nils-herrmann/clean_scirepeval huggingface dataset:
Emotions in human-computer interaction: the role of nonverbal behaviour in interactive systems

Until recently, the area of human-computer interaction was based on a traditional, cognitive approach, which separated the study of usability from that of emotions. The recent research has shown that emotions play an important role in our life, which led to focusing on the need of studying the emotions in the domain of interactive design. This paper underlines the role of emotions as part of the interactive human-computer process, reinstating the importance of nonverbal communication in this domain. The main issues of this paper are concerned with aspects such as: emotional design approach, the importance of nonverbal as an instrument of usability evaluation and the role of emotions in human-computer interaction.
User involvement in the systems design process-a practical guide for users

Increasingly users ® nd themselves `involved’ in IT design projects. This occurs because the organizational culture of the parent organization purports to promote participation, or because structured design methods are being used which require users to play a part. In either case users who ® nd themselves required to participate in IT projects are frequently unclear about what this requires. In most organizations surprisingly little brie® ng on the users’ role in design projects is provided. Users are therefore confused about their brief and concerned about their lack of expertise in computing. Although research reports on participatory design (PD) projects abound, little coherent guidance for the key stakeholders representing users’ interests is available. The contents of this paper go some way towards ® lling the gap. Clear differentiation is made in the paper between the roles of the different players involved. Detailed guidance is provided for meeting the varied requirements of the different roles. For example, the roles of `top’ management and `middle’ management in supporting user involvement are explored, their special responsibilities speci ® ed and required actions listed. The need for an infrastructure to support user involvement and how to create one is discussed. Guidance is provided on, for example, the representation process and the factors to consider in selecting user representatives. The role of user representatives is particularly problematical and therefore receives particularly close attention. Finally guidance is given regarding the common pitfalls in Quality Assurance procedures and especially how to avoid the procedures becoming a meaningless `rubber-stamping’ exercise. The guidance presented is grounded in the extensive experience of the author in participative design processes in a wide variety of contexts including the footwear industry, a major UK government department and a telecommunications and broadcasting company.
A Markov Model for Headway/Spacing Distribution of Road Traffic

In this paper, we link two research directions of road traffic-the mesoscopic headway distribution model and the microscopic vehicle interaction model-together to account for the empirical headway/spacing distributions. A unified car-following model is proposed to simulate different driving scenarios, including traffic on highways and at intersections. Unlike our previous approaches, the parameters of this model are directly estimated from the Next Generation Simulation (NGSIM) Trajectory Data. In this model, empirical headway/spacing distributions are viewed as the outcomes of stochastic car-following behaviors and the reflections of the unconscious and inaccurate perceptions of space and/or time intervals that people may have. This explanation can be viewed as a natural extension of the well-known psychological car-following model (the action point model). Furthermore, the fast simulation speed of this model will benefit transportation planning and surrogate testing of traffic signals.
Lower-extremity resistance training on unstable surfaces improves proxies of muscle strength, power and balance in healthy older adults: a randomised control trial

It is well documented that both balance and resistance training have the potential to mitigate intrinsic fall risk factors in older adults. However, knowledge about the effects of simultaneously executed balance and resistance training (i.e., resistance training conducted on unstable surfaces [URT]) on lower-extremity muscle strength, power and balance in older adults is insufficient. The objective of the present study was to compare the effects of machine-based stable resistance training (M-SRT) and two types of URT, i.e., machine-based (M-URT) and free-weight URT (F-URT), on measures of lower-extremity muscle strength, power and balance in older adults.



Seventy-five healthy community-dwelling older adults aged 65-80 years, were assigned to three intervention groups: M-SRT, M-URT and F-URT. Over a period of ten weeks, all participants exercised two times per week with each session lasting ~60 min. Tests included assessment of leg muscle strength (e.g., maximal isometric leg extension strength), power (e.g., chair rise test) and balance (e.g., functional reach test), carried out before and after the training period. Furthermore, maximal training load of the squat-movement was assessed during the last training week.



Maximal training load of the squat-movement was significantly lower in F-URT in comparison to M-SRT and M-URT. However, lower-extremity resistance training conducted on even and uneven surfaces meaningfully improved proxies of strength, power and balance in all groups. M-URT produced the greatest improvements in leg extension strength and F-URT in the chair rise test and functional reach test.



Aside from two interaction effects, overall improvements in measures of lower-extremity muscle strength, power and balance were similar across training groups. Importantly, F-URT produced similar results with considerably lower training load as compared to M-SRT and M-URT. Concluding, F-URT seems an effective and safe alternative training program to mitigate intrinsic fall risk factors in older adults.
An efficient VLSI architecture for a serial divider

Processors play a very crucial role in every digital systems. The major blocks include the arithmetic logic units where arithmetic divider modules are frequently required for digital signal and image processing applications. SOC designers have to focus on the design of these dividers to increase the performance, accuracy and functionality of the processors. The division operation completes after several clock cycles which is more as compared to other modules in the arithmetic logic unit. This paper describes the architecture of an arithmetic serial divider. The divider circuit is designed using an adder/subtractor cell and non-restoring algorithm is used to the evaluate the result. CADENCE 90nm technology library is used for designing the architecture of the divider module.
Graph-Level Operations: A High-Level Interface for Graph Visualization Technique Specification

Social networks collected by historians or sociologists typically have a large number of actors and edge attributes. Applying social network analysis (SNA) algorithms to these networks produces additional attributes such as degree, centrality, and clustering coefficients. Understanding the effects of this plethora of attributes is one of the main challenges of multivariate SNA. We present the design of GraphDice, a multivariate network visualization system for exploring the attribute space of edges and actors. GraphDice builds upon the ScatterDice system for its main multidimensional navigation paradigm, and extends it with novel mechanisms to support network exploration in general and SNA tasks in particular. Novel mechanisms include visualization of attributes of interval type and projection of numerical edge attributes to node attributes. We show how these extensions to the original ScatterDice system allow to support complex visual analysis tasks on networks with hundreds of actors and up to 30 attributes, while providing a simple and consistent interface for interacting with network data.
Planning With Agents : An Efficient Approach Using Hierarchical Dynamic Decision Networks

To be useful in solving real world problems, agents need to be able to act in environments in which it may not be possible to be completely aware of the current state and where actions do not always work as planned. Additional complexity is added to the problem when one considers groups of agents working together. By casting the agent planning problem as a partially observable Markov decision problem (POMDP), optimal policies can be generated for partially observable and stochastic environments. Exact solutions, however, are notoriously difficult to find for problems of a realistic nature. We introduce a hierarchical decision network-based planning algorithm that can generate high quality plans during execution while demonstrating significant time savings. We also discuss how this approach is particularly applicable to planning in a multiagent environment as compared to other POMDP-based planning algorithms. We present experimental results comparing our algorithm with results obtained by current POMDP and hierarchical POMDP (HPOMDP) methods.
Image quality assessment: A sparse learning way

Full reference image quality assessment is very important for many image processing applications. The challenge of image quality assessment lies in two aspects: 1) formulating perceptual meaningful features and 2) finding a way to pool them into a single quality score. A novel two-step approach is proposed to address these problems. In the first step, sparse representations of local image patches are computed to simulate the low level characteristic of the human vision system (HVS) and represent the meaningful image structures. The differences between the representations of distorted and undistorted patches are utilized to measure the local distortion. In the second step, these local distortion measurements are fused into a single image quality score by using kernel ridge regression (KRR). Kernel ridge regression can mimic the complex high level behaviors of human vision system and is shown to be an effective way to learn the relationship between local quality measurements and quality score. The contributions of this paper would be summarized as follows: 1) Extracting approximate perceptual meaningful features in image quality assessment is transformed as a sparse representation problem. In this case, the sparse representation coefficients can reflect the salient local structures and give local quality assessments. 2) The KRR is utilized to pool the local quality assessments into a single image quality score. Thus, the nonlinear relationship between the objective model outputs and the subjective quality ratings can be learned by exploiting the KRR. 3) Extensive experiments are conducted on six public databases. Compared with other approaches, the proposed approach has achieved the best performance, which demonstrates the effectiveness and robustness of the proposed approach. ∗Corresponding author. Email address: luxq666666@gmail.com (Xiaoqiang Lu)  to neurocomputing February 14, 2015
Suicide rates among physicians: a quantitative and gender assessment (meta-analysis).

Physicians' suicide rates have repeatedly been reported to be higher than those of the general population or other academics, but uncertainty remains. In this study, physicians' suicide rate ratios were estimated with a meta-analysis and systematic quality assessment of recent studies.



Studies of physicians' suicide rates were located in MEDLINE, PsycINFO, AARP Ageline, and the EBM Reviews: Cochrane Database of Systematic Reviews with the terms "physicians," "doctors," "suicide," and "mortality." Studies were included if they were published in or after 1960 and gave estimates of age-standardized suicide rates of physicians and their reference population or reported extractable data on physicians' suicide; 25 studies met the criteria. Reviewers extracted data and scored each study for quality. The studies were tested for heterogeneity and publication bias and were stratified by publication year, follow-up, and study quality. Effect sizes were pooled by using fixed-effects (women) and random-effects (men) models.



The aggregate suicide rate ratio for male physicians, compared to the general population, was 1.41, with a 95% confidence interval (CI) of 1.21-1.65. For female physicians the ratio was 2.27 (95% CI=1.90-2.73). Visual inspection of funnel plots from tests of publication bias revealed randomness for men but some indication of bias for women, with a relative, nonsignificant lack of studies in the lower right quadrant.



Studies on physicians' suicide collectively show modestly (men) to highly (women) elevated suicide rate ratios. Larger studies should help clarify whether female physicians' suicide rate is truly elevated or can be explained by publication bias.
Challenges, Methodologies, and Issues in the Usability Testing of Mobile Applications

Usability testing of software applications developed for mobile devices is an emerging research area that faces a variety of challenges due to unique features of mobile devices, limited bandwidth, unreliability of wireless networks, as well as the changing context (environmental factors). Traditional guidelines and methods used in usability testing of desktop applications may not be directly applicable to a mobile environment. Therefore, it is essential to develop and adopt appropriate research methodologies that can evaluate the usability of mobile applications. The contribution of this paper is to propose a generic framework for conducting usability tests for mobile applications through discussing research questions, methodologies, and usability attributes. The paper provides an overview of existing usability studies and discusses major research questions that have been investigated. Then, it proposes a generic framework and provides detailed guidelines on how to conduct such usability studies.
A robust and fast skew detection algorithm for generic documents

A robust and fast skew detection algorithm based on hierarchical Hough transform is proposed. It is capable of detecting the skew angle for various document images, including technical articles, postal labels, handwritten text, forms, drawings and bar codes. The algorithm is robust even when black margins introduced by photocopying are present in the image and when the document is scanned at a low resolution of 50 dpi. The algorithm consists of two steps. In the first step we quickly extract the centroids of connected components using a graph data structure. Then, a hierarchical Hough transform (at two different angular resolutions) is applied to the selected centroids. The skew angle corresponds to the location of the highest peak in the Hough space. The performance of the algorithm is shown on a number of document images collected from various application domains. The algorithm is not very sensitive to algorithmic parameters. For an A4 size document image scanned at 50 dpi (typically 413 x 575 pixels), our algorithm is able to detect the skew angle with an accuracy of 0.1 ° in 0.4s of CPU time on a SunSparc 20 workstation.
Fingerprint ridge orientation estimation based on neural network

Ridge orientation is one of the fundamental features of a fingerprint image. Orientation estimation serves for feature extraction and matching and is the base of fingerprint recognition. Most existing orientation estimation methods are based on the characteristic of pixel intensity in a block. This paper uses neural network to learn the ridge orientation. The trained network has the property of responding to true ridge orientation with a large value and responding to the false ridge orientation with a small value. When estimating fingerprint ridge orientation, the responded values to each orientation at each image block are used to compute the fingerprint orientation field. The proposed method turns out more robust than the existing method.  Fingerprint recognition, Ridge orientation, Neural network, Filtering, Orientation setection
Detection of Unknown Anomalies in Streaming Videos with Generative Energy-based Boltzmann Models

Abnormal event detection is one of the important objectives in research and practical applications of video surveillance. However, there are still three challenging problems for most anomaly detection systems in practical setting: limited labeled data, ambiguous definition of “abnormal” and expensive feature engineering steps. This paper introduces a unified detection framework to handle these challenges using energy-based models, which are powerful tools for unsupervised representation learning. Our proposed models are firstly trained on unlabeled raw pixels of image frames from an input video rather than hand-crafted visual features; and then identify the locations of abnormal objects based on the errors between the input video and its reconstruction produced by the models. To handle video stream, we develop an online version of our framework, wherein the model parameters are updated incrementally with the image frames arriving on the fly. Our experiments show that our detectors, using Restricted Boltzmann Machines (RBMs) and Deep Boltzmann Machines (DBMs) as core modules, achieve superior anomaly detection performance to unsupervised baselines and obtain accuracy comparable with the state-of-the-art approaches when evaluating at the pixel-level. More importantly, we discover that our system trained with DBMs is able to simultaneously perform scene clustering and scene reconstruction. This capacity not only distinguishes our method from other existing detectors but also offers a unique tool to investigate and understand how the model works.
A UML Based Approach for Modeling ETL Processes in Data Warehouses

Data warehouses (DWs) are complex computer systems whose main goal is to facilitate the decision making process of knowledge workers. ETL (Extraction-Transformation-Loading) processes are responsible for the extraction of data from heterogeneous operational data sources, their transformation (conversion, cleaning, normalization, etc.) and their loading into DWs. ETL processes are a key component of DWs because incorrect or misleading data will produce wrong business decisions, and therefore, a correct design of these processes at early stages of a DW project is absolutely necessary to improve data quality. However, not much research has dealt with the modeling of ETL processes. In this paper, we present our approach, based on the Unified Modeling Language (UML), which allows us to accomplish the conceptual modeling of these ETL processes together with the conceptual schema of the target DW in an integrated manner. We provide the necessary mechanisms for an easy and quick specification of the common operations defined in these ETL processes such as, the integration of different data sources, the transformation between source and target attributes, the generation of surrogate keys and so on. Moreover, our approach allows the designer a comprehensive tracking and documentation of entire ETL processes, which enormously facilitates the maintenance of these processes. Another advantage of our proposal is the use of the UML (standardization, ease-of-use and functionality) and the seamless integration of the design of the ETL processes with the DW conceptual schema. Finally, we show how to use our integrated approach by using a well-known modeling tool such as Rational Rose.
Agent based urban growth modeling framework on Apache Spark

The simulation of urban growth is an important part of urban planning and development. Due to large data and computational challenges, urban growth simulation models demand efficient data analytic frameworks for scaling them to large geographic regions. Agent-based models are widely used to observe and analyze the urban growth simulation at various scales. The incorporation of the agent-based model makes the scaling task even harder due to communication and coordination among agents. Many existing agent-based model frameworks were implemented using traditional shared and distributed memory programming models. On the other hand, Apache Spark is becoming a popular platform for distributed big data in-memory analytics. This paper presents an implementation of agent-based sub-model in Apache Spark framework. With the in-memory computation, Spark implementation outperforms the traditional distributed memory implementation using MPI. This paper provides (i) an overview of our framework capable of running urban growth simulations at a fine resolution of 30 meter grid cells, (ii) a scalable approach using Apache Spark to implement an agent-based model for simulating human decisions, and (iii) the comparative analysis of performance of Apache Spark and MPI based implementations.
Estimation of ECG features using LabVIEW

Various methods have been proposed and used for ECG feature extraction with considerable percentage of correct detection. Nevertheless, the problem remains open especially with respect to higher detection accuracy in noisy ECGs. In this work we have developed an algorithm based on wavelet transform approach using LabVIEW 8.6. LabVIEW (Laboratory Virtual Instrument Engineering Workbench) is a graphical programming language that uses icons instead of lines of text to create programs. Unlike text based programming language, LabVIEW uses the data flow programming, where the flow of data determines execution. The flexibility, modular nature and ease to use programming possible with labview, makes it less complex. The proposed algorithm is executed in two stages. Firstly, it preprocesses (denoises) the signal to remove the noise from the ECG signal. Then it detects P-wave, T-wave, QRS complex and their onsets and offsets. LabVIEW and the related toolkits, advanced signal processing toolkit and math script are used to build the graphical programme for both the stages. The algorithm is evaluated with ECG data files, with different ECG morphologies and noise content taken from CSE multilead data base which contains 5000 samples per ECG, recorded at a sampling frequency of 500Hz.
Resource allocation neural network in portfolio selection

Portfolio selection is a resource allocation problem in a finance market. The investor’s asset optimization requires the distribution of a set of capital (resources) among a set of entities (assets) with the trade-off between risk and return. The ANN with nonlinear capability is proven to solve a large-scale complex problem effectively. It is suitable to solve NP-hard resource allocation problem. However, the traditional ANN model cannot guarantee the summation of produced investment weight always preserves 100% in output layer. This article introduces a resource allocation neural network model to optimize investment weight of portfolio. This model will dynamically adjust the investment weight as a basis of 100% of summing all of asset weights in the portfolio. The experimental results demonstrate the feasibility of optimal investment weights and superiority of ROI of buy-and-hold trading strategy compared with benchmark Taiwan Stock Exchange (TSE).
Arrested development? Reconsidering dual-systems models of brain function in adolescence and disorders

The dual-systems model of a ventral affective system, whose reactivity confers risks and liabilities, and a prefrontal control system, whose regulatory capacities buffer against these vulnerabilities, is an intuitive account that pervades many fields in the cognitive neurosciences--especially in the study of populations that differ from neurotypical adults, such as adolescents or individuals with affective or impulse regulation disorders. However, recent evidence that is inconsistent with dual-systems models illustrates the complexity of developmental and clinical variations in brain function. Building new models to account for this complexity is critical to progress in these fields, and will be facilitated by research that emphasizes network-based approaches and maps relationships between structure and function, as well as brain and behavior, over time.