Miracle-dz/UniFAR-FTUs huggingface dataset:
SEQUENTIAL MONTE CARLO BASED DATA ASSIMILATION FRAMEWORK AND TOOLKIT FOR DYNAMIC SYSTEM SIMULATIONS

Assimilating real-time sensor data into simulations is an effective approach for improving predictive abilities. However, integrating complex simulation models, e.g., discrete event simulation models and agent-based simulation models, is a challenging task. That is because classical data assimilation techniques, such as Kalman Filter, rely on the analytical forms of system transition distribution, which these models do not have. Sequential Monte Carlo methods are a class of most extensively used data assimilation algorithms which recursively estimate system states using Bayesian inference and sampling technique. They are non-parametric filters and thus can work effectively with complex simulation models. Despite of the advantages of Sequential Monte Carlo methods, simulation systems do not automatically fit in data assimilation framework. In most cases, it is a difficult and tedious task to carry out data assimilation for complex simulation models. In addition, Sequential Monte Carlo methods are statistical methods developed by mathematicians while simulation systems are developed by researchers in particular research fields other than math. There is a need to bridge the gap of theory and application and to make it easy to apply SMC methods to simulation applications. This dissertation presents a general framework integrating simulation models and data assimilation, and provides guidance of how to carry out data assimilation for dynamic system simulations. The developed framework formalizes the data assimilation process by defining specifications for both simulation models and data assimilation algorithms. It implements the standard Bootstrap Particle Filtering algorithm and a new Sensor Informed Particle Filter, (SenSim) to support effective data assimilation. The developed framework is evaluated based on the application of wildfire spread simulation, and experiment results show the effectiveness of data assimilation. Besides the framework, we also developed an open source software toolkit named as Data Assimilation Framework Toolkit to make it easy for researchers to carry out data assimilation for their own simulation applications. A tutorial example is provided to demonstrate the data assimilation process using this data assimilation toolkit. INDEX WORDS: Data Assimilation, Sequential Monte Carlo methods, particle filter, simulation, sensor, general framework,toolkit SEQUENTIAL MONTE CARLO BASED DATA ASSIMILATION FRAMEWORK AND TOOLKIT FOR DYNAMIC SYSTEM SIMULATIONS
Multiscale study of the hepatic volume evolution after major hepatectomie in a porcine model

The aim of this project is double. Firstly, relationships between liver function, blood flows and architecture along with liver volume after major hepatectomy will be carefully analyzed by combining experiments and modeling on multiple levels and scales. These results will be used by mathematicians to develop an alternative evaluation procedure based on innovative tools in order to improve identification of possible or likely post-operative liver failure as early as possible. In this way a critical liver function decrease can be duly treated. Preliminary studies in pigs indicate that patients in which liver failure is likely to occur, may significantly benefit from an implantable surgical device capable of modulating the diameter of the portal vein hence controlling the portal venous pressure into the liver.
About TV Stream Macro-Segmentation Approaches and Results

In the last few decades, digital TV broadcasting has witnessed a huge interest from users against the traditional analog transmission. Many facilities have been already provided for capturing, storing, and sharing digital video content. However, navigating within TV streams is still considered as an important challenge to be faced. From a user point of view, a TV stream is represented as a sequence of programs (P) and breaks (B). While from a signal point of view, this stream is seen as a continuous flow of video and audio frames, with no external markers of the start and the end points of the included programs and no apparent structure. Most TV streams have no associated metadata to describe their structure, except the program guides produced by the TV channels. These program guides lack precision, since the TV channels cannot predict the exact duration of live programs for example. In addition, they don't provide any information about breaks like commercials or trailers. To cope with this problem, TV stream macro-segmentation or structuring has been proposed as a promising solution in the domain of video indexing. TV stream macro-segmentation consists in precisely detecting the first and the last frames of all programs and breaks (commercials, trailers, station identification, bumpersâ€¦) of a given stream, and then in annotating all these segments with some metadata. This can be performed by (1) analyzing the metadata provided with the stream (EPG, EITâ€¦), or (2) analyzing the audio-visual stream to detect the precise start and end of programs and breaks. In this chapter we aim at providing a survey of the existing approaches in this field. Then, we discuss and compare the results of the different approaches to see how to benefit from the advantages and to overcome the limitations of each of them.
Implicitation and Deletion in the Translation of Band of Brothers� Subtitle and Their Effect on the Translation Quality

The space for the subtitle has been a limited space for translators to accommodate the whole message from source to target language. However, the combination between textual multimodality and the techniques of how to make it more implicit should bridge the gap between message of the target text and the viewers. The viewers can still catch the meaning of the utterance not only based on the text but also from the other modality such as gesture, audio and other audio-visual effect on the scene.This study aims to find out the effect of implicitation and deletion, used in translating subtitle, on translation quality. The translation quality assessment includes the level accuracy, acceptability and readability of the text. This study is also a pilot study for the master degree thesis exploring the translation techniques, in this case implicitation and deletion, which are commonly used in subtitle to make the source text more implicit in the target text. Based on the findings of the research, the translators tends to apply implicitation rather than deletion to make the text more implicit.Keywords: implicitation, deletion, translation quality assessment
Cognitive memory effects on non-linear video-based learning

ii ACKNOWLEDGEMENTS I would like to express my genuine gratitude to Assistant Professor Craig M. Harvey, my advisor, for his support and encouragement in the research and preparation of this thesis. Through his leadership and supervision, I was able to increase my basic knowledge of analysis and theoretical research. constant support and guidance throughout the project from development and layout to conducting experiments. Appreciation is also extended to Joseph Niles for his help in leading experiments. Help extended by the LSU Department of Psychology and LSU Department of Industrial Engineering is acknowledged. I would like to express my gratitude towards my parents, sister, and fiancé whose devotion, support, and encouragement have inspired me and been my source of motivation for graduate school.
An Approach to Localization and Mapping Based on Centers of Box-detectors

For the next generation of intelligent robots to communicate with human and surroundings, it is necessary to contain both structures and semantics in the map. However, ignoring the relevance between structures and semantics, the substantial part of researches to address this problem focus on SLAM (Simultanous Localization and Mapping) and object detection in parallel. In this work, a novel approach is presented to fusing object detection and mapping together, which takes advantage of object detection based on CNN (Convolution Neural Network) and maps by utilizing these detected objects. The system is capable of capturing indoor comprehensive instances using a RGB-D camera and consistently building point cloud map based on these known objects' centers. The feasibility and scalability of the system are demonstrated through experiments in a real-world scene with abundant recognizable objects.
UML class diagram object-oriented metrics: algorithms of calculation

The paper proposes algorithms of the object oriented UML class diagram metrics calculation. Metrics include Average CBO, Average DIT, Average NOC, Average DAC, Average NLM, Average NOM, DAC2, SIZE2, and DSC. The proposed algorithms have become a part of the UML Refactoring tool providing UML class diagram analysis and transformation.
Classes of experiences: a high level approach to support content experts for the authoring of 3D environments

In spite of the evolution of computer graphics in recent years, the production of 3D environments still reflects an old situation: 3D modelers are often deputed to the whole construction of the 3D interactive worlds, even if in many cases the focus of 3D worlds is not simply on geometry and demands the specific contribution of experts belonging to different areas. This doesn’t permit a full exploitation of the new 3D interactive media potentialities. We propose a new approach to the production of 3D interactive environments, based on the concept of and on the re-definition of the role of .
RMark : an R Interface for analysis of capture-recapture data with MARK

RMark provides a formula-based interface for analysis of capture-recapture data with the program MARK. RMark reduces the development time of models and removes the potential for errors introduced from manual creation of design matrices in MARK. By providing an interface to MARK in R, all of the tools in R can be accessed to extract and manipulate data, plot or simulate. It also provides the potential to include MARK analysis in a reproducible document with Sweave and LTEX. I demonstrate RMark using the commonly analyzed European dipper data.
The Importance of Emotional Usability

As technology evolves rapidly, users of technology products and interactive computing systems are no longer only satisfied with the levels of product efficiency and effectiveness. Users are also looking for emotional satisfaction from using and interacting with the products. With powerful interactive features technology can potentially deliver more and more technology products and interactive computing systems are developed toward satisfying users both efficiently and emotionally. This article reviews different emotions incorporated into usability and design for technology products and interactive computing systems; and discusses the importance of emotional usability and the need for it.
Automatic Detection of Parameter Shielding for Test Case Generation.

Parameter shielding refers to the situation that one test parameter disables others in test execution. The quality of test case generation techniques is limited by the wide existence of parameter shielding. It is challenging to automatically find out conditions that cause the parameter shielding. This paper presents a novel approach for exploring the shielding conditions of test parameters. Our approach executes test inputs and collects runtime information of execution as features of test inputs. Then, a clustering algorithm is used to group test inputs with similar runtime information while a decision tree algorithm is built to extract the conditions in the groups. Finally, our approach identifies the shielding conditions based on the decision tree. Experiments on seven programs show that our approach can effectively detect the parameter shielding and the related conditions.
Consistency in stochastic networks

Stochastic networks are given by graphs, whose vertices (nodes, neurons or spins) can take one out of a finite number of states at any given time. The way each Vertex changes its State over time is determined by the edges connecting it with other vertices. The edges represent probabilistic dependencies. Updating is usually performed in a parallel or sequential way. Stochastic networks are used to model expert sytems; in this context confidence numbers are given as dependencies between vertices and the problem is to see, to what extent they are stochastically consistent. Given a graph and a set (or subset) of con fidence numbers, we give a procedure that, starting from these confidence numbers, leads to local characteristics which are con sistent with the graph.
Resolution of Traffic Queue with Vision-Based Vehicle Detector

This paper proposes methods for solving problems of vehicle queues. The main purpose of the proposed system is to improve the detection accuracy when vehicle queues occur in traffic jams which appear frequently in real traffic conditions. For vision-based vehicle detectors, vehicle candidates will be detected from the foreground image, which is the subtraction between the current image and the background image. The procedure includes background extraction and segmentations of moving objects. However, when traffic jams happen, vehicle queues will occur. This phenomenon will heavily impact the detection accuracy. This paper presents methods for detecting queues, extracting occluded vehicles and splitting vehicles in queues. Finally, the experimental results show the proposed methods can improve the detection ratio effectively both in the urban and on highway.
An Effective Method to Test the Effectiveness of a Reference Template

most, if not all, of the camera surveillance sensing applications require change detection. Change detection is one of the most important computer vision processes. It aims to detect the changes in a scene over time. Also, the moving objects detection and classification in a dynamic scene is an important area of research in computer vision. Such applications deal with visual sensors, usually stability of such sensors' is affected by weather conditions, it may experience some vibrations, and these vibrations may significantly affect the captured samples of a scene and cause defects in the results of samples comparison (pixel-by pixel operations). To form this comparison, it is necessary to spatially register the images samples, and thereby, to correct for relative translation (shifts) under small variations in both scale and rotations. The samples registration requires a template area to be used as a reference block for the assessment of shifting compensation parameters. This template should be unique relative to its surrounding area in order not to mis-match the reference block template with another block and it should have high contrast, it is vital to be easily identified from its surroundings area. In this research an effective method to test the effectiveness of the reference template is proposed. The obtained results from applying the proposed method indicated encouraging performance when implemented on different templates.
Real-Time Multivariate Data Visualization Using Preattentive Processing

A new method is presented for visualizing data as they are generated from real-time applications. These techniques allow viewers to perform simple data analysis tasks such as detection of data groups and boundaries, target detection, and estimation. The goal is to do this rapidly and accurately on a dynamic sequence of data frames. Our techniques take advantage of an ability of the human visual system called preattentive processing. Preattentive processing refers to an initial organization of the visual system based on operations believed to be rapid, automatic, and spatially parallel. Examples of visual features that can be detected in this way include hue, orientation, intensity, size, curvature, and line length. We believe that studies from preattentive processing should be used to assist in the design of visualization tools, especially those for which high speed target, boundary, and region detection are important. Previous work has shown that results from research in preattentive processing can be used to build visualization tools which allow rapid and accurate analysis of individual, static data frames. We extend these techniques to a dynamic real-time environment. This allows users to perform similar tasks on dynamic sequences of frames, exactly like those generated by real-time systems such as visual interactive simulation. We studied two known preattentive features, hue and curvature. The primary question investigated was whether rapid and accurate target and boundary detection in dynamic sequences is possible using these features. Behavioral experiments were run that simulated displays from our preattentive visualization tools. Analysis of the results of the experiments showed that rapid and accurate target and boundary detection is possible with both hue and curvature. A second question, whether interactions occur between the two features in a real-time environment, was answered positively. This suggests that these and perhaps other visual features can be used to create visualization tools that allow high-speed multidimensional data analysis for use in real-time applications. It also shows that care must be taken in the assignment of data elements to preattentive features to avoid creating certain visual interference effects.
Using FMI in a cloud-based Web Application for System Simulation

This paper presents a generic approach to combine cloud computing and system simulation. It shows the benefits of using FMI to deploy self-executing simulation units on multiple machines. Besides managing the calculation itself, we also present a web interface for uploading, managing and analyzing simulation models. To benefit from available hardware resources in the cloud, an engine is integrated which allows the definition of multiple simulations with different parameter sets in a single step.
Multiple clustering models and SVD-QR-based method for reliable gene expression profile analysis

Clustering-based methods have become popular approach to analysing gene expression profiles and to identifying a set of significant genes. However, their conclusions rely on an outcome of a single clustering model. This paper highlights the fact that each model can result in different gene profiles and sets of the most significant genes. Therefore, the result may not be accurate, and most importantly, not reliable. In order to identify a more reliable set of genes, different cluster models were created using fuzzy c-means clustering. Genes were then ranked using SVD-QR-based orthogonal least square method and selected from each cluster model. The results suggest that the genes identified are more reliable and provide more valuable information than those stratified by a single method.
Decoding of motor task difficulty and execution speed from EEE data with application to stroke rehabilitation

Brain-computer interfaces (BCIs) which provide an alternative channel between human brain and computer world are hope for many patients who su er from neurological diseases such as ALS and stroke. Scientists bene t from BCIs not only for communication but also for rehabilitation and e ective use of robotic exoskeletons. While healthy people can decide the speed level and the amount of force applied for daily activities, stroke patients are not able to transfer their intended movement speed and force magnitude to robotic limbs and mechanical devices. Even though brain signals potentially contain such information about task execution, existing BCI systems do not exploit that information. In this thesis, the possibility of decoding intended human activity speed and task di culty levels from an electroencephalography (EEG) based BCI system is investigated. In particular, two experimental setups are designed to collect data while subjects are performing two di erent tasks, and di erent protocols are proposed with their advantages and drawbacks to extract accurate information from these setups. Moreover, the problem of intention level detection is analyzed in response to task difficulty and speed level. As it is not possible to access the true intention level of a human being to execute a particular task, we use the di culty and speed of the executed task as proxies for the true intention level and then analyze the corresponding neural correlates. We have applied several classi cation protocols and our results indicate that some classi cation protocols are able to detect task speed and di culty from EEG signals.
Using cloudsim to model and simulate snapshot and D2D in cloud storage architecture

cloud storage service (STaaS) are increasingly gaining popularity and becoming one of the major service in cloud computing. Nowadays, although many cloud storage services available on the internet they aren’t convenient and they have many challenges to apply it and benefit from it. In this paper, we will implement the model data backup technologies of our cloud storage architecture on a cloud computing simulation toolkit known as cloudsim using snapshot Amazon ec2 and disk-to-disk. We will present the new concept for cloud storage architecture to be able to store and backup data through model controller in layer backup technology that can be accessed through the APIs. The implementation aims to increase the availability of data, modernizing data protection and reduce in loss of data in the cloud storage environments. Also this solution could provide new information and gives addition that is useful for further researches and open new opportunity for the future cloud storage.
Reinforced Intrusion Detection Using Pursuit Reinforcement Competitive Learning

Today, information technology is growing rapidly,all information can be obtainedmuch easier. It raises some new problems; one of them is unauthorized access to the system. We need a reliable network security system that is resistant to a variety of attacks against the system. Therefore, Intrusion Detection System (IDS) required to overcome the problems of intrusions. Many researches have been done on intrusion detection using classification methods. Classification methodshave high precision, but it takes efforts to determine an appropriate classification model to the classification problem. In this paper, we propose a new reinforced approach to detect intrusion with On-line Clustering using Reinforcement Learning. Reinforcement Learning is a new paradigm in machine learning which involves interaction with the environment.It works with reward and punishment mechanism to achieve solution. We apply the Reinforcement Learning to the intrusion detection problem with considering competitive learning using Pursuit Reinforcement Competitive Learning (PRCL). Based on the experimental result, PRCL can detect intrusions in real time with high accuracy (99.816% for DoS, 95.015% for Probe, 94.731% for R2L and 99.373% for U2R) and high speed (44 ms). The proposed approach can help network administrators to detect intrusion, so the computer network security systembecome reliable. Keywords : Intrusion Detection System, On-Line Clustering, Reinforcement Learning, Unsupervised Learning.