nils-herrmann/clean_scirepeval huggingface dataset:
Mobile Apps for Weight Management: A Scoping Review

Obesity remains a major public health concern. Mobile apps for weight loss/management are found to be effective for improving health outcomes in adults and adolescents, and are pursued as a cost-effective and scalable intervention for combating overweight and obesity. In recent years, the commercial market for 'weight loss apps' has expanded at rapid pace, yet little is known regarding the evidence-based quality of these tools for weight control.



To characterize the inclusion of evidence-based strategies, health care expert involvement, and scientific evaluation of commercial mobile apps for weight loss/management.



An electronic search was conducted between July 2014 and July 2015 of the official app stores for four major mobile operating systems. Three raters independently identified apps with a stated goal of weight loss/management, as well as weight loss/management apps targeted to pediatric users. All discrepancies regarding selection were resolved through discussion with a fourth rater. Metadata from all included apps were abstracted into a standard assessment criteria form and the evidence-based strategies, health care expert involvement, and scientific evaluation of included apps was assessed. Evidence-based strategies included: self-monitoring, goal-setting, physical activity support, healthy eating support, weight and/or health assessment, personalized feedback, motivational strategies, and social support.



A total of 393 apps were included in this review. Self-monitoring was most common (139/393, 35.3%), followed by physical activity support (108/393, 27.5%), weight assessment (100/393, 25.4%), healthy eating support (91/393, 23.2%), goal-setting (84/393, 21.4%), motivational strategies (28/393, 7.1%), social support (21/393, 5.3%), and personalized feedback (7/393, 1.8%). Of apps, 0.8% (3/393) underwent scientific evaluation and 0.3% (1/393) reported health care expert involvement. No apps were comprehensive in the assessment criteria, with the majority of apps meeting less than two criteria.



Commercial mobile apps for weight loss/management lack important evidence-based features, do not involve health care experts in their development process, and have not undergone rigorous scientific testing. This calls into question the validity of apps' claims regarding their effectiveness and safety, at a time when the availability and growth in adoption of these tools is rapidly increasing. Collaborative efforts between developers, researchers, clinicians, and patients are needed to develop and test high-quality, evidence-based mobile apps for weight loss/management before they are widely disseminated in commercial markets.
Web Browser Fingerprinting Using Only Cascading Style Sheets

Many commercial Websites employ Web browser fingerprinting to track visitors using Hypertext Transfer Protocol (HTTP) headers, JavaScript, and other methods. Although a user can disable JavaScript or utilize a prevention tool to avoid being tracked, countermeasures against Web browser fingerprinting using Cascading Style Sheets (CSS) have not been established. Therefore, in this paper, we propose a method of fingerprinting that employs only CSS and discuss the effectiveness of our method.
Integration of fuzzy AHP and interval type-2 fuzzy DEMATEL: An application to human resource management

The fuzzy analytic hierarchy process (fuzzy AHP) and fuzzy decision making trial and evaluation laboratory (fuzzy DEMATEL) have been used to obtain weights for criteria and relationships among dimensions and criteria respectively. The two methods could be integrated since it serves different purposes. Previous research suggested that the weights of criteria and the relationships among dimensions and criteria were obtained with the utilization of triangular type-1 fuzzy sets. This study proposes the integration of fuzzy AHP and interval type-2 fuzzy DEMATEL (IT2 fuzzy DEMATEL) where the interval type-2 trapezoidal fuzzy numbers are used predominantly. This new integration model includes linguistic variables in interval type-2 fuzzy sets (IT2 FS) and expected value for normalizing upper and lower memberships of IT2 FS. The integration was made when the weights obtained from fuzzy AHP were multiplied with expected values of IT2 fuzzy DEMATEL. The proposed integration method was tested to a case of human resources management (HRM). The results show that the criterion of education is more critical than the other criteria since it is a cause and directly influence HRM. The case study results verify the feasibility of the proposed method that suggested the criteria of education as the most influential criteria in managing human
Four-Arm Spiral Antenna Fed by Tapered Transmission Line

This letter presents a four-arm Archimedean spiral antenna with a novel integrated tapered transmission line feed structure. The center-fed spiral antenna consists of a radiation region with an Archimedean spiral configuration and a transmission region that comprises four spiral-shaped transmission lines with exponentially decreasing widths. The tapered transmission line, acting as an impedance transformer, provides a broadband matching network between the 50-Ω input impedance and the higher impedance of the radiation region. Using this design, a mode-1 spiral antenna attains a return loss of greater than 15 dB and a 3-dB broadside axial ratio throughout the 0.8–3-GHz band. The fabricated antenna demonstrates good agreement between the simulated and measured results.
Multi-level preference regression for cold-start recommendations

Due to the absence of historical ratings of new users/items, cold-start recommendation remains a challenge for collaborative filtering. Many matrix factorization based methods are used to predict new user’s/ item’s latent profile before predicting ratings. This kind of methods is usually non-convex. In this work, we design a new convex framework for cold-start recommendations, multi-level preference regression (MPR), directly to predict the ratings rather than latent profiles. We suppose that ratings are mainly affected by three components: 1) correlation between user’s attributes (such as age and gender) and item’s attributes (such as genre and producer); 2) each user’s preference on item’s attributes; 3) item’s popularity in a group of users with some attributes. Adjusting the impact of the three components, we can tackle three cold-start scenarios of user, item, and system. In the MPR framework, three different learning strategies are discussed: pointwise regression, pairwise regression, and large-margin learning. Experimental results on three datasets demonstrate that the proposed model can achieve the state of the art in the user cold-start scenario and the best performance in other scenarios.
Attachment Style as a Predictor of Adult Romantic Relationships

Questionnaire measures of attachment style, attachment history, beliefs about relationships, selfesteem, limerence, loving, love addiction, and love styles were administered to 374 undergraduates. Attachment style was related in theoretically expected ways to attachment history and to beliefs about relationships. Securely attached Ss reported relatively positive perceptions of their early family relationships. Avoidant Ss were most likely to report childhood separation from their mother and to express mistrust of others. Anxious-ambivalent subjects were less likely than avoidant Ss to see their father as supportive, and they reported a lack of independence and a desire for deep commitment in relationships. The self-esteem measure and each of the scales measuring forms of love were factor analyzed separately. Analyses based on scale scores derived from the resulting factors indicated that attachment style was also strongly related to self-esteem and to the various forms of love discussed in other theoretical frameworks. The results suggest that attachment theory offers a useful perspective on adult love relationships.
The mnist database of handwritten digits

please note that your browser may uncompress these files without telling you. If the files you downloaded have a larger size than the above, they have been uncompressed by your browser. Simply rename them to remove the .gz extension. Some people have asked me "my application can't open your image files". These files are not in any standard image format. You have to write your own (very simple) program to read them. The file format is described at the bottom of this page.
Effective and efficient correlation analysis with application to Market Basket Analysis and Network Community Detection

Finding the most interesting correlations among items is essential for problems in many commercial, medical, and scientific domains. For example, what kinds of items should be recommended with regard to what has been purchased by a customer? How to arrange the store shelf in order to increase sales? How to partition the whole social network into several communities for successful advertising campaigns? Which set of individuals on a social network should we target to convince in order to trigger a large cascade of further adoptions? When conducting correlation analysis, traditional methods have both effectiveness and efficiency problems, which will be addressed in this dissertation. Here, we explore the effectiveness problem in three ways. First, we expand the set of desirable properties and study the property satisfaction for different correlation measures. Second, we study different techniques to adjust original correlation measure, and propose two new correlation measures: the Simplified χ with Continuity Correction and the Simplified χ with Support. Third, we study the upper and lower bounds of different measures and categorize them by the bound differences. Combining with the above three directions, we provide guidelines for users to choose the proper measure according to their situations. With the proper correlation measure, we start to solve the efficiency problem for a large dataset. Here, we propose a fully-correlated itemset (FCI) framework to decouple the correlation measure from the need for efficient search. By wrapping the desired measure in our FCI framework, we take advantage of the desired measure’s superiority in evaluating itemsets, eliminate itemsets with irrelevant items, and achieve good computational performance. In addition, we identify a 1-dimensional monotone property of the upper bound of any good correlation measure, and different 2-dimensional
A Temporal Sequence Learning for Action Recognition and Prediction

In this work1, we present a method to represent a video with a sequence of words, and learn the temporal sequencing of such words as the key information for predicting and recognizing human actions. We leverage core concepts from the Natural Language Processing (NLP) literature used in sentence classification to solve the problems of action prediction and action recognition. Each frame is converted into a word that is represented as a vector using the Bag of Visual Words (BoW) encoding method. The words are then combined into a sentence to represent the video, as a sentence. The sequence of words in different actions are learned with a simple but effective Temporal Convolutional Neural Network (T-CNN) that captures the temporal sequencing of information in a video sentence. We demonstrate that a key characteristic of the proposed method is its low-latency, i.e. its ability to predict an action accurately with a partial sequence (sentence). Experiments on two datasets, UCF101 and HMDB51 show that the method on average reaches 95% of its accuracy within half the video frames. Results, also demonstrate that our method achieves compatible state-of-the-art performance in action recognition (i.e. at the completion of the sentence) in addition to action prediction.
Using Supervised Learning Methods for Gene Selection in RNA-Seq Case-Control Studies

Whole transcriptome studies typically yield large amounts of data, with expression values for all genes or transcripts of the genome. The search for genes of interest in a particular study setting can thus be a daunting task, usually relying on automated computational methods. Moreover, most biological questions imply that such a search should be performed in a multivariate setting, to take into account the inter-genes relationships. Differential expression analysis commonly yields large lists of genes deemed significant, even after adjustment for multiple testing, making the subsequent study possibilities extensive. Here, we explore the use of supervised learning methods to rank large ensembles of genes defined by their expression values measured with RNA-Seq in a typical 2 classes sample set. First, we use one of the variable importance measures generated by the random forests classification algorithm as a metric to rank genes. Second, we define the EPS (extreme pseudo-samples) pipeline, making use of VAEs (Variational Autoencoders) and regressors to extract a ranking of genes while leveraging the feature space of both virtual and comparable samples. We show that, on 12 cancer RNA-Seq data sets ranging from 323 to 1,210 samples, using either a random forests-based gene selection method or the EPS pipeline outperforms differential expression analysis for 9 and 8 out of the 12 datasets respectively, in terms of identifying subsets of genes associated with survival. These results demonstrate the potential of supervised learning-based gene selection methods in RNA-Seq studies and highlight the need to use such multivariate gene selection methods alongside the widely used differential expression analysis.
Deep convolutional neural network for decoding motor imagery based brain computer interface

Deep convolutional neural networks (CNNs) have made tremendous development in the field of image recognition and natural language processing. However, there is still a lack of knowledge of using CNN models to decode motor imagery based Brain Computer Interface (BCI). This paper presents a method applying CNN to analyze the EEG signals, which are produced by left and right hand motor imagery tasks. EEG signals are transferred into time-frequency images using Short-Time Fourier Transform (STFT) and then those images are fed as input of the network for classification. A comparison is made to verify the performance of three different activation functions during the network's learning procedure. They are rectified linear unit (ReLU), exponential linear unit (ELU) and the newly proposed scaled exponential linear unit (SELU). The results show that our method using CNN model can achieve better accuracy than the conventional method and SELU function shows superior ability for the network to convergence.
Complex suicide by self-stabbing with subsequent drowning in the sea.

The paper presents a unique case of a complex suicide committed by a young man, mostly probably triggered by a disappointment in love. The uniqueness of the suicide lies in the fact that the victim inflicted several deep stab wounds on himself, in the chest and abdomen, while standing partly submerged in the sea and, having done so, he dropped and disappeared in the water. The postmortem examination showed, apart from deep wounds in the trunk, characteristics of drowning that manifested itself in the form of aqueous emphysema of the lungs. Suicide was clearly determined on the basis of the circumstances preceding death, the location, and arrangement of the trunk wounds and the testimony given by a witness of the incident. The circumstances preceding the suicidal act clearly suggest an underlying undiagnosed mental disorder.
Automatic Solution of Jigsaw Puzzles

We present a method for automatically solving apictorial jigsaw puzzles that is based on an extension of the method of differential invariant signatures. Our algorithms are designed to solve challenging puzzles, without having to impose any restrictive assumptions on the shape of the puzzle, the shapes of the individual pieces, or their intrinsic arrangement. As a demonstration, the method was successfully used to solve two commercially available puzzles. Finally we perform some preliminary investigations into scalability of the algorithm for even larger puzzles.
Detection of Emotions during Learning with AutoTutor

The relationship between emotions and learning was investigated by tracking the affective states that college students experienced while interacting with AutoTutor, an intelligent tutoring system with conversational dialogue. An emotionally responsive tutor would presumably facilitate learning, but this would only occur if learner emotions can be accurately identified. After a learning session with AutoTutor, the affective states of the learner were classified by the learner, a peer, and judges trained on Ekman’s Facial Action Coding system. The classification of the trained judges was more reliable and matched the learners much better than the low scores of untrained peers. This result suggests that peer tutors may be limited in detecting the affective states of peer learners. Classification accuracy was poor at constant intervals of polling (every 20 seconds) but much higher when individuals declared that an affect state had been experienced.
Movie trailer quality evaluation using real-time human electroencephalogram

The total US box office revenue exceeds ten billion dollars a year. Inevitably, new product forecasting and diagnosis have high financially stakes. The motion picture industry has a huge incentive to perform early prediction of movie success or failure. In this paper, we present a study that evaluate viewer responses to short movie trailers using a cap instrumented with Electroencephalogram (EEG) sensors. The method can be used to evaluate prerelease movies regarding how engaging they are to viewers. The primary advantage of our approach is that responses of a viewer can be recorded without any manual inputs from the viewer. The data collected, if analyzed properly, can reveal more accurate information regarding the viewer's emotion states while watching the movie than post-viewing surveys. This approach also enables the delivery of personalized entertainment through the brain interface.
Consumer trust in an Internet store

Sirkka L. Jarvenpaa a, Noam Tractinsky b and Michael Vitale c a Department of Management Science and Information Systems, CBA 5.202, B6500, University of Texas at Austin, Austin, TX 78712-1175, USA E-mail: sjarvenpaa@mail.utexas.edu b Industrial Engineering and Management, Ben-Gurion University, Beer Sheva 84105,  noamt@bgumail.bgu.ac.il c Melbourne Business School, The University of Melbourne, Carlton 3053, Victoria, Australia E-mail: m.vitale@mbs.unimelb.edu.au
A Trilateral Weighted Sparse Coding Scheme for Real-World Image Denoising

Most of existing image denoising methods assume the corrupted noise to be additive white Gaussian noise (AWGN). However, the realistic noise in real-world noisy images is much more complex than AWGN, and is hard to be modeled by simple analytical distributions. As a result, many state-of-the-art denoising methods in literature become much less effective when applied to real-world noisy images captured by CCD or CMOS cameras. In this paper, we develop a trilateral weighted sparse coding (TWSC) scheme for robust real-world image denoising. Specifically, we introduce three weight matrices into the data and regularization terms of the sparse coding framework to characterize the statistics of realistic noise and image priors. TWSC can be reformulated as a linear equality-constrained problem and can be solved by the alternating direction method of multipliers. The existence and uniqueness of the solution and convergence of the proposed algorithm are analyzed. Extensive experiments demonstrate that the proposed TWSC scheme outperforms state-of-the-art denoising methods on removing realistic noise.
You are what you eat? Vegetarianism, health and identity.

This paper examines the views of 'health vegetarians' through a qualitative study of an online vegetarian message board. The researcher participated in discussions on the board, gathered responses to questions from 33 participants, and conducted follow-up e-mail interviews with 18 of these participants. Respondents were predominantly from the United States, Canada and the UK. Seventy per cent were female, and ages ranged from 14 to 53 years, with a median of 26 years. These data are interrogated within a theoretical framework that asks, 'what can a vegetarian body do?' and explores the physical, psychic, social and conceptual relations of participants. This provides insights into the identities of participants, and how diet and identity interact. It is concluded that vegetarianism is both a diet and a bodily practice with consequences for identity formation and stabilisation.
A simplified extension of the Area under the ROC to the multiclass domain

The Receiver Operator Characteristic (ROC) plot allows a classifier to be evaluated and optimised over all possible operating points. The Area Under the ROC (AUC) has become a standard performance evaluation criterion in two-class pattern recognition problems, used to compare different classification algorithms independently of operating points, priors, and costs. Extending the AUC to the multiclass case is considered in this paper, called the volume under the ROC hypersurface (VUS). A simplified VUS measure is derived that ignores specific intraclass dimensions, and regards inter-class performances only. It is shown that the VUS measure generalises from the 2-class case, but the bounds between random and perfect classification differ, with the lower bound tending towards zero as the dimensionality increases. A number of experiments with known distributions are used to verify the bounds, and to investigate a numerical integration approach to estimating the VUS. Experiments on real data compare several competing classifiers in terms of both error-rate and VUS. It was found that some classifiers compete in terms of error-rate, but have significantly different VUS scores, illustrating the importance of the VUS approach.
Energy Efficient Smartphone-Based Activity Recognition using Fixed-Point Arithmetic

In this paper we propose a novel energy efficient approach for the recognition of human activities using smartphones as wearable sensing devices, targeting assisted living applications such as remote patient activity monitoring for the disabled and the elderly. The method exploits fixed-point arithmetic to propose a modified multiclass Support Vector Machine (SVM) learning algorithm, allowing to better preserve the smartphone battery lifetime with respect to the conventional floating-point based formulation while maintaining comparable system accuracy levels. Experiments show comparative results between this approach and the traditional SVM in terms of recognition performance and battery consumption, highlighting the advantages of the proposed method.
RegNet: Multimodal sensor registration using deep neural networks

In this paper, we present RegNet, the first deep convolutional neural network (CNN) to infer a 6 degrees of freedom (DOF) extrinsic calibration between multimodal sensors, exemplified using a scanning LiDAR and a monocular camera. Compared to existing approaches, RegNet casts all three conventional calibration steps (feature extraction, feature matching and global regression) into a single real-time capable CNN. Our method does not require any human interaction and bridges the gap between classical offline and target-less online calibration approaches as it provides both a stable initial estimation as well as a continuous online correction of the extrinsic parameters. During training we randomly decalibrate our system in order to train RegNet to infer the correspondence between projected depth measurements and RGB image and finally regress the extrinsic calibration. Additionally, with an iterative execution of multiple CNNs, that are trained on different magnitudes of decalibration, our approach compares favorably to state-of-the-art methods in terms of a mean calibration error of 0.28° for the rotational and 6 cm for the translation components even for large decalibrations up to 1.5 m and 20°.