Miracle-dz/UniFAR-FTUs huggingface dataset:
An Evaluation of Structural Model for Independent Learning Through Connectivism Theory and Web 2.0 Towards Studentsr Achievement

-The purpose of the study was to evaluate an independent learning measurement model based on connectivism theory and Web 2.0 learning tool. The quantitative method was utilised in this study. The data were collected using connectivism and Web 2.0 learning tool instruments among students of Two Year Programme in one of the matriculation colleges in Malaysia. For the purpose of sampling, the cluster sampling was utilised. In this study, the research model was evaluated using a two-step process, those were the evaluation of the measurement model and the evaluation of the structural model. In general, the purpose of model evaluation is to determine whether both measurement and structural model fulfill the quality criteria for empirical work. The findings revealed that there were significant correlationsbetween connectivism theory and Web 2.0 learning tool towards students' achievement in the structural model. The study also showed that the students' achievement was attributed by the principles of connectivism theory and Web 2.0 as a learning tool. In conclusion, this study had successfully evaluated an independent learning model based on connectivism theory and Web 2.0 learning tool with the implementing of Partial Least SquaresStructural Equation Modelling. This study implied that apart from others factor, connectivism theory and Web 2.0 learning tool are also contributed a different perspective to the process of students' learning.
SYNTHESIS OF MODULAR-STRUCTURED SYSTEMS FOR AUTOMATIZATION OF DECISION-MAKING PROCESSES IN TRANSPORT LOGISTICS

The ranging analysis of transport logistics problems and decision-making methods were held in the paper. The problems, which are represented as subsystems of DSS (Decision Support System) module type, were considered. One of which, in particular, evaluation of the quality of transport services to select cargo shipping company, was considered in details.
Identification of usability problems and requirements of elderly Chinese users for smart TV interactions

ABSTRACT With the development of Information and Communications Technology (ICT), smart TV is gradually becoming universal and penetrating daily life. Smart TV has a wide range of user groups, and the elderly is an important group. The special physiological and psychological characteristics of Chinese elderly highlight the usability problems of smart TV interactions for them. The main functions of smart TVs were selected and operated by the elderly in this study. With the help of physiological measurements, behaviour analyses and interviews during natural usage scenarios, we determined the usability issues and user requirements of Chinese elderly for interacting with smart TVs. The research shows that the elderly in China have the intention to use smart TV products, but the usability of interactive systems affects the experience of using a smart TV. The results indicate that different content search methods result in different user experiences. Pinyin search is difficult to operate using a remote control, and some influencing factors are revealed. Voice search results in the best user experience for the elderly, but the recognition accuracy is easily affected by factors such as user accents and environmental noise. Hierarchical search is easy to operate, but it often takes a long time to finish an involved task. Other usability issues of some main functions, i.e. screen mirroring, shopping, playing games, system settings and application downloads, were also obtained, and the requirements of the elderly were well understood. These findings have implications for interaction design and implementation of smart TV service systems.
High Speed Network Anonymity Protocol Using Compression Based Face Change

The major objective of any data communication system is to securely transmit their digital data between two or more computers in the network. Delay Tolerant Networks were proposed to enable communication by leveraging the store-carry-andforward mechanism of message transfer. The proposed approach selects the path according to the feasible number of hops needed to reach the corresponding node. The architecture anonymise every node of the network by giving them fake ids which will be their FaceChange_id’s. This FaceChange_id is used for communication in the network which enhances the data forwarding security. The data is compressed so as to increase the speed of the system and reduce delay and throughput
Corpus Quality Improvement to Improve the Quality of Statistical Translator Machines (Case Study of Indonesian Language to Java Krama)

Bahasa merupakan alat komunikasi yang dijadikan sarana untuk berinteraksi dengan masyarakat sekitar.Kemampuan akan penguasaan banyak bahasa tentunya akan mempermudah untuk berinteraksi dengan orang lain dari berbagai daerah yang berbeda. Oleh karena itu, diperlukan penerjemah untuk menambah pengetahuan akan berbagai bahasa yang ada. Mesin Penerjemah Statistik (Statistical Machine Translation) merupakan sebuah pendekatan mesin penerjemah dengan hasil terjemahan yang dihasilkan atas dasar model statistik yang parameter-parameternya diambil dari hasil analisis korpus paralel. Korpus paralel adalah pasangan korpus yang berisi kalimat-kalimat dalam suatu bahasa dan terjemahannya. Salah satu fitur yang digunakan untuk meningkatkan kualitas hasil terjemahan adalah dengan optimasi korpus. Tujuan yang ingin dicapai dalam penelitian ini adalah melakukan untuk melihat pengaruh kualitas korpus dengan memfilter pasangan kalimat-kalimat dengan terjemahan berkualitas. Filter yang digunakan adalah nilai minimal setiap kalimat yang di uji dengan metode Bilingual Evaluation Understudy (BLEU). Pengujian dilakukan dengan membandingkan nilai akurasi hasil terjemahan sebelum dan setelah optimasi korpus. Dari hasil penelitian, penggunaan optimasi korpus dapat meningkatkan kualitas terjemahan untuk mesin penerjemah bahasa Indonesia ke bahasa Jawa krama. Hal itu terlihat dari hasil pengujian dengan menambahkan optimasi korpus pada 15 kalimat uji diluar korpus terdapat peningkatan rata - rata nilai BLEU sebesar 10.53% dan dengan menggunakan 100 kalimat uji yang berasal dari korpus optimasi terdapat peningkatan rata-rata nilai BLEU sebesar 11.63% pada pengujian otomatis serta 0.03% pada pengujian oleh ahli bahasa. Berdasarkan hal tersebut, mesin penerjemah statistik bahasa Indonesia ke bahasa Jawa krama dengan penggunaan fitur optimasi korpus dapat meningkatkan nilai akurasi hasil terjemahan.
Verification of Randomized Distributed Algorithms under Round-Rigid Adversaries

Randomized fault-tolerant distributed algorithms pose a number of challenges for automated verification: (i) parameterization in the number of processes and faults, (ii) randomized choices and probabilistic properties, and (iii) an unbounded number of asynchronous rounds. The combination of these challenges makes verification hard. Challenge (i) was recently addressed in the framework of threshold automata. We extend threshold automata to model randomized algorithms that perform an unbounded number of asynchronous rounds. For non-probabilistic properties, we show that it is necessary and sufficient to verify these properties under round-rigid schedules, that is, schedules where no process enters round r before all other processes finished round r −1. For almost-sure termination, we analyze these algorithms under round-rigid adversaries , that is, fair adversaries that only generate round-rigid schedules. This allows us to do compositional and inductive reasoning that reduces verification of the asynchronous multi-round algorithms to model checking of a one-round threshold automaton. We apply this framework to classic algorithms: Ben-Or's and Bracha's seminal consensus algorithms for crashes and Byzantine faults, 2-set agreement for crash faults, and RS-Bosco for the Byzantine case.
An evaluation of classification algorithms for prediction of drug interactions: Identification of the best algorithm

Introduction: One of the main causes of medical errors is drug interaction which occurs when a drug decreases or increases the effect of another drug. Drug interactions occur as a result of changes in pharmacodynamics, pharmacokinetics, or a combination of both. Due to the problems caused by these errors and lack of an efficient system of automatic diagnosis of drug interactions, and also since a large amount of these interactions can be prevented, we aimed to search for drug interactions in the medical texts and also classify and identify the best algorithm. Methods: A two-stage classification was used to solve the problem of unbalanced data dispersion in drug interaction classes. A subset of the most suitable features was identified for classification. In the first step of designing a binary classification, pairs of drugs which interact with each other and those which do not be separated. Then, we classified the pairs of drug interactions in one of the following four classes: effect, advice, mechanism, and int. In this study, different algorithms were used in both types of classifications, based on the type of data and expert opinion. To validate the first-stage model, we considered 90% of the data as training data and the rest were considered as the test data. To validate the second-stage model, we used the difference verification method. Weka data analysis software was also used for designing the model; then, the classification was made. Results: The results showed that the most appropriate features were mutual information (obtaining a score of 1000) and parts of speech. The efficiency of J48 algorithm in the stage of separating the drugs with and without interaction (F-measure = 0.914) and also in the multiclass stage of the bagging algorithm (F-measure = 0.915) was the highest among other algorithms. ZeroR algorithm required the shortest time to build the model (less than half a second) in both stages. Conclusion: According to the results of J48 algorithms and random forest, it can be concluded that decision tree is the most appropriate approach in the extraction and automatic classification of drug interactions, using the features derived from the text to be applied in clinical decision support system.
Modelling Oblivious Transfer in EasyCrypt

We describe how to construct an oblivious transfer protocol which security is based on subset membership problems and smooth projective hash functions. A specific protocol based on the two-message oblivious transfer protocols of Kalai (2005) and the encryption schemes presented by Cramer and Shoup (2002) is presented. In addition, the protocol is modelled in EASYCRYPT and we use the program to prove the security, which is based on the Decisional Diffie-Hellman assumption. However, we encountered challenges using the program, which is under development. The stability of EASYCRYPT was a problem. Axioms that were important for our implementation were not available, and the necessary information on syntax needed to define new modules and lemmas was inadequate. We had to rewrite existentand compose many new lemmas. We also found a critical error in EASYCRYPT, which was given high priority by the team behind EASYCRYPT, and has now been corrected. However, our proofs had to surround the problem and became more complicated. We also concluded that EASYCRYPT is not developed primarily for doing algebra, resulting in unnecessary complicated codes for the proof of our protocol.
Service disruption index (SDI): A novel user perspective reliability indicator for modern virtual service networks

In this paper, we propose a service‐independent, user‐perspective reliability analysis index for the modern virtual service networks to quantify the magnitude of failure impacts in terms of actual user‐perceived value. If a virtual service network encounters failures, it will generate disruptions to the demand traffic. The proposed algorithm observes these disrupted demand flows and then maps that value to the overall network reliability. Comparative analysis and simulation results show that our proposed method is suitable for reliability evaluation in modern virtual service networks and can provide acceptable accuracy. Consequently, it can be used by service providers as a reliability indicator to monitor the application‐specific reliability for each virtual service network.
Improving Students Ability in Mathematical Problem Solving Through Problem Based Learning Model in Tenth Grades State Senior High School 7 of Medan

This research aimed to determine that improving the mathematical problem solving ability of students through the PBL model is better than traditional learning. This research is a quasi experimental research. Sampling method is using random sampling method that is from tenth grade at State Senior High School 7 of Medan selected two classes as sample which subjected to random treatment of one as control class that is X2 and the other as experimental class that is X5. Eksperimental class is using PBL model and control class is using traditional learning. Inferensial statistic analysis was performed by Independent Sample t-Test. The math subject is system of linear equations with two variables. Based on the research, it was found that the significance value for mathematical problem solving ability in the experimental and control classes is 0.00. Because 0.00 < 0.05 then H0 is rejected, So improving the mathematical problem solving ability through the PBL model is better than traditional learning. It was concluded that improving the mathematical problem solving ability through the PBL model is better than traditional
Comparison of game-based learning and traditional lecture approaches to improve student engagement and knowledge transfer in STEM education

In the modern educational system, educators are constantly striving to increase student engagement. Improving student engagement leads to an increase in learning motivation, ultimately enhancing students’ ability to grasp complex topic areas. A common strategy to achieve higher engagement levels in the classroom is game-based learning (GBL). GBL has received mixed reviews due to a lack of data comparison and the difficulty of balancing entertainment with educational value. The objective of this study was to investigate how student knowledge transfer compares between a GBL activity and a traditional classroom lecture within STEM education. The GBL activity developed for the study was a cooperative board game called Preservation. During the game, players worked together to mitigate a tide of environmental threats related to the cornwater-ethanol-beef system in the Midwest. The primary learning outcomes measured during the study were student attitudes towards the environment and their capacity for systems thinking. Students in two junior level undergraduate courses completed pre-post-surveys after experiencing one of three treatments: group one – played Preservation, and group two – played Preservation with supporting lecture. Assessment focused on changes in student attitudes and overall understanding of system interactions. Initial findings suggest that the combination treatment provided the greatest change in systems thinking, however, no change occurred with respect to environmental attitudes. The results of this study will be used to direct the development of subsequent games and hands-on activities to promote transformational learning strategies in STEM education.
Multi-objective optimization of an energy hub using artificial intelligence

The present dissertation is focused on the energy optimization of systems which contain multiple energy carriers. The objective of the research is the development of a methodology and the corresponding software tools, which permit to calculate the optimal energy flow of a multi-carrier system in arder to satisfy its energy demands and minimiza a set of established optimization criteria. The topics that are covered in this research include the description of the mathematical formulation of a multi-carrier energy system based on the energy hub concept, the modelling and prediction algorithms that are used to forecast the energetic needs of the plant, as well as the proposed optimization methodology to obtain the system's optimal operating strategy and its complete state, including transmission and conversion of multiple energy carriers within defined security constraints. Digital models and software-basad simulations demonstrate the feasibility of the proposed solution. The testing and validation of the proposal was made under real operating conditions of a car manufacturing plant in the framework of the FP7 European research project EuroEnergest. The findings provided valuable output conclusions of the suitability and usefulness of this methodology to calculate the optimal control of a multi-carrier energy system, as well as the potential energetic, environmental and economic benefits that could be resultad from its application. En la presente Tesis doctoral se estudia la optimización energética en sistemas que contienen múltiples fuentes y demandas energéticas. El objetivo de la investigación es desarrollar una metodología y los correspondientes algoritmos de control, que permitan controlar la operación de sistemas con múltiples portadores energéticos de forma óptima,con fin de satisfacer sus demandas energéticas y minimizar una serie de criterios establecidos. Los temas que se tratan en esta investigación consisten en la descripción, formulación matemática y análisis de sistemas multiportadoras de energía, basándose en el concepto del Hub Energético y en algoritmos adaptativos para el modelado y predicción de las demandas energéticas de estos sistemas. Además, se desarrolla una metodología de optimización para calcular la óptima estrategia de operación de los equipos y el uso de sus energías. Los modelos digitales y las simulaciones realizadas demuestranla viabilidad de la solución. La evaluación y validación de la metodología se ha realizado bajo condiciones de operación reales en una planta de fabricación de automóviles, en el marco del proyecto de investigación Europeo FP7 EuroEnergest. Los resultados de validación presentan un gran potencial de ahorro cuando se aplica la estrategia de operación calculada por el algoritmo, mostrando beneficios energéticos, económicos y medioambientales.
Comparative Analysis of Stock Market Prediction System using SVM and ANN

Support vector machines (SVM) and artificial neural networks (ANN) are machine learning methods that find a wide range of applications both in the field of engineering and social sciences. Here, Artificial Neural Networks (ANN) and Support vector machines (SVM) are employed to predict stock market daily trends: ups and downs. The purpose is to study the variation in certain parameters like accuracy, time efficiency of both classifiers (ANN and SVM) on similar datasets in predicting stock market daily trends. In this study, different SVM and ANN models for the problem of stocks prediction which provide maximum accurate results have been applied on different combinations of data sets which obtained from the historical data of various companies and comparative analysis has been presented. The findings show that SVM and ANN models give meaningful performance results for the stock investment.
Multi-modal route choice modeling in a dynamic schedule-based transit network

Route choice behavior has predominantly been analyzed from the angle of a single mode, most often the car. Considering route choice in the broader context of multimodal networks yet opens the way to more complex policy analysis and wider applications. The main modeling challenges in this context are the limited availability in time of public transport services and the definition of alternatives to the observed path. This paper tackles these challenges by applying the recursive logit to model the choice of transit modes and route in a real network. The model is based on the assumption of a full available schedule. The approach presents numerous advantages. First, route choice preferences can be consistently estimated without generating choice sets of paths. Second, the model can be used to predict fast and accurately path choices in real network by sampling from estimated link choice probabilities. Although the network is much larger than previous applications of the RL model with over 1 million links, we obtain reasonable computational times. Third, the approach allows to include all transit services without restriction in one large-scale network, providing the possibility to estimate realistic rates of substitution between different attributes.
Yelp’s Review Filtering Algorithm

In this paper, we present an analysis of features influencing Yelp's proprietary review filtering algorithm. Classifying or misclassifying reviews as recommended or non-recommended affects average ratings, consumer decisions, and ultimately, business revenue. Our analysis involves systematically sampling and scraping Yelp restaurant reviews. Features are extracted from review metadata and engineered from metrics and scores generated using text classifiers and sentiment analysis. The coefficients of a multivariate logistic regression model were interpreted as quantifications of the relative importance of features in classifying reviews as recommended or non-recommended. The model classified review recommendations with an accuracy of 78%. We found that reviews were most likely to be recommended when conveying an overall positive message written in a few moderately complex sentences expressing substantive detail with an informative range of varied sentiment. Other factors relating to patterns and frequency of platform use also bear strongly on review recommendations. Though not without important ethical implications, the findings are logically consistent with Yelp’s efforts to facilitate, inform, and empower consumer decisions.
ANTENA MIKROSTRIP BERPOLARISASI SIRKULAR DENGAN TEKNIK DUAL-FEED DAN PENAMBAHAN SLOT

Tel-USAT 2 adalah pengembangan sistem satelit mikro di lingkungan Telkom University. Tel-USAT 2 memiliki misi Remote Sensing Payload (RSPL) yang menggunakan sistem SAR. Salah satu komponen SAR adalah S-Band Transmitter yang berfungsi mengirimkan data hasil imaging ke ground station. Gelombang elektromagnetik dari satelit akan melewati lapisan ionosfer yang terdapat Efek Faraday. Untuk mengatasi Efek Faraday digunakan antena dengan polarisasi sirkular yang akan dirancang pada penelitian ini. Pada penelitian ini, antena bekerja pada frekuensi tengah 2,35 GHz dengan bandwidth VSWR < 1,5 sebesar 54,2 MHz. Antena berpolarisasi sirkular dengan nilai axial ratio 0,95 dB pada frekuensi 2,35 GHz dan bandwidth axial ratio < 3 dB sebesar 54,3 MHz. Pola radiasi didapatkan berupa unidirectional serta gain didapatkan sebesar 7,15 dBi.