Search results for: data access

Authors: Jonathan Adeyemi

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Context: This study focuses on El Anatsui and his global acclaim in the art world despite his origins from the global artworld’s margins. It addresses the disparities in the treatment between Western and non-Western artists and questions whether Anatsui’s consecration is a result of exoticism or the growing consensus on decolonization. Research Aim: The aim of this study is to investigate how El Anatsui achieved global acclaim from the margins of the art world and determine if his consecration represents a mark of decolonization or the typical Western desire for exoticism. Methodology: The study utilizes a case study approach, literature analysis, and in-depth interviews. The artist, the organizers of the Venice Biennale, the relevant curators at Tate Modern London, and the October Gallery in London, and other galleries in Nigeria, which represent the artist were interviewed for data collection. Findings: The study seeks to determine the authenticity of the growing consensus on decolonization, inclusion, and diversity in the global artistic field. Preliminary findings show that domestic socio-economic and political factors debilitated the mechanisms for local validation in Nigeria, weakening the domestic foundation for international engagement. However, alternative systems of exhibition, especially in London and the USA contributed critically to providing the initial international visibility, which formed the foundation for his global acclaim. Out of the 21 winners of the Golden Lion for Lifetime Achievement since its inception at the 47th Venice Biennale in 1997, American artists have dominated with 10 recipients, 8 recipients from Europe, 2 recipients from Africa (2007 and 2015) and 1 from Asia. This aligns with Bourdieu’s concept of cultural and economic capital, which prevented Africa countries from participation until recently. Moreover, while the average age of recipients is 76 years, Anatsui received the award at the age of 71, while Malick Sidibé (Mali) was awarded at 72. Thus, the Venice Biennale award for El Anatsui incline more towards a commitment to decolonisation than exoticism. Theoretical Importance: This study contributes to the field by examining the dynamics of the art world's monopoly of legitimation and the role of national, ethnicity and cultural differences in the promotion of artists. It aims to challenge the Westernized hierarchy of valorization and consecration in the art world. The research supports Bourdieu’s artistic field theory, which emphasises the importance of cultural, economic and social capital in determining agents’ position and access to the field resources (symbolic capital). Bourdieu also established that dominated agents can change their position in the field’s hierarchy either by establishing or navigating alternative systems. Data Collection and Analysis Procedures: The opacity of art world’s operations places the required information within the purview of the insiders (agents). Thus, the study collects data through in-depth interviews with relevant and purposively selected individuals and organizations. The data was/will be analyzed using qualitative methods, such as thematic analysis and content analysis. The interpretive analytical approach adopted facilitated the construction of meanings that may not be apparent in the data or responses. Questions Addressed: The study addresses how El Anatsui achieved global acclaim despite being from the margins, whether his consecration represents decolonization or exoticism, and the extent to which the global artistic field embraces decolonization, inclusion, and diversity. Conclusion: The study will contribute to knowledge by providing insights into the extent of commitment to decolonization, inclusion, and diversity in the global artistic field. It also shed light on the mechanisms behind El Anatsui's rise to global acclaim and challenge Western-dominated artistic hierarchies.

Keywords: decolonisation, exorticism, artistic field, culture game

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Authors: Valentin P. Velikov

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Automatic program generation saves time, human resources, and allows receiving syntactically clear and logically correct modules. The 4-th generation programming languages are related to drawing the data and the processes of the subject area, as well as, to obtain a frame of the respective information system. The application can be separated in interface and business logic. That means, for an interactive generation of the needed system to be used an already existing toolkit or to be created a new one.

Keywords: computer science, graphical user interface, user dialog interface, dialog frames, data modeling, subject area modeling

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Authors: Bo Wang

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The geometric processing of multi-source remote sensing data using control data of different scale and different accuracy is an important research direction of multi-platform system for earth observation. In the existing block bundle adjustment methods, as the controlling information in the adjustment system, the approach using single observation scale and precision is unable to screen out the control information and to give reasonable and effective corresponding weights, which reduces the convergence and adjustment reliability of the results. Referring to the relevant theory and technology of quotient space, in this project, several subjects are researched. Multi-layer quotient space of multi-geometric features is constructed to describe and filter control data. Normalized granularity merging mechanism of multi-layer control information is studied and based on the normalized scale factor, the strategy to optimize the weight selection of control data which is less relevant to the adjustment system can be realized. At the same time, geometric positioning experiment is conducted using multi-source remote sensing data, aerial images, and multiclass control data to verify the theoretical research results. This research is expected to break through the cliché of the single scale and single accuracy control data in the adjustment process and expand the theory and technology of photogrammetry. Thus the problem to process multi-source remote sensing data will be solved both theoretically and practically.

Keywords: multi-source image geometric process, high precision geometric positioning, quotient space of multi-geometric features, optimized weight selection

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Authors: Caiwei Chen, Jianhao Li, Jiasong Zhu

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The reform and opening-up stimulated economic and technological take-off in China while resulting in massive urbanization and motorization. Wujiaochang area was set as a secondary business district in Shanghai to meet the growing demand, with the reconstruction of Wujiaochang Plaza in 2005 being a milestone of this intended urban renewal. Wujiaochang is now an economically dynamic area providing much larger traffic and transit capacity transportation-wise. However, this rebuilding has completely changed the face of the district. It is, therefore, appropriate to evaluate its impact on neighborhoods and communities while assessing the overall sustainability of such an operation. In this study, via an online questionnaire survey among local residents and daily visitors, we assess the perceptions and the estimated impact of Wujiaochang Plaza's reconstruction. We then confront these results to the 62 answers from local residents to a questionnaire collected on paper. The analysis of our data, along with observation and other forms of information -such as maps analysis or online applications (Dianping)- demonstrate major improvement in economic sustainability but also significant losses in environmental sustainability, especially in terms of active transportation. As for the social viewpoint, local residents' opinions tend to be rather positive, especially regarding traffic safety and access to consumption, despite the lack of connectivity and radical changes induced by Wujiaochang massive transformations. In general, our investigation exposes the overall positive outcomes of Wujiaochang Plaza reconstruction but also unveils major drawbacks, especially in terms of soft mobility and traffic fluidity. We gather that our approach could be of tremendous help for future major urban interventions, as such approaches in municipal regeneration are widely implemented in Chinese cities and yet still need to be thoroughly assessed in terms of sustainability.

Keywords: China's reform and opening-up, economical revitalization, neighborhood identity, sustainability assessment, urban renewal

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Authors: Joe Dorbor Wuokolo

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Thousands of smallholder farmers in Todee District, Montserrado county, are currently vulnerable to the negative impact of climate change. The district, which is the agricultural hot spot for the county, is faced with unfavorable changes in the daily temperature due to climate change. Farmers in the district have observed a dramatic change in the ratio of rainfall to sunshine, which has caused a chilling effect on their crop yields. However, there is a lack of documentation regarding how farmers perceive and respond to these changes and challenges. A study was conducted in the region to examine the perceptions of smallholder farmers regarding the negative impact of climate change, the adaptation strategies practice, and the barriers that hinder the process of advancing adaptation strategy. On purpose, a sample of 41 respondents from five towns was selected, including five town chiefs, five youth leaders, five women leaders, and sixteen community members. Women and youth leaders were specifically chosen to provide gender balance and enhance the quality of the investigation. Additionally, to validate the barriers farmers face during adaptation to climate change, this study interviewed eight experts from local and international organizations and government ministries and agencies involved in climate change and agricultural programs on what they perceived as the major barrier in both local and national level that impede farmers adaptation to climate change impact. SPSS was used to code the data, and descriptive statistics were used to analyze the data. The weighted average index (WAI) was used to rank adaptation strategies and the perceived importance of adaptation practices among farmers. On a scale from 0 to 3, 0 indicates the least important technique, and 3 indicates the most effective technique. In addition, the Problem Confrontation Index (PCI) was used to rank the barriers that prevented farmers from implementing adaptation measures. According to the findings, approximately 60% of all respondents considered the use of irrigation systems to be the most effective adaptation strategy, with drought-resistant varieties making up 30% of the total. Additionally, 80% of respondents placed a high value on drought-resistant varieties, while 63% percent placed it on irrigation practices. In addition, 78% of farmers ranked and indicated that unpredictability of the weather is the most significant barrier to their adaptation strategies, followed by the high cost of farm inputs and lack of access to financing facilities. 80% of respondents believe that the long-term changes in precipitation (rainfall) and temperature (hotness) are accelerating. This suggests that decision-makers should adopt policies and increase the capacity of smallholder farmers to adapt to the negative impact of climate change in order to ensure sustainable food production.

Keywords: adaptation strategies, climate change, farmers’ perception, smallholder farmers

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Authors: Saeed Hosseini, Ali Reza Tahavvor

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Sound processing is one the subjects that newly attracts a lot of researchers. It is efficient and usually less expensive than other methods. In this paper the flow generated sound is used to estimate the flow speed of free flows. Many sound samples are gathered. After analyzing the data, a parameter named wave power is chosen. For all samples, the wave power is calculated and averaged for each flow speed. A curve is fitted to the averaged data and a correlation between the wave power and flow speed is founded. Test data are used to validate the method and errors for all test data were under 10 percent. The speed of the flow can be estimated by calculating the wave power of the flow generated sound and using the proposed correlation.

Keywords: the flow generated sound, free stream, sound processing, speed, wave power

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Authors: Shengnan Chen, Shuhua Wang

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Successful production of hydrocarbon from unconventional tight oil reserves has changed the energy landscape in North America. The oil contained within these reservoirs typically will not flow to the wellbore at economic rates without assistance from advanced horizontal well and multi-stage hydraulic fracturing. Efficient and economic development of these reserves is a priority of society, government, and industry, especially under the current low oil prices. Meanwhile, society needs technological and process innovations to enhance oil recovery while concurrently reducing environmental impacts. Recently, big data analysis and artificial intelligence become very popular, developing data-driven insights for better designs and decisions in various engineering disciplines. However, the application of data mining in petroleum engineering is still in its infancy. The objective of this research aims to apply intelligent data analysis and data-driven models to exploit unconventional oil reserves both efficiently and economically. More specifically, a comprehensive database including the reservoir geological data, reservoir geophysical data, well completion data and production data for thousands of wells is firstly established to discover the valuable insights and knowledge related to tight oil reserves development. Several data analysis methods are introduced to analysis such a huge dataset. For example, K-means clustering is used to partition all observations into clusters; principle component analysis is applied to emphasize the variation and bring out strong patterns in the dataset, making the big data easy to explore and visualize; exploratory factor analysis (EFA) is used to identify the complex interrelationships between well completion data and well production data. Different data mining techniques, such as artificial neural network, fuzzy logic, and machine learning technique are then summarized, and appropriate ones are selected to analyze the database based on the prediction accuracy, model robustness, and reproducibility. Advanced knowledge and patterned are finally recognized and integrated into a modified self-adaptive differential evolution optimization workflow to enhance the oil recovery and maximize the net present value (NPV) of the unconventional oil resources. This research will advance the knowledge in the development of unconventional oil reserves and bridge the gap between the big data and performance optimizations in these formations. The newly developed data-driven optimization workflow is a powerful approach to guide field operation, which leads to better designs, higher oil recovery and economic return of future wells in the unconventional oil reserves.

Keywords: big data, artificial intelligence, enhance oil recovery, unconventional oil reserves

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Authors: Esmat Noroozi, Elahe Sarfi, Farha Hosseinzadeh Lotfi

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Examining the impacts of data modification is considered as sensitivity analysis. A lot of studies have considered the data modification of inputs and outputs in DEA. The issues which has not heretofore been considered in DEA sensitivity analysis is modification in the number of inputs and (or) outputs and determining the impacts of this modification in the status of efficiency of DMUs. This paper is going to present systems that show the impacts of adding one or multiple inputs or outputs on the status of efficiency of DMUs and furthermore a model is presented for recognizing the minimum number of inputs and (or) outputs from among specified inputs and outputs which can be added whereas an inefficient DMU will become efficient. Finally the presented systems and model have been utilized for a set of real data and the results have been reported.

Keywords: data envelopment analysis, efficiency, sensitivity analysis, input, out put

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Authors: Gong Zhilin, Jing Yang, Jian Yin

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The purpose of this paper is to develop a novel system for credit card fraud detection based on sequential modeling of data using hybrid deep learning models. The projected model encapsulates five major phases are pre-processing, imbalance-data handling, feature extraction, optimal feature selection, and fraud detection with an ensemble classifier. The collected raw data (input) is pre-processed to enhance the quality of the data through alleviation of the missing data, noisy data as well as null values. The pre-processed data are class imbalanced in nature, and therefore they are handled effectively with the K-means clustering-based SMOTE model. From the balanced class data, the most relevant features like improved Principal Component Analysis (PCA), statistical features (mean, median, standard deviation) and higher-order statistical features (skewness and kurtosis). Among the extracted features, the most optimal features are selected with the Self-improved Arithmetic Optimization Algorithm (SI-AOA). This SI-AOA model is the conceptual improvement of the standard Arithmetic Optimization Algorithm. The deep learning models like Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and optimized Quantum Deep Neural Network (QDNN). The LSTM and CNN are trained with the extracted optimal features. The outcomes from LSTM and CNN will enter as input to optimized QDNN that provides the final detection outcome. Since the QDNN is the ultimate detector, its weight function is fine-tuned with the Self-improved Arithmetic Optimization Algorithm (SI-AOA).

Keywords: credit card, data mining, fraud detection, money transactions

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Authors: Sara J. Mills, Sally Howell

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The field of special education has long struggled to bridge the research to practice gap. There is ample evidence from research of effective strategies for students with special needs, but these strategies are not routinely implemented in schools in ways that yield positive results for students. In recent years, the field of special education has turned its focus to implementation science. That is, discovering effective methods of implementing evidence-based practices in school settings. Teacher training is a critical factor in implementation. This study aimed to successfully implement Schema-Based Instruction (SBI) for math problem solving in four classrooms in a special primary school serving students with language deficits, including students with Autism Spectrum Disorders (ASD) and Intellectual Disabilities (ID). Using an action research design that allowed for adjustments and modification to be made over the year-long study, two cohorts of teachers across the school were trained and supported in six-week learning cycles to implement SBI in their classrooms. The learning cycles included a one-day training followed by six weeks of one-on-one or team coaching and three fortnightly cohort group meetings. After the first cohort of teachers completed the learning cycle, modifications and adjustments were made to lesson materials in an attempt to improve their effectiveness with the second cohort. Fourteen teachers participated in the study, including master special educators (n=3), special education instructors (n=5), and classroom assistants (n=6). Thirty-one students participated in the study (21 boys and 10 girls), ranging in age from 5 to 12 years (M = 9 years). Twenty-one students had a diagnosis of ASD, 20 had a diagnosis of mild or moderate ID, with 13 of these students having both ASD and ID. The remaining students had diagnosed language disorders. To evaluate the effectiveness of the implementation approach, both student and teacher data was collected. Student data included pre- and post-tests of math word problem solving. Teacher data included fidelity of treatment checklists and pre-post surveys of teacher attitudes and efficacy for teaching problem solving. Finally, artifacts were collected throughout the learning cycle. Results from cohort 1 and cohort 2 revealed similar outcomes. Students improved in the number of word problems they answered correctly and in the number of problem-solving steps completed independently. Fidelity of treatment data showed that teachers implemented SBI with acceptable levels of fidelity (M = 86%). Teachers also reported increases in the amount of time spent teaching problem solving, their confidence in teaching problem solving and their perception of students’ ability to solve math word problems. The artifacts collected during instruction indicated that teachers made modifications to allow their students to access the materials and to show what they knew. These findings are in line with research that shows student learning can improve when teacher professional development is provided over an extended period of time, actively involves teachers, and utilizes a variety of learning methods in classroom contexts. Further research is needed to evaluate whether these gains in teacher instruction and student achievement can be maintained over time once the professional development is completed.

Keywords: implementation science, mathematics problem solving, research-to-practice gap, schema based instruction

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Authors: Ahmed Abdulla Ashlam, Atta Badii, Frederic Stahl

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In recent years, SQL injection attacks have been identified as being prevalent against web applications. They affect network security and user data, which leads to a considerable loss of money and data every year. This paper presents the use of classification algorithms in machine learning using a method to classify the login data filtering inputs into "SQLi" or "Non-SQLi,” thus increasing the reliability and accuracy of results in terms of deciding whether an operation is an attack or a valid operation. A method Web-App auto-generated twin data structure replication. Shielding against SQLi attacks (WebAppShield) that verifies all users and prevents attackers (SQLi attacks) from entering and or accessing the database, which the machine learning module predicts as "Non-SQLi" has been developed. A special login form has been developed with a special instance of data validation; this verification process secures the web application from its early stages. The system has been tested and validated, up to 99% of SQLi attacks have been prevented.

Keywords: SQL injection, attacks, web application, accuracy, database

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Authors: Zahid Ullah, Atlas Khan

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The rapid growth of data in diverse domains has created an urgent need for effective utilization of mathematical and statistical sciences in data analytics. This abstract explores the journey from theory to practice, emphasizing the importance of harnessing mathematical and statistical innovations to unlock the full potential of data analytics. Drawing on a comprehensive review of existing literature and research, this study investigates the fundamental theories and principles underpinning mathematical and statistical sciences in the context of data analytics. It delves into key mathematical concepts such as optimization, probability theory, statistical modeling, and machine learning algorithms, highlighting their significance in analyzing and extracting insights from complex datasets. Moreover, this abstract sheds light on the practical applications of mathematical and statistical sciences in real-world data analytics scenarios. Through case studies and examples, it showcases how mathematical and statistical innovations are being applied to tackle challenges in various fields such as finance, healthcare, marketing, and social sciences. These applications demonstrate the transformative power of mathematical and statistical sciences in data-driven decision-making. The abstract also emphasizes the importance of interdisciplinary collaboration, as it recognizes the synergy between mathematical and statistical sciences and other domains such as computer science, information technology, and domain-specific knowledge. Collaborative efforts enable the development of innovative methodologies and tools that bridge the gap between theory and practice, ultimately enhancing the effectiveness of data analytics. Furthermore, ethical considerations surrounding data analytics, including privacy, bias, and fairness, are addressed within the abstract. It underscores the need for responsible and transparent practices in data analytics, and highlights the role of mathematical and statistical sciences in ensuring ethical data handling and analysis. In conclusion, this abstract highlights the journey from theory to practice in harnessing mathematical and statistical sciences in data analytics. It showcases the practical applications of these sciences, the importance of interdisciplinary collaboration, and the need for ethical considerations. By bridging the gap between theory and practice, mathematical and statistical sciences contribute to unlocking the full potential of data analytics, empowering organizations and decision-makers with valuable insights for informed decision-making.

Keywords: data analytics, mathematical sciences, optimization, machine learning, interdisciplinary collaboration, practical applications

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Authors: Ishapathik Das, Sumen Sen, N. Rao Chaganty, Pooja Sengupta

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Dependent multivariate count data occur in several research studies. These data can be modeled by a multivariate Poisson or Negative binomial distribution constructed using copulas. However, when some of the counts are inflated, that is, the number of observations in some cells are much larger than other cells, then the copula based multivariate Poisson (or Negative binomial) distribution may not fit well and it is not an appropriate statistical model for the data. There is a need to modify or adjust the multivariate distribution to account for the inflated frequencies. In this article, we consider the situation where the frequencies of two cells are higher compared to the other cells, and develop a doubly inflated multivariate Poisson distribution function using multivariate Gaussian copula. We also discuss procedures for regression on covariates for the doubly inflated multivariate count data. For illustrating the proposed methodologies, we present a real data containing bivariate count observations with inflations in two cells. Several models and linear predictors with log link functions are considered, and we discuss maximum likelihood estimation to estimate unknown parameters of the models.

Keywords: copula, Gaussian copula, multivariate distributions, inflated distributios

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Authors: Lu Zhao, Yanrong Kang, Lili Guo, Yuan Long, Guidong Xing

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Smart watches, as a handy device with rich functionality, has become one of the most popular wearable devices all over the world. Among the various function, the most basic is health monitoring. The monitoring data can be provided as an effective evidence or a clue for the detection of crime cases. For instance, the step counting data can help to determine whether the watch wearer was quiet or moving during the given time period. There is, however, still quite few research on the analysis of human character based on these data. The purpose of this research is to analyze the health monitoring data to distinguish the drinking state from normal state. The analysis result may play a role in cases involving drinking, such as drunk driving. The experiment mainly focused on finding the figures of smart watch health monitoring data that change with drinking and figuring up the change scope. The chosen subjects are mostly in their 20s, each of whom had been wearing the same smart watch for a week. Each subject drank for several times during the week, and noted down the begin and end time point of the drinking. The researcher, then, extracted and analyzed the health monitoring data from the watch. According to the descriptive statistics analysis, it can be found that the heart rate change when drinking. The average heart rate is about 10% higher than normal, the coefficient of variation is less than about 30% of the normal state. Though more research is needed to be carried out, this experiment and analysis provide a thought of the application of the data from smart watches.

Keywords: character analysis, descriptive statistics analysis, drink state, heart rate, smart watch

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Authors: Pallavi Singh

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Developing library knowledge centres involves transforming traditional library spaces into dynamic, interactive environments that support collaborative learning, digital literacy, and access to various resources. Knowledge centres, also known as knowledge hubs or centres of excellence, play a crucial role in organizations and communities by serving as repositories of expertise and information. The Energy and Resources Institute (TERI) is a research organisation dedicated to sustainable community solutions. TERI Knowledge Resource Center is also aligned with the objective of the host organization within TERI; there are several specialized knowledge centers dedicated to various aspects of sustainability, energy, climate change, environmental management, green mobility, etc.

Keywords: knowledge centres, environmental management, green mobility, energy

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Authors: Necati Içer

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Crop-hail insurance plays a vital role in managing risks and reducing the financial consequences of hail damage on crop production. Predicting insurance premium rates with short-term data is a major difficulty in numerous nations because of the unique characteristics of hailstorms. This study aims to suggest a feasible approach for establishing equitable premium rates in crop-hail insurance for nations with short-term insurance data. The primary goal of the rate-making process is to determine premium rates for high and zero loss costs of villages and enhance their credibility. To do this, a technique was created using the author's practical knowledge of crop-hail insurance. With this approach, the rate-making method was developed using a range of temporal and spatial factor combinations with both hypothetical and real data, including extreme cases. This article aims to show how to incorporate the temporal and spatial elements into determining fair premium rates using short-term insurance data. The article ends with a suggestion on the ultimate premium rates for insurance contracts.

Keywords: crop-hail insurance, premium rate, short-term insurance data, spatial and temporal parameters

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Authors: Samy A. Khalil, U. Ali Rahoma

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The measurements of solar radiation, satellite data has been routinely utilize to estimate solar energy. However, the temporal coverage of satellite data has some limits. The reanalysis, also known as "retrospective analysis" of the atmosphere's parameters, is produce by fusing the output of NWP (Numerical Weather Prediction) models with observation data from a variety of sources, including ground, and satellite, ship, and aircraft observation. The result is a comprehensive record of the parameters affecting weather and climate. The effectiveness of reanalysis datasets (ERA-5) for North Africa was evaluate against high-quality surfaces measured using statistical analysis. Estimating the distribution of global solar radiation (GSR) over five chosen areas in North Africa through ten-years during the period time from 2011 to 2020. To investigate seasonal change in dataset performance, a seasonal statistical analysis was conduct, which showed a considerable difference in mistakes throughout the year. By altering the temporal resolution of the data used for comparison, the performance of the dataset is alter. Better performance is indicate by the data's monthly mean values, but data accuracy is degraded. Solar resource assessment and power estimation are discuses using the ERA-5 solar radiation data. The average values of mean bias error (MBE), root mean square error (RMSE) and mean absolute error (MAE) of the reanalysis data of solar radiation vary from 0.079 to 0.222, 0.055 to 0.178, and 0.0145 to 0.198 respectively during the period time in the present research. The correlation coefficient (R2) varies from 0.93 to 99% during the period time in the present research. This research's objective is to provide a reliable representation of the world's solar radiation to aid in the use of solar energy in all sectors.

Keywords: solar energy, ERA-5 analysis data, global solar radiation, North Africa

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Authors: Mujahid Muhammad, Paul Kearney, Adel Aneiba

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The ability for vehicles to communicate with other vehicles (V2V), the physical (V2I) and network (V2N) infrastructures, pedestrians (V2P), etc. – collectively known as V2X (Vehicle to Everything) – will enable a broad and growing set of applications and services within the intelligent transport domain for improving road safety, alleviate traffic congestion and support autonomous driving. The telecommunication research and industry communities and standardization bodies (notably 3GPP) has finally approved in Release 14, cellular communications connectivity to support V2X communication (known as LTE – V2X). LTE – V2X system will combine simultaneous connectivity across existing LTE network infrastructures via LTE-Uu interface and direct device-to-device (D2D) communications. In order for V2X services to function effectively, a robust security mechanism is needed to ensure legal and safe interaction among authenticated V2X entities in the LTE-based V2X architecture. The characteristics of vehicular networks, and the nature of most V2X applications, which involve human safety makes it significant to protect V2X messages from attacks that can result in catastrophically wrong decisions/actions include ones affecting road safety. Attack vectors include impersonation attacks, modification, masquerading, replay, MiM attacks, and Sybil attacks. In this paper, we focus our attention on LTE-based V2X security and access control mechanisms. The current LTE-A security framework provides its own access authentication scheme, the AKA protocol for mutual authentication and other essential cryptographic operations between UEs and the network. V2N systems can leverage this protocol to achieve mutual authentication between vehicles and the mobile core network. However, this protocol experiences technical challenges, such as high signaling overhead, lack of synchronization, handover delay and potential control plane signaling overloads, as well as privacy preservation issues, which cannot satisfy the adequate security requirements for majority of LTE-based V2X services. This paper examines these challenges and points to possible ways by which they can be addressed. One possible solution, is the implementation of the distributed peer-to-peer LTE security mechanism based on the Bitcoin/Namecoin framework, to allow for security operations with minimal overhead cost, which is desirable for V2X services. The proposed architecture can ensure fast, secure and robust V2X services under LTE network while meeting V2X security requirements.

Keywords: authentication, long term evolution, security, vehicle-to-everything

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Authors: Ali Hosseini

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Paralleling is a mechanism to decrease the time necessary to execute the programs. Sorting is one of the important operations to be used in different systems in a way that the proper function of many algorithms and operations depend on sorted data. CRCW_SORT algorithm executes ‘N’ elements sorting in O(1) time on SIMD (Single Instruction Multiple Data) computers with n^2/2-n/2 number of processors. In this article having presented a mechanism by dividing the input string by the hinge element into two less strings the number of the processors to be used in sorting ‘N’ elements in O(1) time has decreased to n^2/8-n/4 in the best state; by this mechanism the best state is when the hinge element is the middle one and the worst state is when it is minimum. The findings from assessing the proposed algorithm by other methods on data collection and number of the processors indicate that the proposed algorithm uses less processors to sort during execution than other methods.

Keywords: CRCW, SIMD (Single Instruction Multiple Data) computers, parallel computers, number of the processors

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Authors: Chris Ewe, Naoum Jamous, Holger Schrödl

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Like most entrepreneurs, data center operators pursue goals such as profit-maximization, improvement of the company’s reputation or basically to exist on the market. Part of those aims is to guarantee a given quality of service. Quality characteristics are specified in a contract called the service level agreement. Central part of this agreement is non-functional properties of an IT service. The system availability is one of the most important properties as it will be shown in this paper. To comply with availability requirements, data center operators can use virtualization technologies. A clear model to assess the effect of virtualization functions on the parts of a data center in relation to the system availability is still missing. This paper aims to introduce a basic model that shows these connections, and consider if the identified effects are positive or negative. Thus, this work also points out possible disadvantages of the technology. In consequence, the paper shows opportunities as well as risks of data center virtualization in relation to system availability.

Keywords: availability, cloud computing IT service, quality of service, service level agreement, virtualization

Procedia PDF Downloads 540

Authors: Maquiling Jhuvie Jane R., Ojastro Regine B., Peroja Loreille Marie B., Pinili Joy Angela., Salve Genial Gail M., Villavicencio Marielle Irene B., Yap Alther Francis Garth B.

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Introduction: This study investigated the impact of prolonged device usage on wrist pain and perceived academic performance among college students in Computer Studies. The research aims to explore the correlation between the frequency of technological device use and the incidence of wrist pain, as well as how this pain affects students' academic performance. The study seeks to provide insights that could inform interventions to promote better musculoskeletal health among students engaged in intensive technology use to further improve their academic performance. Method: The study utilized descriptive-correlational and comparative design, focusing on bona fide students from Silliman University’s College of Computer Studies during the second semester of 2023-2024. Participants were recruited through a survey sent via school email, with responses collected until March 30, 2024. Data was gathered using a password-protected device and Google Forms, ensuring restricted access to raw data. The demographic profile was summarized, and the prevalence of wrist pain and device usage were analyzed using percentages and weighted means. Statistical analyses included Spearman’s rank correlation coefficient to assess the relationship between wrist pain and device usage and an Independent T-test to evaluate differences in academic performance based on wrist pain presence. Alpha was set at 0.05. Results: The study revealed that 40% of College of Computer Studies students experience wrist pain, with 2 out of every 5 students affected. Laptops and desktops were the most frequently used devices for academic work, achieving a weighted mean of 4.511, while mobile phones and tablets received lower means of 4.183 and 1.911, respectively. The average academic performance score among students was 29.7, classified as ‘Good Performance.’ Notably, there was no significant relationship between the frequency of device usage and wrist pain, as indicated by p-values exceeding 0.05. However, a significant difference in perceived academic performance was observed, with students without wrist pain scoring an average of 30.39 compared to 28.72 for those with wrist pain and a p-value of 0.0134 confirming this distinction. Conclusion: The study revealed that about 40% of students in the College of Computer Studies experience wrist pain, but there is no significant link between device usage and pain occurrence. However, students without wrist pain demonstrated better academic performance than those with pain, suggesting that wrist health may impact academic success. These findings imply that physical therapy practices in the Philippines should focus on preventive strategies and ergonomic education to improve student health and performance.

Keywords: wrist pain, frequency of use of technological devices, perceived academic performance, physical therapy

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Authors: Mahmoud Ahmed Farrag, Ali Zain Elabdeen Heikal, Mohamed Shawky Ahmed, Ahmed Osama Amer

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Crowd-sourced data refers to data that is collected and shared by a large number of individuals or organizations, often through the use of digital technologies such as mobile devices and social media. The shortage in crash data collection in developing countries makes it difficult to fully understand and address road safety issues in these regions. In developing countries, crowd-sourced data can be a valuable tool for improving road safety, particularly in urban areas where the majority of road crashes occur. This study is -to our best knowledge- the first to develop safety performance functions using crowd-sourced data by adopting a negative binomial structure model and the Full Bayes model to investigate traffic safety for urban road networks and provide insights into the impact of roadway characteristics. Furthermore, as a part of the safety management process, network screening has been undergone through applying two different methods to rank the most hazardous road segments: PCR method (adopted in the Highway Capacity Manual HCM) as well as a graphical method using GIS tools to compare and validate. Lastly, recommendations were suggested for policymakers to ensure safer roads.

Keywords: crowdsourced data, road crashes, safety performance functions, Full Bayes models, network screening

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Authors: Fikiru Temesgen Gelata

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Food security, adaptation, and climate change mitigation are all problems that can be resolved simultaneously with Climate-Smart Agriculture (CSA). This study examines determinants of climate-smart agriculture (CSA) practices among smallholder farmers, aiming to understand the factors guiding adoption decisions and evaluate the impact of CSA on smallholder farmer income in the study areas. For this study, three-stage sampling techniques were applied to select 230 smallholders randomly. Mann-Kendal test and multinomial endogenous switching regression model were used to analyze trends of decrease or increase within long-term temporal data and the impact of CSA on the smallholder farmer income, respectively. Findings revealed education level, household size, land ownership, off-farm income, climate information, and contact with extension agents found to be highly adopted CSA practices. On the contrary, erosion exerted a detrimental impact on all the agricultural practices examined within the study region. Various factors such as farming methods, the size of farms, proximity to irrigated farmlands, availability of extension services, distance to market hubs, and access to weather forecasts were recognized as key determinants influencing the adoption of CSA practices. The multinomial endogenous switching regression model (MESR) revealed that joint adoption of crop rotation and soil and water conservation practices significantly increased farm income by 1,107,245 ETB. The study recommends that counties and governments should prioritize addressing climate change in their development agendas to increase the adoption of climate-smart farming techniques.

Keywords: climate-smart practices, food security, Oincome, MERM, Ethiopia

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Authors: Shimaa Nagro, Russell Campion

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Mobile devices are becoming ever more widely available, with growing functionality, and are increasingly used as an enabling technology to give students access to educational material anytime and anywhere. However, the design of educational material user interfaces for mobile devices is beset by many unresolved research issues such as those arising from emphasising the information concepts then mapping this information to appropriate media (modelling information then mapping media effectively). This report describes a multimedia user interface design method for mobile learning. The method covers specification of user requirements and information architecture, media selection to represent the information content, design for directing attention to important information, and interaction design to enhance user engagement based on Human-Computer Interaction design strategies (HCI). The method will be evaluated by three different case studies to prove the method is suitable for application to different areas / applications, these are; an application to teach about major computer networking concepts, an application to deliver a history-based topic; (after these case studies have been completed, the method will be revised to remove deficiencies and then used to develop a third case study), an application to teach mathematical principles. At this point, the method will again be revised into its final format. A usability evaluation will be carried out to measure the usefulness and effectiveness of the method. The investigation will combine qualitative and quantitative methods, including interviews and questionnaires for data collection and three case studies for validating the MDMLM method. The researcher has successfully produced the method at this point which is now under validation and testing procedures. From this point forward in the report, the researcher will refer to the method using the MDMLM abbreviation which means Multimedia Design Mobile Learning Method.

Keywords: human-computer interaction, interface design, mobile learning, education

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Authors: Israel Oguche

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Journalism practice has faced several challenges across the globe, particularly in developing countries such as Nigeria. While Nigeria has thrived under democratic experiment for twenty years since the return to democracy in 1999, there is still a great lacuna in freedom of expression, such that the presidents, though elected democratically, have had the tendencies to use military might in clamping down on journalism practices across the country. Under Muhammadu Buhari, it seems Nigeria has returned to the military era when powers were used against who says what, on a media, so today, in Nigeria, there are obvious cases of outright human rights violations and detention of journalists whose offenses were not spelled out. From Abiri Jones to Abba Jalingo and Omoyele Sowore, Nigeria journalists have been placed under the cocoon of the tyrannical administration of Muhammadu Buhari, the president, with subsequent clamping down on the instruments of freedoms such as access to justice and fair hearing. This paper gave vivid analytical and empirical perspectives of journalism practice under the dark days of Muhammadu Buhari as Nigeria’s president. The objectives include; examining the core cases of attacks on journalists since 2015 to date, determining the burden of practicing journalism in a tyrannical government, reeling out the effects of restrictive practices of journalism on freedom of expression among Nigerians and proffering solutions for improvement in the years ahead. Using the cognitive dissonance theory, the survey method was used for the study, with qualitative research analysis as a tool for data presentation. In the findings, the number of journalists in jail for publishing objectively under the Buhari administration remains high while the government has clamped down on freedom of expression among the people. The study concluded that there is a need for repelling of laws made by the Nigeria government in order to save the Nigerian journalism industry from total collapse.

Keywords: communication, developing democracy, press freedom, journalism practices

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Authors: Syed Romat Ali Shah, Bilal Shoaib, Saleem Akhtar, Munib Ahmad, Shahan Sadiqui

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Classification is a data mining technique, which is recognizedon Machine Learning (ML) algorithm. It is used to classifythe individual articlein a knownofinformation into a set of predefinemodules or group. Web mining is also a portion of that sympathetic of data mining methods. The main purpose of this paper to analysis and compare the performance of Naïve Bayse Algorithm, Decision Tree, K-Nearest Neighbor (KNN), Artificial Neural Network (ANN)and Support Vector Machine (SVM). This paper consists of different ML algorithm and their advantages and disadvantages and also define research issues.

Keywords: Data Mining, Web Mining, classification, ML Algorithms

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Authors: Rishabh Srivastav, Divyam Sharma

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We propose an algorithm to cluster categorical data named as ‘Genetic algorithm initialized rough set based fuzzy K-Modes for categorical data’. We propose an amalgamation of the simple K-modes algorithm, the Rough and Fuzzy set based K-modes and the Genetic Algorithm to form a new algorithm,which we hypothesise, will provide better Centroid Model clustering results, than existing standard algorithms. In the proposed algorithm, the initialization and updation of modes is done by the use of genetic algorithms while the membership values are calculated using the rough set and fuzzy logic.

Keywords: categorical data, fuzzy logic, genetic algorithm, K modes clustering, rough sets

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Authors: Ahmad Awajan, Sadam Al Wadi

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In this study, a hybrid method based on Empirical Mode Decomposition and Holt-Winter (EMD-HW) is used to forecast Amman stock market data. First, the data are decomposed by EMD method into Intrinsic Mode Functions (IMFs) and residual components. Then, all components are forecasted by HW technique. Finally, forecasting values are aggregated together to get the forecasting value of stock market data. Empirical results showed that the EMD- HW outperform individual forecasting models. The strength of this EMD-HW lies in its ability to forecast non-stationary and non- linear time series without a need to use any transformation method. Moreover, EMD-HW has a relatively high accuracy comparing with eight existing forecasting methods based on the five forecast error measures.

Keywords: Holt-Winter method, empirical mode decomposition, forecasting, time series

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Authors: Martina Marinelli, Samanta Vellante, Francesco Pilotti, Daniele Di Valerio, Gaetanino Paolone

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Any development in web marketing technology requires changes in information engineering to identify instruments and techniques suitable for the production of software applications for informative marketing. Moreover, for large web solutions, designing an interface that enables human interactions is a complex process that must bridge between informative marketing requirements and the developed solution. A user-friendly interface in web marketing applications is crucial for a successful business. The paper introduces mkInfo - a software platform that implements informative marketing. Informative marketing is a new interpretation of marketing which places the information at the center of every marketing action. The creative team includes software engineering researchers who have recently authored an article on automatic code generation. The authors have created the mkInfo software platform to generate informative marketing web applications. For each web application, it is possible to automatically implement an opt in page, a landing page, a sales page, and a thank you page: one only needs to insert the content. mkInfo implements an autoresponder to send mail according to a predetermined schedule. The mkInfo platform also includes e-commerce for a product or service. The stakeholder can access any opt-in page and get basic information about a product or service. If he wants to know more, he will need to provide an e-mail address to access a landing page that will generate an e-mail sequence. It will provide him with complete information about the product or the service. From this point on, the stakeholder becomes a user and is now able to purchase the product or related services through the mkInfo platform. This paper suggests a possible definition for Informative Marketing, illustrates its basic principles, and finally details the mkInfo platform that implements it. This paper also offers some Informative Marketing models, which are implemented in the mkInfo platform. Informative marketing can be applied to products or services. It is necessary to realize a web application for each product or service. The mkInfo platform enables the product or the service producer to send information concerning a specific product or service to all stakeholders. In conclusion, the technical contributions of this paper are: a different interpretation of marketing based on information; a modular architecture for web applications, particularly for one with standard features such as information storage, exchange, and delivery; multiple models to implement informative marketing; a software platform enabling the implementation of such models in a web application. Future research aims to enable stakeholders to provide information about a product or a service so that the information gathered about a product or a service includes both the producer’s and the stakeholders' point of view. The purpose is to create an all-inclusive management system of the knowledge regarding a specific product or service: a system that includes everything about the product or service and is able to address even unexpected questions.

Keywords: informative marketing, opt in page, software platform, web application

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Authors: Michael Stokes

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Using a disability studies approach, this paper analyzes the American science fiction novel Ballroom of the Skies as way to address and access narratives of American exceptionalism in relation to global struggle. Combined with a critical race studies analysis of identity and cultural practice, this essay seeks to find parallels between the treatment of disability and the treatment of the racialized body in literature to forcibly reread potential for multiple assemblages of identity in the speculated futures of science fiction. Thinking through this relationship, the essay constructs a thematic understanding of social eugenics as practiced in American culture.

Keywords: disability studies, science fiction, eugenics, cultural studies

Procedia PDF Downloads 253