nils-herrmann/clean_scirepeval huggingface dataset:
Population-based norms for the Mini-Mental State Examination by age and educational level.

To report the distribution of Mini-Mental State Examination (MMSE) scores by age and educational level.



National Institute of Mental Health Epidemiologic Catchment Area Program surveys conducted between 1980 and 1984.



Community populations in New Haven, Conn; Baltimore, Md; St Louis, Mo; Durham, NC; and Los Angeles, Calif.



A total of 18,056 adult participants selected by probability sampling within census tracts and households.



Summary scores for the MMSE are given in the form of mean, median, and percentile distributions specific for age and educational level.



The MMSE scores were related to both age and educational level. There was an inverse relationship between MMSE scores and age, ranging from a median of 29 for those 18 to 24 years of age, to 25 for individuals 80 years of age and older. The median MMSE score was 29 for individuals with at least 9 years of schooling, 26 for those with 5 to 8 years of schooling, and 22 for those with 0 to 4 years of schooling.



Cognitive performance as measured by the MMSE varies within the population by age and education. The cause of this variation has yet to be determined. Mini-Mental State Examination scores should be used to identify current cognitive difficulties and not to make formal diagnoses. The results presented should prove to be useful to clinicians who wish to compare an individual patient's MMSE scores with a population reference group and to researchers making plans for new studies in which cognitive status is a variable of interest.
How feasible is the use of magnetic field alone for indoor positioning?

The use of magnetic field variations for positioning and navigation has been suggested by several researchers. In most of the applications, the magnetic field is used to determine the azimuth or heading. However, for indoor applications, accurate heading determination is difficult due to the presence of magnetic field anomalies. Here location fingerprinting methodology can take advantage of these anomalies. In fact, the more significant the local anomalies, the more unique the magnetic “fingerprint”. In general, the more elements in each fingerprint, the better for positioning. Unfortunately, magnetic field intensity data only consists of three components. Since true north (or magnetic north) is generally unknown, even with help of the accelerometer to detect the direction of the gravity, only two components can be extracted, i.e. the horizontal intensity and the vertical intensity (or total intensity and inclination). Furthermore, moving objects containing ferromagnetic materials and electronic devices may affect the magnetic field. Tests were carried out to investigate the feasibility of using magnetic field alone for indoor positioning. Possible solutions are discussed.
Word segmentation for the Myanmar language

This study reports the development of a Myanmar word segmentation method using Unicode standard encoding. Word segmentation is an essential step prior to natural language processing in the Myanmar language, because a Myanmar text is a string of characters without explicit word boundary delimiters. The proposed method has two phases: syllable segmentation and syllable merging. A rule-based heuristic approach was adopted for syllable segmentation, and a dictionary-based statistical approach for syllable merging. Evaluation of test results showed that the method is very effective for the Myanmar language.
Adaptive non-local means denoising of MR images with spatially varying noise levels.

To adapt the so-called nonlocal means filter to deal with magnetic resonance (MR) images with spatially varying noise levels (for both Gaussian and Rician distributed noise).



Most filtering techniques assume an equal noise distribution across the image. When this assumption is not met, the resulting filtering becomes suboptimal. This is the case of MR images with spatially varying noise levels, such as those obtained by parallel imaging (sensitivity-encoded), intensity inhomogeneity-corrected images, or surface coil-based acquisitions. We propose a new method where information regarding the local image noise level is used to adjust the amount of denoising strength of the filter. Such information is automatically obtained from the images using a new local noise estimation method.



The proposed method was validated and compared with the standard nonlocal means filter on simulated and real MRI data showing an improved performance in all cases.



The new noise-adaptive method was demonstrated to outperform the standard filter when spatially varying noise is present in the images.
Understanding and Supporting Cross-Device Web Search for Exploratory Tasks with Mobile Touch Interactions

Mobile devices enable people to look for information at the moment when their information needs are triggered. While experiencing complex information needs that require multiple search sessions, users may utilize desktop computers to fulfill information needs started on mobile devices. Under the context of mobile-to-desktop web search, this article analyzes users’ behavioral patterns and compares them to the patterns in desktop-to-desktop web search. Then, we examine several approaches of using Mobile Touch Interactions (MTIs) to infer relevant content so that such content can be used for supporting subsequent search queries on desktop computers. The experimental data used in this article was collected through a user study involving 24 participants and six properly designed cross-device web search tasks. Our experimental results show that (1) users’ mobile-to-desktop search behaviors do significantly differ from desktop-to-desktop search behaviors in terms of information exploration, sense-making and repeated behaviors. (2) MTIs can be employed to predict the relevance of click-through documents, but applying document-level relevant content based on the predicted relevance does not improve search performance. (3) MTIs can also be used to identify the relevant text chunks at a fine-grained subdocument level. Such relevant information can achieve better search performance than the document-level relevant content. In addition, such subdocument relevant information can be combined with document-level relevance to further improve the search performance. However, the effectiveness of these methods relies on the sufficiency of click-through documents. (4) MTIs can also be obtained from the Search Engine Results Pages (SERPs). The subdocument feedbacks inferred from this set of MTIs even outperform the MTI-based subdocument feedback from the click-through documents.
Addressing Female Genital Mutilation/Cutting (FGM/C) in the Era of Clitoral Reconstruction: Plastic Surgery

The aim of this review is to give an overview of the recent evidence on clitoral reconstruction and other relevant reconstructive plastic surgery measures after female genital mutilation/cutting (FGM/C).



Recent publications present refinements and modifications of the surgical technique of clitoral reconstruction along with reconstruction of the labia majora and clitoral hood. Novel approaches with reposition of the clitoral nerve, anchoring of the labia majora, fat grafting, and full thickness mucosa grafts have been introduced. The current evidence on outcomes of clitoral reconstruction shows potential benefits. However, there is a risk of postoperative complications and a negative outcome. Experts in the field advocate for a multidisciplinary approach including psychosexual counseling and health education with or without subsequent clitoral reconstructive surgery.



The evolution of reconstructive treatment for women with FGM/C is expanding, however at a slow rate. The scarcity of evidence on clitoral reconstruction halters availability of clinical guidelines and consensus regarding best practice. Clitoral reconstruction should be provided by multidisciplinary referral centers in a research setting with long-term follow-up on outcomes of postoperative morbidity and possible benefits.
A Comparative Analysis of Different NoSQL Databases on Data Model, Query Model and Replication Model

Relational databases like SQL have a limitation when it comes to data aggregation, which is used for business intelligence and data mining. Data aggregation becomes impossible on very large volumes of data when it comes to memory and time consumption. NoSQL databases provide an efficient framework to aggregate large volumes of data. There are different NoSQL databases like Key-value stores, Column Family/BigTable clones, Document databases and Graph databases. This paper compares different NoSQL databases against persistence, Replication, Transactions and Implementation language. This paper also discusses on performance and scalability aspects of different NoSQL databases.
On the Design of a Blockchain Platform for Clinical Trial and Precision Medicine

This paper proposes a blockchain platform architecture for clinical trial and precision medicine and discusses various design aspects and provides some insights in the technology requirements and challenges. We identify 4 new system architecture components that are required to be built on top of traditional blockchain and discuss their technology challenges in our blockchain platform: (a) a new blockchain based general distributed and parallel computing paradigm component to devise and study parallel computing methodology for big data analytics, (b) blockchain application data management component for data integrity, big data integration, and integrating disparity of medical related data, (c) verifiable anonymous identity management component for identity privacy for both person and Internet of Things (IoT) devices and secure data access to make possible of the patient centric medicine, and (d) trust data sharing management component to enable a trust medical data ecosystem for collaborative research.
Multivariate analysis of fMRI time series: classification and regression of brain responses using machine learning.

Machine learning and pattern recognition techniques are being increasingly employed in functional magnetic resonance imaging (fMRI) data analysis. By taking into account the full spatial pattern of brain activity measured simultaneously at many locations, these methods allow detecting subtle, non-strictly localized effects that may remain invisible to the conventional analysis with univariate statistical methods. In typical fMRI applications, pattern recognition algorithms "learn" a functional relationship between brain response patterns and a perceptual, cognitive or behavioral state of a subject expressed in terms of a label, which may assume discrete (classification) or continuous (regression) values. This learned functional relationship is then used to predict the unseen labels from a new data set ("brain reading"). In this article, we describe the mathematical foundations of machine learning applications in fMRI. We focus on two methods, support vector machines and relevance vector machines, which are respectively suited for the classification and regression of fMRI patterns. Furthermore, by means of several examples and applications, we illustrate and discuss the methodological challenges of using machine learning algorithms in the context of fMRI data analysis.
Online-specific fear of missing out and Internet-use expectancies contribute to symptoms of Internet-communication disorder

Some of the most frequently used online applications are Facebook, WhatsApp, and Twitter. These applications allow individuals to communicate with other users, to share information or pictures, and to stay in contact with friends all over the world. However, a growing number of users suffer from negative consequences due to their excessive use of these applications, which can be referred to as Internet-communication disorder. The frequent use and easy access of these applications may also trigger the individual's fear of missing out on content when not accessing these applications. Using a sample of 270 participants, a structural equation model was analyzed to investigate the role of psychopathological symptoms and the fear of missing out on expectancies towards Internet-communication applications in the development of symptoms of an Internet-communication disorder. The results suggest that psychopathological symptoms predict higher fear of missing out on the individual's Internet-communication applications and higher expectancies to use these applications as a helpful tool to escape from negative feelings. These specific cognitions mediate the effect of psychopathological symptoms on Internet-communication disorder. Our results are in line with the theoretical model by Brand et al. (2016) as they show how Internet-related cognitive bias mediates the relationship between a person's core characteristics (e.g., psychopathological symptoms) and Internet-communication disorder. However, further studies should investigate the role of the fear of missing out as a specific predisposition, as well as specific cognition in the online context.
A Low-Power Text-Dependent Speaker Verification System with Narrow-Band Feature Pre-Selection and Weighted Dynamic Time Warping

To fully enable voice interaction in wearable devices, a system requires low-power, customizable voice-authenticated wake-up. Existing speaker-verification (SV) methods have shortcomings relating to power consumption and noise susceptibility. To meet the application requirements, we propose a low-power, text-dependent SV system comprising a sparse spectral feature extraction front-end showing improved noise robustness and accuracy at low power, and a back-end running an improved dynamic time warping (DTW) algorithm that preserves signal envelope while reducing misalignments. Without background noise, the proposed system achieves an equal-errorrate (EER) of 1.1%, compared to 1.4% with a conventional Mel-frequency cepstral coefficients (MFCC)+DTW system and 2.6% with a Gaussian mixture universal background (GMMUBM) based system. At 3dB signal-to-noise ratio (SNR), the proposed system achieves an EER of 5.7%, compared to 13% with a conventional MFCC+DTW system and 6.8% with a GMM-UBM based system. The proposed system enables simple, low-power implementation such that the power consumption of the end-to-end system, which includes a voice activity detector, feature extraction front-end, and back-end decision unit, is under 380 μW.
Deep Learning for Multi-task Medical Image Segmentation in Multiple Modalities

Automatic segmentation of medical images is an important task for many clinical applications. In practice, a wide range of anatomical structures are visualised using different imaging modalities. In this paper, we investigate whether a single convolutional neural network (CNN) can be trained to perform different segmentation tasks. A single CNN is trained to segment six tissues in MR brain images, the pectoral muscle in MR breast images, and the coronary arteries in cardiac CTA. The CNN therefore learns to identify the imaging modality, the visualised anatomical structures, and the tissue classes. For each of the three tasks (brain MRI, breast MRI and cardiac CTA), this combined training procedure resulted in a segmentation performance equivalent to that of a CNN trained specifically for that task, demonstrating the high capacity of CNN architectures. Hence, a single system could be used in clinical practice to automatically perform diverse segmentation tasks without task-specific training.
Computational Thinking and Expository Writing in the Middle School

To broaden participation in computing we need to look beyond traditional domains of inquiry and expertise. We present results from a demonstration project in which interactive journalism was used to infuse computational thinking into the standard curriculum and regular classroom experience at a middle school with a diverse population. Outcomes indicate that we were able to develop positive attitudes about computational thinking and programming among students and teachers who did not necessarily view themselves as “math types.” By partnering with language arts, technology and math teachers at Fisher Middle School, Ewing New Jersey, we introduced the isomorphism between the journalistic process and computational thinking to 7th and 8th graders. An intense summer institute, first with the teachers and then with students recruited from the school, immersed them in the “newsroom of the future” where they researched and wrote news stories, shot and edited video, and developed procedural animations in Scratch to support their storylines. An afterschool club sustained the experience. The teachers adapted interactive journalism and Scratch programming to enrich standard language arts curriculum and are infusing computational thinking in classroom experiences throughout the school.
Sensorless current estimation and sharing in multiphase input-parallel output-parallel DC-DC converters

This paper introduces a sensorless current-sharing strategy for multiphase input-parallel output-parallel (IPOP) DC-DC converters. A dual active bridge (DAB) DC-DC converter is chosen as the basic DC-DC converter. With this strategy, by perturbing the duty cycles in (n-1) out of n phases in turn and measuring the changes of duty cycles respectively, the parameters mismatches among phases are estimated. According to the mismatches, a set of variables, which are proportional to the per-phase output currents, are calculated. Then with a current-sharing regulator, parameter mismatches are compensated, thus achieving current sharing without current sensing. The strategy is verified through both simulation and experimental implementation with a 30V-to-70V, 272W, 20kHz, three-phase IPOP DC-DC converter.
I seek you: searching and matching individuals in social networks

The first task any individual faces after joining an online social network (OSN) is locating friends that are present on that particular site. Most OSNs offer some variation of a tool that imports email contact lists to facilitate the task of finding one's friends. However, given that OSNs attempt to reconnect individuals with past acquaintances, one might not have access to the email address for a long lost friend. Furthermore, people tend to utilize a number of aliases online, meaning that an email address cannot always be used to reliably find a friend. Thus, new members must still manually search for friends based on a number of biographical attributes, such as gender, age, hometown, etc. It is not clear, however, what attributes are useful for conducting the search. Even after the search has been performed, the person performing the search might be left with a number of candidate profiles. In this paper, we develop a system for searching and matching individuals in OSNs. We evaluate the efficacy of our person matching techniques by measuring the overlap between social networks, and comparing our results to those published by compete.com. We then look at several interesting properties of overlapping profiles in both networks.
A New Bit-Serial Architecture for Field Multiplication Using Polynomial Bases

Multiplication is the main finite field arithmetic operation in elliptic curve cryptography and its bit-serial hardware implementation is attractive in resource constrained environments such as smart cards, where the chip area is limited. In this paper, a new serial-output bitserial multiplier using polynomial bases over binary extension fields is proposed. It generates a bit of the multiplication in each clock cycle with the latency of one cycle. To the best of our knowledge, this is the first time that such a serial-output bit-serial multiplier architecture using polynomial bases for general irreducible polynomials is proposed.
Personalized click model through collaborative filtering

Click modeling aims to interpret the users' search click data in order to predict their clicking behavior. Existing models can well characterize the position bias of documents and snippets in relation to users' mainstream click behavior. Yet, current advances depict users' search actions only in a general setting by implicitly assuming that all users act in the same way, regardless of the fact that anyone, motivated with some individual interest, is more likely to click on a link than others. It is in light of this that we put forward a novel personalized click model to describe the user-oriented click preferences, which applies and extends matrix / tensor factorization from the view of collaborative filtering to connect users, queries and documents together. Our model serves as a generalized personalization framework that can be incorporated to the previously proposed click models and, in many cases, to their future extensions. Despite the sparsity of search click data, our personalized model demonstrates its advantage over the best click models previously discussed in the Web-search literature, supported by our large-scale experiments on a real dataset. A delightful bonus is the model's ability to gain insights into queries and documents through latent feature vectors, and hence to handle rare and even new query-document pairs much better than previous click models.
Risk-taking bias in human decision-making is encoded via a right–left brain push–pull system

A person's decisions vary even when options stay the same, like when a gambler changes bets despite constant odds of winning. Internal bias (e.g., emotion) contributes to this variability and is shaped by past outcomes, yet its neurobiology during decision-making is not well understood. To map neural circuits encoding bias, we administered a gambling task to 10 participants implanted with intracerebral depth electrodes in cortical and subcortical structures. We predicted the variability in betting behavior within and across patients by individual bias, which is estimated through a dynamical model of choice. Our analysis further revealed that high-frequency activity increased in the right hemisphere when participants were biased toward risky bets, while it increased in the left hemisphere when participants were biased away from risky bets. Our findings provide electrophysiological evidence that risk-taking bias is a lateralized push-pull neural system governing counterintuitive and highly variable decision-making in humans.
Resonant frequency biofeedback training to increase cardiac variability: rationale and manual for training.

Heart rate and blood pressure, as well as other physiological systems, among healthy people, show a complex pattern of variability, characterized by multifrequency oscillations. There is evidence that these oscillations reflect the activity of homeostatic reflexes. Biofeedback training to increase the amplitude of respiratory sinus arrhythmia (RSA) maximally increases the amplitude of heart rate oscillations only at approximately 0.1 Hz. To perform this task people slow their breathing to this rate to a point where resonance occurs between respiratory-induced oscillations (RSA) and oscillations that naturally occur at this rate, probably triggered in part by baroreflex activity. We hypothesize that this type of biofeedback exercises the baroreflexes, and renders them more efficient. A manual is presented for carrying out this method. Supporting data are provided in Lehrer, Smetankin, and Potapova (2000) in this issue.