Miracle-dz/UniFAR-FTUs huggingface dataset:
Data Assimilation for Spatial Temporal Simulations Using Localized Particle Filtering

As sensor data becomes more and more available, there is an increasing interest in assimilating real time sensor data into spatial temporal simulations to achieve more accurate simulation or prediction results. Particle Filters (PFs), also known as Sequential Monte Carlo methods, hold great promise in this area as they use Bayesian inference and stochastic sampling techniques to recursively estimate the states of dynamic systems from some given observations. However, PFs face major challenges to work effectively for complex spatial temporal simulations due to the high dimensional state space of the simulation models, which typically cover large areas and have a large number of spatially dependent state variables. As the state space dimension increases, the number of particles must increase exponentially in order to converge to the true system state. The purpose of this dissertation work is to develop localized particle filtering to support PFs-based data assimilation for large-scale spatial temporal simulations. We develop a spatially dependent particle-filtering framework that breaks the system state and observation data into sub-regions and then carries out localized particle filtering based on these spatial regions. The developed framework exploits the spatial locality property of system state and observation data, and employs the divide-and-conquer principle to reduce state dimension and data complexity. Within this framework, we propose a two-level automated spatial partitioning method to provide optimized and balanced spatial partitions with less boundary sensors. We also consider different types of data to effectively support data assimilation for spatial temporal simulations. These data include both hard data, which are measurements from physical devices, and soft data, which are information from messages, reports, and social network. The developed framework and methods are applied to large-scale wildfire spread simulations and achieved improved results. Furthermore, we compare the proposed framework to existing particle filtering based data assimilation frameworks and evaluate the performance for each of them. INDEX WORDS: Data assimilation, Particle filtering, Hard/Soft data, Spatial temporal simulations, Wildfire simulation. DATA ASSIMILATION FOR SPATIAL TEMPORAL SIMULATIONS USING LOCALIZED PARTICLE FILTERING
Metabolic Correction: Targeted Cost Effective Therapeutic Strategy to Improve Outcomes

Metabolic correction is a clinical strategy directed to address the root biochemical cause of disease that can be used with other treatments and has been proposed to be a cost effective alternative that can potentially reduce complications of the natural course of the disease as well as from the adverse drugs reactions and its costs. Metabolic correction is not part of the current medical guideline, but should they?
Learning to code: from procedural puzzle-based games to creative programming

Learning to code is integrated in a growing number of schools worldwide. However, the learning to code activities shows important differences according to the creative engagement of the learners in the activity. We identify five levels of learning to code activities: (1) teacher-centered explanations or tutorials; (2) procedural, step-by-step programming; (3) creative individual programming; (4) co-creative programming and (5) participatory co-creation of knowledge through programming.
An Empirical Study of the Determinants of Service Level Management and its Performance

Recently, Service Level Management(SLM) including SLA has been developing to manage the level of information systems service. Although previous SLA studies have provided the conceptual model and critical success factors, the studies have not tested the effectiveness of the SLA by way of the empirical study. In this study, we applied an innovation decision making theory to our model and tested an empirical study using of a firm survey data. We suggested a SLM model, which consists of SLA planning, application, management dimensions. We found that the SLM's management stage has an impact on performance, whereas SLM's planning and application has not. At each sub dimensional analysis, there are found that adequate SLA indicators in the application stage influences performance and change management and feedback & reporting in the management stage influence performance.
Context-aware Cloud Computing for Personal Learning Environment

Virtual learning means to learn from social interactions in a virtual platform that enables people to study anywhere and at any time. Current Virtual Learning Environments (VLEs) are normally institution centric and are designed to support formal learning, which do not support lifelong learning. These limitations led to the research of Personal Learning Environments (PLEs), which are learner-centric and provide lifelong access as well as the ability of a user to produce (share) and consume information resources easily. In this research, a context-aware cloud based PLE architecture is proposed, which is driven by a Context-Aware Engine to acquire, filter and interpret context information based on the preferences defined in user profile, where cloud computing is taken as service infrastructure. An illustrative personal learning scenario is investigated to demonstrate the proof of concept implementation. The results show the benefits of the proposed architecture on resource utilisation and user experience.
A fusion algorithm of remote sensing image based on discrete wavelet packet

Image fusion refers to the techniques that integrate complementary information from multiple image sensor data such that the new images are more suitable for the purpose of human visual perception and the compute processing tasks. In this paper, a new image fusion algorithm based on wavelet packet transform to fuse multisensor images is presented. Discrete wavelet transform (DWT) can offer a more precise way for image analysis, than other multi-resolution analysis. It decomposes an image into low frequency band and high frequency band in different level, and it can also be reconstructed gradually in different level. But this method only decomposes low frequency band in a higher scale, so that it omits some useful details of the images. In this paper, we present a new image fusion algorithm. In the algorithm, we use discrete wavelet packet transform (DWPT) to decompose and reconstruct the images. When images are merged in wavelet packet space, different frequency ranges are processed differently. It can merge information from original images adequately and improve abilities of information analysis and feature extraction. This image fusion is performed at the pixel level. In this fusion algorithm, a feature-based fusion rule is used to combine original subimages and to form a pyramid for the fused image. Through merging remote sensing images from multi-sensor to a same object by applying method of wavelet packet analysis, we have obtained a fused picture. In this paper, mutual information is employed as a means of objective assessing image fusion performance. The experiment results show that this fusion algorithm, based on wavelet packet transform, is an effective approach in image fusion area.
In Media We Distrust

The internet allows politicians to deliver controlled messages via campaign websites, but citizens often turn to other sources (e.g., news websites) for political information. Using the cognitive response model as a framework, this article considers how varying contexts may affect the way citizens process candidate messages. We experimentally test the interaction of media trust, media context, and message content on evaluation of and support for a candidate. A national adult sample viewed a candidate profile on either the candidate’s website or CNN.com, and the profile contained either subtle or vivid religious cues. Those high in media trust who saw the CNN profile rated the candidate highest, but only when it contained subtle religious cues. Our findings suggest that while message source and media trust can condition message effects, these factors have less influence in the presence of strong message cues.
A Hybrid Approach to Missing Data: Bayesian Neural Networks, Principal Component Analysis and Genetic Algorithms

The.problem. of.missing.data.in. databases.has. recently.been. dealt.with. through.the. use.computational. intelligence.The.hybrid.of. auto-associative.neural.networks.and. genetic.algorithms. has.proven. to.be. a.successful. approach.to. missing.data. imputation.Similarly,. two.auto-associative.neural.networks.are. developed.to.be.used. in.conjunction. with.genetic.algorithm.to. estimate.missing.data,. and.these. approaches.are. compared.to. a.Bayesian. auto-associative.neural.network.and. genetic.algorithm. approach. One.technique. combines.three. neural.networks. to.form. a.hybrid. auto-associative.network,. while.the. other.merges. principal.component. analysis.and. neural.networks. The.hybrid.of.the. neural.network.and. genetic.algorithm. approach.proves. to.be. the.most. accurate.when. estimating.one. missing.value,. while. a.hybrid.of.principal.component.and. neural.networks. is.more. consistent.and. captures.patterns. in.the. data more efficiently.
SPEED ENHANCEMENT OF DYNAMIC BEM ON INEXPENSIVE HARDWARE WITH PARALLEL FORTRAN 2008

The Boundary Element Method (BEM) has been extensively used in dynamic problems in structural engineering. The method is computationally intensive and traditionally depends on raw computer processing power to solve larger structures. However, advances on processor speed have stagnated due to physical limits. Instead, processing power is now based on parallel computing. The BEM can benefit from parallel computing, in dynamic problems in the Laplace transform domain, as the computation for each (complex) frequency is independent to each other, and can be executed concurrently by a different core or pro- cessor. The volume, in bytes, of the outcome of each frequency computation is small, so that it can be sent through a network with negligible overhead. Thus, the method does not need spe- cial hardware and it can be executed equally well by an ordinary, off-the-shelf, multi-core and/or multiprocessor computer, as well as by a typical computer cluster of low bandwidth. The method is implemented using coarrays as described in the upcoming Fortran 2008 stand- ard. The biggest advantage of Fortran 2008 is that remote memory is referred to directly, like an array with the instance identity as index, instead of having to call a subroutine for loading and storing data. A small but sufficient set of easily learned statements and constructs are defined, which make the program much clearer. The method is tested with typical dynamic problems and shows promising results.
A Survey Paper on Different Data Compression Techniques

This paper describes the different types of compression techniques such as lossless and lossy data compression. This survey paper has been written with the help of different types of algorithm like run length, Huffman coding, Shannon-Fano, Lempel ziv, vector quantization etc. The reference of these algorithms has been taken from various books and research papers onData compression. Todaydata compression is very useful in our life. The main purpose or aim of data compression is to compress any type of data that istransfer over the communication channel, because of the limited channel bandwidthanddata storage capacity. The use of lossless and lossy techniques for data compression means that the numbers of bits are reduced in the original information. By the use of lossless data compression there is no loss in the original information but while using lossy data compression technique some numbers of bits are loss.
ITC Ages Antibacterial Effects of Mouth Gargle on Dental Biofilm

Antibacterial effects confirmed of each mouth rinse on dental biofilm in vitro. In this study, six mouth rinses produced in Korea containing different ingredients (essential oil, cetylpyridinium chloride (CPC), benzethonium chloride (BC), dipotassium glycyrrhizinate (DPZ), and chlorhexidine gluconate (CHX)) were selected. We cultured S. mutans, F. nucleatum and P. intermedia and the plates were incubated with mouth rinses for 30 sec at room temperature according to the manufacturer's instructions. Antibacterial effects of mouth rinses were examined by the ATP, counted colony forming un its (CFU) and scanning electron microscope. SEM after mouth rinses were treated for 5 min on preformed biofilm HA disks, the effects of Hexamedine as an CHX ingredient were confirmed.
Verification of an Experimental Platform Integrating a Tobii Eyetracking System with the HiFi Game Engine

not more than 200 words) Playing a commercial PC or consol game is a highly visual activity, regardless of whether the purpose is entertainment or situated learning as discussed in the Serious Games field. If more information about the visual attention of the player can be recorded and easily analysed, important design information can be extracted. A range of different eyetracking equipment exists on the market and has been used in many studies over the years. However, very few studies describe dynamic stimuli involving the visual interaction of the user/player with a moving 3D object displayed on a computer screen. The reasons for this are that methods and software developed for eyetracking studies of static 2D stimuli are inappropriate for dynamic 3D stimuli, and manual analysis of dynamic 3D visual interaction is extremely time consuming. In order to address this, the authors have developed a software interface between the TobiiTM eyetracking system and the HiFi Game Engine for use in automated logging of dynamic 3D objects of gaze attention. This report describes the verification study performed to assess the performance of this integration between the eyetracker, logging tools and game engine. Detailed analysis shows effective results within the derived accuracy range, which is certainly sufficient for studies from a small scale to large scales necessary for extensive statistical analysis. The work presented in the report has been conducted in collaboration between FOI, Blekinge Institute of Technology and Gotland College.
A Simple and Fast Action Recognition Method Based on AdaBoost Algorithm

A novel feature representation method based on AdaBoost algorithm is put forward for action recognition in this paper. The method can not only adequately describe action in complex scenarios, but also select the most discriminative sample subset from a large amount of raw features of training data. So it can realize a double result, that is, reduce the recognition computational complexity and achieve a good recognition accuracy. The pyramid histogram of oriented gradient feature (PHOG) descriptor is utilized to represent raw feature data. In order to select most discriminative samples subset, AdaBoost algorithm is used to extract the raw feature data. The nearest neighbor classifier algorithm is utilized to test the proposed method on the UCF Sports database. Experiment results show that the method not only achieve the better recognition rate but also greatly improve the speed of recognition.
Atomic Layer Deposition of Nanostructured Materials for Energy and Environmental Applications

Atomic layer deposition (ALD) is a thin film technology that in the past two decades rapidly developed from a niche technology to an established method. It proved to be a key technology for the surface modification and the fabrication of complex nanostructured materials. In this Progress Report, after a short introduction to ALD and its chemistry, the versatility of the technique for the fabrication of novel functional materials will be discussed. Selected examples, focused on its use for the engineering of nanostructures targeting applications in energy conversion and storage, and on environmental issues, will be discussed. Finally, the challenges that ALD is now facing in terms of materials fabrication and processing will be also tackled.
Recognition of lane markings in panoramic images

The detection of road markings has many practical applications, such as advanced driver assistance systems, road maintenance and accurate GPS navigation. In this paper we propose an algorithm to detect and recognize road markings from panoramic images. Our algorithm consists of four steps. First, an inverse perspective mapping is applied to the panoramic image, and the potential road markings are segmented based on their intensity difference compared to the surrounding pixels. Second, we extract the distance between the center and the boundary at regular angular steps of each considered potential road marking segment into a feature vector. Third, each segment is classified using a Support Vector Machine (SVM). Finally, by modeling the lane markings, previous false positive detected segments can be rejected based on their orientation and position relative to the lane markings. Our experiments show that the system is capable of recognizing 93%, 95% and 91% of striped line segments, blocks and arrows respectively, as well as 94% of the lane markings.
A CMOS Compatible Bulk FinFET-Based Ultra Low Energy Leaky Integrate and Fire Neuron for Spiking Neural Networks

The fundamental building block of an artificial spiking neural network (SNN) is an element which can effectively mimic a biological neuron. There are several electronic and spintronic devices which have been demonstrated as a neuron. But the main concern here is the energy consumption and large area of those artificial neurons. In this letter, we propose and demonstrate a highly scalable and CMOS compatible bulk FinFET with an <inline-formula> <tex-math notation="LaTeX">${n}^{+}$ </tex-math></inline-formula> buried layer for ultra low energy artificial neuron using well calibrated TCAD simulations. The proposed device shows the signature spiking frequency versus input voltage curve of a biological neuron. The energy per spike of the integrate block of the proposed leaky integrate and fire (LIF) neuron is <inline-formula> <tex-math notation="LaTeX">${6.3}~\textit {fJ}$ </tex-math></inline-formula>/spike which is the minimum reported till date.
FOCUS: ADAPTING TO CRAWL INTERNET FORUMS

Internet is emergent exponentially and has become progressively more. Now, it is complicated to retrieve relevant information from internet. The rapid growth of the internet poses unprecedented scaling challenges for general purpose crawlers and search engines. In this paper, we present a novel Forum Crawler under Supervision (FoCUS) method, which supervised internet-scale forum crawler. The intention of FoCUS is to crawl relevant forum information from the internet with minimal overhead, this crawler is to selectively seek out pages that are pertinent to a predefined set of topics, rather than collecting and indexing all accessible web documents to be capable to answer all possible ad-hoc questions. FoCUS is continuously keeps on crawling the internet and finds any new internet pages that have been added to the internet, pages that have been removed from the internet. Due to growing and vibrant activity of the internet; it has become more challengeable to navigate all URLs in the web documents and to handle these URLs. We will take one seed URL as input and search with a keyword, the searching result is based on keyword and it will fetch the internet pages where it will find that keyword.
Sperm displacement in drosophila melanogaster.

This thesis presents experiments which investigated the genetics and mechanisms of sperm displacement in the fly Drosophila melanogaster. Sperm displacement allows males to increase their reproductive success relative to other males. Differential reproductive success of males is an important aspect of total fitness. D. melanogaster provides an effective system in which to investigate traits that affect reproductive success. The experimental work presented involved both genetic studies and phenotypic manipulations. Measurements of the repeatability and heritability of sperm displacement ability (SDA) showed both parameters to be low but significantly non­ zero. SDA was correlated with pre-adult viability, suggesting the heritable component of SDA and pre-adult viability may be confounded. Since little is know of the mechanism of sperm displacement in D. melanogaster, experiments were carried out to investigate the effect of variation in factors which may possibly influence SDA. It was determined that males did not displace the sperm of other males in preference to their own sperm. The effect of variation in copulation duration was investigated with both spermless and normal males. The results indicated that levels of sperm displacement were determined by the amount of sperm transferred, a process which appeared to occur rapidly during copulation. The effects of variation in genetically determined male body size was also investigated. Larger males were found to be at a slight disadvantage in virgin-female matings. No significant effect of body size on SDA was observed. Separate experiments were carried out to determine the effect of male accessory gland products on the fertility of subsequent matings. The possibility that "sperm defence" is due to accessory gland fluid physically interfering with the storage of second male sperm was shown to be unlikely.
Research on Smart Campus Based on the Internet of Things and Virtual Reality

Smart campus is an inevitable trend in the development of digital campus construction. With the development of the Internet of Things (IoT) and Virtual Reality (VR) technology, these technologies become the key to the construction of smart campus. The major objective of this study put forward a smart campus system prototype based on Internet technology. This paper firstly introduces the definition and characteristics of the IoT and VR technology. Secondly, presents the architecture and implementation methodology of the system prototype, and analyzes the core idea of smart campus. Finally, discuss the problems should be noticed in the smart campus construction.
Face Analysis Using Local Binary Patterns

Local Binary Pattern (LBP) is a simple yet very efficient texture operator which labels the pixels of an image by thresholding the neighborhood of each pixel with the value of the center pixel and considers the result as a binary number. Due to its discriminative power and computational simplicity, LBP texture operator has become a popular approach in various applications. This chapter presents the LBP methodology and its application to face image analysis problems, demonstrating that LBP features are also efficient in nontraditional texture analysis tasks. We explain how to easily derive efficient LBP based face descriptions which combine into a single feature vector the global shape, the texture and eventually the dynamics of facial features. The obtained representations are then applied to face and eye detection, face recognition and facial expression analysis problems, yielding in excellent performance.