nils-herrmann/clean_scirepeval huggingface dataset:
Researching Mental Health Disorders in the Era of Social Media: Systematic Review

Mental illness is quickly becoming one of the most prevalent public health problems worldwide. Social network platforms, where users can express their emotions, feelings, and thoughts, are a valuable source of data for researching mental health, and techniques based on machine learning are increasingly used for this purpose.



The objective of this review was to explore the scope and limits of cutting-edge techniques that researchers are using for predictive analytics in mental health and to review associated issues, such as ethical concerns, in this area of research.



We performed a systematic literature review in March 2017, using keywords to search articles on data mining of social network data in the context of common mental health disorders, published between 2010 and March 8, 2017 in medical and computer science journals.



The initial search returned a total of 5386 articles. Following a careful analysis of the titles, abstracts, and main texts, we selected 48 articles for review. We coded the articles according to key characteristics, techniques used for data collection, data preprocessing, feature extraction, feature selection, model construction, and model verification. The most common analytical method was text analysis, with several studies using different flavors of image analysis and social interaction graph analysis.



Despite an increasing number of studies investigating mental health issues using social network data, some common problems persist. Assembling large, high-quality datasets of social media users with mental disorder is problematic, not only due to biases associated with the collection methods, but also with regard to managing consent and selecting appropriate analytics techniques.
Local random-phase noise for procedural texturing

Local random-phase noise is a noise model for procedural texturing. It is defined on a regular spatial grid by local noises, which are sums of cosines with random phase. Our model is versatile thanks to separate sampling in the spatial and spectral domains. Therefore, it encompasses Gabor noise and noise by Fourier series. A stratified spectral sampling allows for a faithful yet compact and efficient reproduction of an arbitrary power spectrum. Noise by example is therefore obtained faster than state-of-the-art techniques. As a second contribution we address texture by example and generate not only Gaussian patterns but also structured features present in the input. This is achieved by fixing the phase on some part of the spectrum. Generated textures are continuous and non-repetitive. Results show unprecedented framerates and a flexible visual result: users can control with one parameter the blending between noise by example and structured texture synthesis.
Measuring geographical regularities of crowd behaviors for Twitter-based geo-social event detection

Recently, microblogging sites such as Twitter have garnered a great deal of attention as an advanced form of location-aware social network services, whereby individuals can easily and instantly share their most recent updates from any place. In this study, we aim to develop a geo-social event detection system by monitoring crowd behaviors indirectly via Twitter. In particular, we attempt to find out the occurrence of local events such as local festivals; a considerable number of Twitter users probably write many posts about these events. To detect such unusual geo-social events, we depend on geographical regularities deduced from the usual behavior patterns of crowds with geo-tagged microblogs. By comparing these regularities with the estimated ones, we decide whether there are any unusual events happening in the monitored geographical area. Finally, we describe the experimental results to evaluate the proposed unusuality detection method on the basis of geographical regularities obtained from a large number of geo-tagged tweets around Japan via Twitter.
Implementation of SMS based Heartbeat monitoring system using PSoC Microcontroller

The rapidly growing VLSI technology could reduce the cost of the embedded systems considerably which has motivated a new era of low cost personal health care monitoring systems which meeting common man requirements. Such systems are dedicated to one person and provide the diagnostic information through remotely. This paper proposes a PSoC microcontroller and GSM modules we are eliminating the cables attached to patient. The patient has a freedom of doing daily activities and still be under continuous monitoring (very suitable for old age people). PSoC has inbuilt ADC and Programmable Gain amplifier which enabled single chip implementation. The hardware complexity is also simple and reduces cost. The basic principle of this system is to read the bio medical signals from the biomedical sensor modules and perform the tasks of data conversion, sending SMS using GSM, as well as providing the ability of simple preprocessing such as waveform averaging or rectification. GSM based modem interface through AT commands to PSoC. The Microcontroller will be able to receive and send text messages Heart beat is sensed by using a high intensity type LED and LDR. The finger is placed between the LED and LDR. The skin may be illuminated with visible (red) using transmitted or reflected light for detection. The concept of this paper is builds upon the integration of wireless communications into medical applications to revolutionize personal healthcare. Under this concept, patients are no longer bound to a specific healthcare location where they are monitored by medical instruments—wireless communications would not only provide them with safe and accurate monitoring but also the freedom of movement. Remote patient monitoring will not only redefine hospital care but also work, home, and recreational activities. Imagine the home of the future: elderly individuals no longer have to travel to the hospital, a mother stuck at work will be able to receive email updates of her sick child's temperature, a doctor on the go will have the ability to turn on his cell phone to check the status of his patients. The objective of this project is to build a wireless heart beat monitoring system using GSM Technology, which could potentially be an integral part of a suite of personal healthcare appliances for a large-scale remote patient monitoring system. In this paper a low cost Digital Heart rate meter using PSoC Microcontroller is designed, and also this unit is connecting to GSM Modem for send …
The Development and Validation of a Measure of Student Attitudes Toward Science , Technology , Engineering , and Math ( S-STEM )

Using an iterative design along with multiple methodological approaches and a large representative sample, this study presents reliability, validity, and fairness evidence for two surveys measuring student attitudes toward science, technology, engineering, and math (S-STEM) and interest in STEM careers for (a) 4ththrough 5th-grade students (Upper Elementary S-STEM) and (b) 6ththrough 12th-grade students (Middle/High S-STEM). Findings from exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) suggested the use of a four-factor structure to measure student attitudes toward science, math, engineering/technology, and 21st century skills. Subject matter experts and literature reviews provided evidence of content validity. Reliability levels were high for both versions. Furthermore, both the Upper Elementary S-STEM and Middle/High S-STEM Surveys demonstrated evidence of configural, metric, and scalar invariance across grade levels, races/ethnicities, and genders. The findings support the validity of interpretations and inferences made from scores on the instruments’ items and subscales.
The Spatial Orienting paradigm: How to design and interpret spatial attention experiments

This paper is conceived as a guide that will describe the very well known Spatial Orienting paradigm, used to explore attentional processes in healthy individuals as well as in people suffering from psychiatric disorders and brain-damaged patients. The paradigm was developed in the late 1970s, and since then, it has been used in thousands of attentional studies. In this review, we attempt to describe, the paradigm for the naïf reader, and explain in detail when is it used, which variables are usually manipulated, how to interpret its results, and how can it be adapted to different populations and methodologies. The main goal of this review is to provide a practical guide to researchers who have never used the paradigm that will help them design their experiments, as a function of their theoretical and experimental needs. We also focus on how to adapt the paradigm to different technologies (such as event-related potentials, functional resonance imaging, or transcranial magnetic stimulation), and to different populations by presenting an example of its use in brain-damaged patients.
Multi-Task Learning For Parsing The Alexa Meaning Representation Language

The Alexa Meaning Representation Language (AMRL) is a compositional graph-based semantic representation that includes fine-grained types, properties, actions, and roles and can represent a wide variety of spoken language. AMRL increases the ability of virtual assistants to represent more complex requests, including logical and conditional statements as well as ones with nested clauses. Due to this representational capacity, the acquisition of large scale data resources is challenging, which limits the accuracy of resulting models. This paper has two primary contributions. The first contribution is a linearization of the AMRL parses that aligns it to a related task of spoken language understanding (SLU) and a deep neural network architecture that uses multi-task learning to predict AMRL fine-grained types, properties and intents. The second contribution is a deep neural network architecture that leverages embeddings from the large-scale data resources that are available for SLU. When combined, these contributions enable the training of accurate models of AMRL parsing, even in the presence of data sparsity. The proposed models, which use the linearized AMRL parse, multi-task learning, residual connections and embeddings from SLU, decrease the error rates in the prediction of the full AMRL parse by 3.56%
Vaginal orgasm is associated with vaginal (not clitoral) sex education, focusing mental attention on vaginal sensations, intercourse duration, and a preference for a longer penis.

Evidence was recently provided for vaginal orgasm, orgasm triggered purely by penile-vaginal intercourse (PVI), being associated with better psychological functioning. Common sex education and sexual medicine approaches might undermine vaginal orgasm benefits.



To examine the extent to which women's vaginal orgasm consistency is associated with (i) being told in childhood or adolescence that the vagina was the important zone for inducing female orgasm; (ii) how well they focus mentally on vaginal sensations during PVI; (iii) greater PVI duration; and (iv) preference for above-average penis length.


In a representative sample of the Czech population, 1,000 women reported their vaginal orgasm consistency (from never to almost every time; only 21.9% never had a vaginal orgasm), estimates of their typical foreplay and PVI durations, what they were told in childhood and adolescence was the important zone for inducing female orgasm, their degree of focus on vaginal sensations during PVI, and whether they were more likely to orgasm with a longer than average penis.



The association of vaginal orgasm consistency with the predictors noted above.



Vaginal orgasm consistency was associated with all hypothesized correlates. Multivariate analysis indicated the most important predictors were being educated that the vagina is important for female orgasm, being mentally focused on vaginal sensations during PVI, and in some analyses duration of PVI (but not foreplay) and preferring a longer than average penis.



Focusing attention on penile-vaginal sensation supports vaginal orgasm and the myriad benefits thereof. Brody S, and Weiss P. Vaginal orgasm is associated with vaginal (not clitoral) sex education, focusing mental attention on vaginal sensations, intercourse duration, and a preference for a longer penis.
Review of job recommender system using big data analytics

The war on talent, supply demand issues, shifting growth paradigms across different sectors form part of the ecosystem in which we live and operate, which is constantly evolving, adaptating and increasing the use of advanced technologies to solve problems across the Talent supply chain domain. Innovations and value added extensions and services are required at a corporate and individual level. At present, we have number of online services which impact our lives in different ways. E-recruitment is one of the known and popular online service for the job seekers as well as for the employers. Past proposed job recommender systems worked with small and structured data sets. But today we have large amount of heterogeneous and unstructured data in comparison to structured data which is growing at tremendous speed. Past proposed techniques of job recommender system does not work adequately on Big Data. Hence, we need more advanced techniques for handling Big data. In this paper, we explored recent work on job recommender system and the research gap between current abilities and new requirements were identified. We proposes a new job recommendation framework which handle Big data effectively.
Review on Machine Learning Techniques for Automatic Segmentation of Liver Images

The segmentation of liver from abdominal images has gained huge importance in medical imaging field since it is fundamental step to diagnose liver tumor and other liver diseases, liver volume measurement and 3D liver volume rendering. In this paper, recent automatic methods for liver image segmentation proposed in literature are reviewed. Generally, Liver image segmentation techniques can be semi-automatic and fully automatic. Under fully automatic segmentation category, various methods, approaches, recent advances and related problems has been explained. Brief summary of all discussed methods has been provided. Finally it is concluded that liver image segmentation is still an open problem since various drawbacks of proposed methods can be addressed.  Liver image Segmentation, Automatic Segmentation, Neural Network, Support Vector Machine, Clustering, Hybrid,
Pre-Prosthetic Orthodontic Implant for Management of Congenitally Unerupted Lateral Incisors – A Case Report

The maxillary lateral incisor is one of the most common congenitally missing teeth of the permanent dentition. With the advent of implants in the field of restorative dentistry, a stable and predictable fixed prosthetic replacement has become a reality, especially for young adult patients who suffer from congenital absence of teeth. The dual goals of establishment of functional stability as well as enhancement of esthetic outcomes are made achievable by the placement of implants. A multidisciplinary team approach involving the triad of orthodontist, periodontist and restorative dentist will ensure the successful completion of the integrated treatment approach in these patients. The present case report achieved successful implant based oral rehabilitation in a patient diagnosed with congenital absence of bilateral maxillary lateral incisors utilizing a preprosthetic orthodontic implant site preparation for the purpose of space gain.
A fully balanced pseudo-differential OTA with common-mode feedforward and inherent common-mode feedback detector

A pseudo-differential fully balanced fully symmetric CMOS operational transconductance amplifier (OTA) architecture with inherent common-mode detection is proposed. Through judicious arrangement, the common-mode feedback circuit can be economically implemented. The OTA achieves a third harmonic distortion of 43 dB for 900 mVpp at 30 MHz. The OTA, fabricated in 0.5m CMOS process, is used to design a 100-MHz fourth-order linear phase filter. The measured filter’s group delay ripple is 3% for frequencies up to 100 MHz, and the measured dynamic range is 45 dB for a total harmonic distortion of 46 dB. The filter consumes 42.9 mW per complex pole pair while operating from a 1.65-V power supply.
Unconstrained inverse quadratic programming problem

The paper covers a formulation of the inverse quadratic programming problem in terms of unconstrained optimization where it is required to find the unknown parameters (the matrix of the quadratic form and the vector of the quasi-linear part of the quadratic form) provided that approximate estimates of the optimal solution of the direct problem and those of the target function to be minimized in the form of pairs of values lying in the corresponding neighborhoods are only known. The formulation of the inverse problem and its solution are based on the least squares method. In the explicit form the inverse problem solution has been derived in the form a system of linear equations. The parameters obtained can be used for reconstruction of the direct quadratic programming problem and determination of the optimal solution and the extreme value of the target function, which were not known formerly. It is possible this approach opens new ways in over applications, for example, in neurocomputing and quadric surfaces fitting. Simple numerical examples have been demonstrated. A scenario in the Octave/MATLAB programming language has been proposed for practical implementation of the method.
ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design

Currently, the neural network architecture design is mostly guided by the indirect metric of computation complexity, i.e., FLOPs. However, the direct metric, e.g., speed, also depends on the other factors such as memory access cost and platform characterics. Thus, this work proposes to evaluate the direct metric on the target platform, beyond only considering FLOPs. Based on a series of controlled experiments, this work derives several practical guidelines for efficient network design. Accordingly, a new architecture is presented, called ShuffleNet V2. Comprehensive ablation experiments verify that our model is the stateof-the-art in terms of speed and accuracy tradeoff.
Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position

A neural network model for a mechanism of visual pattern recognition is proposed in this paper. The network is self-organized by “learning without a teacher”, and acquires an ability to recognize stimulus patterns based on the geometrical similarity (Gestalt) of their shapes without affected by their positions. This network is given a nickname “neocognitron”. After completion of self-organization, the network has a structure similar to the hierarchy model of the visual nervous system proposed by Hubel and Wiesel. The network consits of an input layer (photoreceptor array) followed by a cascade connection of a number of modular structures, each of which is composed of two layers of cells connected in a cascade. The first layer of each module consists of “S-cells”, which show characteristics similar to simple cells or lower order hypercomplex cells, and the second layer consists of “C-cells” similar to complex cells or higher order hypercomplex cells. The afferent synapses to each S-cell have plasticity and are modifiable. The network has an ability of unsupervised learning: We do not need any “teacher” during the process of self-organization, and it is only needed to present a set of stimulus patterns repeatedly to the input layer of the network. The network has been simulated on a digital computer. After repetitive presentation of a set of stimulus patterns, each stimulus pattern has become to elicit an output only from one of the C-cell of the last layer, and conversely, this C-cell has become selectively responsive only to that stimulus pattern. That is, none of the C-cells of the last layer responds to more than one stimulus pattern. The response of the C-cells of the last layer is not affected by the pattern's position at all. Neither is it affected by a small change in shape nor in size of the stimulus pattern.
Molecular assembly on two-dimensional materials.

Molecular self-assembly is a well-known technique to create highly functional nanostructures on surfaces. Self-assembly on two-dimensional (2D) materials is a developing field driven by the interest in functionalization of 2D materials in order to tune their electronic properties. This has resulted in the discovery of several rich and interesting phenomena. Here, we review this progress with an emphasis on the electronic properties of the adsorbates and the substrate in well-defined systems, as unveiled by scanning tunneling microscopy. The review covers three aspects of the self-assembly. The first one focuses on non-covalent self-assembly dealing with site-selectivity due to inherent moiré pattern present on 2D materials grown on substrates. We also see that modification of intermolecular interactions and molecule-substrate interactions influences the assembly drastically and that 2D materials can also be used as a platform to carry out covalent and metal-coordinated assembly. The second part deals with the electronic properties of molecules adsorbed on 2D materials. By virtue of being inert and possessing low density of states near the Fermi level, 2D materials decouple molecules electronically from the underlying metal substrate and allow high-resolution spectroscopy and imaging of molecular orbitals. The moiré pattern on the 2D materials causes site-selective gating and charging of molecules in some cases. The last section covers the effects of self-assembled, acceptor and donor type, organic molecules on the electronic properties of graphene as revealed by spectroscopy and electrical transport measurements. Non-covalent functionalization of 2D materials has already been applied for their application as catalysts and sensors. With the current surge of activity on building van der Waals heterostructures from atomically thin crystals, molecular self-assembly has the potential to add an extra level of flexibility and functionality for applications ranging from flexible electronics and OLEDs to novel electronic devices and spintronics.
A Survey of Artificial Intelligence for Cognitive Radios

Cognitive radio (CR) is an enabling technology for numerous new capabilities such as dynamic spectrum access, spectrum markets, and self-organizing networks. To realize this diverse set of applications, CR researchers leverage a variety of artificial intelligence (AI) techniques. To help researchers better understand the practical implications of AI to their CR designs, this paper reviews several CR implementations that used the following AI techniques: artificial neural networks (ANNs), metaheuristic algorithms, hidden Markov models (HMMs), rule-based systems, ontology-based systems (OBSs), and case-based systems (CBSs). Factors that influence the choice of AI techniques, such as responsiveness, complexity, security, robustness, and stability, are discussed. To provide readers with a more concrete understanding, these factors are illustrated in an extended discussion of two CR designs.