Search results for: data acquisition (DAQ)

Authors: G. R. Navaneesh, E. Nagarajan, C. H. Rajam Raju

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Nowadays the multimedia data are used to store some secure information. All previous methods allocate a space in image for data embedding purpose after encryption. In this paper, we propose a novel method by reserving space in image with a boundary surrounded before encryption with a traditional RDH algorithm, which makes it easy for the data hider to reversibly embed data in the encrypted images. The proposed method can achieve real time performance, that is, data extraction and image recovery are free of any error. A secure transmission process is also discussed in this paper, which improves the efficiency by ten times compared to other processes as discussed.

Keywords: secure communication, reserving room before encryption, least significant bits, image encryption, reversible data hiding

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Authors: Fuad M. Alkoot

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DNA data have been used in forensics for decades. However, current research looks at using the DNA as a biometric identity verification modality. The goal is to improve the speed of identification. We aim at using gene data that was initially used for autism detection to find if and how accurate is this data for identification applications. Mainly our goal is to find if our data preprocessing technique yields data useful as a biometric identification tool. We experiment with using the nearest neighbor classifier to identify subjects. Results show that optimal classification rate is achieved when the test set is corrupted by normally distributed noise with zero mean and standard deviation of 1. The classification rate is close to optimal at higher noise standard deviation reaching 3. This shows that the data can be used for identity verification with high accuracy using a simple classifier such as the k-nearest neighbor (k-NN). 

Keywords: biometrics, genetic data, identity verification, k nearest neighbor

Procedia PDF Downloads 249

Authors: Marjila Burhanzoi, Kenta Onohara, Tomoaki Ikegami

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Faults in photovoltaic (PV) modules should be detected to the greatest extent as early as possible. For that conventional fault detection methods such as electrical characterization, visual inspection, infrared (IR) imaging, ultraviolet fluorescence and electroluminescence (EL) imaging are used, but they either fail to detect the location or category of fault, or they require expensive equipment and are not convenient for onsite application. Hence, these methods are not convenient to use for monitoring small-scale PV systems. Therefore, low cost and efficient inspection techniques with the ability of onsite application are indispensable for PV modules. In this study in order to establish efficient inspection technique, correlation between faults and magnetic flux density on the surface is of crystalline PV modules are investigated. Magnetic flux on the surface of normal and faulted PV modules is measured under the short circuit and illuminated conditions using two different sensor devices. One device is made of small integrated sensors namely 9-axis motion tracking sensor with a 3-axis electronic compass embedded, an IR temperature sensor, an optical laser position sensor and a microcontroller. This device measures the X, Y and Z components of the magnetic flux density (Bx, By and Bz) few mm above the surface of a PV module and outputs the data as line graphs in LabVIEW program. The second device is made of a laser optical sensor and two magnetic line sensor modules consisting 16 pieces of magnetic sensors. This device scans the magnetic field on the surface of PV module and outputs the data as a 3D surface plot of the magnetic flux intensity in a LabVIEW program. A PC equipped with LabVIEW software is used for data acquisition and analysis for both devices. To show the effectiveness of this method, measured results are compared to those of a normal reference module and their EL images. Through the experiments it was confirmed that the magnetic field in the faulted areas have different profiles which can be clearly identified in the measured plots. Measurement results showed a perfect correlation with the EL images and using position sensors it identified the exact location of faults. This method was applied on different modules and various faults were detected using it. The proposed method owns the ability of on-site measurement and real-time diagnosis. Since simple sensors are used to make the device, it is low cost and convenient to be sued by small-scale or residential PV system owners.

Keywords: fault diagnosis, fault location, integrated sensors, PV modules

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Authors: R Palson Kennedy, P.Kiran Sai

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This article introduces machine learning (ML) researchers to the hurdles that geoscience problems present, as well as the opportunities for improvement in both ML and geosciences. This article presents a review from the data life cycle perspective to meet that need. Numerous facets of geosciences present unique difficulties for the study of intelligent systems. Geosciences data is notoriously difficult to analyze since it is frequently unpredictable, intermittent, sparse, multi-resolution, and multi-scale. The first half addresses data science’s essential concepts and theoretical underpinnings, while the second section contains key themes and sharing experiences from current publications focused on each stage of the data life cycle. Finally, themes such as open science, smart data, and team science are considered.

Keywords: Data science, intelligent system, machine learning, big data, data life cycle, recent development, geo science

Procedia PDF Downloads 130

Authors: Joseph Bentil

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Successful acquisition of 21st-century competencies needed by students to navigate through the ever-changing world requires that they are taught and molded by 21st-century teachers with the needed professional competencies. Accordingly, this study sought to understand the readiness and how efficacious pre-service Social Studies specialism students are towards the implementation of the Common Core Social Studies Curriculum in the Junior High Schools in Ghana. Theory of Experience served as the theoretical lens for the study. Working within the pragmatist paradigm, this study utilized the cross-sectional descriptive survey design with a mixed method approach where, through census sampling technique, all the 120 pre-service Social Studies specialism students were sampled for the study. A structured questionnaire and an interview guide were the instruments employed for data collection. Descriptive statistics (mean, standard deviation and inferential statistics like independent samples t-test, one-way between groups ANOVA and Pearson Product Moment Correlation) were employed in the analysis the research questions and hypotheses with the aid of version 28 of SPSS while the qualitative data was analyzed using thematic analysis. The findings discovered that pre-service Social Studies teachers were highly ready and efficacious towards implementing the Common Core Junior High School Social Studies curriculum. However, male pre-service teachers were highly efficacious and ready than their female counterparts. Besides, it was disclosed that pre-service teachers within the 31-40 years age bracket were found to be highly efficacious and ready than their colleagues with 20-30 and below 20 years age bracket respectively. The findings further revealed that there was a moderate and statistically significant positive relationship between pre-service teachers’ readiness and efficacy in implementing the Common Core Social Studies curriculum. Therefore, the study recommended that interventional programmes aimed at raising the readiness and efficacy beliefs of pre-service teachers should be targeted towards female preservice teachers and those below 20 years age bracket for successful implementation and realization of the competencies enshrined in the common core social Studies curriculum.

Keywords: pre-service, readiness, social studies, teachers

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Authors: Mustafa Abu Ghalia, Mohammed Seddik

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The design and development of a new class of biomaterial has gained particular interest in producing polymer scaffold for biomedical applications. Improving mechanical properties, biological and controlling pores scaffold are important factors to provide appropriate biomaterial for implement in soft tissue repair and regeneration. In this study, poly-ε-caprolactone (PCL) /polyethylene glycol (PEG) copolymer (80/20) incorporated with CNF scaffolds were made employing solvent casting and particulate leaching methods. Four mass percentages of CNF (1, 2.5, 5, and 10 wt.%) were integrated into the copolymer through a silane coupling agent. Mechanical properties were determined using Tensile Tester data acquisition to investigate the effect of porosity, pore size, and CNF contents. Tensile strength obtained for PCL/PEG- 5 wt.% CNF was 16 MPa, which drastically decreased after creating a porous structure to 7.1 MPa. The optimum parameters of the results were found to be 5 wt.% for CNF, 240 μm for pore size, and 83% for porosity. Scanning electron microscopy (SEM) micrograph reveals that consistent pore size and regular pore shape were accomplished after the addition of CNF-5 wt. % into PCL/PEG. The results of mass loss of PCL/PEG reinforced-CNF 1% have clearly enhanced to double values compared with PCL/PEG copolymer and three times with PCL/PEG scaffold-CNF 1%. In addition, all PCL/PEG reinforced and scaffold- CNF were partially disintegrated under composting conditions confirming their biodegradable behavior. This also provides a possible solution for the end life of these biomaterials.

Keywords: PCL/PEG, cellulose nanofibers, tissue engineering, biodegradation, compost polymers

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Authors: Asmau Zarma Gogaram

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The paper examines adult education and how it can be employed as a strategy for transformation and security challenges in Nigeria. It defines the meaning of adult education and its objectives.The issue of the necessity of employing adult education as a strategy for transformation and security challenges was also examined in the paper.In doing this it discussed the different types of adult education programmes, i.e.continuing education, literacy education, retirement and pre-retirement education and civic education. The paper concluded by stating that if the programmes stated are internalizes and applied they can help to raise awareness. Finally the paper proffered some recommendations one of which was that government should at all levels increase their efforts or promoting acquisition of adult education.

Keywords: adult education, transformation and security challenges, Nigeria, education and human development

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Authors: Marc Bachelet, Abhijit Kumar Chatterjee, José Manuel Avila

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Data quality is a key component of any data-driven organization. Without data quality, organizations cannot effectively make data-driven decisions, which often leads to poor business performance. Therefore, it is important for an organization to ensure that the data they use is of high quality. This is where the concept of data mesh comes in. Data mesh is an organizational and architectural decentralized approach to data management that can help organizations improve the quality of data. The concept of data mesh was first introduced in 2020. Its purpose is to decentralize data ownership, making it easier for domain experts to manage the data. This can help organizations improve data quality by reducing the reliance on centralized data teams and allowing domain experts to take charge of their data. This paper intends to discuss how a set of elements, including data mesh, are tools capable of increasing data quality. One of the key benefits of data mesh is improved metadata management. In a traditional data architecture, metadata management is typically centralized, which can lead to data silos and poor data quality. With data mesh, metadata is managed in a decentralized manner, ensuring accurate and up-to-date metadata, thereby improving data quality. Another benefit of data mesh is the clarification of roles and responsibilities. In a traditional data architecture, data teams are responsible for managing all aspects of data, which can lead to confusion and ambiguity in responsibilities. With data mesh, domain experts are responsible for managing their own data, which can help provide clarity in roles and responsibilities and improve data quality. Additionally, data mesh can also contribute to a new form of organization that is more agile and adaptable. By decentralizing data ownership, organizations can respond more quickly to changes in their business environment, which in turn can help improve overall performance by allowing better insights into business as an effect of better reports and visualization tools. Monitoring and analytics are also important aspects of data quality. With data mesh, monitoring, and analytics are decentralized, allowing domain experts to monitor and analyze their own data. This will help in identifying and addressing data quality problems in quick time, leading to improved data quality. Data culture is another major aspect of data quality. With data mesh, domain experts are encouraged to take ownership of their data, which can help create a data-driven culture within the organization. This can lead to improved data quality and better business outcomes. Finally, the paper explores the contribution of AI in the coming years. AI can help enhance data quality by automating many data-related tasks, like data cleaning and data validation. By integrating AI into data mesh, organizations can further enhance the quality of their data. The concepts mentioned above are illustrated by AEKIDEN experience feedback. AEKIDEN is an international data-driven consultancy that has successfully implemented a data mesh approach. By sharing their experience, AEKIDEN can help other organizations understand the benefits and challenges of implementing data mesh and improving data quality.

Keywords: data culture, data-driven organization, data mesh, data quality for business success

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Authors: Adeyemo Abduljeeel Taiwo

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This research is an attempt that creates a conducive atmosphere of a phonological and morphological compendium of Arabic language in Modern Standard Arabic (MSA) for modern day communications. The research is carried out with the chief aim of grammatical analysis of the two broad fields of Arabic linguistics namely: Applied and Socio-Linguistics. It draws a pictorial record of Applied and Socio-Linguistics in Arabic phonology and morphology. Thematically, it postulates and contemplates to a large degree, the theory of concord in contemporary modern Arabic language acquisition. It utilizes an analytical method while it portrays Arabic as a Semitic language that promotes linguistics and syntax among the scholars of the fields.

Keywords: Arabic language, applied linguistics, socio-linguistics, modern communications

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

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It is almost impossible to store or analyze big data increasing exponentially with traditional technologies. Hadoop is a new technology to make that possible. R programming language is by far the most popular statistical tool for big data analysis based on distributed processing with Hadoop technology. With RHadoop that integrates R and Hadoop environment, we implemented parallel multiple regression analysis with different sizes of actual data. Experimental results showed our RHadoop system was much faster as the number of data nodes increases. We also compared the performance of our RHadoop with lm function and big lm packages available on big memory. The results showed that our RHadoop was faster than other packages owing to paralleling processing with increasing the number of map tasks as the size of data increases.

Keywords: big data, Hadoop, parallel regression analysis, R, RHadoop

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Authors: Haojie Wang, Xun Li, Rui Yin

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In order to solve the memorization overfitting in the meta-learning MAML algorithm, a method of generating mutually exclusive tasks based on data augmentation is proposed. This method generates a mutex task by corresponding one feature of the data to multiple labels, so that the generated mutex task is inconsistent with the data distribution in the initial dataset. Because generating mutex tasks for all data will produce a large number of invalid data and, in the worst case, lead to exponential growth of computation, this paper also proposes a key data extraction method, that only extracts part of the data to generate the mutex task. The experiments show that the method of generating mutually exclusive tasks can effectively solve the memorization overfitting in the meta-learning MAML algorithm.

Keywords: data augmentation, mutex task generation, meta-learning, text classification.

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Authors: Sifat Rahman Ahona, Rifat Tasnim, Naima Hassan

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Data aggregation is a helpful technique for reducing the data communication overhead in wireless sensor network. One of the important tasks of data aggregation is positioning of the aggregator points. There are a lot of works done on data aggregation. But, efficient positioning of the aggregators points is not focused so much. In this paper, authors are focusing on the positioning or the placement of the aggregation points in wireless sensor network. Authors proposed an algorithm to select the aggregators positions for a scenario where aggregator nodes are more powerful than sensor nodes.

Keywords: aggregation point, data communication, data aggregation, wireless sensor network

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Authors: Maria Korman, Carmit Gal, Ella Gabitov, Avi Karni

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The main question addressed in this study was whether the time-of-day wherein training is afforded is a significant factor for motor skill ('how-to', procedural knowledge) acquisition and consolidation into long term memory in the healthy elderly population. Twenty-nine older adults (60-75 years) practiced an explicitly instructed 5-element key-press sequence by repeatedly generating the sequence ‘as fast and accurately as possible’. Contribution of three parameters to acquisition, 24h post-training consolidation, and 1-week retention gains in motor sequence speed was assessed: (a) time of training (morning vs. evening group) (b) sleep quality (actigraphy) and (c) chronotype. All study participants were moderately morning type, according to the Morningness-Eveningness Questionnaire score. All participants had sleep patterns typical of age, with average sleep efficiency of ~ 82%, and approximately 6 hours of sleep. Speed of motor sequence performance in both groups improved to a similar extent during training session. Nevertheless, evening group expressed small but significant overnight consolidation phase gains, while morning group showed only maintenance of performance level attained at the end of training. By 1-week retention test, both groups showed similar performance levels with no significant gains or losses with respect to 24h test. Changes in the tapping patterns at 24h and 1-week post-training were assessed based on normalized Pearson correlation coefficients using the Fisher’s z-transformation in reference to the tapping pattern attained at the end of the training. Significant differences between the groups were found: the evening group showed larger changes in tapping patterns across the consolidation and retention windows. Our results show that morning-oriented older adults effectively acquired, consolidated, and maintained a new sequence of finger movements, following both morning and evening practice sessions. However, time-of-training affected the time-course of skill evolution in terms of performance speed, as well as the re-organization of tapping patterns during the consolidation period. These results are in line with the notion that motor training preceding a sleep interval may be beneficial for the long-term memory in the elderly. Evening training should be considered an appropriate time window for motor skill learning in older adults, even in individuals with morning chronotype.

Keywords: time-of-day, elderly, motor learning, memory consolidation, chronotype

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Authors: Atul Khurana, Rajul Rastogi, Hans-Joachim Gamperl

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The pharmaceutical companies are getting more inclined towards patient support programs (PSPs) which assist patients and/or healthcare professionals (HCPs) in more desirable disease management and cost-effective treatment. The utmost objective of these programs is patient care. The PSPs may include financial assistance to patients, medicine compliance programs, access to HCPs via phone or online chat centers, etc. The PSP has a crucial role in terms of customer acquisition and retention strategies. During the conduct of these programs, Marketing Authorisation Holder (MAH) may receive information related to concerned medicinal products, which is usually reported by patients or involved HCPs. This information may include suspected adverse reaction(s) during/after administration of medicinal products. Hence, the MAH should design PSP to comply with regulatory reporting requirements and avoid non-compliance during PV inspection. The emergence of wireless health devices is lowering the burden on patients to manually incorporate safety data, and building a significant option for patients to observe major swings in reference to drug safety. Therefore, to enhance the adoption of these programs, MAH not only needs to aware patients about advantages of the program, but also recognizes the importance of time of patients and commitments made in a constructive manner. It is indispensable that strengthening the public health is considered as the topmost priority in such programs, and the MAH is compliant to Pharmacovigilance (PV) requirements along with regulatory obligations.

Keywords: drug safety, good pharmacovigilance practice, patient support program, pharmacovigilance

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Authors: Paula Simões, Isabel Natário

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This paper reviews a number of theoretical aspects for implementing an explicit spatial perspective in econometrics for modelling non-continuous data, in general, and count data, in particular. It provides an overview of the several spatial econometric approaches that are available to model data that are collected with reference to location in space, from the classical spatial econometrics approaches to the recent developments on spatial econometrics to model count data, in a Bayesian hierarchical setting. Considerable attention is paid to the inferential framework, necessary for structural consistent spatial econometric count models, incorporating spatial lag autocorrelation, to the corresponding estimation and testing procedures for different assumptions, to the constrains and implications embedded in the various specifications in the literature. This review combines insights from the classical spatial econometrics literature as well as from hierarchical modeling and analysis of spatial data, in order to look for new possible directions on the processing of count data, in a spatial hierarchical Bayesian econometric context.

Keywords: spatial data analysis, spatial econometrics, Bayesian hierarchical models, count data

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Authors: Gueidi Afef, Gharsellaoui Hamza, Ben Ahmed Samir

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This paper deals with the secret of the continual progression data that new data management solutions have been emerged: The NoSQL databases. They crossed several areas like personalization, profile management, big data in real-time, content management, catalog, view of customers, mobile applications, internet of things, digital communication and fraud detection. Nowadays, these database management systems are increasing. These systems store data very well and with the trend of big data, a new challenge’s store demands new structures and methods for managing enterprise data. The new intelligent machine in the e-learning sector, thrives on more data, so smart machines can learn more and faster. The robotics are our use case to focus on our test. The implementation of NoSQL for Robotics wrestle all the data they acquire into usable form because with the ordinary type of robotics; we are facing very big limits to manage and find the exact information in real-time. Our original proposed approach was demonstrated by experimental studies and running example used as a use case.

Keywords: NoSQL databases, database management systems, robotics, big data

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Authors: C. B. Le, V. N. Pham

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In modern data analysis, multi-source data appears more and more in real applications. Multi-source data clustering has emerged as a important issue in the data mining and machine learning community. Different data sources provide information about different data. Therefore, multi-source data linking is essential to improve clustering performance. However, in practice multi-source data is often heterogeneous, uncertain, and large. This issue is considered a major challenge from multi-source data. Ensemble is a versatile machine learning model in which learning techniques can work in parallel, with big data. Clustering ensemble has been shown to outperform any standard clustering algorithm in terms of accuracy and robustness. However, most of the traditional clustering ensemble approaches are based on single-objective function and single-source data. This paper proposes a new clustering ensemble method for multi-source data analysis. The fuzzy optimized multi-objective clustering ensemble method is called FOMOCE. Firstly, a clustering ensemble mathematical model based on the structure of multi-objective clustering function, multi-source data, and dark knowledge is introduced. Then, rules for extracting dark knowledge from the input data, clustering algorithms, and base clusterings are designed and applied. Finally, a clustering ensemble algorithm is proposed for multi-source data analysis. The experiments were performed on the standard sample data set. The experimental results demonstrate the superior performance of the FOMOCE method compared to the existing clustering ensemble methods and multi-source clustering methods.

Keywords: clustering ensemble, multi-source, multi-objective, fuzzy clustering

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Authors: Leah Burton, Sara Cumming, Julianne DiSanto

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Homelessness is a widespread and complex problem in Canada and around the globe. Many Canadians will face homelessness at least once in their lifetime, with several experiencing subsequent bouts or cyclical patterns of housing precarity. While a Housing First approach to homelessness is a long-standing and widely accepted best practice, it is also recognized that the acquisition of life skills is an effective way to reduce cycles of homelessness. Indeed, when individuals are provided with a range of life skills—such as (but not limited to) financial literacy, household management, interpersonal skills, critical thinking, and resource management—they are given the tools required to maintain long-term Housing for a lifetime; thus reducing a repetitive need for services. However, there is limited research regarding the best ways to teach life skills, a problem that has been further complicated in a post-pandemic world, where services are being delivered online or in a hybrid model of care. More than this, it is difficult to provide life skills on a large scale without losing a client-centered approach to services. This lack of client-centeredness is also seen in the lack of attention to culturally sensitive life skills, which consider the diverse needs of individuals and imbed equity, diversity, and inclusion (EDI) within the skills being taught. This study aims to fill these identified gaps in the literature by employing a community-engaged (CER) approach. Academic, government, funders, front-line staff, and clients at 15 not-for-profits from across the Greater Toronto Area in Ontario, Canada, collaborated to co-create a virtual, client-centric, EDI-informed life skill learning management system. A triangulation methodology was utilized for this research. An environmental scan was conducted for current best practices, and over 100 front-line staff (including workers, managers, and executive directors who work with homeless populations) participated in two separate Creative Problem Solving Sessions. Over 200 individuals with experience in homelessness completed quantitative and open-ended surveys. All sections of this research aimed to discover the areas of skills that individuals need to maintain Housing and to ascertain what a more client-driven EDI approach to life skills training should include. This presentation will showcase the findings on which life skills are deemed essential for homeless and precariously housed individuals.

Keywords: homelessness, housing first, life skills, community engaged research, client- centered

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Authors: Du T. Tran, Quyen T. L. Hoang

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The role of language learning strategies in second language acquisition has received increased attention across several disciplines in recent years. Language learning strategies have been shown to occur in many studies over the passing years with the aim of improving the efficiency of language learning. Following previous studies, this study endeavors to scrutinize language learning strategies employed by the students at Vo Minh Duc high school and the effect of motivation on students’ learning strategy choices. The responses are examined quantitatively and qualitatively to enhance their validity and reliability. Data are collected from 342 students’ responses to the questionnaire, interviews with ten teachers and fifteen students, and classroom observations. The findings reveal that students’ motivation has an enormous impact on the choice of language learning strategies. The results simultaneously show that students use many language learning strategies to enhance their communicative competence, but the most frequently used ones are cognitive and affective ones. Significant correlations among types of learning strategies and the influence of motivation on the choices of language learning strategies were consistent with previous studies. The study’s results are expected to be beneficial to teachers of English and students in terms of narrowing the gap between the students' language learning strategies and their teaching methodologies preferences and sketching out the best strategies to enhance students’ speaking skills. The implications of these findings and the importance of viewing learners holistically are discussed, and recommendations are made for ongoing research.

Keywords: learning strategies, speaking skills, memorization strategies, cognitive strategies, affective strategies

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Authors: Eui-Jin Kim, Hasik Lee, Su-Jin Park, Dong-Kyu Kim

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Smart-card data are expected to provide information on activity pattern as an alternative to conventional person trip surveys. The focus of this study is to propose a method for training the person trip surveys to supplement the smart-card data that does not contain the purpose of each trip. We selected only available features from smart card data such as spatiotemporal information on the trip and geographic information system (GIS) data near the stations to train the survey data. XGboost, which is state-of-the-art tree-based ensemble classifier, was used to train data from multiple sources. This classifier uses a more regularized model formalization to control the over-fitting and show very fast execution time with well-performance. The validation results showed that proposed method efficiently estimated the trip purpose. GIS data of station and duration of stay at the destination were significant features in modeling trip purpose.

Keywords: activity pattern, data fusion, smart-card, XGboost

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Authors: Haojie Wang, Xun Li, Rui Yin

Abstract:

In order to solve the memorization overfitting in the model-agnostic meta-learning MAML algorithm, a method of generating mutually exclusive tasks based on data augmentation is proposed. This method generates a mutex task by corresponding one feature of the data to multiple labels so that the generated mutex task is inconsistent with the data distribution in the initial dataset. Because generating mutex tasks for all data will produce a large number of invalid data and, in the worst case, lead to an exponential growth of computation, this paper also proposes a key data extraction method that only extract part of the data to generate the mutex task. The experiments show that the method of generating mutually exclusive tasks can effectively solve the memorization overfitting in the meta-learning MAML algorithm.

Keywords: mutex task generation, data augmentation, meta-learning, text classification.

Procedia PDF Downloads 133

Authors: Despoina Chochtoula, Aristidis Ilias, Yannis Stamatiou

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Modbus is a protocol that enables the communication among devices which are connected to the same network. This protocol is, often, deployed in connecting sensor and monitoring units to central supervisory servers in Supervisory Control and Data Acquisition, or SCADA, systems. These systems monitor critical infrastructures, such as factories, power generation stations, nuclear power reactors etc. in order to detect malfunctions and ignite alerts and corrective actions. However, due to their criticality, SCADA systems are vulnerable to attacks that range from simple eavesdropping on operation parameters, exchanged messages, and valuable infrastructure information to malicious modification of vital infrastructure data towards infliction of damage. Thus, the SCADA research community has been active over strengthening SCADA systems with suitable data protection mechanisms based, to a large extend, on cryptographic methods for data encryption, device authentication, and message integrity protection. However, due to the limited computation power of many SCADA sensor and embedded devices, the usual public key cryptographic methods are not appropriate due to their high computational requirements. As an alternative, Elliptic Curve Cryptography has been proposed, which requires smaller key sizes and, thus, less demanding cryptographic operations. Until now, however, no such implementation has been proposed in the SCADA literature, to the best of our knowledge. In order to fill this gap, our methodology was focused on integrating Modbus, a frequently used SCADA communication protocol, with Elliptic Curve based cryptography and develop a server/client application to demonstrate the proof of concept. For the implementation we deployed two C language libraries, which were suitably modify in order to be successfully integrated: libmodbus (https://github.com/stephane/libmodbus) and ecc-lib https://www.ceid.upatras.gr/webpages/faculty/zaro/software/ecc-lib/). The first library provides a C implementation of the Modbus/TCP protocol while the second one offers the functionality to develop cryptographic protocols based on Elliptic Curve Cryptography. These two libraries were combined, after suitable modifications and enhancements, in order to give a modified version of the Modbus/TCP protocol focusing on the security of the data exchanged among the devices and the supervisory servers. The mechanisms we implemented include key generation, key exchange/sharing, message authentication, data integrity check, and encryption/decryption of data. The key generation and key exchange protocols were implemented with the use of Elliptic Curve Cryptography primitives. The keys established by each device are saved in their local memory and are retained during the whole communication session and are used in encrypting and decrypting exchanged messages as well as certifying entities and the integrity of the messages. Finally, the modified library was compiled for the Android environment in order to run the server application as an Android app. The client program runs on a regular computer. The communication between these two entities is an example of the successful establishment of an Elliptic Curve Cryptography based, secure Modbus wireless communication session between a portable device acting as a supervisor station and a monitoring computer. Our first performance measurements are, also, very promising and demonstrate the feasibility of embedding Elliptic Curve Cryptography into SCADA systems, filling in a gap in the relevant scientific literature.

Keywords: elliptic curve cryptography, ICT security, modbus protocol, SCADA, TCP/IP protocol

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Authors: Elham Azimzadeh, Hamidollah Hassanlouei, Hadi Nobari, Georgian Badicu, Jorge Pérez-Gómez, Luca Paolo Ardigò

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Children with cerebral palsy (CP) have weak physical abilities and their limitations may have an effect on performing everyday motor activities. One of the most important and common debilitating factors in CP is the malfunction in the upper extremities to perform motor skills and there is strong evidence that task-specific training may lead to improve general upper limb function among this population. However, augmented feedback enhances the acquisition and learning of a motor task. Practice conditions may alter the difficulty, e.g., the reduced frequency of PG could be more challenging for this population to learn a motor task. So, the purpose of this study was to investigate the effect of physical guidance (PG) on learning a tracking task in children with cerebral palsy (CP). Twenty-five independently ambulant children with spastic hemiplegic CP aged 7-15 years were assigned randomly to five groups. After the pre-test, experimental groups participated in an intervention for eight sessions, 12 trials during each session. The 0% PG group received no PG; the 25% PG group received PG for three trials; the 50% PG group received PG for six trials; the 75% PG group received PG for nine trials; and the 100% PG group, received PG for all 12 trials. PG consisted of placing the experimenter's hand around the children's hand, guiding them to stay on track and complete the task. Learning was inferred by acquisition and delayed retention tests. The tests involved two blocks of 12 trials of the tracking task without any PG being performed by all participants. They were asked to make the movement as accurate as possible (i.e., fewer errors) and the number of total touches (errors) in 24 trials was calculated as the scores of the tests. The results showed that the higher frequency of PG led to more accurate performance during the practice phase. However, the group that received 75% PG had significantly better performance compared to the other groups in the retention phase. It is concluded that the optimal frequency of PG played a critical role in learning a tracking task in children with CP and likely this population may benefit from an optimal level of PG to get the appropriate amount of information confirming the challenge point framework (CPF), which state that too much or too little information will retard learning a motor skill. Therefore, an optimum level of PG may help these children to identify appropriate patterns of motor skill using extrinsic information they receive through PG and improve learning by activating the intrinsic feedback mechanisms.

Keywords: cerebral palsy, challenge point framework, motor learning, physical guidance, tracking task

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Authors: Milind Chaudhari, Suhail Balasinor

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Due to massive deforestation and an ever-increasing population, the organic content of the soil is depleting at a much faster rate. Due to this, there is a big chance that the entire food production in the world will drop by 40% in the next two decades. Vertical farming can help in aiding food production by leveraging big data and cloud computing to ensure plants are grown naturally by providing the optimum nutrients sunlight by analyzing millions of data points. This paper outlines the most important parameters in vertical farming and how a combination of big data and AI helps in calculating and analyzing these millions of data points. Finally, the paper outlines how different organizations are controlling the indoor environment by leveraging big data in enhancing food quantity and quality.

Keywords: big data, IoT, vertical farming, indoor farming

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Authors: Suwaree Yordchim, Toby J. Gibbs

Abstract:

The linguistic competence of Thai university students majoring in Business English was examined in the context of knowledge of English language inflection, and also various linguistic elements. Errors analysis was applied to the results of the testing. Levels of errors in inflection, tense and linguistic elements were shown to be significantly high for all noun, verb and adjective inflections. Findings suggest that students do not gain linguistic competence in their use of English language inflection, because of interlanguage interference. Implications for curriculum reform and treatment of errors in the classroom are discussed.

Keywords: interlanguage, error analysis, inflection, second language acquisition, Thai students

Procedia PDF Downloads 458

Authors: Sara Fine-Meltzer

Abstract:

An obvious, but ill-addressed hindrance to reading comprehension in academic English is poor vocabulary. Unfortunately, dealing with the problem is usually delayed until university entrance. It is the contention of this paper that the chore should be confronted much earlier and by using a very old-fashioned method. This presentation is accompanied by vocabulary lists for advanced level university students with explanations concerning the content and justification for the 500-word lists: how they change over time in accordance with evolving styles of academic writing. There are also sample quizzes and methods to ensure that the words are “absorbed” over time. There is a discussion of other vocabulary acquisition methods and conclusions drawn from the drawbacks of such methods. The paper concludes with the rationale for beginning the study of “academic” vocabulary earlier than is generally acceptable.

Keywords: academic vocabulary, old-fashioned methods, quizzes, vocabulary lists

Procedia PDF Downloads 118

Authors: A. Anis, W. Bekheet, A. El Hakim

Abstract:

Implementing a Road Safety Management System (SMS) in a crowded developing country such as Egypt is a necessity. Beginning a sustainable SMS requires a comprehensive reliable data system for all information pertinent to road crashes. In this paper, a survey for the available data in Egypt and validating it for using in an SMS in Egypt. The research provides some missing data, and refer to the unavailable data in Egypt, looking forward to the contribution of the scientific society, the authorities, and the public in solving the problem of missing or unreliable crash data. The required data for implementing an SMS in Egypt are divided into three categories; the first is available data such as fatality and injury rates and it is proven in this research that it may be inconsistent and unreliable, the second category of data is not available, but it may be estimated, an example of estimating vehicle cost is available in this research, the third is not available and can be measured case by case such as the functional and geometric properties of a facility. Some inquiries are provided in this research for the scientific society, such as how to improve the links among stakeholders of road safety in order to obtain a consistent, non-biased, and reliable data system.

Keywords: road safety management system, road crash, road fatality, road injury

Procedia PDF Downloads 131

Authors: Oualid Walid Ben Ali

Abstract:

Big Data has become one of the buzzwords today. The recent explosion of digital data has led the organization, either private or public, to a new era towards a more efficient decision making. At some point, business decided to use that concept in order to learn what make their clients tick with phrases like ‘sales funnel’ analysis, ‘actionable insights’, and ‘positive business impact’. So, it stands to reason that Big Data was viewed through green (read: money) colored lenses. Somewhere along the line, however someone realized that collecting and processing data doesn’t have to be for business purpose only, but also could be used for other purposes to assist law enforcement or to improve policing or in road safety. This paper presents briefly, how Big Data have been used in the fields of policing order to improve the decision making process in the daily operation of the police. As example, we present a big-data driven system which is sued to accurately dispatch the patrol cars in a geographic environment. The system is also used to allocate, in real-time, the nearest patrol car to the location of an incident. This system has been implemented and applied in the Emirate of Abu Dhabi in the UAE.

Keywords: big data, big data analytics, patrol car allocation, dispatching, GIS, intelligent, Abu Dhabi, police, UAE

Procedia PDF Downloads 487

Authors: Chelali Mohammed

Abstract:

In the context of the application of the competency-based approach in the system educational Algeria, the price of physical education and sport must privilege the acquisition of learning approaches and especially the approach science, which from problem situations, research and develops him information processing and application of knowledge and know-how in new situations in the words of ‘JOHN DEWEY’ ‘learning by practice’. And to achieve these goals and make teaching more EPS motivating, consistent and concrete, it is appropriate to perform a pedagogical approach freed from the constraints and open to creativity and student-centered in the light of the competency approach adopted in the formal curriculum. This approach is not unusual, but we think it is a highly professional nature requires the competence of the teacher.

Keywords: approach competencies, physical, education, teachers

Procedia PDF Downloads 598

Authors: Xu Du, Aparna S. Varde

Abstract:

In this research, we propose to conduct diagnostic and predictive analysis about the key factors and consequences of urban population relocation. To achieve this goal, urban simulation models extract the urban development trends as land use change patterns from a variety of data sources. The results are treated as part of urban big data with other information such as population change and economic conditions. Multiple data mining methods are deployed on this data to analyze nonlinear relationships between parameters. The result determines the driving force of population relocation with respect to urban sprawl and urban sustainability and their related parameters. Experiments so far reveal that data mining methods discover useful knowledge from the multicity urban data. This work sets the stage for developing a comprehensive urban simulation model for catering to specific questions by targeted users. It contributes towards achieving sustainability as a whole.

Keywords: data mining, environmental modeling, sustainability, urban planning

Procedia PDF Downloads 301