nils-herrmann/clean_scirepeval huggingface dataset:
FAB-MAP: Probabilistic Localization and Mapping in the Space of Appearance

This paper describes a probabilistic approach to the problem of recognizing places based on their appearance. The system we present is not limited to localization, but can determine that a new observation comes from a previously unseen place, and so augment its map. Effectively this is a SLAM system in the space of appearance. Our probabilistic approach allows us to explicitly account for perceptual aliasing in the environment—identical but indistinctive observations receive a low probability of having come from the same place. We achieve this by learning a generative model of place appearance. By partitioning the learning problem into two parts, new place models can be learned online from only a single observation of a place. The algorithm complexity is linear in the number of places in the map, and is particularly suitable for online loop closure detection in mo-
Towards Twitter hashtag recommendation using distributed word representations and a deep feed forward neural network

Hashtags are useful for categorizing and discovering content and conversations in online social networks. However, assigning hashtags requires additional user effort, hampering their widespread adoption. Therefore, in this paper, we introduce a novel approach for hashtag recommendation, targeting English language tweets on Twitter. First, we make use of a skip-gram model to learn distributed word representations (word2vec). Next, we make use of the distributed word representations learned to train a deep feed forward neural network. We test our deep neural network by recommending hashtags for tweets with user-assigned hashtags, using Mean Squared Error (MSE) as the objective function. We also test our deep neural network by recommending hashtags for tweets without user-assigned hashtags. Our experimental results show that the proposed approach recommends hashtags that are specific to the semantics of the tweets and that preserve the linguistic regularity of the tweets. In addition, our experimental results show that the proposed approach is capable of generating hashtags that have not been seen before.
Accelerating Deep Neural Networks with Spatial Bottleneck Modules

This paper presents an efficient module named spatial bottleneck for accelerating the convolutional layers in deep neural networks. The core idea is to decompose convolution into two stages, which first reduce the spatial resolution of the feature map, and then restore it to the desired size. This operation decreases the sampling density in the spatial domain, which is independent yet complementary to network acceleration approaches in the channel domain. Using different sampling rates, we can tradeoff between recognition accuracy and model complexity. As a basic building block, spatial bottleneck can be used to replace any single convolutional layer, or the combination of two convolutional layers. We empirically verify the effectiveness of spatial bottleneck by applying it to the deep residual networks. Spatial bottleneck achieves 2× and 1.4× speedup on the regular and channel-bottlenecked residual blocks, respectively, with the accuracies retained in recognizing low-resolution images, and even improved in recognizing high-resolution images.
The role of mycorrhizae and plant growth promoting rhizobacteria (PGPR) in improving crop productivity under stressful environments.

Both biotic and abiotic stresses are major constrains to agricultural production. Under stress conditions, plant growth is affected by a number of factors such as hormonal and nutritional imbalance, ion toxicity, physiological disorders, susceptibility to diseases, etc. Plant growth under stress conditions may be enhanced by the application of microbial inoculation including plant growth promoting rhizobacteria (PGPR) and mycorrhizal fungi. These microbes can promote plant growth by regulating nutritional and hormonal balance, producing plant growth regulators, solubilizing nutrients and inducing resistance against plant pathogens. In addition to their interactions with plants, these microbes also show synergistic as well as antagonistic interactions with other microbes in the soil environment. These interactions may be vital for sustainable agriculture because they mainly depend on biological processes rather than on agrochemicals to maintain plant growth and development as well as proper soil health under stress conditions. A number of research articles can be deciphered from the literature, which shows the role of rhizobacteria and mycorrhizae alone and/or in combination in enhancing plant growth under stress conditions. However, in contrast, a few review papers are available which discuss the synergistic interactions between rhizobacteria and mycorrhizae for enhancing plant growth under normal (non-stress) or stressful environments. Biological interactions between PGPR and mycorrhizal fungi are believed to cause a cumulative effect on all rhizosphere components, and these interactions are also affected by environmental factors such as soil type, nutrition, moisture and temperature. The present review comprehensively discusses recent developments on the effectiveness of PGPR and mycorrhizal fungi for enhancing plant growth under stressful environments. The key mechanisms involved in plant stress tolerance and the effectiveness of microbial inoculation for enhancing plant growth under stress conditions have been discussed at length in this review. Growth promotion by single and dual inoculation of PGPR and mycorrhizal fungi under stress conditions have also been discussed and reviewed comprehensively.
Sexually transmitted diseases treatment guidelines, 2010.

These guidelines for the treatment of persons who have or are at risk for sexually transmitted diseases (STDs) were updated by CDC after consultation with a group of professionals knowledgeable in the field of STDs who met in Atlanta on April 18-30, 2009. The information in this report updates the 2006 Guidelines for Treatment of Sexually Transmitted Diseases (MMWR 2006;55[No. ). Included in these updated guidelines is new information regarding 1) the expanded diagnostic evaluation for cervicitis and trichomoniasis; 2) new treatment recommendations for bacterial vaginosis and genital warts; 3) the clinical efficacy of azithromycin for chlamydial infections in pregnancy; 4) the role of Mycoplasma genitalium and trichomoniasis in urethritis/cervicitis and treatment-related implications; 5) lymphogranuloma venereum proctocolitis among men who have sex with men; 6) the criteria for spinal fluid examination to evaluate for neurosyphilis; 7) the emergence of azithromycin-resistant Treponema pallidum; 8) the increasing prevalence of antimicrobial-resistant Neisseria gonorrhoeae; 9) the sexual transmission of hepatitis C; 10) diagnostic evaluation after sexual assault; and 11) STD prevention approaches.
Mobile applications for weight management: theory-based content analysis.

The use of smartphone applications (apps) to assist with weight management is increasingly prevalent, but the quality of these apps is not well characterized.



The goal of the study was to evaluate diet/nutrition and anthropometric tracking apps based on incorporation of features consistent with theories of behavior change.



A comparative, descriptive assessment was conducted of the top-rated free apps in the Health and Fitness category available in the iTunes App Store. Health and Fitness apps (N=200) were evaluated using predetermined inclusion/exclusion criteria and categorized based on commonality in functionality, features, and developer description. Four researchers then evaluated the two most popular apps in each category using two instruments: one based on traditional behavioral theory (score range: 0-100) and the other on the Fogg Behavioral Model (score range: 0-6). Data collection and analysis occurred in November 2012.



Eligible apps (n=23) were divided into five categories: (1) diet tracking; (2) healthy cooking; (3) weight/anthropometric tracking; (4) grocery decision making; and (5) restaurant decision making. The mean behavioral theory score was 8.1 (SD=4.2); the mean persuasive technology score was 1.9 (SD=1.7). The top-rated app on both scales was Lose It! by Fitnow Inc.



All apps received low overall scores for inclusion of behavioral theory-based strategies.
A novel pilot-based frequency offset compensation method in MIMO-OFDM Systems

MIMO-OFDM is the promising technique in the future of wireless communications as it combines the properties of Multiple Input Multiple Output (MIMO) systems with those of Orthogonal Frequency Division Multiplexing (OFDM). However, it is very sensitive to frequency offsets which result in inter-carrier interference (ICI). Therefore, to precisely recover the transmitted data, frequency offset compensation is necessary in MIMO-OFDM receivers. This paper mainly focuses on frequency offset estimation and compensation problem. We use channel coefficients to accurately follow all the frequency offsets. In our proposed receiver the initial estimation is done using a polynomial curve fitting method. To improve system performance, an iterative algorithm, i.e. a feedback from the output to the input, is applied. In this way, every single subcarrier in frequency domain is separately compensated for all the frequency offsets that it experiences while passing through the fading channel. Simulation results clearly show that our proposed method precisely recovers the transmitted data symbols in multipath fading channels.
Noise disturbance in open-plan study environments: a field study on noise sources, student tasks and room acoustic parameters.

The aim of this study is to gain more insight in the assessment of noise in open-plan study environments and to reveal correlations between noise disturbance experienced by students and the noise sources they perceive, the tasks they perform and the acoustic parameters of the open-plan study environment they work in. Data were collected in five open-plan study environments at universities in the Netherlands. A questionnaire was used to investigate student tasks, perceived sound sources and their perceived disturbance, and sound measurements were performed to determine the room acoustic parameters. This study shows that 38% of the surveyed students are disturbed by background noise in an open-plan study environment. Students are mostly disturbed by speech when performing complex cognitive tasks like studying for an exam, reading and writing. Significant but weak correlations were found between the room acoustic parameters and noise disturbance of students.  A field study was conducted to gain more insight in the assessment of noise in open-plan study environments at universities in the Netherlands. More than one third of the students was disturbed by noise. An interaction effect was found for task type, source type and room acoustic parameters.
Linking Business Analytics to Decision Making Effectiveness: A Path Model Analysis

While business analytics is being increasingly used to gain data-driven insights to support decision making, little research exists regarding the mechanism through which business analytics can be used to improve decision-making effectiveness (DME) at the organizational level. Drawing on the information processing view and contingency theory, this paper develops a research model linking business analytics to organizational DME. The research model is tested using structural equation modeling based on 740 responses collected from U.K. businesses. The key findings demonstrate that business analytics, through the mediation of a data-driven environment, positively influences information processing capability, which in turn has a positive effect on DME. The findings also demonstrate that the paths from business analytics to DME have no statistical differences between large and medium companies, but some differences between manufacturing and professional service industries. Our findings contribute to the business analytics literature by providing useful insights into business analytics applications and the facilitation of data-driven decision making. They also contribute to managers' knowledge and understanding by demonstrating how business analytics should be implemented to improve DME.
Pornographic image detection utilizing deep convolutional neural networks

Many internet users are potential victims of the pornographic images and a large part of them are underage children. Thus, content-based pornographic images detection is an important task in computer vision and multimedia research. Previous solutions usually rely on hand-engineered visual features that are hard to analyze and select. In this paper, to detect pornographic images in any style accurately and efficiently with a single model, a novel scheme utilizing the deep convolutional neural networks (CNN) is proposed. The training data are obtained from internet followed by an improved sliding window method and some novel data augmentation approaches. Then a highly efficient training algorithm is proposed based on two strategies. The first is the pre-trained midlevel representations non-fixed fine-tuning strategy. The second is adjusting the training data at the appropriate time on the basis of the performance of the proposed network on the validation set. Furthermore, we introduce a fast image scanning method which is also based on the sliding window approach in the test. We further propose a fast forward pass method based on the “fixedpoint algorithm”. So our CNN could detect all scale images so fast by one forward pass. The effectiveness of the proposed method is demonstrated in experiments on the proposed dataset and the comparative results show that our ∗Corresponding author Email address: tenglwy@gmail.com (Teng Li)  2016 method lead to state-of-the-art detection performance.
Gait Recognition Based on Normal Distance Maps

Gait is a commonly used biometric for human recognition. Its main advantage relies on its ability to identify people at distances at which other biometrics fail. In this paper, we develop a new approach for gait recognition that combines the distance transform with curvatures of local contours. We call our gait feature template the normal distance map. Our method encodes both body shapes and boundary curvatures into a novel feature descriptor that is more robust than existing gait representations. We evaluate our approach on the widely used and challenging USF and CASIA-B datasets. Furthermore, we evaluate it on the OU-ISIR gait dataset, the largest one available in the literature, to obtain statistically reliable results. We verify our approach is significantly superior to the current state-of-the-art under most conditions.
Axis-aligned filtering for interactive physically-based diffuse indirect lighting

We introduce an algorithm for interactive rendering of physically-based global illumination, based on a novel frequency analysis of indirect lighting. Our method combines adaptive sampling by Monte Carlo ray or path tracing, using a standard GPU-accelerated raytracer, with real-time reconstruction of the resulting noisy images. Our theoretical analysis assumes diffuse indirect lighting, with general Lambertian and specular receivers. In practice, we demonstrate accurate interactive global illumination with diffuse and moderately glossy objects, at 1-3 fps. We show mathematically that indirect illumination is a structured signal in the Fourier domain, with inherent band-limiting due to the BRDF and geometry terms. We extend previous work on sheared and axis-aligned filtering for motion blur and shadows, to develop an image-space filtering method for interreflections. Our method enables 5--8X reduced sampling rates and wall clock times, and converges to ground truth as more samples are added. To develop our theory, we overcome important technical challenges---unlike previous work, there is no light source to serve as a band-limit in indirect lighting, and we also consider non-parallel geometry of receiver and reflecting surfaces, without first-order approximations.
Automatic Model Generation Strategies for Model Transformation Testing

Testingmodel transformations requires input models which are graphs of inter-connected objects that must conform to a meta-mode l and meta-constraints from heterogeneous sources such as well-formedness rules, tran formation preconditions, and test strategies. Manually specifying such models is tedious since models must simultaneously conform to several meta-constr ai ts. We propose automatic model generation via constraint satisfaction us i g our tool Cartier for model transformation testing. Due to the virtually infinite number of models in the input domain we compare strategies based on input domain partitioning to guide model generation. We qualify the effectiveness of the se strategies by performing mutation analysis on the transformation using gene rated sets of models. The test sets obtained using partitioning strategies gives mutation scores of up to 87% vs. 72% in the case of unguided/random generation. The se scores are based on analysis of 360 automatically generated test model s for the representative transformation of UML class diagram models to RDBMS mod els.
Robust Feature Selection by Mutual Information Distributions

Mutual information is widely used in artificial intelligence, in a descriptive way, to measure the stochastic dependence of discrete random variables. In order to address questions such as the reliability of the empirical value, one must consider sample-to-population inferential approaches. This paper deals with the distribution of mutual information, as obtained in a Bayesian framework by a second-order Dirichlet prior distribution. The exact analytical expression for the mean and an analytical approximation of the variance are reported. Asymptotic approximations of the distribution are proposed. The results are applied to the problem of selecting features for incremental learning and classification of the naive Bayes classifier. A fast, newly defined method is shown to outperform the traditional approach based on empirical mutual information on a number of real data sets. Finally, a theoretical development is reported that allows one to efficiently extend the above methods to incomplete samples in an easy and effective way.
A survey of graph edit distance

Inexact graph matching has been one of the significant research foci in the area of pattern analysis. As an important way to measure the similarity between pairwise graphs error-tolerantly, graph edit distance (GED) is the base of inexact graph matching. The research advance of GED is surveyed in order to provide a review of the existing literatures and offer some insights into the studies of GED. Since graphs may be attributed or non-attributed and the definition of costs for edit operations is various, the existing GED algorithms are categorized according to these two factors and described in detail. After these algorithms are analyzed and their limitations are identified, several promising directions for further research are proposed.
Computational Thinking … and Doing

A t our most recent CiSE meeting, one of the board members approached me during a short break and asked: " Why do you do this? you seem to have better things to do. " seeking clarification , i came to understand that he Was Wondering Why a computer scientist such as myself would want to be on the editorial board of a publication that isn't necessarily focused on the " tradition " of computer science. then and there, i realized that a computer sci-entist's role is something worth explaining and understanding. much has been made recently of computational thinking, a term coined by Jeanette Wing at the us national science foundation. 1 While i have my doubts about the term's longevity, 2 it struck me as an excellent starting point for explaining what a computer scientist is and does, especially in the CiSE context. computer scientists are an increasingly important part of interdisciplinary—or multidisciplinary, a term i prefer—problem solving. We help others understand the merits of bringing the right methods (algorithms), architecture/design (software engineering), and programming tools (languages and systems) and techniques (visualization) to science and engineering applications. so, as i see it, computer scientists bring a panoply of options to the table that enhance multi disciplinary research. if you will, we're about computational doing—and doing it right. as the former chez thiruvathukal in scientific programming , i like to view computer science's various ideas as the masala found in my (and possibly your) favorite curries. this brings me back to why i'm here. i've always had an eye to applying computer science to other disciplines, scientific and otherwise. in the past few months, i've begun working on two new (funded) projects—one addresses healthcare (modeling health issues in social networks using agent-based simulation) and the other addresses digital humanities (developing collaborative tools for tex-tual studies). in both cases, computer science plays a critical role in that both projects have expected software and experimental outcomes. i don't always write about my other disciplinary interests in CiSE, but given my emphasis on programming and emerging technologies , it's comforting to know that i've joined a group of people committed to exploring the intersections with and showing respect for other disciplines. for the sake of scholarship and meaningful discourse, we need more places like CiSE, and we must keep the content fresh and exciting—even during these challenging times when all publications face …
Creating a Strategic IT Architecture Competency: Learning in Stages

IT architecture is often assumed to follow business strategy, to align IT with the business’s strategic objectives. Increasingly, though, many business strategies depend on specific underlying IT capabilities. To develop a synergy between business strategy and IT architecture, firms must develop organizational competencies in IT architecture. My research has identified four IT architectural stages, each with its own requisite competencies. The “application silo architecture stage” consists of IT architectures of individual applications. The “standardized technology architecture stage” has an enterprise-wide IT architecture that provides efficiencies through technology standardization. The “rationalized data architecture stage” extends the enterprise-wide IT standards to data and processes. And the “modular architecture stage” builds onto enterprise-wide global standards with loosely coupled IT components to preserve the global standards while enabling local differences. Each stage demands different organizational competencies to implement the architecture and prepare the firm to move to the next stage.
Emotional Intelligence: A Critical Evaluation of the Literature with Implications for Mental Health Nursing Leadership.

Emotional intelligence (EI) is necessary for the development of interpersonal and professional competence in nurses. We argue that the concept of emotional intelligence has particular relevance for mental health nursing leadership. In this critique, we examine the recent empirical evidence (2010-2014) related to emotional intelligence, in general, and nursing, specifically. Correlations between emotional intelligence and better overall health, increased work satisfaction, higher spiritual well-being, and decreased risk of job burnout are noted. We offer suggestions for mental health nurse leaders in developing successful project management teams and improving retention of current leaders. We also provide suggestions for future research.