Miracle-dz/UniFAR-FTUs huggingface dataset:
Maxmin Fair Routing in Connection-Oriented Networks

The maxmin fair bandwidth allocation has been proposed as a ow/congestion control mechanism for managing the best-eeort data traac in connection-oriented networks. It achieves the fairness by assigning an equal share of bandwidth to each ow passing a link whenever possible; it improves the system throughput by fully utilizing the link capacity whenever possible. This type of fairness-and throughput-aware bandwidth allocation and corresponding routing schemes are important in multimedia connection-oriented networks for a class of services, such as ABR in ATM networks, which will carry heterogeneous types of best-eeort traac from non-real-time multimedia to ASCII text ows. We present two contributions in this paper. The rst contribution is to deene a new set of fairness-throughput relational operators on the set of feasible bandwidth allocations and to show that the maxmin fair allocation maximizes the fairness-throughput performance measured by the new operators. The second contribution is to extend the concept of maxmin from the domain of ow/congestion control to the domain of routing. We propose a maxmin fair routing algorithm which selects the best route for a new ow such that the fairness-throughput performance of the maxmin fair allocation is maximized after the new ow joins in. The proposed algorithm achieves fairness by routing the new ow around congested links, which will improve the overall data throughput as well. Furthermore, an approximation algorithm is presented, which works better for networks with very dynamic states due to its simplicity and more relaxed information requirement on the network state.
REAL-TIME SPEECH-IN-NOISE INTELLIGIBILITY ENHANCEMENT BASED ON SPECTRAL SHAPING AND DYNAMIC RANGE COMPRESSION

We demonstrate a real-time implementation of a speech-innoise intelligibility enhancement algorithm based on spectral shaping and dynamic range compression. The signal is enhanced before presented in a noisy environment, under the constraint of equal global signal power before and after modifications. The demonstrator modifies in real-time prerecorded sentences as well as input from a microphone (live speech), while it runs in a standard laptop. The noise level and type can be controlled by the user using a Java-based graphical interface. The whole architecture produces low-delay and artifact-free signals and it can be deployed on various Java compatible devices.
A study on wavelet-based edge detection for improved stereo matching

Development of stereopsis technology that gives reality is an interesting research area for a long time. Stereopsis technology expresses actuality with effect of two eyes. Correct stereo matching depends on the exact estimation of image disparity that contains depth information. Wavelet transform can be applied to analyze signals in stereo vision, but related research is insufficient. In this paper, we propose a fast and correct stereo image matching method through edge detection that uses multi-resolution wavelet transform. Also, we analyze the effect of noise during the edge detection process, and make a comparison with traditional methods.
Characterization of submicron ring oscillator using the first order design equations

In this paper, we present the characterization of the Ring oscillator for submicron technology in terms of the first order design equations of the MOSFETs, and we arrive at the extent upto which the short channel effects influence the oscillating frequency. In addition, a solution is suggested to reduce the duration of metastability upto 50%. The Ring oscillator is designed from 3-stage till 15-stage, and the circuit simulations are performed, with the oscillation frequencies ranging from 1.03 GHz till 5.38 GHz. Additional observations are noted by varying the device widths and lengths. It is found that the frequency of operation is independent of the device width, and is inversely proportional to the square of the device length. As a result, the equation derived using the first order behavior can be utilized for obtaining the frequency of the Ring oscillator circuits in general, by including an empirical constant.
A Corpus for a Gesture-Controlled Mobile Spoken Dialogue System

Speech and hand gestures offer the most natural modalities for everyday human-to-human interaction. The availability of diverse spoken dialogue applications and the proliferation of accelerometers on consumer electronics allow the introduction of new interaction paradigms based on speech and gestures. Little attention has been paid however to the manipulation of spoken dialogue systems through gestures. Situation-induced disabilities or real disabilities are determinant factors that motivate this type of interaction. In this paper we propose six concise and intuitively meaningful gestures that can be used to trigger the commands in any SDS. Using different machine learning techniques we achieve a classification error for the gesture patterns of less than 5%, and we also compare our own set of gestures to ones proposed by users. Finally, we examine the social acceptability of the specific interaction scheme and encounter high levels of acceptance for public use.
Energy flux density in a DB boundary loaded uniaxial anisotropic chiral fiber

A detailed analytical investigation of the propagation of electromagnetic waves in optical fibers made of uniaxial anisotropic chiral medium is presented. The emphasis is given on the characteristics of flux density patterns in the guide. In the structure of fiber, the outer region is assumed to be as bounded by a DB boundary. The study has been performed considering varying radial parameter of the guide. Also, two different types of anisotropic mediums as well as fiber dimensions are taken into account. The dispersion relation of the guide is deduced which provides the allowed values of propagation constants. This is followed by the evolution of the expression for the flux density in the fiber. A study of the flux density pattern reveals the existence of both the forward as well as the backward waves in the fiber structure under consideration.
On Solving Large-Scale Polynomial Convex Problems by Randomized First-Order Algorithms

One of the most attractive recent approaches to processing well-structured large-scale convex optimization problems is based on smooth convex-concave saddle point reformulation of the problem of interest and solving the resulting problem by a fast first order saddle point method utilizing smoothness of the saddle point cost function. In this paper, we demonstrate that when the saddle point cost function is polynomial, the precise gradients of the cost function required by deterministic first order saddle point algorithms and becoming prohibitively computationally expensive in the extremely large-scale case, can be replaced with incomparably cheaper computationally unbiased random estimates of the gradients. We show that for large-scale problems with favorable geometry, this randomization accelerates, progressively as the sizes of the problem grow, the solution process. This extends significantly previous results on acceleration by randomization, which, to the best of our knowledge, dealt solely with bilinear saddle point problems. We illustrate our theoretical findings by instructive and encouraging numerical experiments.
A distributed multi-objective optimisation framework for energy efficiency in mobile backhaul networks

Mobile data traffic is experiencing unprecedented growth rates due to the rapid proliferation of devices such as smart phones and tablets. Improving the efficiency of mobile networks, both in terms of services and energy consumption, is thus critical for sustaining this growing demand. While the adoption of new technologies such as small cell networks and cognitive radio reduces deployment and operational costs, challenges remain on how the data traffic can be efficiently processed and transported over the mobile backhaul network. In this paper, we aim to improve the energy efficiency of mobile backhaul networks, while simultaneously balancing the traffic load on its various backhaul nodes, in order to maintain the required service quality. We first formulate a multi-objective optimisation problem, then propose a distributed traffic control algorithm to solve it. We show the convergence of the algorithm through Lyapunov analysis; and numerical simulations illustrate the results. We find that the traffic dynamics pose notable challenges for operators to plan, design and operate mobile backhaul networks when aiming to achieve the desired energy-performance tradeoffs.
Effect of Medium Capacity on the Relationship between Chemical Heterogeneity and Linearly Adsorbed Solute Dispersion into Fixed Beds

The paper aims at investigating influence of medium capacity on linear adsorbed solute dispersion into chemically heterogeneous fixed beds. A discrete chemical heterogeneity distribution is considered in the one-dimensional advective- dispersive equation. The partial differential equation is solved using finite volumes method based on the Adam-Bashforth algorithm. Increased dispersion is estimated by comparing breakthrough curves second order moments and keeping identical hydrodynamic properties. As a result, dispersion increase due to chemical heterogeneity depends on the column size and surprisingly on the solid capacity. The more intense capacity is, the more important solute dispersion is. Medium length which is known to favour this effect vanishing according to the linear adsorption in fixed bed seems to create nonmonotonous variation of dispersion because of the heterogeneity. This nonmonotonous behaviour is also favoured by high capacities.
Clock tree synthesis with pre-bond testability for 3D stacked IC Designs

This paper proposes comprehensive solutions to the clock tree synthesis problem that provides pre-bond testability for 3D IC designs. In 3D ICs, it is essential to stack only good dies by testing the individual dies before stacking. For the clock signaling, the pre-bond testing requires a complete 2D clock tree on each die. The previous work enables the pre-bond testability by allocating specially designed resources called TSV-buffers and redundant trees with transmission gates. We proposes viable solutions to the two fundamental problems of the previous work: (1) using much less buffer resources by preventing (potentially ‘bad’) TSV-buffers with a new tree topology generation algorithm; (2) completely removing the transmission gate control lines by using a specially designed component called self controlled clock transmission gate (SCCTG). Compared to the existing 3D tree topology generation algorithms, solution 1 can use 56%–88% less number of TSVs, 53%–67% less number of buffers, 22%–65% less total wirelength, and 26%–43% less clock power for the benchmark circuits with dense sink placements. Moreover, solution 2 reduces the total wirelength of all the benchmark circuits by 17% and 23% on average for the 2-die and 4-die stacked 3D ICs, respectively.
A method for feature extraction of traffic sign detection and the system for real world scene

Traffic sign detection is the significant step before recognizing the class of traffic signs. In the detection, most studies rely on region of interest (ROI) from color information. In practice, however, there is no way to cover the various conditions such as illumination effects or weather conditions. To overcome the problem, this work uses the ROI-free detection by the supervised learning in which the predictor trains the positive examples of traffic sign image and negative examples of non traffic sign image. The proposed method is robust to illumination effects although it searches the traffic sign over the input scene. Because the real world scene often contains occluded or overlapped traffic signs, it is required that the detection algorithm should handle the cases. In this work, we introduce a novel feature extraction method inspired by vision perception theory developed in biological system and by power spectrum in frequency domain. The method was combined with support vector classifier. The proposed method showed accurate classification results (99.32%, 5 fold cross validation) over combined image sets of positive and negative traffic signs samples. Finally, we compared the detection ability of the proposed method and a previous work using ROI on real-world traffic scenes.
Lattice-based unsupervised acoustic model training

Unsupervised acoustic model training has been successfully used to improve the performance of automatic speech recognition systems when only a small amount of manually transcribed data is available for the target domain. The most common approach is use automatic transcriptions to guide acoustic model estimation. However, since the best recognition hypotheses are known to contain errors, we propose to consider multiple transcription hypotheses during training. The idea is that the EM process can benefit from the estimated posterior probabilities of the hypotheses to converge to a better solution. The proposed unsupervised training method is based on lattices. Lattice-based training gives a relative improvement of 2.2% over 1-best training on a Broadcast News transcription task and converges faster with the iterative incremental training.
We-Intention to Use Instant Messaging for Collaboration: A Social Influence Model

The purpose of this study is to introduce the concept of “we-intention” into group technology adoption and diffusion research. In this study, we examined the “we-intention” of using instant messaging for team collaboration. Building upon the social influence framework, a we-intention model is developed and tested with 163 respondents. The research model explained 41.3% of the variance in we-intention. Attitude, group norm and social identity were found to be statistically significant in determining we-intention to use instant messaging for collaboration, and value perception had significant effects on attitude and social influence factors. We believe that the implications of this study are important for both researchers and practitioners.
Context-Dependent Adaptability in Crowd Behavior Simulation

This paper applies a context-dependent behavior selection architecture to agent-based simulation of crowd behavior. In order to generate realistic humanlike crowd behaviors, we consider adaptability of an autonomous agent at two different levels: a behavior level where the agent makes adaptive selections among multiple competitive behaviors, and a behavioral context level where the agent modulates its behavior patterns according to different psychological and/or social states. A case study example of emergency response simulation is provided. The behavior and behavioral context models are described and simulation results are presented
Research on Information Security Enhancement Approaches and the Applications on HCI Systems

With rapid development of computer techniques, the human computer interaction scenarios are becoming more and more frequent. The development history of the human-computer interaction is from a person to adapt to the computer to the computer and continually adapt to the rapid development. Facing the process of human-computer interaction, information system daily operation to produce huge amounts of data, how to ensure human-computer interaction interface clear, generated data safe and reliable, has become a problem to be solved in the world of information. To deal with the challenging, we propose the information security enhancement approaches and the core applications on HCI systems. Through reviewing the other state-of-the-art methods, we propose the data encryption system to deal with the issues that uses mixed encryption system to make full use of the symmetric cipher algorithm encryption speed and encryption intensity is high while the encryption of large amounts of data efficiently. Our method could enhance the general safety of the HCI system, the experimental result verities the feasibility and general robustness of our approach.
HE Plots for Multivariate Linear Models

Multivariate analysis of variance (MANOVA) extends the ideas and methods of univariate ANOVA in simple and straightforward ways. But the familiar graphical methods typically used for univariate ANOVA are inadequate for showing how measures in a multivariate response vary with each other, and how their means vary with explanatory factors. Similarly, the graphical methods commonly used in multiple regression are not widely available or used in multivariate multiple regression (MMRA). We describe a variety of graphical methods for multiple-response (MANOVA and MMRA) data aimed at understanding what is being tested in a multivariate test, and how factor/predictor effects are expressed across multiple response measures. In particular, we describe and illustrate: (a) Data ellipses and biplots for multivariate data; (b) HE plots, showing the hypothesis and error covariance matrices for a given pair of responses, and a given effect; (c) HE plot matrices, showing all pairwise HE plots; and (d) reduced-rank analogs of HE plots, showing all observations, group means, and their relations to the response variables. All of these methods are implemented in a collection of easily used SAS macro programs.
Space Radiation and Cataracts in Astronauts

Abstract Cucinotta, F. A. , Manuel, F. K. , Jones, J., Iszard, G. , Murrey, J., Djojonegro, B. and Wear, M. Space Radiation and Cataracts in Astronauts. Radiat. Res. 156, 460–466 (2001). For over 30 years, astronauts in Earth orbit or on missions to the moon have been exposed to space radiation comprised of high-energy protons and heavy ions and secondary particles produced in collisions with spacecraft and tissue. Large uncertainties exist in the projection of risks of late effects from space radiation such as cancer and cataracts due to the absence of epidemiological data. Here we present the first epidemiological data linking an increased risk of cataracts for astronauts with higher lens doses (>8 mSv) of space radiation relative to other astronauts with lower lens doses (<8 mSv). Our study uses historical data for cataract incidence in the 295 astronauts participating in NASA's Longitudinal Study of Astronaut Health (LSAH) and individual occupational radiation exposure data. These results, while preliminary because of the use of subjective scoring methods, suggest that relatively low doses of space radiation are causative of an increased incidence and early appearance of cataracts.
LE FONCTIONNEMENT RÉFLEXIF MATERNEL ET LES SYMPTÔMES INTÉRIORISÉS ET EXTÉRIORISÉS D’ENFANTS VICTIMES D’UNE AGRESSION SEXUELLE

Cette etude avait pour objectifs d’evaluer les liens entre les agressions sexuelles en enfance (ASE), le fonctionnement reflexif maternel (FRM) et les difficultes psychosociales chez les enfants ainsi que le role moderateur du FRM dans la relation entre l’ASE et les difficultes chez les enfants. L’echantillon incluait 154 meres, dont 64 meres d’un enfant ayant vecu une ASE. Le Parent Development Interview-Revised et le Child Behavior Checklist ont permis de mesurer le FRM et les difficultes psychosociales respectivement. Une analyse acheminatoire montre que seul le FRM etait significativement associe aux comportements interiorises et exteriorises, et qu’il jouait un role moderateur dans la relation entre l’ASE et les difficultes interiorisees, suggerant un effet protecteur du FRM suite a une ASE.
Action Recognition Based on Local Spatio-temporal Oriented Energy Features and Additive Kernel SVM

Spatio-temporal oriented energy features have been proved to be an efficient feature for action recognition. It has satisfied performance on most of public databases. However, the oriented energy features were used as holistic action features for template matching in many literatures. In the paper, we proposed an action representation based on local spatio-temporal oriented energy features, and multiple feature channels are built to convert the features to descriptors. Moreover, inspired by additive kernel Support Vector Machine can offer significant improvements in accuracy on a wide variety of tasks while having the same run-time. We proposed action classifiers based on additive kernels and tested our system on KTH human action dataset for its performance evaluation. The experimental result shows our system outperforms most of recent action classification systems.
IT Artefacts in IT Services: Toward a Taxonomy

This research paper aims at an understanding of services based on information and communication technology that is sharper in its conceptual analysis than is usually presented. We first outline some relevant issues on the research on service marketing and on the information technology artefact. Next, the role of this artefact as a constituent of information technology (IT) services is derived. As an example we make use of historical development banking services. Further, three different categories of services are identified. Finally, potential avenues for further research are proposed.
Securing Passive Replication through Verification

We show how to leverage trusted computing technology to design an efficient fully-passive replicated system tolerant to arbitrary failures. The system dramatically reduces the complexity of a fault-tolerant service, in terms of protocols, messages, data processing and non-deterministic operations. Our replication protocol enables the execution of a single protected service, replicating only its state, while allowing the backup replicas to check the correctness of the results. We implemented our protocol on Trusted Computing (TC) technology and compared it with two recent replication systems.
Pregnancy after breast cancer: population based study

Objectives To identify women who survived breast cancer and subsequently conceived and to determine the rate of pregnancy (proportion), management, outcome of the cancer, and outcome of the first subsequent pregnancy. Design Population based descriptive study with cases identified from the Western Australian data linkage system and validated by review of medical charts. Supplementary data obtained from hospital and clinician records. Setting Western Australia, 1982-2003. Participants Women aged <45 with a diagnosis of breast cancer who subsequently conceived. Main outcome measures Pregnancy outcome and rate, survival, time from diagnosis to pregnancy. Results Sixty two (54%) women with a diagnosis of breast cancer who subsequently conceived did so less than two years after their diagnosis: 29 of them had an abortion, 27 had a live birth, and six miscarried. Within a proportional hazards regression model subsequent pregnancy was associated with improved overall survival (hazard ratio 0.59, 95% confidence interval 0.37 to 0.95). When the model was stratified by time from diagnosis subsequent pregnancy was associated with improved overall survival in women who waited at least 24 months to conceive (0.48, 0.27 to 0.83) and a non-significant protective effect was seen for women who waited at least six months to become pregnant. Conclusions Our study does not support the current medical advice given to premenopausal women with a diagnosis of with breast cancer to wait two years before attempting to conceive. This recommendation may be valid for women who are receiving treatment or have systemic disease at diagnosis, but for women with localised disease early conception, six months after completing their treatment, is unlikely to reduce survival.