Miracle-dz/UniFAR-FTUs huggingface dataset:
Klusterisasi Pola Penyebaran Penyakit Pasien Berdasarkan Usia Pasien Dengan Menggunakan K-Means Clustering

Data mining merupakan suatu proses yang dapat digunakan untuk menemukan hubungan yang berarti dari sekumpulan data dengan memeriksa data yang tersimpan dalam media penyimpanan dengan menggunakan teknik pengenalan pola seperti teknik statistik dan matematika. Salah satu teknik pada data mining adalah teknik clustering. Teknik clustering merupakan teknik pengelompokkan record pada basis data berdasarkan kriteria tertentu. Tujuan dari penelitian ini adalah untuk menemukan pola kecenderungan penyakit pasien berdasarkan kelompok usia pasien. Penelitian ini dilakukan dengan menggunkan metode K-Means Clustering yang diimplementasikan pada data pasien pada suatu periode tertentu.
A COMPARATIVE STUDY OF SECURE SEARCH PROTOCOLS IN PAY - AS - YOU - GO CLOUDS

Cloud computing has emerged as a major driver in reducing the information technology costs incurred by organizations. In a cost-sensitive environment, an organization is willing tolerate a certain threshold of delay while retrieving information from the cloud. In this paper, we focus on the fundamental issues of cost efficiency and privacy. We review three keyword-based secure search protocols, namely Ostrovsky protocol, COPS protocol and EIRQ protocol. Each protocol offers an improvement over the basic keyword search used by today’s cloud users. In Ostrovsky, encryption offers privacy. In COPS, aggregation results in a limited amount of cost efficiency. In EIRQ, users can retrieve the desired percentage of files by assigning ranks to queries. This feature is useful if the user is only interested in a subset of all the matched files. We present a comparison of the performance of these protocols and conclude by listing the future work that could be carried out in this area.
Comparative study of k-anonymity algorithms for privacy preserving datamining

Nowadays, privacy issue becomes one of the main concerns of persons among their raw data. this happens at a time, when more and more historically public information is also electronically available. when these data are linked together, they provide an electronic shadow of a person or organization that is as identifying and personal as a fingerprint even when the information contains no explicit identifiers, such as name and phone number. other distinctive data, such as birth date and zip code, often combine uniquely and can be linked to publicly available information to re-identify individuals. however, there are several kanonymity algorithms available in the literature to solve that problem such as datafly and incognito. nevertheless, their study of performances in terms of efficiency and accuracy is lacking. in this study, we compare these two k-anonymity algorithms. so that users can select which algorithm is more suitable for their data mining. the finding shows that datafly gives higher overall efficiency. comparing with incognito which gives high accuracy. consistent good performance of incognito in kanonymity has made a promising k-anonymity techniques to be used in the privacy preserving technique.
Detective work: Supporting engineering students' learning when reading scientific texts

One of the novel ideas in teaching that heavily relies on current technology is the “flipped classroom” approach. In a flipped classroom the traditional lecture and homework sessions are inverted. Students are provided with online material in order to gain necessary knowledge before class, while class time is devoted to clarifications and application of this knowledge. The hypothesis is that there could be deep and creative discussions when teacher and students physically meet. This paper presents design considerations for flipped classrooms, and discusses how Moodle can facilitate communication and information sharing in such classrooms. Furthermore, it provides guidelines for supporting out-of-class instruction in the flipped model by using quizzes and feedback in Moodle, and comments on the potential to follow student use of resources by using Moodle reports. This paper concludes with a discussion of the opportunities and challenges when implementing the flipped model in a virtual learning environment (VLE) like Moodle.
Scalable Quota Management for High Performance Computing

Quotas are used in file systems to control use of available storage resources. When a large scientific application is run on a large cluster, quotas are essential to avoid catastrophic failures of other long running applications or itself due to lack of disk space. Quota management also contributes to system noise which can decrease the application throughput of High-Performance Computing (HPC) applications due to interrupts, barriers, or messages exchanged in a large cluster. In a digital cash model [4], quotas are tracked and enforced by a single quota server that issues cryptographically signed vouchers. Clients use these vouchers to allocate storage on any available object storage devices. The storage servers are able to ensure that clients do not use more storage than they are allowed using, in our model, only time-insensitive asynchronous communication with the quota server. The scheme does not require synchronized clocks and is able to recover from client and server failures. It is also resilient to transient failures of the various components including network connectivity. In previous work[4], the above scheme was evaluated using simulation; this work also used moving average window to perform prediction of the size of quotas to be requested from the quota server when replenishing quota. In this work, we investigate the quota management approach based on the digital cash model on Ceph, a peta-byte capable file system. We also use an ensemble based prediction algorithm to track a client’s needs for storage; this is especially important with bursty requests that characterize large scientific applications. While the quota management algorithm is scalable, the prediction algorithm further reduces the number of messages to the quota server, reducing jitter. We have evaluated our algorithm on two scientific applications, a fluid dynamics code and a micro
Classifying RNA secondary structures using support vector machines

CLASSIFYING RNA SECONDARY STRUCTURES USING SUPPORT VECTOR MACHINES by PrathyUsha Sunkara In contrast to DNA, RNA prevails as a single strand. As a consequence of small selfcomplementary regions, RNA commonly exhibits an intricate secondary structure, consisting of relatively short, double helical segments alternated with single stranded regions. The amount of sequence data available is rising rapidly day by day. One of the problems encountered on a specific molecule is finding the relevant data between the massive number of other sequences to be done by reading lists with a short description of all new entries in large databases already existing. One of the main objectives of this work is to take the extracted structures of aligned ribosomal RNA sequences and their secondary structures and cluster them. The proposal is to apply existing dimensionality reduction algorithms to these extracted structures and then cluster them in a reduced dimensional space using Support Vector Machines. CLASSIFYING RNA SECONDARY STRUCTURES USING SUPPORT VECTOR MACHINES by PrathyUsha Sunkara A Thesis Submitted to the Faculty of New Jersey Institute of Technology In Partial Fulfillment of Requirements for the Degree of Master of Science in Computer Science Department of Computer Science
An Aircraft Detection Method Based on Convolutional Neural Networks in High-Resolution SAR Images

In the field of image processing using Synthetic Aperture Radar (SAR), aircraft detection is a challenging task. Conventional approaches always extract targets from the background of an image using image segmentation methods. Nevertheless, these methods mainly focus on pixel contrast and neglect the integrity of the target, which leads to locating the object inaccurately. In this study, we build a novel SAR aircraft detection framework. Compared to traditional methods, an improved saliency-based method is proposed to locate candidates coarsely and quickly in large scenes. This proposed method is verified to be more efficient compared with the sliding window method. Next, we design a convolutional neural network fitting in SAR images to accurately identify the candidates and obtain the final detection result. Moreover, to overcome the problem of limited available SAR data, we propose four data augmentation methods comprising translation, speckle noising, contrast enhancement, and small-angle rotation. Experimental results show that our framework achieves excellent performance on the high-resolution TerraSAR-X dataset.
Amélioration du diagnostic d'une machine tournante par la sélection du vecteur d'entrée

Vibration monitoring is a nondestructive testing technique widely used in industry, automating its use represents an important gain for better maintainability of machines. In this work we propose an approach to improve the automated diagnosis of an industrial rotating machine with a selection of the input vector, indicators will be extracted from vibration signals. Hybrid techniques based on filter and wrapper will be used for selecting the input vector, combined with intelligent classifiers namely, a multilayer perceptron, a Radial basis function, a Bayesian classifier and a support vector machine for decisions making. The performance of the different combinations will be compared and discussed. Mots clefs : machines tournantes, diagnostic, intelligence artificielle, analyse vibratoire, selection d’indicateurs
The different levels of intent specifications : analysis and guidelines on tracing

The increase in the use of computers to control complex systems in which a failure can result in the loss of life and property has led to new safety issues, particularly when control processes are the joint responsibility of humans and machines. Intent specifications provide a clear, step-by-step process to developing systems allowing safety and human factors issues to be integrated into the design from the early stages of development. Additionally, the levels of an intent specification are tied to each other through tracing, which links design and implementation decisions to their underlying reasons and vice versa. An intent specification for the minimum safe altitude warning (MSAW) function of a new air traffic control system was successfully created, and it is compared to the function's existing specification documents. Furthermore, described is a step-by-step guide to tracing between the levels of intent specifications as well as guidance on distinguishing between what type of design and implementation information goes into each particular level. Thesis Supervisor: Nancy G. Leveson Title: MIT Professor of Aeronautics and Astronautics
Empirical methods for assessment of seismic system sensitivity

One of the basic performance characteristics of the seismic system is its sensitivity. An appreciation of the spatial variation of mmin is important for the correct interpretation of seismic data and the planning of future expansion of the seismic system. This paper will discuss the empirical evaluation of the system sensitivity applied to the data from two Australian mines. The method discussed here is reliant on enough seismic data being available and is per se not applicable to greenfields sites. A gridless method for the evaluation of mmin with respect to the distance to sensors is proposed as a stable and relatively easy first order approach to the evaluation of the system sensitivity that overcomes some of the problems of the other methods. This method is easy to understand and facilitate the conceptualisation of the influence of the sensor locations on the system sensitivity.
Two approaches for automating the tuning process of fuzzy logic controllers [PWR application]

The design and evaluation by simulation of two automatically tuned fuzzy logic controllers is presented. Typically, fuzzy logic controllers are designed based on an expert's knowledge of the process. However, this approach has its limitations in the fact that the controller is hard to optimize or tune to get the desired control action. In this paper are two methods to automate the tuning process: 1) using a simplified Kalman filter approach, and 2) using a suboptimal filter. Here, the objective is to enable the fuzzy logic controller to track a suitable reference trajectory, i.e., to learn from an expert's actions. To demonstrate an application of this approach, a PWR (pressurized water reactor) type nuclear reactor model (nonlinear), based on assumed point kinetics with six-delayed neutron groups, is controlled using a fuzzy logic controller that utilizes estimated temperatures from a one-delayed neutron group observer. Here, a previously designed robust optimal controller's response is used as a reference trajectory to determine automatically the rules for the fuzzy logic controller. The fuzzy logic controller displayed good stability and performance robustness characteristics for a wide range of operation. <<ETX>>
Support Vector Machine Text Classifier for Arabic Articles

In this book, we have implemented a support vector machine (SVM) text classifier for Arabic articles. Experimental results show that the SVM classifier outperformed Naive Bayesian (NB) and k-nearest neighbor (kNN) classifiers. We investigated the effectiveness of six state-of-the-art feature subset selection (FSS) methods, which are commonly used in text classification (TC) tasks, for our Arabic SVM text classification system. We implemented an Ant Colony Optimization Based-Feature Subset Selection (ACO Based-FSS) method for our Arabic SVM text classifier. The proposed FSS method adapted Chi-square statistic as heuristic information and the effectiveness of the SVM classifier as a guide to improving the selection of features for each category. Compared to the six state-of-the-art FSS methods, our ACO Based-FSS algorithm achieved better TC effectiveness. Evaluation used an in-house Arabic TC corpus that consists of 1445 documents independently classified into nine categories. The experimental results were presented in terms of macro-averaging precision, macro-averaging recall and macro-averaging F1 measures.
Detection of macular diseases in optical coherence tomography image

Vision decreasing and blindness are great threats to health, which can be caused by various kinds of macular diseases. Optical coherence tomography (OCT) is widely used in the diagnosis of macular disease. Ophthalmologists often need to check and analyse lots of OCT images and then give eye disease report for each image, which is time-consuming and inefficient. Thus, the automated classification method is necessary. In this paper, we proposed an approach to determine the status (normal or abnormal) of retina by OCT images. The proposed method can differentiate several kinds of macular diseases from normal image, including macular edema, macular hole and age-related macular degeneration. Besides the geometry features, our method takes advantage of two texture feature (local binary pattern histograms and histogram of oriented gradient) to improve the accuracy rate. The experimental results on a public available dataset demonstrate that our approach is effective and achieve higher accuracy (all accuracy >0.98, AUC can reach 1). Flowchart of the proposed detection method. GRAPHICAL ABSTRACT
Save Time With Software Patterns

Writing a pattern isn't easy (see the sidebar, "What is a Pattern? "). A pattern author has to justify the pattern by giving examples of how to use it in different domains, usually at least two. For example, the abstract factory-design pattern is documented in InterViews and ET++, and the Singleton design pattern is documented in Smalltalk-80, as well as the InterViews UI Toolkit. The author must also provide any related patterns so developers can understand their differences. If developers use the patterns together in some scenarios, the author has to document this in the related-patterns section of the pattern documentation.
Automatisierte Bewertung in der Programmierausbildung

Munster ; New York : Waxmann 2017, 417 S. - (Digitale Medien in der Hochschullehre; 6) Padagogische Teildisziplin: Medienpadagogik; Hochschulforschung und Hochschuldidaktik; als elektronischer Volltext verfugbar
Building a scientific workflow framework to enable real‐time machine learning and visualization

Nowadays, we have entered the era of big data. In the area of high performance computing, large‐scale simulations can generate huge amounts of data with potentially critical information. However, these data are usually saved in intermediate files and are not instantly visible until advanced data analytics techniques are applied after reading all simulation data from persistent storages (eg, local disks or a parallel file system). This approach puts users in a situation where they spend long time on waiting for running simulations while not knowing the status of the running job. In this paper, we build a new computational framework to couple scientific simulations with multi‐step machine learning processes and in‐situ data visualizations. We also design a new scalable simulation‐time clustering algorithm to automatically detect fluid flow anomalies. This computational framework is built upon different software components and provides plug‐in data analysis and visualization functions over complex scientific workflows. With this advanced framework, users can monitor and get real‐time notifications of special patterns or anomalies from ongoing extreme‐scale turbulent flow simulations.
Comparison of two interleaving techniques for CDMA radio communications systems

For code-division multiple access (CDMA) radio communications systems, orthogonal signaling with noncoherent detection has been proposed when a reference phase is not available. Orthogonal signaling using Hadamard signals is considered. Performance of the channel using error-correcting coding together with bit-by-bit interleaving or Hadamard word-by-word interleaving is investigated. It is found for the same M-ary orthogonal signaling, at a given bit error ratio, the second technique with complete soft decision decoding gives significant reduction in the required signal-to-interference ratio over the first technique with slightly added complexity in the decoder. <<ETX>>
Quantifying uncertainties due to chemistry modelling – evaluation of tropospheric composition simulations in the CAMS model (cycle 43R1)

Abstract. We report on an evaluation of tropospheric ozone and its precursor gases in three atmospheric chemistry versions as implemented in the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS), referred to as IFS(CB05BASCOE), IFS(MOZART) and IFS(MOCAGE). While the model versions were forced with the same overall meteorology, emissions, transport and deposition schemes, they vary largely in their parameterisations describing atmospheric chemistry, including the organics degradation, heterogeneous chemistry and photolysis, as well as chemical solver. The model results from the three chemistry versions are compared against a range of aircraft field campaigns, surface observations, ozone-sondes and satellite observations, which provides quantification of the overall model uncertainty driven by the chemistry parameterisations. We find that they produce similar patterns and magnitudes for carbon monoxide (CO) and ozone (O3), as well as a range of non-methane hydrocarbons (NMHCs), with averaged differences for O3 (CO) within 10 % (20 %) throughout the troposphere. Most of the divergence in the magnitude of CO and NMHCs can be explained by differences in OH concentrations, which can reach up to 50 %, particularly at high latitudes. There are also comparatively large discrepancies between model versions for NO2, SO2 and HNO3, which are strongly influenced by secondary chemical production and loss. Other common biases in CO and NMHCs are mainly attributed to uncertainties in their emissions. This configuration of having various chemistry versions within IFS provides a quantification of uncertainties induced by chemistry modelling in the main CAMS global trace gas products beyond those that are constrained by data assimilation.
Research on Particle Swarm Optimization Algorithm Based on Cloud Computing Task Scheduling

Whether home or abroad, both resource allocation and task scheduling is one of the popular problems in the cloud computing environment, and swarm intelligence algorithm is a hot topic. Particle swarm optimization (PSO) is an important swarm intelligent algorithm in solving the task scheduling optimization problem. Based on the research of the basic PSO algorithm, this paper proposes the next improvement strategies: (1) The calculation method of the success value of single particle is improved, and the progressive speed of individual particles is improved; (2) Integrated the correlation between random factors, that improved the global optimization ability of PSO algorithm in the process of optimization, and avoided the particle trapping into the local best. The simulation results show that under the same conditions, the improved algorithm’s execution time is better than the sequential scheduling algorithm, the greedy algorithm, the basic PSO algorithm, the correlation PSO algorithm and the adaptive PSO algorithm for inertia weights.
Evaluation of Pedometer Performance Across Multiple Gait Types Using Video for Ground Truth

This dissertation is motivated by improving healthcare through the development of wearable sensors. This work seeks improvement in the evaluation and development of pedometer algorithms, and is composed of two chapters describing the collection of the dataset and describing the implementation and evaluation of three previously developed pedometer algorithms on the dataset collected. Our goal is to analyze pedometer algorithms under more natural conditions that occur during daily living where gaits are frequently changing or remain regular for only brief periods of time. We video recorded 30 participants performing 3 activities: walking around a track, walking through a building, and moving around a room. The ground truth time of each step was manually marked in the accelerometer signals through video observation. Collectively 60,853 steps were recorded and annotated. A subclass of steps called shifts were identified as those occurring at the beginning and end of regular strides, during gait changes, and during pivots changing the direction of motion. While shifts comprised only . 03% of steps in the regular stride activity, they comprised 10-25% of steps in the semi-regular and unstructured activities. We believe these motions should be identified separately, as they provide different accelerometer signals, and likely result in different amounts of energy expenditure. This dataset will be the first to specifically allow for pedometer algorithms to be evaluated on unstructured gaits that more closely model natural activities. In order to provide pilot evaluation data, a commercial pedometer, the Fitbit Charge 2, and three prior step detection algorithms were analyzed. The Fitbit consistently underestimated the total number of steps taken across each gait type. Because the Fitbit algorithm is proprietary, it could not be reimplemented and examined beyond a raw step count comparison. Three previously published step detection algorithms, however, were implemented and examined in detail on the dataset. The three algorithms are based on three different methods of step detection; peak detection, zero crossing (threshold based), and autocorrelation. The evaluation of these algorithms was performed across 5