Miracle-dz/UniFAR-FTUs huggingface dataset:
Using data science to detect fake news

The purpose of this thesis is to assist in automating the detection of Fake News by identifying which features are more useful for different classifiers. The effectiveness of different extracted features for Fake News detection are going to be examined. When classifying text with machine learning algorithms features have to be extracted from the articles for the classifiers to be trained on. In this thesis, several different features are extracted: word counts, ngram counts, term frequency-inverse document frequency, sentiment analysis, lemmatization, and named entity recognition to train the classifiers. Two classifiers are used, a Random Forest classifier and a Naïve Bayes classifier. Training on different features combined with different machine learning algorithms yields different accuracies. By testing the different features on different classifiers, it can be determined which features are the best for Fake News detection. Classifying news articles as either Fake News or as not Fake News is explored using three datasets, which in total contains over 40,000 articles . One of the datasets is used to partly to train the classifiers and partly to test the classifiers. The remaining two datasets are used purely for testing the classifiers. All the code used in conjunction with thesis can be found in Appendix B.
The Ability of Ferns to Accumulate Heavy Metals (Hg, Pb And Cd) In The Waters of The Gorontalo River

Sustainable development can be caused by more human’s activity. This activity, it will have an impact on the surrounding environment which can disrupt the stability of the ecosystem, one of which is the river water ecosystem. The research aims to determine the ability of ferns in accumulating heavy metals so that they can be used as indicators of the depletion of river waters ecosystem environments and can be used as fitoremediant areas of waters which accumulate heavy metals. This research is descriptive quantitative. Retrieval of initial data or sample in this study used survey methods and random sampling techniques, testing samples by using the Ranger X-ray Flourenciece (XRF) method. Based on the results of the study that, ferns can accumulate heavy metals, the parts that accumulate are located below the root surface and above the soil surface, namely stems and leaves. In the roots have more amount in accumulating heavy metals than in the stem and leaves, this is because the metal is indicated on the soil. From these results, it can be concluded that ferns can be used as water purification plants in river ecosystems that are indicated by heavy metals.
Plant Species Identification based on Plant Leaf Using Computer Vision and Machine Learning Techniques

Plants are very crucial for life on Earth. There is a wide variety of plant species available, and the number is increasing every year. Species knowledge is a necessity of various groups of society like foresters, farmers, environmentalists, educators for different work areas. This makes species identification an interdisciplinary interest. This, however, requires expert knowledge and becomes a tedious and challenging task for the non-experts who have very little or no knowledge of the typical botanical terms. However, the advancements in the fields of machine learning and computer vision can help make this task comparatively easier. There is still not a system so developed that can identify all the plant species, but some efforts have been made. In this study, we also have made such an attempt. Plant identification usually involves four steps, i.e. image acquisition, pre-processing, feature extraction, and classification. In this study, images from Swedish leaf dataset have been used, which contains 1,125 images of 15 different species. This is followed by pre-processing using Gaussian filtering mechanism and then texture and color features have been extracted. Finally, classification has been done using Multiclass-support vector machine, which achieved accuracy of nearly 93.26%, which we aim to enhance further.
FPGA Implementation of NPSF Testing Using Block Code Technique

This paper presents a test structure for high speed memories. Built in self test (BIST) give the solution for testing memories and associate hardware for test pattern generation and application for a variety of test algorithms. Memory test algorithm for neighborhood pattern sensitive faults (NPSF) is developed by using block code technique to identify the base cell and deleted neighborhood cells. Test pattern generation can be done by using LFSR and Euler pattern generation. The testing process is verified using Xilinx ISE 14.2 and implemented on Nexys 4 DDR Artix 7 FPGA board.
Study on radiation characteristics of Au-CuS nanofluids

ABSTRACT The radiation characteristics of Au-CuS nanofluids are been researched and mainly used in seawater desalination.The nanoparticle films are placed on the surface of the water,and seawater evaporates by solar simulator irradiation.Then evaporation rate and evaporation efficiency under different experimental variables are calculated, which are applied to economic analysis. The results prove that Au-CuS nanofluids show a good prospect in seawater desalination.
Rate-Limiting Steps of Dye Degradation over Titania-Silica Core-Shell Photocatalysts

In this work, we employed a step-by-step sol-gel process to controllably deposit ultra-thin layers of SiO2 on anatase nanoparticles in the range between 0 and 1 nm. The deposition was confirmed by TEM, EDX, and ATR-FTIR (e.g., Ti-O-Si band at 960 cm-1). Zeta potential measurements unravelled a continuous change in surface charge density with increasing silica shell thickness. The photocatalysts were evaluated towards adsorption and degradation of positively-charged and negatively-charged dyes (methylene blue, methyl orange) under UV illumination. The growth mechanism follows the Stranski–Krastanov model with three thickness regimes: (a) Flat islands (first step), (b) mono/bilayers (second/third step), and (c) regular thick films (fourth/fifth step). The results suggest different rate limiting processes for these regimes: (a) For the thinnest scenario, acidic triple-phase boundaries (TPBs) increase the activity for both dyes with their accessibility being the rate limiting step; (b) for continuous mono/bilayers, dye adsorption on the negatively-charged SiO2 shells becomes the rate liming step, which leads to a stark increase in activity for the positively-charged MB and a decrease for MO; (c) for thicker shells, the activity decreases for both dyes and is limited by the charge transport through the isolating shells.
Method for the Computer-Aided Schematic Design and Simulation of Hydrogel-Based Microfluidic Systems

We present an approach to the schematic design and time-dependent simulation of microfluidic systems that are composed of basic components, such as pumps, channels, mixing junctions, and valves. For the schematic capture and the simulation, the Cadence Virtuoso design framework is employed, with the components modeled by Verilog-AMS. The pressures and flow rates are calculated with the help of the hydraulic analogy between electronics and fluidics. Signal flow quantities are used to express the flow of chemical concentration. Our focus is on systems employing chemofluidic transistors, i.e., hydrogel-based microvalves that facilitate autonomous flow control without the need for an external control unit. Our approach also covers layout editing by the use of programmed cells. The utility of the approach is exemplified by the successful top-down design of a chemofluidic oscillator circuit and by the functionally correct simulation of a chemofluidic SR flip-flop.
The Study of Electronic Coursework anti-Plagiarism Based on Information Hiding Technique

With the development of network technologies, many universities have gradually realized coursework electronically in many courses. But then copying electronic coursework becomes more and more commonly among the students. This paper proposes a new method of anti-plagiarism for electronic coursework based on information hiding technique, and a system of electronic coursework anti-plagiarism has been realized by programming. The system of anti - plagiarism uses the watermarking algorithm with high embedding capacity, and embeds the information of originators into the characters of coursework. It can detect the plagiarism ’s parts and locate accurately the resource of plagiarism before teachers mark student ’s coursework. The result of experiment shows that the technique can prevent from copying directly other’s coursework.
ITS evaluation in classroom: the case of the AMBRE ITS

This paper describes the evaluation of an Intelligent Tutoring System (ITS) designed within the framework of the multidisciplinary AMBRE project. The aim of this ITS is to teach abstract knowledge based on problem classes thanks to the Case-Based Reasoning paradigm. We present here AMBRE -AWP , an ITS we designed following this principle for additive word problems domain and we describe how we evaluated it. We conducted first a pre-experiment with five users. Then we conducted an experiment in classroom with 76 eight-year-old pupils using comparative methods. We present the quantitative results and we discuss them using results of qualitative analysis.
Crystallization kinetics of evaporation rubidium chloride in the MSMPR crystallizer

In the MSMPR crystallizer, the evaporating crystallization kinetics of rubidium chloride was studied. Based on the theory of population balance and experimental population density data of rubidium chloride, the crystallization process of rubidium chloride obey ΔL law, and the nucleation and growth rate equations of rubidium chloride were obtained. Theoretical analysis of the kinetic equations show that lower magma density, appropriate temperature and supersaturation are three key factors to get rubidium chloride with bigger mean particle size.
Rational Development of Structurally Ordered Platinum Ternary Intermetallic Electrocatalysts for Oxygen Reduction Reaction

Structurally ordered intermetallic structure is an efficient catalyst design strategy to significantly improve the catalytic performance of Pt alloy electrocatalysts for oxygen reduction reaction in fuel cells. However, a high structural ordering degree generally relies on high-temperature annealing, which results in detrimental catalyst particle sintering. Herein, we reveal that the incompatibility between high ordering degree and minimum particle sintering during thermal annealing can be resolved through rational development of structurally ordered Pt ternary alloys. Ordering transformation mediated by high-temperature annealing of three representative Pt ternary alloys (Pt–Fe–Co, Pt–Ni–Co and Pt–Fe–Ni) at a similar Pt composition was systematically studied. It was found that Fe can significantly promote the structural ordering due to a faster atomic diffusion, whereas Co can effectively inhibit the particle sintering. As a result of the synergy between Co and Fe, the ordered PtCoFe catalyst exhibited the highest ordering degree after thermal annealing at 600 °C with the minimum nanoparticle growth, leading to the highest catalytic activity (0.65 A/mgPt at 0.9 V, 4 times that of pure Pt catalyst) and best stability (16% drop after 10,000 potential cycles). This study provides important clues for the rational design of high-performance structurally ordered ternary Pt alloys.
Progressive Algebraic Soft-Decision Decoding of Reed–Solomon Codes Using Module Minimization

The algebraic soft-decision decoding (ASD) of Reed–Solomon (RS) codes yields a competent decoding performance with a polynomial-time complexity. But its complexity remains high due to the interpolation that generates the interpolation polynomial <inline-formula> <tex-math notation="LaTeX">$Q(x,y)$ </tex-math></inline-formula>. The progressive ASD (PASD) algorithm has been introduced to construct <inline-formula> <tex-math notation="LaTeX">$Q(x,y)$ </tex-math></inline-formula> with a progressively enlarged <inline-formula> <tex-math notation="LaTeX">$y$ </tex-math></inline-formula>-degree, adjusting its error-correction capability and computation to the received information. However, this progressive decoding is realized at the cost of memorizing the intermediate decoding information. To overcome this challenge, this paper proposes a new PASD algorithm which is evolved from the ASD using module minimization (MM) interpolation. Polynomial <inline-formula> <tex-math notation="LaTeX">$Q(x,y)$ </tex-math></inline-formula> can be constructed through the image of the progressively enlarged submodule basis without the need of memorizing the intermediate decoding information, eliminating the memory cost of progressive decoding. The MM interpolation also attributes to a remarkably lower complexity than the original PASD algorithm. Furthermore, a complexity reducing variant is proposed based on assessing the degree of Lagrange interpolation polynomials. We also analyze the complexity of the proposed decoding methods and reveal their channel dependent feature. Our simulation results show their low-complexity and advanced decoding performances.
Computation of Reliability, Average Reliability, and Maintainability of Service Demand Fulfilment Dynamically using System Dynamics

An enterprise not only need information about the time-based service performance (reliability and maintenance of services) but also need to know the effect of time variables on service performance and demand dynamically so as to determine the right service time to providing the services level. This article proposes system dynamics approach to simulate the reliability, average reliability and maintainability of services and enterprise based on service time of demand fulfillment. The model created is a dynamic model that forms a closed system containing negative feedbacks that can be used to simulate. Generally, in a dynamic model with negative feedback, the decrease of time variable causes the increase of reliability of service. However, the simulation scenarios of the dynamic model with negative feedbacks in this article show the decrease of time variables (service time of demand fulfillment and its meantime, meantime of reliability function) can cause either decrease, increase or no change to the total of demand as well as number and average of demand reliability, average reliability, and maintainability in the enterprise.
Development of a Face Recognition System Using Deep Convolutional Neural Network in a Multi-view Vision Environment

This paper presents the implementation of Face Recognition System for multi-view vision system consisting of three cameras. It captures input frames from RLC423 camera; the main structure composed of recognizing faces, embeddings computation and database vector comparison. OpenCV, Python dlib, face recognition or Openface and ResNet34 are utilized for this face recognition method. It illustrates the application of face recognition and compare it to different dataset with static and dynamic approaches such as MIT-CBCL face recognition dataset, Caltech faces and EPFL-RLC Multi-Camera Dataset which, obtained 1.5% training loss and 90.3% model accuracy.
Teaching programming using dedicated Arduino Educational Board

This paper presents a new approach to teaching programming to undergraduate computer science students. A dedicated Arduino board along with custom application programming interface (API) was introduced into programming classes with a view to strengthen students’ engagement and improving the attractiveness of the course. The students were presented with basic functionalities of the board, which gave them a possibility to accomplish their own projects—typically video games—without any background in electronics. The level of engagement of the participants was observed by the tutors during classes and also reviewed based on questionnaires filled by 347 first‐, second‐, and third‐year undergraduates. The results indicated that the proposed approach was well received by nearly 80%, while nearly 75% of the participants expressed a wish to continue their Computer Science education using Arduino.
GC-MS Analysis of Chemical Constituents and in vitro Antioxidant Activity of the Organic Extracts from the Stem of Bridelia stipularis

In the present study the stems of the Bridelia stipularis (L.) Blume, which is traditionally used by ethnic communities in Sabah, Malaysia, has been investigated for its chemical composition, total flavonoid content (TFC) and total phenolic content (TPC) via Gas-Chromatography-Mass Spectroscopy (GC-MS) analysis consuming hexane, chloroform and ethyl acetate as extraction solvents and gallic acid and quercetin as internal standards. In vitro antioxidant activity (AA) was determined by the application of 1,1-diphenyl-2-picryl hydrazine (DPPH) radical scavenging assay using tert-butyl-1-hydroxytoluene (BHT) as comparative drug. The GC-MS profiling showed the presence of 1-dodecanol (40.917%), oxalic acid, cyclobutyl octadecyl ester (24.985%), 1-octanol,2-nitro (12.424%), benzaldehyde, 2,4-dimethyl- (9.583%), 4-tridecanol (6.359%) and nitric acid, nonyl ester (5.616%) as major constituents. The TPC (224.62 ± 0.08 mg QE/g) and TFC (160.48 ± 0.08 mg GAE/g) was reported highest for the most polar solvent i.e. ethyl acetate. The in vitro antioxidant study disclosed highest IC50 value for ethyl acetate (2.15 mg/mL), queued by chloroform (1.19 mg/mL) and hexane (0.89 mg/mL), displaying that polar solvents are good extraction solvents for the identification of free radical scavenging properties, TFC and TPC.
Phytochemical studies and anti-ulcerative colitis effect of Moringa oleifera seeds and Egyptian propolis methanol extracts in a rat model

Objective: To analyze the phytochemical constituents, and to explore potential protective effect of the methanol extract of Moringa oleifera (M. oleifera) seeds and Egyptian propolis, each alone or concurrently administered on acetic acid-induced ulcerative colitis in rats. Methods: Eight groups of 5 rats each were used: normal control group with distilled water, model group, two groups with M. oleifera seeds (100 and 200 mg/kg), two groups with propolis (50 and 100 mg/kg), one group with concurrent administration of both, and one group with prednisolone (reference drug). Macro-and microscopic picture, ulcer index and lesion scores, oxidative markers, inflammatory mediators, in vitro activity of the inflammatory enzymes and 1, 1-diphenyl-2-picrylhydrazyl free radicals scavenging activity were evaluated. The phytochemical constituents of both extracts were explored by GC-MS analysis. Results: Both treatments modulated the macro-and microscopic picture, decreased the ulcerative index, lesion score, oxidative markers and inflammatory mediators, and inhibited the COX-1 and COX-2 enzymes. Propolis appeared to be powerful free radicals scavenger. A powerful synergistic effect of both treatments in modulating the course of the disease was reported. GC-MS analysis of methanol extract of M. oleifera seeds and propolis revealed the presence of 50 and 34 compounds, respectively. Conclusions: M. oleifera seeds and propolis methanol extracts have modulated the course of acetic acid-induced ulcerative colitis. Moreover, both treatments induce a good synergistic effect against the disease. Isolation of the active constituents is recommended.
Comparative Study for Text Document Classification Using Different Machine Learning Algorithms

Classification is a supervised learning method: the goal is finding the labels of the unknown object. In the real world, the tedious amounts of manual works are required to label the unknown documents. The system is initially trained by labeled documents by using one of the supervise machine learning algorithm and then applied trained model to predict the label of the unknown documents. The framework of text document classification consists of: input text document, pre-processing, feature extraction and classification. The analysis four common classification methods are performed: Naive Bayes, Decision Tree, Support Vector Machine and K-nearest neighbors for text document classification. The main focus of this paper is to present comparative study of different exiting classification methods for text document classification. The experiment performed different classification methods on the Enron Email Dataset and measure classification accuracy, true positive, true negative, false positive and false negative to compare the performance of different classification methods.
Interactive motorsports broadcasting in a virtual reality environment

Interactive TV in motorsport, specifically Formula 1, is an established service that has been developed over the last 20 years. Market research reveals trends in broadcasting pointing towards, in the future, sports, in general, will be consumed via VR. Broadcasting interactive TV with VR acting as the medium, however, is an unfamiliar territory where a gap in research exists in interaction design. The purpose of this thesis is to research the viewing habits of inexperienced and experienced viewers of F1 through empirical work that includes literature review, observation and interviews. The insights gathered are then conceptualized through workshops and sketching. These, in turn, helps produce a set of iterated prototypes that are usability tested to extract insights which reveal design recommendations for ergonomics and interface and elements for augmenting the viewing experience the designer should keep in mind when developing for VR broadcasted interactive motorsport TV.
Indoor Intelligent Mobile Robot Based on ZigBee Wireless Location

Traditional localization for indoor robot cannot reach expected positioning accuracy in a complex and wide environment.Based on the received signal strength indicator,a method that uses a ZigBee wireless network was proposed.A ZigBee wireless network indoor positioning system was built up.It provides the coordinates of the robot for the main controller to schedule the pathway of robot.Parameters in positioning system were demarcated and factors that affect the positioning accuracy were analyzed.The experimental results show that the system can locate the robot under a complex indoor environment.If there are no obstacles,the accuracy can reach 0.25 m.It can meet the requirements of indoor positioning accuracy.The localization scope can be extended according to actual demands.
Examining Preservice EFL Teachers' TPACK Competencies in Turkey.

The purpose of the study was to understand the TPACK (Technological Pedagogical Content Knowledge) competency of preservice English teachers and to determine whether there were any significant differences in terms of gender and academic achievement. This study was conducted at a state-run university and subjects were 137 preservice English teachers. The results of the research suggest that males’ technological knowledge was higher than females; however, females were better than males in pedagogical knowledge. Moreover, no significant difference was found between TPACK mean and academic achievement in terms of the correlation between TPACK scale and academic achievement of the participants.