Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance Analysis of Unsupervised Machine Learning Techniques for Network Traffic Classification

Network traffic classification is important for QoS, Network management and security monitoring. Current method for traffic classification such as port based or payload based suffered many problems. Newly emerged application uses encryption and dynamic port numbers to avoid detection. So we use unsupervised machine learning approach to classify the network traffic. In this paper unsupervised K-means and Expectation Maximization algorithm are used to cluster the network traffic application based on similarity between them. Performance of these two algorithms is compared in terms of classification accuracy between them. The experiment results show that K-Means and EM perform well but accuracy of K-Means is better than EM and it form better cluster.
Audio in the UCSB CNSI AlloSphere

The UCSB AlloSphere is a joint effort of the California NanoSystems Institute (CNSI) and the graduate program in Media Arts and Technology (MAT) at the University of California Santa Barbara (UCSB). It is currently under construction, with completion scheduled for the first half of 2006. The AlloSphere is designed as an immersive computational interface for 10 to 20 users, featuring surround-sound data sonification and immersive visualization (i.e., 3D audio and video projection) on a spherical surface. It will provide interactive control by the means of microphone arrays, cameras, and mechanical, and magnetic input tracking. The actual shape of the AlloSphere can be described as two hemispheres with 16-foot radii pulled 8 feet apart, placed in a 3-story anechoic chamber. A 7-foot-wide bridge runs across the center, supporting the users. This document describes the requirements for the audio component of the AlloSphere, introduces the three prevalent spatial sound processing technologies in use today, and outlines the AlloSphere audio input and projection design and implementation plan, from low-level transducer elements to high-level network protocols.
Towards Semantic Network Models via Graph Databases for SDN Applications

At the core of any network control and management system is the representation and maintenance of network topology information. Software-Defined Networking (SDN) treats topology abstractions as one of the cornerstones towards rethinking network architectures and the way they are operated. Recently, motivated by the scalability and performance needs of cloud applications, Graph Databases are being adopted as appealing alternatives to traditional relational models when data is highly interconnected and extensible schemas are called for. In addition, the use of metadata to describe how data is interconnected by means of Web Semantic standards is increasingly gaining ground. At the crossroads of these trends, this paper presents an approach to augment SDN network state with a semantic model leveraging graph database technologies. In particular, our proposal imports the Network Mark up Language (NML) model into a scalable graph database (Neo4j). For validation purposes, we evaluate our proof of concept implementation against a representative set of SDN application primitives.
The Effect and Mechanism of Growth Hormone Replacement on Cognitive Function in Rats with Traumatic Brain Injury

Objective The effects of growth hormone on cognitive dysfunction were observed in a controlled cortical impact (CCI) rat model and the underlying mechanism was explored. Method Three-month-old male SD rats were randomly divided into sham (n = 10), control (n = 10), and CCI groups (n = 40) The parameters were set as follows: striking speed, 3.5 m/s; impact depth, 1.5 mm; and dwell time, 400 msec. Eight and ten weeks post-injury, the GH levels were measured the water maze test and novel object recognition test were performed. CCI rats were divided into normal and decreased GH groups, and further randomly divided into two sub-groups (rhGH treatment and saline vehicle groups). All rats were tested for SYN, BDNF, and TrkB mRNA in the prefrontal cortex and hippocampus by RT-PCR. Results CCI rats 8 weeks post-injury had cognitive dysfunction regardless of the GH level (P<0.05). rhGH treatment improved cognitive function in CCI rats. There was a positive correlation between the expression of prefrontal BDNF and SYN mRNA in CCI rats after rhGH therapy and the water maze test score (r = 0.773 and 0.534, respectively; P<0.05). Furthermore, the expression of BDNF, TrkB, and SYN mRNA in the hippocampus was negatively correlated with the water maze test score (r = 0.602, 0.773, 0.672, and 0.783, respectively; P<0.05). There was a difference in the expression of hippocampal and prefrontal BDNF, TrkB, and SYN mRNA (P<0.05) Conclusion rhGH treatment had a positive effect on cognitive function, which was more evident in GH-deficient rats. The increased expression of hippocampal and prefrontal BDNF and TrkB mRNA is implicated in rhGH therapy to improve cognitive function. Changes in the expression of hippocampal SYN mRNA following rhGH therapy may also play a role in improving cognitive function.
Multidimensional Visualization of Communication Networks

In this paper we analyze structure and functionality of communication networks using parallel coordinates visualization. We transform a network with n nodes to n points in an n-dimensional space by using topological measures on the nodes in the network. We study communication networks on different levels of abstraction. We begin with visualization of subgraphs of the Internet AS graph and continue with visualization of networks on the IP level. Our method provides visual interpretation to network properties such as stability in instances of node-link failures, node back-up, node interdependence, and unique topological patterns common in communication networks. We show how these can be utilized in network study.
WDR coding based image compression technique using PCA

Image compression is the technique of reducing the number of bits required to represent a digital image, which can be accomplished by reducing the redundant and visually irrelevant information present in the images. In this study, a Wavelet Difference Reduction (WDR) coding based image compression technique is proposed which uses Principal Component Analysis (PCA). These techniques are combined in order to achieve high compression ratio with an acceptable degradation in the compressed images. The input image is first compressed using PCA. Few of the Principal Components are used to reconstruct the image. The reconstructed image obtained by PCA is further used as an input data for WDR compression. Since WDR is a wavelet based coding technique so it exploits local characteristics of the image and leads to high Compression Ratio (CR). Here, PCA and WDR are combined because PCA gives high image quality but low CR on the other hand WDR works opposite of it as it gives high CR. The proposed technique is applied on several test images and results are compared with WDR and JPEG2000 techniques. Results clearly show that the proposed technique is better than the existing techniques in terms of both objective and subjective fidelity criteria.
Cosmoclimatology: a new theory emerges

Data on cloud cover from satellites, compared with counts of galactic cosmic rays from a ground station, suggested that an increase in cosmic rays makes the world cloudier. This empirical finding introduced a novel connection between astronomical and terrestrial events, making weather on Earth subject to the cosmic-ray accelerators of supernova remnants in the Milky Way. The result was announced in 1996 at the COSPAR space science meeting in Birmingham and published as “Variation of cosmic-ray flux and global cloud coverage – a missing link in solar-climate relationships” (Svensmark and Friis-Christensen 1997). The title reflected a topical puzzle, that of how to reconcile abundant indications of the Sun’s influence on climate (e.g. Herschel 1801, Eddy 1976, Friis-Christensen and Lassen 1991), with the small 0.1% variations in the solar irradiance over a solar cycle measured by satellites. Clouds exert (on average) a strong cooling effect, and cosmic-ray counts vary with the strength of the solar magnetic field, which repels much of the influx of relativistic particles from the galaxy. The connection offers a mechanism for solardriven climate change much more powerful than changes in solar irradiance. During the past 10 years, considerations of the galactic and solar influence on climate have progressed so far, and have found such widespread applications, that one can begin to speak of a new paradigm of climate change. I call it cosmoclimatology and in this article I suggest that it is already at least as secure, scientifically speaking, as the prevailing paradigm of forcing by variable greenhouse gases. It has withstood many attempts to refute it and now has a grounding in experimental evidence for a mechanism by which cosmic rays can affect cloud cover. Cosmoclimatology already interacts creatively with current issues in solar–terrestrial physics and astrophysics and even with astrobiology, in questions about the origin and survival of life in a high-energy universe. All these themes are pursued in a forthcoming book (Svensmark and Calder 2007).
A new internal multi-model controller for a linear process with an uncertain time delay

This document presents a new Internal Multi-Model Command controller design method for a linear system with a limited uncertain time delay. This design method is based on the use of a models collection to approximate the system functioning using Padé approximations; these models are inversed and multiplied by low pass filters in order to obtain a set of controllers to calculate the command value through a fusion procedure. These controllers are obtained by the multiplication of a low pass filters and Models inverses, in order to impose poles and zeros for the considered system and to control the robustness of the command through the filters parameters, which must confirm a compromise between stability and rapidity. In this paper the Multi-Model Command controller design method will be presented through five sections; the first one describes Internal Model Control concepts, the second one describes effects of presence of a time delay on systems dynamics, the third section describes briefly Multi-Model concepts, the fourth section presents the new Internal Multi-Model controller design Method and finally the fifth section presents the obtained results of the new controller design method application for a system with a limited uncertain time delay and the filters parameters variations effects.
Immigration and Occupations in Europe

In this paper we analyze the effect of immigrants on natives' job specialization in Western Europe. We test whether the inflow of immigrants changes employment rates or the chosen occupation of natives with similar education and age. We find no evidence of the first and strong evidence of the second: immigrants take more manual-routine type of occupations and push natives towards more abstract-complex jobs, for a given set of observable skills. We also find some evidence that this occupation reallocation is larger in countries with more flexible labor laws. As abstract-complex tasks pay a premium over manual-routine ones, we can evaluate the positive effect of such reallocation on the wages of native workers. Accounting for the total change in Complex/Non Complex task supply from natives and immigrants we find that immigration does not change much the relative compensation of the two types of tasks but it promotes the specialization of natives into the first type.
Reaccentuation or deaccentuation : a comparative study of Italian and Dutch

This paper reports on a comparative analysis of accentuation strategies within Italian and Dutch NPs. Accent-patterns were obtained in a (semi-)spontaneous way via a simple dialogue-game with 8 Dutch speakers and 4 Italian ones. In this way, target descriptions of all speakers were obtained in the following four contexts: all new, single contrast in the adjective, single contrast in the noun, and double contrast. It was found that the two languages both signal information status prosodically, but in a rather different way. In Dutch, accent distribution is the main discriminative factor: new and contrastive information are accented, while given information is not. Newness and contrastive accents were not intonationally different, yet a post-hoc test revealed that listeners could distinguish a contrastive intonation from a newness one, because contrastive accents generally were the sole accent in the phrase and always had the shape of a nuclear accent even in non-default positions. In Italian, distribution is not a significant factor, since within the elicited NPs both adjective and noun are always accented, irrespective of the status of the information. However, there is a gradient difference in that “given accents” are perceived as less prominent than the other two, while there is no overall perceptual difference between contrastive and newness accents.
Hard thresholding based compressed sensing approach for thermoacoustic tomography

In this paper we present a reconstruction method based on compressed sensing for thermoacoustic tomography. The proposed method combines hard thresholding algorithm with conjugate gradient method to solve the inverse source problem of thermoacoustic tomography. Within the compressed sensing approach, the method is applied to thermoacoustic tomography. Numerical results for two different scenarios verify that the proposed method improves the image reconstruction quality with under-sampled data.
Applicability of Resource-based Machine Translation to Airplane Manuals

Machine translation (MT) has been studied and developed since the advent of computers, and yet is rarely used in actual business. For business use, rule-based MT has been developed, but it requires rules and a domain-specific dictionary that have been created manually. On the other hand, as huge amounts of text data have become available, corpus-based MT has been actively studied, particularly corpus-based statistical machine translation (SMT). In this study, we tested and verified the usefulness of SMT for aviation manuals. Manuals tend to be similar and repetitive, so SMT is powerful even with a small amount of training data. Although our experiments with SMT are at the preliminary stage, the BLEU score is high. SMT appears to be a powerful and promising technique in this domain.
Flexible Architecture for Internet of Things Utilizing an Local Manager

This paper presents an flexible architecture for Internet of things utilizing a local manager with the goal to solve many issues. The solution is described from both the system architecture and example applications. The component design and the communication between these components are introduced. The Local Manager architecture is composed of a gateway, Message Broker, Message Relay Bridge and several small applications (Apps) with different purposes. The Local Manager can be used as a platform for future integration of things into cloud services via the Internet.
Molecular Imaging of Alzheimer ’ s Disease Using PET

Molecular Imaging Techniques Molecular imaging of the brain aims to quantify various biological processes via the modeling of interactions between a molecular probe and a molecule of interest naturally occurring in a living organism. Brain molecular-imaging techniques such as positron emission tomography (PET) and single photon emission tomography (SPECT) allow clinical investigators to record and analyze such interactions in vivo. PET and SPECT associated with specific molecular probes are sensitive to detecting biological processes in the order of pico-molar (10-12), however these techniques have limited spatial resolution. In contrast, magnetic resonance imaging (MRI) has a tremendous spatial resolution (sub-milliliter range) but lower sensitivity (micromolar; 10-6) relative to PET and SPECT (Table 1). Together, these techniques reveal critical information with regards to the alterations in the brain’s anatomy and physiology witnessed in dementia, as well as canalizing diagnostic methods and therapeutic interventions.
A new approach for iterative decoding on MIMO channels via sphere decoding

A soft iterative detection and decoding (IDD) scheme based on the sphere decoding technique for coded multiple-input multiple-output spatial multiplexing (MIMO-SM) systems is considered in this paper. We propose a new approach to approximate the maximum a-posteriori (MAP) cost function by a quadratic function such that the MAP search can be carried out by list sphere decoding (LSD). Performance of the coded MIMO-SM system with this approach is evaluated by simulation. It is shown that the proposed approach can achieve performance comparable to an existing approach.
Trajectory Generation for Bipedal Robots Using Recurrent Neural Networks

Motivation: The idea of learning a feedback control function in a neural network has exciting implications. If we could train a neural network to output the optimal control variables for any position in state space, then the walking problem would be completely solved. Unfortunately, our own recent experiments with using neural networks to directly learn the control variables (torques) worked well in simulations of the hopping robots described in [5], but did not scale well to simulations of the 12 degree of freedom 3D bipedal robot described in [3]. Essentially, small errors in torques during learning can result in very large and very complicated errors in performance, making the learning problem very difficult.
The competent child and the right "to be oneself" : Reflections on children as fellowcitizens in a day-care centre

The aim of the thesis is to explore discourses on "children as participants" by questioning and discussing constructions of children and childhood that seems to be taken for granted, and which in certain contexts seem to have attained a hegemonic position in recent years. One important task is to gain an insight into the existential conditions of these discourses, to contextualise them by exploring the social practices that are developed as part of these discourses. In this context, it is important to discuss how constructions of the child subject in discourses affect children, and how children handle and experience the new identities that are made available to them through the discourses.
Challenges in the management of severe allergic asthma in the elderly

Little is known about the features of asthma and allergy in the elderly. A significant number of elderly patients with asthma have uncontrolled and severe asthma. This review aims to provide an analysis of the literature on the assessment and phenotype of severe allergic asthma in the elderly. Gaps and pitfalls in diagnostic and therapeutic approaches, as well as management of severe allergic asthma in the elderly, are also discussed.
Gauss-Newton approximation to Bayesian learning

This paper describes the application of Bayesian regularization to the training of feedforward neural networks. A Gauss-Newton approximation to the Hessian matrix, which can be conveniently implemented within the framework of the Levenberg-Marquardt algorithm, is used to reduce the computational overhead. The resulting algorithm is demonstrated on a simple test problem and is then applied to three practical problems. The results demonstrate that the algorithm produces networks which have excellent generalization capabilities.
SELECTION FOR RECOMBINATION IN SMALL POPULATIONS

Abstract The reasons that sex and recombination are so widespread remain elusive. One popular hypothesis is that sex and recombination promote adaptation to a changing environment. The strongest evidence that increased recombination may evolve because recombination promotes adaptation comes from artificially selected populations. Recombination rates have been found to increase as a correlated response to selection on traits unrelated to recombination in several artificial selection experiments and in a comparison of domesticated and nondomesticated mammals. There are, however, several alternative explanations for the increase in recombination in such populations, including two different evolutionary explanations. The first is that the form of selection is epistatic, generating linkage disequilibria among selected loci, which can indirectly favor modifier alleles that increase recombination. The second is that random genetic drift in selected populations tends to generate disequilibria such that beneficial alleles are often found in different individuals; modifier alleles that increase recombination can bring together such favorable alleles and thus may be found in individuals with greater fitness. In this paper, we compare the evolutionary forces acting on recombination in finite populations subject to strong selection. To our surprise, we found that drift accounted for the majority of selection for increased recombination observed in simulations of small to moderately large populations, suggesting that, unless selected populations are large, epistasis plays a secondary role in the evolution of recombination. Corresponding Editor: A. Caballero
Wage Moderation and Rising Unemployment

Why, at the onset of a downturn in economic activity and an upswing in unemployment (in the early 2000â€™s), did the leading trade unions in Germany decide to demand-and in some cases strike for- larger wage increases and denounce a government-led policy of wage moderation, rather than strengthen their adherence to such a policy? Two (related) explanations of this apparently nonrational behavior were offered by German unionists at an informal conference on union policy-making in Germany, Japan, and the U.S. In the first place, it was claimed that at the grass roots union members might be expected to accept wage restraint only if they are convinced that they would be punished for failure to do so (e.g. to accept subcontract wages in marginal enterprises) or if acceptance would be virtually certain to ensure reward in the form of increased job security and employment (e.g. by the adoption of more expansionist fiscal or monetary policies). And second, some of the union participants at the conference claimed that the failure of real wages to grow as rapidly as productivity tends to engender feelings of unfair on inequitable treatment among unionists, which, if allowed to persist, could override the requirements of rational self-interest in pursuing wage policies aimed solely at maximizing the net wealth of the membership.
Does Capital Scarcity Matter?

This paper quantifies the welfare impact of a permanent increase in the level of per capita income brought about by a temporary increase in the growth rate of GDP per capita following capital account liberalization. In the immediate aftermath of liberalization, and under a range of assumptions, differences between the autarkic and integrated equilibrium consumption paths are large. Yet the welfare impact of these differences is small when using infinite horizon consumption streams to compute welfare gains. The results suggest that a finite horizon framework may be more appropriate and policy-relevant for evaluating the welfare consequences of economic policy changes that induce temporary growth effects but have a permanent impact on the level of per capita incomes.