nils-herrmann/clean_scirepeval huggingface dataset:
A spatio temporal, Gaussian process regression, real estate price predictor
Venue: SIGSPATIAL/GIS

This paper introduces a novel four stage methodology for real estate valuation. This research shows that space, property, economic, neighbourhood and time features are all contributing factors in producing a house price predictor in which validation shows a 96.6% accuracy on Gaussian Process Regression beating regression kriging, random forests and an M5P decision tree. The output is integrated into a commercial real estate decision engine.
Leadership and the psychosocial work environment in old age care
Venue: International journal of older people nursing

To study leadership factors and their associations with psychosocial work environmental among nursing assistants who are engaged in old age care and to analyse (i) differences in the assessment of leadership factors and the assessment of psychosocial work environmental in nursing homes and home help services and (ii) the association between the psychosocial work environment and factors that are related to leadership in nursing homes and home help services.  Leadership factors are an important element of the psychosocial work environment in old age care. The physical distance between leaders and nursing assistants is larger in home help services than in nursing homes. Therefore, it is important to study leadership separately in nursing homes and home help services.  Assessments from 844 nursing assistants in nursing homes and 288 in home help services (45 nursing homes and 21 home help service units) were analysed.  The data were analysed using linear regression. Age, gender, number of staff at the unit, number of years at the current working unit and educational level were controlled in Model 1. Summarised indexes that were based on all independent variables except the main independent variable were additionally controlled in Model 2.  Psychosocial work environment was related to leadership factors, but stronger associations occurred more frequently in nursing homes than in home help services. Empowering leadership, support from superiors, the primacy of human resources and control over decisions were associated with higher assessments on all the variables that were related to the psychosocial work environment in both the nursing homes and home help services.  Organisational differences in conducting leadership in old age care must be considered. Some leadership characteristics are better prerequisites for creating and maintaining a positive psychosocial work environment for nursing assistants in nursing homes and home help services. Implications for practice Due to the differences in organisational settings, it is important to consider the differences in prerequisites in conducting leadership. To influence nursing assistants' performance and to increase quality in old age care in the long term, appropriate leadership is necessary.
Variability Mitigation in Nanometer CMOS Integrated Systems: A Survey of Techniques From Circuits to Software
Venue: Proceedings of the IEEE

Variation in performance and power across manufactured parts and their operating conditions is an accepted reality in modern microelectronic manufacturing processes with geometries in nanometer scales. This article surveys challenges and opportunities in identifying variations, their effects and methods to combat these variations for improved microelectronic devices. We focus on computing devices and their design at various levels to combat variability. First, we provide a review of key concepts with particular emphasis on timing errors caused by various variability sources. We consider methods to predict and prevent, detect and correct, and finally conditions under which such errors can be accepted; we also consider their implications on cost, performance and quality. We provide a comparative evaluation of methods for deployment across various layers of the system from circuits, architecture, to application software. These can be combined in various ways to achieve specific goals related to observability and controllability of the variability effects, providing means to achieve cross layer or hybrid resilience. We then provide examples of real world resilient single core and parallel architectures. We find that parallel architectures and parallelism in general provide the best means to combat and exploit variability to design resilient and efficient systems. Using programmable accelerator architectures such as clustered processing elements and GP GPUs, we show how system designers can coordinate propagation of timing error information and its effects along with new techniques for memoization (i.e. spatial or temporal reuse of computation) This discussion naturally leads to use of these techniques into emerging area of "approximate computing," and how these can be used in building resilient and efficient computing systems. We conclude with an outlook for the emerging field.
Generation of Vortex Lift Through Reduction of Rotor/Stator Gap in Turbomachinery

The axial gap between blade rows in turbomachinery should be minimized in order to reduce size requirements and increase efficiency; although, there is a tradeoff between gap width and flow steadiness. The aerodynamic interaction between rotors and stators influences system performance under both steady and transient conditions. To investigate the basic physical mechanisms associated with the rotor/stator interaction, an efficient numerical scheme for solving unsteady, viscous flows on a quasi three dimensional coordinate system is established using an immersed boundary method. The data transfer between moving and stationary grids that slip against each other in traditional numerical methods is avoided. The effects of the axial gap between adjacent blade rows are studied by considering the flow evolution through a rotor and stator stage in different Reynolds number regimes. Results indicate that reduced blade gap leads to high lift on the rotor blade and improved stage loading. At the same time, the rotor.
Refining Geometry from Depth Sensors using IR Shading Images
Venue: International Journal of Computer Vision

We propose a method to refine geometry of 3D meshes from a consumer level depth camera, e.g. Kinect, by exploiting shading cues captured from an infrared (IR) camera. A major benefit to using an IR camera instead of an RGB camera is that the IR images captured are narrow band images that filter out most undesired ambient light, which makes our system robust against natural indoor illumination. Moreover, for many natural objects with colorful textures in the visible spectrum, the subjects appear to have a uniform albedo in the IR spectrum. Based on our analyses on the IR projector light of the Kinect, we define a near light source IR shading model that describes the captured intensity as a function of surface normals, albedo, lighting direction, and distance between light source and surface points. To resolve the ambiguity in our model between the normals and distances, we utilize an initial 3D mesh from the Kinect fusion and multi view information to reliably estimate surface details that were not captured and reconstructed by the Kinect fusion. Our approach directly operates on the mesh model for geometry refinement. We ran experiments on our algorithm for geometries captured by both the Kinect I and Kinect II, as the depth acquisition in Kinect I is based on a structured light technique and that of the Kinect II is based on a time of flight technology. The effectiveness of our approach is demonstrated through several challenging real world examples. We have also performed a user study to evaluate the quality of the mesh models before and after our refinements.
Simultaneous Estimation of Near IR BRDF and Fine Scale Surface Geometry
Venue: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Near Infrared (NIR) images of most materials exhibit less texture or albedo variations making them beneficial for vision tasks such as intrinsic image decomposition and structured light depth estimation. Understanding the reflectance properties (BRDF) of materials in the NIR wavelength range can be further useful for many photometric methods including shape from shading and inverse rendering. However, even with less albedo variation, many materials e.g. fabrics, leaves, etc. exhibit complex fine scale surface detail making it hard to accurately estimate BRDF. In this paper, we present an approach to simultaneously estimate NIR BRDF and fine scale surface details by imaging materials under different IR lighting and viewing directions. This is achieved by an iterative scheme that alternately estimates surface detail and NIR BRDF of materials. Our setup does not require complicated gantries or calibration and we present the first NIR dataset of 100 materials including a variety of fabrics (knits, weaves, cotton, satin, leather) and organic (skin, leaves, jute, trunk, fur) and inorganic materials (plastic, concrete, carpet) The NIR BRDFs measured from material samples are used with a shape from shading algorithm to demonstrate fine scale reconstruction of objects from a single NIR image.
The Effect of Participation in Support Groups on Depression, Anxiety and Stress in Family Caregivers of People with Alzheimers: Randomized clinical trial

This study sought to determine the effect of participation in support groups on the depression, anxiety and stress level of caregivers of patients with Alzheimer. This study was a single blind randomized clinical controlled trial (RCT) with 80 family caregivers of people with Alzheimer's (per group=40) The intervention group participated in eight sessions 1.5 2 hours in support groups. The tool used in this study was the DASS 21 questionnaire for measuring depression, anxiety and stress level of the caregivers, analysis of parametric data, using SPSS version 21. Findings showed, participation in support groups showed no significant difference on depression, anxiety and stress in family caregivers of Alzheimer patients in the control group and the intervention group. Given that caring for these patients by their family members are very sensitive and costly issues for policy makers and health service providers, community and families of these patients
Normal Pressure Hydrocephalus, Sleep Apnea Syndrome, and Quality of Life in an Octagenarian
Venue: Journal of the American Geriatrics Society

heimers and dementia the conversation with children/Accessed 2015. 7. Huvent Grelle D, Pagniez C, Puisieux F. La maladie d'Alzheimer expliqu ee aux petits: La souffrance psychologique des petits enfants confront es a la maladie d'Alzheimer d'un grand parent. How to explain Alzheimer's disease to children: Psychological stress in grandchildren faced with Alzheimer's disease affecting a grandparent. La Revue Francophone de G eriatrie et de G erontologie 2011;XVIII:490 492.
The critical nexus: Linked practices in reading, thinking and writing to develop discourse competency

Informed by a conviction that writing is thinking, this paper presents a model for developing student writing within disciplinary curriculum by linking writing practice explicitly to reading non fiction and critical thinking as co constructive practices of disciplinary discourse. The challenge of developing students' written communication is here focalised through reading and enacted through formative exercises that emphasise writing as a discursive practice and iterative process. This pedagogy aims to reveal to students the conventions and codes of disciplinary discourse through shared reading and writing exercises that focus on reading comprehension. Rather than an empirical study, I offer a practitioner's perspective informed by several years of reflective practice and training in the approaches associated with Writing Across the Curriculum. The goal of describing this pedagogy is to demonstrate how writing practice and development can be integrated with existing curriculum without displacing subject content. The example here is drawn from a First Year subject in Screen Media Studies located within a School of Humanities Creative Arts.
Voxelnet An Agent Based System for Spatial Data Analytics

Voxelnet is a proposed voxel based spatialised framework built upon internet communication and associated geospatial data standards. Each Voxelnet block has an individual IP address and functions as a computational agent. The internet of things (IoT) is also based upon objects having individual IP addresses on the internet so they can communicate using an individual unique identifier. IP addresses can roughly reveal where things are via geo locations. The internet, though, is a document based repository with 3D plug ins that is not effective for sharing a unified world of interconnections from a volumetric point of view. The Voxelnet provides volumetric semantics on top of the current internet, within which measurements can be located, shared, interrelated and spatially analysed, supporting smart informed decisions in real and virtual worlds that account for spatial structure. It can also be used to interact with IoT systems using an integrated spatial representation.  Geospatial Data Systems; Block Models; Internet of Things; Data Sharing; Multi Agent Systems.
Las tecnologias de informacion como apoyo al proceso de ensenanza y aprendizaje. Una experiencia en pedagogia universitaria

En este articulo se da cuenta de una experiencia pedagogica realizada en la Facultad de Ingenieria de nuestra Universidad, que tuvo por objeto disenar, implementar, ejecutar y evaluar una experiencia piloto de innovacion curricular que permita mejorar la metodologia y evaluacion de asignaturas de ambito de la Ingenieria Portuaria tanto desde la perspectiva de los metodos utilizados en el Proceso de la Ensenanza Aprendizaje (E A) como tambien desde el punto de vista del conocimiento y competencias requeridas por los estudiantes en su formacion profesional, incorporando tecnologias de informacion consideradas estas como herramientas que facilitan el proceso de aprendizaje y la autonomia del estudiante. Aqui se presentan los resultados que se obtuvieron en esta experiencia, trabajo que se comprende en el contexto de una necesaria innovacion educacional a nivel de la educacion superior. Es claro que en distintas instancias academicas se reflexiona en torno al modelo tradicional de educacion; se piensa, por ejemplo, que algunos elementos del Proceso de E A deben ser modificados, que el conocimiento no debe ser el unico objetivo del aprendizaje, que el proceso educativo no debe estar centrado exclusivamente en la exposicion del profesor, sino que, por el contrario, en la interaccion dialogica entre docente y estudiantes; ademas, se piensa que el proceso debe orientarse mas bien al desarrollo programado de habilidades en los alumnos. Los resultados muestran que es posible concretar una experiencia de cambio metodologico y de evaluacion para la Facultad de Ingenieria. Palabras claves tecnologias de la informacion, innovacion, docencia universitaria, aprendizaje, uso de pagina web
Video Segmentation via Object Flow
Venue: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Video object segmentation is challenging due to fast moving objects, deforming shapes, and cluttered backgrounds. Optical flow can be used to propagate an object segmentation over time but, unfortunately, flow is often inaccurate, particularly around object boundaries. Such boundaries are precisely where we want our segmentation to be accurate. To obtain accurate segmentation across time, we propose an efficient algorithm that considers video segmentation and optical flow estimation simultaneously. For video segmentation, we formulate a principled, multiscale, spatio temporal objective function that uses optical flow to propagate information between frames. For optical flow estimation, particularly at object boundaries, we compute the flow independently in the segmented regions and recompose the results. We call the process object flow and demonstrate the effectiveness of jointly optimizing optical flow and video segmentation using an iterative scheme. Experiments on the SegTrack v2 and Youtube Objects datasets show that the proposed algorithm performs favorably against the other state of the art methods.
Bilateral Space Video Segmentation
Venue: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

In this work, we propose a novel approach to video segmentation that operates in bilateral space. We design a new energy on the vertices of a regularly sampled spatiotemporal bilateral grid, which can be solved efficiently using a standard graph cut label assignment. Using a bilateral formulation, the energy that we minimize implicitly approximates long range, spatio temporal connections between pixels while still containing only a small number of variables and only local graph edges. We compare to a number of recent methods, and show that our approach achieves state of the art results on multiple benchmarks in a fraction of the runtime. Furthermore, our method scales linearly with image size, allowing for interactive feedback on real world high resolution video.
Vehicle detection method based on mean shift clustering
Venue: J. Intell. Fuzzy Syst.

To overcome the missing detection of traditional external feature based stereo vision vehicle detection method while there were multiple vehicles on the road, this paper proposed a road vehicle detection method based on mean shift clustering and semi global disparity map. Firstly, the semi global optimization function was established to find the WTA solution based on the principle of optimal in scan lines, and the sub pixel accurate semi global disparity map was obtained by quadratic curve fitting. Then, the three dimensional environment and the point cloud were reconstructed from the stereo camera calibration data and the disparity map. After that, a road plane extraction method was proposed based on the lane detection, and the ground point was removed. At last, based on the road vehicle feature analysis, the kernel function of Mean Shift clustering algorithm was optimized to realize the multiple vehicle positioning and detection. The real vehicle road experiment results indicated that this method has a good robustness to the road environment. Also it can effectively distinct multiple vehicle even if there is occlusion.
Water Level Declines in the High Plains Aquifer: Predevelopment to Resource Senescence.
Venue: Ground water

A large imbalance between recharge and water withdrawal has caused vital regions of the High Plains Aquifer (HPA) to experience significant declines in storage. A new predevelopment map coupled with a synthesis of annual water levels demonstrates that aquifer storage has declined by approximately 410 km(3) since the 1930s, a 15% larger decline than previous estimates. If current rates of decline continue, much of the Southern High Plains and parts of the Central High Plains will have insufficient water for irrigation within the next 20 to 30 years, whereas most of the Northern High Plains will experience little change in storage. In the western parts of the Central and northern part of the Southern High Plains, saturated thickness has locally declined by more than 50% and is currently declining at rates of 10% to 20% of initial thickness per decade. The most agriculturally productive portions of the High Plains will not support irrigated production within a matter of decades without significant changes in management.
Prediction of Water Level in the Urmia Lake Using the Extreme Learning Machine Approach
Venue: Water Resources Management

Predicting the dynamics of water level in lakes plays a vital role in navigation, water resources planning and catchment management. In this paper, the Extreme Learning Machine (ELM) approach was used to predict the daily water level in the Urmia Lake. Daily water level data from the Urmia Lake in northwest of Iran were used to train, test and validate the employed models. Results showed that the ELM approach can accurately forecast the water level in the Urmia Lake. Outcomes from the ELM model were also compared with those of genetic programming (GP) and artificial neural networks (ANNs) It was found that the ELM technique outperforms GP and ANN in predicting water level in the Urmia Lake. It also can learn the relation between the water level and its influential variables much faster than the GP and ANN. Overall, the results show that the ELM approach can be used to predict dynamics of water level in lakes.
A Probabilistic Nonlinear Model for Forecasting Daily Water Level in Reservoir
Venue: Water Resources Management

Accurate prediction and monitoring of water level in reservoirs is an important task for the planning, designing, and construction of river shore structures, and in taking decisions regarding irrigation management and domestic water supply. In this work, a novel probabilistic nonlinear approach based on a hybrid Bayesian network model with exponential residual correction has been proposed for prediction of reservoir water level on daily basis. The proposed approach has been implemented for forecasting daily water levels of Mayurakshi reservoir (Jharkhand, India) using a historic data set of 22 years. A comparative study has also been carried out with linear model (ARIMA) and nonlinear approaches (ANN, standard Bayesian network (BN) in terms of various performance measures. The proposed approach is comparable with the observed values on every aspect of prediction, and can be applied in case of scarce data, particularly when forcing parameters such as precipitation and other meteorological data are not available.
A Novel Method to Water Level Prediction using RBF and FFA
Venue: Water Resources Management

Water level prediction of rivers, especially in flood prone countries, can be helpful to reduce losses from flooding. A precise prediction method can issue a forewarning of the impending flood, to implement early evacuation measures, for residents near the river, when is required. To this end, we design a new method to predict water level of river. This approach relies on a novel method for prediction of water level named as RBF FFA that is designed by utilizing firefly algorithm (FFA) to train the radial basis function (RBF) and (FFA) is used to interpolation RBF to predict the best solution. The predictions accuracy of the proposed RBF FFA model is validated compared to those of support vector machine (SVM) and multilayer perceptron (MLP) models. In order to assess the models' performance, we measured the coefficient of determination (R2) correlation coefficient (r) root mean square error (RMSE) and mean absolute percentage error (MAPE) The achieved results show that the developed RBF FFA model provides more precise predictions compared to different ANNs, namely support vector machine (SVM) and multilayer perceptron (MLP) The performance of the proposed model is analyzed through simulated and real time water stage measurements. The results specify that the developed RBF FFA model can be used as an efficient technique for accurate prediction of water stage of river.