Miracle-dz/UniFAR-FTUs huggingface dataset:
Iterative decoding of binary block and convolutional codes

Iterative decoding of two-dimensional systematic convolutional codes has been termed "turbo" (de)coding. Using log-likelihood algebra, we show that any decoder can be used which accepts soft inputs-including a priori values-and delivers soft outputs that can be split into three terms: the soft channel and a priori inputs, and the extrinsic value. The extrinsic value is used as an a priori value for the next iteration. Decoding algorithms in the log-likelihood domain are given not only for convolutional codes but also for any linear binary systematic block code. The iteration is controlled by a stop criterion derived from cross entropy, which results in a minimal number of iterations. Optimal and suboptimal decoders with reduced complexity are presented. Simulation results show that very simple component codes are sufficient, block codes are appropriate for high rates and convolutional codes for lower rates less than 2/3. Any combination of block and convolutional component codes is possible. Several interleaving techniques are described. At a bit error rate (BER) of 10/sup -4/ the performance is slightly above or around the bounds given by the cutoff rate for reasonably simple block/convolutional component codes, interleaver sizes less than 1000 and for three to six iterations.
Efficient User-Level Fine-Grained File Update Logging

Fine-grained file update logging, which logs every update to a file system, is an enabling technology to protect file systems against malicious attacks and/or user mistakes, because it allows each file update to be undoable. This paper explores the performance tradeoffs of several user-level approaches to fine-grained file update logging. In particular, it examines four file update logging schemes for NFS servers: FUL-O, FUL-A, FUL-I and FUL-I+, which all work on top of the NFS protocol but entail different performance overhead, implementation complexity and hardware cost. FUL-O takes a naive update-in-place strategy and requires a separate logging server. FUL-A is based on FUL-O but takes a nonoverwrite strategy to reduce the disk I/O overhead. FULI is based on FUL-A but eliminates the need of a separate logging server. FUL-I+ is based on FUL-I but incorporates a kernel-level performance optimization mechanism to reduce the context switch and memory copy overhead associated with user-level file update logging. Measurements from running synthetic benchmarks and real-world file access traces demonstrate that the performance of an NFS server protected by these user-level file update logging schemes is actually comparable to that of an unprotected NFS server.
A service based wireless sensor networks architecture for high value-added process monitoring

Current research in high value-added manufacturing processes, such as the Micro Injection Molding (μIM) process, focuses on the monitoring of the machine process data. , which could potentially affect the quality of the end product. Up until now, machine process monitoring has been limited to the machine level and has often bypassed the ambient environmental factors and pre manufacturing factors, which could have direct impact on the end product quality. In light of this, extending the monitoring of high value-added processes to include the ambient manufacturing environment would have numerous benefits in terms of process monitoring accuracy, material condition and product quality. In this paper, we present a novel Service Orientated Architecture (SOA) for monitoring a high value-added manufacturing process and its ambient environment using Wireless Sensor Networks (WSN). The proposed architecture applies the SOA to a network of wireless sensor nodes installed in the uIM machine and the industrial micro-molding environment. Here we present; the generic architectural design, system integration, user interfaces, and initial test results for an experimental testbed system.
Correlation between Emotive, Descriptive and Naturalness Attributes in Subjective Data Relating to Spatial Sound Reproduction

In an experiment, inspired by aspects of the Repertory Grid Technique, to find the dimensions forming the perceived spatial impression of a sound reproducing system, subjects frequently described their experiences as being either natural or artificial. These results are analyzed using multivariate methods to investigate the correlation between attributes relating to naturalness and other more descriptive attributes.
Negative Trends in Transport-related Mortality Rates in Broiler Chickens

The high incidence of deaths during transport for slaughter is associated with poor welfare and represents a considerable loss to the poultry industry. In the period from 2009 to 2014, all shipments of broiler chickens to poultry processing plants were monitored in the Czech Republic and the numbers of chickens transported and those dying as a result of their transport were recorded and analysed. Overall transport-related mortality of broiler chickens transported for slaughter in the Czech Republic was 0.37%. It ranged from 0.31% to 0.72%, the increase approximately corresponding to the increasing transport distance. Statistically highly significant (p<0.001) differences were found when comparing transport-related mortality rates in individual seasons of the year. The greatest mortality (0.55%) was associated with transports carried out in winter months whereas the lowest death losses (0.30%) were found in chickens transported for slaughter in summer months. Our study revealed greater transport-related mortality rates in broiler chickens transported for slaughter in the Czech Republic than expected when considering earlier studies. The most pronounced increases were found in transports for shorter distances and in winter months. However, an increase was found at all transport distances monitored except for distances exceeding 300 km and all seasons except for summer. Furthermore, a general increasing tendency in chicken losses during the monitored period was found. The particularly alarming finding is that the mortality of broiler chickens being transported to processing plants has been showing a long-term increasing tendency over the last two decades. Further research should focus on the identification of specific factors leading to such high and growing mortality rates and developing practical guidelines to improve the welfare of the birds in transit accordingly.
Improving RBF Kernel Function of Support Vector Machine using Particle Swarm Optimization

Support vector machine (SVM) has become an increasingly popular tool for machine learning tasks involving classification, regression or novelty detection. SVM is able to calculate the maximum margin (separating hyper-plane) between data with and without the outcome of interest if they are linearly separable. To improve the generalisation performance of SVM classifier optimization technique is used. Optimization refers to the selection of a best element from some set of available alternatives. Particle swarm optimization (PSO) is a population based stochastic optimization technique where the potential solutions, called particles, fly through the problem space by following the current optimum particles. In this paper, Principal Component Analysis (PCA) is used for reducing features of breast cancer, lung cancer and heart disease data sets and an empirical comparison of kernel selection using PSO for SVM is used to achieve better performance. This paper focused on SVM trained using linear, polynomial and radial basis function (RBF) kernels and applying PSO to each kernels for each data set to get better accuracy.
SoDA: Dynamic visual analytics of big social data

In this work we apply dynamic visual analytics on big social data by the example of microblogs from Twitter. Thereby, we address current challenges like real-time analytics as well as analyses of unstructured data. To this end, we propose SoDA - a concept enabling the integrated analysis of the dimensions: message, location and time. Furthermore, we introduce a novel design for tag cloud visualizations, the weighted tag network, offering enhanced semantic insights. All concepts are fully implemented and evaluated by a comprehensive software prototype in different application scenarios.
Does the Sun Shrink with Increasing Magnetic Activity?

It has been demonstrated that frequencies of f-modes can be used to estimate the solar radius to a good accuracy. These frequencies have been used to study temporal variations in the solar radius with conflicting results. The variation in f-mode frequencies is more complicated than what is assumed in these studies. If a careful analysis is performed, then it turns out that there is no evidence for any variation in the solar radius.
A Maple Program to Derive New Equations of State for Hard-Sphere Fluids

The hard-sphere fluid is a very commonly used mechanical or thermodynamic model in physics, chemistry and chemical engineering, with its equation of state being its most important thermodynamic property. In this work, we present the implementation of the computer program Maple for developing an algorithm for the derivation of different analytical expressions for both hard-sphere and hard-disk equations of state, i.e., compressibility factors as functions of the density or the packing fraction. The asymptotic expansion method is used, which permits a great number of different expressions to be generated from knowledge of the values of a given number of virial coefficients. Finally, the procedure of selection of the most appropriate expression is explained and examples are given.
DSP&FPGA-based joint impedance controller for DLR/HIT II dexterous robot hand

A position-based joint impedance controller and a DSP&FPGA-based multilevel control structure were developed to improve the capabilities of robot hand performing task in a variety of environments. Based on finite element analysis technologies, the new torque sensors and giant magnetoresistance (GMR) position sensors were designed and applied, and meet the challenge of joint torque measuring and position measuring. The experimental results of joint impedance control show that proposed strategy not only improves the effectiveness of contact task performance, but also provides compliant interaction of robot hand with a person, thus enables robot hand to work in different environments.
Directed Forgetting of Individual Words in Free Recall.

University of MichiganADDISON E. WOODWARD, JR.Governors State UniversityThis article reports an experiment designed to clarify the storage, rehearsal,and retrieval mechanisms that underlie the ability of 5s to recall to-be-remembered words (R words) without intruding to-be-forgotten words(F words). The 5s were presented seven 24-word lists, each consisting of arandom mixture of 12 R words and 12 F words; the cues to remember or forgetwere presented subsequent to each word in turn. Six of the lists were followedby either an immediate test of R-word recall or a distractor activity, and oneof the lists was followed by a prearranged signal to 5s to recall both R wordsand F words from that list. In general, the results implicate rehearsal andorganizational processes at input rather than suppression or selective retrievalprocesses during output as the mechanisms by which 5s achieve the simul-taneous recall and nonrecall of R words and F words, respectively.
Neural network classifiers for language identification using phonotactic and prosodic features

In this paper, we explore phonotactic and prosodic features derived from the speech signal and its transcription for identification of a language. The characteristics of languages represented by phonotactic and prosodic features at the trisyllabic level are used to train feedforward neural network (FFNN) classifiers to discriminate among languages. We demonstrate that these features indeed contain language-specific information. We also show that phonotactic features in terms of broad phonetic categories are sufficient to represent the phonotactic regularities/constraints of languages. The performance of the FFNN classifier based on these features is evaluated for three Indian languages.
A flexible distributed storage integrity auditing mechanism in Cloud Computing

In this paper, we investigate the problem of data security in cloud data storage, which is essentially a distributed storage system. To achieve the assurances of cloud data integrity and availability and enforce the quality of dependable cloud storage service for users, we propose an effective and flexible distributed scheme with explicit dynamic data support, including block update, delete, and append. We rely on erasure-correcting code in the file distribution preparation to provide redundancy parity vectors and guarantee the data dependability. By utilizing the token with distributed verification of coded data, our scheme achieves the integration of storage correctness insurance and data error localization, i.e., whenever data corruption has been detected during the storage correctness verification across the distributed servers, we can almost guarantee the simultaneous identification of the misbehaving server(s). Considering the time, computation resources, and even the related online burden of users, we also provide the extension of the proposed main scheme to support third-party auditing, where users can safely delegate the integrity checking tasks to third-party auditors and be worry-free to use the cloud storage services. Through detailed security and extensive experiment results, we show that our scheme is highly efficient and resilient to Byzantine failure, malicious data modification attack, and even server colluding attacks.
Notice of Violation of IEEE Publication PrinciplesAn pproach to recognize handwritten bengali numerals for postal automation

A recognition approach for handwritten Bengali numerals and its application for Bangladesh Postal system are presented in this paper. The approach consists of preprocessing, feature extraction and recognition. Using the canny edge detector the top left corner of the post-code box has been detected and the four handwritten numeral images have been segmented. Each image then goes through the steps of normalization, filtering and thinning. Kirsch mask has been used to get the edges through the horizontal, vertical, right and left diagonal. Principal component analysis (PCA) has been used for dimension reduction as the final feature vector consists of the four directional feature vector along with the normalized image. Then the output of the PCA is passed to a trained Support Vector Machine (SVM) to determine which class the input belongs to. Experiments demonstrate that the average recognition rate, error rate and reliability achieved by the proposed system are 92.5%, 7.5% and 92.5% respectively.
Assembly automation with evolutionary nanorobots and sensor-based control applied to nanomedicine

The author presents a new approach within advanced graphics simulations for the problem of nanoassembly automation and its application for medicine. The problem under study concentrates its main focus on nanorobot autonomous control for assembly manipulation and the use of evolutionary competitive agents as a suitable way to warranty the robustness of any proposed model. Thereby the presented paper summarizes as well distinct aspects of some techniques required to achieve a successful nanoplanning system design and its simulation visualization in real time.
Analyzing students'data using a classification technique based on genetic algorithm and fuzzy logic

This paper presents a classification technique for analyzing students' data based on genetic algorithm. The classification technique is modified using Fuzzy rules. The combination of genetic algorithm and fuzzy rules is employed to optimize the indices entropy and gini index. The solution obtained by this classification technique is an optimal way to classify the students' database. The results of technique are presented in this paper.
Signal for space-time noncommutativity: the Z -> gamma gamma decay in the renormalizable gauge sector of the theta-expanded NCSM

We propose the Z -> gamma gamma decay, a process strictly forbidden in the standard model, as a signal suitable for the search of noncommutativity of coordinates at very short distances. We compute the Z -> gamma gamma partial widthin the framework of the recently proposed renormalizable gauge sector of the noncommutative standard model. The one-loop renormalizability is obtained for the model containing the usual six representations of matter fields of the first generation. Even more, the noncommutative part is finite or free of divergences, showing that perhaps new interaction symmetry exists in the noncommutative gauge sector of the model. Discovery of such symmetry would be of tremendous importance in further search for the violation of the Lorentz invariance at very high energies. Experimental possibilities of Z -> gamma gamma decay are analyzed and a firm bound to the scale of the noncommutativity parameter is set around 1 TeV.
Market Preferences and Process Selection ( Mapps ) : The Value of Perfect Flexibility

In this paper we investigate the value of perfect flexibility. Perfect flexibility is defined as the increase in profit that can be obtained by responding instantly to changes in market preferences, compared to a “robust” policy of offering only a single product mix over time. We assume that the market evolves over time throughm states or scenarios, defined by the preferences of the market. In response to this evolving marketplace, a producer can respond by switching its facilities to one of t technological states, defined by plant capabilities. We model the evolution of market preferences and policies for process selection as a Markov Decision Process. With an objective of profit maximization, we show that when the cost of switching production processes is very high, the optimal policy is to select a single “robust” process and to never switch from it. In contrast, when process switching costs are zero, we show that the optimal policy is “perfect flexibility” where production processes are immediately matched to market preferences. An optimal production policy will exist between these two extreme policies. We define the value of perfect flexibility as the difference in expected profits between 1Address: College of Business and University of Colorado, Boulder, CO 80309-0419, phone: (303) 492-4351 2Corresponding author. Address: Department of Business Administration, University of Illinois at Urbana-Champaign, 1206 South Sixth Street, Champaign, IL 61820, phone: (217) 333-8270 3Address: Babcock Graduate School of Business, Wake Forest University, Wake Forest, NC 27109-7659, phone: (910) 759-4423
ACCRETION AND OUTFLOW IN INTERACTING BINARY SYSTEMS: FUSE OBSERVATIONS OF THE NOVALIKE CATACLYSMIC VARIABLE, UX URSAE MAJORIS

We present far-ultraviolet (905 – 1182 Å), time-series spectroscopy of the eclipsing, novalike cataclysmic variable, UX UMa, acquired with FUSE. The time-averaged spectrum is complex and is dominated by overlapping spectral features. The most prominent features are emission lines of C III, N III, N IV, and O VI. They are broad (FWHM ≥ 1800 km s) and double-peaked with a central absorption at zero velocity. During eclipse, the spectrum is simpler: the emission lines remain bright, but the absorption components of the lines and the weaker features between the emission lines disappear entirely, leaving a flat continuum. This behavior is also evident in GHRS (1149 – 1660 Å) spectra that we retrieved from the HST archive. The FUV spectra show flickering on time scales of several minutes. The flickering is seen primarily in the continuum and/or the weaker lines rather than in the prominent emission lines. The orbital light curve has a dip in the FUV flux between orbital phases 0.45 – 0.65, similar to a pre-eclipse dip detected in HST observations. The EWs of the line absorption features decrease during the dip. We have detected a systematic wavelength shift of spectral features on the orbital period, but with a phase lag of ≃ 20, a phenomenon that has been reported at optical wavelengths. We discuss the implications of our results in the context of models of an accretion disk with a chromosphere between the disk and the extended wind. Finally, we note that the observed FUV flux is too low to be consistent with the temperature and radius of the WD derived by Baptista et al. (1995), suggesting that their remaining binary parameters, including a mass ratio of 1, ought to be viewed with skepticism. Subject headings: accretion, accretion disks— binaries: close — novae, cataclysmic variables — stars: individual (UX UMa) — ultraviolet: stars
Time Delay and Permittivity Estimation by Ground-Penetrating Radar With Support Vector Regression

In the field of civil engineering, sounding the pavement layers is classically performed using standard ground-penetrating radar, whose vertical resolution is bandwidth dependent. The layer thicknesses are deduced from both the time delays of backscattered echoes and the permittivity of layers. In contrast with conventional spectral analysis approaches, this letter focuses on one of the machine learning algorithms, namely, the support vector machine, to perform time delay estimation and dielectric constant estimation of the medium from backscattered radar signals. This letter shows the super time resolution capability of such technique to resolve overlapping and fully correlated echoes within the context of thin pavement layer testing.
Cross-media within the Facebook newsfeed

This study investigates the role of interpretation in cross-media uses. Highlighting the specificity of cross-media uses within meta-media environments such as Facebook, we argue that interpretative processes play a crucial role in the formation of cross-media repertoires. The methodology relies on a reception analysis for which we conducted interviews with eleven expatriates using Facebook on a daily basis, in conjunction with a commented consultation of their newsfeed. In the analysis, we show that reading Facebook’s newsfeed is an activity that contributes to the construction of the user’s mediated lifeworld. Schutz’s phenomenological approach allows us to explore how users develop reading strategies to typify their experience of the social world within Facebook, to maintain the relevance of their newsfeed and to negotiate the technological features of Facebook, shedding light on how users assemble their cross-mediated experience within meta-media.
Profile, Outcomes, and Determinants of Unsuccessful Tuberculosis Treatment Outcomes among HIV-Infected Tuberculosis Patients in a Nigerian State

Background. Few studies have evaluated the rate of tuberculosis (TB)/human immunodeficiency virus (HIV) coinfection and the determinants of its treatment outcomes in Africa. We aimed to determine the predictors of unsuccessful treatment outcomes in HIV-infected tuberculosis patients in Nigeria. Methods. A retrospective cohort study design was used to assess adult TB/HIV patients who registered for TB treatment in two health facilities in Ebonyi State, Southeast Nigeria, between January 2011 and December 2012. Predictors of unsuccessful treatment outcomes were determined using multivariable logistic regression analysis. Results. Of 1668 TB patients, 342 (20.5%) were HIV coinfected. Of these, 195 (57%) had smear-negative pulmonary TB and 11 (3.2%) had extrapulmonary TB. Overall, 225 (65.8%) patients achieved successful outcomes, while 117 (34.2%) had unsuccessful outcomes. The unsuccessful treatment outcomes were due to “default” (9.9%), “death” (19%), “treatment failure” (1.5%), and “transferring out” (3.8%). Independent determinants for unsuccessful outcomes were receiving care at a public facility and noninitiation of antiretroviral therapy. Conclusion. There is need for the reevaluation of the quality of public sector treatment services provided for TB/HIV patients as well as further expansion of TB/HIV collaborative activities in rural areas, and interventions to reduce mortality and default rates among TB/HIV patients are urgently needed in Nigeria.