Miracle-dz/UniFAR-FTUs huggingface dataset:
Extraction and vectorization of street blocks on scanned topographic maps

Based on feature points extraction and total least squares approximation,a new method is presented for extracting and vectorizing street blocks on topographic maps.Firstly,the outline of the street block is tracked and the original vector data are obtained.Then,the feature points among the original data are extracted.Finally,each sideline of the street blocks is fitting by using total least squares,and the final vector data are obtained.Moreover,three precision criteria are presented and a quantitative analysis of the vector data on the experimental map is given.Results show that the improved precision and data without redundancy can be obtained by using this method.
Implementation of a multiprocessor array for spiking neural network emulation on FPGA

During this project I have described and simulated an Array of Multiprocessors to emulate Spiking Neural Networks in a new architecture. Finally I have synthesized and implemented it on a FPGA to check the correct behaviour. The main described block is the Process Element, which contains several elements that allow the processing of the neuronal algorithm. Inside we find the ALU, which allows to perform calculations, a bank of registers to store values, a LFSR to generate pseudorandom numbers, data memory to store parameters and memories, which allow a dynamic interaction between Processing Elements. Each element connects within a parameterizable array with the necessary hardware for the complete functionality of all of the instruction set. Finally it has been synthesized and modified to meet the time constraints, achieving to generate an array of 12x12 with problemfree timings.
Design of afforestation and decision-making knowledge base and research of inference engine algorithm in the silviculture expert system

Based on a series of studies on forestry expert system during the Tenth Five-Year 863 Plans of China,this study was conducted on the knowledge representation,knowledge base and reasoning mechanism of the silviculture expert system.We proposed a model of knowledge representation and reasoning mechanism adopting the production rule combined with the framework representation method to fit the demands of different regions.This model is in line with the cognitive characteristics of experts to facilitate the maintenance of experts to update and improve the storage way of the knowledge base,to reduce the number of inference rules,to make the expert system adapt to different regions,and to separate the inference engine from the knowledge base.The structure and example of the knowledge base are given,together with the reasoning process of the inference engine based on the knowledge base.The outcome of this study was applied in the "Forest Cultivation Expert System" which comes from the National Eleventh Five-Year research in the subject of "The Research and Application of Key Technologies in the Production and Management Process of Fast-Growing and High-Yield Plantation",and achieved satisfactory results.
Computational methods for comparison and exploration of event sequences

Many types of data, e.g., natural language texts, biological sequences, or time series of sensor data, contain sequential structure. Analysis of such sequential structure is interesting for various reasons, for example, to detect that data consists of several homogeneous parts, that data contains certain recurring patterns, or to find parts that are different or surprising compared to the rest of the data. The main question studied in this thesis is how to identify global and local patterns in event sequences. Within this broad topic, we study several subproblems. The first problem that we address is how to compare event frequencies across event sequences and databases of event sequences. Such comparisons are relevant, for example, to linguists who are interested in comparing word counts between two corpora to identify linguistic differences, e.g., between groups of speakers, or language change over time. The second problem that we address is how to find areas in an event sequence where an event has a surprisingly high or low frequency. More specifically, we study how to take into account the multiple testing problem when looking for local frequency deviations in event sequences. Many algorithms for finding local patterns in event sequences require that the person applying the algorithm chooses the level of granularity at which the algorithm operates, and it is often not clear how to choose that level. The third problem that we address is which granularities to use when looking for local patterns in an event sequence. The main contributions of this thesis are computational methods that can be used to compare and explore (databases of) event sequences with high computational efficiency, increased accuracy, and that offer new perspectives on the sequential structure of data. Furthermore, we illustrate how the proposed methods can be applied to solve practical data analysis tasks, and describe several experiments and case studies where the methods are applied on various types of data. The primary focus is on natural language texts, but we also study DNA sequences and sensor data. We find that the methods work well in practice and that they can efficiently uncover various types of interesting patterns in the data.
Hyperparameter Learning for Graph Based Semi-supervised Learning Algorithms

Research Interests statistical machine learning, data mining and pattern recognition. Specialization in graphical models, kernel methods, semi-supervised learning, exponential families, reinforcement learning, Gaussian processes, and Bregman divergence. Applications: image processing, font and calligraphy learning using image analogy, traffic control, named entity recognition, text categorization, and sensor networks.
EXPLORE THE EFFECTS OF EMOTICONS ON TWITTER SENTIMENT ANALYSIS

In recent years, Twitter Sentiment Analysis (TSA) has become a hot research topic. The target of this task is to analyse the sentiment polarity of the tweets. There are a lot of machine learning methods specifically developed to solve TSA problems, such as fully supervised method, distantly supervised method and combined method of these two. Considering the specialty of tweets that a limitation of 140 characters, emoticons have important effects on TSA. In this paper, we compare three emoticon pre-processing methods: emotion deletion (emoDel), emoticons 2-valued translation (emo2label) and emoticon explanation (emo2explanation). Then, we propose a method based on emoticon-weight lexicon, and conduct experiments based on Naive Bayes classifier, to validate the crucial role emoticons play on guiding emotion tendency in a tweet. Experiments on real data sets demonstrate that emoticons are vital to TSA.
Design And Implementation Of The 4 4 Bsd Operating System

Thank you very much for reading design and implementation of the 4 4 bsd operating system. As you may know, people have look numerous times for their favorite books like this design and implementation of the 4 4 bsd operating system, but end up in infectious downloads. Rather than enjoying a good book with a cup of coffee in the afternoon, instead they juggled with some malicious bugs inside their computer.
Unsupervised and Supervised Classification of PolSAR Image Using Decomposition Techniques: An Analysis from L- Band SIR-C Data

The classification of Polarimetric Synthetic Aperture Radar (PolSAR) image has become a very important task after availability of data by using different techniques. In this paper L-band Quad Pole SIR-C PolSAR dataset of Kolkata City, India is used. We applied different unsupervised and supervised classification techniques on SIR-C dataset. The unsupervised classification techniques include like H-alpha, Wishart H-alpha and Wishart H-A-alpha, whereas in supervised classification classes can be made manually using clustering process. This paper presents the comparison of unsupervised and supervised classification on the basis of four major classes like water, open land, vegetation and settlement. In unsupervised classification we observed that the Wishart H-A-alpha classified image is not only better than H-alpha and Wishart H-alpha classified image but also better than supervised classification for classification PolSAR image analysis.
LTE in the Unlicensed Spectrum: A Survey

ABSTRACT Fueled by the increasing popularity of handheld mobile devices with powerful data processing capabilities, the wireless industry is witnessing an avalanche of mobile traffic. This unprecedented escalation has imposed significant challenges due to the limited licensed spectrum for cellular networks. Any effort to achieve capacity growth through network densification will face the challenge of severe inter-cell interference. Among the most effective solutions to this issue is to make the best use of all spectrum types through matured technology. Long-term evolution (LTE) access to unlicensed spectrum is considered one of the latest groundbreaking innovations to provide high-performance and a seamless user experience under a unified radio technology by extending LTE to the readily available unlicensed spectrum. In this article, we introduce the dawn of LTE access to unlicensed spectrum as a boon to the spectrum scarcity problem. Then, we explore deployment scenarios for LTE in unlicensed spectrum. We further identify key issues in bringing LTE unlicensed spectrum access to reality, together with other technologies for accessing unlicensed spectrum, specifically Wi-Fi. Finally, we discuss some existing coexistence mechanisms for successful, harmonious deployment of LTE and Wi-Fi in the same unlicensed band.
Computer simulation of low-dose CT with clinical lung image database: a preliminary study

Large samples of raw low-dose CT (LDCT) projections on lungs are needed for evaluating or designing novel and effective reconstruction algorithms suitable for lung LDCT imaging. However, there exists radiation risk when getting them from clinical CT scanning. To avoid the problem, a new strategy for producing large samples of lung LDCT projections with computer simulations is proposed in this paper. In the simulation, clinical images from the publicly available medical image database-the Lung Image Database Consortium(LIDC) and Image Database Resource Initiative (IDRI) database (LIDC/IDRI) are used as the projected object to form the noise-free sinogram. Then by adding a Poisson distributed quantum noise plus Gaussian distributed electronic noise to the projected transmission data calculated from the noise-free sinogram, different noise levels of LDCT projections are obtained. At last the LDCT projections are used for evaluating two reconstruction strategies. One is the conventional filtered back projection (FBP) algorithm and the other is FBP reconstruction from the filtered sinogram with penalized weighted least square criterion (PWLS-FBP). Images reconstructed with the LDCT simulations have shown that the PWLS-FBP algorithm performs better than the FBP algorithm in reducing streaking artifacts and preserving resolution. Preliminary results indicate that the feasibility of the proposed lung LDCT simulation strategy for helping to determine advanced reconstruction algorithms.
Placement algorithms for hierarchical cooperative caching and other location problems

In a large-scale information system, such as a digital library or the world wide web, a set of distributed caches can improve their effectiveness by cooperating with one another, both in serving each other's requests as well as in deciding what to store. This dissertation explores the potential of such cooperative caching, and provides basic placement algorithms using which the caches can coordinate their storage decisions. The first part of the dissertation focuses on variants of the placement problem involving a single object. The most well-known of these variants are the facility location problems, which have received considerable attention in the operations research literature due to their widespread applicability. We prove that a simple local search heuristic, proposed about 25 years ago, yields polynomial-time constant-factor approximations for several metric facility location problems. The second part of the dissertation addresses the simultaneous placement of a collection of objects in hierarchical networks. We provide both exact and approximate polynomial-time algorithms for this hierarchical placement problem. Our exact algorithm is based on a reduction to min-cost flow, and does not appear to be practical for large problem sizes. Hence we are motivated to look for simpler approximate algorithms. Our main result is a simple constant-factor approximation algorithm that admits an efficient distributed implementation. The third and final part of the dissertation describes our simulation experiments which were performed to ascertain the practical potential of cooperative caching, and to investigate the practical performance of various placement and replacement algorithms. Driven by both synthetic and web-trace workloads, these experiments compare several traditional as well as novel caching algorithms. They demonstrate that cooperation improves performance significantly, especially when the cache sizes are small. More surprisingly, the experiments show that our amortizing algorithm yields placements that are extremely close, within 5%, to the optimal when the access patterns are stable. We also examine hybrid strategies to cope with changing access patterns.
Multi-camera networks for motion parameter estimation of an aircraft

A multi-camera network is proposed to estimate an aircraft’s motion parameters relative to the reference platform in large outdoor fields. Multiple cameras are arranged to cover the aircraft’s large-scale motion spaces by field stitching. A camera calibration method using dynamic control points created by a multirotor unmanned aerial vehicle is presented under the conditions that the field of view of the cameras is void. The relative deformation of the camera network caused by external environmental factors is measured and compensated using a combination of cameras and laser rangefinders. A series of field experiments have been carried out using a fixed-wing aircraft without artificial makers, and its accuracy is evaluated using an onboard Differential Global Positioning System. The experimental results show that the multi-camera network is precise, robust, and highly dynamic and can improve the aircraft’s landing accuracy.
A Study of the Practical and Tutorial Scheduling Problem

The practical and tutorial allocation problem is a problem encountered at tertiary institutions and essentially involves the allocation of students to practical or tutorial groups for the different courses the student is enrolled in. Practical and tutorial scheduling for first year courses is becoming mor e and more challenging as the number of permissible course combinations and student numbers inc rease at tertiary institutions, and while this has previously been done manually and independently for each course, this is no longer feasible. The paper firstly presents a formal definition of the practical and tutorial scheduling problem. Low - level c onstruction heuristics for this domain are defined and a heuristic approach for solving this problem is proposed. A tool namely, PRATS, incorporating this approach is described. The performance of PRATS on six sets of real - world data is discussed . The paper also reports on a hyper - heuristic implemented to automatically generate low - level construction heuristics and compares the performance of the generated heuristi cs to the human intuitive heuristics used.
Ab initio calculations of CaZrO3, BaZrO3, PbTiO3 and SrTiO3 (001), (011) and (111) surfaces as well as their (001) interfaces

Abstract We carried out ab initio calculations for technologically important ABO3 perovskites, like, CaZrO3, BaZrO3, PbTiO3 and SrTiO3, their (001), (011) and (111) surfaces as well as (001) interfaces. For ABO3 perovskites, such as CaZrO3, BaZrO3, PbTiO3 and SrTiO3, in most of cases, all upper (001) surface layer atoms relax inwards, all second surface layer atoms relax outwards, and again, all third surface layer atoms relax inwards. Our calculated CaZrO3, BaZrO3, PbTiO3 and SrTiO3 (001) surface energies are almost equivalent for both AO and BO2-terminations, and always considerably smaller than the (011) and especially (111) surface energies. Our calculated BaTiO3/SrTiO3, SrZrO3/PbZrO3 and PbTiO3/SrTiO3 (001) heterostructure band gap depends much more strongly from the termination of the upper augmented layer (AO or BO2) than from the number on the substrate augmented layers.
Efficiency and mutation strength adaptation of the (μ/μI, λ)-ES in a noisy environment

Noise is present in many optimization problems. Evolutionary algorithms are frequently reported to be robust with regard to the effects of noise. In many cases, there is a tradeoff between the accuracy with which the fitness of a candidate solution is determined and the number of candidate solutions that are evaluated in every time step. This paper addresses this tradeoff on the basis of recently established results from the analysis of the local performance of a recombinant multi-parent evolution strategy on a noisy sphere. It is shown that, provided that mutation strengths are appropriately adapted, the strategy is indeed able to cope with noise, and that results previously obtained for single-parent evolution strategies do not carry over to multi-parent strategies. Then, the problem of mutation strength adaptation in noisy environments is addressed. Mutative self-adaptation and the cumulative mutation strength adaptation algorithm are compared empirically in a simple fitness environment. The results suggest that both algorithms are prone to failure in the presence of noise.
Dynamic multi-objective cooperative optimization of biochemical process based on kinetic model and MOPSO

Dynamic optimization or optimal control problems are omnipresent in the biochemical industry,which often involve multiple and conflicting objectives. This paper presents a novel dynamic multi-objective cooperative optimization algorithm based on particle swarm optimization and kinetic model for biochemical process. The multi-objective optimization includes two objective functions: as maximizing production concentration and minimizing the production time by optimizing the initial variables. In order to improve the diversity of the Pareto solutions, a dynamic aggregate method is adopted to select the best particle and to guide its fighting. The global exploratory capability is enhanced by using incorporate adaptive inertia weight. The efficiency of the proposed method is demonstrated by some comparison rice fermentation process experiments, the results show the superiority of the proposed method to the conventional one.
An Efficient Smooth Boosting by Filtering(New Trends in Theory of Computation and Algorithm)

Boosting is a general method to construct a highly accurate classiﬁer by combining “weakly” accurate ones. Smooth boosting algorithms are variants of boosting methods which handle only smooth distributions on the data. They are proved to be noise-tolerant and can be used in the “boosting by ﬁltering” scheme, which is suitable for learning over huge data. However, current smooth boosting algorithms have rooms for improvements: A non-smooth boosting algorithm, InfoBoost can perform more eﬃciently than typical boosting algorithmns by using an information-theoretic criterion for choosing hypotheses. In this paper, we propose a new smooth boosting algorithm with an information-theoretic criterion and we show that it inherits the advantages of two approaches, smooth boosting and InfoBoost.
Facial Animation & Interaction During Dialogues

Preface This document is the final thesis that is part of the Master project. This project has been carried out in order to obtain a Master degree in computer science at the chair of Human Media Interaction (HMI). This chair is part of the "Electrical Engineering", "Mathematics" and "Computer Science" division at the University of Twente. HMI is all about the interaction between human and machine and offers a great range of topics. At MIRALab research topics include facial animation, cloth and hair simulation, 3D modeling, personality and emotion simulation, motion capture and body animation. Projects carried out at MIRALab include visualization for medical purposes, reconstruction of cultural heritage and augmented reality. I would like to thank Prof. Nadia Magnenat-Thalmann for allowing me to do a project at her lab and for her support during the project. I had great time at your lab and looking forward to continue that. I would also like to thank my direct supervisors George Papagiannakis and Zerrin Kasap for their supervision, useful comments and extensive help. I also would like to thank Prof. Anton Nijholt and Job Zwiers and Dirk Heylen for supervising me in the Netherlands. A special thank you is for everyone at MIRALab that made my stay pleasant. Thank you all!
Optimizing Join in HIVE Star Schema Using Key/Facts Indexing

ABSTRACT These days Big Data represents complex and an important issue for the extraction/retrieval of information due to the fact that its analysis requires massive computation power. In addition, database star schema can be considered as one of the complicated data models due to the use of joining queries heavily for information extraction and reports generation, which demands scanning for a large amount of data (tera, peta, zeta bytes, etc.). On the other hand, HIVE is considered one of the essential and efficient Big Data SQL-based tools built on the top of Hadoop as a translator from SQL queries into Map/Reduce tasks. In addition, using data indexing techniques with join queries could improve /speed up HIVE join query tasks execution especially in a star schema. According to the work in this paper, Key/Facts indexing methodology was introduced to materialize the star schema and inject a simple index for data. Based on this, Key/Facts indexing methodology SQL queries’ execution time in HIVE improved without changing HIVE framework. TPC-H benchmark was used in order to estimate the performance of Key/Facts methodology. Experimental results prove that Key/Facts methodology out-performs traditional HIVE join execution time. Also, Key/Facts performance is improved by increasing the data size. Generally, Key/Facts can be considered one of the suitable methodologies for Big Data analysis.