nils-herrmann/clean_scirepeval huggingface dataset:
The Effectiveness of Mobile-Health Technology-Based Health Behaviour Change or Disease Management Interventions for Health Care Consumers: A Systematic Review

Mobile technologies could be a powerful media for providing individual level support to health care consumers. We conducted a systematic review to assess the effectiveness of mobile technology interventions delivered to health care consumers.



We searched for all controlled trials of mobile technology-based health interventions delivered to health care consumers using MEDLINE, EMBASE, PsycINFO, Global Health, Web of Science, Cochrane Library, UK NHS HTA (Jan 1990-Sept 2010). Two authors extracted data on allocation concealment, allocation sequence, blinding, completeness of follow-up, and measures of effect. We calculated effect estimates and used random effects meta-analysis. We identified 75 trials. Fifty-nine trials investigated the use of mobile technologies to improve disease management and 26 trials investigated their use to change health behaviours. Nearly all trials were conducted in high-income countries. Four trials had a low risk of bias. Two trials of disease management had low risk of bias; in one, antiretroviral (ART) adherence, use of text messages reduced high viral load (>400 copies), with a relative risk (RR) of 0.85 (95% CI 0.72-0.99), but no statistically significant benefit on mortality (RR 0.79 [95% CI 0.47-1.32]). In a second, a PDA based intervention increased scores for perceived self care agency in lung transplant patients. Two trials of health behaviour management had low risk of bias. The pooled effect of text messaging smoking cessation support on biochemically verified smoking cessation was (RR 2.16 [95% CI 1.77-2.62]). Interventions for other conditions showed suggestive benefits in some cases, but the results were not consistent. No evidence of publication bias was demonstrated on visual or statistical examination of the funnel plots for either disease management or health behaviours. To address the limitation of the older search, we also reviewed more recent literature.



Text messaging interventions increased adherence to ART and smoking cessation and should be considered for inclusion in services. Although there is suggestive evidence of benefit in some other areas, high quality adequately powered trials of optimised interventions are required to evaluate effects on objective outcomes.
3D CAD solid model retrieval based on region segmentation

3D shape retrieval may find the existing models as reference for design reuse. 3D segmentation decomposes models into new elements with large granularity and salient shapes to replace the faces in a solid model. In this way, it may reduce the complexity of a CAD model and make a local salient shape more prominent. Therefore, a retrieval method for 3D CAD solid models based on region segmentation is proposed in this paper. To deal with the problems of poor efficiency and uncertain results, a three-step segmentation method for CAD solid models is introduced. First, face adjacency graph (FAG) descriptions for query models and data models are created from their B-rep models. Second, the FAGs are segmented into a set of convex, concave and planar regions, and the relations among the regions are represented with a region graph. Finally, the sub-graphs are combined recursively to form optimal region sub-graphs with respect to an objective function through an optimal procedure. To avoid using complex graph matching or sub-graph matching for model shape comparison, region property codes are introduced to represent face regions in a CAD model. The similarity between the two compared models is evaluated by comparing their region property codes. The experiments show that the proposed method supports 3D CAD solid model retrieval.
An Experimental Study on Pedestrian Classification

Detecting people in images is key for several important application domains in computer vision. This paper presents an in-depth experimental study on pedestrian classification; multiple feature-classifier combinations are examined with respect to their ROC performance and efficiency. We investigate global versus local and adaptive versus nonadaptive features, as exemplified by PCA coefficients, Haar wavelets, and local receptive fields (LRFs). In terms of classifiers, we consider the popular support vector machines (SVMs), feedforward neural networks, and k-nearest neighbor classifier. Experiments are performed on a large data set consisting of 4,000 pedestrian and more than 25,000 nonpedestrian (labeled) images captured in outdoor urban environments. Statistically meaningful results are obtained by analyzing performance variances caused by varying training and test sets. Furthermore, we investigate how classification performance and training sample size are correlated. Sample size is adjusted by increasing the number of manually labeled training data or by employing automatic bootstrapping or cascade techniques. Our experiments show that the novel combination of SVMs with LRF features performs best. A boosted cascade of Haar wavelets can, however, reach quite competitive results, at a fraction of computational cost. The data set used in this paper is made public, establishing a benchmark for this important problem
Trend Modeling for Traffic Time Series Analysis: An Integrated Study

This paper discusses the trend modeling for traffic time series. First, we recount two types of definitions for a long-term trend that appeared in previous studies and illustrate their intrinsic differences. We show that, by assuming an implicit temporal connection among the time series observed at different days/locations, the PCA trend brings several advantages to traffic time series analysis. We also describe and define the so-called short-term trend that cannot be characterized by existing definitions. Second, we sequentially review the role that trend modeling plays in four major problems in traffic time series analysis: abnormal data detection, data compression, missing data imputation, and traffic prediction. The relations between these problems are revealed, and the benefit of detrending is explained. For the first three problems, we summarize our findings in the last ten years and try to provide an integrated framework for future study. For traffic prediction problem, we present a new explanation on why prediction accuracy can be improved at data points representing the short-term trends if the traffic information from multiple sensors can be appropriately used. This finding indicates that the trend modeling is not only a technique to specify the temporal pattern but is also related to the spatial relation of traffic time series.
Automatic Road Sign Recognition Using Neural Networks

An automatic road sign recognition system first locates road signs within images captured by an imaging sensor on-board of a vehicle, and then identifies road signs assisting the driver of the vehicle to properly operate the vehicle. This paper presents an automatic road sign recognition system capable of analysing live images, detecting multiple road signs within images, and classifying the type of the detected road signs. The system consists of two modules: detection and classification. The detection module segments the input image in the hue-saturation-intensity colour space and locates road signs. The classification module determines the type of detected road signs using a series of one to one architectural Multi Layer Perceptron neural networks. The performances of the classifiers that are trained using Resillient Backpropagation and Scaled Conjugate Gradient algorithms are compared. The experimental results demonstrate that the system is capable of achieving an average recognition hit-rate of 96% using Scaled Conjugate Gradient trained classifiers.
Occluded Imaging with Time-of-Flight Sensors

We explore the question of whether phase-based time-of-flight (TOF) range cameras can be used for looking around corners and through scattering diffusers. By connecting TOF measurements with theory from array signal processing, we conclude that performance depends on two primary factors: camera modulation frequency and the width of the specular lobe (“shininess”) of the wall. For purely Lambertian walls, commodity TOF sensors achieve resolution on the order of meters between targets. For seemingly diffuse walls, such as posterboard, the resolution is drastically reduced, to the order of 10cm. In particular, we find that the relationship between reflectance and resolution is nonlinear—a slight amount of shininess can lead to a dramatic improvement in resolution. Since many realistic scenes exhibit a slight amount of shininess, we believe that off-the-shelf TOF cameras can look around corners.
Forgeries of Fingerprints in Forensic Science

The objective of this chapter is to provide an account of the considerations made in forensic science regarding issues associated with potential forgeries of fingerprints. We will start with a clarification of terms and define the production of forgeries and the fabrication of evidence based on fingerprints. A short historical account will be given to highlight that the raised issues coincide with the early days of fingerprinting. Various methods of production of forged fingers as published in the forensic literature will then be exposed, distinguishing the techniques requiring the cooperation of the donor and the techniques without the cooperation of the donor. Examples of the various types of forgeries with associated images will be shown. The ability of forensic experts to distinguish between genuine marks and fakes will then be discussed. Although manual inspection techniques, they may also provide a reference to biometrics practitioners in their development of computerised techniques.
Towards a Sociological Understanding of Social Media : Theorizing Twitter

This article presents the first steps towards a sociological understanding of emergent social media. This article uses Twitter, the most popular social media website, as its focus. Recently, the social media site has been prominently associated with social movements in Libya, Egypt, Tunisia, and Algeria. Rather than rush to breathlessly describe its novel role in shaping contemporary social movements, this article takes a step back and considers Twitter in historical and broad sociological terms. This article is not intended to provide empirical evidence or a fully formed theoretical understanding of Twitter, but rather to provide a selected literature review and a set of directions for sociologists. The article makes connections specifically to Erving Goffman’s interactionist work, not only to make the claim that some existing sociological theory can be used to think critically about Twitter, but also to provide some initial thoughts on how such theoretical innovations can be developed.
API code recommendation using statistical learning from fine-grained changes

Learning and remembering how to use APIs is difficult. While code-completion tools can recommend API methods, browsing a long list of API method names and their documentation is tedious. Moreover, users can easily be overwhelmed with too much information. We present a novel API recommendation approach that taps into the predictive power of repetitive code changes to provide relevant API recommendations for developers. Our approach and tool, APIREC, is based on statistical learning from fine-grained code changes and from the context in which those changes were made. Our empirical evaluation shows that APIREC correctly recommends an API call in the first position 59% of the time, and it recommends the correct API call in the top five positions 77% of the time. This is a significant improvement over the state-of-the-art approaches by 30-160% for top-1 accuracy, and 10-30% for top-5 accuracy, respectively. Our result shows that APIREC performs well even with a one-time, minimal training dataset of 50 publicly available projects.
Emotion malleability beliefs, emotion regulation, and psychopathology: Integrating affective and clinical science.

Beliefs that individuals hold about whether emotions are malleable or fixed, also referred to as emotion malleability beliefs, may play a crucial role in individuals' emotional experiences and their engagement in changing their emotions. The current review integrates affective science and clinical science perspectives to provide a comprehensive review of how emotion malleability beliefs relate to emotionality, emotion regulation, and specific clinical disorders and treatment. Specifically, we discuss how holding more malleable views of emotion could be associated with more active emotion regulation efforts, greater motivation to engage in active regulatory efforts, more effort expended regulating emotions, and lower levels of pathological distress. In addition, we explain how extending emotion malleability beliefs into the clinical domain can complement and extend current conceptualizations of major depressive disorder, social anxiety disorder, and generalized anxiety disorder. This may prove important given the increasingly central role emotion dysregulation has been given in conceptualization and intervention for these psychiatric conditions. Additionally, discussion focuses on how emotion beliefs could be more explicitly addressed in existing cognitive therapies. Promising future directions for research are identified throughout the review.
Smart attacks in smart grid communication networks

The operations of a smart grid heavily rely on the support of communication infrastructures for efficient electricity management and reliable power distribution. Due to the strong dependency, the robustness of a smart grid communication network against attack is of the utmost importance for the deployment of the smart grid. Notably, the large scale and autonomous features of a smart grid render its cyber security quite vulnerable to adversaries. In this article, we introduce several intelligent attacks and countermeasures in smart grid communication networks, which aim for maximal damage or benefits by taking advantage of the network structure as well as the protocol functionality. We adopt the percolation-based connectivity in statistic mechanics to quantitatively analyze the network robustness. If the attack and defense strategies are involved, the attack can be further smart and complicated. Consequently, a twoplayer zero-sum game is introduced between the adversary and the defender, and the outcome of the game equilibrium is used to evaluate the performance of defense mechanisms with different network configurations. This article therefore offers novel insights and comprehensive analysis on the cyber security of a smart grid.
Wavelength selection for low-saturation pulse oximetry

Conventional pulse oximeters are accurate at high oxygen saturation under a variety of physiological conditions but show worsening accuracy at lower saturation (below 70%). Numerical modeling suggests that sensors fabricated with 735 and 890 nm emitters should read more accurately at low saturation under a variety of conditions than sensors made with conventionally used 660 and 900 nm band emitters. Recent animal testing confirms this expectation. It is postulated that the most repeatable and stable accuracy of the pulse oximeter occurs when the fractional change in photon path lengths due to perturbations in the tissue (relative to the conditions present during system calibration) is equivalent at the two wavelengths. Additionally, the penetration depth (and/or breadth) of the probing light needs to be well matched at the two wavelengths in order to minimize the effects of tissue heterogeneity. At high saturation these conditions are optimally met with 660 and 900 nm band emitters, while at low saturation 735 and 890 nm provide better performance.
Treatment planning of the edentulous mandible

Edentulism can be a debilitating handicap. Zarb described endentulous individuals who could not function as 'denture cripples'. Most difficulty with complete denture prostheses arises from the inability to function with the mandibular prostheses. Factors that adversely affect successful use of a complete denture on the mandible include: 1) the mobility of the floor of the mouth, 2) thin mucosa lining the alveolar ridge, 3) reduced support area and 4) the motion of the mandible (Figs 1,2). These factors alone can explain the difficulty of wearing a denture on the mandibular arch compared to the maxillary arch. The maxilla exhibits much less mobility on the borders of the denture than the mandible, moreover having a stable palate with thick fibrous tissues available to support the prostheses and resist occlusal forces. These differences explain most of the reasons why patients experience difficulty with using a complete denture on the mandibular arch compared to the maxillary arch.
Co-Design of a CMOS Rectifier and Small Loop Antenna for Highly Sensitive RF Energy Harvesters

In this paper, a design method for the co-design and integration of a CMOS rectifier and small loop antenna is described. In order to improve the sensitivity, the antenna-rectifier interface is analyzed as it plays a crucial role in the co-design optimization. Subsequently, a 5-stage cross-connected differential rectifier with a 7-bit binary-weighted capacitor bank is designed and fabricated in standard 90 nm CMOS technology. The rectifier is brought at resonance with a high-Q loop antenna by means of a control loop that compensates for any variation at the antenna-rectifier interface and passively boosts the antenna voltage to enhance the sensitivity. A complementary MOS diode is proposed to improve the harvester's ability to store and hold energy over a long period of time during which there is insufficient power for rectification. The chip is ESD protected and integrated on a compact loop antenna. Measurements in an anechoic chamber at 868 MHz demonstrate a -27 dBm sensitivity for 1 V output across a capacitive load and 27 meter range for a 1.78 W RF source in an office corridor. The end-to-end power conversion efficiency equals 40% at -17 dBm.
A fuzzy neural network approach for quantitative evaluation of mura in TFT-LCD

Mura is a typical region defect of TFT-LCD, which appears as low contrast, non-uniform brightness regions, typically larger than a single pixel. It is caused by a variety of physical factors such as non-uniformly distributed liquid crystal material and foreign particles within the liquid crystal. As compared to point defect and line defect, mura is relatively difficult to be identified due to its low contrast and no particular pattern of shape. Though automatic inspection of mura was discussed in many literatures, there is no an inspection method could be used to practical application because the defect models proposed were not consistent with the real ones. Since mura is of strong complexity and vagueness, so it is difficult to establish the accurate mathematical model of mura. Therefore, a fuzzy neural network approach for quantitative evaluation of mura in TFT-LCD is proposed in this paper. Experimental results show that a fuzzy neural network is very useful in solving such complex recognition problems as mura evaluation
Container-Based Orchestration in Cloud: State of the Art and Challenges

How to effectively manage increasingly complex enterprise computing environments is one of the hardest challenges that most organizations have to face in the era of cloud computing, big data and IoT. Advanced automation and orchestration systems are the most valuable solutions helping IT staff to handle large-scale cloud data centers. Containers are the new revolution in the cloud computing world, they are more lightweight than VMs, and can radically decrease both the start up time of instances and the processing and storage overhead with respect to traditional VMs. The aim of this paper is to provide a comprehensive description of cloud orchestration approaches with containers, analyzing current research efforts, existing solutions and presenting issues and challenges facing this topic.
Self-supervised learning model for skin cancer diagnosis

Automated diagnosis of skin cancer is an active area of research with different classification methods proposed so far. However, classification models based on insufficient labeled training data can badly influence the diagnosis process if there is no self-advising and semi supervising capability in the model. This paper presents a semi supervised, self-advised learning model for automated recognition of melanoma using dermoscopic images. Deep belief architecture is constructed using labeled data together with unlabeled data, and fine tuning done by an exponential loss function in order to maximize separation of labeled data. In parallel a self-advised SVM algorithm is used to enhance classification results by counteracting the effect of misclassified data. To increase generalization capability and redundancy of the model, polynomial and radial basis function based SA-SVMs and Deep network are trained using training samples randomly chosen via a bootstrap technique. Then the results are aggregated using least square estimation weighting. The proposed model is tested on a collection of 100 dermoscopic images. The variation in classification error is analyzed with respect to the ratio of labeled and unlabeled data used in the training phase. The classification performance is compared with some popular classification methods and the proposed model using the deep neural processing outperforms most of the popular techniques including KNN, ANN, SVM and semi supervised algorithms like Expectation maximization and transductive SVM.
Load Control Using Sensitivity Identification by Means of Smart Transformer

The higher variability introduced by distributed generation leads to fast changes in the aggregate load composition, and thus in the power response during voltage variations. The smart transformer, a power electronics-based distribution transformer with advanced control functionalities, can exploit the load dependence on voltage for providing services to the distribution and transmission grids. In this paper, two possible applications are proposed: 1) the smart transformer overload control by means of voltage control action and 2) the soft load reduction method, that reduces load consumption avoiding the load disconnection. These services depend on the correct identification of load dependence on voltage, which the smart transformer evaluates in real time based on load measurements. The effect of the distributed generation on net load sensitivity has been derived and demonstrated with the control hardware in loop evaluation by means of a real time digital simulator.
A coherent projection approach for direct volume rendering

Direct volume rendering offers the opportunity to visualize all of a three-dimensional sample volume in one image. However, processing such images can be very expensive and good quality high-resolution images are far from interactive. Projection approaches to direct volume rendering process the volume region by region as opposed to ray-casting methods that process it ray by ray. Projection approaches have generated interest because they use coherence to provide greater speed than ray casting and generate the image in a layered, informative fashion. This paper discusses two topics: First, it introduces a projection approach for directly rendering rectilinear, parallel-projected sample volumes that takes advantage of coherence across cells and the identical shape of their projection. Second, it considers the repercussions of various methods of integration in depth and interpolation across the scan plane. Some of these methods take advantage of Gouraud-shading hardware, with advantages in speed but potential disadvantages in image quality.