Search results for: social network tools

Authors: Gizem Eser Erdek

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This study is research on predicting the remaining life of industrial cutting tools used in the industrial production process with deep learning methods. When the life of cutting tools decreases, they cause destruction to the raw material they are processing. This study it is aimed to predict the remaining life of the cutting tool based on the damage caused by the cutting tools to the raw material. For this, hole photos were collected from the hole-drilling machine for 8 months. Photos were labeled in 5 classes according to hole quality. In this way, the problem was transformed into a classification problem. Using the prepared data set, a model was created with convolutional neural networks, which is a deep learning method. In addition, VGGNet and ResNet architectures, which have been successful in the literature, have been tested on the data set. A hybrid model using convolutional neural networks and support vector machines is also used for comparison. When all models are compared, it has been determined that the model in which convolutional neural networks are used gives successful results of a %74 accuracy rate. In the preliminary studies, the data set was arranged to include only the best and worst classes, and the study gave ~93% accuracy when the binary classification model was applied. The results of this study showed that the remaining life of the cutting tools could be predicted by deep learning methods based on the damage to the raw material. Experiments have proven that deep learning methods can be used as an alternative for cutting tool life estimation.

Keywords: classification, convolutional neural network, deep learning, remaining life of industrial cutting tools, ResNet, support vector machine, VggNet

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Authors: Zhaojunfeng

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Driven by the national policy of "East Data and Western Calculation", the computing first network will attract a new wave of development. As the foundation of the development of the computing first network, the computing force bearer network has become the key direction of technology research and development in the industry. This article will analyze typical computing force application scenarios and bearing requirements and sort out the SLA indicators of computing force applications. On this basis, this article carries out research and discussion on the key technologies of computing force bearer network in a slice packet network, and finally, gives evolution policy for SPN computing force bearer network to support the development of SPN computing force bearer network technology and network deployment.

Keywords: component-computing force bearing, bearing requirements of computing force application, dual-SLA indicators for computing force applications, SRv6, evolution strategies

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Authors: Siti Azrina B. A. Aziz, Siti Hafizah A. Hamid

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New approaches to analyze and visualize data stream in real-time basis is important in making a prompt decision by the decision maker. Financial market trading and surveillance, large-scale emergency response and crowd control are some example scenarios that require real-time analytic and data visualization. This situation has led to the development of techniques and tools that support humans in analyzing the source data. With the emergence of Big Data and social media, new techniques and tools are required in order to process the streaming data. Today, ranges of tools which implement some of these functionalities are available. In this paper, we present chronological evolution evaluation of technologies for supporting of real-time analytic and visualization of the data stream. Based on the past research papers published from 2002 to 2014, we gathered the general information, main techniques, challenges and open issues. The techniques for streaming text visualization are identified based on Text Visualization Browser in chronological order. This paper aims to review the evolution of streaming text visualization techniques and tools, as well as to discuss the problems and challenges for each of identified tools.

Keywords: information visualization, visual analytics, text mining, visual text analytics tools, big data visualization

Procedia PDF Downloads 373

Authors: Esperanza Garcia-Uceda, Josefina L. Murillo-Luna, M. Pilar Latorre-Martinez, Marta Ferrer-Serrano

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Social entrepreneurship represents an innovation of traditional business models. During the last decade, its important role in contributing to rural and regional development has been widely recognized, due to its capacity to combat the problem of depopulation through the creation of employment. However, the success of this type of innovative business initiatives depends to a large extent on the existence of an adequate ecosystem of support resources. Based on the theoretical framework of the quadruple helix (QH), which highlights the need for collaboration between different interest groups -university, industry, government and civil society- for the development of regional innovations, in this work the network analysis is applied to study the ecosystem of resources to support social entrepreneurship in the rural area of the province of Zaragoza (Spain). It is a quantitative analysis that can be used to measure the interactions between the different actors that make up the quadruple helix, as well as the networks created between the different institutions and support organizations, through the study of the complex networks they form. The results show the importance of the involvement of local governments and the university, as key elements in the development process, but also allow identifying other issues that are susceptible to improvement.

Keywords: ecosystem of support resources, network analysis, quadruple helix, social entrepreneurship

Procedia PDF Downloads 223

Authors: Rosdyana Mangir Irawan Kusuma, Wei-Chun Kao, Ho-Thi Trang, Yu-Yen Ou, Kai-Lung Hua

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Stock market prediction is still a challenging problem because there are many factors that affect the stock market price such as company news and performance, industry performance, investor sentiment, social media sentiment, and economic factors. This work explores the predictability in the stock market using deep convolutional network and candlestick charts. The outcome is utilized to design a decision support framework that can be used by traders to provide suggested indications of future stock price direction. We perform this work using various types of neural networks like convolutional neural network, residual network and visual geometry group network. From stock market historical data, we converted it to candlestick charts. Finally, these candlestick charts will be feed as input for training a convolutional neural network model. This convolutional neural network model will help us to analyze the patterns inside the candlestick chart and predict the future movements of the stock market. The effectiveness of our method is evaluated in stock market prediction with promising results; 92.2% and 92.1 % accuracy for Taiwan and Indonesian stock market dataset respectively.

Keywords: candlestick chart, deep learning, neural network, stock market prediction

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Authors: Abdelhadi Lotfi, Abdelkader Benyettou

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In this work, a training algorithm for probabilistic neural networks (PNN) is presented. The algorithm addresses one of the major drawbacks of PNN, which is the size of the hidden layer in the network. By using a cross-validation training algorithm, the number of hidden neurons is shrunk to a smaller number consisting of the most representative samples of the training set. This is done without affecting the overall architecture of the network. Performance of the network is compared against performance of standard PNN for different databases from the UCI database repository. Results show an important gain in network size and performance.

Keywords: classification, probabilistic neural networks, network optimization, pattern recognition

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Authors: Ethan Nikookar, Yalda Boroushaki, Larissa Statsenko, Jorge Ochoa Paniagua

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Given the serious impact that supply chain disruptions can have on a firm's bottom-line performance, both industry and academia are interested in supply chain resilience, a capability of the supply chain that enables it to cope with disruptions. To date, much of the research has focused on the antecedents of supply chain resilience. This line of research has suggested various firm-level capabilities that are associated with greater supply chain resilience. A consensus has emerged among researchers that supply chain flexibility holds the greatest potential to create resilience. Supply chain flexibility achieves resilience by creating readiness to respond to disruptions with little cost and time by means of reconfiguring supply chain resources to mitigate the impacts of the disruption. Decisions related to supply chain disruptions are made by supply chain managers; however, the role played by supply chain managers' reference networks has been overlooked in the supply chain resilience literature. This study aims to understand the impact of supply chain managers on their firms' supply chain resilience. Drawing on social capital theory and social network theory, this paper proposes a conceptual model to explore the role of supply chain managers in developing the resilience of supply chains. Our model posits that higher level of supply chain managers' embeddedness in their reference network is associated with increased resilience of their firms' supply chain. A reference network includes individuals from whom supply chain managers seek advice on supply chain related matters. The relationships between supply chain managers' embeddedness in reference network and supply chain resilience are mediated by supply chain flexibility.

Keywords: supply chain resilience, embeddedness, reference networks, social capitals

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Authors: Anca Radulescu, Danae Evans

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The relationship between a network’s hardwiring and its emergent dynamics are central to neuroscience. We study the principles of this correspondence in a canonical setup (in which network nodes exhibit well-studied complex quadratic dynamics), then test their universality in biological networks. By extending methods from discrete dynamics, we study the effects of network connectivity on temporal patterns, encapsulating long-term behavior into the rich topology of network Mandelbrot sets. Then elements of fractal geometry can be used to predict and classify network behavior.

Keywords: canonical model, complex dynamics, dynamic networks, fractals, Mandelbrot set, network connectivity

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Authors: Mansur Ahmed Kazaure

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The emergence of sponsorship as a new marketing communication tool and a source of competitive advantage in the marketplace has changed the entire marketing communication process. Sponsorship has overtaken other marketing communication tools in terms of growth and expenditure. This paper seeks to evaluate the role of sponsorship in marketing communication tools. The study recommends that proper measures be taken before the company embarks into sponsorship programs. This is necessary because investment in sponsorship does not always guarantee sustainable competitive advantage in the marketplace.

Keywords: sponsorship, marketing communication theories, marketing communication tools

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Authors: Mohamed Raouf Benmakrelouf, Wafa Karouche, Joseph Rynkiewicz

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In this paper, we propose an alternative method to construct a Bayesian Network (BN). This method relies on a convolutional neural network (CNN classifier), which determinates the edges of the network skeleton. We train a CNN on a normalized empirical probability density distribution (NEPDF) for predicting causal interactions and relationships. We have to find the optimal Bayesian network structure for causal inference. Indeed, we are undertaking a search for pair-wise causality, depending on considered causal assumptions. In order to avoid unreasonable causal structure, we consider a blacklist and a whitelist of causality senses. We tested the method on real data to assess the influence of education on the voting intention for the extreme right-wing party. We show that, with this method, we get a safer causal structure of variables (Bayesian Network) and make to identify a variable that satisfies the backdoor criterion.

Keywords: Bayesian network, structure learning, optimal search, convolutional neural network, causal inference

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Authors: Christopher Godwin Udomboso, Angela Unna Chukwu, Isaac Kwame Dontwi

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In selecting a Statistical Neural Network model, the Network Information Criterion (NIC) has been observed to be sample biased, because it does not account for sample sizes. The selection of a model from a set of fitted candidate models requires objective data-driven criteria. In this paper, we derived and investigated the Adjusted Network Information Criterion (ANIC), based on Kullback’s symmetric divergence, which has been designed to be an asymptotically unbiased estimator of the expected Kullback-Leibler information of a fitted model. The analyses show that on a general note, the ANIC improves model selection in more sample sizes than does the NIC.

Keywords: statistical neural network, network information criterion, adjusted network, information criterion, transfer function

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Authors: P. L. Cheng, I. N. Umar

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Online discussion forum is a popular e-learning technique that allows participants to interact and construct knowledge. This study aims to examine the levels of participation, categories of participants and the structure of their interactions in a forum. A convenience sampling of one course coordinator and 23 graduate students was selected in this study. The forums’ log file and the Social Network Analysis software were used in this study. The analysis reveals 610 activities (including viewing forum’s topic, viewing discussion thread, posting a new thread, replying to other participants’ post, updating an existing thread and deleting a post) performed by them in this forum, with an average of 3.83 threads posted. Also, this forum consists of five at-risk participants, six bridging participants, four isolated participants and five leaders of information. In addition, the network density value is 0.15 and there exist five reciprocal interactions in this forum. The closeness value varied between 28 and 68 while the eigen vector centrality value varied between 0.008 and 0.39. The finding indicates that the participants tend to listen more rather than express their opinions in the forum. It was also revealed that those who actively provide supports in the discussion forum were not the same people who received the most responses from their peers. This study found that cliques do not exist in the forum and the participants are not selective to whom they response to, rather, it was based on the content of the posts made by their peers. Based upon the findings, further analysis with different method and population, larger sample size and a longer time frame are recommended.

Keywords: e-learning, learning management system, online forum, social network analysis

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Authors: Navaneeth Krishnan, Chandan N. Bhagwat, Aparajit P. Utpat

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Cloud Computing provides us a means to upload data and use applications over the internet. As the number of devices connecting to the cloud grows, there is undue pressure on the cloud infrastructure. Fog computing or Network Based Computing or Edge Computing allows to move a part of the processing in the cloud to the network devices present along the node to the cloud. Therefore the nodes connected to the cloud have a better response time. This paper proposes a method of moving the computation from the cloud to the network by introducing an android like appstore on the networking devices.

Keywords: cloud computing, fog computing, network devices, appstore

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Authors: M. Kollar, A. Zieba

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In this paper, we present a method for a time synchronization between the two eNodeBs (eNBs) in E-UTRAN (Evolved Universal Terrestrial Radio Access) network. The two eNBs are cooperating in so-called inter eNB CA (Carrier Aggregation) case and connected via asymmetrical IP network. We solve the problem by using broadcasting signals generated in E-UTRAN as synchronization signals. The results show that the time synchronization with the proposed method is possible with the error significantly less than 1 ms which is sufficient considering the time transmission interval is 1 ms in E-UTRAN. This makes this method (with low complexity) more suitable than Network Time Protocol (NTP) in the mobile applications with generated broadcasting signals where time synchronization in asymmetrical network is required.

Keywords: IP scheduled throughput, E-UTRAN, Evolved Universal Terrestrial Radio Access Network, NTP, Network Time Protocol, assymetric network, delay

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Authors: Kolesnik Nadezda A.

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The research aims to elaborate inter-organizational network relationships management model to maximize value co-creation. We propose a network management framework that requires evaluation of network partners with respect to their position and role in network; and elaboration of appropriate relationship development strategy with partners in network. Empirical research and approval is based on the case study method, including structured in-depth interviews with the companies from b2b market.

Keywords: inter-organizational networks, value co-creation, model, B2B market

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Authors: José L. Jiménez

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The use of technology in the classroom is an issue that is constantly evolving. Digital age students learn differently than their teachers did, so now the teacher should be constantly evolving their methods and teaching techniques to be more in touch with the student. In this paper a case study presents how were used some of these technologies by accompanying a classroom course, this in order to provide students with a different and innovative experience as their teacher usually presented the activities to develop. As students worked in the various activities, they increased their digital skills by employing unknown tools that helped them in their professional training. The twenty-first century teacher should consider the use of Information and Communication Technologies in the classroom thinking in skills that students of the digital age should possess. It also takes a brief look at the history of distance education and it is also highlighted the importance of integrating technology as part of the student's training.

Keywords: digital tools, on-line learning, social networks, technology

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Authors: Sourour Ramzi, Claudia Sarrico

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Little has been done on network DEA in education, and nobody has attempted to model the whole education supply chain using network DEA. As such the contribution of the present paper is to propose a model for measuring the efficiency of education supply chains using network DEA. First, we use a general survey of data envelopment analysis (DEA) to establish the emergent themes for research in DEA, and focus on the theme of Network DEA. Second, we use a survey on two-stage DEA models, and Network DEA to write a state of the art on Network DEA, particularly applied to supply chain management. Third, we use a survey on DEA applications to establish the most influential papers on DEA education applications, in order to establish the state of the art on applications of DEA in education, in general, and applications of DEA to education using network DEA, in particular. Finally, we propose a model for measuring the performance of education supply chains of different education systems (countries or states within a country, for instance). We then use this model on some empirical data.

Keywords: supply chain, education, data envelopment analysis, network DEA

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Authors: Yong Wee Foo, Lai Meng Tang

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Artificial Intelligence (AI), particularly the usage of deep neural networks for hierarchical representations from data, has found numerous complex applications across various domains, including computer vision, robotics, autonomous vehicles, and other scientific fields. However, their inherent “black box” nature can sometimes make it challenging for early researchers or school students of various levels to comprehend and trust the results they produce. Consequently, there has been a growing demand for reliable visualization tools in engineering and science education to help learners understand, trust, and explain a deep learning network. This has led to a notable emphasis on the visualization of AI in the research community in recent years. AI visualization tools are increasingly being adopted to significantly improve the comprehension of complex topics in deep learning. This paper proposes a novel approach to empower students to actively explore the inner workings of deep neural networks by combining the student-centered learning approach of flipped classroom models with the investigative power of AI visualization tools, namely, the TensorFlow Playground, the Local Interpretable Model-agnostic Explanations (LIME), and the SHapley Additive exPlanations (SHAP), for delivering an AI education curriculum. Combining the two factors is vital in fostering ownership, responsibility, and critical thinking skills in the age of AI.

Keywords: deep learning, explainable AI, AI visualization, representation learning

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Authors: Sandeep Singh Waraich

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Wireless sensor network (WSN ) consists of a large number of sensor nodes. The disparity in their energy consumption usually lead to the loss of equilibrium in wireless sensor network which may further results in an energy hole problem in wireless network. In this paper, we have considered the inclusion of orphan nodes which usually remain unutilized as intermediate nodes in multi-hop routing. The Orphan Node Inclusion (ONI) Protocol lets the cluster member to bring the orphan nodes into their clusters, thereby saving important resources and increasing network lifetime in critical applications of WSN.

Keywords: wireless sensor network, orphan node, clustering, ONI protocol

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Authors: Dania Aljouhi

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September 26th, 2017, is a historic date for all women in Saudi Arabia. On that day, Saudi Arabia announced the decree on allowing Saudi women to drive. With the advent of vision 2030 and its goal to empower women and increase their participation in Saudi society, we see how Saudis’ Twitter users deliberate the 2017 decree from different social, cultural, religious, economic and political factors. This topic bridges social media 'Twitter,' gender and social-cultural studies to offer insights into how Saudis’ tweets reflect a broader discourse on Saudi women in the age of social media. The present study aims to explore the meanings and themes that emerge by Saudis’ Twitter users in response to the 2017 royal decree on women driving. The sample used in the current study involves (n= 1000) tweets that were collected from Sep 2017 to March 2019 to account for the Saudis’ tweets before and after implementing the decree. The paper uses semantic and thematic network analysis methods to examine the Saudis’ Twitter discourse on the women driving issue. The paper argues that Twitter as a platform has mediated the discourse of women driving among the Saudi community and facilitated social changes. Finally, framing theory (Goffman, 1974) and Networked framing (Meraz & Papacharissi 2013) are both used to explain the tweets on the decree of allowing Saudi women to drive based on # Saudi women-driving-cars.

Keywords: Saudi Arabia, women, Twitter, semantic network analysis, framing

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Authors: Byung-Ik Kim, Hong-Koo Kang, Tae-Jin Lee, Hae-Ryong Park

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Cyber terrors against specific enterprises or countries have been increasing recently. Such attacks against specific targets are called advanced persistent threat (APT), and they are giving rise to serious social problems. The malicious behaviors of APT attacks mostly affect websites and penetrate enterprise networks to perform malevolent acts. Although many enterprises invest heavily in security to defend against such APT threats, they recognize the APT attacks only after the latter are already in action. This paper discusses the characteristics of APT attacks at each step as well as the strengths and weaknesses of existing malicious code detection technologies to check their suitability for detecting APT attacks. It then proposes a network-based malicious behavior detection algorithm to protect the enterprise or national networks.

Keywords: Advanced Persistent Threat (APT), malware, network security, network packet, exploit kits

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Authors: Pingping Lin, Xudong Luo, Yifan Fan

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Sentiment analysis is a very active research topic. Every day, Facebook, Twitter, Weibo, and other social media, as well as significant e-commerce websites, generate a massive amount of comments, which can be used to analyse peoples opinions or emotions. The existing methods for sentiment analysis are based mainly on sentiment dictionaries, machine learning, and deep learning. The first two kinds of methods rely on heavily sentiment dictionaries or large amounts of labelled data. The third one overcomes these two problems. So, in this paper, we focus on the third one. Specifically, we survey various sentiment analysis methods based on convolutional neural network, recurrent neural network, long short-term memory, deep neural network, deep belief network, and memory network. We compare their futures, advantages, and disadvantages. Also, we point out the main problems of these methods, which may be worthy of careful studies in the future. Finally, we also examine the application of deep learning in multimodal sentiment analysis and aspect-level sentiment analysis.

Keywords: document analysis, deep learning, multimodal sentiment analysis, natural language processing

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Authors: Roberto Feltrero, Sara Osuna-Acedo

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Responsible Research and Innovation have to accomplish one fundamental aim: everybody has to participate in the benefits of innovation, but also innovation has to be democratic; that is to say, everybody may have the possibility to participate in the decisions in the innovation process. Particularly, a democratic and inclusive model of social participation and innovation includes persons with disabilities and people at risk of discrimination. Innovations on Artificial Intelligence for social development have to accomplish the same dual goal: improving equality for accessing fields of public interest like education, training and public services, as well as improving civic and democratic participation in the process of developing such innovations for all. This research aims to develop innovations, policies and policy recommendations to apply and disseminate such artificial intelligence and social model for making educational and administrative processes more accessible. First, designing a citizen participation process to engage citizens in the designing and use of artificial intelligence tools for public services. This will result in improving trust in democratic institutions contributing to enhancing the transparency, effectiveness, accountability and legitimacy of public policy-making and allowing people to participate in the development of ethical standards for the use of such technologies. Second, improving educational tools for lifelong learning with AI models to improve accountability and educational data management. Dissemination, education and social participation will be integrated, measured and evaluated in innovative educational processes to make accessible all the educational technologies and content developed on AI about responsible and social innovation. A particular case will be presented regarding access for all to educational tools and public services. This accessibility requires cognitive adaptability because, many times, legal or administrative language is very complex. Not only for people with cognitive disabilities but also for old people or citizens at risk of educational or social discrimination. Artificial Intelligence natural language processing technologies can provide tools to translate legal, administrative, or educational texts to a more simple language that can be accessible to everybody. Despite technological advances in language processing and machine learning, this becomes a huge project if we really want to respect ethical and legal consequences because that kinds of consequences can only be achieved with civil and democratic engagement in two realms: 1) to democratically select texts that need and can be translated and 2) to involved citizens, experts and nonexperts, to produce and validate real examples of legal texts with cognitive adaptations to feed artificial intelligence algorithms for learning how to translate those texts to a more simple and accessible language, adapted to any kind of population.

Keywords: responsible research and innovation, AI social innovations, cognitive accessibility, public participation

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Authors: Cristobal Miralles Insa

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The network of chairs of the "Valencian Public System of Social Services" (VPSSS) is sponsored by the Valencian Institute of Training, Research, and Quality in Social Services and aims to promote research, dissemination, and evaluation of the needs that arise in the field of the public system of social services. It also seeks to transfer knowledge to foster the development of public policies in this field. Given that it is an Interuniversity Chair among the five public universities in Valencia, there is coordination of complementary themes and roles for this objective, with Universitat Politènica de València focusing primarily on promoting innovation and social entrepreneurship to address multiple social challenges through its platform INSSPIRA. This approach is aimed at the entire university community and its various interest groups, carrying out research, teaching, and dissemination activities that promote social inclusion, personal development, and autonomy for groups in situations of vulnerability, lack of protection, dependence, or social urgency. Although it focuses on the Valencian context, both the issues in this context and the tools in process to address them, often have a universal and scalable character and has been inspiring for the innovation system of UPV. This entrepreneurial incubator goes along from early stages of students on the campus until the so-called “StartUPV” system, where students are challenged with social problems that require creative solutions. Therefore, the Chair is conceived with a holistic spirit and an inspiring vocation that engages the whole university community. In this communication, it is described the entities and individuals participating in this UPV Chair of VPSSS, followed by the presentation of different work lines and objectives for the chair. Subsequently, a description of various activities undertaken to promote innovation in social services are described, where support to teaching and extracurricular activities in this field are exposed. It must be noted that some awareness and dissemination of activities are carried out in a transversal mode as they contribute to different objectives simultaneously; with special focus on Learning-Service approaches that achieved very good results which are also summarized.

Keywords: social innovation, entrepeneurship, university, vulnerable sectors

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Authors: Eileen Y. Wong, Katherine Jin, Anat Talmon

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Background: Help seeking behaviors are highly contingent on socio-cultural factors such as ethnicity. Both Latino and Asian Americans underutilize mental health services compared to their White American counterparts. This difference may be related to the composite of one’s social support system, which includes family cohesion and social networks. Previous studies have found that Latino families are characterized by higher levels of family cohesion and social support, and Asian American families with greater family cohesion exhibit lower levels of help seeking behaviors. While both are broadly considered collectivist communities, within-culture variability is also significant. Therefore, this study aims to investigate the relationship between help seeking behaviors in the two cultures with levels of family cohesion and strength of social network. We also consider such relationships in light of previous traumatic events and diagnoses, particularly post-traumatic stress disorder (PTSD), to understand whether clinically diagnosed individuals differ in their strength of network and help seeking behaviors. Method: An adult sample (N = 2,990) from the National Latino and Asian American Study (NLAAS) provided data on participants’ social network, family cohesion, likelihood of seeking professional help, and DSM-IV diagnoses. T-tests compared Latino American (n = 1,576) and Asian American respondents (n = 1,414) in strength of social network, level of family cohesion, and likelihood of seeking professional help. Linear regression models were used to identify the probability of help-seeking behavior based on ethnicity, PTSD diagnosis, and strength of social network. Results: Help-seeking behavior was significantly associated with family cohesion and strength of social network. It was found that higher frequency of expressing one’s feelings with family significantly predicted lower levels of help-seeking behaviors (β = [-.072], p = .017), while higher frequency of spending free time with family significantly predicted higher levels of help-seeking behaviors (β = [.129], p = .002) in the Asian American sample. Subjective importance of family relations compared to that of one’s peers also significantly predict higher levels of help-seeking behaviors (β = [.095], p = .011) in the Asian American sample. Frequency of sharing one’s problems with relatives significantly predicted higher levels of help-seeking behaviors (β = [.113], p < .01) in the Latino American sample. A PTSD diagnosis did not have any significant moderating effect. Conclusion: Considering the underutilization of mental health services in Latino and Asian American minority groups, it is crucial to understand ways in which help seeking behavior can be encouraged. Our findings suggest that different dimensions within family cohesion and social networks have differential impacts on help-seeking behavior. Given the multifaceted nature of family cohesion and cultural relevance, the implications of our findings for theory and practice will be discussed.

Keywords: family cohesion, social networks, Asian American, Latino American, help-seeking behavior

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Authors: Sylvia Graczka

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Waste related environmental problems are not only exploding but are also spotlighted for capacity shortages in recycling, as China announced its ban on waste imports. According to the waste hierarchy, prevention is the primary solution for waste, and also the cheapest. Waste related environmental pollution as externality puts an ever-growing burden on communities bearing the social costs. Economic policies often claim to be pro-environment, this often appears only theoretically, or at the level of principles. There are few concrete occurrences of tools in economic policies, such as green taxes, that are truly effective in stimulating the shift towards waste reduction. The paper presents theoretical economic policy tools based on literature review, and case studies on applied economic policy tools by analyzing policy papers, strategies in force, in line with ‘polluter pays’ and ‘extended producer responsibility’ principles. The study also emphasizes the differences between the broader notion of waste reduction and that of waste minimization, parallel to the difference between resource efficiency and resource savings. It also puts the issue in the context of neoclassical environmental economics and ecological economics, to present alternatives in approach. The research concludes in identifying effective economic policy tools that support the reduction of material use, and the prevention of waste. Consumer and producer awareness of waste problems and consciousness related to their choices are inevitable to make economic policy tools work effectively.

Keywords: economic policy, producer responsibility, resource efficiency, waste prevention

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Authors: Betül Garda

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Technological developments have diversified marketing activities of the tourism sector and it has increased tourism opportunities to compete on a global scale for tourism businesses. Tourism businesses have been forced to use its core skills and knowledge effectively with the increase in effectiveness of the technology in the global competitive environment. Tourism businesses have been reached beyond the traditional boundaries because of their commercial activities, so, the boundaries of the national market either eliminated or blurred. Therefore, the internet is the alternative promotion tool and distribution channel to providing unlimited facilities for tourism suppliers. For example, the internet provides an opportunity to reach customers on a global scale with direct email marketing, advertising, customer service, promotion, sales, and marketing. Tourism businesses have improved themselves with the continuous information flows and also they have provided the permanence of the changes. Especially in terms of tourism businesses, social media is emerging as an extremely important tool in the use of knowledge effectively. This research paper investigates the impact of social media on the tourism businesses. A social networking site is a type of social media that provides a platform for business and people to connect with each other. Social media is so flexible that it can be used for both leisure and business purposes. In the tourism industry, social networking sites are one of the essential tools that play an important and beneficial role. The topic that will be discussed in this research paper are consumer behavior, connection with consumers, effectiveness in terms of time and cost, creating brand awareness and building the image of the company, promoting company, targeting consumers in a conceptual frame.

Keywords: branding, promoting, social media in tourism, tourism marketing tools

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Authors: Surinder Singh, Meenakshi

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In this paper MZI-FBG based optical add drop multiplexer is designed and its performance is analyzed in the ring network. In the ring network nodes are composed of optical add drop multiplexer, transmitter and receiver. OADM is used to add or drop any frequency at intermediate nodes without affecting other channels. In this paper the performance of the ring network is carried out by varying various kinds of fiber with or without amplifiers.

Keywords: OADM, ring network, MZI-FBG, transmitter

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Authors: Auliya Adzillatin Uzhma, Ismu Rini Dwi Ari, I. Nyoman Suluh Wijaya

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Corporate Social Responsibility (CSR) programme in Pantai Harapan Jaya village is cultivation of mangrove and fishery capital distribution, to achieve the goal the CSR programme needed participation from the society in it. Moeliono in Fahrudin (2011) mentioned that participation from society is based by intrinsic reason from inside people it self and extrinsic reason from the other who related to him or from connection with other people. The fundamental connection who caused more boundaries from action which the organization can do called the social structure. The purpose of this research is to know the form of public participation and the density of the villager and people who is participated in CSR programme. This research use Social Network Analysis method by knew the Rate of Participation and Density. The result of the research is people who is involved in the programme is lived in Dusun Pondok Dua and they work in fisheries field. Rate of Participation is 11,61 and that means people involved in 11 or 12 activites of CSR Programme. The rate of participation of CSR Programme is categorized as high rate participation. The density value from the participant is 0.516 it’s mean that 51.6% of the people that participated is involved in the same step of CSR programme.

Keywords: community participation, social network analysis, corporate social responsibility, urban and regional studies

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Authors: Hajnalka Szarvas

Abstract:

The research examines the effectiveness of e-participation tools and methods from a point of view of cultural fitting to the Hungarian community traditions. Participation can have very different meanings depending on the local cultural and historical traditions, experiences of the certain societies. Generally when it is about e-democracy or e-participation tools most of the researches are dealing with its technological sides and novelties, but there is not much said about the cultural and social context of the different platforms. However from the perspective of their success it would be essential to look at the human factor too, the actual users, how the certain DMS or any online platform is fitting to the way of thought, the way of functioning of the certain society. Therefore the paper will explore that to what extent the different online platforms like Loomio, Democracy OS, Your Priorities EVoks, Populus, miutcank.hu, Liquid Democracy, Brain Bar Budapest Lab are compatible with the Hungarian mental structures and community traditions, the contents of collective mind about community functioning. As a result the influence of cultural embeddedness of the logic of e-participation development tools on success of these methods will be clearly seen. Furthermore the most crucial factors in general which determine the efficiency of e-participation development tools in Hungary will be demonstrated.

Keywords: cultural embeddedness, e-participation, local community traditions, mental structures

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