Miracle-dz/UniFAR-FTUs huggingface dataset:
Design Science Research Methods and Patterns: Innovating Information and Communication Technology

Design research promotes understanding of advanced, cutting-edge information systems through the construction and evaluation of these systems and their components. Since this method of research can produce rigorous, meaningful results in the absence of a strong theory base, it excels in investigating new and even speculative technologies, offering the potential to advance accepted practice. Design Science Research Methods and Patterns presents innovative research methods that help break new ground by applying patterns, reuse, and design science to research. The book relies on familiar patterns to provide the solid fundamentals of various research philosophies and techniques as touchstones that demonstrate how to innovate research methods. Filled with practical examples of applying patterns to IT research with an emphasis on reusing research activities to save time and money, this book describes design science research in relation to other information systems research paradigms such as positivist and interpretivist research.
Automatic modulation classification of communication signals

AUTOMATIC MODULATION CLASSIFICATION OF COMMUNICATION SIGNALS by Zaihe Vu The automatic modulation recognition (AMR) plays an important role in various civilian and military applications. Most of the existing AMR algorithms assume that the input signal is only of analog modulation or is only of digital modulation. In blind environments, however, it is impossible to know in advance if the received communication signal is analogue modulated or digitally modulated. Furthermore, it is noted that the applications of the currently existing AMR algorithms designed for handling both analog and digital communication signals are rather restricted in practice. Motivated by this, an AMR algorithm that is able to discriminate between analog communication signals and digital communication signals is developed in this dissertation. The proposed algorithm is able to recognize the concrete modulation type if the input is an analog communication signal and to estimate the number of modulation levels and the frequency deviation if the input is an exponentially modulated digital communication signal. For linearly modulated digital communication signals, the proposed classifier will classify them into one of several nonoverlapping sets of modulation types. In addition, in Mary FSK (MFSK) signal classification, two classifiers have also been developed. These two classifiers are also capable of providing good estimate of the frequency deviation of a received MFSK signal. For further classification of linearly modulated digital communication signals, it is often necessary to blindly equalize the received signal before performing modulation recognition. This doing generally requires knowing the carrier frequency and symbol rate of the input signal. For this purpose, a blind carrier frequency estimation algorithm and a blind symbol rate estimation algorithm have been developed. The carrier frequency estimator is based on the phases of the autocorrelation functions of the received signal. Unlike the cyclic correlation based estimators, it does not require the transmitted symbols being non-circularly distributed. The symbol rate estimator is based on digital communication signals' cyclostationarity related to the symbol rate. In order to adapt to the unknown symbol rate as well as the unknown excess bandwidth, the received signal is first filtered by using a bank of filters. Symbol rate candidates and their associated confident measurements are extracted from the fourth order cyclic moments of the filtered outputs, and the final estimate of symbol rate is made based on weighted majority voting. Α thorough evaluation of some well-known feature based AMR algorithms is also presented in this dissertation. AUTOMATIC MODULATION CLASSIFICATION OF COMMUNICATION SIGNALS
The Simulator Development for RDE Reactor

BATAN is proposing the construction of experimental power reactor (RDE reactor) for increasing the public acceptance on NPP development plan, proofing the safety level of the most advanced reactor by performing safety demonstration on the accidents such as Chernobyl and Fukushima, and owning the generation fourth (G4) reactor technology. For owning the reactor technology, the one of research activities is RDE’s simulator development that employing standard equation. The development utilizes standard point kinetic and thermal equation. The examination of the simulator carried out comparison in which the simulation’s calculation result has good agreement with assumed parameters and ChemCAD calculation results. The transient simulation describes the characteristic of the simulator to respond the variation of power increase of 1.5%/min, 2.5%/min, and 3.5%/min.
A Novel Compact Cat Swarm Optimization Based on Differential Method

ABSTRACT A compact cat swarm optimization scheme (cCSO) is proposed in this paper, which is designed to solve application domains plagued with limited memory and less-computation power, as a member of cat swarm optimization algorithms (CSO), it composes of two sub-modes, i.e., tracing and seeking modes, so it keeps the same search logic of CSO. On the other hand, cCSO inherits the main feature of compact algorithms, a normal probabilistic model is used to represent the population of solutions instead of processing an actual population, which ensures the cCSO to have the modest memory requirement. The updating vector for the probabilistic model provides a clear moving direction for cats in next step. A cat without historical position and velocity is applied in the algorithm. When the cat is in seeking mode, it employs a differential operator to update the cat’s position, which makes it possible for the cat to have multiple searching directions. Experimental results show that cCSO has pretty performance compared with respect to some population-based testing benchmarks. And it also shows superior performance in convergence rate to some compact optimization algorithms. The case study of gray image segmentation proves that it suits for solving the optimization problem by limited hardware.
Electrochemical Li-Ion battery modeling for electric vehicles

The future development of electric vehicles is mostly dependent of improvements in battery performances. In support of the actual research of new materials having higher performances in terms of energy, power, durability and cost, it is necessary to develop modeling tools. The models are helpful to simulate integration of the battery in the powertrain and crucial for the battery management system, to improve either direct (e.g. preventing overcharges and thermal runaway) and indirect (e.g. state of charge indicators) safety. However, the battery models could be used to understand its physical phenomena and chemical reactions to improve the battery design according with vehicles requirements and reduce the testing phases. One of the most common model describing the porous electrodes of lithium-ion batteries is revisited. Many variants available in the literature are inspired by the works of prof. J Newman and his research group from UC Berkeley. Yet, relatively few works, to the best of our knowledge, analyze in detail its predictive capability. In the present work, to investigate this model, all the physical quantities are set in a dimensionless form, as commonly used in fluid mechanics: the parameters that act in the same or the opposite ways are regrouped, and the total number of simulation parameter is greatly reduced. In a second phase, the influence of the parameter is discussed, and interpreted with the support of the limit cases. The analysis of the discharge voltage and concentration gradients is based on galvanostatic and pulse/relaxation current profiles and compared with tested commercial LGC cells. The simulations are performed with the software Comsol® and the post-processing with Matlab®. Moreover, in this research, the parameters from the literatures are discussed to understand how accurate are the techniques used to parametrize and feed the inputs of the model. Then, our work shows that the electrode isotherms shapes have a significant influence on the accuracy of the evaluation of the states of charges in a complete cell. Finally, the protocols to characterize the performance of commercial cells at different C-rates are improved to guarantee the reproducibility.
Effects of the Position and Orientation Inaccuracies in Haptic Guidance on the Task Performance in Teleoperation Systems : Peg-in-hole Task

This study evaluates the usefulness of haptic guidance and investigates the effect of inaccuracy in haptic guidance on the task performance. Both position inaccuracy and orientation inaccuracy were considered. The peg-in-hole task was conducted as a benchmark test for evaluation, and a psychophysical study was performed and analyzed on eight inaccuracy combinations. In conclusion, the haptic guidance showed outstanding performance in terms of task completion time, position tracking performance, contact force, and total force, but the haptic guidance with severe orientation inaccuracy caused serious complications.
PENERAPAN MODEL PEMBELAJARAN SNOWBALL THROWING UNTUK MENINGKATKAN HASIL BELAJAR IPA PADA SISWA KELAS IV SDN 021 SAMARINDA UTARA

This study aims to know to know the Improvement of Learning Outcomes About Style Through Model Learning Snowball Throwing In Students Class IV SDN 021 North Samarinda. The type of this research is Classroom Action Research (PTK), which is implemented in SDN 021 North Samarinda precisely in class IV which amounts to 17 students. Data collection techniques used in this study is the observation sheet of teacher and student activity when the learning process, written test, documentation. The study was conducted with two cycles and each cycle consisted of two meetings, each cycle consisting of four stages, namely planning, implementation, observation and reflection. Based on the observation data obtained in each cycle, can be seen the student learning outcomes increase well. The results showed that through the model of learning Snowball Throwing can improve student learning outcomes. Improvement of student learning outcomes from prasiklus of 41.2% with an average value of 50.29 subsequently increased in the first cycle of 76.47% with an average value of 70.58. Then on the second cycle has increased 88.23% with an average student 80.58. By applying the Snowball Throwing learning model can increase student activity in teaching and learning process, this can be seen with the increase in each cycle. The percentage in prasiklus is 49.99%. Next cycle I is 72.05%, the percentage in cycle II is 85.29%. The conclusion in this research by using Snowball Throwing learning model can improve learning result and student activity of Class IV SDN 021 North Samarinda.
Active caching for recommender systems

ACTIVE CACHING FOR RECOMMENDER SYSTEMS by Muhammad Umar Qasim Web users are often overwhelmed by the amount of information available while carrying out browsing and searching tasks. Recommender systems substantially reduce the information overload by suggesting a list of similar documents that users might find interesting. However, generating these ranked lists requires an enormous amount of resources that often results in access latency. Caching frequently accessed data has been a useful technique for reducing stress on limited resources and improving response time. Traditional passive caching techniques, where the focus is on answering queries based on temporal locality or popularity, achieve a very limited performance gain. In this dissertation, we are proposing an ‘active caching’ technique for recommender systems as an extension of the caching model. In this approach estimation is used to generate an answer for queries whose results are not explicitly cached, where the estimation makes use of the partial order lists cached for related queries. By answering non-cached queries along with cached queries, the active caching system acts as a form of query processor and offers substantial improvement over traditional caching methodologies. Test results for several data sets and recommendation techniques show substantial improvement in the cache hit rate, byte hit rate and CPU costs, while achieving reasonable recall rates. To ameliorate the performance of proposed active caching solution, a shared neighbor similarity measure is introduced which improves the recall rates by eliminating the dependence on monotinicity in the partial order lists. Finally, a greedy balancing cache selection policy is also proposed to select most appropriate data objects for the cache that help to improve the cache hit rate and recall further. ACTIVE CACHING FOR RECOMMENDER SYSTEMS by Muhammad Umar Qasim A Dissertation Submitted to the Faculty of New Jersey Institute of Technology in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy in Information Systems Department of Information Systems, NJIT May 2011 Copyright c ⃝ 2011 by Muhammad Umar Qasim ALL RIGHTS RESERVED APPROVAL PAGE ACTIVE CACHING FOR RECOMMENDER SYSTEMS Muhammad Umar Qasim Dr. Vincent Oria, Dissertation Co-Advisor Date Associate Professor of Computer Science, NJIT Dr. Yi-Fang Brook Wu, Dissertation Co-Advisor Date Associate Professor of Information Systems, NJIT Dr. Min Song, Committee Member Date Assistant Professor of Information Systems, NJIT Dr. Dimitri Theodoratos, Committee Member Date Associate Professor of Computer Science, NJIT Dr. Il Im, Committee Member Date Associate Professor of Information Systems, Yonsei University, South Korea I would like to dedicate this doctoral dissertation to my father, Muhammad Qasim, without his continuous encouragement I could not have completed this process and to my wife, Munazza, for being a candle flame in dark times and a patient endurer.
The Use of Guided Writing and Sequences of Pictures as Teaching Technique to Enhance the Ability of Writing Narrative of Students in “Different English Course”

Although many techniques are used to improve the writing ability of students, very few studies have investigated the nature of guided writing and sequences of pictures techniques. This present study set out to investigate the use of guided writing and sequences of pictures as teaching techniques to enhance students’ writing ability in narrative at “Different English course”. The action research was used for this study. Three cycles of action research were implemented in this study. Pre-test and post-tests were conducted to monitor the students’ writing improvement. Using Jacobs et al’s (1981) ESL Composition Profile, improvement in students’ writing was analysed analytically. Based on test results, the mean of post-test was higher than the mean score of pre-test. Therefore, it can be concluded that guided writing and sequences of pictures can improve the students’ writing ability. This improvement of the students’ writing ability included a) students enhanced their writing ability in organization and language use, and based on their views to the techniques and b) they chose sequences of pictures as technique which mostly gave contributions to their narrative writing.
Robust Synchronization in Networks of Compartmental Systems

Robust Synchronization in Networks of Compartmental Systems Milad Alekajbaf Master of Applied Science Graduate Department of Electrical and Computer Engineering University of Toronto 2014 This thesis studies robust synchronization in networks of compartmental systems motivated by cellular networks. These networks consist of compartments each of which is composed by interconnected subunits. We investigate conditions on the dynamics of the subunits and on the interconnection topology that guarantee robust synchronization in the presence of external disturbances and possibly nonlinear perturbations on the network. The results are illustrated with several examples, including a network of genetic oscillators.
COMPARATIVE STUDY ON SELECTED SEARCH ENGINES & META SEARCH ENGINES IN RETRIEVING PHYSIOTHERAPY INFORMATION FROM THE WORLD WIDE WEB & DETERMINING OVERLAP BETWEEN THEM SURVEY

Introduction : Search tools including Search Engines, Meta Search Engines and Subject Directory are used to find the needed information in the World Wide Web. The aim of this study was to compare selected Search Engines and Meta Search Engines in retrieving information concerning physiotherapy from the World Wide Web to determine overlap among them. Methods : The current study was carried out using descriptive comparative methods. Seven search engines and seven meta search engines introduced by ";searchinginewatch.com"; as the most frequently used ones, were analyzed. Then subject sub category of physiotherapy in eight subject fields were chosen from Medical Subject Headings (MeSH) as keywords. After choosing keywords, a search was made via using the search and Meta search engines to identify how many results could be obtained by studied search engines for each individual keyword. Finally the rate of overlap among search engines and meta search engines was measure in terms of percentage. The obtained data were then analyzed using descriptive methods and the SPSS. Results : According to the result of this study, ";Altavista";, All the web ";and";Google"; showed the higher most retrieved results. Among the Meta search engines ";Ixquick"; got the first rank. Search results of ";Altavista"; and ";all the web";,"; Vivsimon"; and ";Clusty"; and ";Metacrawler"; and ";Dogpile"; showed large overlaps. Moreover, overlap percentage between search engines and Meta search engines with respect to keyword, was determined in this study. Conclusion : In this study, the percentage of overlap among selected search engines and meta search engines using the selected keywords, was shown to be 40% - 60% . This difference was because of variety of results ranking methods in different search engines and Meta search engines. Keywords : Medical Information Storage and Retrieval; Physical Therapy (Specialty); Internat
How do team conflicts impact knowledge sharing?

Abstract Past studies have shown that conflict in teams is inevitable and even desirable for enhancing team performance. Conflict occurs due to perceived or real differences in teams and is often magnified by demographic and functional diversity. Given that software development teams reflect both types of diversity, it is important to investigate the impact of team conflict on critical team outcomes such as knowledge sharing. The results of the study show that while task conflicts impact knowledge sharing positively, only moderate levels of relationship conflict is good for knowledge sharing. Both high and low levels of relationship conflicts are not conducive for knowledge sharing. Further, high levels of relationship conflicts moderate the direct impacts of task conflicts on knowledge sharing adversely, while low levels of relationship conflicts magnify the impact of task conflicts on knowledge sharing. The complex interactions between task conflict, relationship conflict and knowledge sharing have useful implications for both practice and future research.
Deep learning with limited labeled image data for health informatics

Deep learning is a data-driven technique for developing intelligent systems using a large amount of training data. Amongst the deep learning applications, this thesis focuses on problems in health informatics. Compared to the general deep learning applications, health informatics problems are complex, unique and pose problemspecific challenges. Many of these problems however, face a common challenge: the lack of labeled data. In this thesis, we explore the following three ways to overcome three specific image based health informatics problems: 1) The use of image patches instead of whole images as the input for deep learning. To increase the data size, each image is partitioned into non-overlapping, mid-level patches: This approach is illustrated by addressing the food image recognition problem. Automatic food recognition could be used for nutrition analysis. We propose a novel deep framework for mid-level food image patches. Evaluations on 3 benchmark datasets demonstrate that the proposed approach achieves superior performance over baseline convolutional neural networks (CNN) methods. 2) The use of prior knowledge to reduce the high dimensionality and complexity of raw data: We illustrate this idea on magnetic resonance imaging (MRI) images, for diagnosing a common mental-health disorder, the Attention Deficit Hyperactivity Disorder (ADHD). MRI has been increasingly used in analyzing ADHD with machine learning algorithms. We propose a multi-channel 3D CNN based automatic ADHD diagnosis approach using MRI scans. Evaluations on ADHD200 Competition dataset show that the proposed approach achieves state-of-the-art accuracy. 3) The use of synthetic data pre-training along with real data domain adaptation to increase the available labeled data during training: We illustrate this idea on
The role of word frequency and contextual diversity in visual word recognition: a mini review

Contextual diversity refers to the number of contexts in which a word appears. It is traditionally believed that word frequency is an important factor affecting lexical access, but the presence of contextual diversity challenges the position of word frequency in lexical cognition. The research on contextual diversity and word frequency is mainly focused on two fields, namely word recognition and word learning. However, research outcomes concerning the mapping between contextual diversity and word frequency are inconsistent with each other: 1) Contextual diversity can replace word frequency, and is better than word frequency in terms of word recognition; 2) Word frequency and contextual diversity are two different variables that affect word recognition, both of which independently affect word recognition. No definite conclusion about the relationship between the two has been reached yet. Finally, we highlight topics that are in need of future systematic research.