Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-criteria optimization algorithms for high dose rate brachytherapy

The overall purpose of this thesis is to use the knowledge of radiation physics, computer programming and computing hardware to improve cancer treatments. In particular, designing a treatment plan in radiation therapy can be complex and user-dependent, and this thesis aims to simplify current treatment planning in high dose rate (HDR) prostate brachytherapy. This project was started from a widely used inverse planning algorithm, Inverse Planning Simulated Annealing (IPSA). In order to eventually lead to an ultra-fast and automatic inverse planning algorithm, three multi-criteria optimization (MCO) algorithms were implemented. With MCO algorithms, a desirable plan was selected after computing a set of treatment plans with various trade-offs. In the first study, an MCO algorithm was introduced to explore the Pareto surfaces in HDR brachytherapy. The algorithm was inspired by the MCO feature integrated in the Raystation system (RaySearch Laboratories, Stockholm, Sweden). For each case, 300 treatment plans were serially generated to obtain a uniform approximation of the Pareto surface. Each Pareto optimal plan was computed with IPSA, and each new plan was added to the Pareto surface portion where the distance between its upper boundary and its lower boundary was the largest. In a companion study, or the second study, a knowledge-based MCO (kMCO) algorithm was implemented to shorten the computation time of the MCO algorithm. To achieve this, two strategies were implemented: a prediction of clinical relevant solution space with previous knowledge, and a parallel computation of treatment plans with two six-core CPUs. As a result, a small size (14) plan dataset was created, and one plan was selected as the kMCO plan. The planning efficiency and the dosimetric performance were compared between the physician-approved plans and the kMCO plans for 236 cases. The third and final study of this thesis was conducted in cooperation with Cédric Bélanger. A graphics processing units (GPU) based MCO (gMCO) algorithm was implemented to further speed up the computation. Furthermore, a quasi-Newton optimization engine was implemented to replace simulated annealing in the first and the second study. In this way, one thousand IPSA equivalent treatment plans with various trade-offs were computed in parallel. One plan was selected as the gMCO plan from the calculated plan dataset. The planning time and the
Analysis of cryptosystem recognition scheme based on Euclidean distance feature extraction in three machine learning classifiers

In reality, many cryptographic analysis techniques are based on a specific cryptographic system or a large number of encrypted ciphertext. The identification and detection of cryptographic system is of great significance for evaluating the security of the algorithm and guiding the design and improvement of the algorithm. In this paper, we transcode each character in ciphertext into a decimal number, construct these numbers into one-dimensional arrays, and obtain the Euclidean distance between these one-dimensional arrays. Then we use these distances as features and input them into three machine learning classifiers: random forest, logistic regression and support vector machine to recognize cryptosystem and compare their recognition accuracy. The subjects include 8 common block ciphers (DES, 3DES, AES-128, AES-256, IDEA, SMS4, Blowfish, Camellia-128). The experimental results show that using the feature extraction scheme not only shortens the experimental time, reduces the computational cost, but also improves the recognition accuracy of eight typical block cipher algorithms. The classification accuracy of the ECB mode in the random forest classifier is 75%, which is higher than the existing published literature experimental results. The classification accuracy rate of CBC mode is higher than 13.5%, which is higher than the accuracy of random classification.
AIS Electronic Library (AISeL) AIS Electronic Library (AISeL)

Aligning local business and technology initiatives with enterprise-wide objectives remains a challenge for many organizations. To this end, Enterprise Architecture Management (EAM) imposes formal control mechanisms such as architecture plans and principles aimed at leveraging enterprise-wide standards and harnessing information systems (IS) complexity. Addressing recent calls to complement EAM control portfolios with informal control mechanisms, this study reports on the design, implementation and adoption of an Enterprise Architecture Label at a large multinational engineering company. Based on recent research on nudging, we deliberately designed the choice architecture of local decision makers. The Enterprise Architecture Label aims to influence the decision-making process, so that IS design alternatives that are preferable from an enterprise-wide perspective appear to be more attractive. Following an Action Design Research approach, the paper highlights the process of defining the underlying measurement system, designing an appropriate presentation, and the learnings and theory implications made throughout this process
The effect of game–based immersive virtual reality learning environment on learning outcomes: designing an intrinsic integrated educational game for pre–class learning

ABSTRACT As an emerging learning platform, game-based immersive virtual reality learning environments (GIVRLEs) have the potential to solve difficult teaching problems. This study designed a GIVRLE by integrating knowledge of quadratic functions into gameplay. Forty seventh graders who had never acquired that knowledge played the game and took pre- and posttests. An additional 60 seventh graders took the same math tests as controls. The results showed significant improvements in math achievement and learning motivation between the pre- and posttests among students who played the game. No enhancement of math achievement was found in the control students. The playability survey and user experience questionnaire verified the suitability of the game. The findings indicate that a GIVRLE is a suitable tool for addressing teaching difficulties in K–12. The notion of intrinsic integration between learning content and gameplay based on simulated daily activity tasks is further discussed.
Automatic semblance velocity analysis using Convolutional Neural Networks

Velocity analysis can be a time-consuming task when it is performed manually. Methods have been proposed to automate the process of velocity analysis, which, however, typically requires significant manual effort. We propose using the Convolutional Neural Network (CNN) to estimate stacking velocities directly from the semblance. Our CNN model uses two images as one input data for training. One is the entire semblance (guide image), and the other is a small patch (target image) extracted from the semblance at a specific time step. Labels for each input dataset are the root mean square (RMS) velocities. We generate the training dataset using synthetic data. After training the CNN model with synthetic data, we test the trained model with other synthetic data that was not used in the training step. The result shows that the model can predict a consistent velocity model. One also notices that when the input data is extremely different from the one used for the training, the CNN model will hardly pick the correct velocities. In this case, I propose to adopt transfer learning to update the trained model (base model) with a small portion of the target data. The latter improves the accuracy of the predicted velocity model. The Marmousi dataset and a marine dataset from the Gulf of Mexico are used for validating the proposed automatic velocity analysis algorithm.
What's New in Shock, November 2010?

In this month’s issue of Shock, some interesting experimental and clinical articles are presented that will be put in a wider perspective in this commentary. The articles can be grouped into three major areas: sepsis signaling, cardiopulmonary interaction, and hypertonic saline and prevention of abdominal compartment syndrome. We will discuss these articles in the light of current knowledge and clinical applicability.
SCALE DEVELOPMENT Theory and Applications Third Edition

The book present complex concepts in a way that helps students understand the logic underlying the creation, use, and evaluation of measurement instruments and develop a more intuitive feel for how scales work. Robert F. DeVellis demystifies measurement by relating it to familiar experiences and by emphasizing a conceptual rather than a strictly mathematical understanding.
Pocket Book of English for Graphic Technique As Learning Source and Medium of English for Graphic Technique Course

The rapid growth of information technology, especially by using devices, linear with practical needs, convenience, and affordability in learning media. Today, the presence of android-based applications is quite widespread, especially in the discipline of educational media and overcoming limitations in the context of learning. Pocket books are a source and medium of learning that, if packaged in a practical and sophisticated way, will be able to provide solutions to learning problems as well as to supply impetus for student interest and learning outcomes. This research aims to develop learning resources and learning media in the sort of a decent and useful Android-based pocket book application as an effort to increase student interest and learning achievement. The research method used is R & D with research subjects, namely students of the Department of Graphic Technique in semester 2. The data collection technique in this research was in the kind of documentation by organizing learning materials based on syllabus through relevant book references and conducting FGD with graphic and ESP experts. Furthermore, to ensure that the pocket book application is feasible, validation was carried out by 6 validators with a value of 4.61 with an excellent category then tested with students’ response of 4.57 with a very positive category.Therefore, this application is proven to be able to accommodate the needs of resources and media for learning English for students because it can be a substitute for books that are easy to carry and can be used practically without limitation of time and place.
Pengaruh Program Diabetes Self Management Education Terhadap Manajemen Diri Pada Penderita Diabetes Mellitus Tipe 2

Abstrak Diabetes Self-Management Education (DSME) merupakan salah satu metode pengelolaan diabetes secara berkelajutan dengan memfasilitasi pengetahuan, dan ketrampilan. Tujuan penelitian ini adalah untuk mengetahui pengaruh DSME terhadap manajemen diri penderita DM tipe 2. Penelitian quasi-experimental design ini menggunakan pendekatan pre and post test non equivalent gruop design. Sampel terdiridari 66 responden yang dipilih secara purposive sampling (33 responden kelompok intervensi dan 33 responden kelompok kontrol). Hasil penelitian menunjukkan mayoritas responden adalah perempuan (72.7% kelompok intervensi dan 69.7% kelompok kontrol), berusia 36-45 tahun (78.8% kelompok intervensi dan 87.9% kelompok kontol). Analisis hasil penelitian juga menunjukkan DSME berpengaruh terhadap peningkatan pengetahuan (p=0.000), peningkatan pola makan (p=0.000), peningkatan latihan fisik (p=0.001), peningkatan terapi farmakologis (p=0.000) dan peningkatan monitoring guladarah (p=0.000) pada pasien DM tipe 2 di Puskesmas Trienggadeng Kecematan Trienggadeng Kabupaten Pidie Jaya. Berdasarkan hasi lpenelitian ini dapat disimpulkan bahwa DSME dapat bermanfaat bagi pasien DM tipe 2 untuk mengontrol dan mengelola penyakit yang dialaminya. Kata Kunci : DSME, Pengetahuan, Pola Makan, Latihan Fisik, Monitoring Gula Darah, Terapi Farmakologis Abstract Diabetes Self-Management Education (DSME) is a method of managing diabetes continuously by facilitating knowledge and skills. The purpose of this study was to find out the effect of DSME on self-management in patients with DM type 2. This study used quasi-experimental design with pre and posttest nonequivalent group design approach. The sample consisted of 66 respondents selected with purposive sampling technique (33 respondents for intervention group and 33 respondents for control group). The result of the study showed that the majority of respondents were female (72.7% for intervention group and 69.7% for control group), and 36-45 years old (78.8% for intervention group and 87.9% for control group). The analysis of the result of study showed that DSME affected the increasing of knowledge (P=0.000), dietary habit (P=0.000), physical exercise (P=0.001), pharmacologic therapy (P=0.000), and blood sugar monitoring (P=0.000) in the patients with DM type 2 in Trienggadeng Community Health Center of Trienggadeng Sub-district of Pidie Jaya Regency. Based on the result of this study, it can be concluded that DSME is beneficial for patients with DM type 2 to control and manage their disease. Keywords : DSME, Knowledge, Dieatary Habit, Physical Exercise, Blood Sugar Monitoring, Pharmacologic Therapy
A review of assessment methods for cellular automata models of land-use change and urban growth

ABSTRACT Cellular automata (CA) models are in growing use for land-use change simulation and future scenario prediction. It is necessary to conduct model assessment that reports the quality of simulation results and how well the models reproduce reliable spatial patterns. Here, we review 347 CA articles published during 1999–2018 identified by a Scholar Google search using ‘cellular automata’, ‘land’ and ‘urban’ as keywords. Our review demonstrates that, during the past two decades, 89% of the publications include model assessment related to dataset, procedure and result using more than ten different methods. Among all methods, cell-by-cell comparison and landscape analysis were most frequently applied in the CA model assessment; specifically, overall accuracy and standard Kappa coefficient respectively rank first and second among all metrics. The end-state assessment is often criticized by modelers because it cannot adequately reflect the modeling ability of CA models. We provide five suggestions to the method selection, aiming to offer a background framework for future method choices as well as urging to focus on the assessment of input data and error propagation, procedure, quantitative and spatial change, and the impact of driving factors.
Emoji Powered Capsule Network to Detect Type and Target of Offensive Posts in Social Media

This paper describes a novel research approach to detect type and target of offensive posts in social media using a capsule network. The input to the network was character embeddings combined with emoji embeddings. The approach was evaluated on all three subtasks in Task 6 - SemEval 2019: OffensEval: Identifying and Categorizing Offensive Language in Social Media. The evaluation also showed that even though the capsule networks have not been used commonly in natural language processing tasks, they can outperform existing state of the art solutions for offensive language detection in social media.
Understanding the Quality Determinants that Influence the Intention to Use the Mobile Learning Platforms: A Practical Study

There is a widespread use of Internet technology in the present times, because of which universities are making investments in Mobile learning to augment their position in the face of extensive competition and also to enhance their students’ learning experience and efficiency. Nonetheless, Mobile Learning Platform are only going to be successful when students show acceptance and adoption of this technology. Our literature review indicates that very few studies have been carried out to show how university students accept and employ Mobile Learning Platform. In addition, it is asserted that behavioral models of technology acceptance are not equally applied in different cultures. The purpose of this study is to develop an extension of Technology Acceptance Model (TAM) by including four more constructs: namely, content quality, service quality, information quality and quality of the system. This is proposed to make it more relevant for the developing countries, like the United Arab Emirates (UAE). An online survey was carried out to obtain the data. A total of 221 students from the UAE took part in this survey. Structural equation modeling was used to determine and test the measurement and structural model. Data analysis was carried out, which showed that ten out of a total of 12 hypotheses are supported. This shows that there is support for the applicability of the extended TAM in the UAE. These outcomes suggest that Mobile Learning Platform should be considered by the policymakers and education developers as being not only a technological solution but also as being new e-learning platform especially for distance learning students.
UAV-Aided Two-Way Mobile Relaying Systems

This letter investigates a two-way mobile relaying strategy where a relay mounted on a unmanned aerial vehicle (UAV) helps bidirectional communication of a pair of ground nodes (GNs). In order to deal with the minimum average rate maximization problem between two GNs, UAV trajectory, transmit power, and bandwidth are jointly optimized by fully utilizing the mobility of UAV and communication resources. To solve this problem, we apply a successive convex approximation technique for non-convex constraints by introducing new surrogate functions. Since all variables are jointly updated at the same iteration, we can yield a Karush-Kuhn-Tucker (KKT) point at the convergence point from the proposed algorithm. The effectiveness of the proposed algorithm is demonstrated in numerical results.
Real to H-Space Autoencoders for Theme Identification in Telephone Conversations

Machine learning (ML) and deep learning with deep neural networks (DNN), have drastically improved the performances of modern systems on numerous spoken language understanding (SLU) related tasks. Since most of current researches focus on new neural architectures to enhance the performances in realistic conditions, few recent works investigated the use of different algebras with neural networks (NN), to better represent the nature of the data being processed. To this extent, quaternion-valued neural networks (QNN) have shown better performances, and an important reduction of the number of neural parameters compared to traditional real-valued neural networks, when dealing with multidimensional signal. Nonetheless, the use of QNNs is strictly limited to quaternion input or output features. This article introduces a new unsupervised method based on a hybrid autoencoder (AE) called real-to-quaternion autoencoder (R2H), to extract a quaternion-valued input signal from any real-valued data, to be processed by QNNs. The experiments performed to identify the most related theme of a given telephone conversation from a customer care service (CCS), demonstrate that the R2H approach outperforms all the previously established models, either real- or quaternion-valued ones, in term of accuracy and with up to four times fewer neural parameters.
Multilateral well modeling from compartmentalized reservoirs

The existence of compartmentalization in oil and gas fields have been a major industry challenge for decades. This phenomenon introduces some complexity and uncertainty in the prediction of well productivities and the overall hydrocarbon recovery factor. In the past, multiple vertical wells were drilled to increase recovery. The recent advancement of the multilateral and completions technology made multilateral wells a viable alternative to produce multiple reservoir compartments, most especially offshore. Reservoir compartments may possess a set of unique characteristics, such as average pressures, thicknesses, permeabilities, and porosity distribution. This variation in properties introduced complex dynamics in forecasting the commingled production figures and overall recovery factor from complex reservoir structures using advanced well systems. Existing analytical and semi-analytical productivity models due to the simplifying assumptions in their development are only suitable for first approximations and early estimates in field applications. In this work, a comparison is made between the more widely known finite difference numerical method and the finite volume numerical discretization method in field productivity predictions. The Matlab Reservoir Simulation Toolbox (MRST) which is a collection of open source codes, based on the finite volume discretization methodology is used to develop the reservoir compartment and multilateral well model used in this study. We investigate the pressure drop behavior over time through the lateral for a conventional well completion and compare with the pressure drop behavior for a smart well completion with downhole flow control devices for flow control and optimization. Several cases of compartmentalized reservoirs with faults of varying orientation and sealing capacity is then investigated. The production profile results obtained from the base reservoir case from MRST is compared to those from the IMEX simulator tool in CMG (Computer Modeling Group), a commercial reservoir simulator, based on the finite difference numerical discretization method. The results we obtain show a more accurate production profile prediction based on the finite volume method over the finite difference method, as expected. The ability of the simulation toolbox as well as the importance of using an improved and more efficient numerical discretization scheme in solving an increasingly complex array of reservoir structures and advanced well geometries, with multiphase fluid flow is demonstrated. Finally, an adjoint gradient – based method of optimization implemented in the toolbox is used to investigate the optimization potential of using the smart well completions versus a conventional well completion with the net present value as the objective function. Results obtained show that an investment in smart completions for the multilateral well ultimately yields a higher net cash flow and net present value over a conventional well of equivalent length designed without smart completions.
A Systematic literature Review of the Application of Capability Approach in the ICT4D studies

(( The applicability of the Capability approach (CA) continue to be a lingering problem in the Information and Communication Technology for Development (ICT4D) studies. The aim of this systematic literature review (SLR) is to review the use of the CA and its application in the ICT4D studies. The study synthesized literature from Three ICT4D journals: The Information Technologies & International Development (ITID), Electronic Journal of Information Systems in Developing Countries (EJISDC) and Information Technology for Development (ITD) . We reviewed articles published be-tween January 2004 to January 2019. The study reveals a mismatch and misalignment on the understanding of some of the concepts of the CA such as development; there seems to be lack of dearth knowledge on development and its applicability in the ICT4D domain. This calls for a further examination and contextualisation of the concepts of the CA.