Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhancing Least Squares GNSS Positioning with 3D Mapping without Accurate Prior Knowledge

GNSS positioning performance in dense urban areas is severely degraded due to the obstruction and reflection of the signals by the surrounding buildings. A basic GNSS position solution can exhibit errors of tens of meters, sometimes more. Here, 3D mapping is used to aid conventional ranging-based GNSS positioning. Terrain height-aiding contributes an additional virtual ranging measurement to the position solution. In addition, 3D city models predict Non-Line-Of-Sight (NLOS) reception over a large search area. The resulting NLOS probabilities aid a consistency checking algorithm that selects and weights the signal used for the final position solution. Iteration then refines the position solution. Practical test results demonstrate improvement in the horizontal and vertical accuracy of conventional ranging-based GNSS positioning in urban areas by a factor of 2.5 and 5, respectively. Using the new technique, the Root Mean Square (RMS) position error in dense urban areas was found to be 20.8 m horizontally and 12.2 m vertically. Copyright © 2017 Institute of Navigation
Multi-sensor data fusion using neural networks

A general approach to the use of neural networks for data fusion is outlined. The discussion begins with examples of data fusion problems and a pattern recognition example is given to illustrate the concepts involved in data fusion. The differences between using post- and pre-detection signals and the advantages of using the latter are discussed. How to apply a neural network to the data fusion problem is demonstrated, and experimental results for a character recognition task are given. The general approach applies to a variety of practical situations, including robot navigation and military environment assessment/evaluation. <<ETX>>
Thematic Research Collections: Libraries and the Evolution of Alternative Digital Publishing in the Humanities

Abstract:The growth and evolution of digital scholarship in the humanities has produced new genres of scholarly work and publication, reliant upon new ways of representing and sharing evidence, analysis, and interpretation. Meanwhile, extant systems of scholarly communication, including publication, discovery, access-provision, maintenance, and preservation, too often exclude digital research products, to the potential detriment of the entire scholarly record. This paper considers one genre of digital humanities scholarship: the thematic research collection, a digital collection of primary sources gathered to support research on a theme. This genre is recognizable and increasingly common, yet wildly heterogeneous in precise form, function, and purpose. This typological analysis aims to identify and describe types of collections as a way toward comprehending the range, variation, and complexity of the whole genre. The research considers what thematic research collections are, how they work, and what challenges confront the provision of effective and ongoing access to digital scholarship.
Generative Neural Machine for Tree Structures

Tree structures are commonly used in the tasks of semantic analysis and understanding over the data of different modalities, such as natural language, 2D or 3D graphics and images, or Web pages. Previous studies model the tree structures in a bottom-up manner, where the leaf nodes (given in advance) are merged into internal nodes until they reach the root node. However, these models are not applicable when the leaf nodes are not explicitly specified ahead of prediction. Here, we introduce a neural machine for top-down generation of tree structures that aims to infer such tree structures without the specified leaf nodes. In this model, the history memories from ancestors are fed to a node to generate its (ordered) children in a recursive manner. This model can be utilized as a tree-structured decoder in the framework of "X to tree" learning, where X stands for any structure (e.g. chain, tree etc.) that can be represented as a latent vector. By transforming the dialogue generation problem into a sequence-to-tree task, we demonstrate the proposed X2Tree framework achieves a 11.15% increase of response acceptance ratio over the baseline methods.
Privacy risks of spatio-temporal data transformations (Konum zaman verilerinin dönüşümünde gizlilik riskleri)

In recent years, we witness a great leap in data collection thanks to increasing number of mobile devices. Millions of mobile devices including smart phones, tablets and even wearable gadgets embedded with GPS hardware enable tagging data with location. New generation applications rely heavily on location information for innovative business intelligence which may require data to be shared with third parties for analytics. However, location data is considered to be highly sensitive and its processing is regulated especially in Europe where strong data protection practices are enforced. To preserve privacy of individuals, first precaution is to remove personal identifiers such as name and social security number which was shown to be problematic due to possible linking with public data sources. In fact, location itself may be an identifier, for example the locations in the evening may hint the home address which may be linked to the individual. Since location cannot be shared as it is, data transformation techniques have been developed with the aim of preventing user re-identification. Data transformation techniques transform data points from their initial domain into a new domain while preserving certain statistical properties of data. In this thesis, we show that distance-preserving data transformations may not fully preserve privacy in the sense that location information may be estimated from the transformed data when the attacker utilizes information such as public domain knowledge and known samples. We present attack techniques based on adversaries with various background information. We first focus on spatio-temporal trajectories and propose an attack that can reconstruct a target trajectory using a few known samples from the dataset. We show that it is possible to create many similar trajectories that mimic the target trajectory according to the knowledge (i.e. number of known samples). The attack can identify locations visited or not visited by the trajectory with high confidence. Next, we consider relation-preserving transformations and develop a novel attack technique on transformation of sole location points even when only approximate or noisy distances are present. We experimentally demonstrate that an attacker with a limited background information from the dataset is still able to identify small regions that include the target location points.
Selection of optimal learning rates in CMAC based control schemes

CMAC based control schemes have been studied by many researchers. It is well recognized that properly designed CMAC controllers provide useful and practical tools for precision control of nonlinear systems. For complex trajectories, however, the convergence speed of CMAC can be slow because the CMAC module takes much time in learning the inverse dynamics of the plant. Therefore, one practical difficulty of CMAC based controller design is the selection of appropriate learning rate. In this paper, we present a method for selection of optimal CMAC learning rate. Furthermore, we demonstrate that the proposed GA-based approach to parameter selection can provide a global optimal solution. Computer simulation results confirm the effectiveness of the proposed method.
Use of a robotic platform in dyslexia-affected pupils: the ROBIN project experience

— The ROBIN project was a joint effort that involved private and public entities, aimed to develop a computer-based software platform to promote a multi-faceted representation of data to build learning exercises supported with a feedback provided constantly by an anthropomorphic robotic system. The platform was devised in order to build a playful and stimulating learning environment able to support children affected by dyslexia.
Guidelines for the Choice of Microarray Adjustment Methods for Class Comparison, Class Discovery and Class Prediction

Many data adjustment methods have been proposed in the microarray literature prior to detailed analyses such as class comparison, class discovery or class prediction. But guidelines have not yet been proposed for ensuring that the choice of an adjustment method is appropriate for the type of study envisaged. The aim of that article is to present guidelines for the choice of data pre-processing methods, for each of the three main areas of microarray applications: class comparison, class discovery and class prediction.
An isolated SNM model for high-stability multi-port register file in 65 nm CMOS

In modern microprocessors, the multi-port register file is one of the key modules which provides fast and multiple data access for instructions. As the number of access ports in register files increases, stability becomes a key issue due to the voltage fluctuation on bit lines. We propose to apply an isolated inverter to address the voltage fluctuation. To assess the register stability, we derive a closed-form expression of static noise margin (SNM) for our register file. The proposed SNM model can be used as a guideline to predict the impact of several register parameters on the stability and optimize register file designs. To validate the proposed SNM model, we fabricated a test chip of two-write-four-read (2W4R) 1024 bits register file in a TSMC 65 nm low-power CMOS technology. The experimental result shows that the stability of our register file cells with an isolated inverter improve the conventional cells by approximately 2.4 times. Also, the supply voltage causes a fluctuation of SNM of about 65%, while temperature and transistor mismatch cause a fluctuation of SNM of about 20%.
Tutorial on Minimum Output Sum of Squared Error filter

Tutorial on Minimum Output Sum of Squared Error Filter One of the major challenges in computer vision is visual tracking. Many different techniques have been developed for visual tracking over time. Visual tracking using correlation filters is faster than other techniques. A visual tracking technique using Optimized Correlation Output Filters (OCOF), Average of Synthetic Exact Filter (ASEF) and Minimum Output Sum of Squared Error (MOSSE) filters is presented in this tutorial. The MOSSE filter is a stable correlation filter which can be initialized on a single frame of a video. The MOSSE filter adapts with the changes in the appearance of the object while tracking. Tracking using the MOSSE filter is not dependent on changes in lighting, non-rigid transformations, pose, and scale. This work presents systematic steps towards building the filter and its application as a tracker in visual tracking. A number of experiments are performed to demonstrate the working of the tracker.
A Multiple Objective Approach to Space Planning for Academic Facilities

This paper addresses the office layout problem where existing offices vary considerably as to relevant criteria and yet permanent walls make it impractical to remodel existing spaces. The major objective of this study was the equitable reassignment of 144 offices to 289 faculty and staff members in 6 academic departments with the College of Administrative Science at The Ohio State University. Since the building contains 5 floors and a wide diversity of office quality, six conflicting objectives have been recognized. In order to adequate address these multiple objectives, a large mixed-integer goal programming model was formulated. A companion interactive computer program was also developed to evaluate the performance of each solution with respect to these objectives. The goal programming model served to evaluate several layout strategies and identify the tradeoffs implicit in the problem. The interactive computer program helped translate these insights into a final compromise solution. There are two interesting findings of the study. The first one is desirability of allowing the decision makers to be in command of the solution process. This may be particularly important when several decision makers must compete for the same resources. Another finding is that a linear programming code is sufficient for this particular formulation of a mixed-integer model. Although the model contains over 1,700 integer variables, a very few non-integer values were generated. This avoided the computational burden of a mixed-integer code. The model presented here has its generic roots in the so-called "assignment problems," with the added feature of recognizing multiple objectives. It is felt that with the proper modifications, the ideas presented here may well be extended to location-distribution and other types of assignment problems. These models have been applied to the layout of the College, with the final implementation having occurred during the Summer Quarter, 1978.
Power of Web 2.0 mass collaboration in computational intelligence and its’ uses, an example from finance

One of the main issues of concern within world wide web is the emergence of web 2.0 mass collaboration systems and our understanding of this new phenomenon. Web 2.0 systems have gained enormous popularity in recent years, however as is often case with novel technologies, the real merits sometime stay somewhat obscured to many researchers. In this short paper web 2.0 applications are lightly introduced, with parallels to computational intelligence being drawn and some experimental results from the financial markets presented, to illustrate value of web 2.0. This paper highlights a number of important issues that deserve academic attention. We hope this paper will serve as a light and not too technical introduction towards encouraging others in computational intelligence to consider leveraging web 2.0.
Sparse-array phasing algorithm based on recursive estimation of fringe contrast

This paper describes a novel real-time algorithm for optically phasing sub-apertures of a sparse-array telescope system based on recursive estimation of the sub-aperture placement that maximizes a fringe-contrast metric. The sub-apertures are phased in pairs using broad spectral band flood-illumination of the sparse-array, while blocking reflections from all but two sub-apertures. The resulting Young’s geometry at the pupil produces an interference pattern that is characterized to determine spatial-frequency filters that are utilized to generate a contrast metric from the fringe patterns. This contrast metric is shown to generate a near-Gaussian variation as a function of optical path-length difference (OPD), with the maximum contrast occurring at zero OPD. The functional relationship between fringe contrast and sub-aperture position based on a common-path, laser-based relative-piston measurement system is developed into an estimator for maximization of fringe contrast (and therefore phasing of sub-apertures). The recursive algorithm produces real-time estimates of the zero OPD value of the relative position that improves as additional data is acquired.
Learning and inference algorithms for dynamical system models of dextrous motion

High dimensional time series data such as video sequences, spectral trajectories of a speech signal or the kinematic measurements of skilled human activity are encountered in several engineering applications, and computational models of such data hold considerable interest, particularly models that capture the inherent stochastic variability in the signal. Of particular interest in this dissertation are kinematic measurements of manipulator and tool motion in robot-assisted minimally invasive surgery (RMIS). A set of gesture labelled RMIS data is initially assumed to be given. The primary goal is to develop statistical models for gesture recognition for new RMIS trials from kinematic data, for eventually supporting automatic skill evaluation and surgeon training. The goal of automatically discovering the structure of dextrous motion in an unsupervised manner is also addressed, when an inventory of gestures is not known, or gesture-labeled data are not provided. A number of statistical models to address these problems have been investigated, including hidden Markov models (HMM) with linear discriminant analysis, factor-analyzed hidden Markov models and linear dynamical systems with time varying parameters. Gesture recognition accuracies for three RMIS training tasks—suturing, knot-tying and needle-passing—are shown to improve significantly with increasing model complexity, justifying the concomitant increase in the computation required to estimate model parameters from gesture-labeled data or to perform recognition. Algorithms for unsupervised structure induction have been investigated for discovering gestures used in skilled dexterous motion directly from kinematic data when gesture-labeled data are not available. An improved algorithm based on successive state splitting is presented for discovering the state-topology of a hidden Markov model. The algorithm efficiently explores an enormous space of possible topologies and yields models with a high goodness-of-fit to the RMIS kinematic data. Technical contributions of this dissertations include novel, efficient algorithms for probabilistic principal component analysis, for switching linear dynamical system parameter estimation, and for hidden Markov model topology induction. Other techniques for improving gesture recognition accuracy beyond those mentioned above are also investigated by incorporating ideas such as user-adaptation of the models.
New generation high speed turret and pseudo bang-bang controller

In this paper we report on the development of a new generation high speed turret (HST) and associated pseudo bang-bang controller. The turret was developed as part of a US Army contract and is based on the use of a Lynx T468 motor as the azimuth drive and a similar (smaller) motor for the elevation drive. The T468 motor is a high torque, high-efficiency brushless motor used as direct drive. The pseudo bang-bang controller designed for high-speed maneuvers uses a combination of a high-gain PD controller in the inner loop and a nonlinear strategy in the outer loop to achieve near-minimum-time set point maneuvers with no overshoot and no chattering. Experimental results show an excellent performance with times of less than 600 msec for a 180-degree maneuver.
Computer assisted telephony system

Telephones have high reach both in developed and developing countries that have made it a preferred tool for collecting and disseminating information. Voice based information collection systems offers data collection at low cost and save time that is needed to reach a human. Due to high teledensity, voice based systems have been very effective and have emerged as promising tools for facilitating data collection and community interaction in developing nations where the Internet penetration and literacy level is low. Such voice based telephony systems can be improved by assistance from the computer system integrated with the data collector’s telephone. We are explaining the development and deployment of computer-assisted telephony system (CATS) to conduct interviews with patients at Grant Medical College and Sir Jamshedjee Jeejeebhoy Group of Hospitals (a.k.a. JJ Hospital) Mumbai, India. This work presents the benefit of CATS over standalone telephonic surveys. In order to further take the leverage of CATS, we propose to develop automated telephonic data collection system. A prototype of same is evaluated in a lab study with low-literates. We discuss the results of the study conducted, requirements and benefits of fully automated system.
Creation and content-based retrieval in a radiological documentary record

We present a data model for managing a medical documentary record. The multimedia documents handle various type of information: medical images (X-rays, MRI, tomography.), data on the patient's civil and clinic identity, and examination reports. A retrieval process must answer to queries involving both the explicit alphanumeric data stored in the patient's dossier and information that are carried by the images. Content-based retrieval of medical pictures cannot be solved with a matching process based on image analysis. Thus we propose a data model for describing the semantic content of a medical picture. The image is viewed as pairs of isosemantic regions and signals in respect of an anatomic and a pathological model. This description is used to index the pictorial part of the medical record. A graphical interface is provided for the users to index the images and formulate the queries.
Adaptive Voltage-based Control of Direct-drive Robots Driven by Permanent Magnet Synchronous Motors

Tracking control of the direct-drive robot manipulators in high-speed is a challenging problem. The Coriolis and centrifugal torques become dominant in the high-speed motion control. The dynamical model of the robotic system including the robot manipulator and actuators is highly nonlinear, heavily coupled, uncertain and computationally extensive in non-companion form. In order to overcome these problems, this paper presents a novel adaptive control for direct-drive robot manipulators driven by Permanent Magnet Synchronous Motors (PMSM) in tracking applications. The novelty of this paper is that the proposed adaptive law is free from manipulator dynamics by using the Voltage Control Strategy (VCS). Additionally, a state space model of the robotic system driven by PMSM is presented. The VCS differs from the commonly used control strategy for robot manipulators the so called torque control strategy. The position control of the PMSM is effectively used for the tracking control of the robot manipulator. This idea takes the control problem from the manipulator control to the motor control resulting in a simple yet efficient control design. Compared with the torque control, the control design is simpler, easier to implement with better tracking performance. The control method is verified by stability analysis. Simulation results show superiority of the proposed control to the torque control applied by field oriented control on the direct-drive robot driven by PMSM.
Modeling and Estimation of Patterns of Relationship Formation and Dissolution

This dissertation describes and develops methods for modeling sexual partnership formation and termination using retrospectively collected survey data. Such methods are required to produce information necessary to model propagation of sexually transmitted diseases and the impact of interventions on such processes which are being used both to design and to monitor HIV combination prevention studies. Sexual history data are commonly obtained through surveys that collect information on relationships that are ongoing during a fixed time window. This sampling mechanism leads to incomplete sexual history data and duration data that are left truncated and right censored. In Chapter 1, we describe a common sampling scheme for collecting sexual partnership data, discuss a key assumption required for unbiased estimation, and provide the conditions under which the nonparametric maximum likelihood estimator of the relationship duration distribution is unique and consistent. We also investigate the conditions required for the consistency of the regression coefficient from a Cox proportional hazards model that apply even when the distribution of duration is not completely identifiable due to restrictions on the support of the truncation distribution. Lastly, we will provide some illustrative examples on estimating distribution of most recent partnerships and present spline regression results based on sexual history data from Botswana. In Chapter 2, we present a Markov framework for modeling and estimation of partnership transition probabilities for sexual history data collected under a retrospective sampling scheme. We propose a stochastic expectation maximization algorithm (stEM) coupled with rejection-sampling scheme in order to estimate transition probabilities from a state of celibacy to monogamy and to concurrency (or vice versa). This approach ac-