Miracle-dz/UniFAR-FTUs huggingface dataset:
Spectrum Analysis Tutorial, Part 2: Properties and Applications of the Discrete Fourier Transform

In part one of this tutorial (Jaffe 1987), we introduced the discrete Fourier transform (DFT). To review, the DFT takes a waveform as input and produces as output the spectrum of that waveform. One way to understand this process is to consider the samples of the waveform as a vector and to see the DFT as the projection of this vector onto a set of complex sinusoidal basis vectors. In this manner, the DFT produces a sequence of spectral components equally spaced in frequency, with a length equal to that of the original waveform. Each element of the spectrum is a coefficient of the projection given by the inner product of the waveform with one of the basis sinusoids. This coefficient can be represented in polar coordinates to give the amplitude and phase of the corresponding sinusoid. The equation for the DFT is:
LMI-based robust model predictive control evaluated on an industrial CSTR model

In this paper, robust model predictive control (MPC) is studied for a general class of uncertain linear systems with structured time-varying uncertainties. The controller design is characterized as an optimization problem of the "worst-case" objective function over infinite moving horizon, subject to input and output constraints. A sufficient state-feedback synthesis condition is provided in the form of linear matrix inequality (LMI) optimization, and can be solved online. The stability of such a control scheme is determined by the feasibility of the optimization problem. To demonstrate its usefulness, this robust MPC technique is applied to an industrial continuous stirred tank reactor (CSTR) problem with explicit input and output constraints. Its relative merits to conventional MPC approaches are also discussed.
Modeling Affirmative and Negated Action Processing in the Brain with Lexical and Compositional Semantic Models

Recent work shows that distributional semantic models can be used to decode patterns of brain activity associated with individual words and sentence meanings. However, it is yet unclear to what extent such models can be used to study and decode fMRI patterns associated with specific aspects of semantic composition such as the negation function. In this paper, we apply lexical and compositional semantic models to decode fMRI patterns associated with negated and affirmative sentences containing hand-action verbs. Our results show reduced decoding (correlation) of sentences where the verb is in the negated context, as compared to the affirmative one, within brain regions implicated in action-semantic processing. This supports behavioral and brain imaging studies, suggesting that negation involves reduced access to aspects of the affirmative mental representation. The results pave the way for testing alternate semantic models of negation against human semantic processing in the brain.
An Efficient Broadcast Scheme for Safety-Related Services in Distributed TDMA-Based VANETs

The medium access control (MAC) protocol should provide an efficient broadcast service to support the high-priority safety applications in vehicular ad hoc networks (VANETs). Basic safety messages (BSMs) transmitted through vehicle-to-vehicle and vehicle-to-infrastructure architectures are the most fundamental of these applications. The periodic transmission of BSMs results in a heavy burden on the load of the VANETs when traffic density is high, and the presence of hidden terminals degrade the performance of the network. This letter introduces a distributed time division multiple access (TDMA)-based scheme that controls the transmission range to improve the efficiency of BSM transmissions in VANETs. The transmission collisions can be reduced without lowering the awareness quality of the surrounding nodes at a close range. Meanwhile, merging collisions can be pre-detected and eliminated by cooperation among the nodes. Simulation results demonstrate that our strategies can provide an efficient and reliable BSM broadcast service.
Extramuros: making music in a browser-based, language-neutral collaborative live coding environment

e extramuros soware was developed to explore live coding and network music, bringing live coding musicians together around shared text buffers. Originally developed to support a globally distributed live coding ensemble, the extramuros soware has found additional application in projecting laptop orchestra performances to remote sites, in zero-installation workshop and performance seings, and in facilitating the efficient display of code by an ensemble. As the soware works by connecting shared text buffers to audio programming languages through specific network connections augmented by pipes, it is a language-neutral approach. is paper describes the overall architecture of the extramuros system, relating that architecture to perennial network music concerns for bandwidth, security, and synchronization. Examples of early use in workshops, rehearsals and performances by laptop orchestras and other small telematic ensembles are discussed, leading to a concluding discussion of directions for future work.
and space during locomotor adaptation? How does the motor system correct for errors in time

You might find this additional info useful.This article cites€39 articles, 20 of which you can access for free at: http://jn.physiology. org/content/108/2/672.full#ref-list-1Updated information and services including high resolution figures, can be found at: http://jn.physiology. org/content/108/2/672.fullAdditional material and information about Journal of Neurophysiology can be found at: http://www.the-aps.org/publications/jnThis information is current as of December 12, 2012.
Bio-Inspired Computer Vision: Setting the Basis for a New Departure

Studies in biological vision have always been a great source of inspiration for design of computer vision algorithms. In the past, several successful methods were designed with varying degrees of correspondence with biological vision studies, ranging from purely functional inspiration to methods that utilize models that were primarily developed for explaining biological observations. Even though it seems well recognized that computational models of visual cortex can help in design of computer vision algorithms, it is a non-trivial exercise for a computer vision researcher to mine relevant information from biological literature as very few studies in biology are organized at a task level. This has lead to a widening gap between biological vision and computer vision research. In this paper we aim to bridge this gap by provide insights and methodology to envision a new departure in this area. Not only we revisit some of the main features of biological vision and discuss the foundations of existing computational studies modeling biological vision, but also we revisit three classical computer vision tasks from a biological perspective: image sensing, segmentation and optical flow. Using this task-centric approach, we discuss well-known biological functional principles and compare them with approaches taken by computer vision. Based on this comparative analysis of computer and biological vision, we will present some recent promising approaches in modelling biological vision and we highlight a few novel ideas that we think are promising for future investigations in computer vision. To this extent, this papers provides new insights for the design of biology-based computer vision algorithms and pave a way for much needed interaction between the two communities.
Colour normalisation and spatial comparison : a robust way to integrate the light spectrum

In the physical world no colours exist, only light energy distributions with various spectral compositions. It is well known that there is a direct relationship between spectral power distribution and perceived colour when colour is considered out of context. If a colour is perceived in context, this relationship no longer holds. To integrate the rich amount of spectral information, in order to obtain the triplet necessary to generate colour sensation, the human retina integrates the spectrum by means of cone sensitivity curves. Colorimetry does the same using colour matching functions. Considering colour out of context, small changes in the integration curves result in corresponding changes in colour. In the case of colour in context this does not apply, since human vision colour normalisation mechanisms play an important role. This paper presents experiments that show how colour normalisation and spatial computation can compensate for changes in the integration curves. In other words, the human visual system has developed ways to adjust to changing lighting composition and distribution. Experiments prove that these mechanisms can also compensate for differences in cone sensitivities, or more generally in every set of spectrum integration curves.
Dynamic Service Chaining Method Considering Performance of Middlebox Over SDN

The conventional dynamic routing methods in Software Defined Networks (SDN) set the optimal routing path based on the minimum link cost, and thereby transmits the incoming or outgoing flows to the terminal. However, in this case, flows can bypass the middlebox that is responsible for security service and thus, thus the network can face a threat. That is, while determining the best route for each flow, it is necessary to consider a dynamic service chaining, which routes a flow via a security middlebox. Therefore, int this paper, we propose a new dynamic routing method that considers the dynamic flow routing method combined with the security service functions over the SDN.
Modelling a Collaborative Digital Research Ecosystem Based on a Novel Time-Dependent Matchmaker Model

Improvements in web technologies have motivated a rapidly increasing research interests in online collaborative research frameworks. Such frameworks provide online networks of individuals from the academia, the industry, and the public sector for the purpose of research collaborations; and the use of collective intelligence in tackling societal problems. This paper aims at modelling a collaborative digital research ecosystem which leverages on a novel time-dependent matchmaker model in identifying and linking collaborators with similar research behaviours and interests. In the development of this model, it was considered that a researcher’s interest in a particular topic may change with time. Notably, this system will encourage formation of online research teams; enhance multidisciplinary researches; facilitate co-authoring among researchers regardless of geographical locations; easy identification of domain experts; ideation and so on.
Revealing and Characterizing MPLS Networks

The Internet is a wide network of computers in constant evolution. Each year, more and more organizations are connected to this worldwide network. Each of them has its own structure and administration that are not publicly revealed for economical, political, and security reasons. Consequently, our perception of the Internet structure, and more specifically, its topology, is incomplete. In order to balance this lack of knowledge, the research community relies on network measurements. Most of the time, they are performed based on the well-known tool traceroute. However, in practice, an operator may privilege other technologies than IP to forward packets inside its network. MultiProtocol Label Switching (MPLS) is one them. Even if it is heavily deployed by operators, it has not been really investigated by researchers. Prior to this thesis, only two studies focused on the identification of MPLS tunnels in traceroute data. Moreover, while one of them does not take all possible scenarios into account, the other lack of precision in some of its models. In addition, MPLS tunnels may hide their content to traceroute. Topologies inferred from such data may thus contain false links or nodes with an artificially high degree, leading so to biases in standard graph metrics used to model the network. Even if some researchers already tried to tackle this issue, the revelation of hidden MPLS devices in traceroute data is still an open question. This thesis aims at characterizing MPLS in two different ways. On the one hand, at an architectural level, we will analyze in detail its deployment and use in both IPv4 and IPv6 networks in order to improve its state-of-the-art view. We will show that, in practice, more than one IPv4 trace out of two crosses at least one MPLS tunnel. We will also see that, even if this protocol can simplify the internal architecture of transit networks, it also allows some operators to perform traffic engineering in their domain. On the other hand, MPLS will be studied from a measurement point of view. We will see that routers from different manufacturers may have distinct default behaviors regarding to MPLS, and that these specific behaviors can be exploited to identify MPLS tunnels during traceroute measurements. More precisely, we will focus on new methods able to infer the presence of tunnels that are invisible in traceroute outputs, as well as on mechanisms to reveal their content. We will also show that they can be used in order to improve the inference of Internet graph properties, such as path lengths and node degrees. Finally, these techniques will be integrated into Trace the Naughty Tunnels (TNT), a traceroute extension able to identify all types of MPLS tunnels along a path towards a destination. We will prove that this tool can be used in order to get a detailed quantification of MPLS tunnels in the worldwide network. TNT is publicly available, and can therefore be part of many future studies conducted by the research community.
Analysis of the loss of orthogonality through Doppler spread in OFDM systems

The paper quantifies the significant effects of Doppler spread in a time variant mobile radio channel on the performance of OFDM multi-carrier systems. A suitable model for OFDM is introduced and serves as the basis for the derivation of the inter-carrier interference (ICI) due to the loss of sub-carrier orthogonality. Several Doppler spectra are analyzed and compared, such as the classical and uniform distributions, as well as the case of two distinct values for the Doppler frequency. We can thus employ the computed variance of the ICI process, together with the channel noise, in system analyses and designs, and avoid lengthy time-domain simulations. We also analyze optimal receiver frequency synchronisation in the case of Doppler spreads.
JW300: A Wide-Coverage Parallel Corpus for Low-Resource Languages

Viable cross-lingual transfer critically depends on the availability of parallel texts. Shortage of such resources imposes a development and evaluation bottleneck in multilingual processing. We introduce JW300, a parallel corpus of over 300 languages with around 100 thousand parallel sentences per language pair on average. In this paper, we present the resource and showcase its utility in experiments with cross-lingual word embedding induction and multi-source part-of-speech projection.
Accelerating Rule-matching Systems with Learned Rankers

Infusing machine learning (ML) and deep learning (DL) into modern systems has driven a paradigm shift towards learning-augmented system design. This paper proposes the learned ranker as a system building block, and demonstrates its potential by using rule-matching systems as a concrete scenario. Specifically, checking rules can be time-consuming, especially complex regular expression (regex) conditions. The learned ranker prioritizes rules based on their likelihood of matching a given input. If the matching rule is successfully prioritized as a top candidate, the system effectively achieves early termination. We integrated the learned rule ranker as a component of popular regex matching engines: PCRE, PCRE-JIT, and RE2. Empirical results show that the rule ranker achieves a top-5 classification accuracy at least 96.16%, and reduces the rule-matching system latency by up to 78.81% on a 8-core CPU.
Cuneiform Symbols Recognition Based on K-Means and Neural Network

Cuneiform is the ancient writing system in the world . But, there is no clear interest recognition cuneiform symbol, despite its importance. This research interested in building an algorithm for cuneiform symbol recognition. Firstly, the Sumerian texts were entered through the scanner and make some initial preprocessing operations such as segmentation for the purpose of cutting the text and getting a cuneiform symbol. Then, features were extracted for each symbol by using vertical and horizontal projections, centre of gravity, and connected component. Because of the large number of cuneiform symbols, the similar symbols are clustered by K-means algorithm, then multilayer neural networks are used to classify the symbol within the same cluster. The proposed algorithm gave good results
Semi-Parametric Maximum Likelihood Method for Interaction in Case-Mother Control-Mother Designs: Package SPmlficmcm

The analysis of interaction effects involving genetic variants and environmental exposures on the risk of adverse obstetric and early-life outcomes is generally performed using standard logistic regression in the case-mother and control-mother design. However such an analysis is inefficient because it does not take into account the natural family-based constraints present in the parent-child relationship. Recently, a new approach based on semi-parametric maximum likelihood estimation was proposed. The advantage of this approach is that it takes into account the parental relationship between the mother and her child in estimation. But a package implementing this method has not been widely available. In this paper, we present SPmlficmcm, an R package implementing this new method and we propose an extension of the method to handle missing offspring genotype data by maximum likelihood estimation. Our choice to treat missing data of the offspring genotype was motivated by the fact that in genetic association studies where the genetic data of mother and child are available, there are usually more missing data on the genotype of the offspring than that of the mother. The package builds a non-linear system from the data and solves and computes the estimates from the gradient and the Hessian matrix of the log profile semi-parametric likelihood function. Finally, we analyze a simulated dataset to show the usefulness of the package.
The interpretive and ideal-type approach: Rethinking digital non-use(s) in a weberian perspective

Despite attempts to conceptualize the digital divide through a critical lens that includes different ontologies of non-Internet use, research still tends to dichotomize users and nonusers and to depict non-users as a homogeneous group. This tendency masks multifaceted social and agentic processes that involve status, class, literacy, identity, actions, and practices. In response, we urge researchers to employ a substantive theoretical and analytical framework to critically explore non-use(s) and its different meanings and outcomes. Max Weber’s interpretive perspective and his concept of the ideal type can provide such a framework.
The Syntactic Bits of Nouns: How Prior Syntactic Distributions Affect Comprehension, Production, and Acquisition

Author(s): Lester, Nicholas | Advisor(s): Moscoso del Prado Martin, Fermin | Abstract: Usage-based linguistic theory argues that experience is the fundamental organizing principle of language. Linguistic representations are extracted from – and continuously tuned by – probabilistic features of language use. Much psycholinguistic evidence supports this argument, particularly in the domain of lexical processing. For example, how a word is distributed across its various lexical and morphological contexts influences how quickly it is recognized and produced in isolation. Fewer studies have explored how syntactic distributions affect lexical processing, and of these, even fewer have adopted comprehensive, abstract measurements of syntax. In this dissertation, I present several new information-theoretic tools for measuring the syntactic distributions of words based on the Dependency Grammar formalism. This formalism allows me to contrast two independent dimensions of syntactic structure: hierarchical status and word order. Further, I provide a new method for teasing apart information bound to syntactic and lexical contexts. I compute these measures for nouns based on two large corpora of English.These measures are correlated with behavior in several contexts. First, I re-analyze the noun-based trials of two previously published databases of visual lexical decision response time data, one simple and the other primed. I then turn to production, reporting two picture-naming studies. In the first, participants produce nouns in isolation. This task consitutes a stong attack on the hypothesis that syntactic distributions affect noun production; at least on its face, it does not require participants to access syntactic information in order to successfully complete the task. In a follow up, participants were asked to name the images using a syntactic frame (the + NAME). This task should promote syntactic access, increasing the likelihood that prior syntactic distributions should play a role. Finally, I test whether children are senstive to these syntactic distributions (based on adult speech) as they begin to produce nouns in syntactic contexts for the first time using a large, densely sampled longitudinal corpus of child speech.Results show that isolated noun processing is affected by prior syntactic distributions in both comprehension and production. However, the specific nature of these effects differs across modalities, and in production, as a function of whether the nouns were produced in isolation or within a syntactic frame. The measures also predict the age at which nouns first emerge in the speech of children.
Improved Secure Authentication By Using Image compare and Captcha

The concept of CaRP (Captcha as Graphical Passwords) is easy but generic. CaRP can have manifold instantiations. In presumption, any Captcha plan relying on multiple-object categorization can be rehabilitated to a CaRP scheme. We present exemplary CaRPs built on both text Captcha and image-recognition Captcha. One of them is a text CaRP in which a password is a progression of characters like a text password, but come into by clicking the right character progression on CaRP images. a CaRP password can be create only probabilistically by repeated online guessing attacks even if the password is in the search set.CaRP also proffer a novel move towards to address the well-known image hotspot difficulty in well-liked graphical password systems, such as Pass Points, that frequently leads to feeble password choices.
Application of PID Controller and Nonlinear Sliding Mode Control on Two Link Robotic Manipulator

Robotic manipulators plays an important role in industrial autonomous manufacturing. Non linearity as well as parameter variations or uncertainties caused by inaccurate system modeling, insufficient parameter identification, external disturbances and environmental conditions are the most challenging issues in robot engineering. The dynamic model of the two link robotic manipulator is done using the EulerLagrange equation. A conventional PID controller and a robust nonlinear sliding mode control is introduced to control the motion of the end effector at specific position for pick and place tasks. Comparative study is done and the results shows that Sliding mode control surpass PID control with minimum tracking error. Simulation results validate that the chattering of control input can be mitigated effectively using SMC