Miracle-dz/UniFAR-FTUs huggingface dataset:
Health and Quality of Life Outcomes

Background: Sleep disturbances are a common and bothersome symptom of fibromyalgia (FM). This study reports psychometric properties of a single-item scale to assess sleep quality among individuals with FM. Methods: Analyses were based on data from two randomized, double-blind, placebo-controlled trials of pregabalin (studies 1056 and 1077). In a daily diary, patients reported the quality of their sleep on a numeric rating scale ranging from 0 ("best possible sleep") to 10 ("worst possible sleep"). Test re-test reliability of the Sleep Quality Scale was evaluated by computing intraclass correlation coefficients. Pearson correlation coefficients were computed between baseline Sleep Quality scores and baseline pain diary and Medical Outcomes Study (MOS) Sleep scores. Responsiveness to treatment was evaluated by standardized effect sizes computed as the difference between least squares mean changes in Sleep Quality scores in the pregabalin and placebo groups divided by the standard deviation of Sleep Quality scores across all patients at baseline. Results: Studies 1056 and 1077 included 748 and 745 patients, respectively. Most patients were female (study 1056: 94.4%; study 1077: 94.5%) and white (study 1056: 90.2%; study 1077: 91.0%). Mean ages were 48.8 years (study 1056) and 50.1 years (study 1077). Test re-test reliability coefficients of the Sleep Quality Scale were 0.91 and 0.90 in the 1056 and 1077 studies, respectively. Pearson correlation coefficients between baseline Sleep Quality scores and baseline pain diary scores were 0.64 (p < 0.001) and 0.58 (p < 0.001) in the 1056 and 1077 studies, respectively. Correlations between the Sleep Quality Scale and the MOS Sleep subscales were statistically significant (p < 0.01), except for the MOS Snoring subscale. Across both studies, standardized effect sizes were generally moderate (0.46 to 0.52) for the 300 mg group and moderate (0.59) or moderate-to-large (0.70) for the 450 mg group. In study 1056, the effect size for the 600 mg group was moderate-to-large (0.73). In study 1077, the effect size for the 600 mg group was large (0.82). Conclusion: These results provide evidence of the reproducibility, convergent validity, and responsiveness to treatment of the Sleep Quality Scale and provide a foundation for its further use and evaluation in FM patients. Published: 17 June 2009 Health and Quality of Life Outcomes 2009, 7:54 doi:10.1186/1477-7525-7-54 Received: 4 December 2008 Accepted: 17 June 2009 This article is available from: http://www.hqlo. com/content/7/1/54 © 2009 Cappelleri et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Interpretable machine learning methods for predictions in systems biology from omics data

Machine learning has become a powerful tool for systems biologists, from diagnosing cancer to optimizing kinetic models and predicting the state, growth dynamics, or type of a cell. Potential predictions from complex biological data sets obtained by “omics” experiments seem endless, but are often not the main objective of biological research. Often we want to understand the molecular mechanisms of a disease to develop new therapies, or we need to justify a crucial decision that is derived from a prediction. In order to gain such knowledge from data, machine learning models need to be extended. A recent trend to achieve this is to design “interpretable” models. However, the notions around interpretability are sometimes ambiguous, and a universal recipe for building well-interpretable models is missing. With this work, we want to familiarize systems biologists with the concept of model interpretability in machine learning. We consider data sets, data preparation, machine learning methods, and software tools relevant to omics research in systems biology. Finally, we try to answer the question: “What is interpretability? ” We introduce views from the interpretable machine learning community and propose a scheme for categorizing studies on omics data. We then apply these tools to review and categorize recent studies where predictive machine learning models have been constructed from non-sequential omics data.
A Growing Natural Gas Approach for Evaluating Quality of Software Modules

development life cycle of software project helps the development organization to make efficient use of available resource to produce the product of highest quality. " Whether a module is faulty or not " approach can be used to predict quality of a software module. There are numbers of software quality prediction models described in the literature based upon genetic algorithms, artificial neural network and other data mining algorithms. One of the promising aspects for quality prediction is based on clustering techniques. Most quality prediction models that are based on clustering techniques make use of K-means, Mixture-of-Guassians, Self-Organizing Map, Neural Gas and fuzzy K-means algorithm for prediction. In all these techniques a predefined structure is required that is number of neurons or clusters should be known before we start clustering process. But in case of Growing Neural Gas there is no need of predetermining the quantity of neurons and the topology of the structure to be used and it starts with a minimal neurons structure that is incremented during training until it reaches a maximum number user defined limits for clusters. Hence, in this work we have used Growing Neural Gas as underlying cluster algorithm that produces the initial set of labeled cluster from training data set and thereafter this set of clusters is used to predict the quality of test data set of software modules. The best testing results shows 80% accuracy in evaluating the quality of software modules. Hence, the proposed technique can be used by programmers in evaluating the quality of modules during software development.
A Flexible, High Performance Service-Oriented Architecture for Detecting Cyber Attacks

A high percentage of false positives remains a problem in current network security detection systems. With the growing reliance of industry on computer networks, and the growing variety of attacks that can be directed towards a computer network, it is clear that detection systems must be improved in order to tackle this growing problem. To help minimise the problem of false positives, this paper describes a method and apparatus for security alert analysis that is based on two technologies: (i) event correlation and (ii) a truth maintenance system. This work was undertaken in the context of practical network security management in a large outsourced management service provider in the Asia-Pacific region.
Digital Commons@Becker

The time-to-result for culture-based microorganism recovery and phenotypic antimicrobial susceptibility testing necessitates initial use of empiric (frequently broad-spectrum) antimicrobial therapy. If the empiric therapy is not optimal, this can lead to adverse patient outcomes and contribute to increasing antibiotic resistance in pathogens. New, more rapid technologies are emerging to meet this need. Many of these are based on identifying resistance genes, rather than directly assaying resistance phenotypes, and thus require interpretation to translate the genotype into treatment recommendations. These interpretations, like other parts of clinical diagnostic workﬂows, are likely to be increasingly automated in the future. We set out to evaluate the two major approaches that could be amenable to automation pipelines: rules-based methods and machine learning methods. The rules-based algorithm makes predictions based upon current, curated knowledge of Enterobacteriaceae resistance genes. The machine-learning algorithm predicts resistance and susceptibility based on a model built from a training set of variably resistant isolates. As our test set, we used whole genome sequence data from 78 clinical Enterobacteriaceae isolates, previously identiﬁed to represent a variety of phenotypes, from fully-susceptible to pan-resistant strains for the antibiotics tested. We tested three antibiotic resistance determinant databases for their utility in identifying the complete resistome for each isolate. The predictions of the rules-based and machine learning algorithms for these isolates were compared to results of phenotype-based diagnostics. The rules based and machine-learning predictions achieved agreement with standard-of-care phenotypic diagnostics of 89.0 and 90.3%, respectively, across twelve antibiotic agents from six major antibiotic classes. Several sources of disagreement between the algorithms were identiﬁed. Novel variants of known resistance factors and incomplete genome assembly confounded
Title of Deliverable : Framework for the Support of IPv 6 Wireless LANs , Version 2 Work package contributing to Deliverable : WP 4 Type of Deliverable * : R Deliverable Security Class *

This document aims to provide a framework for the support of wireless LANs (WLANs) in an IPv6-only environment. It is hoped that it will serve as an IPv6 WLAN design and implementation guide for readers that are unfamiliar with IPv6 and/or WLANs. The original focus for this document was to be on access control solutions but we have now expanded the scope to include all aspects of deploying IPv6 WLANs. While authentication and access control still remain a significant part of this deliverable, we also look at the issues in designing and implementing the WLAN together with IPv6 or MIPv6 specific issues that should be taken into consideration. We have also added a section on hardware and software availability. We also focus on IEEE 802.11 based WLANs since these are by far the most widely deployed and are certainly beginning to live up to the ‘wireless Ethernet’ tag. This is the second version of the deliverable
A Framework for 3D Reconstruction of Human Organs from MR Images: A Model and Fuzzy Set Principles Based Approach. Application to Prostate Segmentation and Reconstruction

3D segmentation and reconstruction is a difficult process because they must take into account the different deformations which characterize the object to be segmented. In the medical field, manual body delineation is a time consuming task and often requires prior knowledge to cope absence of contours due to the lack of contrast of images as well as the great variability of body shapes and positions. For these reasons, researches in this field were directed towards methods which combine image information and a priori knowledge about the structure of the studied object. These methods are an extension of active contours ([1], [2]) and can be classified in two classes: Methods with a priori knowledge on the geometry: The first work was that of [3] and its 3D extension in [4], [5] and [6] and m-reps multiscale description introduced by [7]. Methods with a priori knowledge on appearance: In this category, in addition of information about the geometry of the object, information on its appearance, in particular gray levels and textures are included [8] and [9] where knowledge about the probabilities densities of the pixels inside the form is added. In many medical fields, the needs for segmentation are very important. In this paper, we propose a general framework for 3D reconstruction of human organs from MRI. This framework is described here through the process of prostate segmentation and reconstruction for prostate cancer diagnosis and image-guided therapy planning. Because automatic prostate segmentation remains complicated-in particular at apex either from transrectal ultrasound images (used for brachytherapy) or from MRI or CT (used for radiotherapy)-it seems to be an appropriate field of application of our approach. This article is presented as follow: first, we shortly present the context of prostate segmentation for radiotherapy planning, then we detail the model-based approach and the fuzzy set principles method used for reconstruction.
Solar radiation measurements in the United States between 1916 and 1949 document widespread brightening in the mid‐20th century

An analysis is presented of global radiation E g↓ measurements made between 1916 and 1949 at 26 sites in the continental United States. The procedures used to fill gaps in the database are described and the accuracy of the data assessed. During this period E g↓ averaged 172 and 41 W/m2 above the model‐estimated values for the same sites during the 1960–1990 period and 7 W/m2 below the average values estimated between 1991 and 2010; the largest changes were found at two urban sites, New York City and Pittsburgh. The major inter‐annual variation found within the 1916–1949 period was a significant spike lasting a decade which reached a maximum around 1940. This episode of global brightening was attributed to reduced emissions of anthropogenic aerosols during the economic depression together with lesser cloud cover associated with the severe droughts occurring in the same decade. The widespread global brightening which occurred concurrently outside the United States is documented and a link between global brightening and global warming during the first half of the 20th century demonstrated.
What You Corrupt Is Not What You Crash: Challenges in Fuzzing Embedded Devices

—As networked embedded systems are becoming more ubiquitous, their security is becoming critical to our daily life. While manual or automated large scale analysis of those systems regularly uncover new vulnerabilities, the way those systems are analyzed follows often the same approaches used on desktop systems. More speciﬁcally, traditional testing approaches relies on observable crashes of a program, and binary instrumentation techniques are used to improve the detection of those faulty states. In this paper, we demonstrate that memory corruptions, a common class of security vulnerabilities, often result in different behavior on embedded devices than on desktop systems. In particular, on embedded devices, effects of memory corruption are often less visible. This reduces signiﬁcantly the effectiveness of traditional dynamic testing techniques in general, and fuzzing in particular. Additionally, we analyze those differences in several categories of embedded devices and show the resulting impact on ﬁrmware analysis. We further describe and evaluate relatively simple heuristics which can be applied at run time (on an execution trace or in an emulator), during the analysis of an embedded device to detect previously undetected memory corruptions.
Investigating ad transparency mechanisms in social media: A case study of Facebook's explanations

Targeted advertising has been subject to many privacy complaints from both users and policy makers. Despite this attention, users still have little understanding of what data the advertising platforms have about them and why they are shown particular ads. To address such concerns, Facebook recently introduced two transparency mechanisms: a "Why am I seeing this? " button that provides users with an explanation of why they were shown a particular ad (ad explanations), and an Ad Preferences Page that provides users with a list of attributes Facebook has inferred about them and how (data explanations). In this paper, we investigate the level of transparency provided by these two mechanisms. We first define a number of key properties of explanations and then evaluate empirically whether Facebook's explanations satisfy them. For our experiments, we develop a browser extension that collects the ads users receive every time they browse Facebook, their respective explanations, and the attributes listed on the Ad Preferences Page; we then use controlled experiments where we create our own ad campaigns and target the users that installed our extension. Our results show that ad explanations are often incomplete and sometimes misleading while data explanations are often incomplete and vague. Taken together, our findings have significant implications for users, policy makers, and regulators as social media advertising services mature.
Information Sharing Strategy among Particles in Particle Swarm Optimization Using Laplacian Operator

Particle Swarm Optimization (PSO) has been extensively used in recent years for the optimization of nonlinear optimization problems. Two of the most popular variants of PSO are PSO-W (PSO with inertia weight) and PSO-C (PSO with constriction factor). Typically particles in swarm use information from global best performing particle, gbest and their own personal best, pbest. Recently, studies have focused on incorporating influences of other particles other than gbest. In this paper, we develop a methodology to share information between two particles using a Laplacian operator designed from Laplace probability density function. The properties of this operator are analyzed. Two particles share their positional information in the search space and a new particle is formed. The particle, called as Laplacian particle, replaces the worst performing particle in the swarm. Using this new operator, this paper introduces two algorithms namely Laplace Crossover PSO with inertia weight (LXPSO-W) and Laplace Crossover PSO with constriction factor (LXPSO-C). The performance of the newly designed algorithms is evaluated with respect to PSO-W and PSO-C using 15 benchmark test problems. The empirical results show that the new approach improves performance measured in terms of efficiency, reliability and robustness.
Justitia: Software Multi-Tenancy in Hardware Kernel-Bypass Networks

Kernel-bypass networking (KBN) is becoming the new norm in modern datacenters. While hardware-based KBN ofﬂoads all dataplane tasks to specialized NICs to achieve better latency and CPU efﬁciency than software-based KBN, it also takes away the operator’s control over network sharing policies. Providing policy support in multi-tenant hardware KBN brings unique challenges – namely, preserving ultra-low latency and low CPU cost, ﬁnding a well-deﬁned point of mediation, and rethinking trafﬁc shapers. We present Justitia to address these challenges with three key design as-pects: (i) Split Connection with message-level shaping, (ii) sender-based resource mediation together with receiver-side updates, and (iii) passive latency monitoring. Using a latency target as its knob, Justitia enables multi-tenancy policies such as predictable latencies and fair/weighted resource sharing. Our evaluation shows Justitia can effectively isolate latency-sensitive applications at the cost of slightly decreased utilization and ensure that throughput and bandwidth of the rest are not unfairly penalized.
Downscaling annual precipitation with TMPA and land surface characteristics in China

Very high‐resolution (∼1 km) precipitation datasets are needed in various applications, especially in the hydrological, environmental, agricultural, and biological fields, although they also have major roles in the climate sciences. Nevertheless, various studies have concurred that the spatial distribution of precipitation is influenced by land surface characteristics through non‐stationary relationships, and that this characteristic is generally not considered fully by either global or local models using fixed combinations of variables (e.g. the normalized difference vegetation index and/or digital elevation models). This study introduced a new spatial downscaling algorithm, a divide‐and‐conquer method (called Cubist), to downscale Tropical Rainfall Measuring Mission (TRMM) satellite precipitation data over China from 2000 to 2015. In this endeavour, we considered the non‐stationary relationships between precipitation and land surface characteristics at various scales and derived the following conclusions: (1) The relationships and related variables explaining the spatial distribution of precipitation varied with spatial scale. (2) The Cubist algorithm achieved greater accuracy than Multivariate Linear Regression (MLR) models, and it was found to compensate for the disadvantages of MLR overestimation and underestimation at low and high values, respectively. (3) In downscaling, the monthly Version 7 TRMM Multisatellite Precipitation Analysis (TMPA) datasets over China, the entire study area was separated into subregions driven by spatial datasets according to geographic similarities, which were similar to Chinese rainfall zones.
EFAS-Meteo: A European daily high-resolution gridded meteorological data set for 1990 - 2011

Data sets of spatially irregular meteorological observations interpolated to a regular grid are not only important for climate analyses but are also essential in order to derive climatologies for rainfall-runoff models which require meteorological data sets as input forcing. For example, in the European Flood Awareness System long term observed meteorological data are used to drive the hydrological model LISFLOOD to obtain long term time series of simulated discharges at a pan-European scale. Those long term time series of simulated ‘‘proxy’’ discharges can then be used for statistical analysis, e.g., to derive return periods or other time series derivatives. In this report, we present a comprehensive pan European high-resolution gridded daily data set (EFAS-Meteo) of precipitation, surface temperature (mean, minimum and maximum), wind speed, vapour pressure, calculated radiation and evapotranspiration (potential evapotranspiration, bare soil and open water evapotranspiration). The data set was created as part of the development of EFAS and has been continuously updated throughout the last years.
A review of regulatory modeling frameworks supporting numeric water quality criteria development in the United States

The US Environmental Protection Agency (USEPA) has a long history of leveraging environmental models and integrated modeling frameworks to support the regulatory development of numeric ambient water quality criteria for the protection of aquatic life and human health. Primary modeling types include conceptual, mechanistic, and data‐driven empirical models; Bayesian and probabilistic models; and risk‐based modeling frameworks. These models and modeling frameworks differ in their applicability to and suitability for various water quality criteria objectives. They require varying knowledge of system processes and stressor‐response relationships, data availability, and expertise of stakeholders. In addition, models can be distinguished by their ability to characterize variability and uncertainty. In this work, we review USEPA recommendations for model use in existing regulatory frameworks, technical support documents, and peer‐reviewed literature. We characterize key attributes, identify knowledge gaps and opportunities for future research, and highlight where renewed USEPA guidance is needed to promote the development and use of models in numeric criteria derivation. These outcomes then inform a decision‐based framework for determining model suitability under particular scenarios of available knowledge, data, and access to technical resources. Integr Environ Assess Manag 2023;19:191–201. © 2022 SETAC
Semantic Decomposition and Reconstructionf Residential Scenes from LiDAR DataSupplementary Material

This supplemental document provides more technical details of semantic segmentation, texture mapping, plant modeling and object replacement for other common subjects in landscape. 1 Semantic Segmentation The first stage in our system is to segment the input point cloud into semantically distinctive groups. This is done in two steps. The first is to label the input into categories, such as houses or plants. Then points labelled as houses are further refined into more detailed structural classes, such as roofs or walls. We use supervised machine learning techniques to perform these tasks.