Search results for: empirical data

Authors: E. Ghasemi Ardi, K. J. Dong, A. B. Yu, R. Y. Yang

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In current work, a numerical model based on the discrete element method (DEM) was developed which provided information about particle dynamic and impact event condition inside a laboratory scale impact mill (Fritsch). It showed that each particle mostly experiences three impacts inside the mill. While the first impact frequently happens at front surface of the rotor’s rib, the frequent location of the second impact is side surfaces of the rotor’s rib. It was also showed that while the first impact happens at small impact angle mostly varying around 35º, the second impact happens at around 70º which is close to normal impact condition. Also analyzing impact energy revealed that varying mill speed from 6000 to 14000 rpm, the ratio of first impact’s average impact energy and minimum required energy to break particle (Wₘᵢₙ) increased from 0.30 to 0.85. Moreover, it was seen that second impact poses intense impact energy on particle which can be considered as the main cause of particle splitting. Finally, obtained information from DEM simulation along with obtained data from conducted experiments was implemented in semi-empirical equations in order to find selection and breakage functions. Then, using a back-calculation approach, those parameters were used to predict the PSDs of ground particles under different impact energies. Results were compared with experiment results and showed reasonable accuracy and prediction ability.

Keywords: single particle breakage, particle dynamic, population balance model, particle size distribution, discrete element method

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Authors: Yan Cheng, Yijun Shao, James Rudolph, Charlene R. Weir, Beth Sahlmann, Qing Zeng-Treitler

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Supervised predictive models require labeled data for training purposes. Complete and accurate labeled data, i.e., a ‘gold standard’, is not always available, and imperfectly labeled data may need to serve as an alternative. An important question is if the accuracy of the labeled data creates a performance ceiling for the trained model. In this study, we trained several models to recognize the presence of delirium in clinical documents using data with annotations that are not completely accurate (i.e., weakly-supervised learning). In the external evaluation, the support vector machine model with a linear kernel performed best, achieving an area under the curve of 89.3% and accuracy of 88%, surpassing the 80% accuracy of the training sample. We then generated a set of simulated data and carried out a series of experiments which demonstrated that models trained on imperfect data can (but do not always) outperform the accuracy of the training data, e.g., the area under the curve for some models is higher than 80% when trained on the data with an error rate of 40%. Our experiments also showed that the error resistance of linear modeling is associated with larger sample size, error type, and linearity of the data (all p-values < 0.001). In conclusion, this study sheds light on the usefulness of imperfect data in clinical research via weakly-supervised learning.

Keywords: weakly-supervised learning, support vector machine, prediction, delirium, simulation

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Authors: Kenneth Harper

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Blockchain technology presents solutions for managing healthcare data, addressing critical challenges in privacy, integrity, and access. This paper explores how privacy-preserving technologies, such as zero-knowledge proofs (ZKPs) and homomorphic encryption (HE), enhance decentralized healthcare platforms by enabling secure computations and patient data protection. An examination of the mathematical foundations of these methods, their practical applications, and how they meet the evolving demands of healthcare data security is unveiled. Using real-world examples, this research highlights industry-leading implementations and offers a roadmap for future applications in secure, decentralized healthcare ecosystems.

Keywords: blockchain, cryptography, data privacy, decentralized data management, differential privacy, healthcare, healthcare data security, homomorphic encryption, privacy-preserving technologies, secure computations, zero-knowledge proofs

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Authors: Dražen Cvitanić, Biljana Maljković

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This paper presents models for predicting operating speeds on tangent sections of two-lane rural roads developed on continuous speed data. The data corresponds to 20 drivers of different ages and driving experiences, driving their own cars along an 18 km long section of a state road. The data were first used for determination of maximum operating speeds on tangents and their comparison with speeds in the middle of tangents i.e. speed data used in most of operating speed studies. Analysis of continuous speed data indicated that the spot speed data are not reliable indicators of relevant speeds. After that, operating speed models for tangent sections were developed. There was no significant difference between models developed using speed data in the middle of tangent sections and models developed using maximum operating speeds on tangent sections. All developed models have higher coefficient of determination then models developed on spot speed data. Thus, it can be concluded that the method of measuring has more significant impact on the quality of operating speed model than the location of measurement.

Keywords: operating speed, continuous speed data, tangent sections, spot speed, consistency

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Authors: S. Nickolas, Shobha K.

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The treatment of incomplete data is an important step in the data pre-processing. Missing values creates a noisy environment in all applications and it is an unavoidable problem in big data management and analysis. Numerous techniques likes discarding rows with missing values, mean imputation, expectation maximization, neural networks with evolutionary algorithms or optimized techniques and hot deck imputation have been introduced by researchers for handling missing data. Among these, imputation techniques plays a positive role in filling missing values when it is necessary to use all records in the data and not to discard records with missing values. In this paper we propose a novel artificial neural network based clustering algorithm, Adaptive Resonance Theory-2(ART2) for imputation of missing values in mixed attribute data sets. The process of ART2 can recognize learned models fast and be adapted to new objects rapidly. It carries out model-based clustering by using competitive learning and self-steady mechanism in dynamic environment without supervision. The proposed approach not only imputes the missing values but also provides information about handling the outliers.

Keywords: ART2, data imputation, clustering, missing data, neural network, pre-processing

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Authors: Ruixia Liu

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Qinghai-Tibet Plateau has an important influence on the precipitation of its lower reaches. Data from remote sensing has itself advantage and numerical prediction model which assimilates RS data will be better than other. We got the assimilation data of MHS and terrestrial and sounding from GSI, and introduced the result into WRF, then got the result of RH and precipitation forecast. We found that assimilating MHS and terrestrial and sounding made the forecast on precipitation, area and the center of the precipitation more accurate by comparing the result of 1h,6h,12h, and 24h. Analyzing the difference of the initial field, we knew that the data assimilating about Qinghai-Tibet Plateau influence its lower reaches forecast by affecting on initial temperature and RH.

Keywords: Qinghai-Tibet Plateau, precipitation, data assimilation, GSI

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Authors: Edson Vengesai

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Derivative products have become essential instruments in portfolio diversification, price discovery, and, most importantly, risk hedging. Derivatives are complex instruments; their valuation, volatility implications, and real impact on the underlying assets' behaviour are not well understood. Little is documented empirically, with conflicting conclusions on how these instruments affect firm risk exposures. Given the growing interest in using derivatives in risk management and portfolio engineering, this study examines the practical impact of derivative usage on the underlying stock price exposure and systematic risk. The paper uses data from South African listed firms. The study employs GARCH models to understand the effect of derivative uses on conditional stock volatility. The GMM models are used to estimate the effect of derivatives use on stocks' systematic risk as measured by Beta and on the total risk of stocks as measured by the standard deviation of returns. The results provide evidence on whether derivatives use is instrumental in reducing stock returns' systematic and total risk. The results are subjected to numerous controls for robustness, including financial leverage, firm size, growth opportunities, and macroeconomic effects.

Keywords: derivatives use, hedging, volatility, stock price exposure

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Authors: Milda Istiqomah

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Deradicalization program has long been the subject of investigation. There is a steadily growing interest in examining the results on how Islamist terrorists agree to abandon violence and leave radicalism; however, it is argued that de-radicalization program on terrorism in many countries is still questionable for its effectiveness. This article aims to provide an overview of the deradicalization program specifically related to the victim-centred approach conducted by the Indonesian government and investigates critical issues surrounding the analysis of their effectiveness and outcomes. This research employs several case studies of a victim-centred approach conducted by the Indonesian Witness and Victim Protection Agency as well as the Indonesian Counter-terrorism Agency. This paper argues that the victim-centred approach to de-radicalize former terrorist prisoners faces several implemental challenges; however, the initiative may offer promise for future successful de-radicalization program. Furthermore, until more data surrounding the efficacy of this initiative available, the victim-centred approach may also constitute a significant and essential component of disengagement, de-radicalisation, and reintegration of terrorist prisoners. In conclusion, this paper suggests that further empirical research concerning prevention policies and disengagement interventions related to victim-centred approach need to be explored to give more inputs to the Indonesian government to achieve the effectiveness of de-radicalization program.

Keywords: terrorism, victim-centred approach, de-radicalization, Islamist extremism

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Authors: Ibrahim Alkandi

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This study aims at understanding how banks can utilize their resources and capabilities to achieve a competitive advantage. The resource-based approach has been applied to assess the resources and capabilities as well as how the management perceives them as sources of competitive advantages. A quantitative approach was implemented using cross-sectional data. The research population consisted of Top managers in financial companies in Saudi Arabia, and the sample comprised 79 managers. The resources were sub divided into tangible and intangible. Among the variables that will be assessed in the research include propriety rights, trademark which is the brand, communication as well as organizational culture. To achieve the objective of the research, Multivariate analysis through multiple regression was used. The research tool used is a questionnaire whose validity is also assessed. According to the results of the study, there is a significant relationship between bank’s performance and the strategic management of propriety rights, trademark, administrative and financial skills as well as bank culture. Therefore, the research assessed four aspects, among the variables in the model, in relation to the strategic performance of these banks. The aspects considered were trademark, communication, administrative and leadership style as well as the company’s culture. Hence, this paper contributes to the body of literature by providing empirical evidence of the resources influencing both banks’ market and economic performance.

Keywords: competitive advantage, Saudi banks, strategic management, RBV

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Authors: Bello Theodora O., Animola Odunayo V., Owoade Johnson T.

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With the advent of Covid-19, online classroom instruction became a very important mode of instruction delivery during which learners were engaged in both collaborative and online interactive learning process, but along with it are challenges as well as its deliverables. This study therefore investigated the various online platform used by the students for learning among fresh undergraduate students of Obafemi Awolowo University, Ile-Ife, Osun Sate. It also assessed the student’s perception towards online learning in the university and examined the influence of collaborative learning among the students. Lastly, it examined the problems that are associated with collaborative online learning instruction in the university. These were with a view to providing empirical information on problems and prospects of online classroom instruction among fresh undergraduate physical science students of Obafemi Awolowo University, Ile-Ife. The study employed a descriptive survey research technique. The population comprised all the fresh undergraduates in physical science departments of Obafemi Awolowo University, Ile-Ife. The sample consisted two hundred freshmen in physical science departments of Obafemi Awolowo University, Ile-Ife, who were selected using simple random techniques. During the selection, a questionnaire was used to collect data from the respondents. The data were analyzed using appropriate descriptive of frequency, simple percentage, and mean. Results showed that Google Meet 149(74.5%), Telegram 120(60.0%), and Google Classroom 143(71.5%), are the prominent online classroom instruction used by the students in Obafemi Awolowo University, Ile-Ife. The results also showed that the freshmen’s perception towards online classroom instruction in Obafemi Awolowo University, Ile-Ife is low with cluster mean of 2.97. It further revealed that collaborative learning enhances the learning ability of below average learners more than that of the above average and average students (73.6%). Finally, the result showed that they are affirmative of the problems associated with online classroom instruction in Obafemi Awolowo University, Ile-Ife with cluster mean of 3.01. The result concluded that most Online platform used by the fresher’s students in Obafemi Awolowo University, Ile-Ife are Google Meet, Telegram and Google Classroom. The students have negatives perception towards online classroom instruction and the students are affirmative of the problems associated with online classroom instruction among physical science freshmen in Obafemi Awolowo University, Ile-Ife.

Keywords: online, instruction, freshmen, physical science, collaborative

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Authors: Byung Ho Jung, Ji Eun Shin, Dong Hoon Lim

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Rhipe that integrates R and Hadoop environment made it possible to process and analyze massive amounts of data using a distributed processing environment. In this paper, we implemented multiple regression analysis using Rhipe with various data sizes of actual data. Experimental results for comparing the performance of our Rhipe with stats and biglm packages available on bigmemory, showed that our Rhipe was more fast than other packages owing to paralleling processing with increasing the number of map tasks as the size of data increases. We also compared the computing speeds of pseudo-distributed and fully-distributed modes for configuring Hadoop cluster. The results showed that fully-distributed mode was faster than pseudo-distributed mode, and computing speeds of fully-distributed mode were faster as the number of data nodes increases.

Keywords: big data, Hadoop, Parallel regression analysis, R, Rhipe

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Authors: Mona Almansoori, Ahmed Mustafa, Ahmad Elshamy

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Wireless sensor network was formed by a combination of nodes, systematically it transmitting the data to their base stations, this transmission data can be easily compromised if the limited processing power and the data consistency from these nodes are kept in mind; there is always a discussion to address the secure data transfer or transmission in actual time. This will present a mechanism to securely transmit the data over a chain of sensor nodes without compromising the throughput of the network by utilizing available battery resources available in the sensor node. Our methodology takes many different advantages of Z-MAC protocol for its efficiency, and it provides a unique key by sharing the mechanism using neighbor node MAC address. We present a light weighted data integrity layer which is embedded in the Z-MAC protocol to prove that our protocol performs well than Z-MAC when we introduce the different attack scenarios.

Keywords: hybrid MAC protocol, data integrity, lightweight encryption, neighbor based key sharing, sensor node dataprocessing, Z-MAC

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Authors: Madaki Umar Yusuf, Mohd Rizam B. Abubakar

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The survival censored data with incomplete covariate data is a common occurrence in many studies in which the outcome is survival time. With model when the missing covariates are categorical, a useful technique for obtaining parameter estimates is the EM by the method of weights. The survival outcome for the class of generalized linear model is applied and this method requires the estimation of the parameters of the distribution of the covariates. In this paper, we propose some clinical trials with ve covariates, four of which have some missing values which clearly show that they were fully censored data.

Keywords: EM algorithm, incomplete categorical covariates, ignorable missing data, missing at random (MAR), Weibull Distribution

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Authors: Muhammad Tahir, Mushtaq Muhammad

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Capital structure is one of the key decisions taken by the financial managers. This study aims to investigate the factors influencing capital structure decision in Oil and Gas industry of Pakistan using secondary data from published annual reports of listed Oil and Gas Companies of Pakistan. This study covers the time-period from 2008-2014. Capital structure can be affected by profitability, firm size, growth opportunities, dividend payout, liquidity, business risk, and ownership structure. Panel data technique with Ordinary least square (OLS) regression model has been used to find the impact of set of explanatory variables on the capital structure using the Stata. OLS regression results suggest that dividend payout, firm size and government ownership have the most significant impact on financial leverage. Dividend payout and government ownership are found to have significant negative association with financial leverage however firm size indicated positive relationship with financial leverage. Other variables having significant link with financial leverage includes growth opportunities, liquidity and business risk. Results reveal significant positive association between growth opportunities and financial leverage whereas liquidity and business risk are negatively correlated with financial leverage. Profitability and managerial ownership exhibited insignificant relationship with financial leverage. This study contributes to existing Managerial Finance literature with certain managerial implications. Academically, this research study describes the factors affecting capital structure decision of Oil and Gas Companies in Pakistan and adds latest empirical evidence to existing financial literature in Pakistan. Researchers have studies capital structure in Pakistan in general and industry at specific, nevertheless still there is limited literature on this issue. This study will be an attempt to fill this gap in the academic literature. This study has practical implication on both firm level and individual investor/ lenders level. Results of this study can be useful for investors/ lenders in making investment and lending decisions. Further, results of this study can be useful for financial managers to frame optimal capital structure keeping in consideration the factors that can affect capital structure decision as revealed by this study. These results will help financial managers to decide whether to issue stock or issue debt for future investment projects.

Keywords: capital structure, multicollinearity, ordinary least square (OLS), panel data

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Authors: Babek Erdebilli

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The aim of this study is to develop and describe a new methodology for the Multi-Criteria Decision-Making (MCDM) problem using IFE (Elimination Et Choix Traduisant La Realite (ELECTRE) model. The proposed models enable Decision-Makers (DMs) on the assessment and use Intuitionistic Fuzzy Numbers (IFN). A numerical example is provided to demonstrate and clarify the proposed analysis procedure. Also, an empirical experiment is conducted to validation the effectiveness.

Keywords: multi-criteria decision-making, IFE, DM’s, fuzzy electre model

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Authors: Abdeljalil Beniiche

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The limitation with smart contracts is that they cannot access external data that might be required to control the execution of business logic. Oracles can be used to provide external data to smart contracts. An oracle is an interface that delivers data from external data outside the blockchain to a smart contract to consume. Oracle can deliver different types of data depending on the industry and requirements. In this paper, we study and describe the widely used blockchain oracles. Then, we elaborate on his potential role, technical architecture, and design patterns. Finally, we discuss the human oracle and its key role in solving the truth problem by reaching a consensus about a certain inquiry and tasks.

Keywords: blockchain, oracles, oracles design, human oracles

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Authors: Yasser Mohamed Abdelrahman Tarshany

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The emergence of the digital economy has transformed global business and created new opportunities for growth and development. However, as digital technologies continue to reshape the global economy, there is a growing need to ensure that these transformations are guided by ethical principles that serve the common good. In this context, Maqasid, the Islamic ethical framework that focuses on the higher objectives and values of Shariah, offers a valuable lens for examining the ethical implications of digital transformation. The research objective of this study is to explore the opportunities and challenges of integrating Maqasid into the global digital economy from a business and management perspective. Specifically, the study aims to analyze the ethical implications of digital technologies for the economy and to identify strategies for leveraging Maqasid to promote ethical and socially responsible practices in the digital age. The study adopts a qualitative research methodology, drawing on existing literature and empirical data to develop a conceptual framework for understanding the relationship between Maqasid and the global digital economy. The study also employs case studies and interviews with business leaders and policymakers to explore practical strategies for integrating Maqasid into digital business practices. The research findings reveal that Maqasid can serve as a powerful framework for promoting ethical and socially responsible practices in the digital economy. Specifically, the study identifies several key strategies for leveraging Maqasid in digital business practices, including promoting social justice, protecting privacy and personal data, and ensuring transparency and accountability in business operations. The research outcomes of this study provide a valuable contribution to the field of business and management by demonstrating the importance of integrating ethical principles into the digital economy. Furthermore, the study highlights the potential of Maqasid as a powerful framework for promoting ethical and socially responsible practices in the digital age. Finally, the study suggests several avenues for future research, including exploring the role of Maqasid in promoting digital inclusion and reducing inequality in the global economy.

Keywords: Maqasid, global digital, economy, opportunities, challenges for business, management

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Authors: Abdoullah Nyassi, Golam Sarwar, Sarra Baldeh, Mamadou S. K. Jallow, Bai Lamin Dondeh, Isaac Osei, Grant A. Mackenzie

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A randomized controlled trial is the "gold standard" for evaluating the efficacy of an intervention. Large-scale, cluster-randomized trials are expensive and difficult to conduct, though. To guarantee the validity and generalizability of findings, high-quality, dependable, and accurate data management systems are necessary. Robust data management systems are crucial for optimizing and validating the quality, accuracy, and dependability of trial data. Regarding the difficulties of data gathering in clinical trials in low-resource areas, there is a scarcity of literature on this subject, which may raise concerns. Effective data management systems and implementation goals should be part of trial procedures. Publicizing the creative clinical data management techniques used in clinical trials should boost public confidence in the study's conclusions and encourage further replication. In the ongoing pneumococcal vaccine schedule study in rural Gambia, this report details the development and deployment of multi-data management systems and methodologies. We implemented six different data management, synchronization, and reporting systems using Microsoft Access, RedCap, SQL, Visual Basic, Ruby, and ASP.NET. Additionally, data synchronization tools were developed to integrate data from these systems into the central server for reporting systems. Clinician, lab, and field data validation systems and methodologies are the main topics of this report. Our process development efforts across all domains were driven by the complexity of research project data collected in real-time data, online reporting, data synchronization, and ways for cleaning and verifying data. Consequently, we effectively used multi-data management systems, demonstrating the value of creative approaches in enhancing the consistency, accuracy, and reporting of trial data in a poor resource setting.

Keywords: data management, data collection, data cleaning, cluster-randomized trial

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Authors: Pi Hsia Liang

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The rise of Facebook, Twitter, and other social networks significantly changes in interconnections between people, enhancing the process of information dissemination and amplify the influence of that information. Therefore, to develop informational efficiency or signaling equilibrium type of information environment among social networks, without adverse selection effects, becomes an important issue. Thus, someone may post a piece of intentional information in relation to personal interest for trying to create marginal influence. Therefore, economists are seeking to establish theories of informational efficiency under social network environment in order to resolve adverse selection issues. Reputation could be one of the important factors in the process of creating informational efficiency. Additionally, investors how to process green information, or information of corporate social responsibility is a very important study. This study essentially employs experimental study for examining how investors use stock relevant green information in Facebook and various Taiwan local networks. Facebook, and blogs of Money DJ, Technews and cnYES, respectively, are the primary sites for this examination that also allow to differentiate effects between Facebook and other local social networks. Questionnaire is developed for such an experimental testing. Note that questionnaire allows this study to group, for example, decision frequency and length of time duration focusing on social networks that are used for discriminating investor type and competence of informed investor. This study selects 500 investors that can be separated into two respective 250 samples as the control group and 250 samples in such an experimental. The quantity of sample investor sufficiently results in statistic significance of this experimental study. The empirical results of this study can be used for explaining how financial information in relation to corporate social responsibility would be disseminated in social websites. Therefore, we can lead to better interpretation of price/earnings relationship type of study and empirical studies of green information usefulness or informational efficiency Note that the above mentioned empirical studies did not exist any social network and annual report of corporate social responsibility. This study expects to find the results that both network degree and network cluster significantly affected green information dissemination frequency. In other words, investors with more connections and with high clustered connections might exert a greater influence on their green information dissemination process. The preferred users of financial social networks could make better stock decision that could amplify effects of green information. In addition, Facebook would be more influential than other local Taiwan financial social networks, although Facebook is not a specialized financial social network. In other words, the popularity and reputation effects of Facebook significantly contribute to usefulness of green information and influence of green information. Third, it has a better chance to find rumor or cheating information in local Taiwan financial social networks than Facebook. In other words, Facebook possesses reputation effect, or a better informational efficiency. Or, even though Taiwan local financial social networks have marginal informational effects on stock price, because of shortage of informational efficiency or monitoring system, information could be a tool for those whom owning superior information.

Keywords: network effect on financial services, informational efficiency theory, social networks, social websites

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Authors: A. H. M. Zehadul Karim

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Cameron Highlands (4°28’N, 101°23’E) is an attractive mountainous region with steep slopes located in the state of Pahang, Malaysia stretching between 1070 and 1830m above sea level with a total land area of 71,218ha. It is one of the few places in Malaysia that has a tropical highland climate as the mean annual temperature of it is 18 °C (64 °F) thus making the atmosphere perfect for specialized agriculture. Being ecologically suitable, Cameron Highlands has recently been identified as a very strategic farming area, producing multifarious vegetables, flowers and tea with a commercial motive of marketing them to Singapore and all over the urban areas of Malaysia to meet the domestic and international demands. The main intricacies of this plantation agriculture are fully dependent on the policies formulated by a group of emerging entrepreneurs who employ foreign labourers to make these agricultural activities a success in the agrarian sector in Malaysia. Based on the socio-anthropological perspective, the paper entirely relies on empirical field data generated by interviewing 10 farm owners and 200 foreign workers to find out the intricacies of this plantation agriculture which makes the research innovative and pragmatically significant. The paper deals with important issues relating to this productive plantation agriculture of Cameron Highlands and as such, narrates the various exceptional and holistic skills adopted for this type of farming.

Keywords: Cameron Highlands Malaysia, plantation agriculture, issues and challenges, mechanisms

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Authors: Alhadi Bustaman, Soeganda Formalidin, Titin Siswantining

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DNA microarray technology is used to analyze thousand gene expression data simultaneously and a very important task for drug development and test, function annotation, and cancer diagnosis. Various clustering methods have been used for analyzing gene expression data. However, when analyzing very large and heterogeneous collections of gene expression data, conventional clustering methods often cannot produce a satisfactory solution. Biclustering algorithm has been used as an alternative approach to identifying structures from gene expression data. In this paper, we introduce a transform technique based on singular value decomposition to identify normalized matrix of gene expression data followed by Mixed-Clustering algorithm and the Lift algorithm, inspired in the node-deletion and node-addition phases proposed by Cheng and Church based on Agglomerative Hierarchical Clustering (AHC). Experimental study on standard datasets demonstrated the effectiveness of the algorithm in gene expression data.

Keywords: agglomerative hierarchical clustering (AHC), biclustering, gene expression data, lymphoma, singular value decomposition (SVD)

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Authors: Ramya P, Lino Abraham Varghese, S. Bose

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By cloud storage services, the data can be stored in the cloud, and can be shared across multiple users. Due to the unexpected hardware/software failures and human errors, which make the data stored in the cloud be lost or corrupted easily it affected the integrity of data in cloud. Some mechanisms have been designed to allow both data owners and public verifiers to efficiently audit cloud data integrity without retrieving the entire data from the cloud server. But public auditing on the integrity of shared data with the existing mechanisms will unavoidably reveal confidential information such as identity of the person, to public verifiers. Here a privacy-preserving mechanism is proposed to support public auditing on shared data stored in the cloud. It uses group signatures to compute verification metadata needed to audit the correctness of shared data. The identity of the signer on each block in shared data is kept confidential from public verifiers, who are easily verifying shared data integrity without retrieving the entire file. But on demand, the signer of the each block is reveal to the owner alone. Group private key is generated once by the owner in the static group, where as in the dynamic group, the group private key is change when the users revoke from the group. When the users leave from the group the already signed blocks are resigned by cloud service provider instead of owner is efficiently handled by efficient proxy re-signature scheme.

Keywords: data integrity, dynamic group, group signature, public auditing

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Authors: Wendy Lee

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From the mid-twentieth century onward, the notion of ‘attachment’ has been used to define the optimum relationship between young children and their carers; first applied to parents and young children and more recently with early childhood educators and children in their care. However, it is seldom, if ever, asked whether the notion of ‘attachment’ and more especially so-called Attachment Theory, as propounded by John Bowlby and others, provides a sound basis for conceptualising child-adult relationships in early years. Even if appropriate in the context of family, the use of the term raises a number of questions when used in early childhood education. Research has shown that our youngest children (infants) in early childhood centre based care settings, are given the utmost priority to build 'attachments' with their educators. But exactly when, how and why does this priority diminish - and should it (for preschoolers)? This presentation will elaborate on such issues and will argue that there is a need to reconceptualise and redefine how 'quality relationships' should be measured and implemented in the daily practices and pedagogical methods adopted by early childhood educators. Moreover, this presentation will include data collected from the empirical study conducted, that observed various early childhood educators and children in Australian early childhood centres. Lastly, the thoughts, feelings and desires of parents of children in early childhood centre-based care, regarding the term 'attachment' and 'quality relationships' will be shared in the hope that we can take one step closer in bridging the needs of families, children, early childhood centres, educators, and the wider community.

Keywords: attachment, early childhood education, pedagogy, relationships

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Authors: Jerlin George, R. Chitra

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The devices with sensors that can monitor your temperature, heart rate, and other vital signs and link to the internet, known as the Internet of Things (IoT), have completely transformed the way we control health. Providing real-time health data, these sensors improve diagnostics and treatment outcomes. Security and privacy matters when IoT comes into play in healthcare. Cyberattacks on centralized database systems are also a problem. To solve these challenges, the study uses blockchain technology coupled with proxy re-encryption to secure health data. ThingSpeak IoT cloud analyzes the collected data and turns them into blockchain transactions which are safely kept on the DriveHQ cloud. Transparency and data integrity are ensured by blockchain, and secure data sharing among authorized users is made possible by proxy re-encryption. This results in a health monitoring system that preserves the accuracy and confidentiality of data while reducing the safety risks of IoT-driven healthcare applications.

Keywords: internet of things, healthcare, sensors, electronic health records, blockchain, proxy re-encryption, data privacy, data security

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Authors: Nathainail Bashir, Neil Anderson

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The objective of this study site was to investigate the current state of the practice with regards to karst detection methods and recommend the best method and pattern of arrays to acquire the desire results. Proper site investigation in karst prone regions is extremely valuable in determining the location of possible voids. Two geophysical techniques were employed: multichannel analysis of surface waves (MASW) and electric resistivity tomography (ERT).The MASW data was acquired at each test location using different array lengths and different array orientations (to increase the probability of getting interpretable data in karst terrain). The ERT data were acquired using a dipole-dipole array consisting of 168 electrodes. The MASW data was interpreted (re: estimated depth to physical top of rock) and used to constrain and verify the interpretation of the ERT data. The ERT data indicates poorer quality MASW data were acquired in areas where there was significant local variation in the depth to top of rock.

Keywords: dipole-dipole, ERT, Karst terrains, MASW

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Authors: H. W. Meerveld, R. H. A. Lindelauf

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In contemporary warfare, data science is crucial for the military in achieving information superiority. Yet, to the authors’ knowledge, no extensive literature survey on data science in military decision-making has been conducted so far. In this study, 156 peer-reviewed articles were analysed through an integrative, semi-systematic literature review to gain an overview of the topic. The study examined to what extent literature is focussed on the opportunities or risks of data science in military decision-making, differentiated per level of war (i.e. strategic, operational, and tactical level). A relatively large focus on the risks of data science was observed in social science literature, implying that political and military policymakers are disproportionally influenced by a pessimistic view on the application of data science in the military domain. The perceived risks of data science are, however, hardly addressed in formal science literature. This means that the concerns on the military application of data science are not addressed to the audience that can actually develop and enhance data science models and algorithms. Cross-disciplinary research on both the opportunities and risks of military data science can address the observed research gaps. Considering the levels of war, relatively low attention for the operational level compared to the other two levels was observed, suggesting a research gap with reference to military operational data science. Opportunities for military data science mostly arise at the tactical level. On the contrary, studies examining strategic issues mostly emphasise the risks of military data science. Consequently, domain-specific requirements for military strategic data science applications are hardly expressed. Lacking such applications may ultimately lead to a suboptimal strategic decision in today’s warfare.

Keywords: data science, decision-making, information superiority, literature review, military

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Authors: Mahdi Fakheri, Mohammad Mohsen Mahdizadeh Naeini

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This paper addresses an understudied aspect of U.S. foreign aids between the years 2000 and 2020. Despite a growing body of literature on the impacts of U.S. aids, the question about how the United States uses its foreign aids to change developing countries has remained unanswered. As foreign aid is a tool of the United States' foreign policy, answering this very question can reveal the future that the U.S. prefers for developing countries and that secures its national interest. This paper will explore USAID's official dataset, which includes the data of foreign aids to the Middle East, Africa, Latin America, and Southeast Asia from 2000 to 2020. Through an empirical analysis, this paper argues that the focus of U.S. foreign aid is evenly divided between institutional and non-institutional (i.e., slight enhancement of status quo) changes. The former is induced by training and education, funding the initiatives and projects, making capacity and increasing the efficiency of human, operational, and management sectors, and enhancing the living condition of the people. Moreover, it will be demonstrated that the political, military, cultural, economic, and judicial are some of the institutions that the U.S. has planned to change in the aforementioned period and regions.

Keywords: USAID, foreign aid, development, developing countries, Middle East, Africa, Southeast Asia, Latin America

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Authors: Cai Qianyi

Abstract:

In the midst of global digital revolution, the flow of data poses security threats that call China's existing legislative framework for protecting personal information into question. As a preliminary procedure for risk analysis and prevention, the risk assessment of personal data transmission lacks detailed guidelines for support. Existing provisions reveal unclear responsibilities for network operators and weakened rights for data subjects. Furthermore, the regulatory system's weak operability and a lack of industry self-regulation heighten data transmission hazards. This paper aims to compare the regulatory pathways for data information transmission risks between China and Europe from a legal framework and content perspective. It draws on the “Data Protection Impact Assessment Guidelines” to empower multiple stakeholders, including data processors, controllers, and subjects, while also defining obligations. In conclusion, this paper intends to solve China's digital security shortcomings by developing a more mature regulatory framework and industry self-regulation mechanisms, resulting in a win-win situation for personal data protection and the development of the digital economy.

Keywords: personal information data transmission, risk assessment, DPIA, internet service provider, personal information data transimission, risk assessment

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Authors: Habiba Ben Abdessalem

Abstract:

The emergence of giant set of textual data was the push that has encouraged researchers to invest in this field. The purpose of textual data analysis methods is to facilitate access to such type of data by providing various graphic visualizations. Applying these methods requires a corpus pretreatment step, whose standards are set according to the objective of the problem studied. This step determines the forms list contained in contingency table by keeping only those information carriers. This step may, however, lead to noisy contingency tables, so the use of wavelet denoising function. The validity of the proposed approach is tested on a text database that offers economic and political events in Tunisia for a well definite period.

Keywords: textual data, wavelet, denoising, contingency table

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Authors: Tefera Kebede Leyu

Abstract:

The purpose of this study was to estimate the impact of LIVES project participation on the level of technical efficiency of farm households in three regions of Ethiopia. We used household-level data gathered by IRLI between February and April 2014 for the year 2013(retroactive). Data on 1,905 (754 intervention and 1, 151 control groups) sample households were analyzed using STATA software package version 14. Efforts were made to combine stochastic frontier modeling with impact evaluation methodology using the Heckman (1979) two-stage model to deal with possible selectivity bias arising from unobservable characteristics in the stochastic frontier model. Results indicate that farmers in the two groups are not efficient and operate below their potential frontiers i.e., there is a potential to increase crop productivity through efficiency improvements in both groups. In addition, the empirical results revealed selection bias in both groups of farmers confirming the justification for the use of selection bias corrected stochastic frontier model. It was also found that intervention farmers achieved higher technical efficiency scores than the control group of farmers. Furthermore, the selectivity bias-corrected model showed a different technical efficiency score for the intervention farmers while it more or less remained the same for that of control group farmers. However, the control group of farmers shows a higher dispersion as measured by the coefficient of variation compared to the intervention counterparts. Among the explanatory variables, the study found that farmer’s age (proxy to farm experience), land certification, frequency of visit to improved seed center, farmer’s education and row planting are important contributing factors for participation decisions and hence technical efficiency of farmers in the study areas. We recommend that policies targeting the design of development intervention programs in the agricultural sector focus more on providing farmers with on-farm visits by extension workers, provision of credit services, establishment of farmers’ training centers and adoption of modern farm technologies. Finally, we recommend further research to deal with this kind of methodological framework using a panel data set to test whether technical efficiency starts to increase or decrease with the length of time that farmers participate in development programs.

Keywords: impact evaluation, efficiency analysis and selection bias, stochastic frontier model, Heckman-two step

Procedia PDF Downloads 74