nils-herrmann/clean_scirepeval huggingface dataset:
Factors Influencing Cryptocurrency Prices : Evidence from Bitcoin , Ethereum , Dash , Litcoin , and Monero

This paper examines factors that influence prices of most common five cryptocurrencies such Bitcoin, Ethereum, Dash, Litecoin, and Monero over 20102018 using weekly data. The study employs ARDL technique and documents several findings. First, cryptomarket-related factors such as market beta, trading volume, and volatility appear to be significant determinant for all five cryptocurrencies both in shortand long-run. Second, attractiveness of cryptocurrencies also matters in terms of their price determination, but only in long-run. This indicates that formation (recognition) of the attractiveness of cryptocurrencies are subjected to time factor. In other words, it travels slowly within the market. Third, SP500 index seems to have weak positive long-run impact on Bitcoin, Ethereum, and Litcoin, while its sign turns to negative losing significance in short-run, except Bitcoin that generates an estimate of -0.20 at 10% significance level. Lastly, error-correction models for Bitcoin, Etherem, Dash, Litcoin, and Monero show that cointegrated series cannot drift too far apart, and converge to a longrun equilibrium at a speed of 23.68%, 12.76%, 10.20%, 22.91%, and 14.27% respectively.
Combination of 595-nm pulsed dye laser, long-pulsed 755-nm alexandrite laser and microdermabrasion treatment for keratosis pilaris.

mechanobullous epidermolysis bullosa acquisita to combined treatment with immunoadsorption and rituximab (anti-CD20 monoclonal antibodies).  Treatment-resistant classical epidermolysis bullosa acquisita responding to rituximab. . 7 Crichlow SM, Mortimer NJ, Harman KE. A successful therapeutic trial of rituximab in the treatment of a patient with recalcitrant, high-titre epidermolysis bullosa acquisita. Br J Dermatol 2007; 156: 194–196. 8 Saha M, Cutler T, Bhogal B, Black MM, Groves RW. Refractory epidermolysis bullosa acquisita: successful treatment with rituximab.
Why we have switched from building full-fledged taxonomies to simply detecting hypernymy relations

The study of taxonomies and hypernymy relations has been extensive on the Natural Language Processing (NLP) literature. However, the evaluation of taxonomy learning approaches has been traditionally troublesome, as it mainly relies on ad-hoc experiments which are hardly reproducible and manually expensive. Partly because of this, current research has been lately focusing on the hypernymy detection task. In this paper we reflect on this trend, analyzing issues related to current evaluation procedures. Finally, we propose two potential avenues for future work so that is-a relations and resources based on them play a more important role in downstream NLP applications.
Maximizing Constructivist Learning From Multimedia Communications by Minimizing Cognitive Load

Students viewed an animation depicting either the process of lightning formation or how car brakes work and listened to a corresponding narration describing the steps. The entire animation and narration were presented at the same time (concurrent), the entire narration was presented before or after the entire animation (successive large bites), or short portions of the narration were presented before or after corresponding short portions of the animation for each successive portion of the presentation (successive small bites). Overall, the concurrent and successive small bites groups performed significantly better than the successive large bites groups on remembering the explanation in words (retention), generating solutions to transfer problems (transfer), and selecting verbal labels for elements in a line drawing (matching), but they did not differ significantly from each other. Results are consistent with a dual-process model of working memory in which learners are more likely to construct connections between words and corresponding pictures when they are held in working memory at the same time.
Detection of traffic signs in real-world images: The German traffic sign detection benchmark

Real-time detection of traffic signs, the task of pinpointing a traffic sign's location in natural images, is a challenging computer vision task of high industrial relevance. Various algorithms have been proposed, and advanced driver assistance systems supporting detection and recognition of traffic signs have reached the market. Despite the many competing approaches, there is no clear consensus on what the state-of-the-art in this field is. This can be accounted to the lack of comprehensive, unbiased comparisons of those methods. We aim at closing this gap by the “German Traffic Sign Detection Benchmark” presented as a competition at IJCNN 2013 (International Joint Conference on Neural Networks). We introduce a real-world benchmark data set for traffic sign detection together with carefully chosen evaluation metrics, baseline results, and a web-interface for comparing approaches. In our evaluation, we separate sign detection from classification, but still measure the performance on relevant categories of signs to allow for benchmarking specialized solutions. The considered baseline algorithms represent some of the most popular detection approaches such as the Viola-Jones detector based on Haar features and a linear classifier relying on HOG descriptors. Further, a recently proposed problem-specific algorithm exploiting shape and color in a model-based Houghlike voting scheme is evaluated. Finally, we present the best-performing algorithms of the IJCNN competition.
Effects of mindfulness-based stress reduction (MBSR) on emotion regulation in social anxiety disorder.

Mindfulness-based stress reduction (MBSR) is an established program shown to reduce symptoms of stress, anxiety, and depression. MBSR is believed to alter emotional responding by modifying cognitive-affective processes. Given that social anxiety disorder (SAD) is characterized by emotional and attentional biases as well as distorted negative self-beliefs, we examined MBSR-related changes in the brain-behavior indices of emotional reactivity and regulation of negative self-beliefs in patients with SAD. Sixteen patients underwent functional MRI while reacting to negative self-beliefs and while regulating negative emotions using 2 types of attention deployment emotion regulation-breath-focused attention and distraction-focused attention. Post-MBSR, 14 patients completed neuroimaging assessments. Compared with baseline, MBSR completers showed improvement in anxiety and depression symptoms and self-esteem. During the breath-focused attention task (but not the distraction-focused attention task), they also showed (a) decreased negative emotion experience, (b) reduced amygdala activity, and (c) increased activity in brain regions implicated in attentional deployment. MBSR training in patients with SAD may reduce emotional reactivity while enhancing emotion regulation. These changes might facilitate reduction in SAD-related avoidance behaviors, clinical symptoms, and automatic emotional reactivity to negative self-beliefs in adults with SAD.
City-Scale Localization for Cameras with Known Vertical Direction

We consider the problem of localizing a novel image in a large 3D model, given that the gravitational vector is known. In principle, this is just an instance of camera pose estimation, but the scale of the problem introduces some interesting challenges. Most importantly, it makes the correspondence problem very difficult so there will often be a significant number of outliers to handle. To tackle this problem, we use recent theoretical as well as technical advances. Many modern cameras and phones have gravitational sensors that allow us to reduce the search space. Further, there are new techniques to efficiently and reliably deal with extreme rates of outliers. We extend these methods to camera pose estimation by using accurate approximations and fast polynomial solvers. Experimental results are given demonstrating that it is possible to reliably estimate the camera pose despite cases with more than 99 percent outlier correspondences in city-scale models with several millions of 3D points.
Combining Smartphone and Smartwatch Sensor Data in Activity Recognition Approaches: an Experimental Evaluation

Activity recognition has been widely studied in ubiquitous computing since it can be used in several application domains, such as fall detection and gesture recognition. Initially, works in this area were based on research-only devices (bodyworn sensors). However, with advances in mobile computing, current research focuses on mobile devices, mainly, smartphones. These devices provide Internet access, processing, and various sensors, such as accelerometer and gyroscope, which are useful resources for activity recognition. Therefore, many studies use smartphones as data source. Additionally, some works have already considered the use of wristbands and specially-designed watches, but fewer investigate the latest marketable wearable devices, such as smartwatches, which are less intrusive and can provide new opportunities to complement smartphone data. Moreover, for the best of our knowledge, no previous work experimentally evaluates the impact caused by the combination of sensor data from smartwatches and smartphones on the accuracy of activity recognition approaches. Therefore, the main goal of this experimental evaluation is to compare the use of data from smartphones as well as the combination of data from smartphones and smartwatches for activity recognition. We evidenced that the use of smartphone and smartwatch data combined can increase the accuracy of activity recognition.
Investigating users' query formulations for cognitive search intents

This study investigated query formulations by users with {\it Cognitive Search Intents} (CSIs), which are users' needs for the cognitive characteristics of documents to be retrieved, {\em e.g. comprehensibility, subjectivity, and concreteness. Our four main contributions are summarized as follows (i) we proposed an example-based method of specifying search intents to observe query formulations by users without biasing them by presenting a verbalized task description;(ii) we conducted a questionnaire-based user study and found that about half our subjects did not input any keywords representing CSIs, even though they were conscious of CSIs;(iii) our user study also revealed that over 50\% of subjects occasionally had experiences with searches with CSIs while our evaluations demonstrated that the performance of a current Web search engine was much lower when we not only considered users' topical search intents but also CSIs; and (iv) we demonstrated that a machine-learning-based query expansion could improve the performances for some types of CSIs.Our findings suggest users over-adapt to current Web search engines,and create opportunities to estimate CSIs with non-verbal user input.
Computer-Mediated Communication Effects on Disclosure , Impressions , and Interpersonal Evaluations Getting to Know One Another a Bit at a Time

This investigation examined how computer-mediated communication (CMC) partners exchange personal information in initial interactions, focusing on the effects of communication channels on self-disclosure, question-asking, and uncertainty reduction. Unacquainted individuals (N = 158) met either face-to-face or via CMC. Computer-mediated interactants exhibited a greater proportion of more direct and intimate uncertainty reduction behaviors than unmediated participants did, and demonstrated significantly greater gains in attributional confidence over the course of the conversations. The use of direct strategies by mediated interactants resulted in judgments of greater conversational effectiveness by partners. Results illuminate some microstructures previously asserted but unverified within social information processing theory (Walther, 1992), and extend uncertainty reduction theory (Berger & Calabrese, 1975) to CMC interaction.
Toward a statistical mechanics of four letter words

We consider words as a network of interacting letters, and approximate the probability distribution of states taken on by this network. Despite the intuition that the rules of English spelling are highly combinatorial (and arbitrary), we find that maximum entropy models consistent with pairwise correlations among letters provide a surprisingly good approximation to the full statistics of four letter words, capturing ∼ 92% of the multi–information among letters and even ‘discovering’ real words that were not represented in the data from which the pairwise correlations were estimated. The maximum entropy model defines an energy landscape on the space of possible words, and local minima in this landscape account for nearly two–thirds of words used in written English.
Substrate Integrated Folded Waveguide (SIFW) narrow-wall directional coupler

For low microwave band, the size of substrate integrated waveguide (SIW) couplers still need to be further reduced though other advantages of low profile, low insertion loss, low cost etc. are very remarkable. In this letter, novel substrate integrated folded waveguide (SIFW) narrow-wall single-slot and double-slot directional couplers are designed and implemented. The measured results are in good agreement with simulations. A corresponding SIW coupler is also designed for comparison. The SIFW coupler keeps almost all the advantages of the SIW coupler with nearly a half reduction in size.
Text line segmentation of historical documents: a survey

There is a huge amount of historical documents in libraries and in various National Archives that have not been exploited electronically. Although automatic reading of complete pages remains, in most cases, a long-term objective, tasks such as word spotting, text/image alignment, authentication and extraction of specific fields are in use today. For all these tasks, a major step is document segmentation into text lines. Because of the low quality and the complexity of these documents (background noise, artifacts due to aging, interfering lines), automatic text line segmentation remains an open research field. The objective of this paper is to present a survey of existing methods, developed during the last decade and dedicated to documents of historical interest.
Evaluation of symptom checkers for self diagnosis and triage: audit study

To determine the diagnostic and triage accuracy of online symptom checkers (tools that use computer algorithms to help patients with self diagnosis or self triage).



Audit study.



Publicly available, free symptom checkers.



23 symptom checkers that were in English and provided advice across a range of conditions. 45 standardized patient vignettes were compiled and equally divided into three categories of triage urgency: emergent care required (for example, pulmonary embolism), non-emergent care reasonable (for example, otitis media), and self care reasonable (for example, viral upper respiratory tract infection).



For symptom checkers that provided a diagnosis, our main outcomes were whether the symptom checker listed the correct diagnosis first or within the first 20 potential diagnoses (n=770 standardized patient evaluations). For symptom checkers that provided a triage recommendation, our main outcomes were whether the symptom checker correctly recommended emergent care, non-emergent care, or self care (n=532 standardized patient evaluations).



The 23 symptom checkers provided the correct diagnosis first in 34% (95% confidence interval 31% to 37%) of standardized patient evaluations, listed the correct diagnosis within the top 20 diagnoses given in 58% (55% to 62%) of standardized patient evaluations, and provided the appropriate triage advice in 57% (52% to 61%) of standardized patient evaluations. Triage performance varied by urgency of condition, with appropriate triage advice provided in 80% (95% confidence interval 75% to 86%) of emergent cases, 55% (47% to 63%) of non-emergent cases, and 33% (26% to 40%) of self care cases (P<0.001). Performance on appropriate triage advice across the 23 individual symptom checkers ranged from 33% (95% confidence interval 19% to 48%) to 78% (64% to 91%) of standardized patient evaluations.



Symptom checkers had deficits in both triage and diagnosis. Triage advice from symptom checkers is generally risk averse, encouraging users to seek care for conditions where self care is reasonable.
EEG Data Space Adaptation to Reduce Intersession Nonstationarity in Brain-Computer Interface

A major challenge in EEG-based brain-computer interfaces (BCIs) is the intersession nonstationarity in the EEG data that often leads to deteriorated BCI performances. To address this issue, this letter proposes a novel data space adaptation technique, EEG data space adaptation (EEG-DSA), to linearly transform the EEG data from the target space (evaluation session), such that the distribution difference to the source space (training session) is minimized. Using the Kullback-Leibler (KL) divergence criterion, we propose two versions of the EEG-DSA algorithm: the supervised version, when labeled data are available in the evaluation session, and the unsupervised version, when labeled data are not available. The performance of the proposed EEG-DSA algorithm is evaluated on the publicly available BCI Competition IV data set IIa and a data set recorded from 16 subjects performing motor imagery tasks on different days. The results show that the proposed EEG-DSA algorithm in both the supervised and unsupervised versions significantly outperforms the results without adaptation in terms of classification accuracy. The results also show that for subjects with poor BCI performances when no adaptation is applied, the proposed EEG-DSA algorithm in both the supervised and unsupervised versions significantly outperforms the unsupervised bias adaptation algorithm (PMean).
Enhancing K-12 education with alice programming adventures

This paper describes the integration of the Alice 3D virtual worlds environment into many disciplines in elementary school, middle school and high school. We have developed a wide range of Alice instructional materials including tutorials for both computer science concepts and animation concepts. To encourage the building of more complicated worlds, we have developed template Alice classes and worlds. With our materials, teachers and students are exposed to computing concepts while using Alice to create projects, stories, games and quizzes. These materials were successfully used in the summers 2008 and 2009 in training and working with over 130 teachers.
A 3D passive dynamic biped with yaw and roll compensation

Autonomous walking bipedal machines, possibly useful for rehabilitation and entertainment purposes, need a high energy efficiency, offered by the concept of ‘Passive Dynamic Walking’ (exploitation of the natural dynamics of the robot). 2D passive dynamic bipeds have been shown to be inherently stable, but in the third dimension two problematic degrees of freedom are introduced: yaw and roll. We propose a design for a 3D biped with a pelvic body as a passive dynamic compensator, which will compensate for the undesired yaw and roll motion, and allow the rest of the robot to move as if it were a 2D machine. To test our design, we perform numerical simulations on a multibody model of the robot. With limit cycle analysis we calculate the stability of the robot when walking at its natural speed. The simulation shows that the compensator, indeed, effectively compensates for both the yaw and the roll motion, and that the walker is stable.
"I regretted the minute I pressed share": a qualitative study of regrets on Facebook

We investigate regrets associated with users' posts on a popular social networking site. Our findings are based on a series of interviews, user diaries, and online surveys involving 569 American Facebook users. Their regrets revolved around sensitive topics, content with strong sentiment, lies, and secrets. Our research reveals several possible causes of why users make posts that they later regret: (1) they want to be perceived in favorable ways, (2) they do not think about their reason for posting or the consequences of their posts, (3) they misjudge the culture and norms within their social circles, (4) they are in a "hot" state of high emotion when posting, or under the influence of drugs or alcohol, (5) their postings are seen by an unintended audience, (6) they do not foresee how their posts could be perceived by people within their intended audience, and (7) they misunderstand or misuse the Facebook platform. Some reported incidents had serious repercussions, such as breaking up relationships or job losses. We discuss methodological considerations in studying negative experiences associated with social networking posts, as well as ways of helping users of social networking sites avoid such regrets.
Ranking Visualizations of Correlation Using Weber's Law

Despite years of research yielding systems and guidelines to aid visualization design, practitioners still face the challenge of identifying the best visualization for a given dataset and task. One promising approach to circumvent this problem is to leverage perceptual laws to quantitatively evaluate the effectiveness of a visualization design. Following previously established methodologies, we conduct a large scale (n = 1687) crowdsourced experiment to investigate whether the perception of correlation in nine commonly used visualizations can be modeled using Weber's law. The results of this experiment contribute to our understanding of information visualization by establishing that: (1) for all tested visualizations, the precision of correlation judgment could be modeled by Weber's law, (2) correlation judgment precision showed striking variation between negatively and positively correlated data, and (3) Weber models provide a concise means to quantify, compare, and rank the perceptual precision afforded by a visualization.
Lightweight Attribute-Based Encryption for the Internet of Things

The large volume of data produced by the increasingly deployed Internet of Things (IoT), is shifting security priorities to consider data access control from a data-centric perspective. To secure the IoT, it becomes essential to implement a data access control solution that offers the necessary flexibility required to manage a large number of IoT devices. The concept of Ciphertext-Policy Attribute-based Encryption (CP-ABE) fulfills such requirement. It allows the data source to encrypt data while cryptographically enforcing a security access policy, whereby only authorized data users with the desired attributes are able to decrypt data. Yet, despite these manifest advantages; CP-ABE has not been designed taking into consideration energy efficiency. Many IoT devices, like sensors and actuators, cannot be part of CP-ABE enforcement points, because of their resource limitations in terms of CPU, memory, battery, etc. In this paper, we propose to extend the basic CP-ABE scheme using effective pre-computation techniques. We will experimentally compute the energy saving potential offered by the proposed variant of CP-ABE, and thus demonstrate the applicability of CP-ABE in the IoT.
Speaker Verification Using Adapted Gaussian Mixture Models

In this paper we describe the major elements of MIT Lincoln Laboratory’s Gaussian mixture model (GMM)-based speaker verification system used successfully in several NIST Speaker Recognition Evaluations (SREs). The system is built around the likelihood ratio test for verification, using simple but effective GMMs for likelihood functions, a universal background model (UBM) for alternative speaker representation, and a form of Bayesian adaptation to derive speaker models from the UBM. The development and use of a handset detector and score normalization to greatly improve verification performance is also described and discussed. Finally, representative performance benchmarks and system behavior experiments on NIST SRE corpora are presented.