Miracle-dz/UniFAR-FTUs huggingface dataset:
A Algorithm of Path Planning Based on Multiple Mobile Robots

The environment of automated warehouse is complex. Path collision exists conflict between intelligent robot with unknown obstacles and intelligent mobile-robot, increasing the difficulty in multiple mobile robots path planning.To solve the problem, firstly working environmental model is established with traffic rules method and the grid method. Then the whole system adopts the idea of hierarchical cooperation for dynamic local and global path planning.By simulation this method is suitable for dynamic environment with known and unknown obstacles and effectively solve the problem of path planning for multiple mobile robots in automated warehouse.
HYBRID EXPERT SYSTEM OF ROUGH SET AND NEURAL NETWORK

The combination of neural network and expert system can accelerate the process of inference, and then rapidly produce associated facts and consequences. However, neural network still has some problems such as providing explanation facilities, managing the architecture of network and accelerating the training time. Thus to address these issues we develop a new method for preprocessing based on rough set and merge it with neural network and expert system. The resulting system is a hybrid expert system model called a Hybrid Rough Neural Expert System (HRNES).
On the Use of Describing Functions in the Study of Nonlinear Active Microwave Circuits

It is shown that a nonlinear microwave circuit can easily be represented by a feedback model. This formalistic identity with nonlinear control systems suggests that methods and results can be borrowed from nonlinear control theory. The describing function technique, a concept that has been developed to a high degree of sophistication in control theory, is applied to the problem of phase-locking of microwave oscillators. The use of describing functions in the study of nonlinear microwave circuits may give a simple conceptual understanding of locking phenomena, for example, and thereby reduce computational efforts.
High Energy Vision: Processing X-rays

Astronomy is by nature a visual science. The high quality imagery produced by the world's observatories can be a key to effectively engaging with the public and helping to inspire the next generation of scientists. Creating compelling astronomical imagery can, however, be particularly challenging in the non-optical wavelength regimes. In the case of X-ray astronomy, where the amount of light available to create an image is severely limited, it is necessary to employ sophisticated image processing algorithms to translate light beyond human vision into imagery that is aesthetically pleasing while still being scientifically accurate. This paper provides a brief overview of the history of X-ray astronomy leading to the deployment of NASA's Chandra X-ray Observatory, followed by an examination of the specific challenges posed by processing X-ray imagery. The authors then explore image processing techniques used to mitigate such processing challenges in order to create effective public imagery for X-ray astronomy. A follow-up paper to this one will take a more in-depth look at the specific techniques and algorithms used to produce press-quality imagery.
Linear mapping functions for high-dimensional indexing in image databases

The problem of indexing high-dimensional data has received renewed interest because of its necessity in emerging multimedia databases. The limitations of traditional tree-based indexing and the dimensionality curse is well known. We have proposed a hierarchical indexing structure based on linear mapping functions, which is not tree-based, and not necessarily balanced. At each level of the hierarchy, a linear mapping function is used to distribute the data among buckets. The feasibility and the performance of the indexing structure is dependent on finding appropriate mapping functions for any given data set. In this paper we present the approach taken in arriving at a few classes of mapping functions. We have given a heuristic algorithm to determine the choice of the most appropriate mapping function for a given data set. The results of experiments with real life data are presented and they indicate that the proposed indexing structure with linear mapping functions is indeed practical.
Environmental Sound Recognition Using Double-Level Energy Detection

The performance of classic Mel-frequency cepstral coefficients (MFCC) is unsatisfactory in noisy environment with different sound sources from nature. In this paper, a classification approach of the ecological environmental sounds using the double-level energy detection (DED) was presented. The DED was used to detect the existence of the sound signals under noise conditions. In addition, MFCC features from the frames which were detected the presence of the sound signals by DED were extracted. Experimental results show that the proposed technology has better noise immunity than classic MFCC, and also outperforms time-domain energy detection (TED) and frequency-domain energy detection (FED) respectively.
Foundations for an Empirically Determined Scale of Trust in Automated Systems

One component in the successful use of automated systems is the extent to which people trust the automation to perform effectively. In order to understand the relationship between trust in computerized systems and the use of those systems, we need to be able to effectively measure trust. Although questionnaires regarding trust have been used in prior studies, these questionnaires were theoretically rather than empirically generated and did not distinguish between three potentially different types of trust: human-human trust, human-machine trust, and trust in general. A 3-phased experiment, comprising a word elicitation study, a questionnaire study, and a paired comparison study, was performed to better understand similarities and differences in the concepts of trust and distrust, and among the different types of trust. Results indicated that trust and distrust can be considered opposites, rather than different concepts. Components of trust, in terms of words related to trust, were similar across the three.
Improved Principal Component Analysis for Face Image Recognition

Principal cmponent analysis is applied to face recognition problem to shorten image processing time in this paper. Based on the analysis of basic principal cmponent analysis algorithm, a new strategy which can improve algorithm efficiency by reducing the dimension of face images to be processed is introduced to face recognition problem, and the detailed computer implementation procedure is given. Numerical experiments have been performed to evaluate the efficiency of improved principal cmponent analysis algorithm for the problem of face recognition.
Adaptive Volterra equalizer for optical OFDM modem

This paper addresses OFDM (orthogonal frequency division multiplexing) transmission over optical links with high spectral efficiency, i.e. by using high-order QAM-modulation schemes as a mapping method prior to the OFDM multicarrier representation. Here we address especially coherent optical OFDM modem in long distance which is affected by a nonlinear distortion caused by fiber nonlinearity as a major performance-limiting factor in advanced optical communication systems. We proposed a nonlinear electrical equalization scheme based on the Volterra model. Compared with other popular linear compensation technique such as the LMS (least Mean Square) and RLS (Recursive Least square), simulation results are presented to demonstrate the capability of a Volterra model based electrical equalizer used in a coherent optical orthogonal frequency division multiplexing system. It is shown that the Volterra model based equalizer can significantly reduce nonlinear distortion.
Hierarchical rule generalisation for speaker identification in fiction books

This paper presents a hierarchical pattern matching and generalisation technique which is applied to the problem of locating the correct speaker of quoted speech found in fiction books. Patterns from a training set are generalised to create a small number of rules, which can be used to identify items of interest within the text. The pattern matching technique is applied to finding the Speech-Verb, Actor and Speaker of quotes found in fiction books. The technique performs well over the training data, resulting in rule-sets many times smaller than the training set, but providing very high accuracy. While the rule-set generalised from one book is less effective when applied to different books than an approach based on hand coded heuristics, performance is comparable when testing on data closely related to the training set.
TECHNICAL SECURITY METRICS MODEL IN COMPLIANCE WITH ISO/IEC 27001 STANDARD

Technical security metrics provide measurements in ensuring the effectiveness of technical security controls or technology devices/objects that are used in protecting the information systems. However, lack of understanding and method to develop the technical security metrics may lead to unachievable security control objectives and inefficient implementation. This paper proposes a model of technical security metrics to measure the effectiveness of network security management. The measurement is based on the security performance for (1) network security controls such as firewall, Intrusion Detection Prevention System (IDPS), switch, wireless access point and network architecture; and (2) network services such as Hypertext Transfer Protocol Secure (HTTPS) and virtual private network (VPN). The methodology used is Plan-Do-Check-Act process model. The proposed technical security metrics provide guidance for organizations in complying with requirements of ISO/IEC 27001 Information Security Management System (ISMS) standard. The proposed model should also be able to provide a comprehensive measurement and guide to use ISO/IEC 27004 ISMS Measurement standard.
Reducing Computational Complexity of Network Analysis using Graph Compression Method for Brand Awareness Effort

Online� social� media� provides� platform� for� social� interactions. � This� platform� produce� large-scale� data� generated� mostly� from� online� conversations. � Network� analysis� can� help� us� to� mine� knowledge� and� pattern� from� the� relationship� between� actors� inside� the� network. � This� approach� has� been� crucial� in� supporting� prediction� and� decision-making� process. � In� marketing� context� such� as� branding� effort,� using� large-scale� conversation� data� is� cheaper,� faster� and� reliable� comparing� mainstream� approaches� such� as� questionnaire� and� sampling. � Social� network� analysis� provides� several� metrics,� which� was� built� with� no� scalability� in� minds,� thus� it� is� computationally� exhaustive. � Some� metrics� such� as� centrality� and� community� detections� has� exponential� time� and� space� complexity. � With� the� availability� of� cheap� but� large-scale� data,� our� challenge� is� how� to� measure� social� interactions� based� on� those� large-scale� data. � In� this� paper,� we� present� our� approach� to� reduce� the� computational� complexity� of� social� network� analysis� metrics� based� on� graph� compression� method� to� solve� real� world� brand� awareness� effort. �
Augmented Reality Aided 3D Modelling

The purpose of the article is to introduce possibilities of using Augmented Reality instructions in order to better understand technical drawings. The aim of these instructions based on 3D models is to create AR training applications and analyze their impact on the users. These instructions consist in a 3D model, superimposed over the technical drawing. The AR application also offers step by step instructions for 3D modelling in CAD software, which are very useful in creating the 3D model starting from a technical drawing. The AR application was tested by students from the Faculty of Engineering in Sibiu, obtaining great improvements in their 3D modelling times. The results of a survey that the students completed reveal that the AR modelling instructions also aids their spatial visualization capability.
Coupling technologies for Earth System Modelling

Abstract. This paper presents a review of the software currently used in climate modelling in general and in CMIP5 in particular to couple the numerical codes representing the different components of the Earth System. The coupling technologies presented show common features, such as the ability to communicate and regrid data, and also offer different functions and implementations. Design characteristics of the different approaches are discussed as well as future challenges arising from the increasing complexity of scientific problems and computing platforms.
An Improvement Measure to Human Body Detection Based on Kinect Using Integral Images

Human body detection of intelligent system is an important and crucial issue, and this work has been studied for many years. Luciano Spinello achieved the real-time detection performance of human body based on Kinect with the help of GPU to accelerate to computation of features . But when its algorithm is realized in CPU, it can’t still achieve the real-time detection performance. This paper put forword a improvement measure to accelerate the computation of features. Features can be rapidly calculated by integral images, which was proposed by Qiang Zhu to detect object rapidly in 2001, abandoning the previous procession of using three line interpolation and Gauss filter, the improved algorithm, in CPU 3.10Ghz, RAM 2.85GB, 640*480 detection window, can achieve the average detection rate of 40 FPS. Performance gets promotion greatly.
The Design and Implementation of Collaboration Service Integration Platform Based on Context-Aware Role Based Access Model

In recent years, the effective management of organization’s business processes has become more and more important to support the business workflows. Although some collaboration platforms have been created to support business processes, the user and role authorities in these platforms cannot automatically change in accordance with context information (e.g., time and location, and environment information). Therefore, this study designs and implements the Collaboration Service Integration Platform (CSIP) which combines Business Process Model and Notation (BPMN) and Context-Aware Role-Based Access Control (CARBAC) model. The context information is considered to enhance RBAC and adjust the authority of user role dynamically. For implementation, the logic gateways and connectors are added in the tasks and workflows for adjusting the authority dynamically. The right information can be accessed by the right user in CSIP based on CA-RBAC.
Publisher Names in Bibliographic Data: An Experimental Authority File and a Prototype Application

The cataloging community has long acknowledged the value of investing in authority control. As bibliographic systems become more global, the need for authority control becomes even more pressing. The publisher description area of the catalog record is notoriously difficult to control, yet often necessary for collection analysis and development. The research presented in this paper details a project to build a database of authorized names for major publishers worldwide. The authors used ISBN prefix data to cluster bibliographic records by publisher; the resulting database contains thousands of variant forms of each publisher's name and data about their publishing output. Profiles of four large publishers were compared. Each publisher's languages of publication, formats, and subjects demonstrated their distinctive publishing output and validated the record clusters.
Robust digital image inpainting algorithm in the wireless environment

Image or video inpainting is the process/art of retrieving missing portions of an image without introducing undesirable artifacts that are undetectable by an ordinary observer. An image/video can be damaged due to a variety of factors, such as deterioration due to scratches, laser dazzling effects, wear and tear, dust spots, loss of data when transmitted through a channel, etc. Applications of inpainting include image restoration (removing laser dazzling effects, dust spots, date, text, time, etc.), image synthesis (texture synthesis), completing panoramas, image coding, wireless transmission (recovery of the missing blocks), digital culture protection, image de-noising, fingerprint recognition, and film special effects and production. Most inpainting methods can be classified in two key groups: global and local methods. Global methods are used for generating large image regions from samples while local methods are used for filling in small image gaps. Each method has its own advantages and limitations. For example, the global inpainting methods perform well on textured image retrieval, whereas the classical local methods perform poorly. In addition, some of the techniques are computationally intensive; exceeding the capabilities of most currently used mobile devices. In general, the inpainting algorithms are not suitable for the wireless environment. This paper presents a new and efficient scheme that combines the advantages of both local and global methods into a single algorithm. Particularly, it introduces a blind inpainting model to solve the above problems by adaptively selecting support area for the inpainting scheme. The proposed method is applied to various challenging image restoration tasks, including recovering old photos, recovering missing data on real and synthetic images, and recovering the specular reflections in endoscopic images. A number of computer simulations demonstrate the effectiveness of our scheme and also illustrate the main properties and implementation steps of the presented algorithm. Furthermore, the simulation results show that the presented method is among the state-of-the-art and compares favorably against many available methods in the wireless environment. Robustness in the wireless environment with respect to the shape of the manually selected “marked” region is also illustrated. Currently, we are working on the expansion of this work to video and 3-D data.
Improvement of automatic Chinese text classification by combining multiple features

In this paper, we present an effective way of combining character‐based (N‐gram) and word‐based approaches for Chinese text classification. Uni‐gram and bi‐gram features are considered as the baseline model, which are then combined with word features of length greater than or equal to 3. A weight coefficient that can be used to give higher weights to word features is also introduced. We further employ a serial approach based on feature transformation and dimension reduction techniques. The results of McNemar's test indicate that the performance is significantly improved by our proposed method. © 2014 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.
Fault Tolerant Web Services Composition as Planning

Fault existence is unavoidable in Web services (WS) providing and calling, so fault tolerant WS has drawn attention of researchers. But while AI planning is known as a promising techniques for WS composition (WSC) problem, few study focus on fault tolerant WSC. So in this paper we first use fault tolerant planning (FTP) approaches to address fault tolerant WSC. We begin by translating WSC into planning problem and than further translate it into FTP, finally we use FTP planners on WSC. We demonstrate our methods through examples in our work, and we believe this work outlines an exciting direction.
SAR Target Detection Method Based on Empirical Mode Decomposition

Empirical mode decomposition (EMD) is a new signal processing theory, and it is very much fitting for non-stationary signal processing, such as radar signal. So this paper proposes the new synthetic aperture radar (SAR) image target detection algorithm after analyzing the characteristics of EMD and SAR images. The proposed method performs the EMD operation, feature extraction, election and fusion, which can reduce the affection of speckle. Experimental results show that the proposed method is very effective.
Mapping the MPM maximum flow algorithm on GPUs

The GPU offers a high degree of parallelism and computational power that developers can exploit for general purpose parallel applications. As a result, a significant level of interest has been directed towards GPUs in recent years. Regular applications, however, have traditionally been the focus of work on the GPU. Only very recently has there been a growing number of works exploring the potential of irregular applications on the GPU. We present a work that investigates the feasibility of Malhotra, Pramodh Kumar and Maheshwari's "MPM" maximum flow algorithm on the GPU that achieves an average speedup of 8 when compared to a sequential CPU implementation.
On Dimensioning Asymmetric Access Links

Asymmetric forward and reverse link bandwidths in access networks are characteristic of many access technologies and commonly seen in Internet Access service offerings. For many existing and emerging Internet-based applications, TCP is the predominant transport-layer protocol and its performance is critical to the usability of many of these applications. Thus, the ability of many Internet access service providers to control the bandwidth asymmetries seen by TCP flows— whose throughputs can be degraded by such asymmetry—presents the problem of how asymmetry should be controlled to ensure optimal TCP performance. Through the formulation of an optimization problem and the generation of a series of simulation results, guidelines for dimensioning access links are developed and insights into the efficiency of TCP in asymmetric access networks are obtained.
LITHP - an ALGOL List Processor

A list processing package was written in collaboration with Mr. P. Hammersley of Northampton College of Advanced Technology for use on the Elliott 503, and the Ferranti Pegasus, though further development was halted on the Pegasus due to lack of store and also on the 503 due to limitations of the Elliott ALGOL. The list processing methods used here depend very heavily on those of Woodward and Jenkins of The Royal Radar Establishment (Woodward and Jenkins, 1961; Jenkins, 1964)—in fact, the processes used in this paper are a slight variation on their exposition of McCarthy's LISP (McCarthy, 1960). The object of this paper is to facilitate the implementation of a list processing procedure on any machine that has an adequate ALGOL compiler, and for descriptive purposes the program is called LITHP. As list processing is essentially recursive, an ALGOL implementation that is not fully recursive itself, such as that of the 503, may impose severe restrictions on the capability of the machine to handle LITHP. However, no such difficulties should occur with machines which have a full ALGOL implementation.
Taking Stock of the Toolkit

When analyzing digital journalism content, journalism scholars are confronted with a number of substantial differences compared to traditional journalistic content. The sheer amount of data and the unique features of digital content call for the application of valuable new techniques. Various other scholarly fields are already applying computational methods to study digital journalism data. Often, their research interests are closely related to those of journalism scholars. Despite the advantages that computational methods have over traditional content analysis methods, they are not commonplace in digital journalism studies. To increase awareness of what computational methods have to offer, we take stock of the toolkit and show the ways in which computational methods can aid journalism studies. Distinguishing between dictionary-based approaches, supervised machine learning, and unsupervised machine learning, we present a systematic inventory of recent applications both inside as well as outside journalism studies. We conclude with suggestions for how the application of new techniques can be encouraged.