Miracle-dz/UniFAR-FTUs huggingface dataset:
In search of dependable design

This invention relates to hot melt adhesive compositions which includes a hydrogenated block copolymer block such as Kraton G, about 25 to about 250 parts by weight of a hydrocarbon resin made from a petroleum or coal tar distillate, aliphatic dienes and mono and diolefins, cyclic olefins of 5 to 6 carbon atoms and hydrogenated polycyclics per 100 parts by weight of the hydrogenated block copolymer, and about 25 to about 200 parts by weight of a polybutene or polyisobutylene per 100 parts of the hydrogenated block copolymer.
DSRC using OFDM for roadside-vehicle communication system

We propose to apply DSRC using OFDM to roadside-vehicle communication system as a means to overcome frequency selective fading, shadowing and high-speed hand-over difficulties. We optimized the transmission rate of a subcarrier which is a transmission parameter of the OFDM system. Our goal is to achieve a BER of less than 10/sup -5/ under the conditions of transmission rate 2.048 Mbps, carrier frequency 5.8 GHz, vehicle speed 180 km/h, simulcasting of several roadside transmitters, maximum delay of less than 1 /spl mu/sec and utilizing forward error correction (FEC). We developed the transmitter and receiver of DQPSK-OFDM system in 5.8 GHz band and measured the BER performance to show that OFDM system was applicable to roadside-vehicle communication system.
A study of TCP performance and buffer occupancy over a fading wireless link

This paper presents a use of M/G/1 queuing theory to study the impact of fading, and also the benefits of link layer retransmission mechanisms on the performance of the TCP protocol. Our results show that both TCP throughput and buffer occupancy vary as a function of the speed of a mobile host, and TCP performance improves with higher vehicle speed. We also show that when the propagation delay in the wireline network is large, link layer re-transmissions can significantly improve the TCP performance, though it also increases the buffer occupancy.
An Efficient Privacy Preserving Approach Using Id3 Decision Tree Learning Algorithm

Privacy-preserving is an important issue in the areas of data mining and security. The aim of privacy preserving data mining is to develop algorithms to modify the original dataset so that the privacy of confidential information remains preserved and as such, no confidential information could be revealed as a result of applying data mining tasks. In existing system they introduced a new privacy preserving approach via data set complementation which confirms the utility of training data sets for decision tree learning. This approach converts the original data sets, TS, into some unreal data sets such that any original data set is not constructable if an unauthorized party were to steal some portion of unrealized datasets. Meanwhile, there remains only a low probability of random matching of any original data set to the stolen data sets, TL. This work covers the application of new privacy preserving approach with the ID3 decision tree learning algorithm. The problem in existing system is insufficient storage mechanism and this ID3 only can be implemented for discrete-valued attributes. To support continuous-valued attributes c5.0 algorithm is used.
Seeking Health Information on the Internet

The primary objective of this research is to obtain insight into information seeking behaviour on the Internet with regard to health information. Theories from two research areas are used to explain the use of the Internet for health information: health behaviour and the adoption and use of new technologies. The data was gathered through a pencil-and-paper questionnaire (N=123), between August and October 2003. All respondents were experienced with using the Internet to seek health information. Using both lineair and logistic regression we investigate how demographic differences (e.g. age, gender, marital status), internet accessibility (e.g. physical, use patterns) and user needs (e.g. general health situation, limitations) influence: 1) the frequency of going online for health information 2) the kind of health information that is sought after, 3) the online sources that people use for health information (e.g. portals, newsletters, various websites). Results show that gender, Internet experience and physical limitations are significant for the frequency of going online. For the kinds of information that people look for only gender has significant influence. Furthermore, the places that people look for health information are influenced significantly by gender and (to lesser extent) whether people have an Internet connection at home. There is also a significant difference between men and women with regard to the number of websites visited and the way they use the gathered information. This investigation shows that men and women seek information differently. As a consequence, different groups are confronted with different risks.
Evaluation of a Teleoperated Haptic Forklift

A haptic teleoperation scheme has been designed and implemented on a hydraulically actuated lifter which is laboratory version of a forklift truck(1), (2). This control scheme has two novel features. It produces virtual fixtures based on measurements from a proximity sensor located on its end effector. Secondly, the control system has the ability to switch between position and rate modes during normal operation. In order to evaluate the effectiveness of these features, a series of human factors tests were conducted. Twenty test subjects participated in these experiments. Statistical analysis of the results indicates that both of the aforementioned novel features could enhance an operator's ability to conduct pick and place maneuvers using a forklift truck.
Research in Collegiate Mathematics Education. I

The present volume of Research in Collegiate Mathematics Education, like previous volumes in this series, reflects the importance of research in mathematics education at the collegiate level. The editors in this series encourage communication between mathematicians and mathematics educators, and as pointed out by the International Commission of Mathematics Instruction (ICMI), much more work is needed in concert with these two groups. Indeed, editors of RCME are aware of this need and the articles published in this series are in line with that goal. Nine papers constitute this volume. The first two examine problems students experience when converting a representation from one particular system of representations to another. The next three papers investigate students learning about proofs. In the next two papers, the focus is instructor knowledge for teaching calculus. The final two papers in the volume address the nature of "conception" in mathematics. Whether they are specialists in education or mathematicians interested in finding out about the field, readers will obtain new insights about teaching and learning and will take away ideas that they can use. This series is published in cooperation with the Mathematical Association of America.
Research on Information Sharing and Fusion for Distributed Command and Control Decision-Making

Institute of Command Informat ion System PLA Univ. of Sci. & Tech. Nanjing, China e-mail: yanyuyiye@sina.cn Abstract—Compared with the mode of information interaction among command and control (C2) deciders in the traditional high centralized hierarchical structure, the mode of information sharing and fusion in distributed structure has many different features to adapt the information grid environment. The key problems are information visualization, decision preference aggregation and information exchange. Accordingly, a concept of information component, an aggregating algorithm based on linguistic methods, and a way of information exchange through SNS (Social Network Services) are proposed to solve above problems.
Fuzzy decision neural networks and application to data fusion

A decision-based neural network (DBNN) is extended to a fuzzy-decision neural network (FDNN), which is shown to offer classification/generalization performance improvements, especially when the data are not clearly separable. The hierarchical structure adopted make the computation process very efficient. The learning rule and some key properties of FDNN are described. A Bayesian paradigm offers an optimal approach to data fusion. This approach is explored. DBNN, together with a Bayesian approach, is proposed to formulate the data fusion process. <<ETX>>
Acquisition, representation of characteristics of prescription samples of Chinese medicine and experiments on knowledge mining

In this paper, firstly, we introduce a feasible method formalising the calculation of information of prescription of Chinese medicine, and discuss quantified representation of attribute characteristics in prescription samples. Based on aforesaid method and representation, prescription samples are classified by pattern recognition system on the basis of SVM. The acquired results are satisfactory, which are also consistent with or close to the general cognition of principal theory of Chinese medicine. Our experience has shown that machine learning method is a feasible solution to mine thinking mode of Chinese medicine experts when they write prescriptions. This method of pattern recognition can be applied in many research fields of Chinese medicine.
An Identity-Based Fault-Tolerant Conference Key Distribution Scheme

A fault-tolerant conference key distribution scheme based on mechanism of identity-based cryptography and (t,n) threshold secret sharing is proposed in this paper This scheme is much different to traditional ones, its secret shadows are not brought from the sponsor of conference, but from each server's private key signature. By getting together these n secret shadows, the sponsor can construct polynomial. Any of conferees invited by the sponsor can request these secret shadows from t of these n servers, and then restitute conference key by them. In all courses of conference key distribution and reconstruction, every member's identity can be easily validated, so it can be prevented from all kinds of cheat
Impact Evaluation of Customer Knowledge Process on Customer Knowledge Expansion: An Empirical Study in Jordanian Banking Sector

The paper aim is to investigate how Customer Knowledge Processes used in practice by Jordanian banks to achieve customer knowledge expansion. The empirical study is based on a sample of the data collected from 165 respondents, drawn randomly from six banks. The results show that the seven selected factors Customer Knowledge Codification, Customer Knowledge Representation, Customer Knowledge Sharing, Customer Knowledge Application, Design of Customer Knowledge, Execution of Knowledge from Customer, and Verify of Knowledge from Customer have a significant impact on Customer Knowledge Expansion. The findings did reveal the potential relationship between the customer knowledge processes and customer knowledge expansion. It also provides advice for the Information Technology IT Industry as to how an analytical knowledge process from customers should be taken into account in developing countries to attain proper customer knowledge expansion because of cultural, social and educational disparities.
Face Recognition Using Laplacianfaces and Artificial Neural Network

Face recognition has recently become a hot research topic. In order to do more in that area, several algorithms have emerged. However, even the most efficient algorithm has limitations. To overcome this problem, the combination of algorithms is sometimes used. In this paper a methodology based on two approaches is presented. Firstly, we use Locality Preserving Projection (LPP) for feature extraction. Secondly, the Back Propagation Neural Network (BPNN) is used for recognition. Experiments have been done using 400 images of ORL database. Experimental results show that the algorithm is performs well and achieves good recognition.
Mobile Receiver Localization with An Enhanced Received Signal Level Method Signal Level Method

The outcome of this work is the development of a new location determination method for the localization of a mobile receiver in a cellular communications environment. Accurate positioning is confirmed to be the main obstacle to the provision of Location Based Services (LBS), i.e. services that are relevant to the user’s location. Many methods have been proposed in the literature to attain this accuracy, however the nature of mobile communications impedes this effort. Among these methods, some achieve good or fair accuracy for LBS, however their implementation requires undesirable costly modifications of the mobile network and/or handsets. The proposed method, called Enhanced-Received Signal Level (E-RSL) is aiming to attain high positioning accuracy without imposing substantial investments. To this end, the E-RSL employs power measurements of the received signal at the handset, which are innate in any cellular system. The E-RSL method is a two-stage RSL method. The first step is the well-known RSL method employing Maximum Likelihood Estimation for the BS-MS range estimation and trilateration for the location calculation. The second step is utilizing the initial RSL estimate to enhance the estimate accuracy via a Kalman Filter on instantaneous field measurements. A large-scale propagation model is used in the RSL method, while the multipath propagation model of Aulin is utilized in the second stage of the E-RSL method. Simulations of the RSL method comprising the first step of the method, as well as simulations of the E-RSL method are presented to demonstrate their potential for accurate positioning of mobile terminals. Results in urban, suburban and rural environments reveal that the plain RSL method achieves adequate accuracy only in urban areas, whilst the E-RSL attains high accuracy in all environments. This suggests a new method for solving the localization problem, by invoking advanced models describing the handset’s activity in the network. Given that these models are well designed and the network characteristics residing in them can be tracked, accurate localization is becoming a reality.
Recognition of Facial Expression via Kernel PCA Network

In this work, a kernel principle component analysis network (KPCANet) is proposed for classification of the facial expression in unconstrained images, which comprises only the very basic data processing components: cascaded kernel principal component analysis (KPCA), binary hashing, and block-wise histograms. In the proposed model, KPCA is employed to learn multistage filter banks. It is followed by simple binary hashing and block histograms for indexing and pooling. For comparison and better understanding, We have tested these basic networks extensively on many benchmark visual datasets ( such as the JAFFE [13] database, the CMU AMP face expression database, a part of the Extended Cohn-Kanade (CK+) database), The results demonstrate the potential of the KPCANet serving as a simple but highly competitive baseline for facial expression recognition.
Real-time adaptive filters for time-delay estimation

The development of real-time solutions for time-delay estimation problems is addressed. Two algorithms using adaptive digital filters are considered. The first uses the well-known LMs (least mean squares) algorithm, and the second is a novel algorithm using gradient adaptive lattice structures as prewhitening filters, followed by an LMS time-delay estimator. The filters are implemented on a TMS320-C25 system, and detailed performance comparisons are given. <<ETX>>
Software Project Visualization Using Task Oriented Metaphors

This paper presents T-Cube and MetroMap, two new graphical representation models for controlling and managing the processes of software project development. They both use metaphors and visual representation techniques to address typical project management tasks. T-Cube uses a metaphor with the Rubik-Cube whereas MetroMap uses a metaphor with a metro map. The tools have been tested on real project data and a qualitative assessment shows the results of testing the visualizations with users attempting several information retrieval tasks. The utility of the tools has been positively evaluated and the article demonstrates the possibilities of visual approaches in project management.
Smart, flexible, and future-proof data converters

This paper shows a vision on the future of AD and DA converters. Digitization of systems shifts the physics-related problems to the analog front-end, which is becoming a major bottleneck. Instead of the conventional system-function oriented structure we foresee the front-ends to become dictated by the conversion function including all associated signal conditioning. This conversion will take place in a close and concerted cooperation of analog and digital sub functions. Boundaries between signal conditioning and converter sub functions and between analog and digital will fade. It is further argued that current trends show a lot of conflicts for the future, and that a vision-based approach and new concepts are required to find a way out. The smart converter concept is proposed as a way to overcome the problems and to provide flexible and future proof converters. The way to smart converters is illustrated with research activities in which the author is involved.
STAD-a system for testing and debugging: user perspective

A recently developed experimental integrated system for testing and debugging (STAD) is presented. Its testing part supports three data-flow coverage criteria. The debugging part guides the programmer in the localization of faults by generating and interactively verifying hypotheses about their location. An example is given to illustrate the process, followed by a debugging session, a discussion of the principles of data-flow testing, a structural testing scenario, and an introduction to the debugging principles in STAD. <<ETX>>
Self-referred texture coding for segmentation-based codec

This paper describes a texture coding technique mainly suitable for segmentation-based coding schemes. The main features of the proposed technique are its efficiency in terms of bits per pixel for homogeneous regions and its ability to deal with local inhomogeneities that may be present in the image. The basic idea of the coding strategy is to divide the image into blocks and to classify the blocks in two categories: Referable and Nonreferable. Referable means that the block can be approximated by one block of the already transmitted texture and nonreferable is defined by opposition. Nonreferable blocks are transmitted with a general purpose coding scheme (for example a DCT-based technique) and referable blocks are transmitted by means of a simple transition vector indicating which sample of the transmitted texture has to be translated. We show that this technique is suitable for texture but produces distortions for strong contours. As a result, we propose to use it within a segmentation-based coding scheme where contours are transmitted by another strategy. Finally, the application to sequence coding is discussed. It is shown that this technique is particularly attractive to code the prediction error within a motion compensated video coding scheme.
A Runtime Fault Detection Method for HPC Cluster

As the number of nodes keeps increasing, faults have become commonplace for HPC cluster. For fast recovery from faults, the fault detection method is necessary. Based on the usage patterns of HPC cluster, a automatic runtime fault detection mechanism is proposed in this paper: First, the normal activities for nodes in HPC cluster are modeled using runtime state by clustering analysis, Second, the fault detection process is implemented by comparing the current runtime state of nodes with normal activity models. A fault alarm is made immediately when the current runtime state deviates from the normal activity models. In the experiments, the faults are simulated by fault injection methods and the experimental results show that the runtime fault detection method in this paper can detect faults with high accuracy.
Zynq All Programmable SoC Sobel Filter Implementation Using the Vivado HLS Tool

other designated brands included herein are trademarks of Xilinx in the United States and other countries. ARM and Cortex are registered trademarks of ARM in the EU and other countries. All other trademarks are the property of their respective owners. Summary This application note describes how to generate the Sobel edge detection filter in the Zynq™-7000 All Programmable SoC ZC702 Base Targeted Reference Design (TRD) using the Vivado™ High-Level Synthesis (HLS) tool. The techniques described in this application note present the fundamental flow for integrating an IP block generated by the Vivado HLS tool into a Zynq AP SoC-based system.
Development and evaluation of an Earth-System model - HadGEM2

Abstract. We describe here the development and evaluation of an Earth system model suitable for centennial-scale climate prediction. The principal new components added to the physical climate model are the terrestrial and ocean ecosystems and gas-phase tropospheric chemistry, along with their coupled interactions. The individual Earth system components are described briefly and the relevant interactions between the components are explained. Because the multiple interactions could lead to unstable feedbacks, we go through a careful process of model spin up to ensure that all components are stable and the interactions balanced. This spun-up configuration is evaluated against observed data for the Earth system components and is generally found to perform very satisfactorily. The reason for the evaluation phase is that the model is to be used for the core climate simulations carried out by the Met Office Hadley Centre for the Coupled Model Intercomparison Project (CMIP5), so it is essential that addition of the extra complexity does not detract substantially from its climate performance. Localised changes in some specific meteorological variables can be identified, but the impacts on the overall simulation of present day climate are slight. This model is proving valuable both for climate predictions, and for investigating the strengths of biogeochemical feedbacks.
InfoVis Is So Much More: A Comment on Gelman and Unwin and an Invitation to Consider the Opportunities

I welcome the opportunity to respond to Andrew Gelman and Antony Unwin’s article, Infovis and Statistical Graphics: Different Goals, Different Looks. Their view of information visualization (InfoVis) is very distorted, but unfortunately not uncommon. In the following, I will try to give readers a sense of what InfoVis is really about, show some recent contributions, list some challenges, and show that there is a lot of opportunity for collaboration between InfoVis and statistics. Gelman and Unwin base their selection of work on the blog Flowing Data, run by Nathan Yau. While Yau has a large number of readers, his blog does not represent the state of the art in InfoVis research. He tends to focus on communication-oriented and artistic pieces, and rarely delves into the depth of data analysis using visualization on the blog itself. He does some of that in the paid members-only section, as well as in his book Yau (2011). Yau’s focus clearly colors Gelman and Unwin’s perception of visualization and is the basis for their claim that, “[on] the infovis side, computer scientists and designers are interested in grabbing the readers’ attention and telling them a story. ” This is, quite simply, not true.