Miracle-dz/UniFAR-FTUs huggingface dataset:
The effects of different programming trainings on the computational thinking skills

ABSTRACT In this study, the effects of Arduino IDE and C++ programming languages were investigated on the computational thinking skills of preservice teachers. The Computational Thinking Skills Scale was administered to preservice teachers. Firstly, a basic programming training was given and then it was asked to create group projects on a voluntary basis. Six of twelve groups created a project using the Arduino IDE platform and the other six created one using the C++ programming language. Accordingly, a significant difference, which is in favour of the post-test, was found in the scores of pre-tests and post-tests of Arduino IDE groups in the scale factors; creativity, algorithmic thinking, critical thinking and the total score of the scale. No significant difference was found in cooperativity and problem solving factors. Also, the results of the study did not reveal any significant difference between the scores of pre and post-tests of C++ project groups and overall scores of different groups.
Small Area Estimation: Informative Sampling and Two-fold Models

Small area estimation (SAE) has become a dynamic field of research over the last couple of decades. The increasing demand for small area estimates in both public and private sectors has opened the door to further investigation of more applied problems. In this thesis, we consider three problems in the context of small area estimation: (i) small area estimation under informative sampling, (ii) small area estimation under unmatched two-fold subarea models, and (iii) variable selection under two-fold subarea model. In survey sampling, often samples are drawn with unequal probabilities to improve the statistical efficiency of the sampling strategy. When the selection probabilities are correlated with the variables of interest, even after conditioning on the auxiliary information, the sampling scheme is called informative. Under informative sampling, the population model does not hold for the sample. Hence, to avoid biased results, the selection effects should be accounted in the inference procedures. To address this problem, we propose a semi-parametric approach by including a P-spline term of selection probabilities in the model. The basic area-level model assumes that a linear model relates the small area means to area specific auxiliary information. However, in some SAE applications that deal with logit relationships the associated function is non-linear. In this situation, the linking model and the sampling model do not match and cannot be combined to produce the basic area-level model. We propose an empirical best unbiased predictor approach to estimation of small subarea parameters under a two-fold subarea-level model consisting of a sampling model and an unmatched linking model. The main assumption behind standard variable selection methods is that the variable of interest is observed, which is not the case for small area models, where dependent variables are not directly observed, due to the sampling errors. In particular, for a matched two-fold subarea level model, direct estimates of subarea means are measured as proxies for the unknown variable of interest. Applying the standard regression model selection naively may result in inefficient model selection methods. To tackle this issue, we propose a two-step adjustment to the standard variable selection method.
A Secured Frame Work for Searching and Sharing of Datain Cloud Based Services using IoT

— The internet technologies evolved many new innovations in communication technologies for searching and sharing data over the IoT. The importance of both software and hardware technologies became an important for the service providers and users as well. Searching and data sharing is contemporary task for the services providers because security aspects. Efficient searching and secured data sharing is an open issue till now. Information leakage costs more for the organizations. In this paper we proposes an efficient frame work for secured cloud based services where the data can shared among multiple devices of IoT. Our scheme allows all smart devices interact and share data among users in Internet of Things securely. We also attempted to develop a useful searching mechanism for having required data by the users over distributed storage domains to share. We conducted an abundant survey and study them in depth for better improvement of our work.
Effect of Edible Coatings (Xanthan gum and Carob gum) on the Physicochemical and Sensory Properties of French Fries Potatoes

In the present study, French fries potatoes were coated with combination of xanthan gum and carob gum or each of them separately at different concentrations of 0.25, 0.5, 0.75 and 1.5%( w/v) beside the control sample which was considered without coating. Samples were fried at 180 for 6 min. The percentage of hydrocolloids coating, moisture content, oil uptake, textural characteristic (hardness index), frying efficiency, colorimetric characteristics (brightness, redness and yellowness indexes plus color difference) and sensory evaluation (Flavor, aroma, appearance, color and overall acceptance) of potato strips were measured. The results indicated that when the hydrocolloids coating of potato strips applied, the moisture retention capacity increased, oil uptake decreased, frying efficiency increased, brightness index reduced and redness/yellowness index increased. Also by increasing the hydrocolloid initial concentration over 1%, sensory evaluation of the samples decreased. Sensory and physiochemical properties significantly decreased in the control sample (p≥0.05). As a final point, the sample contained 0.25% carob gum and 0.75% xanthan gum was introduced as superior treatment.
Deteksi dan Pencegahan Serangan Wormhole pada Protokol Routing AOMDV menggunakan Gabungan Metode Delphi dan RTT-TC pada Jaringan MANET

Mobile Ad Hoc Network (MANET) is a network consisting of a group of mobile nodes that don't have a fixed infrastructure. This causes the MANET to be vulnerable to attacks that can interfere with the process of communication and data transmission on the network. One type of attack that can attack the MANET is a wormhole attack. The wormhole is an attack on a network where there are two or more wormhole nodes that are interconnected and work together to attack network traffic or discard data packets it receives. Each wormhole node will be connected to each other using a wormhole link. To prevent wormhole attacks, the method that can be used is the Delay Per Hop Indicator (Delphi) method and the Round Trip Time and Topological Comparison (RTT-TC) method. The Delphi detects wormhole attacks by calculating the delay in each hop from the source node to the destination node. Whereas in the RTT-TC, wormhole attacks are detected by calculating the value of round trip time and topological comparison. The Delphi and RTT-TC methods have their advantages and disadvantages. In this study, it was proposed to combine the Delphi and RTT-TC methods to cover the disadvantage of each method.
Comparative study on protein digestibility, protein patterns, antioxidant activities of raw, cooked and fermented soybeans

Soybean ( Glycine max L. Merr.) is a versatile food source with protein, polyunsaturated fats, fiber, vitamins, minerals and other essential nutrients for humans. The purpose of this study is to compare the protein digestibility among raw, cooked and fermented soybeans (Natto) inoculated with Bacillus subtilis var. natto using simulated in vitro gastrointestinal digestion technique (IVD). Amino acid composition and antioxidant activity change during IVD of Natto were also investigated. It was found that trichloroacetic (TCA) soluble peptide yield increased around 46 and 62% in Natto and cooked soybean after IVD, respectively. The soluble protein fractions of cooked soybean and Natto during IVD were digested from large protein fraction into smaller protein sub-fraction observed by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE). After IVD, all essential amino acid especially Arg was found to remarkable increase for 6-fold and 49-fold in cooked soybean and Natto, respectively ( P <0.05). Interestingly, antioxidant properties increased when IVD times were increased ( P <0.05). The increment of antioxidant activities was 2-4 fold changing between before and after IVD. This can be supported by an increment in antioxidant amino acid composition (Trp, Tyr, Met, Cys, His, Phe and Pro) in IVD digested fractions especially in Natto. The highest antioxidant activity was found in fraction after IVD of Natto. These results indicated that Natto had better digestibility and antioxidant activity compared to raw and cooked soybeans.
Association Sorting Algorithm Design for Error Searching System

For the searching results of error searching system for Integrated Decision Information System (IDIS), this paper proposed a method to carry out association sorting according to degree of association of searched results by keywords, which can priorily recommend user interested searching results. Errors of IDIS platform are occurred very often. Because those errors belong to different stages like setup, configuration, and operation, or those errors may occurred in different services, applications, or IP ports, or may be happened in different system software, different version of software, and those errors are also can be classified into different types. As there will be many error information are searched out, it is required to sort them according to degree of association, and provide user interested results. This paper proposed a method to carry out association sorting for the searching results according to the times of searched repeated results by keywords, which effectively put results with high degree of association in the front and increase the searching efficiency.
Optimization of linseed cultivation, a promising way to enhance its secoisolariciresinol diglucoside lignan content

There is strong evidence on the anticancer properties of secolariciresinol diglucoside (SDG), the predominant flaxseed lignan, especially for mammary and prostatic carcinomas. Although the effect of cultivar and location on flaxseed SDG level has been reported, much less is traced regarding the influence of agronomical treatments. The purpose of this study is to determine if agricultural management practices could enhance SDG accumulation in flaxseed. We studied the effect of irrigation frequencies (I), added nutrients (N, zinc and humic acid) and fertilization treatments (F) and their interactions on SDG level and seed yield of the Egyptian flax cultivar Giza 9. Samples were extracted and analyzed for SDG, after alkaline hydrolysis, using HPLC-UV method. The concentration of SDG and its yield varied between 415.55-845.95 mg/100 g whole seeds and 1.77-4.05 Kg/feddan on a dry weight basis, respectively according to different agricultural treatments. I, N and F factors and their interactions had a significant effect on SDG. I × N × F interaction contributed to the majority of SDG variation (31.81 %), followed by I factor (27.96 %), whereas seed yield was more affected by F factor (72.7 %). The I × F interaction was the only significant interaction on the seed yield contributing only for 0.43% of the variance. Moderate irrigation frequency with the humic acid foliar application and mineral nitrogen fertilizer was the best combination for achieving the highest SDG levels. Using optimum agronomic practice would benefit the nutraceutical industry by optimizing flaxseed lignan content and hence enhancing its anticancer, health-promoting effect.
Migrating a web application to serverless architecture

Serverless computing is a novel cloud computing model based on auto-scaling, ephemeral resources billed at a millisecond granularity. Serverless has gained interest in the industry but literature on how the model’s characteristics drive application design is still scarce. This thesis aims to fill the gap by first defining the paradigm along with its origins and surveying for applicable design patterns. The patterns are then applied in an experimental migration process through which 5 new patterns are introduced. Finally the migration outcome is evaluated in terms of development ease, performance and costs. The serverless model is found to deliver on its promises of elasticity and reduced operational overhead; cost benefit however depends largely on expected traffic shape.
Predicting the Upcoming Services of Vacant Taxis near Fixed Locations Using Taxi Trajectories

Emerging on-line reservation services and special car services have greatly affected the development of the taxi industry. Surprisingly, taking a taxi is still a significant problem in many large cities. In this paper, we present an effective solution based on the Hidden Markov Model to predict the upcoming services of vacant taxis that appear at some fixed locations and at specific times. The model introduces a weighted confusion matrix and a modified Viterbi algorithm, combining the factors of time of day and traffic conditions. In our framework, the hotspot or hidden states extraction is implemented through kernel density estimation (KDE) and fuzzy partitioning of traffic zones is done via a Fuzzy C Means (FCM) algorithm. We implement the proposed model on a large-scale dataset of taxi trajectories in Beijing. In this use case, tests demonstrate the high accuracy of the modeling framework in predicting the upcoming services of vacant taxis. We further analyze the factors affecting the predictive accuracy via a prediction accuracy analysis and prediction location evaluation. The findings of this paper can provide intelligence for the improvement of taxi services, to increase the passenger capacity of taxis and also to improve the probability of passengers finding taxis.
Analysis on Different Parameters of Encryption Algorithms for Information Security

With the fast evolution of digital data exchange, security information is very important in data storage and transmission. Data encryption is widely used to ensure security in open networks such as the internet. Security is a very important factor in every field such as Government Agencies (CBI, FBI), Research Organization, E-commerce and etc. where internet is being used. Each type of data has its own features, therefore, different techniques should be used to protect confidential image data from unauthorized access. Cryptography is a technique to secure data on the network from unauthorized user. There are different types of a cryptography algorithm (a) symmetric and (b) asymmetric has been designed. To secure data it is necessary to know which algorithm provides better security, efficiency, accuracy and effectiveness. This paper presents the complete analysis of various symmetric key encryption algorithms (DES, 3DES, AES, and RC4) based on different parameters.
Discriminative Dictionary Learning With Spatial Constraints

In this thesis, we investigate the use of dictionary learnin g for discriminative tasks on natural images. Our contributions can be summarized as follows : • We introduce discriminative deviation based learning to ac hieve principled handling of the reconstruction-discrimination tradeoff that is inherent to discriminative dictionary learning. • Since natural images obey a strong smoothness prior, we show how spatial smoothness constraints can be incorporated into the learning formulation by embedding dictionary learning into Conditional Random Field (CRF) learning. We demonstra te that such smoothness constraints can lead to state-of-the-art performance for pixe l-classification tasks. • Finally, we lay down the foundations of super-latent learni ng. By treating sparse codes on a CRF as latent variables, dictionary learning can also be per formed via the Latent (Structural) SVM formulation for jointly learning a classifier over the sp arse codes. The dictionary is treated as asuper-latentvariable that generates the latent variables.
Biodegradable Samarium-153–labelled microspheres for hepatic radioembolization: preparation, characterization and radiolabelling evaluation after neutron activation

A biocompatible and biodegradable radioactive samarium-153 (153Sm)-labelled Poly-(L-lactic acid) (PLLA) microspheres was developed for hepatic radioembolization. Samarium acetylacetonate was encapsulated in PLLA microspheres using oil-in-water solvent evaporation method. Physicochemical characterization of the microspheres were analysed using Field Emission Scanning Electron Microscopy, Energy Dispersive X-ray spectroscopy and particle size analyser. The prepared microspheres were irradiated in a nuclear reactor with a neutron flux of 1.49 × 1012 n.cm-2.s-1, converting 152Sm to 153Sm (Emax = 807.6 keV, half-life = 46.3 h). Gamma spectroscopy was carried out to determine the presence of radionuclide impurities while the in-vitro radiolabelling efficiency was performed to analyse the retention of 153Sm on the microspheres. The Sm-labelled PLLA microspheres was found spherical with the diameters within 20-60 μm, as indicated by the scanning electron microscopy and particle size distribution results. Gamma spectrometry suggested that no long half-life radioimpurities present after neutron activation. The 153Sm-labelled PLLA microspheres has achieved a nominal activity of 5.9 GBq.g-1 after 6 h neutron activation. The formulation showed more than 97% radiolabelling efficiency in saline and human blood plasma over 550 h. The 153Sm-labelled PLLA microspheres are potentially useful for hepatic radioembolization due to their biodegradability, favourable radiation characteristics and excellent radiolabelling efficiency. The preparation of the formulation does not involve ionizing radiation and hence reduces the costs of production.
Performance, resource and cost aware virtual machine adaptation

Performance, Resource and Cost aware Virtual Machine Adaptation Cloud Computing has increasingly become an attractive paradigm for computing during recent years. In the current Infrastructure as a Service (IaaS) cloud landscape users pay for statically con gured Virtual Machine sizes irrespective of usage. Although the auto-scaling features o ered by current cloud providers enable cloud hosted applications to dynamically scale the amount of resources allocated, the adopted con gurations are often sub-optimal owing to the lack of exibility involved in resoure provisioning. This results in higher costs and di culty in meeting performance targets for clients. It would be more favorable for users to consume (and be billed for) just the right amount of resources necessary to satisfy the performance requirement of their applications. Although prior work have suggested a variety of approaches to the auto-scaling problem, the bene ts of these approaches remain restricted to applications that mainly depend on CPU and memory. The reason is partly due to cloud operators not providing guarantees on resource types that are di cult to partition such as IO and networking performance in their typical VM o erings (although specialized instances for these types of resources are available). We take a novel perspective in addressing this problem where we assume that the cloud operator exposes a small, dynamic fraction (for security and privacy reasons) of its infrastructure and the corresponding resource speci cations and constraints to each application. Assuming such a scenario we propose a dynamic VM recon guration scheme which comprises an Application Performance Model, a Cost Model and a Recon guration algorithm. The performance model helps estimate the performance of an application given speci c resources. The Cost model assigns a numerical cost value to resource candidates made available to the application considering the lease expense, recon guration penalty and operating income. A recon guration algorithm assisted by the cost model makes optimal recon guration decisions. Simulation results for the RUBiS and lebenchleserver applications and the worldcup workload show signi cant cost savings can be achieved while meeting performance targets compared to rule-based scaling systems. Our proposed framework has the advantages of being simple, generic and computationally e cient. This framework is also attractive from a cloud operator's perspective as it indirectly assists the operator with the problem of e cient datacenter utilization.
Modified Visibility Restoration-Based Contrast Enhancement Algorithm for Colour Foggy Images

Abstract The visibility enhancement of colour foggy images is a very challenging task for many real-time applications. In this paper, we have proposed an efficient and robust algorithm for visibility enhancement of colour foggy images. The proposed algorithm works in two steps: in the first step, modified visibility restoration algorithm is applied for visibility enhancement, and in the second step, sigmoid-function-based contrast enhancement technique is applied for colour contrast enhancement of images. The quantitative and qualitative results of proposed and other state-of-the-art algorithms for colour foggy images are obtained in terms of fog aware density evaluator, fog reduction factor (FRF), measure of enhancement (EME), and measure of enhancement factor (EMF) on different colour foggy image databases. Results reveal the strength of proposed algorithm mathematically on the basis of fog thickness estimation from original image and output-enhanced image, FRF, EME, and EMF for colour foggy images. Experimental results shows that the proposed algorithm provides better quantitative and qualitative results as compared to other state-of-the-art algorithms for colour foggy images. Finally, the proposed algorithm is highly efficient for visibility enhancement of colour foggy images.
Hydraulic Servo System Control Using Differential Evolution Based Robust Structure Specified H∞ Controller

Synthesis of an H∞ controller usually produces a very high order of controller, and one which is much higher than the plant, causing difficulty in implementing the controller in practical applications, especially those using small microcontrollers. However, systems are always subject to disturbances and un-modeled dynamics. This research introduces modeling and simulations of lower order structures, specifically an H∞ robust controller, to control a hydraulic servo system. A low order controller shortens the gap between the complicated H∞ controllers to the practical embedded control system application. Simulation results show that the proposed controller gives satisfactory results with small settling times and no steady state error. The resulting controller also produced better responses than that of a full order H∞ controller generated using the Matlab Robust Control Toolbox.
Validation Methods for Phenolic Components with RP-HPLC-UV in Various Bee Products

In this study, a simple and a fast method were developed using RP-HPLC-UV for the separation and quantitative determination of phenolic compounds in various bee products. Fourteen phenolic compounds were identified using 70% acetonitrile in water, and 2% acetic acid in water as a mobile phase with a gradient elution mode. The validation method exhibited linearity (R 2 >0.994), limits of detection (0.022-0.062 mg/L) and quantification (0.030-0.187mg/L). Under the optimized conditions, 14 commercially available phenolic compounds were analyzed in less than 50 min. This method was successfully employed to study the phenolic profiles of bee products as well as other natural samples.