Miracle-dz/UniFAR-FTUs huggingface dataset:
Parallel Sorting on the Heterogeneous AMD Fusion Accelerated Processing Unit

Parallel Sorting on the Heterogeneous AMD Fusion Accelerated Processing Unit Michael Christopher Delorme Master of Applied Science Graduate Department of Electrical and Computer Engineering University of Toronto 2013 We explore efficient parallel radix sort for the AMD Fusion Accelerated Processing Unit (APU). Two challenges arise: efficiently partitioning data between the CPU and GPU and the allocation of data in memory regions. Our coarse-grained implementation utilizes both the GPU and CPU by sharing data at the begining and end of the sort. Our fine-grained implementation utilizes the APU’s integrated memory system to share data throughout the sort. Both these implementations outperform the current state of the art GPU radix sort from NVIDIA. We therefore demonstrate that the CPU can be efficiently used to speed up radix sort on the APU. Our fine-grained implementation slightly outperforms our coarse-grained implementation. This demonstrates the benefit of the APU’s integrated architecture. This performance benefit is hindered by limitations in the APU’s architecture and programming model. We believe that the performance benefits will increase once these limitations are addressed in future generations of the APU.
A method for identifying predictive markers of mental illness in social media data

Undiagnosed mental illness poses a significant health risk. In-person screenings to identify individuals at-risk of mental illness are expensive, time-consuming, and often inaccurate. This report presents an array of computational methods that can be used to identify predictive markers of mental illness specifically, depression and Post-Traumatic Stress Disorder by scanning and interpreting text and images posted on social media. Separate analyses of Twitter and Instagram data are presented. Predictive features were extracted from social media posts (NTwitter = 279,951, NInstagram = 43,950) using a variety of techniques, including color analysis, face detection, semantic analysis, and Natural Language Processing. Resulting models successfully discriminated between depressed and healthy content, and compared favorably to general practitioners’ average success rates in diagnosing depression. Results held even when the analysis was restricted to content posted before first depression diagnosis. In the case of Twitter data, state-space temporal analysis suggests that onset of depression may be detectable from Twitter data several months prior to diagnosis. These methods offer a datadriven, predictive approach for early screening and detection of mental illness.
Pattern Discovery in Internet Background Radiation

Internet Background Radiation (IBR) is observed in empty network address spaces. No traffic should arrive there, but it does in overwhelming quantities, gathering evidences of attacks, malwares and misconfigurations. The study of IBR helps to detect spreading network problems, common vulnerabilities and attack trends. However, network traffic data evolves quickly and is of high volume and diversity, i.e., an outstanding big data challenge. When used to assist network security, it also requires the online classification of dynamic streaming data. In this paper, we introduce an AGgregation & Mode (AGM) vector to represent network traffic. The AGM format characterizes IP hosts by extracting aggregated and mode values of IP header fields, and without inspecting payloads. We performed clustering and statistical analysis to explore six months of IBR from 2012 with the AGM mapping. The discovered patterns allow building a classification of IBR, which identifies phenomena that have been actively polluting the Internet for years. The AGM representation is light and tailored for monitoring and pattern discovery. We show that AGM vectors are suitable to analyze large volumes of network traffic: they capture permanent operations, such as long term scanning, as well as bursty events from targeted attacks and short term incidents.
Uji Keterbacaan pada Pengembangan Buku Ajar Kalkulus Berbantuan Geogebra untuk Meningkatkan Kemampuan Pemecahan Masalah dan Representasi Matematis

Kalkulus Diferensial merupakan salah satu mata kuliah yang didapat mahasiswa di semester awal perkuliahan dan merupakan prasarat dari berbagai mata kuliah di semester selanjutnya. Jika mahasiswa kurang menguasai materi Kalkulus Diferensial, dimungkinkan mahasiswa akan mengalami kesulitan di berbagai mata kuliah yang menggunakan Kalkulus Diferensial sebagai prasaratnya. Karakteristik mata kuliah Kalkulus Diferensial yang banyak menggunakan grafik dan gambar membuat materi Kalkulus Diferensial lebih mudah disampaikan dengan bantuan Software Geogebra. Untuk itu pengembangan Buku Ajar Kalkulus Diferensial Berbantuan Software Geogebra dirasa perlu. Langkah-langkah pengembangan Buku Ajar Kalkulus Diferensial Berbantuan Geogebra menggunakan 4-D Model, di mana pada salah satu langkahnya adalah uji keterbacaan. Dari hasil uji keterbacaan didapatkan bahwa Buku Ajar Kalkulus Diferensial Berbantuan Geogebra ini valid dan dapat digunakan untuk langkah selanjutnya yaitu ujicoba di kelas.
Constrained Time-Dependent Adaptive Eco-Routing Navigation System

Eco-routing is a vehicle navigation method that selects those paths to a destination that minimize fuel consumption, energy consumption or pollutant emissions. It is one of the techniques that attempt to lower vehicle's operational cost and environmental footprint. This work reviews the current eco-routing methods and proposes a new method designed to overcome their shortcomings. Most current methods assign every road in the road network some constant cost that represents either vehicle's consumption there or the amount of emitted pollutants. An optimal routing algorithm is then used to find the path that minimizes the sum of these costs. Various extensions are considered in the literature. Constrained eco-routing allows imposing limits on travel time, energy consumption, and pollutant emissions. Time-dependent eco-routing allows routing on a graph with costs that are functions of time. Adaptive eco-routing allows updating the eco-routing solution in case it becomes invalid due to some unexpected development on the road. There exist published optimal eco-routing methods that solve either the time-dependent eco-routing, or constrained eco-routing, or adaptive eco-routing. Each comes with considerably higher computational overhead with respect to the standard eco-routing and, to author's best knowledge, no published method supports the combination of all three: constrained time-dependent adaptive eco-routing. It is argued in this work that the routing costs are uncertain because of their dependence on immediate traffic around the vehicle, on driver's behavior, and other perturbations. It is further argued that since these costs are uncertain, there is little benefit in using optimal routing because the optimality of the solution holds only as long as the routing costs are correct. Instead, an approximation method is proposed in this work. The computational overhead is lower since the solution is not required to be optimal. This enables the constrained time-dependent adaptive eco-routing.
Application of a distributed 2D overland flow model for rainfall/runoff and erosion simulation in a Mediterranean watershed

Soil erosion has reemerged as an environmental problem associated with climate change that requires the help of simulation tools for forecasting future consequences. This topic becomes even more relevant in Mediterranean catchments due to the highly variable and irregular rainfall regime. Hence, an approach that includes the rainfall/runoff and erosion phenomena is required for quantifying the amount of soil the catchments are transferring to the rivers. As the calibration process of the infiltration and erosion parameters can become cumbersome in terms of iterations to the optimal values to fit experimental data, a Simplified Catchment Model (SCM) is introduced as a first approach. The set of tuning constants that provides the best fit are used as input for re-calibrating the parameters by means of the simulation of the real catchment. The modeling effort here presented opens its application to the analysis of the hydro-sedimentary processes at larger temporal and spatial scales.
Test Case Selection and Prioritization for Regression Testing using Fault Severity

Regression testing is a significant but a very expensive testing process . Test case prioritization is a technique to schedule and execute the test cases in such an order that results in increasing their ability to meet some performance goal. One of the main goal is to increase the rate of fault detection –i.e. to detect the faults as early as possible during the testing process. Test case prioritization is used to minimize the expenses of regression testing. This paper proposes a technique to select and prioritize the test cases and results in improving the rate of fault detection.
Employee Acceptance of Employer Control Over Personal Devices

Organizations face new and growing security challenges as consumer technology continues to be integrated into organizational workflows. Bring your own device (BYOD) is a phenomenon that is here to stay; however, securing employee’s personally-owned devices may require the organizations to consider exerting some control over the employee’s device. For organizations to secure access to their sensitive information in this way, they must first garner the employee’s consent. This research seeks to examine employee acceptance of employer control by constructing a model of employee acceptance based upon the extant acceptance literature. The model is then empirically tested through the use of structural equation modeling. The results indicated that social influence and to a lesser extent habit play a crucial role in employee’s desire to accept employer control over personally owned devices.
Just-in-time vehicle scheduling with capacity constraints

ABSTRACT This article treats a scheduling problem where timely departures of vehicles executing Just-In-Time (JIT) deliveries between a single supplier and a single receiver along with the assignment of supplies to vehicles are to be determined. This problem, for instance, arises in the automotive industry where parts are to be delivered JIT from a central distribution center to a car manufacturer. We define different subproblems and provide an analysis of computational complexity along with suitable solution procedures. Furthermore, we apply these procedures to an industry case, so that managerial implications are revealed; e.g., with regard to the impact of standardized containers on delivery costs.
Multi-touch and Tangible Interface: Two Different Interaction Modes in the Same System

We present here a system built around the idea of giving to several users the possibility to interact with a cheap and solid hardware, at the same time, using natural gestures supported by an intuitive user interface. A projector and a camera are placed underneath a Plexiglas sheet, framed with an array of infrared LEDs, all set into a wooden table box. This allows for multiple users (up to four or five) to freely move around the box and manipulate the objects retro-projected on the screen, using a tangible interface designed aiming at offering few simple operations: geometric transforms (rotations, translations and scales), drawing, erasing and color se-lections. All of these are executed through the use of either a cus-tom built IR LED pen and/or directly the fingers. The main purpose of the project is to offer an instrument of tangible interaction to classrooms of näıve users (i.e.: neither technology nor science professors and students) in a university environment.
Une méthode de réduction exacte pour la segmentation par graph cuts

Les graph cuts sont desormais un standard au sein de la communaute de la vision par ordinateur. Neanmoins, leur grande consommation memoire reste un probleme majeur : les graphes sous-jacents contiennent des milliards de noeuds et davantage d'arcs. Excepte quelques methodes [14, 10, 5] exactes, les heuristiques presentes dans la litterature ne permettent d'obtenir qu'une solution approchee [12, 8]. Dans un premier temps, nous presentons une nouvelle strategie pour reduire exactement ces graphes : le graphe est construit en ajoutant les noeuds qui satisfont localement une condition donnee et correspond a une bande etroite autour des contours de l'objet a segmenter. Les experiences presentees pour segmenter des images en niveaux de gris et en couleur mettent en evidence une faible consommation memoire tout en garantissant une faible distance sur les segmentations. Nous presentons aussi une application de cette methode pour segmenter des tumeurs dans des images scanner.
Hadoop Application Architectures

Get expert guidance on architecting end-to-end data management solutions with Apache Hadoop. While many sources explain how to use various components in the Hadoop ecosystem, this practical book takes you through architectural considerations necessary to tie those components together into a complete tailored application, based on your particular use case.To reinforce those lessons, the books second section provides detailed examples of architectures used in some of the most commonly found Hadoop applications. Whether youre designing a new Hadoop application, or planning to integrate Hadoop into your existing data infrastructure, Hadoop Application Architectures will skillfully guide you through the process.This book covers:Factors to consider when using Hadoop to store and model dataBest practices for moving data in and out of the systemData processing frameworks, including MapReduce, Spark, and HiveCommon Hadoop processing patterns, such as removing duplicate records and using windowing analyticsGiraph, GraphX, and other tools for large graph processing on HadoopUsing workflow orchestration and scheduling tools such as Apache OozieNear-real-time stream processing with Apache Storm, Apache Spark Streaming, and Apache FlumeArchitecture examples for clickstream analysis, fraud detection, and data warehousing
Conception D’une Architecture Pour La Composition Des Services Web Byzantins

Existing Byzantine-resilient replication protocols satisfy two standard correctness criteria, safety and liveness, even in the presence of Byzantine faults. The performance of these protocols is most commonly assessed in the absence of processor faults and is usually good in that case. However, faulty processes can significantly degrade the performance of some protocols, limiting their practical utility in adversarial environments. This work demonstrates the extent of performance degradation possible in some existing protocols that do satisfy liveness and that do perform well absent Byzantine faults. We propose a new Byzantine fault-tolerant replication protocol that meets the new correctness criterion and evaluate its performance in fault-free executions and when under attack.
Microarray Gene Retrieval System Based on LFDA and SVM

The DNA microarray technology enables the biologists to observe the expressions of multiple thousands of genes in parallel fashion. However, processing and gaining knowledge from the voluminous microarray gene data is serious issue. It is necessary for the biologists to retrieve the required data in a reasonable time. In order to address this issue, this work presents a gene retrieval system, which is based on feature dimensionality minimization and classification of the microarray gene data. The feature dimensionality minimization is achieved by Local Fisher Discriminant Analysis (LFDA), which inherits the merits of both Fisher Discriminant Analysis (FDA) and Locality Preserving Projection (LPP). Support Vector Machine (SVM) is employed as the classifier to classify between the genes. The LFDA is chosen for reducing the dimensionality of the features, owing to its better performance on multimodal data. The SVM is trained with the feature dimensionality reduced microarray gene data, which improves the efficiency and overthrows the computational complexity. The performance of the proposed approach is compared with the LPP and FDA. Additionally, the performance of SVM is compared with the k-Nearest Neighbour (k-NN) classifier. The combination of LFDA and SVM serves better in terms of accuracy, sensitivity and specificity.
OPTIMUM CHOICE OF GAS UNIT GEOMETRICAL CHARACTERISTICS TO ENHANCE ITS EFFICIENCY

Automatic weapons are considered as an internal combustion engine, where the driving energy is supplied from the burning of the smokeless propellant charge, it burns producing tremendous power of hot gases. This power is extensively used in driving of projectile through the barrel, and the motion of weapon mechanisms that leads to the full automation of weapon functions. In order to achieve the second goal of gases usage, a constructional arrangement is fixed on weapon barrel, this arrangement allows gases to branch and drive the main moving weapon component, this component is known as the breech block carrier [1]. The motion of which is responsible of moving all other mechanisms in automatic weapon. The regulations and controlling over the output from the gas unit are affected basically by the changes of the geometrical characteristics of the gas unit. In this work, the variations of these geometrical parameters of the gas unit and their reflections on its efficiency have been studied. The present study facilitates the choice of effective parameters specially when designing a new weapon or modifying the power of an existing weapon. -----------------------------------------------------------------------------------------------* Egyptian armed forces 49 BL Proceedings of the 15 Int. AMME Conference, 29-31 May, 2012
Scaling seismic imaging algorithms to petascale computing and beyond

Matthieu Lefebvre, Ebru Bozdağ, Henri Calandra, Dimitri Komatitsch, Wenjei Lei, Daniel Peter, Herurisa Rusmanugroho, James Smith and Jeroen Tromp 1 Princeton University, ml15@princeton.edu 2 Université de Nice Sophia Antipolis, bozdag@princeton.edu 3 Total E&P, henri.calandra@total.com 4 CNRS, komatitsch@lma.cnrs-mrs.fr 5 Princeton University, lei@princeton.edu 6 ETH Zürich peterda@ethz.ch 7 Princeton University, herurisa@princeton.edu 8 Princeton University, jas11@princeton.edu 9 Princeton University, jtromp@princeton.edu
PCA and SVM Based on Multiple Kernels for Breast Cancer Classification

In this paper, we propose an efficient algorithm based on Principal Component Analysis (PCA) and Support Vector Machines(SVM) with multiple kernels in the process of breast cancer classification. The first step, we use PCA to project high dimensional breast cancer data into much lower dimensional space. Second, we use SVM with multiple kernels to classify the lower dimensional breast cancer data. Finally, the experimental and analytical results show that the proposed algorithm has a better performance than traditional SVM for breast cancer classification.
Application of the Haar Classifier in Obstacle Target Detection

Aimed at obstacles on 500kV high voltage transmission lines, such as shockproof hammers, spacers and suspension clamps, a strong classifier with high recognition accuracy and fast speed is proposed in this paper by using a collection of video files, extracting Haar-like features of the target object, and combining the optimal weak classifier through a filter cascade. In the process of feature extraction, extended Haar-like features and the integral graph method with high efficiency are applied. The experimental results show that the Haar classifier can achieve high accuracy and fast speed in the detection of obstacles on high voltage transmission lines.
Week texture objects pose estimation based on 3D model

This paper proposes a 3D pose estimation method for week texture objects, by performing point matching of a test image to a matched rendering image of an object rather than its 3D model. Give a 3D model of an object, we use an exemplar based 2D-3D matching method to estimate the coarse pose of the object. We first obtain the 2D rendering images of each view of the object using its 3D model, and build an exemplar based model using all the rendering images. For a test image, we then perform 2D-3D matching using the proposed model, and the rendering image with the highest score is the best match to the test image. The coarse pose can be obtained using the view parameters of the rending images. Finally, we perform point matching between the matched rendering image and the test image to estimate pose more accurately. The proposed coarse-to- fine pose estimation method can provide stronger constraint, which makes pose estimation more accurate. The experimental results demonstrate the effectiveness of the proposed method.
Simulation Service Providing

Die Konvergenz der Simulation und der Technologie des Internets, im Besonderen der Webtechnologie, ist bereits seit ca. 10 Jahren Teil der Forschung und Entwicklung im Bereich der Simulation. Die aktuellen Schlagworter in der Webtechnologie sind XML, Web Services, Grid Computing und Semantic Web. Sie bieten die Moglichkeit, webbasierte, verteilte Simulation zu automatisieren und unter Einhaltung von Qualitats- und Sicherheitsrichtlinien Simulation als Dienstleistung uber das Internet anzubieten. Die vorliegende Arbeit untersucht die Verbindung der Simulationstechnik mit der Technologie der Web Services um damit Simulation Service Providing zu ermoglichen. Es wurde ein Framework entwickelt, das es erlaubt, Simulationsdienste als Web Service anzubieten. Es wird eine kurze Einfuhrung in die Simulation und speziell die Webbasierten Simulation gegeben und die im Rahmen dieser Arbeit benotigten Internettechnologien werden ausfuhrlich erlautert. Danach wird das im Rahmen dieser Arbeit entwickelte SimASP Framework vorgestellt und anhand einiger Beispiele aus dem Bereich der Discrete Event Simulation validiert.
A Hybrid Grey Based Artificial Neural Network and C&R Tree for Project Portfolio Selection

Purpose The purpose of this paper is twofold: the selection of project portfolios through hybrid artificial neural network algorithms, feature selection based on grey relational analysis, decision tree and regression; and the identification of the features affecting project portfolio selection using the artificial neural network algorithm, decision tree and regression. The authors also aim to classify the available options using the decision tree algorithm. Design/methodology/approach In order to achieve the research goals, a project-oriented organization was selected and studied. In all, 49 project management indicators were chosen from A Guide to the Project Management Body of Knowledge (PMBOK Guide), and the most important indicators were identified using a feature selection algorithm and decision tree. After the extraction of rules, decision rule-based multi-criteria decision making matrices were produced. Each matrix was ranked through grey relational analysis, similarity to ideal solution method and multi-criteria optimization. Finally, a model for choosing the best ranking method was designed and implemented using the genetic algorithm. To analyze the responses, stability of the classes was investigated. Findings The results showed that projects ranked based on neural network weights by the grey relational analysis method prove to be better options for the selection of a project portfolio. The process of identification of the features affecting project portfolio selection resulted in the following factors: scope management, project charter, project management plan, stakeholders and risk. Originality/value This study presents the most effective features affecting project portfolio selection which is highly impressive in organizational decision making and must be considered seriously. Deploying sensitivity analysis, which is an innovation in such studies, played a constructive role in examining the accuracy and reliability of the proposed models, and it can be firmly argued that the results have had an important role in validating the findings of this study.