Miracle-dz/UniFAR-FTUs huggingface dataset:
On the uncertainty in sequential hypothesis testing

We consider the problem of sequential hypothesis testing when the exact pdfs are not known but instead a set of iid samples are used to describe the hypotheses. We modify the classical test by introducing a likelihood ratio interval which accommodates the uncertainty in the pdfs. The test finishes when the whole likelihood ratio interval crosses one of the thresholds and reduces to the classical test as the number of samples to describe the hypotheses tend to infinity. We illustrate the performance of this test in a medical image application related to tuberculosis diagnosis. We show in this example how the test confidence level can be accurately determined.
Linköping University Electronic Press Report Tool Position Estimation of a Flexible Industrial Robot Using Recursive Bayesian Methods Tool Position Estimation of a Flexible Industrial Robot Using Recursive Bayesian Methods Tool Position Estimation of a Flexible Industrial Robot Using Recursive Bayes

A sensor fusion method for state estimation of a exible industrial robot is presented. By measuring the acceleration at the end-eector, the accuracy of the arm angular position is improved signicantly when these measurements are fused with motor angle observation. The problem is formulated in a Bayesian estimation framework and two solutions are proposed; one using the extended Kalman lter (ekf) and one using the particle lter (pf). The technique is veried on experiments on the ABB IRB4600 robot, where the accelerometer method is showing a signicant better dynamic performance, even when model errors are present. Abstract— A sensor fusion method for state estimation of a flexible industrial robot is presented. By measuring the acceleration at the end-effector, the accuracy of the arm angular position is improved significantly when these measurements are fused with motor angle observation. The problem is formulated in a Bayesian estimation framework and two solutions are proposed; one using the extended Kalman filter (EKF) and one using the particle filter (PF). The technique is verified on experiments on the ABB IRB4600 robot, where the accelerometer method is showing a significant better dynamic performance, even when model errors are present.
Modelling the Security of Recognition-Based Graphical Passwords

Recognition-based graphical passwords are a suggested alternative authentication mechanism which have received substantial attention in research literature. The literature often presents new schemes, usability studies or propose countermeasures for specific attacks. Whilst this is beneficial, it does not allow for consistent comparison of the security of recognition-based graphical password schemes. This thesis contributes a proposed solution to this problem. Presented in this thesis are models for estimating the number of attacks required before success for four aspects of the security of a recognition-based graphical password scheme. This includes two types of guessing attacks and two types of observation attacks. These models combine to provide an overall metric of the security of recognition-based graphical password schemes. Attacks to be incorporated into the metric were established by reviewing the literature and establishing the scope and context. The literature review allowed extraction of the variables of a recognition-based graphical password scheme which represent the scheme. The first aspect examined was that of guessing attacks. The first guessing attack considered was random guessing, the model for this aspect was an adaption of the frequently reported mathematical model. The second guessing attack was a newly proposed attack which prioritised images from more popular semantic categories e.g. animals. The model for this attack was constructed as a further adaption of the random guessing model based on the success rates for the attack which were established by simulations which incorporated user selected images. The observability attacks modelled were shoulder surfing and frequency attacks. The observability attack models were constructed by simulation of the attacks for a wide range of potential configurations of the recognition-based graphical password schemes. A mathematical model was fitted to the resulting data. The final metric combined these models and was evaluated against a list of metric requirements established from relevant literature. The metric results in a consistent, repeatable, and quantitative method for comparing recognition-based graphical password schemes. It can be directly applied to a subset of schemes which allows their security levels to be compared in a way not possible previously. Also presented are details on how the metric could be extended to incorporate other recognition-based graphical password schemes. The approach detailed also allows the possibility of extension to incorporate different attack types and authentication contexts. The metric allows appropriate selection of a recognition-based scheme and contributes to a detailed analysis of the security aspects of recognition-based graphical passwords.
How did you change my view? A corpus-based study of concessions’ argumentative role

In everyday communicative arenas, we engage in critical discussions to persuade others to change their views about issues of personal as well as public interest. Discourse analysts have deemed concessions as privileged strategies to manage disagreement and reach consensus. However, a coherent and comprehensive account of the argumentative functions played by different concessive relations is lacking: do concessions always bear an argumentative role? By which semantic and pragmatic properties? What type of argumentative moves do they instantiate? To answer these questions, we propose a corpus-based study of lexical markers of concessions in the subreddit Changemyview. Drawing from a multilevel semantic and pragmatic annotation, polysemous markers with contrast are disambiguated, and an empirically motivated typology of concessive relations is proposed. The results of the analysis highlight two types of concessions that are specialized in introducing and/or preventing counterarguments.
Self-Learning MapReduce Scheduler in Multi-job Environment

Hadoop, as the most widely adopted open-source implementation of MapReduce framework, makes MapReduce widely accessible. However, it is currently limited by its default MapReduce scheduler. To achieve better performance, the scheduler should take into consideration nodes' computing power and system resources in heterogeneous environment. Further more, from job perspective, tasks' non-linear progress is also an important factor. Some research work has been carried out to enhance the performance of MapReduce, but they are not satisfactory in terms of considering characteristics of both nodes and jobs. To overcome this drawback, we propose a Self-Learning MapReduce Scheduler (SLM), which outperforms the existing schedulers in multi-job environment. Since competitions on system resources may make a task's progress unpredictable, SLM determines the progress of each job based on its own historical information. In particular, on the self-learning stage of a job, with the feedback information from the first few tasks, SLM calculates the task phase weights. With these phase weights, SLM can obtain more accurate execution time estimation, which is the most important condition to finding stragglers (slow tasks). Experimental results show that, SLM can effectively improve the accuracy of execution time estimation and straggler identification, leading to the rational utilization of resources and shortening jobs' execution time especially in multi-job environment.
Application of a commercial kit for detection of PCR products to quantification of human immunodeficiency virus type 1 RNA and proviral DNA

Quantitative tests for human immunodeficiency virus type 1 (HIV-1) RNA in plasma and proviral DNA in peripheral blood mononuclear cells (PBMC) provide valuable information on the status of HIV-1 infection. This paper describes tests that were carried out with commercially available materials and an enzyme-linked immunosorbent assay reader for detecting spectrophotometric changes. Samples consisted of 100 microliters of plasma or 200,000 PBMC. The procedure involved sample preparation, PCR-based amplification with the primer pair SK39 (biotinylated at the 5' end) and SK38, hybridization of the cDNA PCR product to an RNA probe, capture of the RNA-DNA hybrid on a solid phase by means of strepavidin, binding to an alkaline phosphatase-conjugated antibody directed against RNA-DNA hybrids, and incubation with p-nitrophenylphosphate. Spectrophotometric changes were recorded at four intervals over a period of 20 h. The inclusion of HIV-1 RNA or proviral DNA standards in each run was an integral part of the procedure. The dynamic ranges afforded by these assays--500 to 1 million RNA copies per ml and 10 to 5,000 proviral DNA copies per 10(6) PBMC--were applicable to most plasma specimens and to all PBMC specimens from HIV-1-infected patients. Correlations of log-transformed HIV-1 RNA and proviral DNA concentrations with those found by reference methods were, respectively, 0.88 and 0.80. The between-run coefficients of variation for the detection method were < or = 25% (range, 9.1 to 24.7) and < or = 15% (range, 10.9 to 15.1), respectively, for HIV-1 RNA and proviral DNA. The reproducibility of the overall procedure for HIV-1 RNA in plasma (including sample preparation, amplification, and detection) was given by a duplicate standard deviation of log10 copies per ml of 0.11. Thus, the method was sufficiently precise to allow the detection of fourfold changes in plasma HIV-1 RNA concentrations, with a power of 0.95.
Youth fans of global popular culture: Between prosumer and free digital labourer

The subculture of urban global popular-culture youth fans is believed to be a resistance-subculture against the hegemony of dominant power. This study, however, found that the subculture given rise to by the urban, global popular-culture youth fans of ‘the Mortal Instruments’ in Indonesia is in opposition to the Neo-Gramscian thought which has become the foundation of popular-culture studies. In constructing their identity, some of the digital fandoms of global popular culture have been critical of the content of cultural texts as a form of resistance against texts produced by cultural industries. However, they have only been developing artificial forms of resistance within the system, that is, in fan sites. This study found that the urban youths joining digital fandoms are not free from the hegemony of capitalism because they have become playlabourers, engaging in free digital labour for the powers of the global culture industry. This critical attitude of urban youths, in building their digital fandom-subculture identity, is incapable of standing against the system. They even position themselves within the network of cultural-industry capitalism – identified by the Frankfurt School as the domination and superiorization of the industry power of global entertainment that is continually self-restoring.
Machine Learning-Based Framework for Resource Management and Modelling for Video Analytic in Cloud-Based Hadoop Environment

Hadoop framework has recently been adapted for use by the video analytics community for intensive, distributed video processing, storage. However, the challenge is to estimate the required amount of resources to be used in such an environment to fulfil the requirements of a user with requirements constraints. Therefore, it is important to understand how to model the performance of a Hadoop based implementation of video analytic applications in terms of meeting their performance goals. In this paper we propose the use of machine learning approachs in modelling the execution time based on the given resources. The prediction is based on parameters related to typical video analytic applications such as video file characteristics (e, g, resolution, file size, frame rate. etc.), cluster resource consumption,, Hadoop configuration values (reducer slots, tasks). The investigation carried out compares the use of different machine learning classifiers with regard to their best obtainable performance accuracies, show that a decision based model (M5P) outperforms a Linear Regression model, while the Ensemble Classifier, Bagging, out-performs these standard single classifiers. The research conducted bridges an existing research gap in video analytic-related performance predictions, whereby current research focuses on different application types, is largely limited to using standard learning algorithms such as SVM, Linear Regression, Multilayer Perceptron (MLP).
Hand pose estimation using voxel-based individualized hand model

A robust and adaptive hand pose estimation technique is necessary to create a natural and non-contact manmachine interface system. In this regard, individualizing the system's hand model is key to improving hand pose estimation. This paper addresses the issue of calibrating the hand model of the system for every user in order to get a more robust pose estimation results. Our system is a vision-based model-based approach that uses a skeletal hand model composed of a finger link structure and a surface structure. The surface structure is composed of voxel which is derived from silhouette images obtained by multi-viewpoint cameras. The finger link structure is estimated by searching for the optimum lengths from a set of values generated from the calibration motion of the fingers. We compared the output of using a standard (non-calibrated) hand model with our proposed individualized hand model in the pose estimation system. Results show that using the calibrated hand model has a more robust result than the one using the standard model.
Performance evaluation of opportunistic temporal-pairing access network (OPAN)

Content distribution from wired to wireless domain has traditionally being perceived as the problem of wireless last-hop extension. We argue that increasing the physical transmission capability at the last hop bottleneck alone will not be sufficient to cater for throughput demands and come up with a network architecture which can be tailored favorably for content distribution within mobile and wireless milieu called Opportunistic temporal-Pairing Access Network (OPAN). By carefully coordinate direct node-to-node ambient burst data communication “inside” the wireless access (infrastructure) network, it is possible to create a structure where the traffic load on the last-hop downlink path is greatly minimized. If such setup is then augmented with the knowledge of end users' mobility and the exploitation of spatial reuse techniques and careful packet scheduling, the effective system throughput capacity as well as the data diffusion time can be improved significantly compared to the simple unicasting in infrastructure networks. We show that OPAN is not only capable of achieving a downlink capacity improvement of, conservatively, one order of magnitude, but also able to maintain a faster overall data diffusion time.
A new closed-loop converter for sinusoidal encoders — Preliminary results

Sinusoidal position transducers provide accurate and reliable representation of the mechanical position of their shafts. The sine/cosine transducers signals may be used in open loop or closed loop arrangements for the determination of position. The present work describes a novel implementation of the classical PLL closed loop method without using VCO, DAC, counter and look-up tables. The proposed method makes use of the excitation signal of the resolver to estimate the sine/cosine of the estimated angle. The proposed method is simpler than the available techniques, and may be implemented easily using digital or basic analogue electronic circuitry. Beside simplicity of implementation, the proposed schemes offer the advantage of robustness to amplitude fluctuation of the transducer excitation signal. This paper describes the concept of the new method, simulation results and preliminary experimental results.
Laser-scanner grid map building based on Dempster-Shafer evidence theory

A method of constructing a grid map based on Dempster-Shafer evidence theory (D-S evidence theory) is proposed with respect to the problem of unstructured unknown environment mapping. Mobile robot explores environment with a laser scanner. D-S evidence theory is used to fuse information. The problem that D-S evidence theory can't be applied to information fusion under certain circumstances and the matter that D-S evidence theory have counter-intuitive behaviors in some cases are discussed. An approximate process algorithm is advanced to avoid above problems. Finally, A two-dimensional grid map is built and a way of evaluating the environment and environment exploration effect is suggested. Simulation result shows that this method is appropriate for unstructured unknown environment mapping.
A smart scarf for pulse signal monitoring using a flexible pressure nanosensor

Real-time, long-term pulse signal monitoring plays a significant role in monitoring chronic diseases for elder people, pregnant women etc. For users with weak pulse, a carotid pulse signal monitoring system can be appropriate. However, till today no unobtrusive solution is available. Therefore, we suggest a novel flexible and planar pressure nanosensor weaved in a smart scarf system for pulse signal monitoring with better user experience called Smart-SP (smart scarf for pulse signal monitoring system). To our knowledge, this is the first time applying a flexible pressure nanosensor on a wearable carotid pulse monitoring system. To meet the needs of the application, a method is proposed to design the size of the sensor. An interface allows third-party analysis software and provides raw data. A database of pulse signal for diagnostic purposes is set-up. Digital low-pass filter improves the signal accuracy.
COSMOLOGICAL CONSTRAINTS FROM SUNYAEV–ZEL'DOVICH-SELECTED CLUSTERS WITH X-RAY OBSERVATIONS IN THE FIRST 178 deg2 OF THE SOUTH POLE TELESCOPE SURVEY

We use measurements from the South Pole Telescope (SPT) Sunyaev–Zel'dovich (SZ) cluster survey in combination with X-ray measurements to constrain cosmological parameters. We present a statistical method that fits for the scaling relations of the SZ and X-ray cluster observables with mass while jointly fitting for cosmology. The method is generalizable to multiple cluster observables, and self-consistently accounts for the effects of the cluster selection and uncertainties in cluster mass calibration on the derived cosmological constraints. We apply this method to a data set consisting of an SZ-selected catalog of 18 galaxy clusters at z > 0.3 from the first 178 deg2 of the 2500 deg2 SPT-SZ survey, with 14 clusters having X-ray observations from either Chandra or XMM-Newton. Assuming a spatially flat ΛCDM cosmological model, we find the SPT cluster sample constrains σ8(Ω m /0.25)0.30 = 0.785 ± 0.037. In combination with measurements of the cosmic microwave background (CMB) power spectrum from the SPT and the seven-year Wilkinson Microwave Anisotropy Probe data, the SPT cluster sample constrains σ8 = 0.795 ± 0.016 and Ω m = 0.255 ± 0.016, a factor of 1.5 improvement on each parameter over the CMB data alone. We consider several extensions beyond the ΛCDM model by including the following as free parameters: the dark energy equation of state (w), the sum of the neutrino masses (Σm ν), the effective number of relativistic species (N eff), and a primordial non-Gaussianity (f NL). We find that adding the SPT cluster data significantly improves the constraints on w and Σm ν beyond those found when using measurements of the CMB, supernovae, baryon acoustic oscillations, and the Hubble constant. Considering each extension independently, we best constrain w = −0.973 ± 0.063 and the sum of neutrino masses Σm ν < 0.28 eV at 95% confidence, a factor of 1.25 and 1.4 improvement, respectively, over the constraints without clusters. Assuming a ΛCDM model with a free N eff and Σm ν, we measure N eff = 3.91 ± 0.42 and constrain Σm ν < 0.63 eV at 95% confidence. We also use the SPT cluster sample to constrain f NL = −220 ± 317, consistent with zero primordial non-Gaussianity. Finally, we discuss the current systematic limitations due to the cluster mass calibration, and future improvements for the recently completed 2500 deg2 SPT-SZ survey. The survey has detected ∼500 clusters with a median redshift of ∼0.5 and a median mass of ∼2.3 × 1014 M ☉ h −1 and, when combined with an improved cluster mass calibration and existing external cosmological data sets will significantly improve constraints on w.
Combination of Linear Prediction and Phase Decomposition for Glottal Source Analysis on Voiced Speech

Some glottal analysis approaches based upon linear prediction or complex cepstrum approaches have been proved to be effective to estimate glottal source from real speech utterances. We propose a new approach employing both an all-pole odd-order linear prediction to provide a coarse estimation and phase decomposition based causality/anti-causality separation to generate further refinements. The obtained measures show that this method improved performance in terms of reducing source-filter separation in estimation of glottal flow pulses (GFP). No glottal model fitting is required by this method, thus it has wide and flexible adaptation to retain fidelity of speakers's vocal features with computationally affordable resource. The method is evaluated on real speech utterances to validate it.
TCP Implementation in Linux : A Brief Tutorial

Figures 1 and 2 show the internals of the TCP implementation in Linux kernel. Fig. 1 shows the path taken by a new packet from the the wire to a user application. The Linux kernel uses an sk buff data structure to describe each packet. When a packet arrives at the NIC, it invokes the DMA engine to place the packet into the kernel memory via empty sk buffs stored in a ring buffer called rx ring. An incoming packet is dropped if the ring buffer is full. When a packet is processed at higher layers, packet data remains in the same kernel memory, avoiding any extra memory copies. Once a packet is successfully received, the NIC raises an interrupt to the CPU, which processes each incoming packet and passes it to the IP layer. The IP layer performs its processing on each packet, and passes it up to the TCP layer if it is a TCP packet. The TCP process is then scheduled to handle received packets. Each packet in TCP goes through a series of complex processing steps. The TCP state machine is
Pathways to Poly-Victimization

Some children, whom we have labeled poly-victims, experience very high levels of victimizations of different types. This article finds support for a conceptual model suggesting that there may be four distinct pathways to becoming such a poly-victim: (a) residing in a dangerous community, (b) living in a dangerous family, (c) having a chaotic, multiproblem family environment, or (d) having emotional problems that increase risk behavior, engender antagonism, and compromise the capacity to protect oneself. It uses three waves of the Developmental Victimization Survey, a nationally representative sample of children aged 2—17 years. All four hypothesized pathways showed significant independent association with poly-victim onset. For the younger children, the symptom score representing emotional problems was the only significant predictor. For the older children, the other three pathway variables were significant predictors—dangerous communities, dangerous families, and problem families—but not symptom score. Poly-victimization onset was also disproportionately likely to occur in the year prior to children’s 7th and 15th birthday, corresponding roughly to the entry into elementary school and high school. The identification of such pathways and the ages of high onset should help practitioners design programs for preventing vulnerable children from becoming poly-victims.