Miracle-dz/UniFAR-FTUs huggingface dataset:
UAMA: a unified architecture for active measurements in IP networks; End-to-end objetive quality indicators

The necessity to keep acceptable service quality levels in IP networks is undeniable nowadays, particularly with the improvement of the next generation networks. This promotes the use and justifies the need for solutions that enable network measurement and monitoring. Network measurement and performance data acquisition is currently a target of many research and development efforts, mainly due to its applicability in different situations, including network traffic engineering. In this field, there are two measurement approaches: passive and active. Passive measurement is based on the observation of ordinary traffic of an operational network, without the introduction of any kind of traffic in the monitoring probe. On the other hand, the active approach tries to acquire performance indicators using a controlled traffic generation and transmission through the analyzed network Knowing the potentialities and deficiencies of such techniques, and specifically directing our interest to IP network traffic engineering, this paper emphasizes the active measurement particularities, proposing the project, modeling and implementation of a standardized and extensible architecture for this kind of measurement. We try to join together different metric acquisition processes in a unified system. To do so, we present the architecture entities and the conceived and implemented protocol that are used for communication between these entities, highlighting the technique used to minimize the traffic impact in the ongoing network operation. Finally, we also introduce the idea behind the architecture flexibility through its plug-in approximation.
Data Aggregation in Body Sensor Networks: A Power Optimization Technique for Collaborative Signal Processing

Body sensor networks (BSNs) have proved their viability to greatly improve quality of medical care by providing continuous and in-home monitoring solutions. Highly constrained nature of the platform demands a design that efficiently utilizes limited resources of the system. Energy optimization techniques are especially desirable as the system lifetime is constrained by small batteries that power sensor nodes in a BSN. In this paper, we introduce a novel data-centering routing model to minimize communication energy, taking collaborative nature of signal processing for healthcare applications into consideration. Transmission energy for a path is determined as a compromise between the path length and the amount of data being transmitted along the path. Data produced by different nodes are aggregated to form packets of large size that consume smaller energy per bit. We formulate the problem as a minimum concave cost multicommodity flow problem and propose two approaches to find both optimal and approximate solutions. We evaluate performance of our energy minimization techniques on a variety of synthesized signal processing task graphs, as well as a real application for evaluating human postural control system. The results show an average of 35% energy saving with our proposed routing against a simple shortest path approach.
Computer support for cooperative design (invited paper)

Computer support for design as cooperative work is the subject of our discussion in the context of our research program on Computer Support in Cooperative Design and Communication. We outline our theoretical perspective on design as cooperative work, and we exemplify our approach with reflections from a project on computer support for envisionment in design — the APLEX and its use. We see envisionment facilities as support for both experiments with and communication about the future use situation. As a background we sketch the historical roots of our program — the Scandinavian collective resource approach to design and use of computer artifacts, and make some critical reflections on the rationality of computer support for cooperative work.
Implementing calendars and temporal rules in next generation databases

In applications like financial trading, scheduling, manufacturing and process control, time based predicates in queries and rules are very important. There is also a need to define lists of time points or intervals. The authors refer to these lists as calendars. The authors present a system of calendars that allow specification of natural-language time-based expressions, maintenance of valid time in databases, specification of temporal conditions in database queries and rules, and user-defined semantics for date manipulation. A simple list based language is proposed to define, manipulate and query calendars. The design of the parser and an algorithm for efficient evaluation of calendar expressions is also described. The paper also describes the implementation of time-based rules in POSTGRES using the proposed system of calendars. <<ETX>>
A ug 2 00 4 A Generalized Approach to Complex Networks

This work reports on how the formalization of complex networ k concepts in terms of discrete mathematics, especially mathematical morphology, allows a ser ie of generalizations and important results ranging from new measurements of the network topology to new network grow th models. First, the concepts of node degree and clustering coefficient are extended in order to characte rize not only specific nodes, but any generic subnetwork. Second, the consideration of distance transform and rings a re used to further extend those concepts in order to obtain a signature, instead of a single scalar measurement, ra nging from the single node to whole graph scales. The use of the closing operation is also proposed as a means to ana lyze the proximity of 3-cycles along a complex network. The potential of such concepts is illustrated with res pect to the random and Barabási-Albert models, as well as distance-based preferential-attachment networks. A gene ralization of the Barabási-Albert network, characterize d by preferential attachment with respect to the generalized de gree, is also proposed and shown to exhibit properties which are intermediate between the Barabási-Albert and random m dels. Each of these cases is characterized and analyzed with respect to particularly relevant subnetworks contain ed in the original complex network, namely hubs, 3-cycles and outmost nodes.
Stressful first impressions in job interviews

Stress can impact many aspects of our lives, such as the way we interact and work with others, or the first impressions that we make. In the past, stress has been most commonly assessed through self-reported questionnaires; however, advancements in wearable technology have enabled the measurement of physiological symptoms of stress in an unobtrusive manner. Using a dataset of job interviews, we investigate whether first impressions of stress (from annotations) are equivalent to physiological measurements of the electrodermal activity (EDA). We examine the use of automatically extracted nonverbal cues stemming from both the visual and audio modalities, as well EDA stress measurements for the inference of stress impressions obtained from manual annotations. Stress impressions were found to be significantly negatively correlated with hireability ratings i.e individuals who were perceived to be more stressed were more likely to obtained lower hireability scores. The analysis revealed a significant relationship between audio and visual features but low predictability and no significant effects were found for the EDA features. While some nonverbal cues were more clearly related to stress, the physiological cues were less reliable and warrant further investigation into the use of wearable sensors for stress detection.
Time and alternation: an automata based framework to software model checking

In this paper, we present a class of powerful canonical timed alternating automata and a formalism for describing timed linear temporal logic to software model checking. Time and alternation, these two "metaphors" have dominated automata theory research in recent years. For real-time systems, it is important to augment untimed and asynchronous models of computation with the notion of time. Nevertheless, alternation is a powerful parallelism feature that has the potential to improve and reduce the state-space explosion problem in building large software model checking systems. We show that the dual connection between timed automata-theoretical and propositional-logic frameworks support and model software specifications. This can be established through languages over infinite timed words; and has a direct impact for expressing logical aspects and properties of model checking software systems.
Pain Management in the Elderly: Transdermal Fentanyl for the Treatment of Pain Caused by Osteoarthritis of the Knee and Hip

This study was designed to evaluate the utility of transdermal fentanyl (transdermal fentanyl, TDF) for the treatment of pain due to osteoarthritis (osteoarthritis, OA) of the knee and hip, which was not adequately controlled by nonopioid analgesics or weak opioids. WOMAC is a reliable, valid, and responsive multidimensional, self-administrated outcome measure designed specifically to evaluate patients with OA of the knee or hip. TDF significantly increased pain control and improved functioning and quality of life. Metoclopramide appeared to be of limited value in preventing nausea and vomiting.
Modeling and reduction of gate leakage during behavioral synthesis of nanoCMOS circuits

For a nanoCMOS of sub-65mn technology, where the gate oxide (SiO/sub 2/) thickness is very low, the gate leakage is one of the major components of power dissipation. In this paper, we provide analytical models to describe the tunneling current and propagation delay of behavioral level components considering various physical effects in the absence of foundry data. Subsequently, we explore the use of multiple oxide thickness resources as a technique for the reduction of gate leakage. In particular, we introduce a behavioral datapath scheduler that maximizes the utilization of higher gate oxide thickness resources. We characterize behavioral components for both 65nm and 45nm technologies in order to study the trend of tunneling current as technology scales, and provide them as inputs to the scheduler. We carried out extensive experiments for several benchmarks and observed significant reduction in gate leakage.
Experimental study of a tip leakage flow: wavelet analysis of pressure fluctuations

A wavelet-based conditional analysis of unsteady flow and sound signals highlights the role of intermittent perturbations both in the sound generation and the unsteady field of an aerofoil tip leakage flow experiment. It is shown how the most probable flow perturbations generated at the pressure side tip edge are convected through the gap and swept downstream along the suction side past the trailing edge tip corner, where they radiate sound. The nascent sound sources are identified and localized in the clearance between 40% and 60% of the chord. It is also found that the time dependence of the averaged intermittent structures scales with the inverse of the square root of the mean velocity and a physical interpretation based on a simple potential vortex model is proposed. The data are retrieved from an experiment that has been carried out at low Mach number (Ma < 0.3) in an anechoic test facility. A single motionless instrumented NACA 5510 aerofoil was mounted into the potential core of an open rectangular jet between two plates with an adjustable clearance. The tip leakage flow was ensured by the 5% camber and a 15° angle of attack. A large database obtained by a variety of measurement techniques is thus available for the present analysis. More specifically, the conditional approach is applied to joint far field, wall pressure and particle image velocimetry (PIV) measurements. The wall pressure probes are located along the suction side tip edge and on the tip inside the gap, whereas the PIV plane is parallel to the mid-gap plane. Additional joint wall pressure and single hot-wire anemometry (HWA) measurements are also analysed with a hot-wire probe located near the trailing edge tip corner. The conditional averaging is triggered by high-energy wavelet events selected in a reference signal by setting a threshold to the so-called local intermittency measure.
Language model adaptation using Random Forests

In this paper we investigate random forest based language model adaptation. Large amounts of out-of-domain data are used to grow the decision trees while very small amounts of in-domain data are used to prune them back, so that the structure of the trees are suitable for the desired domain while the probabilities in the tree nodes are reliably estimated. Extensive experiments are carried out and results are reported on a particular task of adapting Broadcast News language model to the MIT computer science lecture domain. We show 0.80% and 0.60% absolute WER improvement over language model interpolation and count merging techniques, respectively.
Deep factorisation of the stable process

The Lamperti–Kiu transformation for real-valued self-similar Markov processes (rssMp) states that, associated to each rssMp via a space-time transformation, is a Markov additive process (MAP). In the case that the rssMp is taken to be an α-stable process with α ∈ (0, 2), [12] and [20] have computed explicitly the characteristics of matrix exponent of the semi-group of the embedded MAP, which we henceforth refer to as the Lamperti-stable MAP. Specifically, the matrix exponent of the Lamperti-stable MAP’s transition semi-group can be written in a compact form using only gamma functions. Just as with Levy processes, there exists a factorisation of the (matrix) exponents of MAPs, with each of the two factors uniquely characterising the ascending and descending ladder processes, which themselves are again MAPs. To the author’s knowledge, not a single example of such a factorisation currently exists in the literature. In this article we provide a completely explicit Wiener–Hopf factorisation for the Lampertistable MAP. As a consequence of our methodology, we also get additional new results concerning the space-time invariance properties of stable processes. Accordingly we develop some new fluctuation identities therewith.
Efficient Updates for Web-Scale Indexes over the Cloud

In this paper, we present a distributed system which enables fast and frequent updates on web-scale Inverted Indexes. The proposed update technique allows incremental processing of new or modified data and minimizes the changes required to the index, significantly reducing the update time which is now independent of the existing index size. By utilizing Hadoop MapReduce, for parallelizing the update operations, and HBase, for distributing the Inverted Index, we create a high-performance, fully distributed index creation and update system. To the best of our knowledge, this is the first open source system that creates, updates and serves large-scale indexes in a distributed fashion. Experiments with over 23 million Wikipedia documents demonstrate the speed and robustness of our implementation: It scales linearly with the size of the updates and the degree of change in the documents and demonstrates a constant update time regardless of the size of the underlying index. Moreover, our approach significantly increases its performance as more computational resources are acquired: It incorporates a 15.4GB update batch to a 64.2GB indexed dataset in about 21 minutes using just 12 commodity nodes, 3.3 times faster compared to using two nodes.
An improved blind adaptive MMSE receiver for fast fading DS-CDMA channels

In this paper, an improved correlation matrix estimation scheme for blind adaptive MMSE receivers is provided. The new scheme takes advantage of the fact that the desired linear receiver can be expressed as a function of the interference correlation matrix only, rather than the total data correlation matrix. A theoretical analysis is performed for the flat fading case which predicts that the new estimation scheme will result in significant performance improvement. Blind adaptive MMSE receivers with the new estimation scheme appear to achieve performance comparable to the training-sequence based adaptive MMSE receivers. Detailed computer simulations for the fast multipath fading environment verify that the proposed scheme yields strong performance gain over previous methods.
VOICE ACTIVITY DETECTION USING A CONTEXTUAL INFORMATION AND MULTIPLE HYPOTHESIS TESTING

This paper shows a revised statistical test for voice activity detection in noise adverse environments. The method is based on a revised contextual likelihood ratio test (LRT) defined over a multiple observation window. The new approach not only evaluates the two hypothesis consisting on all the observations to be speech or non-speech but all the possible hypothesis defined over the individual observations. The implicit hangover mechanism artificially added by the original method was not found in the revised method so its design can be further improved. With these and other innovations the proposed method showed a high speech/non-speech discrimination over a wide range of SNR conditions. The experimental framework showed that the revised method yields significant improvements over standardized VADs for discontinous voice transmission and distributed speech recognition, as well as over recently reported methods.
TwinPCG: Dual Thread Redundancy with Forward Recovery for Preconditioned Conjugate Gradient Methods

Even though iterative solvers like the Preconditioned Conjugate Gradient method (PCG) have been studied for over fifty years, fault tolerance for such solvers has seen much attention in recent years. For iterative solvers, two major reliable strategies of recovery exist: checkpoint-restart for backward recovery, or some type of redundancy technique for forward recovery. Efficient low-overhead redundancy techniques like algorithm-based fault tolerance for sparse matrix-vector products (SpMxV) have recently been proposed. These techniques add resilience with a good, but limited scope; state-of-the-art techniques correct at most 1 fault within a SpMxV. In this work, we study a more powerful resilience concept, which is redundant multithreading. It offers more generic and stronger recovery guarantees, including any soft faults in PCG iterations (among others covering SpMxV), but also requires more resources. We carefully study this redundancy-efficiency conflict. We propose a fault-tolerant PCG method, called TwinPCG, which introduces very small wall-clock time overhead, and significant advantages in detection and correction strategies. Our method uses Dual Modular Redundancy instead of the more expensive Triple Modular Redundancy (TMR); still, it retains the TMR advantages of fault correction. We describe, implement, and benchmark our iterative solver, and compare it in terms of efficiency and fault tolerance capabilities to state-of-the-art techniques. We find that before multithreading in BLAS, TwinPCG introduces 5-6% runtime overhead compared to reference PCG implementations, and can exploit BLAS multithreading well. In the presence of faults, it reliably performs forward recovery for a range of problems, showing all the strengths of TMR techniques.
A Machine Learning-based Color Image Quality Metric

A quality metric based on a classification process is introduced. The main idea of the proposed method is to avoid the error pooling step of many factors (in frequential and spatial domain) commonly applied to obtain a final quality score. A classification process based on the Support Vector Machine method is designed to obtain the final quality class with respect to the standard quality scale provided by the UIT. Thus, for each degraded color image, a feature vector is computed including several Human Visual System characteristics, such as, contrast masking effect, color correlation, and so on. In that way, a machine learning expert, providing a final class number is designed. Introduction When designing a quality metric (including or not Human Visual System features), the main weakness is the computation of the final score. Actually, to develop a quality metric, the usually applied scheme consists in performing 1) a color space transformation to obtain decorrelated color coordinates and 2) a decomposition of these new coordinates towards perceptual channels. An error is then estimated for each one of these channels. The final quality score is obtained by pooling these errors in both spatial and frequential domain. Nevertheless, the pooling stage is based on the use of the Minkowski error metric. Recent studies [1] have shown that this summation does not perform well even if it is the most widely used. One can obtained the same Minkowski value for two different distorted images while the visual quality drastically differs from one distorted image to the other. This can be explained by the fact that the implicit assumption of this metric is that all signal samples are independant. Yet, this is not the case when one uses perceptual channels. When applying an “error pooling” of the obtained estimated errors within each perceptual channels, the Minkowski metric fails to generate a good final score. Actually, the final goal of each metric is to score by a single note the quality of an image. Then, to assess how the metric performs well, a correlation measure between the designed quality metric value and the Mean Opinion Score (MOS) is computed. The MOS is obtained from a set of human observers scores with respect to a normalized scale defined by the UIT [2]. The higher degree, the more the metric proceeds as the human observer does. From the above remarks, one main constatation can be formulated: the need to obtain a final quality score is not necessary the best way to quantify the quality. Actually, in the recommendations given by the UIT [2], the human observers have to choose a quality class from a scale containing five notes (cf. Table 1). Those notes characterize the quality of the reconstructed images. In that way, the human observers make then neither more nor less one classification. In this paper, the quality measure is based on a learned classification process in order to respect the one of human observers. Instead of computing a final note, our method classifies the qualTable 1: Quality scale of the UIT-R.
Face Tracking and Pose Estimation Using Affine Motion Parameters

We describe a method for tracking a person’s face through an image sequence and estimating the 3-D facial pose within each frame. The technique is based on an affine approximation to the motion of projected facial features such as eyes, mouth and nose. Tracking stability is maintained by enforcing the affine relationship amongst the motion of the features using linear regression and application of a Kalman filter to the estimated affine parameters. Facial pose is estimated using an ellipse-circle correspondence technique based on the affine transformation between the features in the current view and those in a fronto-parallel view. The method has the advantage of being simple to implement and not relying on assumed facial characteristics. Experiments on both synthetic and real sequences illustrate the effectiveness of the approach.
Optimal route selection in complex multi-stage supply chain networks using SARSA(λ)

Success of any supply chain highly depends on the appropriate use of transportation. It is always a tough decision to select an optimal route in the stages of a supply chain that will balance the reverse condition of cost and time. Q-learning is already getting implied to optimize routes, but it greatly lacks in designing a complete Markov Decision Process (MDP) with enough number of states and actions sets. In this paper, we propose an MDP that can fit the design of a proper multi-stage supply chain network and intend to solve the MDP with SARSA (X) algorithm. The algorithm was simulated in a java based environment and experimental results show that Q-learning produced higher chunks of rewards but SARSA (X) was faster in case of convergence speed.