Miracle-dz/UniFAR-FTUs huggingface dataset:
New Wars Rational, Old War Expectations

The violent cycle between Israel and the Palestinians in Gaza, which took place in the summer of 2014, was understood and perceived by Israeli society as having characteristics of a new war: it was seen as part of an ongoing and never-ending conflict, with no clear starting point or finish, in which one side is not an official state that has an organized military, and in which it is hard to determine what is the ideal outcome for either side. By studying Israeli newspapers’ reports of the war, this research report shows that although embracing the mind-set of new wars, it was also framed and perceived in Israeli society as having old war characteristics: There was a belief that a tie breaker can be found; that this violent cycle can fundamentally change the future of the Israeli-Palestinian conflict; and there was hope for a clear and decisive end to the war in which one side will be the winner. It is therefore claimed that societies, even when rationally understanding what new wars are, still hold a collective memory of old wars. This shared memory makes them think and hope in ways that fit their past experiences, even when knowing that the nature of conflicts has changed.
German Speech Recognition: A Solution for the Analysis and Processing of Lecture Recordings

Since recording technology has become more robust and easier to use, more and more universities are taking the opportunity to record their lectures and put them on the Web in order to make them accessable by students. The automatic speech recognition (ASR) techniques provide a valueable source for indexing and retrieval of lecture video materials. In this paper, we evaluate the state-of-the-art speech recognition software to find a solution for the automatic transcription of German lecture videos. Our experimental results show that the word error rates (WERs) was reduced by 12.8% when the speech training corpus of a lecturer is increased by 1.6 hours.
Structured connectivity in an associative memory model

High capacity associative memory models with dilute structured connectivity are trained using naturalistic bitmap patterns. The connectivity of the model is chosen to reflect the local spatial continuity of the data. The results show that the structured connectivity gives the networks a higher effective capacity than equivalent randomly diluted networks. Moreover the locally connected networks have a much lower mean connection length than the randomly connected network. It is shown that a small amount of additional connectivity can correct any neurons that fail to train.
Evaluation of interpolation kernels for SAR interferometry

Interpolation is required in interferometric synthetic aperture radar (SAR) processing for coregistration of complex signals. Straightforward system theoretical considerations provide objective figures of merit for interpolators, such as interferometric decorrelation and phase noise. Theoretical and simulation results are given for nearest neighbor, piecewise linear, four- and six-point cubic convolution, and truncated sinc kernels.
An approach to topology synthesis of analog circuits using hierarchical blocks and symbolic analysis

This paper presents a method of design automation for analog circuits, focusing on topology generation and quick performance evaluation. First we describe mechanisms to generate circuit topologies with hierarchical blocks. Those blocks are specialized by adding terminal information. The connection between blocks is in compliance with a set of synthesis rules, which are extracted from typical schematics in the literature. Symbolic analysis has been used to select an appropriate topology quickly and to help the designer gain a better understanding of a circuit's behavior. Finally, experimental results show the creativity and efficiency of our method
Efficient adaptive modulation and power allocation algorithm for OFDMA cellular systems

An adaptive transmission scheme using QAM and LDPC code is proposed for an OFDMA cellular system employing FDD. Also, adaptive algorithms for active user selection, subchannel transmission power allocation, and modulation and coding set selection are proposed. The performance of the proposed scheme is obtained from computer simulation and is compared with that of the conventional scheme using mean SNR only. It is shown that the proposed scheme can provide up to 5.1 dB gain over the conventional scheme at the expense of only 3 more bits in feedback information.
Perspectives on Reuse in the Era of Java Enterprise

In this report we investigate into the idea of having a developer coach for the application development with object oriented technology in an organisation. Although the myth of a developer coach in the form of an evangelist, guru, etc. is very common, we will show that this approach is not well founded. In particular we will examine two scenarios which are refinements of the classical scenario of having a project management, a human resources department, domain experts and application developers. In the first scenario we will simply add already bespoken developer coach. In the second scenario the charges will be more elaborate, instead of application developers we will have component developers. We will also assume a technology instructor and an application architect. Theoretical considerations will lead us to the conclusion that the second scenario is more efficient. We will close the report with some empirical data that confirms our theoretical considerations and some speculations on the implications.
Compact Timed Automata for PLC Programs

H.X. Willems University of Nijmegen Computing Science Institute P.O. Box 9010 6500 GL Nijmegen, The Netherlands November 24, 1999 Abstract In this work a set of tools is developed to convert programs for Programmable Logic Controllers (PLCs) into timed automata in order to facilitate the veri cation of such programs. It is shown that our timed automata models of PLC programs can be dissected into a timed and an untimed part. Typically, the untimed part is much larger than the timed part and can be reduced in size by using the CADP toolset. The reduction in state space is substantial, even for small PLC programs.
Title Stata.com Mixed — Multilevel Mixed-effects Linear Regression

Syntax Menu Description Options Remarks and examples Stored results Methods and formulas Acknowledgments References Also see Syntax mixed depvar fe equation || re equation || re equation. , options where the syntax of fe equation is indepvars if in weight , fe options and the syntax of re equation is one of the following: for random coefficients and intercepts levelvar: varlist , re options for random effects among the values of a factor variable levelvar: R.varname , re options levelvar is a variable identifying the group structure for the random effects at that level or is all representing one group comprising all observations. fe options Description Model noconstant suppress constant term from the fixed-effects equation re options Description Model covariance(vartype) variance–covariance structure of the random effects noconstant suppress constant term from the random-effects equation collinear keep collinear variables fweight(exp) frequency weights at higher levels pweight(exp) sampling weights at higher levels 1 2 mixed — Multilevel mixed-effects linear regression options Description Model mle fit model via maximum likelihood; the default reml fit model via restricted maximum likelihood pwscale(scale method) control scaling of sampling weights in two-level models residuals(rspec) structure of residual errors SE/Robust vce(vcetype) vcetype may be oim, robust, or cluster clustvar Reporting level(#) set confidence level; default is level(95) variance show random-effects and residual-error parameter estimates as variances and covariances; the default stddeviations show random-effects and residual-error parameter estimates as standard deviations noretable suppress random-effects table nofetable suppress fixed-effects table estmetric show parameter estimates in the estimation metric noheader suppress output header nogroup suppress table summarizing groups nostderr do not estimate standard errors of random-effects parameters nolrtest do not perform likelihood-ratio test comparing with linear regression display options control column formats, row spacing, line width, display of omitted variables and base and empty cells, and factor-variable labeling EM options emiterate(#) number of EM iterations; default is emiterate(20) emtolerance(#) EM convergence tolerance; default is emtolerance(1e-10) emonly fit model exclusively using EM emlog show EM iteration log emdots show EM iterations as dots Maximization maximize options control the maximization process; seldom used matsqrt parameterize variance components using matrix square roots; the default matlog parameterize variance components using matrix logarithms coeflegend display legend instead of statistics mixed — Multilevel mixed-effects linear regression 3 vartype Description independent one unique variance parameter per random effect, all covariances 0; the default unless the R. notation is used exchangeable equal variances for random effects, and one …
Smoke detection using spatio-temporal analysis, motion modeling and dynamic texture recognition

In this paper, we propose a novel method for video-based smoke detection, which aims to discriminate smoke from smoke-colored moving objects by applying spatio-temporal analysis, smoke motion modeling and dynamic texture recognition. Initially, candidate smoke regions in a frame are identified using background subtraction and color analysis based on the HSV model. Subsequently, spatio-temporal smoke modeling consisting of spatial energy analysis and spatio-temporal energy analysis is applied in the candidate regions. In addition, histograms of oriented gradients and optical flows (HOGHOFs) are computed to take into account both appearance and motion information, while dynamic texture recognition is applied in each candidate region using linear dynamical systems and a bag of systems approach. Dynamic score combination by mean value is finally used to determine whether there is smoke or not in each candidate image region. Experimental results presented in the paper show the great potential of the proposed approach.
Medical Image Compression Using Fuzzy C-Means Based Contourlet Transform

Problem statement: To meet the demand for high speed transmission of image in efficient image storage and remote medical treatment, the eff icient image compression is essential. The contourlet transform along with wavelet theory has great potential in medical image compression. Approach: The significant portion of the medical image appli ed with Fuzzy C-means based contourlet transform. DWT applied to the rest of th e image. Finally modified EZW of six symbols differing from normal EZW was applied to the whole image. This technique increases PSNR and gives better compression ratio. Results: The MATLAB simulation showed that the method of separate transforms to the two regions proves bette r results compared to the ordinary way of applying only single transforms to the whole image. The resu lts revealed that proposed algorithm was simple and computationally fewer complexes based on embedded block coding with coefficient truncation. Conclusion: The compression of the proposed algorithm is super ior to EZW, SPIHT. Our new method of compression algorithm can be used to improve the performance of Compression Ratio (CR) and Peak Signal to Noise Ratio (PSNR). In future this study can be extended to real time applications for video compression in medical image s.
The Role of NF-κB in PPARα-Mediated Hepatocarcinogenesis

In this review, the role of NF-κB in the induction of hepatocarcinogenesis by peroxisome proliferators is examined. The administration of peroxisome proliferators for more than a three-day period leads to the activation of NF-κB in the livers of rats and mice. On the other hand, peroxisome proliferator activated receptor-α (PPARα) activation in non-hepatic tissues can lead to the inhibition of NF-κB activation. Several lines of evidence support the hypothesis that the activation of NF-κB by peroxisome proliferators in the liver is mediated by oxidative stress. The role of NF-κB in peroxisome proliferator-induced hepatocarcinogenesis has been examined using NF-κB knockout models. Specifically, the induction of cell proliferation and the promotion of liver carcinogenesis are inhibited in mice lacking the p50 subunit of NF-κB. Overall, the activation of NF-κB appears to be important in the carcinogenic activity of peroxisome proliferators.
A new approach for very dark video denoising and enhancement

The paper presents a novel three-stage algorithm for very-low-light video denoising and enhancement. The proposed technique invokes twice, in the first and the third stages, the well-known Non-Local Means (NLM) method for spatial and temporal denoising: it is well adapted to the application, leading to the definition of a novel NLM tool. The intermediate stage performs a custom tone adjustment specifically aimed at enlarging the dynamic range of very dark videos. The overall approach transforms very dark videos into more watchable ones, effectively reduces very high noise, and all in all, produces high quality restored image sequences outperforming the recent state-of-the-art results. Additionally, the first and third stages can be combined as a two-step filtering scheme for normal-light videos: the novel denoising solution achieves a heavy noise removal, while reducing motion blur artifacts and preserving image details.
Ultra-Fast Matrix Multiplication: An Empirical Analysis of Highly

33 Matrices play an important role in mathematics and computer science, but more importantly, they are ubiquitous in our daily lives as they are instrumental in the efficient manipulation and storage of digital data. Matrix multiplication is essential not only in graph theory but also in applied fields, such as computer graphics and digital signal processing (DSP). DSP chips are found in all cell phones and digital cameras, as matrix operations are the processes by which DSP chips are able to digitize sounds or images so that they can be stored or transmitted electronically. Fast matrix multiplication is still an open problem, but implementation of existing algorithms [5] is a more common area of development than the design of new algorithms [6]. Strassen’s algorithm is an improvement over the naive algorithm in the case of multiplying two 2×2 matrices, because it uses only seven scalar multiplications as opposed to the usual eight. Even though it has been shown that Strassen’s algorithm is optimal for two-by-two matrices [6], there have been asymptotic improvements to the algorithm for very large matrices. Thus, the search for improvements over Strassen’s algorithm for smaller matrices is still being conducted. Even Strassen’s algorithm is not considered an efficient reduction as it requires the size of the multiplicand matrices to be large powers of two. The following two sections present the naive, Winograd’s, and Strassen’s algorithms along with discussions of the theoretical bounds for each algorithm. We then present a confirmation of the theoretical study on the running times of the algorithms, followed by the results of an empirical study. In the final two sections, we present an improved Hybrid algorithm, which incorporates Strassen’s asymptotical advantage with Winograd’s practical performance, and discuss the stunning performance of the AltiVecoptimized Strassen’s algorithm. Naive Algorithm
A 1.2 V low-power true single-phase clock CMOS 2/3 prescalers

This paper presents a low-power true single-phase clock 2/3 prescalers at 1.2 V supply voltage. The true single-phase clock divider was developed to help achieve low power and highspeed. All simulation results have been carried out by using Hspice program simulator based on 0. 13μm CMOS technology. The power consumption of the proposed circuit is less than that of the previous circuits. The prescaler is capable of operating from 0.5 to 9.7 GHz.
An Advanced Wireless Sensor Network for Health Monitoring

—In this paper we propose a system architecture for smart healthcare based on an advanced Wireless Sensor Network (WSN). It specifically targets assisted-living residents and others who may benefit from continuous, remote health monitoring. We present the advantages, objectives, and status of the design. An experimental living space has been constructed at the Department of Computer Science at UVA for evaluation. Early results suggest a strong potential for WSNs to open new research perspectives for low-cost, ad hoc deployment of multi-modal sensors for an improved quality of medical care.
Dynamic background subtraction using moments

Detection of moving objects is known to be a critical first step in many vision-based applications such as video surveillance. Background Subtraction Algorithms (BSA) offer a solution to detect all foreground pixels in a frame by comparing them with a background model. However, this is still a very challenging task especially when dynamic scenes are involved (camera jitter, noise, etc). In this paper, we present a new method for dynamic background subtraction operation based on invariant moments (Hu Set). Each pixel is modeled as a set of moments calculated from its neighborhood and stored using codebook construction. Experimental results on a set of outdoor scenes show that our method outperforms traditional BSA.
New technique for inverse kinematics problem using Simulated Annealing

One interesting problem in robotics is the inverse kinematics; It consists in easy words to find the correct values for each one of actuators (or degrees of freedom) in a robotic system knowing the final position. Nowadays exist many techniques to solve this kind of problem but unfortunately some techniques require a lot of time and bring a restricted solution that in some cases (like redundant mechanism) is not the best one. This paper presents a new technique for inverse kinematics problem using Simulated Annealing technique; the authors implement a multi-objective cost function in order to find the intermediate set of angles for the transitional points in a trajectory, from the initial position to the final one. This methodology is compared with the classical inverse kinematics solutions in a different kind of examples and is presented the advantages of this technique like reduction of time (CPU processing) and the actuator’s energy consumption using this tool, besides, and comparison with other new techniques, is make and the results of the study is also presented showing the different advantages for the simulated annealing approach. Finally a future work is proposed for improving the results and to implement this technique in industrial problems.
A proposal for estimating consumer behaviour models based on fuzzy association rules

The present paper tries to point out the necessity that firms focused on consumer markets have to understand and predict in a higher efficiently manner the behaviour of their target population. Thus, it is convenient that models of consumer behaviour in which firms are based to take their decisions to be closer to what a real Marketing Management Support System should offer. In this sense, we propose a new application for consumer behaviour modelling, based on fuzzy association rules (FAS) for adjusting the data, as a complementary way to the results obtained by using the classic technique of model estimation based on Structural Equation Modelling (SEM). With this aim, an online behavioural model is proposed being later tested making use of both FAS and SEM. Finally, a comparative analysis of the results is done, focusing specially on our proposal of methodological application.
GMM and kernel-based speaker recognition with the ISIP toolkit

This paper describes an open source framework for developing speaker recognition systems. Among other features, it supports kernel classifiers, such as the support and relevance vector machines. The paper also presents results for the IME corpus using Gaussian mixture models, which outperforms previously published ones, and discusses strategies for applying discriminative classifiers to speaker recognition
Experimental Evaluation for Impact Factors of Online Signature Verification

A study of online signature verification has been carried out based on experimental evaluation. We started by presenting a brief survey of the recent literatures on important factors of online signature verification. Four main issues have been addressed in the work: 1) assessment for signature consistency caused by different physiological factor. 2) Analysis for variation and complexity of online signature. 3) Performance evaluation for features widely used in several datasets. 4) Comparative analysis for algorithms applied on acquired datasets. From detailed experimental analysis, we identified and characterized some important aspects of online signatures.
The Return of the Gating Network: Combining Generative Models and Discriminative Training in Natural Image Priors

In recent years, approaches based on machine learning have achieved state-of-the-art performance on image restoration problems. Successful approaches include both generative models of natural images as well as discriminative training of deep neural networks. Discriminative training of feed forward architectures allows explicit control over the computational cost of performing restoration and therefore often leads to better performance at the same cost at run time. In contrast, generative models have the advantage that they can be trained once and then adapted to any image restoration task by a simple use of Bayes' rule. In this paper we show how to combine the strengths of both approaches by training a discriminative, feed-forward architecture to predict the state of latent variables in a generative model of natural images. We apply this idea to the very successful Gaussian Mixture Model (GMM) of natural images. We show that it is possible to achieve comparable performance as the original GMM but with two orders of magnitude improvement in run time while maintaining the advantage of generative models.