 as part of the financing for the Maritime Silk Road Initiative (MSRI). China has also become the top trade partner of 11 states in the MENA region, as well as its top source of machine imports, surpassing the US and achieving an overall trade surplus almost double that of Washington’s. These are among other features outlined in world-systems analysis (WSA) literature which correspond with the emergence of a new hegemon. WSA is further utilized to gauge other facets of China’s increasing involvement in MENA and assess whether bifurcation is unfolding therein. These features of hegemony include the adoption of China’s modi operandi, economic dominance in production, trade, and finance, military capacity, cultural hegemony in ideology, education, and language, and the promotion of a general interest around which to rally potential peripheries (MENA states in this case). China’s modi operandi has seen some adoption with regards to support against the United Nations Convention on the Law of the Sea, oil bonds denominated in the yuan, and financial institutions such as the Shanghai Gold Exchange enjoying increasing Arab patronage. However, recent elections in Qatar, as well as liberal reforms in Saudi Arabia, demonstrate Washington’s stronger normative influence. Meanwhile, Washington’s economic dominance is challenged by China’s sizable machine exports, increasing overall imports, and widening trade surplus, but retains some clout via dominant arms and transport exports, as well as free-trade deals across the region. Militarily, Washington bests Beijing’s arms exports, has a dominant and well-established presence in the region, and successfully blocked Beijing’s attempt to penetrate through the UAE. Culturally, Beijing enjoys higher favorability in Arab public opinion, and its broadcast networks have found some resonance with Arab audiences. In education, the West remains MENA students’ preferred destination. Further, while Mandarin has become increasingly available in schools across MENA, its usage and availability still lag far behind English. Finally, Beijing’s general interest in infrastructure provision and prioritizing economic development over social justice and democracy provides an avenue for increased incorporation between Beijing and the MENA region. The overall analysis shows solid progress towards bifurcation in MENA.

Keywords: belt and road initiative, hegemony, Middle East and North Africa, world-systems analysis

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Authors: Kemal Izci, Ahmet Oguz Akturk

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Learning is dependent on cognitive and physical differences as well as other differences such as ethnicity, language, and culture. Furthermore, these differences also influence how students show their learning. Assessment is an integral part of learning and teaching process and is essential for effective instruction. In order to provide effective instruction, teachers need to provide equal assessment opportunities for all students to see their learning difficulties and use them to modify instruction to aid learning. Successful assessment practices are dependent upon the knowledge and value of teachers. Therefore, in order to use assessment to assess and support diverse students learning, preservice and inservice teachers should hold an appropriate understanding of equitable assessment. In order to prepare teachers to help them support diverse student learning, as a first step, this study aims to explore how preservice teachers’ understand equitable assessment. 105 preservice science teachers studying at teacher preparation program in a large university located at Eastern part of Turkey participated in the current study. A questionnaire, preservice teachers’ reflection papers and interviews served as data sources for this study. All collected data qualitatively analyzed to develop themes that illustrate preservice science teachers’ understanding of equitable assessment. Results of the study showed that preservice teachers mostly emphasized fairness including fairness in grading and fairness in asking questions not out of covered concepts for equitable assessment. However, most of preservice teachers do not show an understanding of equity for providing equal opportunities for all students to display their understanding of related content. For some preservice teachers providing different opportunities (providing extra time for non-native speaking students) for some students seems to be unfair for other students and therefore, these kinds of refinements do not need to be used. The results of the study illustrated that preservice science teachers mostly understand equitable assessment as fairness and less highlight the role of using equitable assessment to support all student learning, which is more important in order to improve students’ achievement of science. Therefore, we recommend that more opportunities should be provided for preservice teachers engage in a more broad understanding of equitable assessment and learn how to use equitable assessment practices to aid and support all students learning trough classroom assessment.

Keywords: science teaching, equitable assessment, assessment literacy, preservice science teachers

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Authors: H. E. Keshta, A. A. Ali

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Three phase grid-tied inverters are widely employed in micro-grids (MGs) as interphase between DC and AC systems. These inverters are usually controlled through standard decoupled d–q vector control strategy based on proportional integral (PI) controllers. Recently, advanced meta-heuristic optimization techniques have been used instead of deterministic methods to obtain optimum PI controller parameters. This paper provides a comparative study between the performance of the global Porcellio Scaber algorithm (GPSA) based PI controller and Manta Ray foraging optimization (MRFO) based PI controller.

Keywords: micro-grids, optimization techniques, grid-tied inverter control, PI controller

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Authors: Charlotte-Rose Kennedy

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In a political climate divided by Brexit, it is crucial to be critical of the press, as it is the apparatus which political authorities use to impose their laws and shape public opinion. Although large protests have the power to shake and disrupt policy-making by making it difficult for governments to ignore their goals, the British press historically constructs protesters as delegitimate, deviant, and criminal, which could limit protests’ credibility and democratic power. This paper explores how the remain supporting daily UK press (The Mirror, Financial Times, The Independent, The Guardian) and the leave supporting daily UK press (The Daily Mail, The Daily Star, The Sun, The Express, The Telegraph) discursively constructed every pro- and anti-Brexit demonstration from 2016 to 2019. 702 instances of the terms ‘protester’, ‘protesters’, ‘protestor’ and ‘protestors’ were analyzed through both transitivity analysis and critical discourse analysis. This mixed-methods approach allowed for the analysis of how the UK press perpetuated and upheld social ideologies about protests through their specific grammatical and language choices. The results of this analysis found that both remain and leave supporting press utilized the same discourses to report on protests they oppose and protests they support. For example, the remain backing The Mirror used water metaphors regularly associated with influxes of refugees and asylum seekers to support the protesters on the remain protest ‘Final Say’, and oppose the protesters on the leave protest ‘March to Leave’. Discourses of war, violence, and victimhood are also taken on by both sides of the press Brexit debate and are again used to support and oppose the same arguments. Finally, the paper concludes that these analogous discourses do nothing to help the already marginalized social positions of protesters in the UK and could potentially lead to reduced public support for demonstrations. This could, in turn, facilitate the government in introducing increasingly restrictive legislation in relation to freedom of assembly rights, which could be detrimental to British democracy.

Keywords: Brexit, critical discourse analysis, protests, transitivity analysis, UK press

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Authors: Dylan Santos De Pinho, Nabil Ouerhani

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Energy consumption of machine-tools is becoming critical for machine-tool builders and end-users because of economic, ecological and legislation-related reasons. Many machine-tool builders are seeking for solutions that allow the reduction of energy consumption of machine-tools while preserving the same productivity rate and the same quality of machined parts. In this paper, we present the first results of a project conducted jointly by academic and industrial partners to reduce the energy consumption of a Swiss-Type lathe. We employ genetic algorithms to find optimal machining parameters – the set of parameters that lead to the best trade-off between energy consumption, part quality and tool lifetime. Three main machining process parameters are considered in our optimization technique, namely depth of cut, spindle rotation speed and material feed rate. These machining process parameters have been identified as the most influential ones in the configuration of the Swiss-type machining process. A state-of-the-art multi-objective genetic algorithm has been used. The algorithm combines three fitness functions, which are objective functions that permit to evaluate a set of parameters against the three objectives: energy consumption, quality of the machined parts, and tool lifetime. In this paper, we focus on the investigation of the fitness function related to energy consumption. Four different energy consumption related fitness functions have been investigated and compared. The first fitness function refers to the Kienzle cutting force model. The second fitness function uses the Material Removal Rate (RMM) as an indicator of energy consumption. The two other fitness functions are non-deterministic, learning-based functions. One fitness function uses a simple Neural Network to learn the relation between the process parameters and the energy consumption from experimental data. Another fitness function uses Lasso regression to determine the same relation. The goal is, then, to find out which fitness functions predict best the energy consumption of a Swiss-Type machining process for the given set of machining process parameters. Once determined, these functions may be used for optimization purposes – determine the optimal machining process parameters leading to minimum energy consumption. The performance of the four fitness functions has been evaluated. The Tornos DT13 Swiss-Type Lathe has been used to carry out the experiments. A mechanical part including various Swiss-Type machining operations has been selected for the experiments. The evaluation process starts with generating a set of CNC (Computer Numerical Control) programs for machining the part at hand. Each CNC program considers a different set of machining process parameters. During the machining process, the power consumption of the spindle is measured. All collected data are assigned to the appropriate CNC program and thus to the set of machining process parameters. The evaluation approach consists in calculating the correlation between the normalized measured power consumption and the normalized power consumption prediction for each of the four fitness functions. The evaluation shows that the Lasso and Neural Network fitness functions have the highest correlation coefficient with 97%. The fitness function “Material Removal Rate” (MRR) has a correlation coefficient of 90%, whereas the Kienzle-based fitness function has a correlation coefficient of 80%.

Keywords: adaptive machining, genetic algorithms, smart manufacturing, parameters optimization

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Authors: Brandon Foggo, Nanpeng Yu

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Power distribution circuits undergo frequent network topology changes that are often left undocumented. As a result, the documentation of a circuit’s connectivity becomes inaccurate with time. The lack of reliable circuit connectivity information is one of the biggest obstacles to model, monitor, and control modern distribution systems. To enhance the reliability and efficiency of electric power distribution systems, the circuit’s connectivity information must be updated periodically. This paper focuses on one critical component of a distribution circuit’s topology - the secondary transformer to phase association. This topology component describes the set of phase lines that feed power to a given secondary transformer (and therefore a given group of power consumers). Finding the documentation of this component is call Phase Identification, and is typically performed with physical measurements. These measurements can take time lengths on the order of several months, but with supervised learning, the time length can be reduced significantly. This paper compares several such methods applied to Phase Identification for a large range of real distribution circuits, describes a method of training data selection, describes preprocessing steps unique to the Phase Identification problem, and ultimately describes a method which obtains high accuracy (> 96% in most cases, > 92% in the worst case) using only 5% of the measurements typically used for Phase Identification.

Keywords: distribution network, machine learning, network topology, phase identification, smart grid

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Authors: Walid Wahballah, Taher Bazan, Fawzy Eltohamy

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One of the important functions of the satellite attitude control system is to provide the required pointing accuracy and attitude stability for optical remote sensing satellites to achieve good image quality. Although offering noise reduction and increased sensitivity, time delay and integration (TDI) charge coupled devices (CCDs) utilized in high-resolution satellites (HRS) are prone to introduce large amounts of pixel smear due to the instability of the line of sight. During on-orbit imaging, as a result of the Earth’s rotation and the satellite platform instability, the moving direction of the TDI-CCD linear array and the imaging direction of the camera become different. The speed of the image moving on the image plane (focal plane) represents the image motion velocity whereas the angle between the two directions is known as the drift angle (β). The drift angle occurs due to the rotation of the earth around its axis during satellite imaging; affecting the geometric accuracy and, consequently, causing image quality degradation. Therefore, the image motion velocity vector and the drift angle are two important factors used in the assessment of the image quality of TDI-CCD based optical remote sensing satellites. A model for estimating the image motion velocity and the drift angle in HRS is derived. The six satellite attitude control parameters represented in the derived model are the (roll angle φ, pitch angle θ, yaw angle ψ, roll angular velocity φ֗, pitch angular velocity θ֗ and yaw angular velocity ψ֗ ). The influence of these attitude parameters on the image quality is analyzed by establishing a relationship between the image motion velocity vector, drift angle and the six satellite attitude parameters. The influence of the satellite attitude parameters on the image quality is assessed by the presented model in terms of modulation transfer function (MTF) in both cross- and along-track directions. Three different cases representing the effect of pointing accuracy (φ, θ, ψ) bias are considered using four different sets of pointing accuracy typical values, while the satellite attitude stability parameters are ideal. In the same manner, the influence of satellite attitude stability (φ֗, θ֗, ψ֗) on image quality is also analysed for ideal pointing accuracy parameters. The results reveal that cross-track image quality is influenced seriously by the yaw angle bias and the roll angular velocity bias, while along-track image quality is influenced only by the pitch angular velocity bias.

Keywords: high-resolution satellites, pointing accuracy, attitude stability, TDI-CCD, smear, MTF

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Authors: Wang Shuai, Su Rong, K. J.Tseng, V. Viswanathan, S. Ramakrishna

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The More-Electric-Aircraft concept in aircraft industry levies an increasing demand on the embedded starter/generators (ESG). The high-speed and high-temperature environment within an engine poses great challenges to the operation of such machines. In view of such challenges, squirrel cage induction machines (SCIM) have shown advantages due to its simple rotor structure, absence of temperature-sensitive components as well as low torque ripples etc. The tight operation constraints arising from typical ESG applications together with the detailed operation principles of SCIMs have been exploited to derive the mathematical interpretation of the ESG-SCIM design process. The resultant non-linear mathematical treatment yielded unique solution to the SCIM design problem for each configuration of pole pair number p, slots/pole/phase q and conductors/slot zq, easily implemented via loop patterns. It was also found that not all configurations led to feasible solutions and corresponding observations have been elaborated. The developed mathematical procedures also proved an effective framework for optimization among electromagnetic, thermal and mechanical aspects by allocating corresponding degree-of-freedom variables. Detailed 3D FEM analysis has been conducted to validate the resultant machine performance against design specifications. To obtain higher power ratings, electrical machines often have to increase the slot areas for accommodating more windings. Since the available space for embedding such machines inside an engine is usually short in length, axial air gap arrangement appears more appealing compared to its radial gap counterpart. The aforementioned approach has been adopted in case studies of designing series of AFIMs and RFIMs respectively with increasing power ratings. Following observations have been obtained. Under the strict rotor diameter limitation AFIM extended axially for the increased slot areas while RFIM expanded radially with the same axial length. Beyond certain power ratings AFIM led to long cylinder geometry while RFIM topology resulted in the desired short disk shape. Besides the different dimension growth patterns, AFIMs and RFIMs also exhibited dissimilar performance degradations regarding power factor, torque ripples as well as rated slip along with increased power ratings. Parametric response curves were plotted to better illustrate the above influences from increased power ratings. The case studies may provide a basic guideline that could assist potential users in making decisions between AFIM and RFIM for relevant applications.

Keywords: axial flux induction machine, electrical starter/generator, finite element analysis, squirrel cage induction machine

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Authors: Tania Viju, Bimal P., Naseer M. A.

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This study aims to develop a conceptual framework for the decision support system (DSS), that helps the decision-makers to dynamically choose appropriate safety measures for each identified accident blackspot. An accident blackspot is a segment of road where the frequency of accident occurrence is disproportionately greater than other sections on roadways. According to a report by the World Bank, India accounts for the highest, that is, eleven percent of the global death in road accidents with just one percent of the world’s vehicles. Hence in 2015, the Ministry of Road Transport and Highways of India gave prime importance to the rectification of accident blackspots. To enhance road traffic safety and reduce the traffic accident rate, effectively identifying and rectifying accident blackspots is of great importance. This study helps to understand and evaluate the existing methods in accident blackspot identification and prediction that are used around the world and their application in Indian roadways. The decision support system, with the help of IoT, ICT and smart systems, acts as a management and planning tool for the government for employing efficient and cost-effective rectification strategies. In order to develop a decision criterion, several factors in terms of quantitative as well as qualitative data that influence the safety conditions of the road are analyzed. Factors include past accident severity data, occurrence time, light, weather and road conditions, visibility, driver conditions, junction type, land use, road markings and signs, road geometry, etc. The framework conceptualizes decision-making by classifying blackspot stretches based on factors like accident occurrence time, different climatic and road conditions and suggesting mitigation measures based on these identified factors. The decision support system will help the public administration dynamically manage and plan the necessary safety interventions required to enhance the safety of the road network.

Keywords: decision support system, dynamic management, road accident blackspots, road safety

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Authors: Christian Luarca

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The study aims to define the efficiency of e-Trainings by the use of cloud platform as part of the onboarding process for IT support engineers. Traditional lecture based trainings involves human resource to guide and assist new hires as part of onboarding which takes time and effort. The use of electronic medium as a platform for training provides a two-way basic communication that can be done in a repetitive manner. The study focuses on determining the most efficient manner of learning the basic knowledge on IT support in the shortest time possible. This was determined by conducting the same set of knowledge transfer categories in two different approaches, one being the e-Training and the other using the traditional method. Performance assessment will be done by the use of Service Tracker Assessment (STA) Tool and Service Manager. Data gathered from this ongoing study will promote the utilization of e-Trainings in the IT onboarding process.

Keywords: cloud platform, e-Training, efficiency, onboarding

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Authors: Pınar Yardımcı

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This paper tries to answer to the questions whether or not trade openness cause economic growth and trade policy changes is good for Turkey as a developing country in global economy before and after 1980. We employ Johansen cointegration and Granger causality tests with error correction modelling based on vector autoregressive. Using WDI data from the pre-1980 and the post-1980, we find that trade openness and economic growth are cointegrated in the second term only. Also the results suggest a lack of long-run causality between our two variables. These findings may imply that trade policy of Turkey should concentrate more on extra complementary economic reforms.

Keywords: globalization, trade policy, economic growth, openness, cointegration, Turkey

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Authors: Shreyansh Singh, Afroz Ahmed

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Sentiment Analysis intends to naturally reveal the hidden mentality that we hold towards an entity. The total of this assumption over a populace addresses sentiment surveying and has various applications. Current text-based sentiment analysis depends on the development of word embeddings and Machine Learning models that take in conclusion from enormous text corpora. Sentiment Analysis from text is presently generally utilized for consumer loyalty appraisal and brand insight investigation. With the expansion of online media, multimodal assessment investigation is set to carry new freedoms with the appearance of integral information streams for improving and going past text-based feeling examination using the new transforms methods. Since supposition can be distinguished through compelling follows it leaves, like facial and vocal presentations, multimodal opinion investigation offers good roads for examining facial and vocal articulations notwithstanding the record or printed content. These methodologies use the Recurrent Neural Networks (RNNs) with the LSTM modes to increase their performance. In this study, we characterize feeling and the issue of multimodal assessment investigation and audit ongoing advancements in multimodal notion examination in various spaces, including spoken surveys, pictures, video websites, human-machine, and human-human connections. Difficulties and chances of this arising field are additionally examined, promoting our theory that multimodal feeling investigation holds critical undiscovered potential.

Keywords: sentiment analysis, RNN, LSTM, word embeddings

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Authors: Wided Batita, Stéphane Roche, Claude Caron

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Geodesign is an emergent term related to a new and complex process. Hence, it needs to rethink tools, technologies and platforms in order to efficiently achieve its goals. A few tools have emerged since 2010 such as CommunityViz, GeoPlanner, etc. In the era of Web 2.0 and collaboration, WikiGIS has been proposed as a new category of tools. In this paper, we present WikiGIS functionalities dealing mainly with the iteration and traceability management to support the collaboration of the Geodesign process. Actually, WikiGIS is built on GeoWeb 2.0 technologies —and primarily on wiki— and aims at managing the tracking of participants’ editing. This paper focuses on a simplified simulation to illustrate the strength of WikiGIS in the management of traceability and in the access to history in a Geodesign process. Indeed, a cartographic user interface has been implemented, and then a hypothetical use case has been imagined as proof of concept.

Keywords: geodesign, history, traceability, tracking of participants’ editing, WikiGIS

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Authors: Elizabeth Howarth, Dawn Green, Sean Connolly, Geena Vabulas, Sara Smolley

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Through the EU Horizon 2020 Nuvoic Project, the project team recruited 70 individuals in the UK and Ireland to test the Voiceitt speech recognition app and provide user feedback to developers. The app is designed for people with dysarthric speech, to support communication with unfamiliar people and access to speech-driven technologies such as smart home equipment and smart assistants. Participants with atypical speech, due to a range of conditions such as cerebral palsy, acquired brain injury, Down syndrome, stroke and hearing impairment, were recruited, primarily through organisations supporting disabled people. Most had physical or learning disabilities in addition to dysarthric speech. The project team worked with individuals, their families and local support teams, to provide access to the app, including through additional assistive technologies where needed. Testing was user-led, with participants asked to identify and test use cases most relevant to their daily lives over a period of three months or more. Ongoing technical support and training were provided remotely and in-person throughout the testing period. Structured interviews were used to collect feedback on users' experiences, with delivery adapted to individuals' needs and preferences. Informal feedback was collected through ongoing contact between participants, their families and support teams and the project team. Focus groups were held to collect feedback on specific design proposals. User feedback shared with developers has led to improvements to the user interface and functionality, including faster voice training, simplified navigation, the introduction of gamification elements and of switch access as an alternative to touchscreen access, with other feature requests from users still in development. This work offers a case-study in successful and inclusive co-design with the disabled community.

Keywords: co-design, assistive technology, dysarthria, inclusive speech recognition

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Authors: Sushil Kumar Singh, Vivek Tewarson, Sarvesh Kumar, Shobhit Kumar

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Objective: ‘Beating Heart coronary artery bypass grafting on Intermittent Pump Support’ is a more reliable method of coronary revascularization that takes advantage of off and on-pump CABG while eliminating the disadvantage of both techniques. Methods: From January 2015 to December 2021, a new technique, “Intermittent On pump beating heart CABG” using a suction stabilizer was used by putting aortic and venous cannulas electively in all the patients. Patients were supported by a pump intermittently, as and when required (Group 1, n=254). Retrospective data were collected from our record of the patients who underwent off-pump CABG electively by the same surgeon and team (Group 2, n=254). Results: Significant advantage was noted in Group 1 patients in terms of the number of grafts (3.31 ± 1.16 vs. 2.30 ±0.66), grafting of lateral vessels (316 vs.202), mean operating time (1.37 ± 0.23 hrs vs. 2.22 ± 0.45 hrs) and postoperative blood loss (406.30 ± 257.90 ml vs. 567.41 ± 265.20 ml).CPB support time was less than 15 minutes in the majority of patients (n=179, 70.37 %), with a mean of 16.81 minutes. It was required, particularly during the grafting of lateral vessels. A rise in enzymes level (CRP, CKMB, Trop I, and NTPro BNP) was noted in Group 1 patients. But, these did not affect the postoperative course in patients. There was no mortality in Group 1 patients, while four patients in Group 2 died. Coclusions: Intermittent on-pump CABG technique is a promising method of surgical revascularization for all patients requiring CABG. It has shown its superiority in terms of safety, the number of grafts, operating time, and better perioperative course.

Keywords: cardiopulmonary bypass, CABG, beating heart CABG, on-pump CABG

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Authors: Rachel Cherner

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Introduction: The current study investigates several user characteristics that may predict the adoption of digital mental health supports. The extent to which individual characteristics predict preferences for functional elements of computer-mediated social support (CMSS) platforms and mental health (MH) apps is relatively unstudied. Aims: The present study seeks to illuminate the relationship between broad user characteristics and perceived attraction to CMSS platforms and MH apps. Methods: Participants (n=353) were recruited using convenience sampling methods (i.e., digital flyers, email distribution, and online survey forums). The sample was 68% male, and 32% female, with a mean age of 29. Participant racial and ethnic breakdown was 75% White, 7%, 5% Asian, and 5% Black or African American. Participants were asked to complete a 25-minute self-report questionnaire that included empirically validated measures assessing a battery of characteristics (i.e., subjective levels of anxiety/depression via PHQ-9 (Patient Health Questionnaire 9-item) and GAD-7 (Generalized Anxiety Disorder 7-item); attachment style via MAQ (Measure of Attachment Qualities); personality types via TIPI (The 10-Item Personality Inventory); growth mindset and mental health-seeking attitudes via GM (Growth Mindset Scale) and MHSAS (Mental Help Seeking Attitudes Scale)) and subsequent attitudes toward CMSS platforms and MH apps. Results: A stepwise linear regression was used to test if user characteristics significantly predicted attitudes towards key features of CMSS platforms and MH apps. The overall regression was statistically significant (R² =.20, F(1,344)=14.49, p<.000). Conclusion: This original study examines the clinical and sociocultural factors influencing decisions to use CMSS platforms and MH apps. Findings provide valuable insight for increasing adoption and engagement with digital mental health support. Fostering a growth mindset may be a method of increasing participant/patient engagement. In addition, CMSS platforms and MH apps may empower under-resourced and minority groups to gain basic access to mental health support. We do not assume this final model contains the best predictors of use; this is merely a preliminary step toward understanding the psychology and attitudes of CMSS platform/MH app users.

Keywords: computer-mediated social support platforms, digital mental health, growth mindset, health-seeking attitudes, mental health apps, user characteristics

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Authors: Martyn Queen, Diane Crone, Andrew Parker, Saul Bloxham

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Rationale: There is a growing body of evidence that supports the use of physical activity during and after cancer treatment. However, activity levels for patients remain low. As more cancer patients are treated successfully, and treatment costs continue to escalate, physical activity may be a promising adjunct to a person-centred healthcare approach to recovery. Aim: The aim was to further understand how physical activity may enhance the recovery process for a group of mixed-site cancer patients. Objectives: The research investigated longitudinal changes in physical activity and perceived the quality of life between two and six month’s post-exercise interventions. It also investigated support systems that enabled patients to sustain these perceived changes. Method: The respondent cohort comprised 14 mixed-site cancer patients aged 43-70 (11 women, 3 men), who participated in a two-phase physical activity intervention that took place at a university in the South West of England. Phase 1 consisted of an eight-week structured physical activity programme; Phase 2 consisted of four months of non-supervised physical activity. Semi-structured interviews took place three times over six months with each participant. Grounded theory informed the data collection and analysis which, in turn, facilitated theoretical development. Findings: Our findings propose three theories on the impact of physical activity for recovering cancer patients: 1) Knowledge gained through a structured exercise programme can enable recovering cancer patients to independently sustain physical activity to four-month follow-up. 2) Sustaining physical activity for six months promotes positive changes in the quality of life indicators of chronic fatigue, self-efficacy, the ability to self-manage and energy levels. 3) Peer support from patients facilitates adherence to a structured exercise programme and support from a spouse, or life partner facilitates independently sustained physical activity to four-month follow-up. Conclusions: This study demonstrates that qualitative research can provide an evidence base that could be used to support future care plans for cancer patients. Findings also demonstrate that a physical activity intervention can be effective at helping cancer patients recover from the side effects of their treatment, and recommends that physical activity should become an adjunct therapy alongside traditional cancer treatments.

Keywords: physical activity, health, cancer recovery, quality of life, support systems, qualitative, grounded theory, person-centred healthcare

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Authors: Ande Nesmith

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There is an ongoing shortage of foster parents worldwide to take on a growing population of children in need of out-of-home care. Currently, resources are primarily aimed at recruitment rather than retention. Retention rates are extraordinarily low, especially in the first two years of fostering. Qualitative interviews with 19 foster parents averaging 20 years of service provided insight into the challenges they faced and how they overcame them. Thematic analysis of interview transcripts identified sources of stress and resiliency. Key stressors included lack of support and responsiveness from the children’s social workers, false maltreatment allegations, and secondary trauma from children’s destructive behaviors and emotional dysregulation. Resilient parents connected with other foster parents for support, engaged in creative problem-solving, recognized that positive feedback from children usually arrives years later, and through training, understood the neurobiological impact of trauma on child behavior. Recommendations include coordinating communication between the foster parent licensing agency social workers and the children’s social workers, creating foster parent support networks and mentoring, and continuous training on trauma including effective parenting strategies. Research is needed to determine whether these resilience indicators in fact lead to long-term retention. Policies should include a mechanism to develop a cohesive line of communication and connection between foster parents and the children’s social workers as well as their respective agencies.

Keywords: foster care stability, foster parent burnout, foster parent resiliency, foster parent retention, trauma-informed fostering

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Authors: Pyung Soo Kim, Eung Hyuk Lee, Mun Suck Jang

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In the current paper, a residual generation filter with finite memory structure is proposed for fault detection. The proposed finite memory residual generation filter provides the residual by real-time filtering of fault vector using only the most recent finite observations and inputs on the window. It is shown that the residual given by the proposed residual generation filter provides the exact fault for noise-free systems. Finally, to illustrate the capability of the proposed residual generation filter, numerical examples are performed for the discretized DC motor system having the multiple sensor faults.

Keywords: residual generation filter, finite memory structure, kalman filter, fast detection

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Authors: Wenquan Gan, Peiyu Zhao, Xinyu Zhao

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Under the background of the rapid development of China’s ageing population, ageing-in-place has become a primary strategy to cope with this problem promoted by the Chinese government. However, most older adults currently living in old residential communities are insufficient to support their ageing-in-place. Therefore, exploring how to retrofit existing communities towards ageing-friendly standards to support older adults is essential for healthy ageing. To better cope with this issue, this study aims to shed light on the inter-relationship among the built environment, daily activities, and well-being of older adults in urban China. Using mixed research methods including GPS tracking, structured observation, and in-depth interview to examine: (a) what specific places or facilities are most commonly used by the elderly in the ageing-in-place process; (b) what specific built environment characteristics attract older adults in these frequently used places; (c) how has the use of these spaces impacted the well-being of older adults. Specifically, structured observation and GPS are used to record and map the older residents’ behaviour and movement in Suzhou, China, a city with a highly aged population and suitable as a research case. Subsequently, a follow-up interview is conducted to explore what impact of activities and the built environment on their well-being. Results showed that for the elderly with good functional ability, the facilities promoted by the Chinese government to support ageing-in-place, such as community nursing homes for the aged, day-care centre, and activity centres for the aged, are rarely used by older adults. Additionally, older adults have their preferred activities and built environment characteristics that contribute to their well-being. Our findings indicate that a complex interrelationship between the built environment and activities can influence the well-being of the elderly. Further investigations are needed to understand how to support healthy ageing-in-place, especially in addition to providing permanent elder-ly-care facilities, but to attend to the design interventions that can enhance these particularly built environment characteristics to facilitate a healthy lifestyle in later life.

Keywords: older adults, built environment, spatial behavior, community activity, healthy ageing

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Authors: Douglas A. Menezes, Isabel D. Nunes, Ulrich Schiel

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Most Virtual Learning Environments do not provide support mechanisms for the integrated planning, construction and follow-up of Instructional Design supported by Learning Analytic results. The present work aims to present an authoring tool that will be responsible for constructing the structure of an Instructional Design (ID), without the data being altered during the execution of the course. The visual interface aims to present the critical situations present in this ID, serving as a support tool for the course follow-up and possible improvements, which can be made during its execution or in the planning of a new edition of this course. The model for the ID is based on High-Level Petri Nets and the visualization forms are determined by the specific kind of the data generated by an e-course, a population of students generating sequentially dependent data.

Keywords: educational data visualization, high-level petri nets, instructional design, learning analytics

Procedia PDF Downloads 238