Miracle-dz/UniFAR-FTUs huggingface dataset:
Spectrum and infrastructure sharing in the MIMO interference relay channels

In this paper, single-stream transmission in the MIMO interference relay channel is studied. Two independent transceiver pairs with multiple antennas belonging to different operators communicate with the assistance of one relay, which operates in half-duplex mode and employs an amplify-and-forward strategy. The relay that is shared between the two operators also has multiple antennas. First, the interference relay channel is converted to the conventional interference channel via a preliminarily determined relay amplification matrix. Various relay amplification matrices are investigated for this conversion. Then, the flexible coordinated beamforming for the interference relay channel (IRC Flex-CoBF) is proposed for the transceivers. The IRC FlexCoBF algorithm is compared to the alternative schemes proposed in the literature. Simulations show that IRC FlexCoBF achieves a better sum rate performance. Furthermore, a higher robustness to the interferences is demonstrated for IRC FlexCoBF compared to the state of the art. Simulation results show that by sharing a relay between two operators a significant gain in sum rate can be achieved compared to the relay channel.
Tamil Speech Recognition Using Hybrid Technique of EWTLBO and HMM

-Speech Recognition technology is one of the fast growing engineering technologies. The task of speech recognition is to convert speech into a sequence of words by a computer program. As the most natural communication modality for humans, the ultimate dream of speech recognition is to enable people to communicate more naturally and effectively. The accuracy of automatic speech recognition remains one of the most important research challenges after years of research and development. There are a number of well-known factors that determine the accuracy of a speech recognition system. Several speech recognition algorithms were adapted for different language. In this research continuous speech recognition is concentrated over Tamil language. This paper divides the proposed framework into three stages namely preprocessing, feature extraction and classification. Feature extraction is the process of retaining useful information of the signal while discarding redundant and unwanted information. In feature extraction stage, the proposed framework uses MFCC for transforming the signal into a form appropriate for classification. Classification is done by combination of EWTLBO and HMM. Teaching–Learning-Based Optimization (TLBO) is recently being used as a reliable, accurate and robust optimization technique scheme for global optimization over continuous spaces. This research presents an improved variant of TLBO algorithm, called Enhanced Weighted Teaching–Learning-Based Optimization (EWTLBO). A performance comparison of the proposed method is provided against the original TLBO and some other algorithms. An additional parameter “weight” is introduced to the existing TLBO algorithm to increase convergence rate. To enhance the search space of the algorithm an elitist concept is introduced to improve the performance of existing TLBO algorithm. EWTLBO is used to find the optimization value which is passed through the HMM to get the recognized output.
Motion synthesis and editing in low‐dimensional spaces

Human motion is difficult to create and manipulate because of the high dimensionality and spatiotemporal nature of human motion data. Recently, the use of large collections of captured motion data has added increased realism in character animation. In order to make the synthesis and analysis of motion data tractable, we present a low‐dimensional motion space in which high‐dimensional human motion can be effectively visualized, synthesized, edited, parameterized, and interpolated in both spatial and temporal domains. Our system allows users to create and edit the motion of animated characters in several ways: The user can sketch and edit a curve on low‐dimensional motion space, directly manipulate the character's pose in three‐dimensional object space, or specify key poses to create in‐between motions. Copyright © 2006 John Wiley & Sons, Ltd.
Evaluation and Extension of Maximum Entropy Models with Inequality Constraints

A maximum entropy (ME) model is usually estimated so that it conforms to equality constraints on feature expectations. However, the equality constraint is inappropriate for sparse and therefore unreliable features. This study explores an ME model with box-type inequality constraints, where the equality can be violated to reflect this unreliability. We evaluate the inequality ME model using text categorization datasets. We also propose an extension of the inequality ME model, which results in a natural integration with the Gaussian MAP estimation. Experimental results demonstrate the advantage of the inequality models and the proposed extension.
Learning from Default Mode Network: The Predictive Value of Resting State in Traumatic Brain Injury

In the 1880s, the Italian physiologist Angelo Mosso hypothesized that an attentional or cognitive task increases cerebral blood flow ([Mosso, 1884][1]). Lacking current technology, he tried to measure the blood flow of a supine live subject with a “delicately balanced table which could tip
CONTOURLET BASED TEXTURE ANALYSIS AND CLASSIFICATION OF MAMMOGRAM IMAGES

In this paper we have proposed a fully automated Computer Aided Diagnostic (CADx) system that can aid the radiologists in reading vast number of mammograms generated during screening procedures. The aim of the proposed system is to minimize the number of false positives and the number of false negatives. The remarkable potential of contourlet transform in extracting texture features of images with smooth contours is exploited in the proposed method. Mammogram images are taken from MIAS (Mammographic Image Analysis Society) database and a Probabilistic Neural Network (PNN) is trained to classify the image as normal, benign or malignant. The Region of Interest (ROI) of size 256 x 256 pixels is segmented from the mammogram image using Otsu’s N segmentation method and the Contourlet Coefficient Co-occurrence Matrix (CCCM) features are extracted. The texture feature set comprising the dominant features is fed to a PNN for the purpose of classification. To evaluate the efficiency of the proposed system 3 fold cross validation is performed and a classification accuracy of 91.1% is obtained. The performance of the proposed method is compared against other methods in terms of sensitivity, specificity and classification accuracy. The results obtained prove that the texture analysis of mammogram images using CCCM features outperforms other methods in terms of classification accuracy and hence can be successfully applied for the classification of mammogram images.
The Application of Wavelet De-noising in the Power System Fault Recording

In this paper, base on the basic wavelet transform knowledge and that signal and noise have different characteristics performance at different scales, combining threshold de-noising and the spatially selective de-noising of wavelet de-nosing methods, a new de-noising method based on the wavelet transform has been proposed and applied to deal with the fault recording signals which measured in the actual power system. The experiment proved that this way could remove the noise effectively and enhance the stability of the fault recording system.
MASSES AND ORBITAL CONSTRAINTS FOR THE OGLE-2006-BLG-109Lb,c JUPITER/SATURN ANALOG PLANETARY SYSTEM

We present a new analysis of the Jupiter+Saturn analog system, OGLE-2006-BLG-109Lb,c, which was the first double planet system discovered with the gravitational microlensing method. This is the only multi-planet system discovered by any method with measured masses for the star and both planets. In addition to the signatures of two planets, this event also exhibits a microlensing parallax signature and finite source effects that provide a direct measure of the masses of the star and planets, and the expected brightness of the host star is confirmed by Keck AO imaging, yielding masses of , Mb = 231 ± 19 M⊕, and Mc = 86 ± 7 M⊕. The Saturn-analog planet in this system had a planetary light-curve deviation that lasted for 11 days, and as a result, the effects of the orbital motion are visible in the microlensing light curve. We find that four of the six orbital parameters are tightly constrained and that a fifth parameter, the orbital acceleration, is weakly constrained. No orbital information is available for the Jupiter-analog planet, but its presence helps to constrain the orbital motion of the Saturn-analog planet. Assuming co-planar orbits, we find an orbital eccentricity of and an orbital inclination of . The 95% confidence level lower limit on the inclination of i > 49° implies that this planetary system can be detected and studied via radial velocity measurements using a telescope of ≳30 m aperture.
Challenges in Real-Time Synchronization

Upcoming multi-core processors force developers of realtime systems to meet the challenges in real-time synchronization. This paper sketches some results of a novel wait-free, linearizable, and disjoint-access parallel NCAS library, called RTNCAS. It focuses the use in massive parallel real-time systems and enables developers to implement arbitrarily complex data structures in an easy way, while ensuring linearizability, wait-freedom, as well as disjoint-access parallelism. It allows, furthermore, developers to re-use their sequential algorithms without any modifications and care about concurrency. Thereby, the degree of disjoint-access parallelism can be used to trade a low jitter for a higher average-case performance.
Worst-Case Value at Risk of Nonlinear Portfolios

Portfolio optimization problems involving value at risk VaR are often computationally intractable and require complete information about the return distribution of the portfolio constituents, which is rarely available in practice. These difficulties are compounded when the portfolio contains derivatives. We develop two tractable conservative approximations for the VaR of a derivative portfolio by evaluating the worst-case VaR over all return distributions of the derivative underliers with given first-and second-order moments. The derivative returns are modelled as convex piecewise linear or---by using a delta--gamma approximation---as possibly nonconvex quadratic functions of the returns of the derivative underliers. These models lead to new worst-case polyhedral VaR WPVaR and worst-case quadratic VaR WQVaR approximations, respectively. WPVaR serves as a VaR approximation for portfolios containing long positions in European options expiring at the end of the investment horizon, whereas WQVaR is suitable for portfolios containing long and/or short positions in European and/or exotic options expiring beyond the investment horizon. We prove that---unlike VaR that may discourage diversification---WPVaR and WQVaR are in fact coherent risk measures. We also reveal connections to robust portfolio optimization. This paper was accepted by Dimitris Bertsimas, optimization.
Fault-tolerance in the Advanced Automation System

The Advanced Automation System (AAS), a distributed real-time system intended to replace the present en-route and terminal approach US air traffic control computer systems over the next decade, is discussed. High availability of air traffic control services is an essential requirement of the system. The authors discuss the general approach to fault tolerance adopted in the AAS by reviewing some of the questions asked during the system design, various alternative solutions considered, and the reasons for the design choices made. <<ETX>>
The art of chicken sexing

Expert chick sexers are able to quickly and reliably determine the sex of day-old chicks on the basis of very subtle perceptual cues. They claim that in many cases they have no idea how they make their decisions. They just look at the rear end of a chick, and ‘see’ that it is either male or female. This is somewhat reminiscent of those expert chess players, often cited in the psychological literature, who can just ‘see’ what the next move should be; similar claims have been made for expert wine tasters and experts at medical diagnosis. All of these skills are hard-earned and not accessible to introspection. But is there really anything unusual about the chicken sexer, the chess grand master, the wine buff or the medical expert? I argue that there is not. In fact, we are all constantly making categorizations of this sort: we are highly accurate at categorizing natural kinds, substances, artefacts, and so on. We do so quickly and subconsciously, and the process is completely inaccessible to introspection. The question is, why is it so difficult to acquire skills such as chicken sexing, when we automatically acquire the ability to categorize other objects. In this paper, I argue that we have mechanisms for learning the cues necessary for categorization, but that these mechanisms require selective attention to be given to the relevant features. We automatically acquire the ability to categorize certain objects because we have inbuilt attention directors causing us to attend to diagnostic cues. In cases such as chicken sexing, where we do not automatically develop categorization abilities, our inbuilt attention directors do not cause us to attend to diagnostic cues, and out attention therefore has to be drawn to these cues in another way, such as through training.
Characterization and retinal neuron differentiation of WERI-Rb1 cancer stem cells

Purpose The evidence is increasing that cancer stem cells (CSCs) expressing embryonic and neuronal stem cell markers are present in human retinoblastoma (Rb). This study was conducted to determine whether stem-like cancer cells (SLCCs) in Rb express retinal stem cell–related genes and whether SLCCs can directly differentiate into retinal neurons. Methods The cancer stem cell characteristics in WERI-Rb1 cells were determined with Hoechst 33,342 staining, clone formation assay, and CD133 flow cytometry. The expression of embryonic stem cell and retinal stem cell–related genes was analyzed with real-time PCR and immunofluorescence. The SLCCs were induced to differentiate into retinal neurons by the addition of Dickkopf-related protein 1 and Lefty-A. Results A small but persistent population of cells excluding Hoechst dye in a verapamil-sensitive manner exhibited a cancer stem cell–like phenotype. The SLCCs displayed highly clonogenic abilities and increased CD133 expression with isolation and expansion in culture in serum-free medium. By comparing the expression of stem cell markers, we found Oct3/4 was more highly expressed in the SLCCs than in human embryonic stem cells. Together with the properties of intrinsic retinal stem cell–related gene expression, we found SLCCs can be induced into neuron-like cells that express glial fibrillary acidic protein and rhodopsin (a photoreceptor cell marker). Conclusions These findings provide new insight into cancer stem cells and used a strategy of an artificial change of cancer stem cell fate with transcription factors.
On the implication problem for probabilistic conditional independency

The implication problem is to test whether a given set of independencies logically implies another independency. This problem is crucial in the design of a probabilistic reasoning system. We advocate that Bayesian networks are a generalization of standard relational databases. On the contrary, it has been suggested that Bayesian networks are different from the relational databases because the implication problem of these two systems does not coincide for some classes of probabilistic independencies. This remark, however, does not take into consideration one important issue, namely, the solvability of the implication problem. In this comprehensive study of the implication problem for probabilistic conditional independencies, it is emphasized that Bayesian networks and relational databases coincide on solvable classes of independencies. The present study suggests that the implication problem for these two closely related systems differs only in unsolvable classes of independencies. This means there is no real difference between Bayesian networks and relational databases, in the sense that only solvable classes of independencies are useful in the design and implementation of these knowledge systems. More importantly, perhaps, these results suggest that many current attempts to generalize Bayesian networks can take full advantage of the generalizations made to standard relational databases.
Traffic-based modeling of electric vehicle charging load and its impact on the distribution network and railway station parking lots

In this paper, a traffic-based modeling for electric vehicle charging profile is proposed. An optimization process will find the best-fitted probability distribution function to the real traffic data. The impacts of electric vehicles charging profile on the regional distribution network, and a typical railway station, proposing EVs parking lots facilities, are discussed. The results show a significant increment of annual energy purchase price for the downstream consumers that would be possibly reduced by proposing supervision system aimed to charging coordination.
Relaxing correctness criteria in real-time DBMSs

When manipulating traditional databases, Database Management Systems (DBMSs) must maintain data logical consistency while attempting to enhance concurrency execution of transactions. In real-time DBMSs, transactions have deadlines and data have often validity duration. Then real-time DBMSs must further allow transactions to respect their temporal constraints. However, these objectives are difficult to reach simultaneously. Therefore, some proposed works attempt to bring new concepts for relaxing transactions ACID properties in real-time context, that might be respected in traditional DBMSs. To this purpose, in this paper we introduce data-imprecision levels and transaction-deadline levels, which are materialized by respectively two notions : epsilon-data (ε-data) which takes into account the possible data imprecision and delta-deadline (∆-deadline) which takes into account possible data or transaction deadline overtaken. We then present transactions concurrency-control and scheduling (CC&S) protocols designed to control database temporal and logical inconsistencies in realtime DBMSs, and we show that these protocols bring some added value to real-time DBMSs and may outperform classical transaction CC&S protocols already proposed i.e. more transactions meet their deadlines
Intrusion Detection Based On Fuzzy Logic Approach Using Simplified Swarm Optimization

--The intrusion is becoming more essential for effective defense against attacks that are constantly changing in magnitude and complexity. Mainly intrusion detection relies on the extensive knowledge of security experts. The paper proposed a new detection mechanism as Fuzzy Intrusion Detection Engine (FIDE) that uses fuzzy logic to access network data. FIDE uses fuzzy analyzer engine to evaluate inputs and generate alerts for security administrators. The FIDE act as a fuzzy classifier, whose knowledge base is act as fuzzy “if-then” rule. This paper describes the components of FIDE architecture, and explains the benefit of fuzzy rule that improve fuzzy sets. Finally, in order to obtain the best result Simplified Swarm Optimization is used to optimize the structure of FIDE. The simulation of the proposed system is trained and tested with actual real time network data. The FIDE IDS can detect a wide range of common attack types. The proposed system shows high accuracy in identifying attacks.
Evaluation and performance analysis of graph theoretical methods for image segmentation

Image segmentation plays vital role in computer vision for image retrieval, visual summary, image base modeling, and for many other purposes. Despite many years of research and significant contributions, image segmentation is still a very challenging task to suit for variety of applications. Among the different segmentation approaches, graph theoretical approach is the most popular since it has capabilities of organizing the image elements into accurate mathematical structures and makes the formulation computationally efficient. This paper critically reviews recent graph based segmentation methods along with their detailed analysis, experimental performance and evaluation on the basis of Berkeley benchmark. The study and evaluation is useful in improving the performance of existing methods as well as helpful in the development of new methods.
How does coarse gravel augmentation affect early-stage Chinook salmon Oncorhynchus tshawytscha embryonic survivorship?

Early-stage Chinook salmon Oncorhynchus tshawytscha embryos were incubated in artificial redds that mimicked hyporheic conditions in gravel-augmented habitat to assess survivorship. Two complementary experiments were conducted where units varied along gradients of (1) increasing interstitial flow velocity (0·05-2·50 cm s⁻¹) in a uniformly coarse (particles ≥22 mm) sediment mixture and (2) increasing sediment porosity with interstitial flow velocity held constant. Embryonic survivorship increased moderately along a gradient of interstitial flow velocity, while survivorship among units with varying sediment porosities was consistent. No evidence for flow-induced agitation and mortality was observed. Results suggest that high interstitial flow velocities may confer a moderate advantage for incubating salmonid embryos when conditions that typically reduce embryonic mortality (i.e. low concentrations of fine particles) are ideal.
Edge-preserving nonlinear iterative image resampling method

In this paper, an edge-preserving nonlinear iterative regularization-based image resampling method for a single noise-free image is proposed. Several aspects of the resampling algorithm are investigated: choice of discrepancy and regularization norms, improvements of convergence speed using edge-directional steepest-descent method and patch-based details synthesis. A model of a downsampling operator based on a camera observation model is considered.
Using Association Rules to Make Rule-based Classifiers Robust

Rule-based classification systems have been widely used in real world applications because of the easy interpretability of rules. Many traditional rule-based classifiers prefer small rule sets to large rule sets, but small classifiers are sensitive to the missing values in unseen test data. In this paper, we present a larger classifier that is less sensitive to the missing values in unseen test data. We experimentally show that it is more accurate than some benchmark classifies when unseen test data have missing values.
Challenges and Perspectives in Security Measures for the SCADA System

: In the past few years the security issues in the supervisory control and data acquisition (SCADA) system have been widely investigated, and many security mechanisms have been proposed from research communities. The international standard organizations also have published several standard documents for secured SCADA systems. In this paper, we overview the SCADA system architecture and consider the constraints due to the system’s own characteristics. And then, we explain the technological challenges for the SCADA security and summarize the current results which have been brought out by the efforts from the international bodies as well as research communities.