Miracle-dz/UniFAR-FTUs huggingface dataset:
Near Real-Time Prediction of Zenith Tropospheric Delays for Position Estimation Over Long-Baseline CORS Network

This paper presents the preliminary results in establishing a strategy for predicting Zenith Tropospheric Delay (ZTD) and relative ZTD (rZTD) between Continuous Operating Reference Stations (CORS) in near real-time. It is anticipated that the predicted ZTD or rZTD can assist the network-based Real-Time Kinematic (RTK) performance over long inter-station distances, ultimately, enabling a cost effective method of delivering precise positioning services to sparsely populated regional areas, such as Queensland. This research firstly investigates two ZTD solutions: 1) the post-processed IGS ZTD solution and 2) the near Real-Time ZTD solution. The near Real-Time solution is obtained through the GNSS processing software package (Bernese) that has been deployed for this project. The predictability of the near Real-Time Bernese solution is analyzed and compared to the post-processed IGS solution where it acts as the benchmark solution. The predictability analyses were conducted with various prediction time of 15, 30, 45, and 60 minutes to determine the error with respect to timeliness. The predictability of ZTD and relative ZTD is determined (or characterized) by using the previously estimated ZTD as the predicted ZTD of current epoch. This research has shown that both the ZTD and relative ZTD predicted errors are random in nature; the STD grows from a few millimeters to sub-centimeters while the predicted delay interval ranges from 15 to 60 minutes. Additionally, the RZTD predictability shows very little dependency on the length of tested baselines of up to 1000 kilometers. Finally, the comparison of near Real-Time Bernese solution with IGS solution has shown a slight degradation in the prediction accuracy. The less accurate NRT solution has an STD error of 1cm within the delay of 50 minutes. However, some larger errors of up to 10cm are observed.
ACTION SYNTHESIS USING BODY SEGMENTATION

Recognizing action is an important part of a video surveillance and video retrieval system, in which motion information extracted from video is useful. This paper proposes an action synthesis method, in which Efros et al.’s action recognition method, Chen et al.’s body segmentation method, and Fujiyosi et al.’s method are combined. Chen et al.’s method and Fujiyosi et al.’s method are used as preprocessing of action synthesis. Experimental results with a number of test sequences show that the proposed method works efficiently for human action synthesis.
Empirical analysis of a parallel data mining algorithm on a graphic processor

In this thesis, we analyze in an empirical way a di↵erent approach of the algorithm SPAM (Sequential PAttern Mining using A Bitmap Representation) made by J. Ayres, J. Gehrke, T. Yiu and J. Flannick from Cornell University, exploiting GPUs. SPAM is a novel approach for FSM (Frequent Sequence Mining) where the algorithm is not looking for a single item during the mining process but for a set of items that customers have taken in their transactions. Basically we can think of Spam as a supermarket where the customers have the ”Fidelity card” and that the supermarket can track the transactions. What Spam does is to calculate which are the most bought items per customer. But the question is: Why graphic cards ? The answer is pretty easy: graphic cards can benefit of a rich amount of data parallelism allowing many arithmetic operations to be safely performed on program data structures in a simultaneous manner. In our tests we noticed that performing a massive amount of multiplications (or any other kind of operation) with the CPU took at least the 60% more rather than the GPU. Just with this simple case, we could understand the potential of GPUs. ACM Classification: B.2. 1, B.2. 2, B.2. 4, B.3. 2, B.3. 3, H.2. 8
The Effects of Caching on Reconfigurable Adaptive Computing Systems

Adaptive computing systems have proven useful for implementing a wide range of algorithms. A limitation of current systems is the relatively small amount of reconfigurable hardware resources. Many algorithms require more hardware resources than are available. One solution to this problem is runtime reconfiguration (RTR). Using RTR techniques, a large algorithm is implemented as a collection of configurations for the reconfigurable hardware. These configurations are loaded onto the reconfigurable hardware as necessary to implement the algorithm. A primary limitation of RTR is that the reconfiguration process is slow. Therefore, methods of decreasing reconfiguration time are desirable. Another method of implementing large algorithms on small hardware is to use multiple configurable computing platforms connected via a communication network. RTR techniques can be used in conjunction with this method to further increase hardware availability. In this case reconfiguration time is increased by the overhead of transmitting data across the communication network. Methods of decreasing network overhead are desirable. This thesis discusses the use of caching techniques to decrease reconfiguration time. An architecture for caching configurations is implemented on a configurable computing system platform. The use of caching to decrease network overhead is discussed and exhibited. An example application is implemented and used to evaluate the effects of caching on reconfig-uration time and algorithm performance. who all provided technical assistance and worked on projects related to this research. Thanks must also go to all of my friends and family.
SEISMIC DESIGN OF DOUBLE LAYER GRIDS BY NEURAL NETWORKS

The main contribution of the present paper is to train efficient neural networks for seismic design of double layer grids subject to multiple-earthquake loading. As the seismic analysis and design of such large scale structures require high computational efforts, employing neural network techniques substantially decreases the computational burden. Square-onsquare double layer grids with the variable length of span and height are considered. Backpropagation (BP), radial basis function (RBF) and generalized regression (GR) neural networks are trained for efficiently prediction of the seismic design of the structures. The numerical results demonstrate the superiority of the GR over the BP and RBF neural networks.
Gaining Competitive Advantages through a Functionality Grid for Website Evaluation

As firms become more mature and competitive in deploying their Web sites, gaining competitive advantage through innovative Web site functions has become increasingly difficult. Previous studies lacked comprehensive analysis of the functionality of commercial Web sites. This article proposes a framework in the form of a two-dimensional grid for analyzing, comparing, and improving specifically the functionality of commercial Web sites. The grid classifies Web site functions into four types: information, communication, downloading, and transaction. It addresses a wide range of electronic commerce activities including advertising, sales, customer service, distribution, financing, and market research. A set of metrics are defined for objectively measuring the functionality of each type of function. Use of a functionality grid and the associated metrics are illustrated in a case study on the comparative analysis of three oil companies' Web sites. The results provide insights into how commercial Web sites differ in functionality.
Conservation Law of Mass

Copyright: © 2013 Tamir A. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. “Mass” is a basic property of a physical body which describes the amount of matter in it. In contrast to “weight” that is the force of gravitational attraction between an object and earth or some other astronomical body mass is not depending in the location of the body in space. We feel our weight while standing on the floor due to reaction of the floor on the bottom of our legs as dictated by Newton’s third law, action = reaction, where the action is our weight. While mass cannot change under ordinary circumstances, weight changes dependent where the object is.
Experience based navigation : theory, practice and implementation

For robotic systems to realise lifelong autonomy they must be able to navigate accurately in changing environments. In this thesis we describe, implement and validate a new approach to the problem of long-term navigation. To begin, we present our stereo visual odometry system which provides highly accurate pose estimation. Our approach combines several techniques found in existing implementations and a recently published image descriptor that simplifies the solution architecture. The performance and versatility of our system is demonstrated through testing on multiple datasets. Equipped with our visual odometry system, we describe a new approach to the problem of lifelong navigation. We learn a model whose complexity varies naturally in accordance with the variation of scene appearance. As the robot repeatedly traverses its workspace, it accumulates distinct visual experiences that, in concert, implicitly represent the scene variation - each experience captures a visual mode. When operating in a previously visited area, we continually try to localise in these previous experiences while simultaneously running the visual odometry. Failure to localise in a sufficient number of prior experiences indicates an insufficient model of the workspace and instigates the laying down of the live image sequence as a new distinct experience. In this way, over time we can capture the typical temporally varying appearance of an environment and the number of experiences required tends to a constant. Although we focus on vision as a primary sensor, the ideas we present here are equally applicable to other sensor modalities. We demonstrate our approach working on a road vehicle operating over a three month period at different times of day, in different weather and lighting conditions.
OFEX 2.0 : DATABASE FOR PERFORMANCE EVALUATION OF OBJECT FEATURE EXTRACTION ALGORITHMS

In this paper, we present the revised database for the performance evaluation of object feature extraction algorithms. The aim is to establish evaluation procedure and performance measures for these algorithms utilizing image database. The image database, OFEX(Obejct Feature EXtraction) 1.0, is updated to OFEX 2.0, adding 3 more objects with various conditions. Experimental results are presented when SIFT (Scale Invariant Feature Transform) is employed.
Automated and interactive composition of ubiquitous applications

Application composition refers to the creation of applications by using Web Services and resources such as mobile devices, displays, and various augmented everyday objects as building blocks. This approach is especially useful for ubiquitous applications which focus on supporting users' needs and everyday activities. This thesis proposes to compose these applications by choosing the appropriate set of resources and services and their configuration as dictated by the users themselves, together with their needs and other contexts. This thesis studies automated and interactive application composition. The first approach enables the system to act upon users' needs and intentions, while the second enables users themselves to specify their needs and achieve their goals. The research was carried out by designing, implementing and evaluating eight prototypes for automated and interactive application composition. The evaluation methods used included performance analysis and user experiments. The main results include (1) the design and implementation of automated composition mechanisms which rely on evolutionary computing and genetic algorithms, (2) a detailed performance analysis of these mechanisms using synthesized datasets and in a real networking environment; (3) the design and implementation of interactive application composition prototypes which rely on graphical and physical (i.e. touch-based) user interfaces for mobile devices and utilize various contexts; (4) an evaluation of these prototypes in a series of user experiments. The evaluation studied the following issues: users' attitudes towards an automated composition system which makes decisions on their behalf; users' attitudes towards a context-aware composition interface; and the issue of balancing user control and system autonomy.
Multivariate PCA Analysis Combined with Ward's Clustering for Verification of Psychological Characterization in Visually and Acoustically Social Contexts

The development of the nervous system is a process of gene-environment interaction through prenatal and neonatal stages. The chemical environment may cause mal-development of the social brain network leading to developmental disorders. Thus, we need to develop a new behavioral method which distinguishes the subtle difference in early stage development of sensory-motor, socio-emotional, and cognitive processing in humans and animal models. Here, we have developed an innovative tool for signal processing to structuralize the behavioral elements from a large body of complex information via the multivariate analysis (principal components analysis combined with Ward’s clustering) using an animal model (domestic chicks) and found dynamical differences of combined behavioral parameters over social contexts between socially affiliated chicks and isolated chicks. We prepared two groups each containing six subjects, each group had its peer-social environments regulated, either isolated or grouped, and then their social interaction was tested through a series of social contexts consisting of four serial contexts, 1) firstly isolation, 2) acoustical interaction, 3) acoustical and visual meeting with the reference peers, and lastly 4) isolation again. Results show the juveniles’ social cognition and emotion was visualized as specific clustering structures with active and affective behavioral factors that were seen only in context three with unfamiliar peers visually and acoustically but were not seen in isolation or the social auditory without visual context. These results suggest the usefulness of the combined multivariate analysis under sensory cued-restricted contexts to quantitatively reveal a subtle difference of socio-emotional behavior.
OPTIMIZING THE VIDEO EXPERIENCE IN MOOCS

Massive Open Online Courses have gained more and more popularity in the recent years. Video Content contributes a vital aspect of the learning experience in MOOCs. The paper at hand proposes ways to optimize the video experience in MOOCs. Single stream videos will be considered as well as the openHPI's dual stream video player. openHPI is the MOOC platform of the Hasso Plattner Institute, providing MOOCs to thousands of users since 2012. One of the unique features of our video player is the possibility to play two synchronized video streams. Based on collected usage data of our html5 based video player we evaluate the learners acceptance of features, such as adaptive playback speed, dual video scaling, full-screen mode, slide navigation and subtitles. Furthermore, we will discuss the impact on the users learning outcome.
PLASTIC ANALYSIS OF FRAMES USING GENETIC AND ANT COLONY ALGORITHMS

In the recent years heuristic algorithms such as genetic algorithms, ant colony algorithms and simulated annealing have found many applications in optimization problems. The essence of these algorithms lies in the fact that they do not depend on the specific search space to which they are applied and consequently this extends their generality. In this paper, genetic and ant colony algorithms are used to find the collapse load factor of twodimensional frames and their efficiency is compared to a direct approach. It is shown that when these algorithms are tuned finely and their parameters are adjusted carefully, very good results can be obtained. Four examples are presented to illustrate the efficiency of algorithms. Keyword: Plastic load factor; analysis; design; genetic algorithm; ant colony algorithm; planar frames
Measuring Word Similarity Based on the Optimal Transformation and Combination of Features

The semantic similarity between two words can be determined based on their common and distinctive features after transforming them into measurable values. Up to now, a variety of transfer functions with respect to the transformation and the combination of features have been proposed. However, none of them have ever processed and combined those features properly, thus making them incapable of making a judgment of similarity that is close to human judgments of similarity. This paper offers a method that represents the optimal combination of the optimal transformation of two types of widely used features. The results obtained from a standard data set show that the proposed solution outperforms all of its benchmarks significantly.
The AOPOntology: A Semantic Artificial Intelligence Tool for Predictive Toxicology

Introduction Toxicology needs artificial intelligence tools that can automate the prediction of toxicity. Today we are at an interesting nexus. We have thousands of chemicals in the environment that lack regulatory thresholds for determining risk. New high throughput in vitro testing methods are becoming available to test these chemicals. Causal Adverse Outcome Pathway Networks (CAOPN) are emerging that will allow us to make predictions based on perturbations of specific key events within the network. The AOPOntology was developed as infrastructure for this nexus, providing the ability to model and marry the data from the in vitro tests for the thousands of chemicals and place them within the CAOPN framework to facilitate adverse outcome predictions. Materials and Methods The AOPN is a functional specialized ontology that creates classes that model biological pathways and CAOPNs. Adverse outcome predictions are based on mathematical determinations of key events that are sufficient to infer adverse outcomes will occur, or biological information. These sufficiency relationships are captured in the AOPOntology and used by the semantic reasoners to make predictions. Results The AOPOntology version 1.0 architecture is in place, and a CAOPN for steatosis demonstrates how causal network theory is used to make predictions. The AOPOntology is available at https://github.com/DataSciBurgoon/aop-ontology. Discussion The AOPOntology is a knowledge base for CAOPNs that one can use to make predictions about a chemical’s potential toxicity using in vitro high throughput and other assays. Conclusions Using CAOPNs and causal network theory one is able to predict potential toxicity for chemicals using in vitro high throughput and various high content screens.
A Study on PAPR Reduction for MU-MIMO using Blind Selected Mapping

Peak-to-average power ratio (PAPR) of single-carrier (SC) waveform in multi-user multiple-input multiple-output (MU-MIMO) uplink transmission becomes higher than that of single-antenna transmission due to the use of transmit filtering. An antenna-wise selected mapping (SLM) can effectively lower the PAPR of MIMO uplink transmit signal waveform, but side-information transmission is mandatory. In this paper, we study an SLM without side-information transmission (called blind SLM) for SC-MU-MIMO uplink with MMSE-SVD which uses singular value decomposition (SVD) transmit filtering and minimum mean-square error (MMSE) receive filtering. To realize a simple data detection without side-information, we recommend that the phase rotation sequence multiplication should be applied to transmit data streams before applying the transmit filtering (called stream-wise SLM). Phase rotation sequence selection is done so as to minimize the maximum instantaneous PAPR associated with all transmit antennas. At the receiver, the phase rotation sequence estimation is done for each user’s data streams after applying the receive filtering. Simulation results confirm that the stream-wise blind SLM for uplink MMSE-SVD can effectively reduce the PAPR of transmit waveforms, while there is no significant bit-error rate (BER) degradation although the data detection is carried out without side-information. Keyword MU-MIMO, peak-to-average power ratio (PAPR), selected mapping (SLM)
Just in time information Retrieval Agent to support Text creation, based on the Mixed Initiative User Interface Desing

Information is seen as a valuable resource, but the quality and quantity of informationretrieved at the moment of web information searching depend on both: the user's query and the search engine's features. This master thesis presents a just in time information retrieval (JITIR) agent that is based on the Google Search API to implement a web application. The thesis consists of the implementation and evaluation of the Pandora Editor which has as part of its main functionality an information search and retrieval agent. The motivation is to provide a new interaction paradigm to support documents writing. The interaction is oriented to enhance the text creation activity by having automated query formulation and reducing the user's memory overload. The writer can use those queries to get relevant web results from Google search and improve his document content and quality.
Embedded Databases on Flash Memories: Performance and Lifetime Issues, the case of SQLite

Databases are more and more used in embedded system applications and especially in consumer electronics. This comes from the need to structure user and/or system data to be more efficiently managed and accessed. SQLite is one of the most used database applications. This paper presents a micro benchmarking methodology and results for SQLite database requests on embedded flash specific file systems. Indeed, flash file systems behavior are very specific to flash memory intricacies and the objective of this study is to highlight the interactions between flash memory, flash file systems, and SQLite based applications.
“SCATTMAT: A Mode Matching and Generalized Scattering Matrix Code for Personal Computers in a Windows Environment”

In this paper, a computer aided design tool is presented to simulate symmetrical radially deformed circular waveguide microwave components. This tool works with the well-known “Mode Matching and Scattering Matrix” techniques, and it has been developed in C++ language in a Windows environment, to obtain the most friendly interface between the user and the code. The most important feature of the presented code is that it includes the possibility to display the internal mode power evolution along the component. The code has been successfully validated with numerical and experimental results from different authors.