Miracle-dz/UniFAR-FTUs huggingface dataset:
Fast plane segmentation with line primitives for RGB-D sensor

This article presents a fast and robust plane segmentation approach for RGB-D type sensor, which detects plane candidates by line segments extracted from 2-D scanline projected from row or column points. It neither requires high computation to calculate local normals for the entire point cloud as most of approaches do nor randomly chooses plane candidates such as RANSAC-like approaches. First, a line extraction algorithm is utilized to extract line segments. Second, the plane candidates are detected by estimating local normal of points lying on line segments. Finally, the plane having most inlier is recursively extracted from the plane candidates as the result plane. Experiments were conducted with different data sets and the segmentation performances were evaluated quantitatively and qualitatively. We demonstrated the efficiency and robustness of our proposed approach, especially in the none plane scenario, the approach needs little computational cost.
ALTERNATING SSFP PERMITS RAPID, BANDING-ARTIFACT-FREE BALANCED SSFP FMRI

x List of Abbreviations and Symbols Used . xi Acknowledgements . xv Chapter
Cellular Automata (CA) As a Basic Method for Studying Network Dynamics

Macromolecular networks in living cell are the main focus of postgenomic biology and medicine. While network structure is well characterized by means of graph theory and information theory, problems are still encountered in describing and modeling dynamics of cellular networks. The present work summarizes the studies initiated at the Center for the Study of Biological Complexity at VCU, Richmond, aiming at establishing cellular automata as a standard for performing modeling of processes within metabolic, protein, and gene regulatory networks. The CA methodology is presented along with applications to specific pathways of biomedical importance. It is shown that the method enables the identification of characteristic dynamic patterns, and devising of strategies for pathway control. Preliminary results are also shown from a more general approach relating network topology and dynamics.
Dynamic crowding distance based diversity maintenance strategy in MOEAs

This paper proposed a dynamic crowding distance(DCD) based on diversity maintenance strategy,in which the definition of individual's DCD was based on the degree of difference between the crowding distances on different objectives.The proposed strategy computed individuals' DCD dynamically during the process of population maintenance.Compared with NSGA-II and e-MOEA,the experimental results demonstrate that DCD can improve diversity at a high level.
A study on the prediction of driver's pedestrian detectability considering characteristics of human fields-of-view while driving

Recently, advances in pedestrian detection technology have resulted in the development of driving assistance systems that notify the drivers of the presence of pedestrians. However, warning of all existing pedestrians would interfere with the driver’s concentration. Therefore, it is necessary to develop a method to predict the detectability of a pedestrian by the driver. This report proposes a method that predicts the pedestrian detectability considering the characteristics of human fields-of-view. In order to predict the pedestrian detectability precisely, the proposed method constructs a predictor specific to each fields-of-view. Comparison between the output of the proposed method and the ground-truth of pedestrian detectability showed that the proposed method significantly reduced the prediction error in comparison with the existing methods.
Comparative Chukotko Kamchatkan Dictionary

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A NEW GENERATION OF LOS ALAMOS OPACITY TABLES

We present a new, publicly available set of Los Alamos OPLIB opacity tables for the elements hydrogen through zinc. Our tables are computed using the Los Alamos ATOMIC opacity and plasma modeling code, and make use of atomic structure calculations that use fine-structure detail for all the elements considered. Our equation of state model, known as ChemEOS, is based on the minimization of free energy in a chemical picture and appears to be a reasonable and robust approach to determining atomic state populations over a wide range of temperatures and densities. In this paper we discuss in detail the calculations that we have performed for the 30 elements considered, and present some comparisons of our monochromatic opacities with measurements and other opacity codes. We also use our new opacity tables in solar modeling calculations and compare and contrast such modeling with previous work.
Mastering Machine Learning With scikit-learn

Apply effective learning algorithms to real-world problems using scikit-learn About This BookDesign and troubleshoot machine learning systems for common tasks including regression, classification, and clusteringAcquaint yourself with popular machine learning algorithms, including decision trees, logistic regression, and support vector machinesA practical example-based guide to help you gain expertise in implementing and evaluating machine learning systems using scikit-learnWho This Book Is ForIf you are a software developer who wants to learn how machine learning models work and how to apply them effectively, this book is for you. Familiarity with machine learning fundamentals and Python will be helpful, but is not essential. In Detail This book examines machine learning models including logistic regression, decision trees, and support vector machines, and applies them to common problems such as categorizing documents and classifying images. It begins with the fundamentals of machine learning, introducing you to the supervised-unsupervised spectrum, the uses of training and test data, and evaluating models. You will learn how to use generalized linear models in regression problems, as well as solve problems with text and categorical features.You will be acquainted with the use of logistic regression, regularization, and the various loss functions that are used by generalized linear models. The book will also walk you through an example project that prompts you to label the most uncertain training examples. You will also use an unsupervised Hidden Markov Model to predict stock prices.By the end of the book, you will be an expert in scikit-learn and will be well versed in machine learning
Automatic scaling of Cassandra clusters

The purpose of this thesis is to create an automatic scaling implementation for Cassandra clusters. The automatic scaler should never lower the overall performance of the cluster in a way that results in a bad user experience. It should also be able to successfully scale up and down nodes, and the cluster should continue as if nothing happened. Last but not least, it is desirable that the automatic scaler performs equally, or better than, the person who is in charge of administrating the database. In this thesis we have developed an early version of an autoscaler that may run alongside a Cassandra instance. The implementation is split into two separate implementations: a master-, and an agent-implementation. The master will be deployed to the same server as the application using the cluster, even though this is not required. The agent implementation will be deployed to, and run alongside, all nodes that are a part of the cluster. The agent will monitor the node’s resource usage, and send messages back to the master if the usage increases above, or decreases below certain thresholds. We performed a set of test cases to prove that the implementation works as intended. The test cases recorded the nodes resource-usage to determine the impact our implementation makes to the overall performance.
A Scalable Framework for Distributed Object Tracking across a Many-camera Network

Advances in deep neural networks (DNN) and computer vision (CV) algorithms have made it feasible to extract meaningful insights from large-scale deployments of urban cameras. Tracking an object of interest across the camera network in near real-time is a canonical problem. However, current tracking platforms have two key limitations: 1) They are monolithic, proprietary and lack the ability to rapidly incorporate sophisticated tracking models; and 2) They are less responsive to dynamism across wide-area computing resources that include edge, fog and cloud abstractions. We address these gaps using Anveshak, a runtime platform for composing and coordinating distributed tracking applications. It provides a domain-specific dataflow programming model to intuitively compose a tracking application, supporting contemporary CV advances like query fusion and re-identification, and enabling dynamic scoping of the camera network's search space to avoid wasted computation. We also offer tunable batching and data-dropping strategies for dataflow blocks deployed on distributed resources to respond to network and compute variability. These balance the tracking accuracy, its real-time performance and the active camera-set size. We illustrate the concise expressiveness of the programming model for $4$ tracking applications. Our detailed experiments for a network of 1000 camera-feeds on modest resources exhibit the tunable scalability, performance and quality trade-offs enabled by our dynamic tracking, batching and dropping strategies.
An Efficient Scheme for GPS Data Compression

According to the characteristics of GPS data in static measurement, information source coding was carried out for the received GPS data collected from the observation spot with the help of the combination of eliminating redundancy of data, dictionary coding and differential Huffman coding method. Compared with the existing data compression technology, the proposed scheme demands less hardware resources. The coding speed is faster and preferable compression ratio can be achieved. About 25 percent of GPS data can be (reduced) in (receiving) device with an 8-bit microprocessor. So the lossless compression of GPS data can be (realized) and the communication loads can be released in low performance receiving device without any additional change of hardware resource.
Trust-Based Service Management for Service-Oriented Mobile Ad Hoc Networks and Its Application to Service Composition and Task Assignment with Multi-Objective Optimization Goals

With the proliferation of fairly powerful mobile devices and ubiquitous wireless technology, traditional mobile ad hoc networks (MANETs) now migrate into a new era of service-oriented MANETs wherein a node can provide and receive service from other nodes it encounters and interacts with. This dissertation research concerns trust management and its applications for service-oriented MANETs to answer the challenges of MANET environments, including no centralized authority, dynamically changing topology, limited bandwidth and battery power, limited observations, unreliable communication, and the presence of malicious nodes who act to break the system functionality as well as selfish nodes who act to maximize their own gain. We propose a context-aware trust management model called CATrust for serviceoriented ad hoc networks. The novelty of our design lies in the use of logit regression to dynamically estimate trustworthiness of a service provider based on its service behavior patterns in a context environment, treating channel conditions, node status, service payoff, and social disposition as “context” information. We develop a recommendation filtering mechanism to effectively screen out false recommendations even in extremely hostile environments in which the majority recommenders are malicious. We demonstrate desirable convergence, accuracy, and resiliency properties of CATrust. We also demonstrate that CATrust outperforms contemporary peer-to-peer and Internet of Things trust models in terms of service trust prediction accuracy against collusion recommendation attacks. We validate the design of trust-based service management based on CATrust with a node-to-service composition and binding MANET application and a node-to-task assignment MANET application with multi-objective optimization (MOO) requirements. For either application, we propose a trust-based algorithm to effectively filter out malicious nodes exhibiting various attack behaviors by penalizing them with trust loss, which ultimately leads to high user satisfaction. Our trust-based algorithm is efficient with polynomial runtime complexity while achieving a close-to-optimal solution. We demonstrate that our trust-based algorithm built on CATrust outperforms a non-trust-based counterpart using blacklisting techniques and trust-based counterparts built on contemporary peer-to-peer trust protocols. We also develop a dynamic table-lookup method to apply the best trust model parameter settings upon detection of rapid MANET environment changes to maximize MOO performance. Trust-Based Service Management for ServiceOriented Mobile Ad Hoc Networks and Its Application to Service Composition and Task Assignment with Multi-Objective Optimization Goals
Research on service matching and composition in networked manufacturing environmentt

In order to integrate various services in networked manufacturing environment,a semantic-based framework to support the matching,composition and operation of manufacturing services was proposed.This framework concentrated on the modeling,operation,monitoring and improvement of networked manufacturing processes.And it was pointed out that the semantic-based service matching and composition algorithm was the key technology in implementation of this framework.A formal definition of manufacturing service was provided,and an integrated service matching algorithm framework was built based on semantics,considering the service functionality,input/output parameters and Quality of Service(QoS). The service composition algorithm optimized the service composition by converting the problem to the shortest path problem.And an example was given to illustrate the application of the proposed algorithm.Finally,the future research trend was also discussed.
Generation and Validation of Workflows for On-demand Mapping

The paper presents a method to automatically select and sequence the tasks required to build maps according to user requirements. Workflows generated are analysed using Petri nets to assess their validity before execution. Although further work is required to select the optimal method for generating the workflow and to execute the workflow, the proposed method can be used on any workflow to assess its validity.
Brain tumor classification of microscopy images using deep residual learning

The crisis rate of brain tumor is about one point four in ten thousands. In general, cytotechnologists take charge of cytologic diagnosis. However, the number of cytotechnologists who can diagnose brain tumors is not sufficient, because of the necessity of highly specialized skill. Computer-Aided Diagnosis by computational image analysis may dissolve the shortage of experts and support objective pathological examinations. Our purpose is to support a diagnosis from a microscopy image of brain cortex and to identify brain tumor by medical image processing. In this study, we analyze Astrocytes that is a type of glia cell of central nerve system. It is not easy for an expert to discriminate brain tumor correctly since the difference between astrocytes and low grade astrocytoma (tumors formed from Astrocyte) is very slight. In this study, we present a novel method to segment cell regions robustly using BING objectness estimation and to classify brain tumors using deep convolutional neural networks (CNNs) constructed by deep residual learning. BING is a fast object detection method and we use pretrained BING model to detect brain cells. After that, we apply a sequence of post-processing like Voronoi diagram, binarization, watershed transform to obtain fine segmentation. For classification using CNNs, a usual way of data argumentation is applied to brain cells database. Experimental results showed 98.5% accuracy of classification and 98.2% accuracy of segmentation.
CauseInfer: Automated End-to-End Performance Diagnosis with Hierarchical Causality Graph in Cloud Environment

Modern computing systems especially cloud-based and cloud-centric systems always consist of a mass of components running in large distributed environments with complicated interactions. They are vulnerable to performance problems due to the highly dynamic runtime environment changes (e.g., overload and resource contention) or software bugs (e.g., memory leak). Unfortunately, it is notoriously difficult to diagnose the root causes of these performance problems in a fine granularity due to complicated interactions and a large cardinality of potential cause set. In this paper, we build an automated, black-box and end-to-end cause inference system named CauseInfer to pinpoint the root causes or at least provide some hints. CauseInfer can automatically map a distributed system to a two-layer hierarchical causality graph and infer the root causes along the causal paths in the causality graph. CauseInfer models the fault propagation paths in an explicit way and works without instrumentation to the running production system, which makes CauseInfer more effective and practical than previous approaches. The experimental evaluations in two benchmark systems show that CauseInfer can identify the root causes in a high accuracy. Compared to several state-of-the-art approaches, CauseInfer can achieve over 10 percent improvement. Moreover, CauseInfer is lightweight and flexible enough to readily scale out in large distributed systems. With CauseInfer, the mean time to recovery (MTTR) of the cloud systems can be significantly reduced.
Simulation Research on Mechanical Properties of Three-phase Asynchronous Motor Based on COM Hybrid Programming

Aiming at the problem of slow speed operation of Matlab used in the research and analysis of power system,hybrid programming is introduced.An example about the mechanical properties of the three-phase asynchronous motor simulation is introduced to emphasize that using COM(Component Object Model) Builder,the program of Matlab is integrated into COM component which is called by VB.The results of the hybrid programming method used in power system simulation show its improved speed and convenience.
Channel Coding for Multi-Carrier Wireless Partial Duplex

In order to leverage the spectrum resources, several forms of wireless duplex have been introduced and investigated in recent years. In Partial Duplex (PD) schemes, part of the band is transmitted in Full-Duplex (FD) and the rest in Half-Duplex (HD); therefore, some transmitted symbols will be characterized, at the receiver, by high SNR (Signal-to-Noise Ratio) and others by low SNR because of the residual self-interference (SI) in the FD part. Combining properly the patterns of these high and low SNR symbols affects the performance of the encoding schemes used in the system; in order to overcome this issue, different encoding and allocation schemes can be adopted for achieving a satisfactory solution. This paper investigates the performance of Low-Density Parity-Check (LDPC), turbo, polar codes for wireless PD. Orthogonal Frequency Division Multiplexing (OFDM) is an efficient multicarrier modulation technique, used in 4G and in the upcoming 5G, and it can be exploited for realizing a proper symbol allocation according to the SNR on each subcarrier. In this context, performance of LDPC, polar, and turbo codes derived from existing specifications has been studied when the system faces a mixture of high and low SNRs on the bits and hence on the symbols coming from the same codeword and this unbalanced SNR distribution is known a-priori at the transmitter, a condition associated with a scheme in which part of the symbols is subject to FD interference.
Animation for Visualization: Opportunities and Drawbacks

The use of Java, Flash, Silverlight, and JavaScript on the Web has made it easier to distribute animated, interactive visualizations. Many visualizers are beginning to think about how to make their visualizations more compelling with animation. There are many good guides on how to make static visualizations more effective, and many applications support interactivity well. But animated visualization is still a new area; there is little consensus on what makes for a good animation.
A branch-and-cut-and-price approach for a two-level hierarchical location problem

In many telecommunication networks a given set of client nodes must be served by different sets of facilities, which must be located and dimensioned in designing the network. We provide a compact and an extended formulation for that problem and we design an exact branch-and-cut-and-price optimization algorithm. We test our approach on a set of instances derived from the facility location literature.
Pilot Design for Sparse Channel Estimation in Large-Scale MIMO-OFDM System

The pilot design problem in large-scale multi-input-multioutput orthogonal frequency division multiplexing (MIMO-OFDM) system is investigated from the perspective of compressed sensing (CS). According to the CS theory, the success probability of estimation is dependent on the mutual coherence of the reconstruction matrix. Specifically, the smaller the mutual coherence is, the higher the success probability is. Based on this conclusion, this paper proposes a pilot design algorithm based on alternating projection and obtains a nonorthogonal pilot pattern. Simulation results show that applying the proposed pattern gives the better performance compared to applying conventional orthogonal one in terms of normalized mean square error (NMSE) of the channel estimate. Moreover, the bit error rate (BER) performance of the large-scale MIMO-OFDM system is improved.
Quality estimation for text simplification

The quality of the output generated by automatic Text Simplification (TS) systems is traditionally assessed by human annotators. In spite of the fact that the automatisation of that process would enable faster and more consistent evaluation, there have been almost no studies addressing this problem. We propose several decision-making procedures for automatic classification of the simplified sentences into three classes (bad, OK, good) depending on their grammaticality, meaning preservation, and simplicity. We experiment with ten different classification algorithms and 12 different feature sets on three TS datasets obtained using different text simplification strategies, achieving the results significantly above the state of the art. Additionally, we propose to use an unique measure (Total2 or Total3) for classifying the quality of the automatically simplified sentences into two (discard or keep) or three (bad, OK, good) classes.
Improved Synthesis of Three Brominated Analogues of the Potent Environmental Mutagen 3-Chloro-4-(dichloromethyl)-5-hydroxy-2(5H)-furanone (MX).

Abstract The synthesis of three brominated analogues of the environmental mutagen 3-chloro-4-(dichloromethyl)-5-hydroxy-2(5H)-furanone (MX), namely, 3-chloro-4-(bromochloromethyl)-5-hydroxy-2(5H)-furanone (BMX-1), 3-chloro-4-(dibromomethyl)-5-hydroxy-2(5H)-furanone (BMX-2) and 3-bromo-4-(dibromomethyl)-5-hydroxy-2(5H)-furanone (BMX-3), by employing a simple procedure from a common precursor, is described.