nils-herrmann/clean_scirepeval huggingface dataset:
Adaptation Patterns in Agile Information Systems Development Teams

This research draws on team adaptation theory to study how agile information systems development (ISD) teams respond to non-routine events in their work environment. Based on our findings from a qualitative case study of three ISD teams, we identified non-routine events that could be distinguished according to the three categories task volatility, technological disruption, and team instability. In addition, we found three patterns of reacting to these events that differed regarding complexity and team learning. Our results show that the theoretical link between different types of events and adaption patterns depends on the type of event and the reach of the events’ impact as well as on the extent to which the teams followed an iterative development approach. While previous literature either examined ISD team agility as the extent to which agile techniques and methods are applied, or as a capability to adapt to changes, this research is the first to study how more or less agile teams react to non-routine events. By taking a process view and examining the influence of iterativeness on the link between events and adaptation patterns, this study helps reconcile the behavioral and capability perspectives on agility that have so far been disconnected.
DISCO Nets: DISsimilarity COefficient Networks

We present a new type of probabilistic model which we call DISsimilarity COefficient Networks (DISCO Nets). DISCO Nets allow us to efficiently sample from a posterior distribution parametrised by a neural network. During training, DISCO Nets are learned by minimising the dissimilarity coefficient between the true distribution and the estimated distribution. This allows us to tailor the training to the loss related to the task at hand. We empirically show that (i) by modeling uncertainty on the output value, DISCO Nets outperform equivalent non-probabilistic predictive networks and (ii) DISCO Nets accurately model the uncertainty of the output, outperforming existing probabilistic models based on deep neural networks.
A self-supervised learning system for object detection using physics simulation and multi-view pose estimation

Progress has been achieved recently in object detection given advancements in deep learning. Nevertheless, such tools typically require a large amount of training data and significant manual effort to label objects. This limits their applicability in robotics, where solutions must scale to a large number of objects and variety of conditions. This work proposes an autonomous process for training a Convolutional Neural Network (CNN) for object detection and pose estimation in robotic setups. The focus is on detecting objects placed in cluttered, tight environments, such as a shelf with multiple objects. In particular, given access to 3D object models, several aspects of the environment are physically simulated. The models are placed in physically realistic poses with respect to their environment to generate a labeled synthetic dataset. To further improve object detection, the network self-trains over real images that are labeled using a robust multi-view pose estimation process. The proposed training process is evaluated on several existing datasets and on a dataset collected for this paper with a Motoman robotic arm. Results show that the proposed approach outperforms popular training processes relying on synthetic — but not physically realistic — data and manual annotation. The key contributions are the incorporation of physical reasoning in the synthetic data generation process and the automation of the annotation process over real images.
Evaluation of a non-point source pollution model, AnnAGNPS, in a tropical watershed

Impaired water quality caused by human activity and the spread of invasive plant and animal species has been identified as a major factor of degradation of coastal ecosystems in the tropics. The main goal of this study was to evaluate the performance of AnnAGNPS (Annualized NonPoint Source Pollution Model), in simulating runoff and soil erosion in a 48 km watershed located on the Island of Kauai, Hawaii. The model was calibrated and validated using 2 years of observed stream flow and sediment load data. Alternative scenarios of spatial rainfall distribution and canopy interception were evaluated. Monthly runoff volumes predicted by AnnAGNPS compared well with the measured data (R 1⁄4 0.90, P < 0.05); however, up to 60% difference between the actual and simulated runoff were observed during the driest months (May and July). Prediction of daily runoff was less accurate (R 1⁄4 0.55, P < 0.05). Predicted and observed sediment yield on a daily basis was poorly correlated (R 1⁄4 0.5, P < 0.05). For the events of small magnitude, the model generally overestimated sediment yield, while the opposite was true for larger events. Total monthly sediment yield varied within 50% of the observed values, except for May 2004. Among the input parameters the model was most sensitive to the values of ground residue cover and canopy cover. It was found that approximately one third of the watershed area had low sediment yield (0e1 t ha 1 y ), and presented limited erosion threat. However, 5% of the area had sediment yields in excess of 5 t ha 1 y . Overall, the model performed reasonably well, and it can be used as a management tool on tropical watersheds to estimate and compare sediment loads, and identify ‘‘hot spots’’ on the landscape.
Performance analysis of two-degree of freedom fractional order PID controllers for robotic manipulator with payload.

The robotic manipulators are multi-input multi-output (MIMO), coupled and highly nonlinear systems. The presence of external disturbances and time-varying parameters adversely affects the performance of these systems. Therefore, the controller designed for these systems should effectively deal with such complexities, and it is an intriguing task for control engineers. This paper presents two-degree of freedom fractional order proportional-integral-derivative (2-DOF FOPID) controller scheme for a two-link planar rigid robotic manipulator with payload for trajectory tracking task. The tuning of all controller parameters is done using cuckoo search algorithm (CSA). The performance of proposed 2-DOF FOPID controllers is compared with those of their integer order designs, i.e., 2-DOF PID controllers, and with the traditional PID controllers. In order to show effectiveness of proposed scheme, the robustness testing is carried out for model uncertainties, payload variations with time, external disturbance and random noise. Numerical simulation results indicate that the 2-DOF FOPID controllers are superior to their integer order counterparts and the traditional PID controllers.
Artefacts in magnetic resonance imaging caused by dental material

A common problem in computer tomography (CT) based imaging of the oral cavity is artefacts caused by dental restorations. The aim of this study was to investigate whether magnetic resonance imaging (MRI) of the oral cavity would be less affected than CT by artefacts caused by typical dental restorative alloys. In order to assess the extent of artefact generation, corresponding MRI scans of the same anatomic region with and without dental metal restorations were matched using a stereotactic frame. MRI imaging of the oral and maxillofacial region could be performed without reduction of the image quality by metallic dental restorations made from titanium, gold or amalgam. Dental restorations made from titanium, gold or amalgam did not reduce the image quality of the MRI sequence used in imaging of the oral and maxillofacial region for dental implant planning. In this respect MRI is superior to CT in implant planning.
Caregiver-assisted music events in psychogeriatric care.

An ethnographic approach to the study of caregiver-assisted music events was employed with patients suffering from dementia or suspected dementia. The aim of this study was to illuminate the importance of music events and the reactions and social interactions of patients with dementia or suspected dementia and their caregivers before, during and after such events, including the remainder of the day. The results showed that the patients experienced an ability to sing, play instruments, perform body movements, and make puns during such music events. While singing familiar songs, some patients experienced the return of distant memories, which they seemed to find very pleasurable. During and after the music events, the personnel experienced bonding with the patients, who seemed easier to care for. Caregiver-assisted music events show a great potential for use in dementia care.
PATCH-BASED IMAGE INTERPOLATION : ALGORITHMS AND APPLICATIONS

Motivated by recent advances in adaptive sparse representations and nonlocal image modeling, we propose a patch-based image interpolation algorithm under a set theoretic framework. Our algorithm alternates the projection onto two convex sets: one is given by the observation data and the other defined by a sparsity-based nonlocal prior similar to BM3D. In order to optimize the design of observation constraint set, we propose to address the issue of sampling pattern and model it by a spatial point process. A Monte-Carlo based algorithm is proposed to optimize the randomness of sampling patterns to better approximate homogeneous Poisson process. Extensive experimental results in image interpolation and coding applications are reported to demonstrate the potential of the proposed algorithms.
The effectiveness of an accessibility-enhanced multimedia informational educational programme in reducing anxiety and increasing satisfaction of patients undergoing cardiac catheterisation.

To evaluate the effectiveness of an accessibility-enhanced multimedia informational educational programme in reducing anxiety and increasing satisfaction with the information and materials received by patients undergoing cardiac catheterisation.



Cardiac catheterisation is one of the most anxiety-provoking invasive procedures for patients. However, informational education using multimedia to inform patients undergoing cardiac catheterisation has not been extensively explored.



A randomised experimental design with three-cohort prospective comparisons.



In total, 123 consecutive patients were randomly assigned to one of three groups: regular education; (group 1), accessibility-enhanced multimedia informational education (group 2) and instructional digital videodisc education (group 3). Anxiety was measured with Spielberger's State Anxiety Inventory, which was administered at four time intervals: before education (T0), immediately after education (T1), before cardiac catheterisation (T2) and one day after cardiac catheterisation (T3). A satisfaction questionnaire was administrated one day after cardiac catheterisation. Data were collected from May 2009-September 2010 and analysed using descriptive statistics, chi-squared tests, one-way analysis of variance, Scheffe's post hoc test and generalised estimating equations.



All patients experienced moderate anxiety at T0 to low anxiety at T3. Accessibility-enhanced multimedia informational education patients had significantly lower anxiety levels and felt the most satisfied with the information and materials received compared with patients in groups 1 and 3. A statistically significant difference in anxiety levels was only found at T2 among the three groups (p = 0·004).



The findings demonstrate that the accessibility-enhanced multimedia informational education was the most effective informational educational module for informing patients about their upcoming cardiac catheterisation, to reduce anxiety and improve satisfaction with the information and materials received compared with the regular education and instructional digital videodisc education.


 TO CLINICAL PRACTICE
As the accessibility-enhanced multimedia informational education reduced patient anxiety and improved satisfaction with the information and materials received, it can be adapted to complement patient education in future regular cardiac care.
Mixture Factorized Ornstein-Uhlenbeck Processes for Time-Series Forecasting

Forecasting the future observations of time-series data can be performed by modeling the trend and fluctuations from the observed data. Many classical time-series analysis models like Autoregressive model (AR) and its variants have been developed to achieve such forecasting ability. While they are often based on the white noise assumption to model the data fluctuations, a more general Brownian motion has been adopted that results in Ornstein-Uhlenbeck (OU) process. The OU process has gained huge successes in predicting the future observations over many genres of time series, however, it is still limited in modeling simple diffusion dynamics driven by a single persistent factor that never evolves over time. However, in many real problems, a mixture of hidden factors are usually present, and when and how frequently they appear or disappear are unknown ahead of time. This imposes a challenge that inspires us to develop a Mixture Factorized OU process (MFOUP) to model evolving factors. The new model is able to capture the changing states of multiple mixed hidden factors, from which we can infer their roles in driving the movements of time series. We conduct experiments on three forecasting problems, covering sensor and market data streams. The results show its competitive performance on predicting future observations and capturing evolution patterns of hidden factors as compared with the other algorithms.
DETERMINANTS OF ORGANIZATIONAL COMMITMENT

The paper deals with a topic of organizational commitment. It is viewed as one of the basic concepts describing the relationship between an employee and an organization. According to Meyer and Allen (1997), all dimensions of commitment concern a relationship between an individual and an organization and an individual’s desire to remain in it or to abandon it, however, the strength of each dimension is conditioned by different factors. Model of organizational commitment consists in distinguishing following three commitment components: affective, continuance, and normative commitment. From this point of view, paper also deals determinants of organizational commitment in the light of research. The main objective of presented research was to search for determinants of organizational commitment. As was mentioned above, in the research, Meyer and Allen three-component model of organizational commitment was used. The data for this study were collected from 330 Polish employees holding different forms of employment: classic employment (N = 198) and temporary work (N = 132). Participants were recruited from two organizations: state company (N = 205) and a private company (N = 125). The research has shown that work locus of control, breach of psychological contract and psychological climate had a significant influence on organizational commitment.
A Gaussian noise generator for hardware-based simulations

Hardware simulation offers the potential of improving code evaluation speed by orders of magnitude over workstation or PC-based simulation. We describe a hardware-based Gaussian noise generator used as a key component in a hardware simulation system, for exploring channel code behavior at very low bit error rates (BERs) in the range of 10/sup -9/ to 10/sup -10/. The main novelty is the design and use of nonuniform piecewise linear approximations in computing trigonometric and logarithmic functions. The parameters of the approximation are chosen carefully to enable rapid computation of coefficients from the inputs while still retaining high fidelity to the modeled functions. The output of the noise generator accurately models a true Gaussian Probability Density Function (PDF) even at very high /spl sigma/ values. Its properties are explored using: 1) several different statistical tests, including the chi-square test and the Anderson-Darling test, and 2) an application for decoding of low-density parity-check (LDPC) codes. An implementation at 133 MHz on a Xilinx Virtex-II XC2V4000-6 FPGA produces 133 million samples per second, which is seven times faster than a 2.6 GHz Pentium-IV PC; another implementation on a Xilinx Spartan-IIE XC2S300E-7 FPGA at 62 MHz is capable of a three times speedup. The performance can be improved by exploiting parallelism: an XC2V4000-6 FPGA with nine parallel instances of the noise generator at 105 MHz can run 50 times faster than a 2.6 GHz Pentium-IV PC. We illustrate the deterioration of clock speed with the increase in the number of instances.
Explainable Time Series Tweaking via Irreversible and Reversible Temporal Transformations

Time series classification has received great attention over the past decade with a wide range of methods focusing on predictive performance by exploiting various types of temporal features. Nonetheless, little emphasis has been placed on interpretability and explainability. In this paper, we formulate the novel problem of explainable time series tweaking, where, given a time series and an opaque classifier that provides a particular classification decision for the time series, we want to find the minimum number of changes to be performed to the given time series so that the classifier changes its decision to another class. We show that the problem is NP-hard, and focus on two instantiations of the problem, which we refer to as reversible and irreversible time series tweaking. The classifier under investigation is the random shapelet forest classifier. Moreover, we propose two algorithmic solutions for the two problems along with simple optimizations, as well as a baseline solution using the nearest neighbor classifier. An extensive experimental evaluation on a variety of real datasets demonstrates the usefulness and effectiveness of our problem formulation and solutions.
Multirate Simulation for High Fidelity Haptic Interaction with Deformable Objects in Virtual Environments

Haptic interaction is an increasingly common form of interaction in virtual environment (VE) simulations. This medium introduces some new challenges. In this paper we study the problem arising from the difference between the sampling rate requirements of haptic interfaces and the significantly lower update rates of the physical models being manipulated. We propose a multirate simulation approach which uses a local linear approximation. The treatment includes a detailed analysis and experimental verification of the approach. The proposed method is also shown to improve the stability of the haptic interaction.
Design and Implementation of a Framework for Efficient and Programmable Sensor Networks

Wireless ad hoc sensor networks have emerged as one of the key growth areas for wireless networking and computing technologies. So far these networks/systems have been designed with static and custom architectures for specific tasks, thus providing inflexible operation and interaction capabilities. Our vision is to create sensor networks that are open to multiple transient users with dynamic needs. Working towards this vision, we propose a framework to define and support lightweight and mobile control scripts that allow the computation, communication, and sensing resources at the sensor nodes to be efficiently harnessed in an application-specific fashion. The replication/migration of such scripts in several sensor nodes allows the dynamic deployment of distributed algorithms into the network. Our framework, SensorWare, defines, creates, dynamically deploys, and supports such scripts. Our implementation of SensorWare occupies less than 180Kbytes of code memory and thus easily fits into several sensor node platforms. Extensive delay measurements on our iPAQ-based prototype sensor node platform reveal the small overhead of SensorWare to the algorithms (less than 0.3msec in most high-level operations). In return the programmer of the sensor network receives compactness of code, abstraction services for all of the node's modules, and in-built multi-user support. SensorWare with its features apart from making dynamic programming possible it also makes it easy and efficient without restricting the expressiveness of the algorithms.
Ford of Europe ’ s Product Sustainability Index

Automotive industry is facing a multitude of challenges towards sustainability that can be partly also addressed by product design: o Climate change and oil dependency. The growing weight of evidence holds that manmade greenhouse gas emissions are starting to influence the world’s climate in ways that affect all parts of the globe (IPCC 2007) – along with growing concerns over the use and availability of fossil carbon. There is a need for timely action including those in vehicle design. o Air Quality and other emissions as noise. Summer smog situa tions frequently lead to traffic restrictions for vehicles not compliant to most recent emission standards. Other emissions as noise affect up to 80 million citizens – much of it caused by the transport sector (roads, railway, aircraft, etc.) (ERF 2007). o Mobility Capability. Fulfilling the societal mobility demand is a key factor enabling (sustainable) development. This is challenged where the infrastructure is not aligned to the mobility demand and where the mobility capability of the individual transport mode (cars, trains, etc.) are not fulfilling these needs – leading to unnecessary travel time and emissions (traffic jams, non-direct connections, lack of parking opportunities, etc.). In such areas, insufficient infrastructure is the reason for 38% of CO2 vehicle emissions (SINTEF 2007). Industry has also to consider changing mobility needs in aging societies. o Safety. Road accidents (including all related transport modes as well as pedestrians) result to 1.2 million fatalities globally according to the World Bank. o Affordability. As mobility is an important precondition for any development it is important that all the mobility solutions are affordable for the targeted regions and markets. All these challenges are both, risks and business opportunities.
A survey of the sleep status among military pilots

[摘要] 目的 调查军队飞行人员的睡眠习惯、常见睡眠问题、白天嗜睡程度和飞行前夜睡眠情况。方法 采取 整群随机抽样方法对1380名军队飞行人员进行问卷调查,对其中1328份有效问卷进行统计分析。结果 飞行人员平均 夜间睡眠时间6.99h,24h平均睡眠时间8.10h。4.10%的飞行人员24h睡眠时间不足6h。中重度打鼾发生率22.80%,易醒 或(和)早醒和入睡困难≥3次/周的发生率为7.65%和5.81%,夜尿患病率4.03%。平均ESS评分5.59±4.40分,以≥11分评 估嗜睡发生率为14.99%。飞行中困倦发生率14.20%。62.53%的飞行人员飞行前夜较平时睡眠发生变化。结论 飞行人 员中存在着多种睡眠问题,并且飞行前夜睡眠可发生变化,这些都可能影响飞行安全,应进行研究和干预。 [关键词] 问卷调查;数据收集;飞行;睡眠;嗜睡 [中图分类号] R856.74 [文献标志码] A [文章编号] 0577-7402(2012)02-0141-05