Search results for: offline social network

Authors: Michal Kozubík, Svetlana Síthová

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Aim: Aim of this study is to introduce the method of instrumental enrichment to people who works in the helping professions, and show further possibilities of its realization with children from socially disadvantaged backgrounds into society. Methods: We focused on Feuerstein’s Instrumental Enrichment method, its theoretical grounds and practical implementation. We carried out questionnaires and directly observed children from the disadvantaged background in Partizánske district. Results: We outlined the issues of children from disadvantaged social environment and their opportunity of social integration using the method. The findings showed the utility of Feuerstein method. Conclusions: We conclude that Feuerstein methods are very suitable for children from socially disadvantaged background and importance of social workers and special educator co-operation.

Keywords: Feuerstein, inclusion, education, socially disadvantaged background

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Authors: Daniel Gredig, Annabelle Bartelsen-Raemy

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Social work professionals should underpin their decisions with scientific knowledge and research findings. Hence, research is used as a framework for social work education and research courses have become a taken-for-granted component of study programmes. However, it has been acknowledged that social work students have negative beliefs and attitudes as well as frequently feelings of fear of research courses. Against this background, the present study aimed to establish the relationship between student’s fear of research courses, their research orientation and interest in research courses. We hypothesized that fear predicts the interest in research courses. Further, we hypothesized that research orientation (perceived importance and attributed usefulness for research for social work practice and perceived unbiased nature of research) was a mediating variable. In the years 2014, 2015 and 2016, we invited students enrolled for a bachelor programme in social work in Switzerland to participate in the study during their introduction day to the school taking place two weeks before their programme started. For data collection, we used an anonymous self-administered on-line questionnaire filled in on site. Data were analysed using descriptive statistics and structural equation modelling (generalized least squares estimates method). The sample included 708 students enrolled in a social work bachelor-programme, 501 being female, 184 male, and 5 intersexual, aged 19–56, having various entitlements to study, and registered for three different types of programme modes (full time programme; part time study with field placements in blocks; part time study involving concurrent field placement). Analysis showed that the interest in research courses was predicted by fear of research courses (β = -0.29) as well as by the perceived importance (β = 0.27), attributed usefulness of research (β = 0.15) and perceived unbiased nature of research (β = 0.08). These variables were predicted, in turn, by fear of research courses (β = -0.10, β = -0.23, and β = -0.13). Moreover, interest was predicted by age (β = 0.13). Fear of research courses was predicted by age (β = -0.10) female gender (β = 0.28) and having completed a general baccalaureate (β = -0.09). (GFI = 0.997, AGFI = 0.988, SRMR = 0.016, CMIN/df = 0.946, adj. R2 = 0.312). Findings evidence a direct as well as a mediated impact of fear on the interest in research courses in entering first-year students in a social work bachelor-programme. It highlights one of the challenges social work education in a research framework has to meet with. It seems, there have been considerable efforts to address the research orientation of students. However, these findings point out that, additionally, research anxiety in terms of fear of research courses should be considered and addressed by teachers when conceptualizing research courses.

Keywords: research anxiety, research courses, research interest, research orientation, social work students, teaching

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Authors: Shuen-Tai Wang, Fang-An Kuo, Chau-Yi Chou, Yu-Bin Fang

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2016 has become the year of the Artificial Intelligence explosion. AI technologies are getting more and more matured that most world well-known tech giants are making large investment to increase the capabilities in AI. Machine learning is the science of getting computers to act without being explicitly programmed, and deep learning is a subset of machine learning that uses deep neural network to train a machine to learn  features directly from data. Deep learning realizes many machine learning applications which expand the field of AI. At the present time, deep learning frameworks have been widely deployed on servers for deep learning applications in both academia and industry. In training deep neural networks, there are many standard processes or algorithms, but the performance of different frameworks might be different. In this paper we evaluate the running performance of two state-of-the-art distributed deep learning frameworks that are running training calculation in parallel over multi GPU and multi nodes in our cloud environment. We evaluate the training performance of the frameworks with ResNet-50 convolutional neural network, and we analyze what factors that result in the performance among both distributed frameworks as well. Through the experimental analysis, we identify the overheads which could be further optimized. The main contribution is that the evaluation results provide further optimization directions in both performance tuning and algorithmic design.

Keywords: artificial intelligence, machine learning, deep learning, convolutional neural networks

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Authors: Charles Rutikanga

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As part of the effort to reconstruct Rwanda and foster a shared national identity after the 1994 genocide against Tutsi, the government of Rwanda has drawn on aspects of indigenous culture and traditional practices. One of these traditional practices and cultural values is Ubudehe, which has been re-introduced after it has been gradually lost since colonial times. It is a form of collective action at the village level, which is inclusive, covering men, women, and the most marginalized community members. The philosophy behind Ubudehe is to increase the level of participation and institutional problem-solving capacity at the local level by citizens and local government. Since the early 2000s, the government re-introduced Ubudehe as a neo-traditional cultural institution in order to support the implementation of the country’s poverty reduction and development programs. An empirical study on indigenous and innovative models of social work practice was conducted under the framework of the ‘Professional Social Work in East Africa’ (PROSOWO II) project. Field data were collected on traditional/indigenous approaches, including Ubudehe, from different categories of informants through focus group discussions (FGDs) and personal interviews. The research showed that professional social workers play a significant role in the whole Ubudehe process. While there have been some challenges in the administration and implementation, overall it has contributed to poverty reduction in a post-genocide Rwanda.

Keywords: development, indigenous approach, social work, Ubudehe

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Authors: Unity Nkateng

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New forms of academic writing such as apprenticeship genres are developing in the field of applied linguistics. However, these perspectives have not adequately addressed the issue of social work students in Botswana. The paper addresses the issue of academic writing with special attention to the types of documents written by University of Botswana (UB) social work students on their fieldwork placement. The research method for this study combines two major research tools in the qualitative inquiry which are text analysis and interviews in order to investigate the context in which the texts are produced. 12 students were consulted and gave their consent for the study. 26 case reports were collected from the Department of Social work at the University of Botswana. The findings show that the case reports students write during their fieldwork placements have 6 moves, which focus on the clients’ story and describe what the students have done and achieved. The significance is that the discrepancy between professional writing and students writing raise questions about the extent to which students are being prepared for professional writing. Students have indicated that their academic writing varies according to the preferences of individual lecturers rather than the requirement of the work situation.

Keywords: apprenticeship genres, client's voice, material processes, relational possesive processes

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Authors: Christa L. Arnold, Margaret C. Stewart

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This research shares the results of an exploratory study examining Statement Analysis (SA) to detect deception in online truthful and untruthful social media posts. Applying a Law Enforcement methodology SA, used in criminal interview statements, this research analyzes what is stated to assist in evaluating written deceptive information. Preliminary findings reveal qualitative and quantitative nuances for SA in online deception detection and uncover insights regarding digital deceptive behavior. Thus far, findings reveal truthful statements tend to differ from untruthful statements in both content and quality.

Keywords: deception detection, online deception, social media content, statement analysis

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Authors: Manasi Roy, Raju Mondal

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During last two decades the synthesis and design of MOFs or novel coordination polymers (CPs) has flourished as an emerging area of research due to their role as functional materials. Accordingly, ten new cobalt-based MOFs have been synthesized using a simple bispyrazole ligand, 4,4′-methylene-bispyrazole (H2MBP), and isophthalic acid (H2IPA) and its four 5-substituted derivatives R-H2IPA (R = COOH, OH, tBu, NH2). The major aim of this study was to validate the mutual influence of temperature and substitutions on the final structural self-assembly. Five different isophthalic acid derivatives were used to study the influence of substituents while each reaction was carried out at two different temperatures to assess the temperature effect. A clear correlation was observed between the reaction temperature and the coordination number of the cobalt atoms which consequently changes the self assembly pattern. Another fact that the periodical change in coordination number did bring about some systematic changes in the structural network via secondary building unit selectivity. With the presence of a tunable cavity inside the network, and unsaturated metal centers, MOFs show highly encouraging photocatalytic degradation of toxic dye with a potential application in waste water purification. Another fascinating aspect of this work is the construction of magnetic coordination polymers with the occurrence of a not-so-common MCE behavior of cobalt-based MOF.

Keywords: MOFs, temperature effect, MCE, dye degradation

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Authors: David Oluigbo, Erik Hemberg, Nathan Shwatal, Wenqi Ding, Yin Yuan, Susanna Mierau

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Introduction: Calcium imaging is an established technique in neuroscience research for detecting activity in neural networks. Bursts of action potentials in neurons lead to transient increases in intracellular calcium visualized with fluorescent indicators. Manual identification of cell bodies and their contours by experts typically takes 10-20 minutes per calcium imaging recording. Our aim, therefore, was to design an automated pipeline to facilitate and optimize calcium imaging data analysis. Our pipeline aims to accelerate cell body and contour identification and production of graphical representations reflecting changes in neuronal calcium-based fluorescence. Methods: We created a Python-based pipeline that uses OpenCV (a computer vision Python package) to accurately (1) detect neuron contours, (2) extract the mean fluorescence within the contour, and (3) identify transient changes in the fluorescence due to neuronal activity. The pipeline consisted of 3 Python scripts that could both be easily accessed through a Python Jupyter notebook. In total, we tested this pipeline on ten separate calcium imaging datasets from murine dissociate cortical cultures. We next compared our automated pipeline outputs with the outputs of manually labeled data for neuronal cell location and corresponding fluorescent times series generated by an expert neuroscientist. Results: Our results show that our automated pipeline efficiently pinpoints neuronal cell body location and neuronal contours and provides a graphical representation of neural network metrics accurately reflecting changes in neuronal calcium-based fluorescence. The pipeline detected the shape, area, and location of most neuronal cell body contours by using binary thresholding and grayscale image conversion to allow computer vision to better distinguish between cells and non-cells. Its results were also comparable to manually analyzed results but with significantly reduced result acquisition times of 2-5 minutes per recording versus 10-20 minutes per recording. Based on these findings, our next step is to precisely measure the specificity and sensitivity of the automated pipeline’s cell body and contour detection to extract more robust neural network metrics and dynamics. Conclusion: Our Python-based pipeline performed automated computer vision-based analysis of calcium image recordings from neuronal cell bodies in neuronal cell cultures. Our new goal is to improve cell body and contour detection to produce more robust, accurate neural network metrics and dynamic graphs.

Keywords: calcium imaging, computer vision, neural activity, neural networks

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Authors: Elif Berna Var

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Cultural, physical, socio-economic, or politic changes occurred in urban areas might be resulted in the decaying period which may cause social problems. As a solution to that, urban renewal projects have been used in European countries since World War II whereas they have gained importance in Turkey after the 1980s. The first attempts were mostly related to physical or economic aspects which caused negative effects on social pattern later. Thus, social concerns have also started to include in renewal processes in developed countries. This integrative approach combining social, physical, and economic aspects promotes creating more sustainable neighbourhoods for both current and future generations. However, it is still a new subject for developing countries like Turkey. Concentrating on Trabzon-Turkey, this study highlights the importance of socially sustainable urban renewal processes especially in historical neighbourhoods where protecting the urban identity of the area is vital, as well as social structure, to create sustainable environments. Being in the historic city centre and having remarkable traditional houses, Ortahisar is an important image for Trabzon. Because of the fact that architectural and historical pattern of the area is still visible but need rehabilitations, it is preferred to use 'urban rehabilitation' as a way of urban renewal method for this study. A project is developed by the local government to create a secondary city centre and a new landmark for the city. But it is still ambiguous if this project can provide social sustainability of area which is one of the concerns of the research. In the study, it is suggested that social sustainability of an area can be achieved by several factors. In order to determine the factors affecting the social sustainability of an urban rehabilitation project, previous studies have been analysed and some common features are attempted to define. To achieve this, firstly, several analyses are conducted to find out social structure of Ortahisar. Secondly, structured interviews are implemented to 150 local people which aims to measure satisfaction level, awareness, the expectation of them, and to learn their demographical background in detail. Those data are used to define the critical factors for a more socially sustainable neighbourhood in Ortahisar. Later, the priority of those factors is asked to 50 experts and 150 local people to compare their attitudes and to find common criterias. According to the results, it can be said that social sustainability of Ortahisar neighbourhood can be improved by considering various factors like quality of urban areas, demographical factors, public participation, social cohesion and harmony, proprietorial factors, facilities of education and employment. In the end, several suggestions are made for Ortahisar case to promote more socially sustainable urban neighbourhood. As a pilot study highlighting the importance of social sustainability, it is hoped that this attempt might be the contributory effect on achieving more socially sustainable urban rehabilitation projects in Turkey.

Keywords: urban rehabilitation, social sustainability, Trabzon, Turkey

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Authors: A. Fahim, Mutahira N. Naseem

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SPAM is any unwanted electronic message or material in any form posted to many people. As the world is growing as global world, social networking sites play an important role in making world global providing people from different parts of the world a platform to meet and express their views. Among different social networking sites facebook become the leading one. With increase in usage different users start abusive use of facebook by posting or creating ways to post spam. This paper highlights the potential spam types nowadays facebook users faces. This paper also provide the reason how user become victim to spam attack. A methodology is proposed in the end discusses how to handle different types of spam.

Keywords: artificial neural networks, facebook spam, social networking sites, spam filter

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Authors: Longfei Wang, Selçuk Köse

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Hardware Trojan becomes great concern as integrated circuit (IC) technology advances and not all manufacturing steps of an IC are accomplished within one company. Real-time hardware Trojan detection is proven to be a feasible way to detect randomly activated Trojans that cannot be detected at testing stage. On-chip sensors serve as a great candidate to implement real-time hardware Trojan detection, however, the optimization of on-chip sensors has not been thoroughly investigated and the location of Trojan has not been carefully explored. On-chip sensor ellipse distribution method and equivalent mapping technique are proposed based on the characteristics of on-chip power delivery network in this paper to address the optimization and distribution of on-chip sensors for real-time hardware Trojan detection as well as to estimate the location and current consumption of hardware Trojan. Simulation results verify that hardware Trojan activation can be effectively detected and the location of a hardware Trojan can be efficiently estimated with less than 5% error for a realistic power grid using our proposed methods. The proposed techniques therefore lay a solid foundation for isolation and even deactivation of hardware Trojans through accurate location of Trojans.

Keywords: hardware trojan, on-chip sensor, power distribution network, power/ground noise

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Authors: Apu Kumar Saha, Mrinmoy Majumder

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The increase in the concentration of Green House gases all over the World has induced global warming phenomena whereby the average temperature of the world has aggravated to impact the pattern of climate in different regions. The frequency of extreme event has increased, early onset of season and change in an average amount of rainfall all are engrossing the conclusion that normal pattern of climate is changing. Sophisticated and complex models are prepared to estimate the future situation of the climate in different zones of the Earth. As hydro-energy is directly related to climatic parameters like rainfall and evaporation such energy resources will have to sustain the onset of the climatic abnormalities. The present investigation has tried to assess the impact of climatic abnormalities upon hydropower potential of different regions of the World. In this regard multi-criteria, decision making, and the neural network is used to predict the impact of the change cognitively by an index. The results from the study show that hydro-energy potential of Asian region is mostly vulnerable with respect to other regions of the world. The model results also encourage further application of the index to analyze the impact of climate change on the potential of hydro-energy.

Keywords: hydro-energy potential, neural networks, multi criteria decision analysis, environmental and ecological engineering

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Authors: Colm Barcoe, Garvan Whelan

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The purpose of this paper is to investigate the impact that US TV and movies have had on Irish tourism. This study examines how a destination marketing organisation (DMO) can use social media channels to capitalise upon the opportunities created by film tourism as it pertains to North American TV and movie productions. The findings are based on a combination of two qualitative methods, in-depth interviews with 20 industry professionals and a Netnographic analysis of social media activity between Tourism Ireland and the North American audience on Facebook and Twitter. The qualitative data were analysed in order to provide insights into the effectiveness of using North American pop culture as part of a digital marketing strategy when creating awareness of Ireland as a brand in the US and Canada. This study addresses a gap in the literature in relation to the use of social media when attracting the North American holidaymaker to Ireland. The findings from this investigation will extend an under-researched body of literature pertaining to Ireland as a destination and the successful digital marketing campaigns that have achieved exponential growth in this sector over the past five years. The empirical evidence presented also illustrates how the innovative use of social media has assisted the DMO to engage with the North American holidaymaker as part of an effective digital marketing strategy. This paper will be of value to academics and industry practitioners interested in film-induced tourism and indeed tourism in general, as well as students.

Keywords: digital marketing, tourism, strategies, movies, US TV

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Authors: Ahmet Kayabasi, Ali Akdagli

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In this study, three robust predicting methods, namely artificial neural network (ANN), adaptive neuro fuzzy inference system (ANFIS) and support vector machine (SVM) were used for computing the resonant frequency of A-shaped compact microstrip antennas (ACMAs) operating at UHF band. Firstly, the resonant frequencies of 144 ACMAs with various dimensions and electrical parameters were simulated with the help of IE3D™ based on method of moment (MoM). The ANN, ANFIS and SVM models for computing the resonant frequency were then built by considering the simulation data. 124 simulated ACMAs were utilized for training and the remaining 20 ACMAs were used for testing the ANN, ANFIS and SVM models. The performance of the ANN, ANFIS and SVM models are compared in the training and test process. The average percentage errors (APE) regarding the computed resonant frequencies for training of the ANN, ANFIS and SVM were obtained as 0.457%, 0.399% and 0.600%, respectively. The constructed models were then tested and APE values as 0.601% for ANN, 0.744% for ANFIS and 0.623% for SVM were achieved. The results obtained here show that ANN, ANFIS and SVM methods can be successfully applied to compute the resonant frequency of ACMAs, since they are useful and versatile methods that yield accurate results.

Keywords: a-shaped compact microstrip antenna, artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), support vector machine (SVM)

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Authors: Clive Sealey

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This article will analyse the impact that the increasingly multicultural nature of the UK has had on the nature and direction of social policy. The increasingly multicultural nature of the UK is being driven by a variety of demographic changes, particularly increased net migration from EU10 and the EU 2 enlargement. This has become an increasingly political issue, as exemplified by the specific rise of the United Kingdom Independence Party as a political force with the primary intention of restricting such migration. Perhaps not surprisingly, this has also had a significant impact on the nature and direction of social policies, as evident in the prominence given to efforts to reducing immigration and to restrict welfare benefits paid to such migrants. These policies have largely reflected the retreat away from the emphasis in UK policy on multiculturalism towards assimilation for all migrants, both prior and newly domiciled. Linking these two main policy emphases of reducing immigration and limiting entitlement to benefits is the concept of citizenship. An important point that this article will highlight, is that this changed citizenship does not just relate to new migrants, but also to existing domiciled migrants, such as in relation to specifying the assimilation of ‘Britishness’ and ‘British values’ in their daily life. Additionally, the article also analyses how the changes in welfare entitlements for new migrants is also impacting in an exclusionary way on the living standards of the native population, and therefore also their social rights as citizens. The article discusses the implication that this change presents for social work practice, particularly in terms of both migrants and native population changed citizenship.

Keywords: migration, citizenship, exclusion, social policy, migrant welfare

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Authors: Zhiwei Wang

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Much emphasis has been placed on the political dimension of digitised Chinese national(ist) discourses and their embodied national identities, which neglects other important dimensions constitutive of their discursive nature. A further investigation into how Chinese national(ist) discourses are daily (re)shaped online by diverse socio-political actors (especially ordinary users) is crucial, which can contribute to not only deeper understandings of Chinese national sentiments on China’s Internet beyond the excessive focus on their passionate, political-charged facet but also richer insights into the socio-technical ecology of the contemporary Chinese digital (and physical) world. This research adopts an ethnographic methodology, by which ‘fieldsites’ are Sina Weibo and bilibili. The primary data collection method is virtual ethnographic observation on everyday national(ist) discussions on both platforms. If data obtained via observations do not suffice to answer research questions, in-depth online qualitative interviews with ‘key actors’ identified from those observations in discursively (re)producing Chinese national identity on each ‘fieldsite’ will be conducted, to complement data gathered through the first method. Critical discourse analysis is employed to analyse data. During the process of data coding, NVivo is utilised. From November 2021 to December 2022, 35 weeks’ digital ethnographic observations have been conducted, with 35 sets of fieldnotes obtained. The strategy adopted for the initial stage of observations was keyword searching, which means typing into the search box on Sina Weibo and bilibili any keywords related to China as a nation and then observing the search results. Throughout 35 weeks’ online ethnographic observations, six keywords have been employed on Sina Weibo and two keywords on bilibili. For 35 weeks’ observations, textual content created by ordinary users have been concentrated much upon. Based on the fieldnotes of the first week’s observations, multifarious national(ist) discourses on Sina Weibo and bilibili have been found, targeted both at national ‘Others’ and ‘Us’, both on the historical and real-world dimension, both aligning with and differing from or even conflicting with official discourses, both direct national(ist) expressions and articulations of sentiments in the name of presentation of national(ist) attachments but for other purposes. Second, Sina Weibo and bilibili users have agency in interpreting and deploying concrete national(ist) discourses despite the leading role played by the government and the two platforms in deciding on the basic framework of national expressions. Besides, there are also disputes and even quarrels between users in terms of explanations for concrete components of ‘nation-ness’ and (in)direct dissent to officially defined ‘mainstream’ discourses to some extent, though often expressed much more mundanely, discursively and playfully. Third, the (re)production process of national(ist) discourses on Sina Weibo and bilibili depends upon not only technical affordances and limitations of the two sites but also, to a larger degree, some established socio-political mechanisms and conventions in the offline China, e.g., the authorities’ acquiescence of citizens’ freedom in understanding and explaining concrete elements of national discourses while setting the basic framework of national narratives to the extent that citizens’ own national(ist) expressions do not reach political bottom lines and develop into mobilising power to shake social stability.

Keywords: national identity, national(ist) discourse(s), everyday nationhood/nationalism, Chinese nationalism, digital nationalism

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Authors: Godswill Nwabuisi Osuafor, Sonto Maria Maputle

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Background: Abounding studies found that religiosity and social factors modulate alcohol use among university students. However, there is a scarcity of empirical studies examining the protective effects of religiosity and other social factors on alcohol use and abuse in South African universities. The aim of this study was therefore to assess the protective effects of religiosity and roles of social factors on alcohol use among university students. Methodology: A survey on the use of alcohol among 416 university students was conducted using structured questionnaire in 2014. Data were sourced on religiosity and contextual variables. Students were classified as practicing intrinsic religiosity or extrinsic religiosity based on the response to the measures of religiosity. Descriptive, chi square and binary logistic analyses were used in processing the data. Result: Results revealed that alcohol use was associated with religiosity, religion, sex, family history of alcohol use and experimenting with alcohol. Reporting alcohol abuse was significantly predicted by sex, family history of alcohol use and experimenting with alcohol. Religiosity mediated lower alcohol use whereas family history of alcohol use and experimenting with alcohol promoted alcohol use and abuse. Conclusion: Families, religious groups and societal factors may be the specific niches for intervention on alcohol use among university students.

Keywords: religiosity, alcohol use, protective factors, university students

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Authors: Ola Hafez

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This study examines the way selected Egyptian digitally mediated memes utilize intertextuality as a means of expression. It is motivated by the emerging digital socio-political humorous practice using various forms of political commentary in Egyptian social media. One of these forms involves the use of memes incorporating (often doctored) video frames taken from Egyptian plays, films and songs, and relocated in a different socio-political context, often with a caption that re-appropriates the frame for the purpose of critical commentary, thus juxtaposing the socio-political phenomena being addressed and the Egyptian artistic and cultural heritage. The paper presents a discourse study of a convenience sample of a recent social media campaign and carries out two levels of analysis. At the micro level, the study pinpoints the various modes of intertextuality employed, including verbal as well as visual intertextuality in the light of the work of social semiotics by Kress and van Leeuwen. At the macro level, the paper sheds light on the socio-political implications of such practice in the light of Political Discourse Analysis.

Keywords: digitally mediated discourse, discourse analysis, Egyptian Arabic, intertextuality, memes, multimodality, political discourse analysis

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Authors: Amanda Kavner, Richard Lamb

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Faster growth in science and technology of other nations may make staying globally competitive more difficult without shifting focus on how science is taught in US classes. An integral part of learning science involves visual and spatial thinking since complex, and real-world phenomena are often expressed in visual, symbolic, and concrete modes. The primary barrier to spatial thinking and visual literacy in Science, Technology, Engineering, and Math (STEM) fields is representational competence, which includes the ability to generate, transform, analyze and explain representations, as opposed to generic spatial ability. Although the relationship is known between the foundational visual literacy and the domain-specific science literacy, science literacy as a function of science learning is still not well understood. Moreover, the need for a more reliable measure is necessary to design resources which enhance the fundamental visuospatial cognitive processes behind scientific literacy. To support the improvement of students’ representational competence, first visualization skills necessary to process these science representations needed to be identified, which necessitates the development of an instrument to quantitatively measure visual literacy. With such a measure, schools, teachers, and curriculum designers can target the individual skills necessary to improve students’ visual literacy, thereby increasing science achievement. This project details the development of an artificial neural network capable of measuring science literacy using functional Near-Infrared Spectroscopy (fNIR) data. This data was previously collected by Project LENS standing for Leveraging Expertise in Neurotechnologies, a Science of Learning Collaborative Network (SL-CN) of scholars of STEM Education from three US universities (NSF award 1540888), utilizing mental rotation tasks, to assess student visual literacy. Hemodynamic response data from fNIRsoft was exported as an Excel file, with 80 of both 2D Wedge and Dash models (dash) and 3D Stick and Ball models (BL). Complexity data were in an Excel workbook separated by the participant (ID), containing information for both types of tasks. After changing strings to numbers for analysis, spreadsheets with measurement data and complexity data were uploaded to RapidMiner’s TurboPrep and merged. Using RapidMiner Studio, a Gradient Boosted Trees artificial neural network (ANN) consisting of 140 trees with a maximum depth of 7 branches was developed, and 99.7% of the ANN predictions are accurate. The ANN determined the biggest predictors to a successful mental rotation are the individual problem number, the response time and fNIR optode #16, located along the right prefrontal cortex important in processing visuospatial working memory and episodic memory retrieval; both vital for science literacy. With an unbiased measurement of science literacy provided by psychophysiological measurements with an ANN for analysis, educators and curriculum designers will be able to create targeted classroom resources to help improve student visuospatial literacy, therefore improving science literacy.

Keywords: artificial intelligence, artificial neural network, machine learning, science literacy, neuroscience

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Authors: Maud Mthembu, Tanusha Raniga, Michael Boecker

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In South Africa and Germany, countless number of children are raised by single mothers with little or no support from the biological fathers. As evidenced in literature, having an involved father living at home can have a positive influence in the life of a child and the mother can be supported in her role. Often single parenting is seen as a causative factor in numerous psychological and social challenges which are faced by children from single-parent households, which is an indication of a pathological lens of viewing single parenting. The empirical data from our study reveals that single mothers in formal employment experience social, economic and emotional hardships of parenting. However, a sense of determination to raise healthy and well-balanced children using economic and social capital accessible to them was one of the key findings. The participants reported visible and invisible sources of support which creates an enabling environment for them to negotiate the challenges of parenting without support from non-residence fathers. Using a qualitative paradigm, a total of twenty professional single mothers were interviewed in Germany and South Africa. Four key themes emerged from the data analysis namely; internal locus of control, positive new experiences, access to economic capital and dependable social support. This study suggests that single mothers who are economically self-reliant and have access to bonding social capital are able to cope with the demands of single parenting. Understanding this multi-dimensional experience of parenting by single parents in formal employment is important to advocate for supportive working conditions for mothers.

Keywords: child parenting, child protection, single parenting, social capital

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Authors: Y. Zheng, M. Busi, A. F. Pedersen, M. A. Beltran, C. Gundlach

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X-ray phase contrast imaging has shown to yield a better contrast compared to conventional attenuation X-ray imaging, especially for soft tissues in the medical imaging energy range. This can potentially lead to better diagnosis for patients. However, phase contrast imaging has mainly been performed using highly brilliant Synchrotron radiation, as it requires high coherence X-rays. Many research teams have demonstrated that it is also feasible using a laboratory source, bringing it one step closer to clinical use. Nevertheless, the requirement of fine gratings and high precision stepping motors when using a laboratory source prevents it from being widely used. Recently, a random phase object has been proposed as an analyzer. This method requires a much less robust experimental setup. However, previous studies were done using a particular X-ray source (liquid-metal jet micro-focus source) or high precision motors for stepping. We have been working on a much simpler setup with just small modification of a commercial bench-top micro-CT (computed tomography) scanner, by introducing a piece of sandpaper as the phase analyzer in front of the X-ray source. However, it needs a suitable algorithm for speckle tracking and 3D reconstructions. The precision and sensitivity of speckle tracking algorithm determine the resolution of the system, while the 3D reconstruction algorithm will affect the minimum number of projections required, thus limiting the temporal resolution. As phase contrast imaging methods usually require much longer exposure time than traditional absorption based X-ray imaging technologies, a dynamic phase contrast micro-CT with a high temporal resolution is particularly challenging. Different reconstruction methods, including neural network based techniques, will be evaluated in this project to increase the temporal resolution of the phase contrast micro-CT. A Monte Carlo ray tracing simulation (McXtrace) was used to generate a large dataset to train the neural network, in order to address the issue that neural networks require large amount of training data to get high-quality reconstructions.

Keywords: micro-ct, neural networks, reconstruction, speckle-based x-ray phase contrast

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Authors: André Pasti

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Historically, the communication sector in Argentina is characterized by intense monopolization and geographical concentration in the city of Buenos Aires. In 2000, the four major media conglomerates in operation – Clarín, Telefónica, America and Hadad – controlled 84% of the national media market. By 2009, new policies were implemented as a result of civil society organizations demands. Legally, a new regulatory framework was approved: the law 26,522 of Audiovisual Communications Services. Supposedly, these policies intend to create new conditions for the development of the audiovisual economy in the territory of Argentina. The regionalization of audiovisual production and the democratization of channels and access to media were among the priorities. This paper analyses the main changes and continuities in the organization of the spatial circuit of the audiovisual industry in Argentina provoked by these new policies. These new policies aim at increasing the diversity of audiovisual producers and promoting regional audiovisual industries. For this purpose, a national program for the development of audiovisual centers within the country was created. This program fostered a federalized production network, based on nine audiovisual regions and 40 nodes. Each node has created technical, financial and organizational conditions to gather different actors in audiovisual production – such as SMEs, social movements and local associations. The expansion of access to technical networks was also a concern of other policies, such as ‘Argentina connected’, whose objective was to expand access to broadband Internet. The Open Digital Television network also received considerable investments. Furthermore, measures have been carried out in order to impose limits on the concentration of ownership as well as to eliminate the oligopolies and to ensure more competition in the sector. These actions intended to force a divide of the media conglomerates into smaller groups. Nevertheless, the corporations that compose these conglomerates resist strongly, making full use of their economic and judiciary power. Indeed, the absence of effective impact of such measures can be testified by the fact that the audiovisual industry remains strongly concentrated in Argentina. Overall, these new policies were designed properly to decentralize audiovisual production and expand the regional diversity of the audiovisual industry. However, the effective transformation of the organization of the audiovisual circuit in the territory faced several resistances. This can be explained firstly and foremost by the ideological and economic power of the media conglomerates. In the second place, there is an inherited inertia from the unequal distribution of the objects needed for the audiovisual production and consumption. Lastly, the resistance also relies on financial needs and in the excessive dependence of the state for the promotion of regional audiovisual production.

Keywords: Argentina, audiovisual industry, communication policies, geographic concentration, regionalization, spatial circuit

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Authors: Ligia Alberto

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This study examined the underrepresentation of women of color in school leadership roles. Using social capital as the theoretical framework, this study explored the role of mentoring relationships in the career advancement and promotion of Latina school leaders. This study showed that informal mentoring relationships are essential to the promotion of women of color. Most of the mentoring relationships were established through close work with their immediate supervisors. This study suggests having informal mentors facilitated Latina women's aspirations to become school leaders and counteract the pattern of underrepresentation of Latinas in such roles.

Keywords: women of color, school leadership, social capital, mentoring

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Authors: Charleen von Kolpinski, Karina Cagarman, Alina Blaute

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The circular economy concept is often framed by politicians, scientists and practitioners as being the solution to sustainability problems of our times. However, the focus of these discussions and publications is very often set on environmental and economic aspects. In contrast, the social dimension of sustainability has been neglected and only a few recent and mostly conceptual studies targeted the inclusion of social aspects and the SDGs into circular economy research. All stakeholders of this new circular system have to be included to represent a truly sustainable solution to all the environmental, economic and social challenges caused by the linear economic system. Hence, this empirical research aims to analyse, next to the environmental and economic dimension, also explicitly the social dimension of a sustainable circular business model. This inductive and explorative approach applies the single case study method. A multi-stakeholder view is adopted to shed light on social aspects of the circular business model. Different stakeholder views, tensions between stakeholders and conflicts of interest are detected. In semi-structured interviews with different stakeholders of the company, this study compares the different stakeholder views to profile the success factors of its business model in terms of sustainability implementation and to detect its shortcomings. These findings result in the development of propositions which cover different social aspects of sustainable circular business model implementation. This study is an answer to calls for future empirical research about the social dimension of the circular economy and contributes to sustainable business model thinking in entrepreneurial contexts of the circular economy. It helps identifying all relevant stakeholders and their needs to successfully and inclusively implement a sustainable circular business model. The method of a single case study has some limitations by nature as it only covers one enterprise with its special business model. Therefore, more empirical studies are needed to research sustainable circular business models from multiple stakeholder perspectives, in different countries and industries. Future research can build upon the developed propositions of this study and develop hypotheses to be tested.

Keywords: circular economy, single case study, social dimension, sustainable circular business model

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Authors: Mohammad Besharatloo

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Intrusion detection is an important research topic in network security because of increasing growth in the use of computer network services. Intrusion detection is done with the aim of detecting the unauthorized use or abuse in the networks and systems by the intruders. Therefore, the intrusion detection system is an efficient tool to control the user's access through some predefined regulations. Since, the data used in intrusion detection system has high dimension, a proper representation is required to show the basis structure of this data. Therefore, it is necessary to eliminate the redundant features to create the best representation subset. In the proposed method, a hybrid model of differential evolution and firefly algorithms was employed to choose the best subset of properties. In addition, decision tree and support vector machine (SVM) are adopted to determine the quality of the selected properties. In the first, the sorted population is divided into two sub-populations. These optimization algorithms were implemented on these sub-populations, respectively. Then, these sub-populations are merged to create next repetition population. The performance evaluation of the proposed method is done based on KDD Cup99. The simulation results show that the proposed method has better performance than the other methods in this context.

Keywords: intrusion detection system, differential evolution, firefly algorithm, support vector machine, decision tree

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Authors: Lokes Parvatha Kumaran S., Sakthi Jay Mahenthar C., Sathyaprakash P., Jayakumar V., Shobanadevi A.

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With the evolution of technology, the need to solve complex computational problems like machine learning and deep learning has shot up. But even the most powerful classical supercomputers find it difficult to execute these tasks. With the recent development of quantum computing, researchers and tech-giants strive for new quantum circuits for machine learning tasks, as present works on Quantum Machine Learning (QML) ensure less memory consumption and reduced model parameters. But it is strenuous to simulate classical deep learning models on existing quantum computing platforms due to the inflexibility of deep quantum circuits. As a consequence, it is essential to design viable quantum algorithms for QML for noisy intermediate-scale quantum (NISQ) devices. The proposed work aims to explore Variational Quantum Circuits (VQC) for Deep Reinforcement Learning by remodeling the experience replay and target network into a representation of VQC. In addition, to reduce the number of model parameters, quantum information encoding schemes are used to achieve better results than the classical neural networks. VQCs are employed to approximate the deep Q-value function for decision-making and policy-selection reinforcement learning with experience replay and the target network.

Keywords: quantum computing, quantum machine learning, variational quantum circuit, deep reinforcement learning, quantum information encoding scheme

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Authors: Sukhleen Kaur

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In security groundwork, Intrusion Detection System (IDS) has become an important component. The IDS has received increasing attention in recent years. IDS is one of the effective way to detect different kinds of attacks and malicious codes in a network and help us to secure the network. Data mining techniques can be implemented to IDS, which analyses the large amount of data and gives better results. Data mining can contribute to improving intrusion detection by adding a level of focus to anomaly detection. So far the study has been carried out on finding the attacks but this paper detects the malicious files. Some intruders do not attack directly, but they hide some harmful code inside the files or may corrupt those file and attack the system. These files are detected according to some defined parameters which will form two lists of files as normal files and harmful files. After that data mining will be performed. In this paper a hybrid classifier has been used via Naive Bayes and Ripper classification methods. The results show how the uploaded file in the database will be tested against the parameters and then it is characterised as either normal or harmful file and after that the mining is performed. Moreover, when a user tries to mine on harmful file it will generate an exception that mining cannot be made on corrupted or harmful files.

Keywords: data mining, association, classification, clustering, decision tree, intrusion detection system, misuse detection, anomaly detection, naive Bayes, ripper

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Authors: Gladys Oppong, Saumya Singh, Pramod Pathak

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Women entrepreneurship has started establishing itself globally. Despite various social hurdles, Indian women have proved their strength in the area of entrepreneurship. Rising pattern of women entrepreneurship in Indian context make it significant to know the reason behind it. It’s a normal perception that women with financially strong backgrounds are highly motivated to progress in the area of entrepreneurship while lack of money becomes a major restraint for others. The proposed study attempts to identify the motivational factors for becoming women entrepreneur. The research work is to be conducted on women entrepreneurs. For this purpose, factor analysis will be used. The study has identified a set of motivational factors namely family business, social status, education and qualification, self-fulfillment and achievement among others that give momentum to the women to become an entrepreneur. The outcome of the study will be helpful in developing women entrepreneurship in India.

Keywords: women entrepreneurship, motivation, family business, social status

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Authors: Bandar Alahmadi, Manohar Mareboyana, Lethia Jackson

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Today, there are many applications that are using computer vision models, such as face recognition, image classification, and object detection. The accuracy of these models is very important for the performance of these applications. One challenge that facing the computer vision models is the adversarial examples attack. In computer vision, the adversarial example is an image that is intentionally designed to cause the machine learning model to misclassify it. One of very well-known method that is used to attack the Convolution Neural Network (CNN) is Fast Gradient Sign Method (FGSM). The goal of this method is to find the perturbation that can fool the CNN using the gradient of the cost function of CNN. In this paper, we introduce a novel model that can attack Regional-Convolution Neural Network (R-CNN) that use FGSM. We first extract the regions that are detected by R-CNN, and then we resize these regions into the size of regular images. Then, we find the best perturbation of the regions that can fool CNN using FGSM. Next, we add the resulted perturbation to the attacked region to get a new region image that looks similar to the original image to human eyes. Finally, we placed the regions back to the original image and test the R-CNN with the attacked images. Our model could drop the accuracy of the R-CNN when we tested with Pascal VOC 2012 dataset.

Keywords: adversarial examples, attack, computer vision, image processing

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Authors: Lukasz Mierczak, Patrick Denke, Piotr Klimczyk, Stefan Siebert

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Due to the high complexity of the magnetization in electrical machines and influence of the manufacturing processes on the magnetic properties of their components, the assessment and prediction of hysteresis and eddy current losses has remained a challenge. In the design process of electric motors and generators, the power losses of stators and rotors are calculated based on the material supplier’s data from standard magnetic measurements. This type of data does not include the additional loss from non-sinusoidal multi-harmonic motor excitation nor the detrimental effects of residual stress remaining in the motor laminations after manufacturing processes, such as punching, housing shrink fitting and winding. Moreover, in production, considerable attention is given to the measurements of mechanical dimensions of stator and rotor cores, whereas verification of their magnetic properties is typically neglected, which can lead to inconsistent efficiency of assembled motors. Therefore, to enable a comprehensive characterization of motor materials and components, Brockhaus Measurements developed a range of in-line and offline measurement technologies for testing their magnetic properties under actual motor operating conditions. Multiple sets of experimental data were obtained to evaluate the influence of various factors, such as elevated temperature, applied and residual stress, and arbitrary magnetization on the magnetic properties of different grades of non-oriented steel. Measured power loss for tested samples and stator cores varied significantly, by more than 100%, comparing to standard measurement conditions. Quantitative effects of each of the applied measurement were analyzed. This research and applied Brockhaus measurement methodologies emphasized the requirement for advanced characterization of magnetic materials used in electric drives and automotive applications.

Keywords: magnetic materials, measurement technologies, permanent magnets, stator and rotor cores

Procedia PDF Downloads 132