nils-herrmann/clean_scirepeval huggingface dataset:
Characteristics of child commercial sexual exploitation and sex trafficking victims presenting for medical care in the United States.

The objective of the study is to describe distinguishing characteristics of commercial sexual exploitation of children/child sex trafficking victims (CSEC) who present for health care in the pediatric setting. This is a retrospective study of patients aged 12-18 years who presented to any of three pediatric emergency departments or one child protection clinic, and who were identified as suspected victims of CSEC. The sample was compared with gender and age-matched patients with allegations of child sexual abuse/sexual assault (CSA) without evidence of CSEC on variables related to demographics, medical and reproductive history, high-risk behavior, injury history and exam findings. There were 84 study participants, 27 in the CSEC group and 57 in the CSA group. Average age was 15.7 years for CSEC patients and 15.2 years for CSA patients; 100% of the CSEC and 94.6% of the CSA patients were female. The two groups significantly differed in 11 evaluated areas with the CSEC patients more likely to have had experiences with violence, substance use, running away from home, and involvement with child protective services and/or law enforcement. CSEC patients also had a longer history of sexual activity. Adolescent CSEC victims differ from sexual abuse victims without evidence of CSEC in their reproductive history, high risk behavior, involvement with authorities, and history of violence.
Multilingual NMT with a language-independent attention bridge

In this paper, we propose a multilingual encoder-decoder architecture capable of obtaining multilingual sentence representations by means of incorporating an intermediate attention bridge that is shared across all languages. That is, we train the model with language-specific encoders and decoders that are connected via self-attention with a shared layer that we call attention bridge. This layer exploits the semantics from each language for performing translation and develops into a language-independent meaning representation that can efficiently be used for transfer learning. We present a new framework for the efficient development of multilingual NMT using this model and scheduled training. We have tested the approach in a systematic way with a multi-parallel data set. We show that the model achieves substantial improvements over strong bilingual models and that it also works well for zero-shot translation, which demonstrates its ability of abstraction and transfer learning.
Development and validation of a survey to assess patient-perceived medication knowledge and confidence in medication use.

The primary objective of this study is to establish the validity and reliability of a perceived medication knowledge and confidence survey instrument (Okere-Renier Survey).



Two-stage psychometric analyses were conducted to assess reliability (Cronbach's alpha > .70) of the associated knowledge scale. To evaluate the construct validity, exploratory and confirmatory factor analyses were performed.



Exploratory factor analysis (EFA) revealed three subscale measures and confirmatory factor analysis (CFA) indicated an acceptable fit to the data (goodness-of-fit index [GFI = 0.962], adjusted goodness-of-fit index [AGFI = 0.919], root mean square residual [RMR = 0.065], root mean square error of approximation [RMSEA] = 0.073). A high internal consistency with Cronbach's a of .833 and .744 were observed in study Stages 1 and 2, respectively.



The Okere-Renier Survey is a reliable instrument for predicting patient-perceived level of medication knowledge and confidence.
CNN-based in-loop filtering for coding efficiency improvement

A recent video coding standard, called High Efficiency Video Coding (HEVC), adopts two in-loop filters for coding efficiency improvement where the in-loop filtering is done by a de-blocking filter (DF) followed by sample adaptive offset (SAO) filtering. The DF helps improve both coding efficiency and subjective quality without signaling any bit to decoder sides while SAO filtering corrects the quantization errors by sending offset values to decoders. In this paper, we first present a new in-loop filtering technique using convolutional neural networks (CNN), called IFCNN, for coding efficiency and subjective visual quality improvement. The IFCNN does not require signaling bits by using the same trained weights in both encoders and decoder. The proposed IFCNN is trained in two different QP ranges: QR1 from QP = 20 to QP = 29; and QR2 from QP = 30 to QP = 39. In testing, the IFCNN trained in QR1 is applied for the encoding/decoding with QP values less than 30 while the IFCNN trained in QR2 is applied for the case of QP values greater than 29. The experiment results show that the proposed IFCNN outperforms the HEVC reference mode (HM) with average 1.9%-2.8% gain in BD-rate for Low Delay configuration, and average 1.6%-2.6% gain in BD-rate for Random Access configuration with IDR period 16.
Tracking modularity in citation networks

Citation network analysis is an effective tool to analyze the structure of scientific research. Clustering is often used to visualize scientific domain and to detect emerging research front there. While we often set arbitrarily clustering threshold, there is few guide to set appropriate threshold. This study analyzed basic process how clustering of citation network proceeds by tracking size and modularity change during clustering. We found that there are three stages in clustering of citation networks and it is universal across our case studies. In the first stage, core clusters in the domain are formed. In the second stage, peripheral clusters are formed, while core clusters continue to grow. In the third stage, core clusters grow again. We found the minimum corpus size around one hundred assuring the clustering. When the corpus size is less than one hundred, clustered network structure tends to be more random. In addition even for the corpus whose size is larger than it, the clustering quality for some clusters formed in the later stage is low. These results give a fundamental guidance to the user of citation network analysis.
Usability guidelines for designing mobile learning portals

Mobile learning presumes the use of mobile Internet technology to facilitate the learning process. The growth and rapid evolution of the wireless technology have created new opportunities for anytime and anywhere learning paradigm. As a result, numerous mobile learning portals have been developed to gain the advantages of it. Nonetheless, there is little research and exploration has been initiated in proposing usability guidelines in designing mobile learning portals to achieve efficiency, effectiveness and satisfaction of learning. Thus, this paper seeks to present usability guidelines by grounding the user interface on usability theoretical framework, possible constraints, and unique properties of mobile computing. Three categories of usability have been formulated: user analysis, interaction and interface design. Ten golden usability guidelines have been suggested which aims for designing highly efficacious, user friendly and usable mobile interface to support dynamicity of mobile and handheld devices. Moreover, Mobile Learning Course Manager (MLCM) portal has been developed to demonstrate and exemplify the usability guidelines proposed.
Comparative study of two 3-CRU translational parallel manipulators

Two types of 3-CRU translational parallel manipulators (TPM) are compared and investigated in this paper. The two 3-CRU TPMs have identical kinematics, but some differences exist in terms of the chain arrangement, one is in fully symmetrical chain arrangement, called symmetrical 3-CRU TPM, the other one is in asymmetrical chain arrangement, called asymmetrical 3-CRU TPM. This paper focuses on discussing the differences between the two 3-CRU TPMs in kinematics, workspace and performance. This study provides insights into parallel manipulators with identical kinematics arranged differently.
Lost in quantization: Improving particular object retrieval in large scale image databases

The state of the art in visual object retrieval from large databases is achieved by systems that are inspired by text retrieval. A key component of these approaches is that local regions of images are characterized using high-dimensional descriptors which are then mapped to ldquovisual wordsrdquo selected from a discrete vocabulary.This paper explores techniques to map each visual region to a weighted set of words, allowing the inclusion of features which were lost in the quantization stage of previous systems. The set of visual words is obtained by selecting words based on proximity in descriptor space. We describe how this representation may be incorporated into a standard tf-idf architecture, and how spatial verification is modified in the case of this soft-assignment. We evaluate our method on the standard Oxford Buildings dataset, and introduce a new dataset for evaluation. Our results exceed the current state of the art retrieval performance on these datasets, particularly on queries with poor initial recall where techniques like query expansion suffer. Overall we show that soft-assignment is always beneficial for retrieval with large vocabularies, at a cost of increased storage requirements for the index.
The Effect of Note Taking Media and Preference on the Cognitive Processes Involved in Learning

This study seeks to evaluate how student information retention and comprehension can be influenced by their preferred note taking medium. One-hundred and nine college students watched lectures and took notes with an assigned medium: longhand or computer. Prior to watching the lectures, participants self-reported their preferred note taking medium. These lectures were pre-recorded and featured PowerPoint presentations containing information relating to the lecture. After the lectures, students were able to review their notes briefly before they engaged in activities unrelated to the lecture. They then took two tests based on the lecture material and completed a questionnaire further inquiring about their note taking tendencies. Tests contained two types of questions: conceptual and specific. A main effect of question type was found, with both computer and longhand note takers performing better on specific questions. Further, computer-preferred note takers who were forced to take notes by hand performed worst overall on the tests. Regardless of preference and question type, computer and longhand users performed equally well overall, and the interaction of medium and question type on test performance was not significant. For transcription tendencies, computer note takers generated more words and more 3-word verbatim sequences than longhand note takers. For note taking tendencies, the use of computer notes somewhat positively correlated with the use of no notes. The results of this study help to further understand how students’ preferred note taking medium can influence performance on subsequent tests.
21.6 A 12nW always-on acoustic sensing and object recognition microsystem using frequency-domain feature extraction and SVM classification

IoT devices are becoming increasingly intelligent and context-aware. Sound is an attractive sensory modality because it is information-rich but not as computationally demanding as alternatives such as vision. New applications of ultra-low power (ULP), ‘always-on’ intelligent acoustic sensing includes agricultural monitoring to detect pests or precipitation, infrastructure health tracking to recognize acoustic symptoms, and security/safety monitoring to identify dangerous conditions. A major impediment for the adoption of always-on, context-aware sensing is power consumption, particularly for ultra-small IoT devices requiring long-term operation without battery replacement. To sustain operation with a 1mm2 solar cell in ambient light (100lux) or achieve a lifetime of 10 years using a button cell battery (2mAh), <20nW power consumption must be achieved, which is more than 2 orders of magnitude lower than current state-of-the-art acoustic sensing systems . More broadly a previous ULP signal acquisition IC  consumes just 3nW while 64nW ECG monitoring system [4] includes back-end classification, however there are no sub-20nW complete sensing systems with both analog frontend and digital backend.
Beyond Shared Hierarchies: Deep Multitask Learning through Soft Layer Ordering

Existing deep multitask learning (MTL) approaches align layers shared between tasks in a parallel ordering. Such an organization significantly constricts the types of shared structure that can be learned. The necessity of parallel ordering for deep MTL is first tested by comparing it with permuted ordering of shared layers. The results indicate that a flexible ordering can enable more effective sharing, thus motivating the development of a soft ordering approach, which learns how shared layers are applied in different ways for different tasks. Deep MTL with soft ordering outperforms parallel ordering methods across a series of domains. These results suggest that the power of deep MTL comes from learning highly general building blocks that can be assembled to meet the demands of each task.
Light as a central modulator of circadian rhythms, sleep and affect

Light has profoundly influenced the evolution of life on earth. As widely appreciated, light enables us to generate images of our environment. However, light — through intrinsically photosensitive retinal ganglion cells (ipRGCs) — also influences behaviours that are essential for our health and quality of life but are independent of image formation. These include the synchronization of the circadian clock to the solar day, tracking of seasonal changes and the regulation of sleep. Irregular light environments lead to problems in circadian rhythms and sleep, which eventually cause mood and learning deficits. Recently, it was found that irregular light can also directly affect mood and learning without producing major disruptions in circadian rhythms and sleep. In this Review, we discuss the indirect and direct influence of light on mood and learning, and provide a model for how light, the circadian clock and sleep interact to influence mood and cognitive functions.
Beavers (Castor fiber and Castor canadensis), the founders of habitats and phytophages

A review of the literature on the influence of beavers on the environment has been presented with regard to following aspects: (1) specific features of the ecology of beavers crucial for understanding their effects on the environment: (2) changes in the physical characteristics of habitats due to the activity of beavers (beavers as engineers); (3) the role of the beaver as a phytophage; (4) long-term changes of vegetation in beavers’ habitats and the possible consequences of these changes for beavers.
A novel shape octagonal koch fractal MIMO antenna for WLAN and WiMAX application

In this paper, a tri band compact octagonal fractal monopole MIMO antenna is presented. The proposed antenna is microstrip line fed and its structure is based on fractal geometry where the resonance frequency of antenna is lowered by applying iteration techniques. The simulated bandwidth of the antenna are 2.3706GHz to 2.45GHz, 3.398GHz to 3.677GHz and 4.9352GHz to 5.8988GHz (S11 <; -10 dB), covering the bands of WLAN and WiMAX. The characteristics of small size, nearly omnidirectional radiation pattern and moderate gain make the proposed MIMO antenna entirely applicable to WLAN and WiMAX applications. The proposed antenna has compact size of 50 mm × 50 mm. Details of the proposed antenna design and performance are presented and discussed.
"Out of the Fr-Eye-ing Pan": Towards Gaze-Based Models of Attention during Learning with Technology in the Classroom

Attention is critical to learning. Hence, advanced learning technologies should benefit from mechanisms to monitor and respond to learners' attentional states. We study the feasibility of integrating commercial off-the-shelf (COTS) eye trackers to monitor attention during interactions with a learning technology called GuruTutor. We tested our implementation on 135 students in a noisy computer-enabled high school classroom and were able to collect a median 95% valid eye gaze data in 85% of the sessions where gaze data was successfully recorded. Machine learning methods were employed to develop automated detectors of mind wandering (MW) -- a phenomenon involving a shift in attention from task-related to task-unrelated thoughts that is negatively correlated with performance. Our student-independent, gaze-based models could detect MW with an accuracy (F1 of MW = 0.59) significantly greater than chance (F1 of MW = 0.24). Predicted rates of mind wandering were negatively related to posttest performance, providing evidence for the predictive validity of the detector. We discuss next steps towards developing gaze-based, attention-aware, learning technologies that can be deployed in noisy, real-world environments.
Adaptive Deep Learning Through Visual Domain Localization

A commercial robot, trained by its manufacturer to recognize a predefined number and type of objects, might be used in many settings, that will in general differ in their illumination conditions, background, type and degree of clutter, and so on. Recent computer vision works tackle this generalization issue through domain adaptation methods, assuming as source the visual domain where the system is trained and as target the domain of deployment. All approaches assume to have access to images from all classes of the target during training, an unrealistic condition in robotics applications. We address this issue proposing an algorithm that takes into account the specific needs of robot vision. Our intuition is that the nature of the domain shift experienced mostly in robotics is local. We exploit this through the learning of maps that spatially ground the domain and quantify the degree of shift, embedded into an end-to-end deep domain adaptation architecture. By explicitly localizing the roots of the domain shift we significantly reduce the number of parameters of the architecture to tune, we gain the flexibility necessary to deal with subset of categories in the target domain at training time, and we provide a clear feedback on the rationale behind any classification decision, which can be exploited in human-robot interactions. Experiments on two different settings of the iCub World database confirm the suitability of our method for robot vision.
One-shot Face Recognition by Promoting Underrepresented Classes

In this paper, we study the problem of training largescale face identification model with imbalanced training data. This problem naturally exists in many real scenarios including large-scale celebrity recognition, movie actor annotation, etc. Our solution contains two components. First, we build a face feature extraction model, and improve its performance, especially for the persons with very limited training samples, by introducing a regularizer to the cross entropy loss for the multinomial logistic regression (MLR) learning. This regularizer encourages the directions of the face features from the same class to be close to the direction of their corresponding classification weight vector in the logistic regression. Second, we build a multiclass classifier using MLR on top of the learned face feature extraction model. Since the standard MLR has poor generalization capability for the one-shot classes even if these classes have been oversampled, we propose a novel supervision signal called underrepresented-classes promotion loss, which aligns the norms of the weight vectors of the one-shot classes (a.k.a. underrepresented-classes) to those of the normal classes. In addition to the original cross entropy loss, this new loss term effectively promotes the underrepresented classes in the learned model and leads to a remarkable improvement in face recognition performance. We test our solution on the MS-Celeb-1M low-shot learning benchmark task. Our solution recognizes 94.89% of the test images at the precision of 99% for the one-shot classes. To the best of our knowledge, this is the best performance among all the published methods using this benchmark task with the same setup, including all the participants in the recent MS-Celeb-1M challenge at ICCV 2017.
Biomechanical evaluation against calcaneofibular ligament repair in the Brostrom procedure: a cadaveric study

The modified Brostrom procedure is commonly recommended for reconstruction of the anterior talofibular ligament (ATF) and calcaneofibular ligament (CF) with an advancement of the inferior retinaculum. However, some surgeons perform the modified Bostrom procedure with an semi-single ATF ligament reconstruction and advancement of the inferior retinaculum for simplicity. This study evaluated the initial stability of the modified Brostrom procedure and compared a two ligaments (ATF + CF) reconstruction group with a semi-single ligament (ATF) reconstruction group. Sixteen paired fresh frozen cadaveric ankle joints were used in this study. The ankle joint laxity was measured on the plane radiographs with 150 N anterior drawer force and 150 N varus stress force. The anterior displacement distances and varus tilt angles were measured before and after cutting the ATF and CF ligaments. A two ligaments (ATF + CF) reconstruction with an advancement of the inferior retinaculum was performed on eight left cadaveric ankles, and an semi-single ligament (ATF) reconstruction with an advancement of the inferior retinaculum was performed on eight right cadaveric ankles. The ankle instability was rechecked after surgery. The decreases in instability of the ankle after surgery were measured and the difference in the decrease was compared using a Mann–Whitney U test. The mean decreases in anterior displacement were 3.4 and 4.0 mm in the two ligaments reconstruction and semi-single ligament reconstruction groups, respectively. There was no significant difference between the two groups (P = 0.489). The mean decreases in the varus tilt angle in the two ligaments reconstruction and semi-single ligament reconstruction groups were 12.6° and 12.2°, respectively. There was no significant difference between the two groups (P = 0.399). In this cadaveric study, a substantial level of initial stability can be obtained using an anatomical reconstruction of the anterior talofibular ligament only and reinforcement with the inferior retinaculum. The modified Brostrom procedure with a semi-single ligament (Anterior talofibular ligament) reconstruction with an advancement of the inferior retinaculum can provide as much initial stability as the two ligaments (Anterior talofibular ligament and calcaneofibular ligament) reconstruction procedure.
Single-inductor multiple-output switching converters with time-multiplexing control in discontinuous conduction mode

An integrated single-inductor dual-output boost converter is presented. This converter adopts time-multiplexing control in providing two independent supply voltages (3.0 and 3.6 V) using only one 1H off-chip inductor and a single control loop. This converter is analyzed and compared with existing counterparts in the aspects of integration, architecture, control scheme, and system stability. Implementation of the power stage, the controller, and the peripheral functional blocks is discussed. The design was fabricated with a standard 0.5m CMOS n-well process. At an oscillator frequency of 1 MHz, the power conversion efficiency reaches 88.4% at a total output power of 350 mW. This topology can be extended to have multiple outputs and can be applied to buck, flyback, and other kinds of converters.