Search results for: open data

Authors: Asteria Nsamba

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Social networking applications and tools have become compelling platforms for generating and sharing knowledge across the world. Social networking applications and tools refer to a variety of social media platforms which include Facebook, Twitter WhatsApp, blogs and Wikis. The most popular of these platforms are Facebook, with 2.41 billion active users on a monthly basis, followed by WhatsApp with 1.6 billion users and Twitter with 330 million users. These communication platforms have not only impacted social lives but have also impacted students’ learning, across different delivery modes in higher education: distance, conventional and blended learning modes. With this amount of interest in these platforms, knowledge sharing has gained importance within the context in which it is required. In open distance learning (ODL) contexts, social networking platforms can offer students and teachers the platform on which to create and share knowledge, and form learning collaborations. Thus, they can serve as support mechanisms to increase interactions and reduce isolation and loneliness inherent in ODL. Despite this potential and opportunity, research indicates that many ODL teachers are not inclined to using social media tools in learning. Although it is unclear why these tools are uncommon in these environments, concerns raised in the literature have indicated that many teachers have not mastered the art of teaching with technology. Using technological, pedagogical content knowledge (TPCK) and product quality theory, and Bloom’s Taxonomy as lenses, this paper is aimed at; firstly, assessing the quality of three social media applications: Facebook, Twitter and WhatsApp, in order to determine the extent to which they are suitable platforms for teaching and learning, in terms of content generation, information sharing and learning collaborations. Secondly, the paper demonstrates the application of teaching, learning and assessment using Bloom’s Taxonomy.

Keywords: distance education, quality, social networking tools, TPACK

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Authors: Srinivas Vangari

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In the era of big data, ensuring the accuracy, consistency, and reliability of critical data assets is crucial for data-driven enterprises. Master Data Management (MDM) plays a crucial role in this endeavor. This paper investigates the role of Artificial Intelligence (AI) in enhancing MDM, focusing on how AI-driven solutions can automate and optimize various stages of the master data lifecycle. By integrating AI (Quantitative and Qualitative Analysis) into processes such as data creation, maintenance, enrichment, and usage, organizations can achieve significant improvements in data quality and operational efficiency. Quantitative analysis is employed to measure the impact of AI on key metrics, including data accuracy, processing speed, and error reduction. For instance, our study demonstrates an 18% improvement in data accuracy and a 75% reduction in duplicate records across multiple systems post-AI implementation. Furthermore, AI’s predictive maintenance capabilities reduced data obsolescence by 22%, as indicated by statistical analyses of data usage patterns over a 12-month period. Complementing this, a qualitative analysis delves into the specific AI-driven strategies that enhance MDM practices, such as automating data entry and validation, which resulted in a 28% decrease in manual errors. Insights from case studies highlight how AI-driven data cleansing processes reduced inconsistencies by 25% and how AI-powered enrichment strategies improved data relevance by 24%, thus boosting decision-making accuracy. The findings demonstrate that AI significantly enhances data quality and integrity, leading to improved enterprise performance through cost reduction, increased compliance, and more accurate, real-time decision-making. These insights underscore the value of AI as a critical tool in modern data management strategies, offering a competitive edge to organizations that leverage its capabilities.

Keywords: artificial intelligence, master data management, data governance, data quality

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Authors: Inigo de Miguel Beriain

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Genetic data of deceased persons are of great interest for both biomedical research and clinical use. This is due to several reasons. On the one hand, many of our diseases have a genetic component; on the other hand, we share genes with a good part of our biological family. Therefore, it would be possible to improve our response considerably to these pathologies if we could use these data. Unfortunately, at the present moment, the status of data on the deceased is far from being satisfactorily resolved by the EU data protection regulation. Indeed, the General Data Protection Regulation has explicitly excluded these data from the category of personal data. This decision has given rise to a fragmented legal framework on this issue. Consequently, each EU member state offers very different solutions. For instance, Denmark considers the data as personal data of the deceased person for a set period of time while some others, such as Spain, do not consider this data as such, but have introduced some specifically focused regulations on this type of data and their access by relatives. This is an extremely dysfunctional scenario from multiple angles, not least of which is scientific cooperation at the EU level. This contribution attempts to outline a solution to this dilemma through an alternative proposal. Its main hypothesis is that, in reality, health data are, in a sense, a rara avis within data in general because they do not refer to one person but to several. Hence, it is possible to think that all of them can be considered data subjects (although not all of them can exercise the corresponding rights in the same way). When the person from whom the data were obtained dies, the data remain as personal data of his or her biological relatives. Hence, the general regime provided for in the GDPR may apply to them. As these are personal data, we could go back to thinking in terms of a general prohibition of data processing, with the exceptions provided for in Article 9.2 and on the legal bases included in Article 6. This may be complicated in practice, given that, since we are dealing with data that refer to several data subjects, it may be complex to refer to some of these bases, such as consent. Furthermore, there are theoretical arguments that may oppose this hypothesis. In this contribution, it is shown, however, that none of these objections is of sufficient substance to delegitimize the argument exposed. Therefore, the conclusion of this contribution is that we can indeed build a general framework on the processing of personal data of deceased persons in the context of the GDPR. This would constitute a considerable improvement over the current regulatory framework, although it is true that some clarifications will be necessary for its practical application.

Keywords: collective data conceptual issues, data from deceased people, genetic data protection issues, GDPR and deceased people

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Authors: Krizette Ladera

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Fathers are there not only to provide the financial stability of a family but a father is also there to provide the love and support that usually people would see as the mother’s responsibility. To describe the lived experiences and how fathers make sense of their lived experiences with their children who have cerebral palsy is the main objective of the study. A qualitative research using a thematic analysis was used for the study. The qualitative research focused on the personal narratives, self-report and expression of the participant’s memory in terms of how they tell their stories. The interpretative phenomenological analysis was used to focus on the experience of the participants on how they will describe their experiences, and to also add on that the IPA will also attempt to describe and explain the meaning of human experiences using interview, specifically on the father who have a child that suffers from cerebral palsy. For the sampling technique, the snowball technique was used to gather participants from the referral of other participants. The five non-randomly selected fathers will be served as the participants for the research. A self-made interview with an open-ended question was used as the research instrument; it includes profiling of the respondent as well as their experiences in taking care of their child that suffers from cerebral palsy. In analyzing a data, the researcher used the thematic analysis where in the interview was made into a transcript, then it was organized and divided themes. After that, the relations of each themes, was identified and it was later documented and translated into written text format using thematic grouping. Finally, the researcher analyzed each data according to its themes and put it in a table to be presented in the result section of the study And as for the result of the study, the researcher was able to come up with the four (4) main themes that most of the participants experienced and those are: The experiences in finding out about the condition of the Child, disclosing the condition of the child to the family and its emotional effect, The experiences of living the day of day realities in providing the physical, financial, emotional and a well balanced environment to the child, and the religious perspectives of the fathers. Along with those four (4) themes comes the subtheme which explains the themes in a more detailed explanation.

Keywords: cerebral palsy, children, fathers, lived experiences

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Authors: Abdullah I. Alkraiji, Thomas W. Jackson, Ian Murray

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The proliferation of health data standards today is somewhat overlapping and conflicting, resulting in market confusion and leading to increasing proprietary interests. The government role and support in standardization for health data are thought to be crucial in order to establish credible standards for the next decade, to maximize interoperability across the health sector, and to decrease the risks associated with the implementation of non-standard systems. The normative literature missed out the exploration of the different steps required to be undertaken by the government towards the development of national health data standards. Based on the lessons learned from a qualitative study investigating the different issues to the adoption of health data standards in the major tertiary hospitals in Saudi Arabia and the opinions and feedback from different experts in the areas of data exchange and standards and medical informatics in Saudi Arabia and UK, a list of steps required towards the development of national health data standards was constructed. Main steps are the existence of: a national formal reference for health data standards, an agreed national strategic direction for medical data exchange, a national medical information management plan and a national accreditation body, and more important is the change management at the national and organizational level. The outcome of this study can be used by academics and practitioners to develop the planning of health data standards, and in particular those in developing countries.

Keywords: interoperabilty, medical data exchange, health data standards, case study, Saudi Arabia

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Authors: SangWon Han, MuWook Pyeon, Sujung Moon, DaeKyo Seo

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Recently, technologies based on three-dimensional (3D) space information are being developed and quality of life is improving as a result. Research on real-time digital map (RDM) is being conducted now to provide 3D space information. RDM is a service that creates and supplies 3D space information in real time based on location/shape detection. Research subjects on RDM include the construction of 3D space information with matching image data, complementing the weaknesses of image acquisition using multi-source data, and data collection methods using big data. Using RDM will be effective for space analysis using 3D space information in a U-City and for other space information utilization technologies.

Keywords: RDM, multi-source data, big data, U-City

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Authors: Saeko Toyoshima

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This research will discuss if performance tasks with technology would enhance students' willingness to communicate. The present study investigated how Japanese learners of English would change their attitude to communication in their target language by experiencing a performance task, called 'digital portfolio', in the classroom, applying the concepts of action research. The study adapted questionnaires including four-Likert and open-end questions as mixed-methods research. There were 28 students in the class. Many of Japanese university students with low proficiency (A1 in Common European Framework of References in Language Learning and Teaching) have difficulty in communicating in English due to the low proficiency and the lack of practice in and outside of the classroom at secondary education. They should need to mediate between themselves in the world of L1 and L2 with completing a performance task for communication. This paper will introduce the practice of CALL class where A1 level students have made their 'digital portfolio' related to the topics of TED® (Technology, Entertainment, Design) Talk materials. The students had 'Portfolio Session' twice in one term, once in the middle, and once at the end of the course, where they introduced their portfolio to their classmates and international students in English. The present study asked the students to answer a questionnaire about willingness to communicate twice, once at the end of the first term and once at the end of the second term. The four-Likert questions were statistically analyzed with a t-test, and the answers to open-end questions were analyzed to clarify the difference between them. They showed that the students had a more positive attitude to communication in English and enhanced their willingness to communicate through the experiences of the task. It will be the implication of this paper that making and presenting portfolio as a performance task would lead them to construct themselves in English and enable them to communicate with the others enjoyably and autonomously.

Keywords: action research, digital portfoliio, computer-assisted language learning, ELT with CALL system, mixed methods research, Japanese English learners, willingness to communicate

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Authors: Nieto Bernal Wilson, Carmona Suarez Edgar

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The organizations have structured and unstructured information in different formats, sources, and systems. Part of these come from ERP under OLTP processing that support the information system, however these organizations in OLAP processing level, presented some deficiencies, part of this problematic lies in that does not exist interesting into extract knowledge from their data sources, as also the absence of operational capabilities to tackle with these kind of projects.  Data Warehouse and its applications are considered as non-proprietary tools, which are of great interest to business intelligence, since they are repositories basis for creating models or patterns (behavior of customers, suppliers, products, social networks and genomics) and facilitate corporate decision making and research. The following paper present a structured methodology, simple, inspired from the agile development models as Scrum, XP and AUP. Also the models object relational, spatial data models, and the base line of data modeling under UML and Big data, from this way sought to deliver an agile methodology for the developing of data warehouses, simple and of easy application. The methodology naturally take into account the application of process for the respectively information analysis, visualization and data mining, particularly for patterns generation and derived models from the objects facts structured.

Keywords: data warehouse, model data, big data, object fact, object relational fact, process developed data warehouse

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Authors: Go Bong Choi, Shin Je Lee, Sung Jin Yoo, Gibaek Lee, Jong Min Lee

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In South Korea, it is difficult to obtain data for statistical pipe assessment. In this paper, to address these issues, we find that various statistical model presented before is how data mixed with noise and are whether apply in South Korea. Three major type of model is studied and if data is presented in the paper, we add noise to data, which affects how model response changes. Moreover, we generate data from model in paper and analyse effect of noise. From this we can find robustness and applicability in Korea of each model.

Keywords: proportional hazard model, survival model, water main deterioration, ecological sciences

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Authors: Anusha Konduru, Zhiyong Shan, Preethi Santhanam, Vinod Namboodiri, Rajiv Bagai

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Mobile applications are increasing in a significant amount, each to address the requirements of many users. However, the quick developments and enhancements are resulting in many underlying defects. Android apps create and handle a large variety of 'instance' data that has to persist across runs, such as the current navigation route, workout results, antivirus settings, or game state. Due to the nature of Android, an app can be paused, sent into the background, or killed at any time. If the instance data is not saved and restored between runs, in addition to data loss, partially-saved or corrupted data can crash the app upon resume or restart. However, it is difficult for the programmer to manually test this issue for all the activities. This results in the issue of data loss that the data entered by the user are not saved when there is any interruption. This issue can degrade user experience because the user needs to reenter the information each time there is an interruption. Automated testing to detect such data loss is important to improve the user experience. This research proposes a tool, DroidDL, a data loss detector for Android, which detects the instance data loss from a given android application. We have tested 395 applications and found 12 applications with the issue of data loss. This approach is proved highly accurate and reliable to find the apps with this defect, which can be used by android developers to avoid such errors.

Keywords: Android, automated testing, activity, data loss

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Authors: M. Foysul Alam

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Land diluvion is a common phenomenon in Bangladesh. According to Bangladesh land administration manual, land gained by diluvion and alluvion are considered as Khas (state) lands. Distributing khas lands to the poorest landless people was suggested as a way of improving the livelihood by mobilizing their economic activity and improving their social status in Bangladesh. The impact of this initiative, which started in 1980 as a state policy, is perhaps one of the least experimentally documented ones in the country. The primary aim of this study was to investigate the economic and social status of some landless farmers focusing on determining whether the distribution of khas lands provide part of the explanation for possible improvement of their livelihood during this period of almost four decades. If not, the secondary objective of this study was to identify the issues and challenges of this policy, whether in strategic planning or implementation efforts. Both subjectively observable and objectively measurable data were collected from 150 households from 30 villages of 6 districts worse suffering from landlessness. A well-organized questionnaire, including 10 open ended question, were used to collect specific data and experiences of the landless farmers. Special attention was given to those who left peasantry becoming involved in other economic activities. The study found that Khas lands have very nominal or even zero influence on landless farmers due to 1. Lack of Khas land inventory 2. Lack of availability of information to the farmers 3. Complexity in application procedure for demanding Khas land and 4. Political nexus of local influential farmers makes it nearly impossible for those farmers who actually deserve it. It also found that most of the lucky farmers who eventually got a piece of Khas land had links with the local elites of their community. The research concludes that it is too early to decide how good the Khas land is for the marginalized landless farmers in Bangladesh, with actually a few numbers of them given the possession of one. This document provides crucial recommendations for overcoming the prevailing challenges.

Keywords: Khas land, land distribution, marginalized farmers, rural livelihood

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Authors: James Moir

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The mainstay of Big Data is prediction in that it allows practitioners, researchers, and policy analysts to predict trends based upon the analysis of large and varied sources of data. These can range from changing social and political opinions, patterns in crimes, and consumer behaviour. Big Data has therefore shifted the criterion of success in science from causal explanations to predictive modelling and simulation. The 19th-century science sought to capture phenomena and seek to show the appearance of it through causal mechanisms while 20th-century science attempted to save the appearance and relinquish causal explanations. Now 21st-century science in the form of Big Data is concerned with the prediction of appearances and nothing more. However, this pulls social science back in the direction of a more rule- or law-governed reality model of science and away from a consideration of the internal nature of rules in relation to various practices. In effect Big Data offers us no more than a world of surface appearance and in doing so it makes disappear any context-specific conceptual sensitivity.

Keywords: big data, appearance, disappearance, surface, epistemology

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Authors: Stepan Papacek, Jiri Jablonsky, Radek Kana, Ctirad Matonoha, Stefan Kindermann

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FRAP (Fluorescence Recovery After Photobleaching) is a widely used measurement technique to determine the mobility of fluorescent molecules within living cells. While the experimental setup and protocol for FRAP experiments are usually fixed, data processing part is still under development. In this paper, we formulate and solve the problem of data selection which enhances the processing of FRAP images. We introduce the concept of the irrelevant data set, i.e., the data which are almost not reducing the confidence interval of the estimated parameters and thus could be neglected. Based on sensitivity analysis, we both solve the problem of the optimal data space selection and we find specific conditions for optimizing an important experimental design factor, e.g., the radius of bleach spot. Finally, a theorem announcing less precision of the integrated data approach compared to the full data case is proven; i.e., we claim that the data set represented by the FRAP recovery curve lead to a larger confidence interval compared to the spatio-temporal (full) data.

Keywords: FRAP, inverse problem, parameter identification, sensitivity analysis, optimal experimental design

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Authors: Tran Duong Nguyen, Marwan Shagar, Qinghao Zeng, Aras Maqsoodi, Pardis Pishdad, Eunhwa Yang

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Construction supply chain management (CSCM) involves the collaboration of many disciplines and actors, which generates vast amounts of data. However, inefficient, fragmented, and non-standardized data storage often hinders this data exchange. The industry has adopted building information modeling (BIM) -a digital representation of a facility's physical and functional characteristics to improve collaboration, enhance transmission security, and provide a common data exchange platform. Still, the volume and complexity of data require tailored information categorization, aligning with stakeholders' preferences and demands. To address this, artificial intelligence (AI) can be integrated to handle this data’s magnitude and complexities. This research aims to develop an integrated and efficient approach for data exchange in CSCM by utilizing AI. The paper covers five main objectives: (1) Investigate existing framework and BIM adoption; (2) Identify challenges in data exchange; (3) Propose an integrated framework; (4) Enhance data transmission security; and (5) Develop data exchange in CSCM. The proposed framework demonstrates how integrating BIM and other technologies, such as cloud computing, blockchain, and AI applications, can significantly improve the efficiency and accuracy of data exchange in CSCM.

Keywords: construction supply chain management, BIM, data exchange, artificial intelligence

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Authors: Nabilah Filzah Mohd Radzuan, Zalinda Othman, Azuraliza Abu Bakar, Abdul Razak Hamdan

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Uncertain data is believed to be an important issue in building up a prediction model. The main objective in the time series uncertainty analysis is to formulate uncertain data in order to gain knowledge and fit low dimensional model prior to a prediction task. This paper discusses the performance of a number of techniques in dealing with uncertain data specifically those which solve uncertain data condition by minimizing the loss of compression properties.

Keywords: compression properties, uncertainty, uncertain time series, mining technique, weather prediction

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Authors: Ma. Rosario Concepcion O. Ang, Luis Caezar Ian K. Panganiban, Charmyne B. Mamador, Oliver Dan G. De Luna, Michael D. Bausas, Joselito P. Cruz

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PhilSHORE is a multi-site, multi-device and multi-criteria decision support tool designed to support the development of tidal current energy in the Philippines. Its platform is based on Geographic Information Systems (GIS) which allows for the collection, storage, processing, analyses and display of geospatial data. Combining GIS tools with open source web development applications, PhilSHORE becomes a webGIS-based marine spatial planning tool. To date, PhilSHORE displays output maps and graphs of power and energy density, site suitability and site-device analysis. It enables stakeholders and the public easy access to the results of tidal current energy resource assessments and site suitability analyses. Results of the initial development shows PhilSHORE is a promising decision support tool for ORE project developments.

Keywords: gis, site suitability analysis, tidal current energy resource assessment, webgis

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Authors: Maram Saleh

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Knowledge has become an essential resource in today’s economy and become the most important asset of maintaining competition advantage in organizations. The importance of knowledge has made organizations to manage their knowledge assets and resources through all multiple knowledge management stages such as: Knowledge Creation, knowledge storage, knowledge sharing and knowledge use. Researches on data mining are continues growing over recent years on both business and educational fields. Data mining is one of the most important steps of the knowledge discovery in databases process aiming to extract implicit, unknown but useful knowledge and it is considered as significant subfield in knowledge management. Data miming have the great potential to help organizations to focus on extracting the most important information on their data warehouses. Data mining tools and techniques can predict future trends and behaviors, allowing businesses to make proactive, knowledge-driven decisions. This review paper explores the applications of data mining techniques in supporting knowledge management process as an effective knowledge discovery technique. In this paper, we identify the relationship between data mining and knowledge management, and then focus on introducing some application of date mining techniques in knowledge management for some real life domains.

Keywords: Data Mining, Knowledge management, Knowledge discovery, Knowledge creation.

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Authors: Murat Yazici

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Anomalies are irregularities found in data that do not adhere to a well-defined standard of normal behavior. The identification of outliers or anomalies in data has been a subject of study within the statistics field since the 1800s. Over time, a variety of anomaly detection techniques have been developed in several research communities. The cluster analysis can be used to detect anomalies. It is the process of associating data with clusters that are as similar as possible while dissimilar clusters are associated with each other. Many of the traditional cluster algorithms have limitations in dealing with data sets containing categorical properties. To detect anomalies in categorical data, fuzzy clustering approach can be used with its advantages. The fuzzy k-Mode (FKM) clustering algorithm, which is one of the fuzzy clustering approaches, by extension to the k-means algorithm, is reported for clustering datasets with categorical values. It is a form of clustering: each point can be associated with more than one cluster. In this paper, anomaly detection is performed on two simulated data by using the FKM cluster algorithm. As a significance of the study, the FKM cluster algorithm allows to determine anomalies with their abnormality degree in contrast to numerous anomaly detection algorithms. According to the results, the FKM cluster algorithm illustrated good performance in the anomaly detection of data, including both one anomaly and more than one anomaly.

Keywords: fuzzy k-mode clustering, anomaly detection, noise, categorical data

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Authors: George Obinna Akuaka

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Solid waste disposal techniques in Nigeria pose an environmental threat to the environment and to nearby resident. The presence of microbial physical and chemical concentration in boreholes samples nearby dumpsite implies that groundwater is normally contaminated by leachate infiltration from an open landfill dumpsite. In this study, the physicochemical and microbial analyses of water samples from hand dug well in the site and boreholes were carried out around the active landfill and from different distances (50 m to 200 m). leachate samples collected were used to ascertain the effect or extent of contamination on the groundwater quality. A total of 5 leachate samples and 5 samples of groundwater were collected, and all samples were analyzed for various physical and chemical parameters according to the standard methods. These include pH, Electrical conductivity, Total dissolved solid, BOD, OD, Temperature, major cations such as Mg²+ Ca²+, Fe²+ Cu²+, major anions NO³-, Cl-,SO⁴- PO⁴-, Zn, Ar, Cd, Cr, Hg, Pb, Ni are the heavy metals and metalloids. The mean values of the physical and chemical parameters obtained from both sites were compared with the established of the World Health Organization (WHO). The leachate samples were found to be higher in the concentration of the results obtained than that of the boreholes water, and the recorded mean values of heavy metals were above approved standard minimum limits. The results indicated that mercury and copper were not found in all the borehole water samples. Microbial analyses showed that total heterotrophic bacteria mean count ranged from 10.6 X10⁷ cfu/ml to 2.04x10⁷cfu/ml and 9.5 X 10⁷ cfu/ml to 18.9 X 10⁷ cfu/ml in leachate and borehole samples respectively. It also revealed that almost at the bacteria isolated in the leachate were also found in the water samples. This results indicated that heavy pollution in all the samples with most physicochemical parameters and microbes showed traceable pollution, which occurred as a result of leachate infiltration into the ground water.

Keywords: physicochemical, landfill dumpsite, microbial, leachate, groundwater

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Authors: Victor Onomza Waziri, John K. Alhassan, Idris Ismaila, Noel Dogonyara

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This paper describes the problem of building secure computational services for encrypted information in the Cloud. Computing without decrypting the encrypted data; therefore, it meets the yearning of computational encryption algorithmic aspiration model that could enhance the security of big data for privacy or confidentiality, availability and integrity of the data and user’s security. The cryptographic model applied for the computational process of the encrypted data is the Fully Homomorphic Encryption Scheme. We contribute a theoretical presentations in a high-level computational processes that are based on number theory that is derivable from abstract algebra which can easily be integrated and leveraged in the Cloud computing interface with detail theoretic mathematical concepts to the fully homomorphic encryption models. This contribution enhances the full implementation of big data analytics based on cryptographic security algorithm.

Keywords: big data analytics, security, privacy, bootstrapping, Fully Homomorphic Encryption Scheme

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Authors: Mohammad Noor Hisham Osman, Ahmed Razman Abdul Latiff, Zaidi Mat Daud, Zulkarnain Muhamad Sori

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The problem that Malaysian auditors do not issue going concern opinion (GC opinion) to seriously financially distressed companies is still a pressing issue. Policy makers, particularly the Financial Statement Review Committee (FSRC) of Malaysian Institute of Accountant, have raised this issue as early as in 2009. Similar problem happened in the US, UK, and many developing countries. It is important for auditors to issue GC opinion properly because such opinion is one signal about the viability of a company much needed by stakeholders. There are at least two unanswered questions or research gaps in the literature on determinants of GC opinion. Firstly, is client’s probability of failure associated with GC opinion issuance? Secondly, to what extent influential owners (management, family, and institution) moderate the association between client probability of failure and GC opinion issuance. The objective of this study is, therefore, twofold; (1) To examine the extent of the relationship between the probability of client failure and the issuance of GC opinion and (2) To examine the level of management, family, and institutional ownerships moderate the association between client probability of failure and the issuance of GC opinion. This study is quantitative in nature, and the sources of data are secondary (mainly company’s annual reports). A total of four hypotheses have been developed and tested on data accumulated from annual reports of seriously financially distressed Malaysian public listed companies. Data from 2006 to 2012 on a sample of 644 observations have been analyzed using panel logistic regression. It is found that certainty (rather than probability) of client failure affects the issuance of GC opinion. In addition, it is found that only the level of family ownership does positively moderate the relationship between client probability of failure and GC opinion issuance. This study is a contribution to auditing literature as its findings can enhance our understanding about audit quality; particularly on the variables that are associated with the issuance of GC opinion. The findings of this study shed light on the roles family owners in GC opinion issuance process, and this would open ways for the researcher to suggest measures that can be used to tackle the problem of auditors do not want to issue GC opinion to financially distressed clients. The measures to be suggested can be useful to policy makers in formulating future promulgations.

Keywords: audit quality, auditing, auditor characteristics, going concern opinion, Malaysia

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Authors: Shraddha Chaudhary, Raksha Agarwal, Niladri Chatterjee

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This work demonstrates a web crawler-based generalized end-to-end open domain Question Answering (QA) system. An efficient QA system requires a significant amount of domain knowledge to answer any question with the aim to find an exact and correct answer in the form of a number, a noun, a short phrase, or a brief piece of text for the user's questions. Analysis of the question, searching the relevant document, and choosing an answer are three important steps in a QA system. This work uses a web scraper (Beautiful Soup) to extract K-documents from the web. The value of K can be calibrated on the basis of a trade-off between time and accuracy. This is followed by a passage ranking process using the MS-Marco dataset trained on 500K queries to extract the most relevant text passage, to shorten the lengthy documents. Further, a QA system is used to extract the answers from the shortened documents based on the query and return the top 3 answers. For evaluation of such systems, accuracy is judged by the exact match between predicted answers and gold answers. But automatic evaluation methods fail due to the linguistic ambiguities inherent in the questions. Moreover, reference answers are often not exhaustive or are out of date. Hence correct answers predicted by the system are often judged incorrect according to the automated metrics. One such scenario arises from the original Google Natural Question (GNQ) dataset which was collected and made available in the year 2016. Use of any such dataset proves to be inefficient with respect to any questions that have time-varying answers. For illustration, if the query is where will be the next Olympics? Gold Answer for the above query as given in the GNQ dataset is “Tokyo”. Since the dataset was collected in the year 2016, and the next Olympics after 2016 were in 2020 that was in Tokyo which is absolutely correct. But if the same question is asked in 2022 then the answer is “Paris, 2024”. Consequently, any evaluation based on the GNQ dataset will be incorrect. Such erroneous predictions are usually given to human evaluators for further validation which is quite expensive and time-consuming. To address this erroneous evaluation, the present work proposes an automated approach for evaluating time-dependent question-answer pairs. In particular, it proposes a metric using the current timestamp along with top-n predicted answers from a given QA system. To test the proposed approach GNQ dataset has been used and the system achieved an accuracy of 78% for a test dataset comprising 100 QA pairs. This test data was automatically extracted using an analysis-based approach from 10K QA pairs of the GNQ dataset. The results obtained are encouraging. The proposed technique appears to have the possibility of developing into a useful scheme for gathering precise, reliable, and specific information in a real-time and efficient manner. Our subsequent experiments will be guided towards establishing the efficacy of the above system for a larger set of time-dependent QA pairs.

Keywords: web-based information retrieval, open domain question answering system, time-varying QA, QA evaluation

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Authors: Sarisa Pinkham, Kanyarat Bussaban

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The research aims to approximate the amount of daily rainfall by using a pixel value data approach. The daily rainfall maps from the Thailand Meteorological Department in period of time from January to December 2013 were the data used in this study. The results showed that this approach can approximate the amount of daily rainfall with RMSE=3.343.

Keywords: daily rainfall, image processing, approximation, pixel value data

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Authors: Kenneth Harper

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The rapid growth of data-driven decision-making across various industries necessitates advanced solutions to ensure data integrity, scalability, and security. This study introduces a decentralized data platform built on blockchain technology to improve data management processes in high-volume environments such as healthcare and financial services. The platform integrates blockchain networks using Cosmos SDK and Polkadot Substrate alongside decentralized storage solutions like IPFS and Filecoin, and coupled with decentralized computing infrastructure built on top of Avalanche. By leveraging advanced consensus mechanisms, we create a scalable, tamper-proof architecture that supports both structured and unstructured data. Key features include secure data ingestion, cryptographic hashing for robust data lineage, and Zero-Knowledge Proof mechanisms that enhance privacy while ensuring compliance with regulatory standards. Additionally, we implement performance optimizations through Layer 2 scaling solutions, including ZK-Rollups, which provide low-latency data access and trustless data verification across a distributed ledger. The findings from this exercise demonstrate significant improvements in data accessibility, reduced operational costs, and enhanced data integrity when tested in real-world scenarios. This platform reference architecture offers a decentralized alternative to traditional centralized data storage models, providing scalability, security, and operational efficiency.

Keywords: blockchain, cosmos SDK, decentralized data platform, IPFS, ZK-Rollups

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Authors: Lisbeth Amhag

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The aim of the study is to describe and analyze the design of mobile teaching for students collaborative learning in distance higher education with a focus on mobile technologies as online webinars (web-based seminars or conferencing) by using laptops, smart phones, or tablets. These multimedia tools can provide face-to-face interactions, recorded flipped classroom videos and parallel chat communications. The data collection consists of interviews with 22 students and observations of online face-to-face webinars, as well two surveys. Theoretically, the study joins the research tradition of Computer Supported Collaborative learning, CSCL, as well as Computer Self-Efficacy, CSE concerned with individuals’ media and information literacy. Important conclusions from the study demonstrated mobile interactions increased student centered learning. As the students were appreciating the working methods, they became more engaged and motivated. The mobile technology using among student also contributes to increased flexibility between space and place, as well as media and information literacy.

Keywords: computer self-efficacy, computer supported collaborative learning, distance and open learning, educational design and technologies, media and information literacy, mobile learning

Procedia PDF Downloads 358

Authors: Mohammad Javad Mollakazemi, Farhad Asadi, Aref Ghafouri

Abstract:

In this paper, we considered and applied parametric modeling for some experimental data of dynamical system. In this study, we investigated the different distribution of output measurement from some dynamical systems. Also, with variance processing in experimental data we obtained the region of nonlinearity in experimental data and then identification of output section is applied in different situation and data distribution. Finally, the effect of the spanning the measurement such as variance to identification and limitation of this approach is explained.

Keywords: Gaussian process, nonlinearity distribution, particle filter, system identification

Procedia PDF Downloads 516

Authors: Jaya Mathew

Abstract:

Many organizations are faced with the challenge of how to analyze and build Machine Learning models using their sensitive telemetry data. In this paper, we discuss how users can leverage the power of R without having to move their big data around as well as a cloud based solution for organizations willing to host their data in the cloud. By using ScaleR technology to benefit from parallelization and remote computing or R Services on premise or in the cloud, users can leverage the power of R at scale without having to move their data around.

Keywords: predictive maintenance, machine learning, big data, cloud based, on premise solution, R

Procedia PDF Downloads 379

Authors: Sven Gehrke, Johannes Ruhland

Abstract:

Data is the oil of our time, without them progress would come to a hold [1]. On the other hand, the mistrust of data mining is increasing [2]. The paper at hand shows different aspects of the concept of trust and describes the information asymmetry of the typical stakeholders of a data mining project using the CRISP-DM phase model. Based on the identified influencing factors in relation to trust, problematic aspects of the current approach are verified using various interviews with the stakeholders. The results of the interviews confirm the theoretically identified weak points of the phase model with regard to trust and show potential research areas.

Keywords: trust, data mining, CRISP DM, stakeholder management

Procedia PDF Downloads 94

Authors: K. Thiagarajan, K. Saranya, A. Veeraiah, B. Sudha

Abstract:

This paper presents a novel algorithm for secure, reliable and flexible transmission of big data in two hop wireless networks using cooperative jamming scheme. Two hop wireless networks consist of source, relay and destination nodes. Big data has to transmit from source to relay and from relay to destination by deploying security in physical layer. Cooperative jamming scheme determines transmission of big data in more secure manner by protecting it from eavesdroppers and malicious nodes of unknown location. The novel algorithm that ensures secure and energy balance transmission of big data, includes selection of data transmitting region, segmenting the selected region, determining probability ratio for each node (capture node, non-capture and eavesdropper node) in every segment, evaluating the probability using binary based evaluation. If it is secure transmission resume with the two- hop transmission of big data, otherwise prevent the attackers by cooperative jamming scheme and transmit the data in two-hop transmission.

Keywords: big data, two-hop transmission, physical layer wireless security, cooperative jamming, energy balance

Procedia PDF Downloads 490

Authors: Romeo Botes, Imelda Smit

Abstract:

In today’s modern age of technology vast amounts of data needs to be processed in real-time to keep users satisfied. This data comes from various sources and in many formats, including electronic and mobile devices such as GPRS modems and GPS devices. They make use of different protocols including TCP, UDP, and HTTP/s for data communication to web servers and eventually to users. The data obtained from these devices may provide valuable information to users, but are mostly in an unreadable format which needs to be processed to provide information and business intelligence. This data is not always current, it is mostly historical data. The data is not subject to implementation of consistency and redundancy measures as most other data usually is. Most important to the users is that the data are to be pre-processed in a readable format when it is entered into the database. To accomplish this, programmers build processing programs and scripts to decode and process the information stored in databases. Programmers make use of various techniques in such programs to accomplish this, but sometimes neglect the effect some of these techniques may have on database performance. One of the techniques generally used,is to pull data from the database server, process it and push it back to the database server in one single step. Since the processing of the data usually takes some time, it keeps the database busy and locked for the period of time that the processing takes place. Because of this, it decreases the overall performance of the database server and therefore the system’s performance. This paper follows on a paper discussing the performance increase that may be achieved by utilizing array lists along with a pull-process-push data processing technique split in three steps. The purpose of this paper is to expand the number of clients when comparing the two techniques to establish the impact it may have on performance of the CPU storage and processing time.

Keywords: performance measures, algorithm techniques, data processing, push data, process data, array list

Procedia PDF Downloads 244