nils-herrmann/clean_scirepeval huggingface dataset:
diarrhoea: meta-analysis Probiotics in prevention of antibiotic associated

To evaluate efficacy of probiotics in prevention and treatment of diarrhoea associated with the use of antibiotics. Design Meta-analysis; outcome data (proportion of patients not getting diarrhoea) were analysed, pooled, and compared to determine odds ratios in treated and control groups.  Studies identified by searching Medline between 1966 and 2000 and the Cochrane Library. Studies reviewed Nine randomised, double blind, placebo controlled trials of probiotics.  Two of the nine studies investigated the effects of probiotics in children. Four trials used a yeast (Saccharomyces boulardii), four used lactobacilli, and one used a strain of enterococcus that produced lactic acid. Three trials used a combination of probiotic strains of bacteria. In all nine trials, the probiotics were given in combination with antibiotics and the control groups received placebo and antibiotics. The odds ratio in favour of active treatment over placebo in preventing diarrhoea associated with antibiotics was 0.39 (95% confidence interval 0.25 to 0.62; P < 0.001) for the yeast and 0.34 (0.19 to 0.61; P < 0.01 for lactobacilli. The combined odds ratio was 0.37 (0.26 to 0.53; P < 0.001) in favour of active treatment over placebo.  The meta-analysis suggests that probiotics can be used to prevent antibiotic associated diarrhoea and that S boulardii and lactobacilli have the potential to be used in this situation. The efficacy of probiotics in treating antibiotic associated diarrhoea remains to be proved. A further large trial in which probiotics are used as preventive agents should look at the costs of and need for routine use of these agents.
Reinforced Random Walk

This thesis aim is to present results on a stochastic model called reinforced random walk. This process was conceived in the late 1980’s by Coppersmith and Diaconis and can be regarded as a generalization of a random walk on a weighted graph. These reinforced walks have non-homogeneous transition probabilities, which arise from an interaction between the process and the weights. We survey articles on the subject, perform simulations and extend a theorem by Pemantle.
Visual privacy protection methods: A survey

Recent advances in computer vision technologies have made possible the development of intelligent monitoring systems for video surveillance and ambientassisted living. By using this technology, these systems are able to automatically interpret visual data from the environment and perform tasks that would have been unthinkable years ago. These achievements represent a radical improvement but they also suppose a new threat to individual’s privacy. The new capabilities of such systems give them the ability to collect and index a huge amount of private information about each individual. Next-generation systems have to solve this issue in order to obtain the users’ acceptance. Therefore, there is a need for mechanisms or tools to protect and preserve people’s privacy. This paper seeks to clarify how privacy can be protected in imagery data, so as a main contribution a comprehensive classification of the protection methods for visual privacy as well as an up-to-date review of them are provided. A survey of the existing privacy-aware intelligent monitoring systems and a valuable discussion of important aspects of visual privacy are also provided.
Characterization and modeling of PIDX parallel I/O for performance optimization

Parallel I/O library performance can vary greatly in response to user-tunable parameter values such as aggregator count, file count, and aggregation strategy. Unfortunately, manual selection of these values is time consuming and dependent on characteristics of the target machine, the underlying file system, and the dataset itself. Some characteristics, such as the amount of memory per core, can also impose hard constraints on the range of viable parameter values. In this work we address these problems by using machine learning techniques to model the performance of the PIDX parallel I/O library and select appropriate tunable parameter values. We characterize both the network and I/O phases of PIDX on a Cray XE6 as well as an IBM Blue Gene/P system. We use the results of this study to develop a machine learning model for parameter space exploration and performance prediction.
Smart Maps through Semantic Web, Social Media, and Sentiment Analysis

In this paper we describe a new approach to creating rich, dynamic and customized maps for business or leisure activities, and demonstrate how the approach can be implemented through a prototype system. The approach is aimed at changing the way we map the world by providing a meaningful and personalized context that is augmented with the semantic web, social media integration and sentiment analysis. In our approach, smart search for an entity on a map is assisted through the semantic web. Once a map entity is identified, real-time and dynamic information about properties of the entity is gathered from social media and is integrated into the map. Sentiment analysis with regard to the map entity can be conducted and its results displayed or used as filters. The main benefit of the proposed approach is two-fold: it allows users to define their own user experience or context by selecting specific properties they want to display on their maps (ratings, comments, pictures and other specific business information), and it organizes interactive maps through the hierarchy of entities/markers/layers/timeframes. We also compare our approach with related work.
Deep auto-encoder neural networks in reinforcement learning

This paper discusses the effectiveness of deep auto-encoder neural networks in visual reinforcement learning (RL) tasks. We propose a framework for combining the training of deep auto-encoders (for learning compact feature spaces) with recently-proposed batch-mode RL algorithms (for learning policies). An emphasis is put on the data-efficiency of this combination and on studying the properties of the feature spaces automatically constructed by the deep auto-encoders. These feature spaces are empirically shown to adequately resemble existing similarities and spatial relations between observations and allow to learn useful policies. We propose several methods for improving the topology of the feature spaces making use of task-dependent information. Finally, we present first results on successfully learning good control policies directly on synthesized and real images.
A current-mode buck converter with a pulse-skipping soft-start circuit

This paper presents a soft-start circuit that adopts a pulse-skipping control to prevent inrush current and output voltage overshoot during the start-up period of dc-dc converters. The purpose of the pulse-skipping control is to significantly restrain the increasing rate of the reference voltage of the error amplifier. Thanks to the pulse-skipping mechanism and the duty cycle minimization, the soft-start-up time can be extended and the restriction of the charging current and the capacitance can be relaxed. The proposed soft-start circuit is fully integrated on chip without external components, leading to a reduction in PCB area and cost. A current-mode buck converter is implemented with TSMC 0.35-μm 2P4M CMOS process. Simulation results show the output voltage of the buck converter increases smoothly and inrush current is less than 300 mA.
Stream Compilation for Real-Time Embedded Multicore Systems

Multicore systems have not only become ubiquitous in the desktop and server worlds, but are also becoming the standard in the embedded space. Multicore offers programability and flexibility over traditional ASIC solutions. However, many of the advantages of switching to multicore hinge on the assumption that software development is simpler and less costly than hardware development. However, the design and development of correct, high-performance, multi-threaded programs is a difficult challenge for most programmers. Stream programming is one model that has wide applicability in the multimedia, signal processing, and networking domains. Streaming is convenient for developers because it separates the creation of actors, or functions that operate on packets of data, from the flow of data through the system. However, stream compilers are generally ineffective for embedded systems because they do not handle strict resource or timing constraints. Specifically, real-time deadlines and memory size limitations are not handled by conventional stream partitioning and scheduling techniques. This paper introducesthe SPIR compiler that orchestrates the execution of streaming applications with strict memory and timing constraints. Software defined radio or SDR is chosen as the application space to illustrate the effectiveness of the compiler for mapping applications onto the IBM Cell platform.
Walking while Performing Working Memory Tasks Changes the Prefrontal Cortex Hemodynamic Activations and Gait Kinematics

Increasing evidence suggests that walking while performing a concurrent task negatively influences gait performance. However, it remains unclear how higher-level cognitive processes and coordination of limb movements are altered in challenging walking environments. This study investigated the influence of cognitive task complexity and walking road condition on the neutral correlates of executive function and postural control in dual-task walking.



Twenty-four healthy young adults completed a series of overground walks with three walking road conditions (wide, narrow, with obstacles) with and without the concurrent n-back working memory tasks of two complexity levels (1-back and 3-back). Prefrontal brain activation was assessed by functional near-infrared spectroscopy. A three-dimensional motion analysis system was used simultaneously to measure gait performance and lower-extremity kinematics. Repeated measures analysis of variance were performed to examine the differences between the conditions.



In comparison with standing still, participants showed lower n-back task accuracy while walking, with the worst performance from the road with obstacles. Spatiotemporal gait parameters, lower-extremity joint movements, and the relative changes in oxygenated hemoglobin (HbO) concentration levels were all significantly different across the task complexity and walking path conditions. While dual-tasking participants were found to flex their hips and knees less, leading to a slower gait speed, longer stride time, shorter step length, and greater gait variability than during normal walking. For narrow-road walking, smaller ankle dorsiflexion and larger hip flexion were observed, along with a reduced gait speed. Obstacle negotiation was mainly characterized by increased gait variability than other conditions. HbO levels appeared to be lower during dual-task walking than normal walking. Compared to wide and obstacle conditions, walking on the narrow road was found to elicit a smaller decrement in HbO levels.



The current study provided direct evidence that, in young adults, neural correlates of executive function and dynamic postural control tend to be altered in response to the cognitive load imposed by the walking environment and the concurrent task during ambulation. A shift of brain activation patterns between functionally connected networks may occur when facing challenging cognitive-motor interaction.
A Journey through the Land of Model-View-Design Patterns

Every software program that interacts with a user requires a user interface. Model-View-Controller (MVC) is a common design pattern to integrate a user interface with the application domain logic. MVC separates the representation of the application domain (Model) from the display of the application's state (View) and user interaction control (Controller). However, studying the literature reveals that a variety of other related patterns exists, which we denote with Model-View- (MV) design patterns. This paper discusses existing MV patterns classified in three main families: Model-View-Controller (MVC), Model-View-View Model (MVVM), and Model-View-Presenter (MVP). We take a practitioners' point of view and emphasize the essentials of each family as well as the differences. The study shows that the selection of patterns should take into account the use cases and quality requirements at hand, and chosen technology. We illustrate the selection of a pattern with an example of our practice. The study results aim to bring more clarity in the variety of MV design patterns and help practitioners to make better grounded decisions when selecting patterns.
Bayesian Inference for Generalized Additive Mixed Models Based on Markov Random ®eld Priors

Most regression problems in practice require ¯exible semiparametric forms of the predictor for modelling the dependence of responses on covariates. Moreover, it is often necessary to add random effects accounting for overdispersion caused by unobserved heterogeneity or for correlation in longitudinal or spatial data. We present a uni®ed approach for Bayesian inference via Markov chain Monte Carlo simulation in generalized additive and semiparametric mixed models. Different types of covariates, such as the usual covariates with ®xed effects, metrical covariates with non-linear effects, unstructured random effects, trend and seasonal components in longitudinal data and spatial covariates, are all treated within the same general framework by assigning appropriate Markov random ®eld priors with different forms and degrees of smoothness. We applied the approach in several case-studies and consulting cases, showing that the methods are also computationally feasible in problems with many covariates and large data sets. In this paper, we choose two typical applications.
AWZone: Ad hoc WiFi Zonification in Post-Disaster Settings

Indoor localization or zonification in disaster affected settings is a challenging research problem. Existing studies encompass localization and tracking of first-responders or fire fighters using wireless sensor networks. In addition to that, fast evacuation, routing, and planning have also been proposed. However, the problem of locating survivors or victims is yet to be explored to the full potential. State-of-the-art literature often employ infrastructure dependent solutions, for example, WiFi localization using WiFi access points exploiting fingerprinting techniques, Pedestrian Dead Reckoning (PDR) starting from known locations, etc. Owing to unpredictable and dynamic nature of disaster affected environments, infrastructure dependent solutions are seldom useful. Therefore, in this study, we propose an ad hoc WiFi zonification technique (named as AWZone) that is independent of any infrastructural settings. AWZone attempts to perform localization through exploiting commodity smartphones as a beaconing device and successively searching and narrowing down the search space. We perform two testbed experiments. The results reveal that, for a single survivor or victim, AWZone can identify the search space and estimate a location with an approximate 1.5m localization error through eliminating incorrect zones from a set of possible results.
Intelligent legged climbing service robot for remote maintenance applications in hazardous environments

-Wheeled-tracked vehicles are undoubtedly the most popular means of transportation. However, these vehicles are mainly suitable for relatively flat terrain. Legged vehicles, on the other hand, have the potential to handle wide variety of terrain. Robug IIs is a legged climbing robot designed to work in relatively unstructured and rough terrain. It has the capability of walking, climbing vertical surfaces and performing autonomous floor to wall transfer. The sensing technique used in Robug IIs is mainly tactile and ultrasonic sensing. A set of reflexive rules have been developed for the robot to react to the uncertainty of the working environment. The robot also has the intelligence to seek and verify its own foot-holds. It is envisaged that the main application of robot is for remote inspection and maintenance in hazardous environments. Legged robot, climbing service robot, insect inspired robot, pneumatic control, fuzzy logic.
Blind Roll-Off Factor and Symbol Rate Estimation Using IFFT and Least Squares Estimator

To investigate the blind roll-off factor and symbol rate estimation of any linear digital modulation scheme, such as phase-shift keying (PSK) and quadrature amplitude modulation (QAM), an algorithm based on the inverse Fourier transform and the least squares estimator is proposed. First, the magnitude of the raised cosine pulse is provided by the inverse Fourier transform of the preprocessed power spectrum. The roll-off factor is then estimated based on the ratio of the second peak to the main peak of the magnitude. A coarse estimation of the symbol rate is extracted from the baseband shape. Based on the coarse estimation, a least squares estimator is used to improve the accuracy of the symbol rate estimation. Computer simulations show the efficiency of the proposed algorithm.
Audio Surveillance: A Systematic Review

Despite surveillance systems becoming increasingly ubiquitous in our living environment, automated surveillance, currently based on video sensory modality and machine intelligence, lacks most of the time the robustness and reliability required in several real applications. To tackle this issue, audio sensory devices have been incorporated, both alone or in combination with video, giving birth in the past decade, to a considerable amount of research. In this article, audio-based automated surveillance methods are organized into a comprehensive survey: A general taxonomy, inspired by the more widespread video surveillance field, is proposed to systematically describe the methods covering background subtraction, event classification, object tracking, and situation analysis. For each of these tasks, all the significant works are reviewed, detailing their pros and cons and the context for which they have been proposed. Moreover, a specific section is devoted to audio features, discussing their expressiveness and their employment in the above-described tasks. Differing from other surveys on audio processing and analysis, the present one is specifically targeted to automated surveillance, highlighting the target applications of each described method and providing the reader with a systematic and schematic view useful for retrieving the most suited algorithms for each specific requirement.
Neural Decision Forests for Semantic Image Labelling

In this work we present Neural Decision Forests, a novel approach to jointly tackle data representation- and discriminative learning within randomized decision trees. Recent advances of deep learning architectures demonstrate the power of embedding representation learning within the classifier -- An idea that is intuitively supported by the hierarchical nature of the decision forest model where the input space is typically left unchanged during training and testing. We bridge this gap by introducing randomized Multi- Layer Perceptrons (rMLP) as new split nodes which are capable of learning non-linear, data-specific representations and taking advantage of them by finding optimal predictions for the emerging child nodes. To prevent overfitting, we i) randomly select the image data fed to the input layer, ii) automatically adapt the rMLP topology to meet the complexity of the data arriving at the node and iii) introduce an l1-norm based regularization that additionally sparsifies the network. The key findings in our experiments on three different semantic image labelling datasets are consistently improved results and significantly compressed trees compared to conventional classification trees.
Pomalidomide alone or in combination with low-dose dexamethasone in relapsed and refractory multiple myeloma: a randomized phase 2 study.

This multicenter, open-label, randomized phase 2 study assessed the efficacy and safety of pomalidomide (POM) with/without low-dose dexamethasone (LoDEX) in patients with relapsed/refractory multiple myeloma (RRMM). Patients who had received ≥2 prior therapies (including lenalidomide [LEN] and bortezomib [BORT]) and had progressed within 60 days of their last therapy were randomized to POM (4 mg/day on days 1-21 of each 28-day cycle) with/without LoDEX (40 mg/week). The primary end point was progression-free survival (PFS). In total, 221 patients (median 5 prior therapies, range 1-13) received POM+LoDEX (n = 113) or POM (n = 108). With a median follow-up of 14.2 months, median PFS was 4.2 and 2.7 months (hazard ratio = 0.68, P = .003), overall response rates (ORRs) were 33% and 18% (P = .013), median response duration was 8.3 and 10.7 months, and median overall survival (OS) was 16.5 and 13.6 months, respectively. Refractoriness to LEN, or resistance to both LEN and BORT, did not affect outcomes with POM+LoDEX (median PFS 3.8 months for both; ORRs 30% and 31%; and median OS 16 and 13.4 months). Grade 3-4 neutropenia occurred in 41% (POM+LoDEX) and 48% (POM); no grade 3-4 peripheral neuropathy was reported. POM+LoDEX was effective and generally well tolerated and provides an important new treatment option for RRMM patients who have received multiple prior therapies.
CMOS Differential Structure with Improved Linearity and Increased Frequency Response

An original differential structure using exclusively MOS devices working in the saturation region will be further presented. Performing the great advantage of an excellent linearity, obtained by a proper biasing of the differential core (using original translation and arithmetical mean blocks), the proposed circuit is designed for low-voltage low- power operation. The estimated linearity is obtained for an extended range of the differential input voltage and in the worst case of considering second-order effects that affect MOS transistors operation. The frequency response of the new differential structure is strongly increased by operating all MOS devices in the saturation region. The circuit is implemented in 0.35 mum CMOS technology, SPICE simulations confirming the theoretical estimated results.
Software Comprehension - A Review & Research Direction

Comprehending computer programs is one of the core software engineering activities. Software comprehension is required when a programmer maintains, reuses, migrates, reengineers, or enhances software systems. Due to this, a large amount of research has been carried out, in an attempt to guide and support software engineers in this process. Several cognitive models of program comprehension have been suggested, which attempt to explain how a software engineer goes about the process of understanding code. However, research has suggested that there is no one ‘all encompassing’ cognitive model that can explain the behavior of ‘all’ programmers, and that it is more likely that programmers, depending on the particular problem, will swap between models (Letovsky, 1986). This paper identifies the key components of program comprehension models, and attempts to evaluate currently accepted models in this framework. It also highlights the commonalities, conflicts, and gaps between models, and presents possibilities for future research, based on its findings.