nils-herrmann/clean_scirepeval huggingface dataset:
Kettlebell swing, snatch, and bottoms-up carry: back and hip muscle activation, motion, and low back loads.

The intent of this study was to quantify spine loading during different kettlebell swings and carries. No previously published studies of tissue loads during kettlebell exercises could be found. Given the popularity of kettlebells, this study was designed to provide an insight into the resulting joint loads. Seven male subjects participated in this investigation. In addition, a single case study of the kettlebell swing was performed on an accomplished kettlebell master. Electromyography, ground reaction forces (GRFs), and 3D kinematic data were recorded during exercises using a 16-kg kettlebell. These variables were input into an anatomically detailed biomechanical model that used normalized muscle activation; GRF; and spine, hip, and knee motion to calculate spine compression and shear loads. It was found that kettlebell swings create a hip-hinge squat pattern characterized by rapid muscle activation-relaxation cycles of substantial magnitudes (∼50% of a maximal voluntary contraction [MVC] for the low back extensors and 80% MVC for the gluteal muscles with a 16-kg kettlebell) resulting in about 3,200 N of low back compression. Abdominal muscular pulses together with the muscle bracing associated with carries create kettlebell-specific training opportunities. Some unique loading patterns discovered during the kettlebell swing included the posterior shear of the L4 vertebra on L5, which is opposite in polarity to a traditional lift. Thus, quantitative analysis provides an insight into why many individuals credit kettlebell swings with restoring and enhancing back health and function, although a few find that they irritate tissues.
Data mining based analysis to explore the effect of teaching on student performance

Analysing the behaviour of student performance in classroom education is an active area in educational research. Early prediction of student performance may be helpful for both teacher and the student. However, the influencing factors of the student performance need to be identified first to build up such early prediction model. The existing data mining literature on student performance primarily focuses on student-related factors, though it may be influenced by many external factors also. Superior teaching acts as a catalyst which improves the knowledge dissemination process from teacher to the student. It also motivates the student to put more effort on the study. However, the research question, how the performance or grade correlates with teaching, is still relevant in present days. In this work, we propose a quantifiable measure of improvement with respect to the expected performance of a student. Furthermore, this study analyses the impact of teaching on performance improvement in theoretical courses of classroom-based education. It explores nearly 0.2 million academic records collected from an online system of an academic institute of national importance in India. The association mining approach has been adopted here and the result shows that confidence of both non-negative and positive improvements increase with superior teaching. This result indeed establishes the fact that teaching has a positive impact on student performance. To be more specific, the growing confidence of non-negative and positive improvements indicate that superior teaching facilitates more students to obtain either expected or better than expected grade.
AccurateML: Information-aggregation-based approximate processing for fast and accurate machine learning on MapReduce

The growing demands of processing massive datasets have promoted irresistible trends of running machine learning applications on MapReduce. When processing large input data, it is often of greater values to produce fast and accurate enough approximate results than slow exact results. Existing techniques produce approximate results by processing parts of the input data, thus incurring large accuracy losses when using short job execution times, because all the skipped input data potentially contributes to result accuracy. We address this limitation by proposing AccurateML that aggregates information of input data in each map task to create small aggregated data points. These aggregated points enable all map tasks producing initial outputs quickly to save computation times and decrease the outputs' size to reduce communication times. Our approach further identifies the parts of input data most related to result accuracy, thus first using these parts to improve the produced outputs to minimize accuracy losses. We evaluated AccurateML using real machine learning applications and datasets. The results show: (i) it reduces execution times by 30 times with small accuracy losses compared to exact results; (ii) when using the same execution times, it achieves 2.71 times reductions in accuracy losses compared to existing approximate processing techniques.
Estimation of dynamical invariants without embedding by recurrence plots.

In this paper we show that two dynamical invariants, the second order Renyi entropy and the correlation dimension, can be estimated from recurrence plots (RPs) with arbitrary embedding dimension and delay. This fact is interesting as these quantities are even invariant if no embedding is used. This is an important advantage of RPs compared to other techniques of nonlinear data analysis. These estimates for the correlation dimension and entropy are robust and, moreover, can be obtained at a low numerical cost. We exemplify our results for the Rossler system, the funnel attractor and the Mackey-Glass system. In the last part of the paper we estimate dynamical invariants for data from some fluid dynamical experiments and confirm previous evidence for low dimensional chaos in this experimental system.
The Influence of Contour on Similarity Perception of Star Glyphs

We conducted three experiments to investigate the effects of contours on the detection of data similarity with star glyph variations. A star glyph is a small, compact, data graphic that represents a multi-dimensional data point. Star glyphs are often used in small-multiple settings, to represent data points in tables, on maps, or as overlays on other types of data graphics. In these settings, an important task is the visual comparison of the data points encoded in the star glyph, for example to find other similar data points or outliers. We hypothesized that for data comparisons, the overall shape of a star glyph-enhanced through contour lines-would aid the viewer in making accurate similarity judgments. To test this hypothesis, we conducted three experiments. In our first experiment, we explored how the use of contours influenced how visualization experts and trained novices chose glyphs with similar data values. Our results showed that glyphs without contours make the detection of data similarity easier. Given these results, we conducted a second study to understand intuitive notions of similarity. Star glyphs without contours most intuitively supported the detection of data similarity. In a third experiment, we tested the effect of star glyph reference structures (i.e., tickmarks and gridlines) on the detection of similarity. Surprisingly, our results show that adding reference structures does improve the correctness of similarity judgments for star glyphs with contours, but not for the standard star glyph. As a result of these experiments, we conclude that the simple star glyph without contours performs best under several criteria, reinforcing its practice and popularity in the literature. Contours seem to enhance the detection of other types of similarity, e. g., shape similarity and are distracting when data similarity has to be judged. Based on these findings we provide design considerations regarding the use of contours and reference structures on star glyphs.
Plantbot: A New ROS-based Robot Platform for Fast Building and Developing

In recent years, the Robot Operating System (ROS) is developing rapidly and has been widely used in robotics research because of its flexible, open source, and easy-to-expand advantages. In scientific research, the corresponding hardware platform is indispensable for the experiment. In the field of mobile robots, PR2, Turtlebot2, and Fetch are commonly used as research platforms. Although these platforms are fully functional and widely used, they are expensive and bulky. What's more, these robots are not easily redesigned and expanded according to requirements. To overcome these limitations, we propose Plantbot, an easy-building, low-cost robot platform that can be redesigned and expanded according to requirements. It can be applied to not only fast algorithm verification, but also simple factory inspection and ROS teaching. This article describes this robot platform from several aspects such as hardware design, kinematics, and control methods. At last two experiments, SLAM and Navigation, on the robot platform are performed. The source code of this platform is available: https://github.com/marooncn/plantbot.
Exfoliative Cheilitis a male patient - a case report

Exfoliative cheilitis, one of a spectrum of diseases that affect the vermilion border of the lips, is uncommon and has no known cause. It is a chronic superficial inflammatory disorder of the vermilion borders of the lips characterized by persistent scaling; it can be a difficult condition to manage. The diagnosis is now restricted to those few patients whose lesions cannot be attributed to other causes, such as contact sensitization or light.  We present a 17 year-old male presented to the out clinic in Baghdad with the chief complaint of a persistent scaly on his lower lips. The patient reported that the skin over the lip thickened gradually over a 3 days period and subsequently became loose, causing discomfort. Once he peeled away the loosened layer, a new layer began to form again.  The lack of specific treatment makes exfoliative cheilitis a chronic disease that radically affects a person’s life. The aim of this paper is to describe a case of recurrent exfoliative cheilitis successfully treated with intralesional corticosteroids and to present possible hypotheses as to the cause.
Conditional Text Paraphrasing: A Survey and Taxonomy

This work introduces a survey for the Text Paraphrasing task. The survey covers the different types of tasks around text paraphrasing and mentions the techniques and models that are regularly used when approaching towards it, alongside the datasets that are used while training and evaluating the models. Text paraphrasing has an effective impact when it is used in other applications, so, the paper mentions some text paraphrasing applications. Also, this work proposes a new taxonomy that it is called Conditional Text Paraphrasing. To the best of our knowledge, this is the first work that shows varieties and sub-problems of the original text paraphrasing task. The target of this taxonomy is to expand the definition of the text paraphrasing by adding some conditional constraints as features that either control the paraphrase generation or discrimination. This expanded definition opens in mind a new domain for research in Natural Language Processing (NLP) and Machine Learning. Finally, some useful applications for the conditional text paraphrasing are represented. Natural Language Processing; Text Paraphrasing; Conditional Text Paraphrasing
Analysis and design of average current mode control using describing function-based equivalent circuit model

A small signal model for average current mode control based on equivalent circuit is proposed. The model uses the three-terminal equivalent circuit model based on linearized describing function method to include the feedback effect of the side-band frequency components of inductor current. It extends the results obtained in peak-current model control to average current mode control. The proposed small signal model is accurate up to half switching frequency, predicting the sub-harmonic instability. The proposed model is verified using SIMPLIS simulation and hardware experiments, showing good agreement with the measurement results. Based on the proposed model, a new feedback design guideline is presented. The proposed design guideline is compared with several conventional, widely used design criteria to highlight its virtue. By designing the external ramp following the proposed design guideline, quality factor of the double poles at half of switching frequency in control-to-output transfer function can be precisely controlled. This helps the feedback design to achieve widest control bandwidth and proper damping.
Now let's do it in practice: user experience evaluation methods in product development

As the selection of products and services becomes profuse in the technology market, it is often the delighting user experience (UX) that differentiates a successful product from the competitors. Product development is no longer about implementing features and testing their usability, but understanding users' daily lives and evaluating if a product resonates with the in-depth user needs. Although UX is a widely adopted term in industry, the tools for evaluating UX in product development are still inadequate. Based on industrial case studies and the latest research on UX evaluation, this workshop forms a model for aligning the used UX evaluation methods to product development processes. The results can be used to advance the state of "putting UX evaluation into practice".
Physical Layer Security in Wireless Ad Hoc Networks Under A Hybrid Full-/Half-Duplex Receiver Deployment Strategy

This paper studies physical layer security in a wireless ad hoc network with numerous legitimate transmitter–receiver pairs and eavesdroppers. A hybrid full-duplex (FD)/half-duplex receiver deployment strategy is proposed to secure legitimate transmissions, by letting a fraction of legitimate receivers work in the FD mode sending jamming signals to confuse eavesdroppers upon their information receptions, and letting the other receivers work in the half-duplex mode just receiving their desired signals. The objective of this paper is to choose properly the fraction of FD receivers for achieving the optimal network security performance. Both accurate expressions and tractable approximations for the connection outage probability and the secrecy outage probability of an arbitrary legitimate link are derived, based on which the area secure link number, network-wide secrecy throughput, and network-wide secrecy energy efficiency are optimized, respectively. Various insights into the optimal fraction are further developed, and its closed-form expressions are also derived under perfect self-interference cancellation or in a dense network. It is concluded that the fraction of FD receivers triggers a non-trivial tradeoff between reliability and secrecy, and the proposed strategy can significantly enhance the network security performance.
Group-based multi-trajectory modeling.

Identifying and monitoring multiple disease biomarkers and other clinically important factors affecting the course of a disease, behavior or health status is of great clinical relevance. Yet conventional statistical practice generally falls far short of taking full advantage of the information available in multivariate longitudinal data for tracking the course of the outcome of interest. We demonstrate a method called multi-trajectory modeling that is designed to overcome this limitation. The method is a generalization of group-based trajectory modeling. Group-based trajectory modeling is designed to identify clusters of individuals who are following similar trajectories of a single indicator of interest such as post-operative fever or body mass index. Multi-trajectory modeling identifies latent clusters of individuals following similar trajectories across multiple indicators of an outcome of interest (e.g., the health status of chronic kidney disease patients as measured by their eGFR, hemoglobin, blood CO2 levels). Multi-trajectory modeling is an application of finite mixture modeling. We lay out the underlying likelihood function of the multi-trajectory model and demonstrate its use with two examples.
Unsupervised and supervised learning to evaluate event relatedness based on content mining from social-media streams

0957-4174/$ see front matter 2012  A http://dx.doi.org/10.1016/j.eswa.2012.05.068 ⇑ Tel. E-mail address: leechung@mail.ee.kuas.edu.tw Due to the explosive growth of social-media applications, enhancing event-awareness by social mining has become extremely important. The contents of microblogs preserve valuable information associated with past disastrous events and stories. To learn the experiences from past events for tackling emerging real-world events, in this work we utilize the social-media messages to characterize real-world events through mining their contents and extracting essential features for relatedness analysis. On one hand, we established an online clustering approach on Twitter microblogs for detecting emerging events, and meanwhile we performed event relatedness evaluation using an unsupervised clustering approach. On the other hand, we developed a supervised learning model to create extensible measure metrics for offline evaluation of event relatedness. By means of supervised learning, our developed measure metrics are able to compute relatedness of various historical events, allowing the event impacts on specified domains to be quantitatively measured for event comparison. By combining the strengths of both methods, the experimental results showed that the combined framework in our system is sensible for discovering more unknown knowledge about event impacts and enhancing event awareness.
A Novel PMU Fog Based Early Anomaly Detection for an Efficient Wide Area PMU Network

Based on phasor measurement units (PMUs), a synchronphasor system is widely recognized as a promising smart grid measurement system. It is able to provide high-frequency, high-accuracy phasor measurements sampling for Wide Area Monitoring and Control (WAMC) applications.However,the high sampling frequency of measurement data under strict latency constraints introduces new challenges for real time communication. It would be very helpful if the collected data can be prioritized according to its importance such that the existing quality of service (QoS) mechanisms in the communication networks can be leveraged. To achieve this goal, certain anomaly detection functions should be conducted by the PMUs. Inspired by the recent emerging edge-fog-cloud computing hierarchical architecture, which allows computing tasks to be conducted at the network edge, a novel PMU fog is proposed in this paper. Two anomaly detection approaches, Singular Spectrum Analysis (SSA) and K-Nearest Neighbors (KNN), are evaluated in the PMU fog using the IEEE 16-machine 68-bus system. The simulation experiments based on Riverbed Modeler demonstrate that the proposed PMU fog can effectively reduce the data flow end-to-end (ETE) delay without sacrificing data completeness.
Penile squamous cell carcinoma with urethral extension treated with Mohs micrographic surgery.

Penile squamous cell carcinoma (SCC) with considerable urethral extension is uncommon and difficult to manage. It often is resistant to less invasive and nonsurgical treatments and frequently results in partial or total penectomy, which can lead to cosmetic disfigurement, functional issues, and psychological distress. We report a case of penile SCC in situ with considerable urethral extension with a focus of cells suspicious for moderately well-differentiated and invasive SCC that was treated with Mohs micrographic surgery (MMS). A review of the literature on penile tumors treated with MMS also is provided.
A copula-based joint deficit index for droughts

In most cases authors are permitted to post their version of the article (e.g. in Word or Tex form) to their personal website or institutional repository. Authors requiring further information regarding Elsevier's archiving and manuscript policies are encouraged to visit: s u m m a r y Current drought information is based on indices that do not capture the joint behaviors of hydrologic variables. To address this limitation, the potential of copulas in characterizing droughts from multiple variables is explored in this study. Starting from the standardized index (SI) algorithm, a modified index accounting for seasonality is proposed for precipitation and streamflow marginals. Utilizing Indiana stations with long-term observations (a minimum of 80 years for precipitation and 50 years for streamflow), the dependence structures of precipitation and streamflow marginals with various window sizes from 1-to 12-months are constructed from empirical copulas. A joint deficit index (JDI) is defined by using the distribution function of copulas. This index provides a probability-based description of the overall drought status. Not only is the proposed JDI able to reflect both emerging and prolonged droughts in a timely manner, it also allows a month-by-month drought assessment such that the required amount of precipitation for achieving normal conditions in future can be computed. The use of JDI is generalizable to other hydrologic variables as evidenced by similar drought severities gleaned from JDIs constructed separately from precipitation and streamflow data. JDI further allows the construction of an inter-variable drought index, where the entire dependence structure of precipitation and streamflow marginals is preserved.  Drought, as a prolonged status of water deficit, has been a challenging topic in water resources management. It is perceived as one of the most expensive and least understood natural disasters. In monetary terms, a typical drought costs American farmers and businesses $6–8 billion each year (WGA, 2004), more than damages incurred from floods and hurricanes. The consequences tend to be more severe in areas such as the mid-western part of the United States, where agriculture is the major economic driver. Unfortunately , though there is a strong need to develop an algorithm for characterizing and predicting droughts, it cannot be achieved easily either through physical or statistical analyses. The main obstacles are identification of complex drought-causing mechanisms, and lack of a precise (universal) scientific definition for droughts. When a drought event occurs, moisture deficits are observed in many hydrologic variables, such as precipitation, …
A novel big-data processing framwork for healthcare applications: Big-data-healthcare-in-a-box

Herein we present a novel big-data framework for healthcare applications. Healthcare data is well suited for bigdata processing and analytics because of the variety, veracity and volume of these types of data. In recent times, many areas within healthcare have been identified that can directly benefit from such treatment. However, setting up these types of architecture is not trivial. We present a novel approach of building a big-data framework that can be adapted to various healthcare applications with relative use, making this a one-stop “Big-Data-Healthcare-in-a-Box”.