Miracle-dz/UniFAR-FTUs huggingface dataset:
EmailTime: visual analytics and statistics for temporal email

Although the discovery and analysis of communication patterns in large and complex email datasets are difficult tasks, they can be a valuable source of information. We present EmailTime, a visual analysis tool of email correspondence patterns over the course of time that interactively portrays personal and interpersonal networks using the correspondence in the email dataset. Our approach is to put time as a primary variable of interest, and plot emails along a time line. EmailTime helps email dataset explorers interpret archived messages by providing zooming, panning, filtering and highlighting etc. To support analysis, it also measures and visualizes histograms, graph centrality and frequency on the communication graph that can be induced from the email collection. This paper describes EmailTime's capabilities, along with a large case study with Enron email dataset to explore the behaviors of email users within different organizational positions from January 2000 to December 2001. We defined email behavior as the email activity level of people regarding a series of measured metrics e.g. sent and received emails, numbers of email addresses, etc. These metrics were calculated through EmailTime. Results showed specific patterns in the use email within different organizational positions. We suggest that integrating both statistics and visualizations in order to display information about the email datasets may simplify its evaluation.
Semi-automatic photo clustering with distance metric learning

Photo clustering has been widely explored in many applications such as album management. But automatic clustering can hardly achieve satisfying performance due to the large variety of photos' content. This paper proposes a semi-automatic photo clustering scheme that attempts to improve clustering performance with users' interactions. Users can adjust the results of automatic clustering, and a set of constraints among photos are generated accordingly. A distance metric is then learned with these constraints and we can re-implement clustering with this metric. We conduct experiments on different photo albums, and experimental results have demonstrated that our approach is able to improve automatic photo clustering results, and it is better than pure manual adjustment approach by exploring distance metric learning.
Encryption techniques: A theoretical overview and future proposals

The usage of encrypted messages has a long history. Today, however, we are able to maintain the security and privacy of information, which is essential to current research in encryption. Encrypted information also forms a key part of scientific projects that involve human behavior, bank transactions, and economic, political and social phenomena. In this paper, we provide a theoretical overview of various encryption techniques, namely those that range from changes in the position of a letter or word, to word transformations and transposition. It may also include computer algorithms that have binary functions, which produce coding of a message. Thirdly, it encapsulates the latest advances in science, which are used to obtain the maximum objective of an encrypted message, i.e. the secret-coded message. For the latter, algorithms are used to determine quantum calculations. For this study, the concepts and processes that are discussed are based on algorithms. These not only provide security when sending messages, but also security for passwords that are used to decipher and to encrypt. The conclusions in this study examine the current theoretical framework, and propose the usage of methods to prevent online security breaches. These methods are presented in the proposal. Here we also argue that it is possible to achieve a mixture of cryptography algorithms that foster an improved performance and enhance the speed of safe transactions.
Review of objective topographic facial nerve evaluation methods.

OBJECTIVE This study is a critical review of the described methods for objective topographic evaluation of facial nerve function to identify areas of consensus and point to future research topics. SOURCES AND STUDY SELECTION: Original research articles on the subject were identified through the Medline database and reference cross-checking. DATA EXTRACTION AND SYNTHESIS The articles were grouped according to the methodology used for topographic facial nerve evaluation. The advantages and shortcomings of each method are evaluated. The results obtained in each publication are presented in light of the method used. CONCLUSION Measurements localized around the facial area under investigation show maximal displacement, whereas other sites exhibit much smaller displacements. Large displacements in these locations can be used to assess synkinesis and contractures. Large intersubject variability of the same measure is found. Both linear measurement and image-subtracting techniques hold promise. but until comparative studies are performed, the best method will remain controversial. Simple systems, accurately evaluating facial motor function, are yet to be developed.
Learning the Prior in Minimal Peer Prediction

Many crowdsourcing applications rely on the truthful elicitation of information from workers; e.g., voting on the quality of an image label, or whether a website is inappropriate for an advertiser. Peer prediction provides a theoretical mechanism for eliciting truthful reports. However, its application depends on knowledge of a full probabilistic model: both a distribution on votes, and a posterior for each possible single vote received. In earlier work, Witkowski and Parkes [2012b], relax this requirement at the cost of “nonminimality,” i.e., users would need to both vote and report a belief about the vote of others. Other methods insist on minimality but still require knowledge of the distribution on votes, i.e., the signal prior but not the posterior [Jurca and Faltings 2008, 2011; Witkowski and Parkes 2012a]. In this paper, we develop the theoretical foundation for learning the signal prior in combination with these minimal peer-prediction methods. To score an agent, our mechanism uses the empirical frequency of reported signals against which to “shadow” [Witkowski and Parkes 2012a], delaying payments until the empirical frequency is accurate enough. We provide a bound on the number of samples required for the resulting mechanism to provide strict incentives for truthful reporting.
Evolving a Scalable Multirobot Controller Using an Artificial Neural Tissue Paradigm

We present an "artificial neural tissue" (ANT) architecture as a control system for autonomous multirobot tasks. This architecture combines a typical neural-network structure with a coarse-coding strategy that permits specialized areas to develop in the tissue which in turn allows such emergent capabilities as task decomposition. Only a single global fitness function and a set of allowable basis behaviors need be specified. An evolutionary (Darwinian) selection process is used to derive controllers for the task in simulation. This process results in the emergence of novel functionality through the task decomposition of mission goals. ANT-based controllers are shown to exhibit self-organization, employ stigmergy and make use of templates (unlabeled environmental cues). These controllers have been tested on a multirobot resource-collection task in which teams of robots with no explicit supervision can successfully avoid obstacles, explore terrain, locate resource material and collect it in a designated area by using a light beacon for reference and interpreting unlabeled perimeter markings. The issues of scalability and antagonism are addressed
Direct 4D parametric imaging for linearized models of reversibly binding PET tracers using generalized AB-EM reconstruction

Due to high noise levels in the voxel kinetics, development of reliable parametric imaging algorithms remains one of most active areas in dynamic brain PET imaging, which in the vast majority of cases involves receptor/transporter studies with reversibly binding tracers. As such, the focus of this work has been to develop a novel direct 4D parametric image reconstruction scheme for such tracers. Based on a relative equilibrium (RE) graphical analysis formulation (Zhou et al 2009b Neuroimage 44 661–70), we developed a closed-form 4D EM algorithm to directly reconstruct distribution volume (DV) parametric images within a plasma input model, as well as DV ratio (DVR) images within a reference tissue model scheme (wherein an initial reconstruction was used to estimate the reference tissue time–activity curves). A particular challenge with the direct 4D EM formulation is that the intercept parameters in graphical (linearized) analysis of reversible tracers (e.g. Logan or RE analysis) are commonly negative (unlike for irreversible tracers, e.g. using Patlak analysis). Subsequently, we focused our attention on the AB-EM algorithm, derived by Byrne (1998, Inverse Problems 14 1455–67) to allow inclusion of prior information about the lower (A) and upper (B) bounds for image values. We then generalized this algorithm to the 4D EM framework, thus allowing negative intercept parameters. Furthermore, our 4D AB-EM algorithm incorporated and emphasized the use of spatially varying lower bounds to achieve enhanced performance. As validation, the means of parameters estimated from 55 human 11C-raclopride dynamic PET studies were used for extensive simulations using a mathematical brain phantom. Images were reconstructed using conventional indirect as well as proposed direct parametric imaging methods. Noise versus bias quantitative measurements were performed in various regions of the brain. Direct 4D EM reconstruction resulted in notable qualitative and quantitative accuracy improvements (over 35% noise reduction, with matched bias, in both plasma and reference-tissue input models). Similar improvements were also observed in the coefficient of variation of the estimated DV and DVR values even for relatively low uptake cortical regions, suggesting the enhanced ability for robust parameter estimation. The method was also tested on a 90 min 11C-raclopride patient study performed on the high-resolution research tomograph wherein the proposed method was shown across a variety of regions to outperform the conventional method in the sense that for a given DVR value, improved noise levels were observed.
The Wintertime Covariation of CO2 and Criteria Pollutants in an Urban Valley of the Western United States

Numerous mountain valleys experience wintertime particulate pollution events, when persistent cold air pools (PCAPs) develop and inhibit atmospheric mixing, leading to the accumulation of pollutants. Here we examine the relationships between trace gases and criteria pollutants during winter in Utah's Salt Lake Valley, in an effort to better understand the roles of transport versus chemical processes during differing meteorological conditions as well as insights into how targeted reductions in greenhouse gases will impact local air quality in varying meteorological conditions. CO2 is a chemically inert gas that is coemitted during fossil fuel combustion with pollutants. Many of these coemitted pollutants are precursors that react chemically to form secondary particulate matter. Thus, CO2 can serve as a stable tracer and potentially help distinguish transport versus chemical influences on pollutants. During the winter of 2015–2016, we isolated enhancements in CO2 over baseline levels due to urban emissions (“CO2ex”). CO2ex was paired with similar excesses in other pollutant concentrations. These relationships were examined during different wintertime conditions and stages of pollution episodes: (a) Non‐PCAP, (b) beginning, and (c) latter stages of an episode. We found that CO2ex is a good indicator of the presence of gaseous criteria pollutants and a reasonable indicator of PM2.5. Additionally, the relationships between CO2ex and criteria pollutants differ during different phases of PCAP events which provide insight into meteorological and transport processes. Lastly, we found a slight overestimation of CO:CO2 emission ratios and a considerable overestimation of NOx:CO2 by existing inventories for the Salt Lake Valley.
Optimum robust control of nonlinear hydraulic servo system

This paper concerns the design of robust and high precision performance controller. Optimum fuzzy PID controller is developed with the aim to achieve high performance and to reduce tracking errors. Mathematical model is developed that includes a dynamic friction model to accounts for main nonlinearities. To acquire the global optimal state proposed controller is optimized by using genetic algorithm. Effectiveness and robustness of the proposed controller are compared with PID and fuzzy control. Extensive simulation results verify optimum fuzzy PID method is fabulous with high effectiveness, robustness and good performance under parametric uncertainties, unknown modeling error and external disturbances.
Complex network analysis of climate change in the Tarim River Basin, Northwest China

The complex network theory provides an approach for understanding the complexity of climate change from a new perspective. In this study, we used the coarse graining process to convert the data series of daily mean temperature and daily precipitation from 1961 to 2011 into symbol sequences consisting of five characteristic symbols ( i.e. , R , r , e , d and D ), and created the temperature fluctuation network (TFN) and precipitation fluctuation network (PFN) to discover the complex network characteristics of climate change in the Tarim River Basin of Northwest China. The results show that TFN and PEN both present characteristics of scale-free network and small-world network with short average path length and high clustering coefficient. The nodes with high degree in TFN are RRR , dRR and ReR while the nodes with high degree in PFN are rre , rrr , eee and err , which indicates that climate change modes represented by these nodes have large probability of occurrence. Symbol R and r are mostly included in the important nodes of TFN and PFN, which indicate that the fluctuating variation in temperature and precipitation in the Tarim River Basin mainly are rising over the past 50 years. The nodes RRR , DDD , ReR , RRd , DDd and Ree are the hub nodes in TFN, which undertake 19.71% betweenness centrality of the network. The nodes rre , rrr , eee and err are the hub nodes in PFN, which undertake 13.64% betweenness centrality of the network.
Software Reliability Modeling and Evaluation under Incomplete Knowledge on Fault Distribution

In this paper we consider non-parametric estimation methods for software reliability assessment without specifying the fault distribution, where the underlying stochastic process to describe software fault-counts in the system testing is given by a non-homogeneous Poisson process. A comprehensive approach based on the kernel estimation is provided with several kernel functions and bandwidth estimations. Next, we develop interval estimation methods via the non-parametric bootstrap, and derive the confidence regions of several reliability measures such as the expected cumulative number of software faults, software intensity function, quantitative software reliability as well. The resulting data-driven methodology can give the useful probabilistic information on the software reliability prediction under the incomplete knowledge on fault distribution. In illustrative examples with a real software fault data, it is shown that the proposed methods provide useful software reliability measures under uncertainty from the view point of frequentist analysis.
Changes in economic difficulties and subsequent sickness absence: a prospective register-linkage study

Objectives People's economic difficulties are associated with their health, but consequences of changes in economic difficulties are less understood. We aimed to examine the associations between changes in economic difficulties and subsequent sickness absence while considering socioeconomic circumstances and other covariates. Design A prospective cohort study. Setting Helsinki, Finland. Participants Municipal employees of the City of Helsinki, Finland (n=3859), who were respondents to the baseline (2000–2002) and follow-up (2007) questionnaire surveys and had register-based follow-up data on sickness absence until the end of 2010. Primary and secondary outcome measures Self-certified short (1–3 days) and medically certified intermediate (4–14 days) and long (15+ days) sickness absence spells were examined using employer's personnel register data. Results Persistent frequent economic difficulties predicted short (rate ratios (RR) 1.66 95% CI 1.49 to 1.86), intermediate (RR 2.13 95% CI 1.85 to 2.45) and long (RR 2.18 95% CI 1.75 to 2.70) sickness absence spells. Increasing economic difficulties similarly predicted sickness absence spells. The risks were somewhat stronger the longer the absence, and remained although attenuated somewhat after full adjustment. Weak risks were found also for persistent occasional economic difficulties and decreasing economic difficulties, and they attenuated further after full adjustments. Conclusions Changes in economic difficulties predict subsequent sickness absence even after considering income, baseline health and other covariates. Thus economic difficulties should be considered when addressing causes of sickness absence.
Solaris Zones: Operating System Support for Consolidating Commercial Workloads

Server consolidation, which allows multiple workloads to run on the same system, has become increasingly important as a way to improve the utilization of computing resources and reduce costs. Consolidation is common in mainframe environments, where technology to support running multiple workloads and even multiple operating systems on the same hardware has been evolving since the late 1960's. This technology is now becoming an important differentiator in the UNIX and Linux server market as well, both at the low end (virtual web hosting) and high end (traditional data center server consolidation). This paper introduces Solaris Zones (zones), a fully realized solution for server consolidation projects in a commercial UNIX operating system. By creating virtualized application execution environments within a single instance of the operating system, the facility strikes a unique balance between competing requirements. On the one hand, a system with multiple workloads needs to run those workloads in isolation, to ensure that applications can neither observe data from other applications nor affect their operation. It must also prevent applications from over-consuming system resources. On the other hand, the system as a whole has to be flexible, manageable, and observable, in order to reduce administrative costs and increase efficiency. By focusing on the support of multiple application environments rather than multiple operating system instances, zones meets isolation requirements without sacrificing manageability.
Designing the Human Machine Interface in the ATIS Domain

Spoken language systems for the near future will not handle all of English, but, rather, will be limited to a domain-specific sub-language. Accurate modeling of the sub-language will depend on analysis of domain-specific data. Since no spoken language systems currently have a wide range of users, and since variability across users is expected to be large, we are simulating applications in which a large population of potential users can be sampled. The data resulting from the simulations can be used for system development and for system evaluation. The application discussed here is the air travel domain using the Official Airline Guide (OAG) reformatted in a relational structure.This study assesses the effects of changes in the simulations on the speech and language of the experimental subjects. These results are relevant to both the experimental conditions for data collection and the design of the human interface for spoken language systems. We report here on five experiments: (1) the effect of longer instructions with examples vs. shorter instructions, using our earlier data collection system, (2) a baseline experiment using a functional equivalent of the data collection effort at Texas Instruments (TI), (3) the use of a more specific version of the scenario used in the baseline experiment, (4) the use of a short, simple familiarization scenario before the main scenario, and (5) in addition to the short familiarization scenario, the use of a finite vocabulary with rejection of sentences with extra-lexical items.
NADPH‐oxidase activity and lipid peroxidation in neutrophils from rats fed fat‐rich diets

In order to investigate the effect of fat‐rich diets on neutrophil functions, 21 day‐aged rats were fed for 6 weeks with a control diet consisting of a regular laboratory rodent chow (4 per cent final fat content), a control diet supplied with soybean oil (15 per cent final fat content), or a control diet supplied with coconut oil (15 per cent final fat content). Glycogen‐elicited peritoneal neutrophils from rats fed soybean and coconut oil‐enriched diets presented a reduction in spontaneous and PMA‐stimulated H2O2 generation relative to neutrophils from rats fed the control diet. The activity of superoxide dismutase, glutathione peroxidase and catalase did not change in animals fed fat‐rich diets. In addition, the capacity to generate O ·−2 spontaneously or in response to PMA, did not change in neutrophils from animals fed fat‐rich diets. Values attained matched those observed in animals fed the control diet, regardless of the method used to measure O ·−2 the superoxide dismutase‐inhibitable reduction of cytochrome c or the lucigenin‐dependent chemiluminescence. However, the initial rate of O ·−2 generation both in resting neutrophils and in PMA‐stimulated cells was significantly reduced when animals were fed with coconut or soybean oil‐enriched diets due, at least in part, to a reduction in the activity of glucose‐6‐phosphate dehydrogenase. The concentration of thiobarbituric acid reactive substances, an index of lipid peroxidation, was increased in animals fed both fat‐rich diets. This was accompanied by an increase in arachidonic acid content in these cells. Results presented suggest that lipid peroxidation in neutrophils from animals fed fat‐rich diets may be associated with a consumption of H2O2 yielding more reactive oxygen‐derived species such as the hydroxyl radical. Copyright © 1999 John Wiley & Sons, Ltd.
Productive potential of upland rice under conventional and organic farming systems

This paper aimed to evaluate the genetic diversity of upland rice varieties, determine the components that contributes most to productivity and evaluate the yield of varieties grown in conventional and organic farming systems. The experiment was carried out during the 2011/2012, 2012/2013 and 2013/2014 crop years, with 11 local upland rice varieties (Agulha, Rosa 15, Mato Grosso, Gomes, Preto, Argentino, Kinsel, Camilo, Piriquito, Casca Roxa and Caipira) and two commercial varieties (Primavera and Cambará). Genetic diversity was observed among the varieties for all evaluated variables and the thousand grain weight was the character that contributed most to the divergence between varieties. The number of panicles m-2 was the yield component that presented the highest association with grain yield of varieties in organic farming systems. The average yield of the varieties was 2.303, 1.711 and 3.534 kg ha-1 for the seasons 1, 2 and 3 respectively, and the highest yields were obtained with earlier sowing and temperature during grains maturation about 20°C. The ‘Piriquito’, ‘Argentino’, ‘Gomes’ and ‘Kinsel’ varieties presented yield above 2.000 kg ha-1 for all seasons, in both systems, indicating better adaptation to the region of the study and can be indicated as the most promising for the crop under organic cultivation.