Miracle-dz/UniFAR-FTUs huggingface dataset:
Augmented Reality: Application to In-Vehicle Navigation

Even with today’s technically advanced navigation systems, user experience situations where announcements are difficult to understand and misleading. Augmented reality – the integration of computer generated content into the vehicle surrounding – can provide an intuitive and unambiguous way to communicate navigation information; it can even serve as a novel user interface that allows interaction with the surrounding. In this paper, challenges, constraints, and possible solutions for AR in-vehicle applications are discussed. Details of the technical and design decisions of the “first in-vehicle augmented video system” are explained, as well as features and possible future upgrades.
Reliable computation in computing systems designed from unreliable components

This is the second of two papers which present information-theory-type results pertaining to the reliability of computing systems designed from unreliable components. Two models for component malfunctions are considered. The first is based on the assumption that malfunctions of a particular component are statistically independent from one use to another. The second is based on the assumption that components fail permanently but that the components which have failed are periodically replaced with good ones. In both cases, malfunctions in different components are assumed to be independent. Just as a channel capacity is defined for communication channels, a computing capacity is defined for computing systems. For both component failure models, it is shown that there are computing systems, designed entirely from unreliable components of the assumed type, which have nonzero computing capacities.
Automatable Annotations – Image Processing and Machine Learning for Script in 3D and 2D with GigaMesh

Libraries, archives and museums hold vast numbers of objects with script in 3D such as inscriptions, coins, and seals, which provide valuable insights into the history of humanity. Cuneiform tablets in particular provide access to information on more than three millennia BC. Since these clay tablets require an extensive examination for transcription, we developed the modular GigaMesh software framework to provide high-contrast visualization of tablets captured with 3D acquisiton techniques. This framework was extended to provide digital drawings exported as XML-based Scalable Vector Graphics (SVG), which are the fundamental input of our approach inspired by machine-learning techniques based on the principle of word spotting. This results in a versatile symbol-spotting algorithm to retrieve graphical elements from drawings enabling automated annotations. Through data homogenization, we achieve compatibility to digitally born manual drawings, as well as to retro-digitized drawings. The latter are found in large Open Access databases, e.g. provided by the Cuneiform Database Library Initiative (CDLI). Ongoing and future work concerns the adaptation of filtering and graphical query techniques for two-dimensional raster images widely used within Digital Humanities research.
Neurally-Guided Semantic Navigation in Knowledge Graph

In this big data era, knowledge becomes increasingly linked, along with the rapid growth in data volume. Connected knowledge is naturally represented and stored as knowledge graphs, which are of more and more importance for many frontier research areas such as machine intelligence. Effectively finding relations between entities in a large knowledge graph plays a key role in many knowledge graph applications, as the most valuable part of a knowledge graph is its rich connectedness, which captures rich information about the objects in the real world. However, due to the intrinsic complexity of real-world knowledge, finding semantically close relations by navigation in a large knowledge graph is very challenging. Canonical graph exploration methods inevitably result in combinatorial explosion especially when the paths connecting two entities are long: the search space is <inline-formula><tex-math notation="LaTeX">$O(d^l)$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>O</mml:mi><mml:mo>(</mml:mo><mml:msup><mml:mi>d</mml:mi><mml:mi>l</mml:mi></mml:msup><mml:mo>)</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="he-ieq1-2805363.gif"/></alternatives></inline-formula>, where <inline-formula><tex-math notation="LaTeX">$d$</tex-math><alternatives><mml:math><mml:mi>d</mml:mi></mml:math><inline-graphic xlink:href="he-ieq2-2805363.gif"/></alternatives></inline-formula> is the average graph node degree and <inline-formula><tex-math notation="LaTeX">$l$</tex-math><alternatives><mml:math><mml:mi>l</mml:mi></mml:math><inline-graphic xlink:href="he-ieq3-2805363.gif"/></alternatives></inline-formula> is the length of the path. In this paper, we will systematically study the semantic navigation problem for large knowledge graphs. Inspired by AlphaGo, which was overwhelmingly successful in the game Go, we designed an efficient semantic navigation method based on a well-tailored Monte Carlo Tree Search algorithm with the unique characteristics of knowledge graphs considered. Extensive experiments on different real-life knowledge bases show that our method is not only effective but also very efficient.
Optical image encryption using multilevel Arnold transform and noninterferometric imaging

Information security has attracted much current attention due to the rapid development of modern technologies, such as computer and internet. We propose a novel method for optical image encryption using multilevel Arnold transform and rotatable-phase-mask noninterferometric imaging. An optical image encryption scheme is developed in the gyrator transform domain, and one phase-only mask (i.e., phase grating) is rotated and updated during image encryption. For the decryption, an iterative retrieval algorithm is proposed to extract high-quality plaintexts. Conventional encoding methods (such as digital holography) have been proven vulnerably to the attacks, and the proposed optical encoding scheme can effectively eliminate security deficiency and significantly enhance cryptosystem security. The proposed strategy based on the rotatable phase-only mask can provide a new alternative for data/image encryption in the noninterferometric imaging.
Command Search for CAD System

AbstractIn this paper, a Command Search Interface is proposed for contemporary CAD systems. The proposed Interface can help users quickly locate the desired commands by a list of key words and thus helping newbie users boost modeling efficiencies without the need of substantial familiarization of graphical user interfaces. In order to enhance the search intelligence, accuracy and robustness, we developed a novel search mechanism based on a proposed CAD Lexicon List, which is constructed from the help manuals of CAD systems and integrated with synonym word sets. This search engine has been initially designed to search CAD modeling commands from textual input, and further incorporated in our Voice-Enabled CAD systems. The definitions and structure of the CAD Lexicon List is presented in detail. The algorithms of the proposed search engine and its applications in our prototype system are also demonstrated.
Flexible flow line scheduling problems with re-entrant flows and queue-time constraints

We consider scheduling problems in a flexible flow line (FFL) with re-entrant flows and queue time constraints. The objective of the scheduling problem is to minimize the number of tardy jobs. The problem considered in the study is a NP-hard problem in the strong sense. Since much computation time is required to find the optimal solution, heuristics are an acceptable practice for finding good solutions. In this research, rule-based heuristics are proposed to solve FFL problems with re-entrant flows and queue time constraints. To evaluate the performance of the proposed heuristics, a large number of test scenarios are designed to simulate practical shop floor problems. For these candidate problems, computational experiments are performed to compare the performance of the proposed heuristics against several well-known dispatching rules. The results show that the proposed heuristics outperform other heuristics.
Dual modality tomography for the monitoring of constituent volumes in multi-component flows.

This thesis describes an investigation into the use of dual modality tomography to monitor multi-component flows. The concept of combining two modalities for this purpose evolved from a desire by the Water Research Council to determine volume flow rates of the major components of sewage. No single sensing method is capable of detecting all suspended solids in sewage flows therefore a decision was taken to combine the technologies of electrical resistance and optical tomography to produce a single measurement system. Sensors for both were positioned around the periphery of a static, circular phantom to allow comparisons between dual and single modalities. Modelling was carried out to determine the behaviour of the electrical impedance and optical tomography technologies in a three dimensional situation, where a variety of flow components exist. This provided a greater understanding of the problems involved in combining these technologies and an appreciation of the potential benefits. The remainder of the work can be divided into three areas. Firstly, hardware was constructed to make voltage measurements for both modalities. Secondly, software was written to perform data acquisition, data manipulation and image reconstruction using a simple back projection algorithm developed for this purpose. Finally, an integration of the individual hardware and software components was performed to produce a dual modality system on which tests were carried out to determine the resulting benefits over single modality alternatives.
Feature tracking of 3D scalar datasets in the 'visit' environment

OF THE THESIS Feature Tracking of 3D Scalar Datasets in the ‘VisIt’ Environment by Rohini M Pangrikar Thesis Director: Professor Deborah Silver Visualization of 3D scientific time varying datasets is a challenging task due to the large amount of data to be processed. Such datasets can contain evolving patterns, visualization of which can give an intuitive interpretation of a dataset under study. The Vizlab at Rutgers has pioneered in the development of feature tracking of 3D scalar datasets by isolating features or region of interest and tracking them over subsequent time steps by using spatial matching. Once these features are extracted and tracked, their evolutionary information can be used for iso-surface visualization by color coding each feature such that evolving patterns are easy to follow. This thesis presents the development of Feature Tracking of 3D scalar datasets in VisIt, a free interactive parallel visualization and graphical analysis tool for viewing scientific data. The implementation is divided into two modules. The first module, implemented as ‘FeatureTrack’ operator plugin, performs feature extraction, quantification and stores iso-surface information for each time step, then tracks features over subsequent timesteps. The second module, implemented in conjunction with the ‘polyfile’ reader plugin, ‘TrackPoly’ operator plugin and ‘PolyDataPlot’ plot plugin, performs visualization. Separation of visualization from feature tracking enables ‘selective enhanced visualization’ so only features of interest are selected and tracked over time.
Effect Research of Aspects Extraction for Chinese Hotel Reviews Based on Machine Learning Method

With the development of Web, there are more and more online reviews. These reviews have become the important information for people. Therefore, automatic analysis of online reviews has become the active research area in natural language processing and management sciences. Apart from the researches about document sentiment classification and sentence sentiment classification, there are an increasing number researches about aspect-based sentiment analysis. This paper tries to use machine learning to extract aspect from Chinese hotel reviews. Through a lot of experiments, we find that machine learning methods are suitable for the aspect extraction of Chinese reviews. This paper adopts different dimensions of features, feature representation methods and classifiers to analyze the integral effect of aspect extraction. The experiment result shows that ME is the best classifiers and presence is most suitable feature representation method for aspect extraction.
A New Approach for Thresholding Finger Print Images based on Second Order Statistical Methods

Thresholding methods are important in any image processing technique. It is used to convert the input gray level image to binary image. Hence, they are the essential preprocessing methods. The present paper proposes various methods for thresholding based on second order statistics (CTH, ETH, HTH, ATH). The proposed four methods are applied on various finger print images and compared with various first order statistics based methods (max, min, (max-min)/2, standard deviation, mean). The results clearly indicate the efficiency of the proposed algorithms.
MICROPROCESSOR BASED HARDWARE-SOFTWARE COMPLEX FOR INVESTIGATING THE MAGNETIC SURFACES OF TORSATRON URAGAN-2M

One of the methods of studying the structure of closed magnetic surfaces in stellarators / torsatrons is a method of scanning fluorescent rod [1-6]. This method has been used to study the torsatron with additional toroidal magnetic field of torsatron URAGAN-2M [4-7]. The experimental session in 2008 of the automation of this method was developed on hardware-software complex described in the article [7]. The hardware of the complex has been implemented on a personal computer and a special module L-783 by L-card. Software of the complex was written in the language Borland C++ Builder 6. To solve the problem of creation the stellarator-mirror magnetic system by switching off one toroidal coil in the torsatron URAGAN-2M described in [8, 9] it was need to conduct a new series of experiments to measure the magnetic surfaces. To perform this work a new safe, protected from the noise, portable device based on microcontroller, which completely replaced the personal computer and an expensive unit L-783 was created. The new complex has been successfully applied in a series of experiments in 2012. This paper describes a new hardware-software control complex of the rod scanning motion in the chamber (Fig. 1) and presents some results of measuring (Fig. 2, Fig. 3) of the magnetic surfaces in the torsatron URAGAN-2M.
Motion‐Vector Refinement for Video Error Concealment Using Downhill Simplex Approach

In error‐prone wireless environments, it is difficult to realize video coding systems that are robust to various types of data loss. In this paper, a novel motion‐vector refinement approach is presented for video error concealment. A traditional boundary‐matching approach is exploited to reduce blocky effects along the block boundary. More specifically, a downhill simplex approach is combined with a boundary‐matching approach to fine‐tune the motion vectors, reducing the blocky effects along the prediction unit block boundary, and minimizing the computational cost. Extensive simulations are performed, and the results obtained verify the robustness and effectiveness of the proposed approach.
A Scheduling Algorithm based on PSO Heuristic in Cloud Computing

The goal of the SaaS provider is the most protable; the user's goal is to meet requirements as quickly as possible but still within budget and deadline. The algorithm's aim gives the schedule to satisfy the objectives of the agents, this is a very difficult problem. This article studies heuristic PSO (Particle Swarm Optimization) and model of components in the cloud computing to propose a model of PaaS providers; admission control algorithm and scheduling for the user’s requirements towards multi-objective optimization of time.The schedule given by the algorithm in order to: (1) optimizing the time for the user, (2) providing the greatest benefits for SaaS providers, (3) satisfying for the constraints of QoS (Quality of Service ) of the user. The result of the algorithm is installed and compared with other algorithms on CloudSim.
A machine learning approach to the classification of technical abstracts in a two-level ontology

This paper describes an approach to multi-label hierarchical document classiﬁcation on an open-source corpus of 30,000 grant proposals. After text cleaning and feature extraction, an array of linear classiﬁers are trained and evaluated with a number of metrics, and found to classify unseen documents into 34 categories with a precision of 80%. Acknowledgements With thanks to L. Gilbert and F. Kayumba on the library staﬀ at STFC, for their advice and feedback on categorisation and ontologies.
Knowledge mapping analysis of CHD research in Web of Science

Based on web of science retrieval tools, bibliometric analysis published on the world through the research field of 2007-2017 years of the coronary heart disease, combined with CiteSpace and Excel analyze the development of coronary heart disease in the research field of Excel software, the research status (including research methods, basic theoretical research and applied research, etc.) and the future development trend, key areas summarize the research of coronary heart disease, and puts forward some suggestions on the future research focus.
GPU parallelization strategies for metaheuristics: a survey

ABSTRACT Metaheuristics have been showing interesting results in solving hard optimization problems. However, they become limited in terms of effectiveness and runtime for high dimensional problems. Thanks to the independency of metaheuristics components, parallel computing appears as an attractive choice to reduce the execution time and to improve solution quality. By exploiting the increasing performance and programability of graphics processing units (GPUs) to this aim, GPU-based parallel metaheuristics have been implemented using different designs. Recent results in this area show that GPUs tend to be effective co-processors for leveraging complex optimization problems. In this survey, mechanisms involved in GPU programming for implementing parallel metaheuristics are presented and discussed through a study of relevant research papers. Metaheuristics can obtain satisfying results when solving optimization problems in a reasonable time. However, they suffer from the lack of scalability. Metaheuristics become limited ahead complex high-dimensional optimization problems. To overcome this limitation, GPU based parallel computing appears as a strong alternative. Thanks to GPUs, parallel metaheuristics achieved better results in terms of computation, and even solution quality. GRAPHICAL ABSTRACT
Design A Maintenance Tool For High-speedSignalling

This project, called Elodi project, aims at realize a framework to produce a maintenance tool (diagnosing and repairing) of a complex system using the design data. This study is applied to a large scale and complex system: the wayside equipment of the high speed signaling system TVM 430. Since 1993, this device is used to bring the on-board signalling of the TGV. The TVM system is presented in this paper, following by the description of the problem of the model-based diagnoses. The discussion is ended with modelling problem to obtain a tool for the maintenance task. 1 A description of the TVM 430 Up to the speed of 220 km/h, the lineside signalling is used to drive a train. With a higher speed, the perception of such a signalling becomes harder and also risky. In order to solve this problem the cab signalling has been designed. The Track-Train transmission [9, 8] does this by the way of safety electrical signals sent from the tracks to the cab. The TVM can be split into two distinct subsystems shown at fig. 1 : the on-board subsystem and the wayside subsystem. Our work Transactions on the Built Environment vol 34, © 1998 WIT Press, www.witpress. com, ISSN 1743-3509
Text Mining Using N-Grams

Text mining is the art of turning free text into numerical variables and then analyzing them with statistical techniques. We introduce the Stata command ngram which implements the most common approach to text mining, "bag of words''. An n-gram is a contiguous sequence of words in a text. Broadly speaking, ngram creates hundreds or thousands of variables each recording how often the corresponding n-gram occurs in a given text. This is more useful than it sounds. Ngram is illustrated with the categorization of text answers from two open-ended questions.
Examining Thematic Variation in a Phenomenographical Study on Computational Physics

Nathaniel T. Hawkins,1 Michael J. Obsniuk,1, 2 Paul W. Irving,1 and Marcos D. Caballero1, 3, 4 Department of Physics and Astronomy, Michigan State University, East Lansing, MI, USA Department of Physics, Kettering University, Flint, MI, USA CREATE for STEM Institute, Michigan State University, East Lansing, MI, USA Department of Physics and Center for Computing in Science Education, University of Oslo, Oslo, Norway
Wireless relay networks

Two network architectures for connecting fixed wireless nodes to a central concentrator-relay and star-are combined into a structured network architecture called a relay star network. A relay star network is characterized by the property that there is a unique communication path between any two nodes; that is, the graph of the network is a rooted tree. In this article we present a tutorial discussion of relay star networks using examples and an extended discussion of a protocol for wireless data collection on relay star networks. The target application for this work is data collection on networks of fixed low-cost, low-data-rate environmental monitors such as urban terminals (parking meters, pay telephones, fire alarm boxes, distress call stations, etc.) and utility meters (water, electricity, gas, steam, etc.).
Video Surveillance System for Extra-high Voltage Power Transmission Lines with an Optimal Image Compression Method

This paper presents a real-time online monitoring system scheme for power transmission line to achieve the objective of giving early warning to disaster and making decision for extra-high voltage power transmission line. Monitoring terminals with a S3C6410 ARM11 processor as core is equipped on the line towers to get the video data captured by the camera on board and other important information. An image optimal compression ratio is discussed based on JPEG2000 baseline compression in RGB space, and then a mathematical model for co-relationship of three color components in RGB is developed. The system has been installed on the power transmission line and works for a long time, and then it has indicated that the work efficiency of detecting the transmission line is raised. Keywords-extra-high voltage power transmission line; image compression; ARM; 4G
A Cost-Effective Semi-Automated Approach for Comprehensive Event Extraction

RESEARCH INTERESTS I am interested in Applied Machine Learning and Data Visualization with special focus on Information Extraction, Natural Language Processing, Intelligence Analysis, and Social Media Analysis. I have received international awards for both Machine Learning and Data Visualization. I am also a Full Stack Developer and have an industry experience of four years. I am graduating in May 2018.