An Extended Network Coding Opportunity Discovery Scheme in Wireless Networks

Network coding is known as a promising approach to improve wireless network performance. How to discover the coding opportunity in relay nodes is really important for it. There are more coding chances, there are more times it can improve network throughput by network coding operation. In this paper, an extended network coding opportunity discovery scheme (ExCODE) is proposed, which is realized by appending the current node ID and all its 1-hop neighbors' IDs to the packet. ExCODE enables the next hop relay node to know which nodes else have already overheard the packet, so it can discover the potential coding opportunities as much as possible. ExCODE expands the region of discovering coding chance to n-hops, and have more opportunities to execute network coding operation in each relay node. At last, we implement ExCODE over the AODV protocol, and efficiency of the proposed mechanism is demonstrated with NS2 simulations, compared to the existing coding opportunity discovery scheme.Comment: 15 pages and 7 figure

How should a small company interact in its business network to sustain its exchange effectiveness?

This paper investigates the dynamic alignment of network and business\ud development of two small firms in the printing industry. Developments are\ud followed over more than 8 years. The aim of the paper is to understand how\ud small firms can manage their network relations by maintaining both their\ud efficiency in existing business and flexibility to develop new business. The case comparison suggests that different networking approaches drive business\ud development. For successful business development both strong and varied ties\ud as well as the existence of different intermediary functions of partners are\ud necessary

Knowledge Discovery in the SCADA Databases Used for the Municipal Power Supply System

This scientific paper delves into the problems related to the develop-ment of intellectual data analysis system that could support decision making to manage municipal power supply services. The management problems of mu-nicipal power supply system have been specified taking into consideration modern tendencies shown by new technologies that allow for an increase in the energy efficiency. The analysis findings of the system problems related to the integrated computer-aided control of the power supply for the city have been given. The consideration was given to the hierarchy-level management decom-position model. The objective task targeted at an increase in the energy effi-ciency to minimize expenditures and energy losses during the generation and transportation of energy carriers to the Consumer, the optimization of power consumption at the prescribed level of the reliability of pipelines and networks and the satisfaction of Consumers has been defined. To optimize the support of the decision making a new approach to the monitoring of engineering systems and technological processes related to the energy consumption and transporta-tion using the technologies of geospatial analysis and Knowledge Discovery in databases (KDD) has been proposed. The data acquisition for analytical prob-lems is realized in the wireless heterogeneous medium, which includes soft-touch VPN segments of ZigBee technology realizing the 6LoWPAN standard over the IEEE 802.15.4 standard and also the segments of the networks of cellu-lar communications. JBoss Application Server is used as a server-based plat-form for the operation of the tools used for the retrieval of data collected from sensor nodes, PLC and energy consumption record devices. The KDD tools are developed using Java Enterprise Edition platform and Spring and ORM Hiber-nate technologies

Sentiment analysis and artificial neural networks-based econometric models for tourism demand forecasting

Purpose \u2013 This is the second step of a previous paper (Folgieri et al., 2017), where we modelled and applied a backpropagation Artificial Neural Network (ANN) to forecast tourists arrivals in Croatia. Tourism is a very important sector of current Countries\u2019 economies, and forcasting assumes even more an significant issue to lead the local tourist offer. In this context, early prediction on the tourist inflow represents a challenge as it is an opportunity in developing tourist income. Applying a Machine Learning Method for Decision Support and Pattern Discovery such as ANN, represents an occasion to achieve a greater accuracy if compared to results usually obtained by other methods, such as Linear Regression. Design \u2013 In this paper, we extended the model of the previously used backpropagation Artificial Neural Network, including data from sentiment analysis collected through social networks on the Internet. Methodology \u2013The accuracy of the neural network has been measured by the Mean Squared Error (MSE) and compared to results obtained applying the ANN without data coming from the sentiment analysis. Approach \u2013 Our approach consists in combining ideas from Tourism Economics and Information Technology, in particular Artificial Intelligence methods, such as Machine Learning and sentiment analysis, throught the Artificial Neural Networks (ANN) we used in our study. Findings \u2013 The results showed that including also data from sentiment analysis, the neural network model to predict tourists arrivals outperforms the previous obtained results. Originality of the research \u2013The idea to use ANN as a Decision Making tool to improve tourist services in a proactive way or in case of unexpected events is innovative. Adding data from sentiment analysis, we can add also tourists' preferences so considering collective intelligence and collective trends as factors which could influence a prediction

Bridging the biodiversity data gaps: Recommendations to meet users’ data needs

A strong case has been made for freely available, high quality data on species occurrence, in order to track changes in biodiversity. However, one of the main issues surrounding the provision of such data is that sources vary in quality, scope, and accuracy. Therefore publishers of such data must face the challenge of maximizing quality, utility and breadth of data coverage, in order to make such data useful to users. Here, we report a number of recommendations that stem from a content need assessment survey conducted by the Global Biodiversity Information Facility (GBIF). Through this survey, we aimed to distil the main user needs regarding biodiversity data. We find a broad range of recommendations from the survey respondents, principally concerning issues such as data quality, bias, and coverage, and extending ease of access. We recommend a candidate set of actions for the GBIF that fall into three classes: 1) addressing data gaps, data volume, and data quality, 2) aggregating new kinds of data for new applications, and 3) promoting ease-of-use and providing incentives for wider use. Addressing the challenge of providing high quality primary biodiversity data can potentially serve the needs of many international biodiversity initiatives, including the new 2020 biodiversity targets of the Convention on Biological Diversity, the emerging global biodiversity observation network (GEO BON), and the new Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES)

Ethics of undergraduate students: a study in Malaysian public universities

This paper aims to determine the ethics of undergraduate students in four aspects of moral processes; awareness, judgement, intention and behaviour. It further explores the impact of gender and academic disciplines on these four moral processes. A total of 2000 undergraduate students from six public universities in Malaysia involved in this study. Data were collected through survey consists of 14 ethical statements developed based on previous studies. Descriptive analysis (such as mean), t-test and Analysis of Variance (ANOVA) were employed for the data analysis. Overall, results reveal that student’s ethical level was mixed. The results also demonstrate that engineering students have low level of ethics, as compared to social science and science students. In terms of gender differences, female students appear to have higher level of ethics than their male counterparts. The findings of this study provide some educational and theoretical implications

National Systems of Innovation and Entrepreneurship: In Search of a Missing Link

The literature on national systems of innovation (NIS) has neglected the issue of entrepreneurship because of several incompatibilities between the two notions. The Schumpeterian legacy, the current person-centric view of entrepreneurship, and methodological problems related to treating entrepreneurship at the macro-level, have made it difficult to integrate entrepreneurship into the NIS perspective. At national level it is more appropriate to treat entrepreneurship as a 'property' (dimension) of NIS. In order to link NIS and entrepreneurship we must establish a common conceptual basis. Our argument is that the functional view of NIS and entrepreneurship presents a common basis for such an approach. We develop criteria for the entrepreneurial NIS which we define as being those that can change balance between individual and cooperative entrepreneurship; that enhance both the opportunity and skill aspects of entrepreneurship; and that can balance generation of uncertainty with support to business models and other organisations which pool uncertainty. From the NIS perspective, we explain entrepreneurship as a systemic phenomenon driven by complementarities between technological, market and institutional opportunities. This framework builds on three research traditions in the entrepreneurship/NIS literature (Schumpeterian, Kirznerian and Listian) which jointly form a multi-level, multi-dimensional framework for understanding entrepreneurship from a NIS perspective. This framework could be useful as a heuristic for empirical research on entrepreneurship. Finally, we analyse policies for entrepreneurship and find that they are highly dependent on underlying and previously discussed conceptions of entrepreneurship

The Performance of a Second Generation Service Discovery Protocol In Response to Message Loss

We analyze the behavior of FRODO, a second generation service discovery protocol, in response to message loss in the network. Earlier protocols, like UPnP and Jini rely on underlying network layers to enhance their failure recovery. A comparison with UPnP and Jini shows that FRODO performs more efficiently in maintaining consistency, with shorter latency, not relying on lower network layers for robustness and therefore functions correctly on a simple lightweight protocol stack

Mining Public Opinion on COVID-19 Vaccines using Unstructured Social Media Data

The emergence of the novel coronavirus (COVID-19), and the necessary separation of populations led to an unprecedented number of new social media users seeking information related to the pandemic. Nowadays, with an estimated 4.5 billion users worldwide, social media data offer an opportunity for near real-time analysis of large bodies of text related to disease outbreaks and vaccination. This study investigated and compared public discourse related to COVID-19 vaccines expressed on two popular social media platforms, Reddit and Twitter. Approximately 9.5 million Tweets and 70 thousand Reddit comments were analyzed from dates January 1, 2020, to March 1, 2022, and analyzed through topic modeling, sentiment analysis, and semantic network analysis. Sentiment analysis through the fine-tuned DistilRoBERTa model revealed that even though Twitter content was overall more negative than content expressed on Reddit, relatively similar changes in sentiment occurred among users of both online platforms. Reversals in sentiment trends typically occurred within relative proximity to events such as vaccine development news, vaccine release, frequent discussion of side-effects, the discovery of new variants, and pandemic fatigue. Topic modeling and semantic network analysis provided insight into how public discourse related to COVID-19 and vaccinations, misinformation, and vaccine hesitancy evolved over 26 months. Though misinformation and mention of conspiracy theories were detected with the analysis, the occurrence of both was less frequent than expected. This work provides a framework that could be scaled and utilized by public health officials to monitor disease outbreaks in near real-time in large communities as well as smaller local groups. Hopefully, the results from this study will help to guide and facilitate the implementation of targeted digital interventions among vaccine-hesitant populations and provide insights to public health officials to inform decision-making and effective policy development