nils-herrmann/clean_scirepeval huggingface dataset:
Classifying Visual Helmet-Mounted Display Designs

In order to fully understand the sensory, perceptual, and cognitive issues associated with helmet-/head-mounted displays (HMDs), it is essential to possess an understanding of exactly what constitutes an HMD, the various design types, their advantages and limitations, and their applications. It also is useful to explore the developmental history of these systems. Such an exploration can reveal the major engineering, human factors, and ergonomic issues encountered in the development cycle. These identified issues usually are indicators of where the most attention needs to be placed when evaluating the usefulness of such systems. New HMD systems are implemented because they are intended to provide some specific capability or performance enhancement. However, these improvements always come at a cost. In reality, the introduction of technology is a tradeoff endeavor. It is necessary to identify and assess the tradeoffs that impact overall system and user sensory systems performance. HMD developers have often and incorrectly assumed that the human visual and auditory systems are fully capable of accepting the added sensory and cognitive demands of an HMD system without incurring performance degradation or introducing perceptual illusions. Situation awareness (SA), essential in preventing actions or inactions that lead to catastrophic outcomes, may be degraded if the HMD interferes with normal perceptual processes, resulting in misinterpretations or misperceptions (illusions). As HMD applications increase, it is important to maintain an awareness of both current and future programs. Unfortunately, in these developmental programs, one factor still is often minimized. This factor is how the user accepts and eventually uses the HMD. In the demanding rigors of warfare, the user rapidly decides whether using a new HMD, intended to provide tactical and other information, outweighs the impact the HMD has on survival and immediate mission success. If the system requires an unacceptable compromise in any aspect of mission completion deemed critical to the Warfighter, the HMD will not be used. Technology in which the Warfighter does have confidence or determines to be a liability will go unused.
Calcium sparks in smooth muscle.

Local intracellular Ca(2+) transients, termed Ca(2+) sparks, are caused by the coordinated opening of a cluster of ryanodine-sensitive Ca(2+) release channels in the sarcoplasmic reticulum of smooth muscle cells. Ca(2+) sparks are activated by Ca(2+) entry through dihydropyridine-sensitive voltage-dependent Ca(2+) channels, although the precise mechanisms of communication of Ca(2+) entry to Ca(2+) spark activation are not clear in smooth muscle. Ca(2+) sparks act as a positive-feedback element to increase smooth muscle contractility, directly by contributing to the global cytoplasmic Ca(2+) concentration ([Ca(2+)]) and indirectly by increasing Ca(2+) entry through membrane potential depolarization, caused by activation of Ca(2+) spark-activated Cl(-) channels. Ca(2+) sparks also have a profound negative-feedback effect on contractility by decreasing Ca(2+) entry through membrane potential hyperpolarization, caused by activation of large-conductance, Ca(2+)-sensitive K(+) channels. In this review, the roles of Ca(2+) sparks in positive- and negative-feedback regulation of smooth muscle function are explored. We also propose that frequency and amplitude modulation of Ca(2+) sparks by contractile and relaxant agents is an important mechanism to regulate smooth muscle function.
The Prevalence and Risk Factors of Functional Dyspepsia in a Multiethnic Population in the United States

The prevalence of functional dyspepsia (FD) in the general population is not known. The aim of this study is to measure the prevalence of FD and its risk factors in a multiethnic volunteer sample of the U.S. population.One thousand employees at the Houston VA Medical Center were targeted with a symptom questionnaire asking about upper abdominal symptoms, followed by a request to undergo endsocopy. Dyspepsia was defined by the presence of epigastric pain, fullness, nausea, or vomiting, and FD was defined as dyspepsia in the absence of esophageal erosions, gastric ulcers, or duodenal ulcers or erosions. The presence of dyspepsia and FD was examined in multiple logistic regression analyses.A total of 465 employees completed the relevant questions and of those 203 had endoscopic examination. The age-adjusted prevalence rate of dyspepsia was 31.9 per 100 (95% CI: 26.7–37.1), and 15.8 per 100 (95% CI: 9.6–22.0) if participants with concomitant heartburn or acid regurgitation were excluded. Subjects with dyspepsia were more likely to report smoking, using antacids, aspirin or nonsteroidal antiinflammatory drugs (NSAIDs), and consulting a physician for their symptoms (p < 0.05) than participants without dyspepsia. Most (64.5%) participants with dyspepsia who underwent endoscopy had FD. The age-adjusted prevalence rate of FD was 29.2 per 100 (95% CI: 21.9–36.5), and 15.0 per 100 (6.7–23.3) if subjects with GERD were excluded. Apart from a trend towards association with older age in the multiple regression analysis, there were no significant predictors of FD among participants with dyspepsia.Most subjects with dyspepsia have FD. The prevalence of FD is high but predictors of FD remain poorly defined.
Combining Weakly and Webly Supervised Learning for Classifying Food Images

Food classification from images is a fine-grained classification problem. Manual curation of food images is cost, time and scalability prohibitive. On the other hand, web data is available freely but contains noise. In this paper, we address the problem of classifying food images with minimal data curation. We also tackle a key problems with food images from the web where they often have multiple cooccuring food types but are weakly labeled with a single label. We first demonstrate that by sequentially adding a few manually curated samples to a larger uncurated dataset from two web sources, the top-1 classification accuracy increases from 50.3% to 72.8%. To tackle the issue of weak labels, we augment the deep model with Weakly Supervised learning (WSL) that results in an increase in performance to 76.2%. Finally, we show some qualitative results to provide insights into the performance improvements using the proposed ideas.
An Efficient DBSCAN using Genetic Algorithm based Clustering

Data mining is widely employed in business management and engineering. The major objective of data mining is to discover helpful and accurate information among a vast quantity of data, providing a orientation basis for decision makers. Data clustering is currently a very popular and frequently applied analytical method in data mining. DBSCAN is a traditional and widely-accepted density-based clustering method. It is used to find clusters of arbitrary shapes and sizes yet may have trouble with clusters of varying density. In this paper an efficient DBSCAN clustering using genetic algorithm is proposed. DBSCAN clustering provides some problem such as the algorithm is not efficient for noisy clusters; hence it is enhanced using genetic algorithm. The proposed technique is efficient in terms of accuracy and execution time.
Smart parking pricing: A machine learning approach

Crowded streets are a major problem in large cities. A large part of the problem stems from drivers seeking on-street parking. Cities such as San Francisco, Los Angeles and Seattle have tackled this problem with smart parking systems that aim to maintain the on-street parking occupancy rates around a target level, thus ensuring that empty spots are spread across the city rather than clustered in a single area. In this study, we use the San Francisco's SFpark system as a case study. Specifically, in each given parking area, the SFpark uses occupancy rate data from the previous month to adjust the price in the current month. Instead, we propose a machine learning approach that predicts the occupancy rate of a parking area based on past occupancy rates and prices from an entire neighborhood (which covers many parking areas). We further formulate an optimization problem for the prices in each parking area that minimize the root mean squared error (RMSE) between the predicted occupancy rates of all areas in the neighborhood and the target occupancy rates. This approach is novel in that 1) it responds to a predicted level of occupancy rate rather than past data and 2) it find prices that optimize the total occupancy rate of all neighborhoods, taking under account that prices in one area can impact the demand in adjacent areas. We conduct a numerical study, using data collected from the SFpark study, that shows that the prices obtained from our optimization lead to occupancy rates that are very close to the desired target level.
Uncovering Divergent Linguistic Information in Word Embeddings with Lessons for Intrinsic and Extrinsic Evaluation

Following the recent success of word embeddings, it has been argued that there is no such thing as an ideal representation for words, as different models tend to capture divergent and often mutually incompatible aspects like semantics/syntax and similarity/relatedness. In this paper, we show that each embedding model captures more information than directly apparent. A linear transformation that adjusts the similarity order of the model without any external resource can tailor it to achieve better results in those aspects, providing a new perspective on how embeddings encode divergent linguistic information. In addition, we explore the relation between intrinsic and extrinsic evaluation, as the effect of our transformations in downstream tasks is higher for unsupervised systems than for supervised ones.
A Convolutional Click Prediction Model

The explosion in online advertisement urges to better estimate the click prediction of ads. For click prediction on single ad impression, we have access to pairwise relevance among elements in an impression, but not to global interaction among key features of elements. Moreover, the existing method on sequential click prediction treats propagation unchangeable for different time intervals. In this work, we propose a novel model, Convolutional Click Prediction Model (CCPM), based on convolution neural network. CCPM can extract local-global key features from an input instance with varied elements, which can be implemented for not only single ad impression but also sequential ad impression. Experiment results on two public large-scale datasets indicate that CCPM is effective on click prediction.
Examining the Critical Success Factors of Mobile Website Adoption

The purpose of this research is to examine the critical success factors of mobile web site adoption.  Based on the valid responses collected from a questionnaire survey, the structural equation modelling technique was employed to examine the research model.  The results indicate that system quality is the main factor affecting perceived ease of use, whereas information quality is the main factor affecting perceived usefulness. Service quality has significant effects on trust and perceived ease of use. Perceived usefulness, perceived ease of use and trust determine user satisfaction.  Mobile service providers need to improve the system quality, information quality and service quality of mobile web sites to enhance user satisfaction.  Previous research has mainly focused on e-commerce web site success and seldom examined the factors affecting mobile web site success. This research fills the gap. The research draws on information systems success theory, the technology acceptance model and trust theory as the theoretical bases.
A generalized rule based tracker for dialogue state tracking

Dialogue state tracking plays an important role in statistical dialogue management. Domain-independent rule-based approaches are attractive due to their efficiency, portability and interpretability. However, recent rule-based models are still not quite competitive to statistical tracking approaches. In this paper, a novel framework is proposed to formulate rule-based models in a general way. In the framework, a rule is considered as a special kind of polynomial function satisfying certain linear constraints. Under some particular definitions and assumptions, rule-based models can be seen as feasible solutions of an integer linear programming problem. Experiments showed that the proposed approach can not only achieve competitive performance compared to statistical approaches, but also have good generalisation ability. It is one of the only two entries that outperformed all the four baselines in the third Dialog State Tracking Challenge.
A scoping review on the conduct and reporting of scoping reviews

Scoping reviews are used to identify knowledge gaps, set research agendas, and identify implications for decision-making. The conduct and reporting of scoping reviews is inconsistent in the literature. We conducted a scoping review to identify: papers that utilized and/or described scoping review methods; guidelines for reporting scoping reviews; and studies that assessed the quality of reporting of scoping reviews.



We searched nine electronic databases for published and unpublished literature scoping review papers, scoping review methodology, and reporting guidance for scoping reviews. Two independent reviewers screened citations for inclusion. Data abstraction was performed by one reviewer and verified by a second reviewer. Quantitative (e.g. frequencies of methods) and qualitative (i.e. content analysis of the methods) syntheses were conducted.



After searching 1525 citations and 874 full-text papers, 516 articles were included, of which 494 were scoping reviews. The 494 scoping reviews were disseminated between 1999 and 2014, with 45% published after 2012. Most of the scoping reviews were conducted in North America (53%) or Europe (38%), and reported a public source of funding (64%). The number of studies included in the scoping reviews ranged from 1 to 2600 (mean of 118). Using the Joanna Briggs Institute methodology guidance for scoping reviews, only 13% of the scoping reviews reported the use of a protocol, 36% used two reviewers for selecting citations for inclusion, 29% used two reviewers for full-text screening, 30% used two reviewers for data charting, and 43% used a pre-defined charting form. In most cases, the results of the scoping review were used to identify evidence gaps (85%), provide recommendations for future research (84%), or identify strengths and limitations (69%). We did not identify any guidelines for reporting scoping reviews or studies that assessed the quality of scoping review reporting.



The number of scoping reviews conducted per year has steadily increased since 2012. Scoping reviews are used to inform research agendas and identify implications for policy or practice. As such, improvements in reporting and conduct are imperative. Further research on scoping review methodology is warranted, and in particular, there is need for a guideline to standardize reporting.
Revising the evolutionary computation abstraction: minimal criteria novelty search

Though based on abstractions of nature, current evolutionary algorithms and artificial life models lack the drive to complexity characteristic of natural evolution. Thus this paper argues that the prevalent fitness-pressure-based abstraction does not capture how natural evolution discovers complexity. Alternatively, this paper proposes that natural evolution can be abstracted as a process that discovers many ways to express the same functionality. That is, all successful organisms must meet the same minimal criteria of survival and reproduction. This abstraction leads to the key idea in this paper: Searching for novel ways of meeting the same minimal criteria, which is an accelerated model of this new abstraction, may be an effective search algorithm. Thus the existing novelty search method, which rewards any new behavior, is extended to enforce minimal criteria. Such minimal criteria novelty search prunes the space of viable behaviors and may often be more efficient than the search for novelty alone. In fact, when compared to the raw search for novelty and traditional fitness-based search in the two maze navigation experiments in this paper, minimal criteria novelty search evolves solutions more consistently. It is possible that refining the evolutionary computation abstraction in this way may lead to solving more ambitious problems and evolving more complex artificial organisms.
Preference for Women's Body Mass and Waist-to-Hip Ratio in Tsimane' Men of the Bolivian Amazon: Biological and Cultural Determinants

The issue of cultural universality of waist-to-hip ratio (WHR) attractiveness in women is currently under debate. We tested men's preferences for female WHR in traditional society of Tsimane'(Native Amazonians) of the Bolivian rainforest (N = 66). Previous studies showed preferences for high WHR in traditional populations, but they did not control for the women's body mass.We used a method of stimulus creation that enabled us to overcome this problem. We found that WHR lower than the average WHR in the population is preferred independent of cultural conditions. Our participants preferred the silhouettes of low WHR, but high body mass index (BMI), which might suggest that previous results could be an artifact related to employed stimuli. We found also that preferences for female BMI are changeable and depend on environmental conditions and probably acculturation (distance from the city). Interestingly, the Tsimane' men did not associate female WHR with age, health, physical strength or fertility. This suggests that men do not have to be aware of the benefits associated with certain body proportions - an issue that requires further investigation.
The Clojure programming language

Customers and stakeholders have substantial investments in, and are comfortable with the performance, security and stability of, industry-standard platforms like the JVM and CLR. While Java and C# developers on those platforms may envy the succinctness, flexibility and productivity of dynamic languages, they have concerns about running on customer-approved infrastructure, access to their existing code base and libraries, and performance. In addition, they face ongoing problems dealing with concurrency using native threads and locking. Clojure is an effort in pragmatic dynamic language design in this context. It endeavors to be a general-purpose language suitable in those areas where Java is suitable. It reflects the reality that, for the concurrent programming future, pervasive, unmoderated mutation simply has to go. Clojure meets its goals by: embracing an industry-standard, open platform - the JVM; modernizing a venerable language - Lisp; fostering functional programming with immutable persistent data structures; and providing built-in concurrency support via software transactional memory and asynchronous agents. The result is robust, practical, and fast. This talk will focus on the motivations, mechanisms and experiences of the implementation of Clojure.
Transfer of several phytopathogenic Pseudomonas species to Acidovorax as Acidovorax avenae subsp. avenae subsp. nov., comb. nov., Acidovorax avenae subsp. citrulli, Acidovorax avenae subsp. cattleyae, and Acidovorax konjaci.

DNA-rRNA hybridizations, DNA-DNA hybridizations, polyacrylamide gel electrophoresis of whole-cell proteins, and a numerical analysis of carbon assimilation tests were carried out to determine the relationships among the phylogenetically misnamed phytopathogenic taxa Pseudomonas avenae, Pseudomonas rubrilineans, "Pseudomonas setariae," Pseudomonas cattleyae, Pseudomonas pseudoalcaligenes subsp. citrulli, and Pseudomonas pseudoalcaligenes subsp. konjaci. These organisms are all members of the family Comamonadaceae, within which they constitute a separate rRNA branch. Only P. pseudoalcaligenes subsp. konjaci is situated on the lower part of this rRNA branch; all of the other taxa cluster very closely around the type strain of P. avenae. When they are compared phenotypically, all of the members of this rRNA branch can be differentiated from each other, and they are, as a group, most closely related to the genus Acidovorax. DNA-DNA hybridization experiments showed that these organisms constitute two genotypic groups. We propose that the generically misnamed phytopathogenic Pseudomonas species should be transferred to the genus Acidovorax as Acidovorax avenae and Acidovorax konjaci. Within Acidovorax avenae we distinguished the following three subspecies: Acidovorax avenae subsp. avenae, Acidovorax avenae subsp. cattleyae, and Acidovorax avenae subsp. citrulli. Emended descriptions of the new taxa are presented.
Logarithmic Time One-Against-Some

We create a new online reduction of multiclass classification to binary classification for which training and prediction time scale logarithmically with the number of classes. We show that several simple techniques give rise to an algorithm which is superior to previous logarithmic time classification approaches while competing with one-against-all in space. The core construction is based on using a tree to select a small subset of labels with high recall, which are then scored using a one-against-some structure with high precision.
Ontology and semantic wiki for an Intangible Cultural Heritage inventory

The increasing globalization has made the preservation of the Intangible Cultural Heritage (ICH) an urgent need, and the UNESCO's states parties have compromised to make collaborative inventories of ICH. Many traditional inventories become obsolete quickly because they present rigid data models and/or because data adquisition from scarce specialists are required. This article proposes to introduce a participative inventory as a semantic wiki, combining the familiarity of the audience with the free text, the expressive power of ontologies, and the benefits of wikis for the controled social enrichment. The scope has been validated in a pilot inventory with public access, that combine an Ontowiki instance, a new ontology based on UNESCO's clasification, and the data of an existent Chilean ICH inventory. Preliminary results indicate that the semantic-based catalog is much more flexible than existing traditional catalogs. Using these collaborative enrichment will enable active involvement to citizenship in recording their immaterial heritage.
RARD II: The 2nd Related-Article Recommendation Dataset

The main contribution of this paper is to introduce and describe a new recommender-systems dataset (RARD II). It is based on data from a recommender-system in the digital library and reference management software domain. As such, it complements datasets from other domains such as books, movies, and music. The RARD II dataset encompasses 89m recommendations, covering an item-space of 24m unique items. RARD II provides a range of rich recommendation data, beyond conventional ratings. For example, in addition to the usual ratings matrices, RARD II includes the original recommendation logs, which provide a unique insight into many aspects of the algorithms that generated the recommendations. In this paper, we summarise the key features of this dataset release, describing how it was generated and discussing some of its unique features.