Search results for: distributed jobs framework

Authors: Neda Jalaliyoon, Nooh Abu Bakar, Hamed Taherdoost

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The present research recommended balanced scorecard (BSC) framework to appraise the performance of the universities. As the original model of balanced scorecard has four perspectives in order to implement BSC in present research the same model with “financial perspective”, “customer”,” internal process” and “learning and growth” is used as well. With applying fuzzy Delphi method (FDM) and questionnaire sixteen measures of performance were identified. Moreover, with using the analytic network process (ANP) the weights of the selected indicators were determined. Results indicated that the most important BSC’s aspect were Internal Process (0.3149), Customer (0.2769), Learning and Growth (0.2049), and Financial (0.2033) respectively. The proposed BSC framework can help universities to enhance their efficiency in competitive environment.

Keywords: balanced scorecard, higher education, fuzzy delphi method, analytic network process (ANP)

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Authors: Jasmin Cowin

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Augmented reality digital technologies, big data, and the need for a teacher workforce able to meet the demands of a knowledge-based society are poised to lead to major changes in the field of education. This paper explores applications and educational use cases of augmented reality digital technologies for educational organizations during the Fourth Industrial Revolution. The Fourth Industrial Revolution requires vision, flexibility, and innovative educational conduits by governments and educational institutions to remain competitive in a global economy. Educational organizations will need to focus on teaching in and for a digital age to continue offering academic knowledge relevant to 21st-century markets and changing labor force needs. Implementation of contemporary disciplines will need to be embodied through learners’ active knowledge-making experiences while embracing ubiquitous accessibility. The power of distributed ledger technology promises major streamlining for educational record-keeping, degree conferrals, and authenticity guarantees. Augmented reality digital technologies hold the potential to restructure educational philosophies and their underpinning pedagogies thereby transforming modes of delivery. Structural changes in education and governmental planning are already increasing through intelligent systems and big data. Reimagining and redesigning education on a broad scale is required to plan and implement governmental and institutional changes to harness innovative technologies while moving away from the big schooling machine.

Keywords: fourth industrial revolution, artificial intelligence, big data, education, augmented reality digital technologies, distributed ledger technology

Procedia PDF Downloads 275

Authors: Serdar Öge, Alpay Karasoy, Özlem Kara

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Although women have merit in their jobs, they still are located very few in the top management in many sectors. There are many causes of such situation. Such a situation creates obstacles; especially invisible ones are called “glass ceiling syndrome”. Also, studies which handle this subject in academic community are very few. The aim of this research is to reach the results about glass ceiling obstacles in terms of female teaching staff (academics) working in higher education institutions. To this end, our study was performed on female academics working at Selcuk University, Konya / Turkey. Our study's main aim can be expressed as to determine whether there are glass ceiling obstacles for female academics working at the higher education institution in question, to measure their glass ceiling perceptions and, thus, to identify what the glass ceiling barrier components for them to promotion to senior management positions are.

Keywords: career, career barriers, glass ceiling syndrome, academics

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Authors: César Coelho, Nikolai Wiegand

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The advancement of nanosatellite technology has opened new avenues for cost-effective and faster space missions. The NanoSat MO Framework (NMF) from the European Space Agency (ESA) provides a modular and simpler approach to the development of flight software and operations of small satellites. This paper presents a methodology using the NMF together with Docker for simulating constellations of satellites. By leveraging Docker containers, the software environment of individual satellites can be easily replicated within a simulated constellation. This containerized approach allows for rapid deployment, isolation, and management of satellite instances, facilitating comprehensive testing and development in a controlled setting. By integrating the NMF lightweight simulator in the container, a comprehensive simulation environment was achieved. A significant advantage of using Docker containers is their inherent scalability, enabling the simulation of hundreds or even thousands of satellites with minimal overhead. Docker's lightweight nature ensures efficient resource utilization, allowing for deployment on a single host or across a cluster of hosts. This capability is crucial for large-scale simulations, such as in the case of mega-constellations, where multiple traditional virtual machines would be impractical due to their higher resource demands. This ability for easy horizontal scaling based on the number of simulated satellites provides tremendous flexibility to different mission scenarios. Our results demonstrate that leveraging Docker containers with the NanoSat MO Framework provides a highly efficient and scalable solution for simulating satellite constellations, offering not only significant benefits in terms of resource utilization and operational flexibility but also enabling testing and validation of ground software for constellations. The findings underscore the importance of taking advantage of already existing technologies in computer science to create new solutions for future satellite constellations in space.

Keywords: containerization, docker containers, NanoSat MO framework, satellite constellation simulation, scalability, small satellites

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Authors: Mohit Taneja, Ravi Kiran, S. C. Bose

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Entrepreneurs’ role in achieving Sustainable development goals is crucial as the growth potential of any region depends upon the number and the success rate of entrepreneurial firms. This paper is an effort to examine the relationship between Sustainable growth (SG) with Entrepreneurial attitude (EA) and Entrepreneurial intention (EI) in the context of the Indian economy. The mediation effect of EI between EA and SG has been considered. Partial least square (PLS) –Structural Equation Model (SEM) software was used to design the framework. Students enrolled in entrepreneurship courses of higher educational institutes (HEI) of Punjab, Haryana, and the National Capital Region NCR were contacted for data collection. The National Institutional Ranking Framework (NIRF) framework was used in selecting HEIs and data collected from 589 students was considered for analysis. McGee’s multi-dimensional scale for measuring ESE and the scale of Linan & Chen for measuring EI & ES (SG) was used. Results highlight that EA has a strong impact on EI (p≤ 0.001) and EI has a positive and strong relationship with SG (ES) as β value for the same is 0.683 (p≤ 0.001). The current study also reflects the mediating effect of EI among EA and ES, as the results show that the combined β value of both EA and EI (i.e.0.684*0.683= 0.467) is more than the direct influence of EA on ES (β=0.265). EA, with the mediating effect of EI can enhance the opportunity for achieving SG, which suggests that in order to increase the venture success rate and to attain SG, emphasis should be given to EI along with EA. The study has been investigated in three regions of India. Future studies can be extended to other South Asian countries for generalization.

Keywords: entrepreneurship, sustainable growth, entrepreneurship intention, entrepreneurship attitude

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Authors: Xianwei Zheng, Yuan Yan Tang

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Classical window Fourier transform has been widely used in signal processing, image processing, machine learning and pattern recognition. The related Gabor transform is powerful enough to capture the texture information of any given dataset. Recently, in the emerging field of graph signal processing, researchers devoting themselves to develop a graph signal processing theory to handle the so-called graph signals. Among the new developing theory, windowed graph Fourier transform has been constructed to establish a time-frequency analysis framework of graph signals. The windowed graph Fourier transform is defined by using the translation and modulation operators of graph signals, following the similar calculations in classical windowed Fourier transform. Specifically, the translation and modulation operators of graph signals are defined by using the Laplacian eigenvectors as follows. For a given graph signal, its translation is defined by a similar manner as its definition in classical signal processing. Specifically, the translation operator can be defined by using the Fourier atoms; the graph signal translation is defined similarly by using the Laplacian eigenvectors. The modulation of the graph can also be established by using the Laplacian eigenvectors. The windowed graph Fourier transform based on these two operators has been applied to obtain time-frequency representations of graph signals. Fundamentally, the modulation operator is defined similarly to the classical modulation by multiplying a graph signal with the entries in each Fourier atom. However, a single Laplacian eigenvector entry cannot play a similar role as the Fourier atom. This definition ignored the relationship between the translation and modulation operators. In this paper, a new definition of the modulation operator is proposed and thus another time-frequency framework for graph signal is constructed. Specifically, the relationship between the translation and modulation operations can be established by the Fourier transform. Specifically, for any signal, the Fourier transform of its translation is the modulation of its Fourier transform. Thus, the modulation of any signal can be defined as the inverse Fourier transform of the translation of its Fourier transform. Therefore, similarly, the graph modulation of any graph signal can be defined as the inverse graph Fourier transform of the translation of its graph Fourier. The novel definition of the graph modulation operator established a relationship of the translation and modulation operations. The new modulation operation and the original translation operation are applied to construct a new framework of graph signal time-frequency analysis. Furthermore, a windowed graph Fourier frame theory is developed. Necessary and sufficient conditions for constructing windowed graph Fourier frames, tight frames and dual frames are presented in this paper. The novel graph signal time-frequency analysis framework is applied to signals defined on well-known graphs, e.g. Minnesota road graph and random graphs. Experimental results show that the novel framework captures new features of graph signals.

Keywords: graph signals, windowed graph Fourier transform, windowed graph Fourier frames, vertex frequency analysis

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Authors: Hussain Sajid, Jung-Hun Shin, Kum-Won Cho

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Image super-resolution is the most common computer vision problem with many important applications. Generative adversarial networks (GANs) have promoted remarkable advances in single-image super-resolution (SR) by recovering photo-realistic images. However, high memory requirements of GAN-based SR (mainly generators) lead to performance degradation and increased energy consumption, making it difficult to implement it onto resource-constricted devices. To relieve such a problem, In this paper, we introduce an optimized and highly efficient architecture for SR-GAN (generator) model by utilizing model compression techniques such as Knowledge Distillation and pruning, which work together to reduce the storage requirement of the model also increase in their performance. Our method begins with distilling the knowledge from a large pre-trained model to a lightweight model using different loss functions. Then, iterative weight pruning is applied to the distilled model to remove less significant weights based on their magnitude, resulting in a sparser network. Knowledge Distillation reduces the model size by 40%; pruning then reduces it further by 18%. To accelerate the learning process, we employ the Horovod framework for distributed training on a cluster of 2 nodes, each with 8 GPUs, resulting in improved training performance and faster convergence. Experimental results on various benchmarks demonstrate that the proposed compressed model significantly outperforms state-of-the-art methods in terms of peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and image quality for x4 super-resolution tasks.

Keywords: single-image super-resolution, generative adversarial networks, knowledge distillation, pruning

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Authors: Cailyn Green

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Substance use counselors have a variety of fast-paced tasks and responsibilities. Professional resources are designed to support professionals in making their job duties easier and less stressful. The purpose of this research was to identify what types of resources would support addiction counselors in performing their job duties. Counselors often must jump in and facilitate a group counseling session with little to no time for prep. This causes stress and creates pressure to come up with a clinical group activity in little time. The researcher utilized qualitative interviews focused on identifying what types of resources would support addiction counselors in doing their jobs easier and effectively. The researcher visited 23 different addiction counseling facilities seeking participants for the interviews. Altogether 15 interviews were collected across six different substance-use counseling facilities. The interviews guided the researcher toward creating an open education resource (OER) of group activities for addiction counselors to utilize.

Keywords: addiction, counseling, resources, OER, treatment

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Authors: Kolade Olawande Owoeye, George R. S. Weir

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The widespread of terrorism and extremism activities on the internet has become a major threat to the government and national securities due to their potential dangers which have necessitated the need for intelligence gathering via web and real-time monitoring of potential websites for extremist activities. However, the manual classification for such contents is practically difficult or time-consuming. In response to this challenge, an automated classification system called composite technique was developed. This is a computational framework that explores the combination of both semantics and syntactic features of textual contents of a web. We implemented the framework on a set of extremist webpages dataset that has been subjected to the manual classification process. Therein, we developed a classification model on the data using J48 decision algorithm, this is to generate a measure of how well each page can be classified into their appropriate classes. The classification result obtained from our method when compared with other states of arts, indicated a 96% success rate in classifying overall webpages when matched against the manual classification.

Keywords: extremist, web pages, classification, semantics, posit

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Authors: Ismael Ávila, José Reynaldo F. Filho, Vasco Varanda Picchi

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The ever-growing consumer awareness of food provenance in Brazil is driving the creation of a trusted ecosystem around the animal protein supply chain. The traceability and accountability requirements of such an ecosystem demand a blockchain layer to strengthen the weak links in that chain. For that, direct involvement of the companies in the blockchain transactions, including as validator nodes of the network, implies formalizing a partnership with the consortium behind the ecosystem. Yet, their compliance standards usually require that a formal governance structure is in place before they agree with any membership terms. In light of such a strategic role of blockchain governance, the paper discusses a framework for tailoring a governance model for a blockchain-based solution aimed at the meat supply chain and evaluates principles and attributes in terms of their relevance to the development of a robust trust ecosystem.

Keywords: blockchain, governance, trust ecosystem, supply chain, traceability

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Authors: Rohit Khankhoje

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The rapid evolution of software development practices has given rise to an increasing demand for efficient and effective test automation. The paper titled "Mastering Test Automation: Bridging Gaps for Seamless QA" delves into the crucial aspects of test automation, addressing the obstacles faced by organizations in achieving flawless quality assurance. The paper highlights the importance of bridging knowledge gaps within organizations, emphasizing the necessity for management to acquire a deeper comprehension of test automation scenarios, coverage, report trends, and the importance of communication. To tackle these challenges, this paper introduces innovative solutions, including the development of an automation framework that seamlessly integrates with test cases and reporting tools like TestRail and Jira. This integration facilitates the automatic recording of bugs in Jira, enhancing bug reporting and communication between manual QA and automation teams as well as TestRail have all newly added automated testcases as soon as it is part of the automation suite. The paper demonstrates how this framework empowers management by providing clear insights into ongoing automation activities, bug origins, trend analysis, and test case specifics. "Mastering Test Automation" serves as a comprehensive guide for organizations aiming to enhance their quality assurance processes through effective test automation. It not only identifies the common pitfalls and challenges but also offers practical solutions to bridge the gaps, resulting in a more streamlined and efficient QA process.

Keywords: automation framework, API integration, test automation, test management tools

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Authors: Katalin Bándy

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The European emigration of highly educated immigrants draws attention to the problem of brain drain. Due to the Eastern European countries joining the EU and the opening of the Western European labour market the west-wards migration brisked up. By now another problem has been intensified correlated to migration: the migration of highly skilled workers related to brain waste tendencies. With some exceptions, educated immigrants from Eastern European countries are more likely to end up in unskilled jobs than residents. This paper is about to reveal the above-mentioned problems and this study is supported by the results of secondary pieces of research and the own survey made in the EU-15 among the Hungarian highly skilled (especially economics graduated) migrants, and it also examines the causes and in the focus there are the migrant motivations of the high-skilled young generation after the crisis.

Keywords: brain drain, brain waste, migration of highly-skilled, underemployment

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Authors: Todd Zhou, Mikhail Yurochkin

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Out-of-distribution (OOD) detection is receiving increasing amounts of attention in the machine learning research community, boosted by recent technologies, such as autonomous driving and image processing. This newly-burgeoning field has called for the need for more effective and efficient methods for out-of-distribution generalization methods. Without accessing the label information, deploying machine learning models to out-of-distribution domains becomes extremely challenging since it is impossible to evaluate model performance on unseen domains. To tackle this out-of-distribution detection difficulty, we designed a model selection pipeline algorithm and developed a model selection framework with different impurity and randomness measurements to evaluate and choose the best-performing models for out-of-distribution data. By exploring different randomness scores based on predicted probabilities, we adopted the out-of-distribution entropy and developed a custom-designed score, ”CombinedScore,” as the evaluation criterion. This proposed score was created by adding labeled source information into the judging space of the uncertainty entropy score using harmonic mean. Furthermore, the prediction bias was explored through the equality of opportunity violation measurement. We also improved machine learning model performance through model calibration. The effectiveness of the framework with the proposed evaluation criteria was validated on the Folktables American Community Survey (ACS) datasets.

Keywords: model selection, domain generalization, model fairness, randomness measurements, bias index

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Authors: Dipo Dunsin, Mohamed C. Ghanem, Karim Ouazzane

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Digital investigators often have a hard time spotting evidence in digital information. It has become hard to determine which source of proof relates to a specific investigation. A growing concern is that the various processes, technology, and specific procedures used in the digital investigation are not keeping up with criminal developments. Therefore, criminals are taking advantage of these weaknesses to commit further crimes. In digital forensics investigations, artificial intelligence is invaluable in identifying crime. It has been observed that an algorithm based on artificial intelligence (AI) is highly effective in detecting risks, preventing criminal activity, and forecasting illegal activity. Providing objective data and conducting an assessment is the goal of digital forensics and digital investigation, which will assist in developing a plausible theory that can be presented as evidence in court. Researchers and other authorities have used the available data as evidence in court to convict a person. This research paper aims at developing a multiagent framework for digital investigations using specific intelligent software agents (ISA). The agents communicate to address particular tasks jointly and keep the same objectives in mind during each task. The rules and knowledge contained within each agent are dependent on the investigation type. A criminal investigation is classified quickly and efficiently using the case-based reasoning (CBR) technique. The MADIK is implemented using the Java Agent Development Framework and implemented using Eclipse, Postgres repository, and a rule engine for agent reasoning. The proposed framework was tested using the Lone Wolf image files and datasets. Experiments were conducted using various sets of ISA and VMs. There was a significant reduction in the time taken for the Hash Set Agent to execute. As a result of loading the agents, 5 percent of the time was lost, as the File Path Agent prescribed deleting 1,510, while the Timeline Agent found multiple executable files. In comparison, the integrity check carried out on the Lone Wolf image file using a digital forensic tool kit took approximately 48 minutes (2,880 ms), whereas the MADIK framework accomplished this in 16 minutes (960 ms). The framework is integrated with Python, allowing for further integration of other digital forensic tools, such as AccessData Forensic Toolkit (FTK), Wireshark, Volatility, and Scapy.

Keywords: artificial intelligence, computer science, criminal investigation, digital forensics

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Authors: Nhabhat Chaimongkol

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Human capital is one of the factors that are vital for economic growth. This is especially true as humans will face increasingly more forms of disruptive technology in the near future. Therefore, there is a need to develop human capital in order to overcome the current uncertainty in the global economy and the future of jobs. In recent years, Thailand has increasingly devoted more attention to developing its human capital. The Thai government has raised this issue in its national agenda, which is part of its 20-year national strategy. Currently, there are multiple challenges and solutions regarding this issue. This study aims to find out what are the challenges and solutions to human capital development in Thailand. The research in this study uses mixed methods consisting of quantitative and qualitative research methods. The results show that while Thailand has many plans to develop human capital, it is still lacking the necessary actions and integrations that are required to achieve its goals. Finally, the challenges and solutions will be discussed in detail.

Keywords: challenges, human capital, solutions, Thailand

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Authors: Babatunde Dosumu, Obuks Ejohwomu, Akilu Yunusa-Kaltungo

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Cost estimation in construction is often difficult, particularly when dealing with risks and uncertainties, which are inevitable and peculiar to developing countries like Nigeria. Direct consequences of these are major deviations in cost, duration, and quality. The fundamental aim of this study is to develop a framework for assessing the impacts of risk on cost estimation, which in turn causes variabilities between contract sum and final account. This is very important, as initial estimates given to clients should reflect the certain magnitude of consistency and accuracy, which the client builds other planning-related activities upon, and also enhance the capabilities of construction industry professionals by enabling better prediction of the final account from the contract sum. In achieving this, a systematic literature review was conducted with cost variability and construction projects as search string within three databases: Scopus, Web of science, and Ebsco (Business source premium), which are further analyzed and gap(s) in knowledge or research discovered. From the extensive review, it was found that factors causing deviation between final accounts and contract sum ranged between 1 and 45. Besides, it was discovered that a cost estimation framework similar to Building Cost Information Services (BCIS) is unavailable in Nigeria, which is a major reason why initial estimates are very often inconsistent, leading to project delay, abandonment, or determination at the expense of the huge sum of money invested. It was concluded that the development of a cost estimation framework that is adjudged an important tool in risk shedding rather than risk-sharing in project risk management would be a panacea to cost estimation problems, leading to cost variability in the Nigerian construction industry by the time this ongoing Ph.D. research is completed. It was recommended that practitioners in the construction industry should always take into account risk in order to facilitate the rapid development of the construction industry in Nigeria, which should give stakeholders a more in-depth understanding of the estimation effectiveness and efficiency to be adopted by stakeholders in both the private and public sectors.

Keywords: cost variability, construction projects, future studies, Nigeria

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Authors: Saeed Nayeri

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This paper explores the feasibility of openness within firms' boundaries during process innovation and identifies the key determinants of open process innovation (OPI). Through a systematic review of 78 research studies published between 2001 and 2024, the author synthesized diverse findings into a comprehensive framework detailing OPI attributes and pillars. The identified OPI attributes encompass themes such as technology intensity, significance, magnitude, and locus of exploitation, while the OPI pillars include mechanisms, partners, achievements, and antecedents. Additionally, the author critically analysed gaps in the literature, proposing future research directions that advocate for a broader methodological approach, increased emphasis on theory development and testing, and more cross-national and cross-sectoral studies to advance understanding in this field.

Keywords: open innovation, process innovation, OPI attributes, systematic literature review, organizational openness

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Authors: Jeremy Berry, Shannon Rodrigue, Caroline Norris

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This study explores the impact of tattoos and/or brands on the recovery of sex trafficking survivors. Many victims of sex trafficking are forced or coerced to take markings of ownership while in the sex trafficking trade in the form of painful tattoos or brands. As a result, victims who are rescued and in recovery often must live with permanent reminders of their traumatic experiences or are left to resort to expensive cosmetic or cover-up jobs, which for many are out of reach. As is often true of domestic violence victims who are left with scars from their abusers, the impact of these permanent markers can delay the healing process and contribute to post-traumatic stress. This study tells the story from the perspectives of the survivors of sex trafficking, how these specific permanent reminders impacted their healing. The study employs a thematic analysis of interviews with sex trafficking victims via focus group interviews.

Keywords: sex trafficking, tattoos, trauma, healing

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Authors: Maria Florencia Ferrer, Saulo Fabiano Amancio-Vieira

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The use of the blockchain in the public sector is present today and no longer the future of disruptive institutional and technological models. There are still some cultural barriers and resistance to the proper use of its potential. This research aims to present the strengths and weaknesses of using a public-permitted and distributed network in the context of the public sector. Therefore, bibliographical/documentary research was conducted to raise the main aspects of the studied platform, focused on the use of the main demands of the public sector. The platform analyzed was LACChain, which is a global alliance composed of different actors in the blockchain environment, led by the Innovation Laboratory of the Inter-American Development Bank Group (IDB Lab) for the development of the blockchain ecosystem in Latin America and the Caribbean. LACChain provides blockchain infrastructure, which is a distributed ratio technology (DLT). The platform focuses on two main pillars: community and infrastructure. It is organized as a consortium for the management and administration of an infrastructure classified as public, following the ISO typologies (ISO / TC 307). It is, therefore, a network open to any participant who agrees with the established rules, which are limited to being identified and complying with the regulations. As benefits can be listed: public network (open to all), decentralized, low transaction cost, greater publicity of transactions, reduction of corruption in contracts / public acts, in addition to improving transparency for the population in general. It is also noteworthy that the platform is not based on cryptocurrency and is not anonymous; that is, it is possible to be regulated. It is concluded that the use of record platforms, such as LACChain, can contribute to greater security on the part of the public agent in the migration process of their informational applications.

Keywords: blockchain, LACChain, public sector, technological disruptions

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Authors: Neda Seyyedi, Reza Berangi

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Voice over IP (VOIP) network, also known as Internet telephony, is growing increasingly having occupied a large part of the communications market. With the growth of each technology, the related security issues become of particular importance. Taking advantage of this technology in different environments with numerous features put at our disposal, there arises an increasing need to address the security threats. Being IP-based and playing a signaling role in VOIP networks, Session Initiation Protocol (SIP) lets the invaders use weaknesses of the protocol to disable VOIP service. One of the most important threats is denial of service attack, a branch of which in this article we have discussed as flooding attacks. These attacks make server resources wasted and deprive it from delivering service to authorized users. Distributed denial of service attacks and attacks with a low rate can mislead many attack detection mechanisms. In this paper, we introduce a mechanism which not only detects distributed denial of service attacks and low rate attacks, but can also identify the attackers accurately. We detect and prevent flooding attacks in SIP protocol using Shannon (FDP-S), Renyi (FDP-R) and Tsallis (FDP-T) entropy. We conducted an experiment to compare the percentage of detection and rate of false alarm messages using any of the Shannon, Renyi and Tsallis entropy as a measure of disorder. Implementation results show that, according to the parametric nature of the Renyi and Tsallis entropy, by changing the parameters, different detection percentages and false alarm rates will be gained with the possibility to adjust the sensitivity of the detection mechanism.

Keywords: VOIP networks, flooding attacks, entropy, computer networks

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Authors: Chipo Mugova, Zuko Mjobo

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The aim of this study was to evaluate the cost of quality (COQ) at a local foundry business to identify the contribution of its units and processes to quality costs within the foundry’s operations. The foundry selected for detailed case study is one of major businesses that have been targeted by the government to produce components for building and re-furbishing wagons and trains. The study aimed at identifying areas in the foundry’s processes in which investment needs to be made to reduce quality costs. This is in alignment with government’s vision of promoting local business to support local markets leading to creation of jobs, and hence reduction of unemployment rate in South Africa. The methodology adopted used cost of quality models. Results from the study indicated that internal failure costs were significantly higher than all other cost of quality categories, taking more than 60% of the business’s income.

Keywords: appraisal costs, cost of quality, failure costs, local content, prevention costs

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Authors: Arshia Aflaki, Hadis Karimipour, Anik Islam

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Generative Adversarial Attacks (GAAs) threaten critical sectors, ranging from fingerprint recognition to industrial control systems. Existing Deep Learning (DL) algorithms are not robust enough against this kind of cyber-attack. As one of the most critical industries in the world, the power grid is not an exception. In this study, a Deep Reinforcement Learning-based (DRL) framework assisting the DL model to improve the robustness of the model against generative adversarial attacks is proposed. Real-world smart grid stability data, as an IIoT dataset, test our method and improves the classification accuracy of a deep learning model from around 57 percent to 96 percent.

Keywords: generative adversarial attack, deep reinforcement learning, deep learning, IIoT, generative adversarial networks, power system

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Authors: S. Halioui, I. Arikan, M. Schmidt

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Tourism sector in Tunisia faces several problems that range from economic challenges to environmental degradation and social instability. These problems have been intensified because of the increased competition in the tourism market, the political instability, financial crises, and recently terrorism problems have aggravated the situation. As a consequence, a new framework that promotes sustainable tourism in the country and increases its competitiveness is urgently needed. Planning for sustainable tourism sector requires the integration of complex interactions between economic, social and environmental aspects. Sustainable tourism principles can be implemented with the help of Strategic Environmental Assessment (SEA) process, which ensures the full integration of economic, social and environmental considerations while planning for the tourism sector in Tunisia. Results of the paper have broad implications for policy makers and tourism professionals.

Keywords: sustainable tourism, strategic environmental assessment, tourism planning, policy

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Authors: Penelope Paliadelis, Asheley Jones, Glenn Campbell

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This paper discusses the development of co-designed capability frameworks for two large multinational organizations led by a university department. The aim was to create evidence-based, integrated capability frameworks that could define, identify, and measure human skill capabilities independent of specific work roles. The frameworks capture and cluster human skills required in the workplace and capture their application at various levels of mastery. Identified capability gaps inform targeted learning opportunities for workers to enhance their employability skills. The paper highlights the value of this evidence-based framework development process in capturing, defining, and assessing desired human-focused capabilities for organizational growth and success.

Keywords: capability framework, human skills, work-integrated learning, credentialing, digital badging

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Authors: Krzysztof Pomorski

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In the given work we present universal mathematical framework for modeling of cattle farm system that can set and validate various hypothesis that can be tested against experimental data. The presented work is preliminary but it is expected to be valid tool for future deeper analysis that can result in new class of prediction methods allowing early detection of cow dieseaes as well as cow performance. Therefore the presented work shall have its meaning in agriculture models and in machine learning as well. It also opens the possibilities for incorporation of certain class of biological models necessary in modeling of cow behavior and farm performance that might include the impact of environment on the farm system. Particular attention is paid to the model of coupled oscillators that it the basic building hypothesis that can construct the model showing certain periodic or quasiperiodic behavior.

Keywords: coupled ordinary differential equations, cattle farm system, numerical methods, stochastic differential equations

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Authors: Jihad Daba, Jean-Pierre Dubois

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Multi path fading noise degrades the performance of cellular communication, most notably in femto- and pico-cells in 3G and 4G systems. When the wireless channel consists of a small number of scattering paths, the statistics of fading noise is not analytically tractable and poses a serious challenge to developing closed canonical forms that can be analysed and used in the design of efficient and optimal receivers. In this context, noise is multiplicative and is referred to as stochastically local fading. In many analytical investigation of multiplicative noise, the exponential or Gamma statistics are invoked. More recent advances by the author of this paper have utilized a Poisson modulated and weighted generalized Laguerre polynomials with controlling parameters and uncorrelated noise assumptions. In this paper, we investigate the statistics of multi-diversity stochastically local area fading channel when the channel consists of randomly distributed Rayleigh and Rician scattering centers with a coherent specular Nakagami-distributed line of sight component and an underlying doubly stochastic Poisson process driven by a lognormal intensity. These combined statistics form a unifying triply stochastic filtered marked Poisson point process model.

Keywords: cellular communication, femto and pico-cells, stochastically local area fading channel, triply stochastic filtered marked Poisson point process

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Authors: Amany ElShazly, Lubna A. Sherif

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The entanglement of humans with the environment has always been inevitable and often causes destruction. In this respect, ‘Ecolinguistics’ helps humans to understand the link between languages and the environment. Stibbe (2014a) has indicated that ‘linguistics’, particularly, Critical Discourse Studies (CDS), provides an interpretation of language which shapes world views, while the ‘eco’ side maintains the life-sustaining interactions of humans and the physical environment. This paper considers two key ecological instances, namely: The Grand Ethiopian Renaissance Dam (GERD) as a focal point of political dispute and THE LINE project as well as Etthadar lel Akhdar (Go Green Initiative) as two examples of combating ecological degradation. ‘Ecosophy’ as explained by Naess (1996) is used to describe the ecolinguistic framework, which assesses discourse where the linguistic lens focuses on the use of metaphor, and ‘Positive Discourse’ framework, which resonates with respect and care for the natural world.

Keywords: ecosophy, critical discourse studies, metaphor, positive discourse, social semiotics, ecolinguistics

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Authors: Maria Elisa B. Manuel

Abstract:

The Philippine housing sector is bracing challenges with the massive housing backlog and the adamant cycle of relocation, resettlement and returns to the cities of informal settler families due to the vast inaccessibility of necessities and opportunities in the past off-city housing projects. Bistekville 2 has been established as a model socialized housing project by utilizing government partnerships with private developers and individuals in the first in-city and onsite resettlement effort in the country. The study looked into the resilience of the residents to idiosyncratic economic shocks by analyzing their vulnerabilities, assets and coping strategies. The study formulated an economic resilience framework to identify how these factors that interact to build the household’s capacity to positively adapt to sudden expenses in their households. The framework is supplemented with a scale that presents the proximity of the household to resilience by identifying through its indicators whether the households are in the level of subsistence, coping, adaptive or transformative. Survey interviews were conducted with 91 households from Bistekville 2 on the components that have been identified by the framework that was processed with qualitative and quantitative processes. The study has found that the households are highly vulnerable due to their family composition and other conditions such as unhealthy loans, inconsistent amortization payment. Along with their high vulnerability, the households have inadequate strategies to anticipate shocks and primarily react to the shock. This has led to the conclusion that the households do not reflect resilience to idiosyncratic economic shocks and are still at the level of coping.

Keywords: idiosyncratic economic shocks, socialized housing, economic resilience, economic vulnerability, adaptive capacity

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Authors: Ujjal Chattaraj, P. C. Dasbebartta, S. Bhuyan

Abstract:

This paper aims to propose a method to detect the action of the driver’s eyes, using the concept of face detection. There are three major key contributing methods which can rapidly process the framework of the facial image and hence produce results which further can program the reactions of the vehicles as pre-programmed for the traffic safety. This paper compares and analyses the methods on the basis of their reaction time and their ability to deal with fluctuating images of the driver. The program used in this study is simple and efficient, built using the AdaBoost learning algorithm. Through this program, the system would be able to discard background regions and focus on the face-like regions. The results are analyzed on a common computer which makes it feasible for the end users. The application domain of this experiment is quite wide, such as detection of drowsiness or influence of alcohols in drivers or detection for the case of identification.

Keywords: AdaBoost learning algorithm, face detection, framework, traffic safety

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

Abstract:

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

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

Procedia PDF Downloads 156