nils-herrmann/clean_scirepeval huggingface dataset:
A large scale study of text-messaging use

Text messaging has become a popular form of communication with mobile phones worldwide. We present findings from a large scale text messaging study of 70 university students in the United States. We collected almost 60, 000 text messages over a period of 4 months using a custom logging tool on our participants' phones. Our re- sults suggest that students communicate with a large number of contacts for extended periods of time, engage in simultaneous conversations with as many as 9 contacts, and often use text messaging as a method to switch between a variety of communication mediums. We also explore the content of text messages, and ways text message habits have changed over the last decade as it has become more popular. Finally, we offer design suggestions for future mobile communication tools.
Nonlocality-Reinforced Convolutional Neural Networks for Image Denoising

We introduce a paradigm for nonlocal sparsity reinforced deep convolutional neural network denoising. It is a combination of a local multiscale denoising by a convolutional neural network (CNN) based denoiser and a nonlocal denoising based on a nonlocal filter (NLF), exploiting the mutual similarities between groups of patches. CNN models are leveraged with noise levels that progressively decrease at every iteration of our framework, while their output is regularized by a nonlocal prior implicit within the NLF. Unlike complicated neural networks that embed the nonlocality prior within the layers of the network, our framework is modular, and it uses standard pretrained CNNs together with standard nonlocal filters. An instance of the proposed framework, called NN3D, is evaluated over large grayscale image datasets showing state-of-the-art performance.
We know what you want to buy: a demographic-based system for product recommendation on microblogs

Product recommender systems are often deployed by e-commerce websites to improve user experience and increase sales. However, recommendation is limited by the product information hosted in those e-commerce sites and is only triggered when users are performing e-commerce activities. In this paper, we develop a novel product recommender system called METIS, a MErchanT Intelligence recommender System, which detects users' purchase intents from their microblogs in near real-time and makes product recommendation based on matching the users' demographic information extracted from their public profiles with product demographics learned from microblogs and online reviews. METIS distinguishes itself from traditional product recommender systems in the following aspects: 1) METIS was developed based on a microblogging service platform. As such, it is not limited by the information available in any specific e-commerce website. In addition, METIS is able to track users' purchase intents in near real-time and make recommendations accordingly. 2) In METIS, product recommendation is framed as a learning to rank problem. Users' characteristics extracted from their public profiles in microblogs and products' demographics learned from both online product reviews and microblogs are fed into learning to rank algorithms for product recommendation. We have evaluated our system in a large dataset crawled from Sina Weibo. The experimental results have verified the feasibility and effectiveness of our system. We have also made a demo version of our system publicly available and have implemented a live system which allows registered users to receive recommendations in real time.
A Neural Clickbait Detection Engine

In an age where people are becoming increasing likely to trust information found through online media, journalists have begun employing techniques to lure readers to articles by using catchy headlines, called clickbait. These headlines entice the user into clicking through the article whilst not providing information relevant to the headline itself. Previous methods of detecting clickbait have explored techniques heavily dependent on feature engineering, with little experimentation having been tried with neural network architectures. We introduce a novel model combining recurrent neural networks, attention layers and image embeddings. Our model uses a combination of distributed word embeddings derived from unannotated corpora, character level embeddings calculated through Convolutional Neural Networks. These representations are passed through a bidirectional LSTM with an attention layer. The image embeddings are also learnt from large data using CNNs. Experimental results show that our model achieves an F1 score of 65.37% beating the previous benchmark of 55.21%.
Inflammatory Bowel Disease: Autoimmune or Immune-mediated Pathogenesis?

The pathogenesis of Crohn's disease (CD) and ulcerative colitis (UC), the two main forms of inflammatory bowel disease (IBD), is still unclear, but both autoimmune and immune-mediated phenomena are involved. Autoimmune phenomena include the presence of serum and mucosal autoantibodies against intestinal epithelial cells in either form of IBD, and against human tropomyosin fraction five selectively in UC. In addition, perinuclear antineutrophil cytoplasmic antibodies (pANCA) are common in UC, whereas antibodies against Saccharomyces cerevisiae (ASCA) are frequently found in CD. Immune-mediate phenomena include a variety of abnormalities of humoral and cell-mediated immunity, and a generalized enhanced reactivity against intestinal bacterial antigens in both CD and UC. It is currently believed that loss of tolerance against the indigenous enteric flora is the central event in IBD pathogenesis. Various complementary factors probably contribute to the loss of tolerance to commensal bacteria in IBD. They include defects in regulatory T-cell function, excessive stimulation of mucosal dendritic cells, infections or variants of proteins critically involved in bacterial antigen recognition, such as the products of CD-associated NOD2/CARD15 mutations.
Memristive Fully Convolutional Network: An Accurate Hardware Image-Segmentor in Deep Learning

As well known, fully convolutional network (FCN) becomes the state of the art for semantic segmentation in deep learning. Currently, new hardware designs for deep learning have focused on improving the speed and parallelism of processing units. This motivates memristive solutions, in which the memory units (i.e., memristors) have computing capabilities. However, designing a memristive deep learning network is challenging, since memristors work very differently from the traditional CMOS hardware. This paper proposes a complete solution to implement memristive FCN (MFCN). Voltage selectors are firstly utilized to realize max-pooling layers with the detailed MFCN deconvolution hardware circuit by the massively parallel structure, which is effective since the deconvolution kernel and the input feature are similar in size. Then, deconvolution calculation is realized by converting the image into a column matrix and converting the deconvolution kernel into a sparse matrix. Meanwhile, the convolution realization in MFCN is also studied with the traditional sliding window method rather than the large matrix theory to overcome the shortcoming of low efficiency. Moreover, the conductance values of memristors are predetermined in Tensorflow with ex-situ training method. In other words, we train MFCN in software, then download the trained parameters to the simulink system by writing memristor. The effectiveness of the designed MFCN scheme is verified with improved accuracy over some existing machine learning methods. The proposed scheme is also adapt to LFW dataset with three-classification tasks. However, the MFCN training is time consuming as the computational burden is heavy with thousands of weight parameters with just six layers. In future, it is necessary to sparsify the weight parameters and layers of the MFCN network to speed up computing.
Chemical Composition, Antioxidant, Antimicrobial And Acetylcholinesterase Inhibitory Properties of Lannea Barteri (Anacardiaceae)

Lannea barteri (Oliv.) Engl (Anacardiaceae) is a medicinal plant used in west African countries such as Côte d’Ivoire for the treatment of various diseases (wound, rheumatic, diarrhoea). Dichloromethane and methanol extracts from the roots and stem bark of L. barteri were screened for their antibacterial, antifungal, radical scavenging and acetylcholinesterase inhibitory activities. TLC bioautography and agar overlay assay for antifungal activity were run with Cladosporium cucumerinum, Fusarium oxysporum f. sp. vasinfectum, Fusarium oxysporum f. sp. lycopersici and Candida albicans respectively. Also extracts were tested on bacteria (Staphylococcus aureus, Staphylococcus epidermis, Enterococcus faecalis, Proteus mirabilis, Pseudomonas aeruginosa and Escherichia coli), some of which were multidrug resistant bacteria. DPPH and Acetylcholinesterase solutions sprayed on TLC plates were used for radical scavengers and acetylcholinesterase inhibitors. L. barteri gave high positive responses in all four tests, exhibiting activity against bacteria, fungi, free radicals and acetycholinesterase. The phytochemical screening showed that all the extracts contained at least trace amount of steroids, terpenoïds, saponins, quinones, tannins and flavonoïds. This study which is the first report on the biological activities and phytochemicals of Lannea barteri, supports its traditional uses in the treatment of infectious and non infectious diseases.
Survey on Evaluation of Student's Performance in Educational Data Mining

Educational information mining is rising field that spotlights on breaking down educational information to create models for enhancing learning encounters and enhancing institutional viability. Expanding enthusiasm for information mining and educational frameworks, make educational information mining as another developing exploration group. Educational Data Mining intends to remove the concealed learning from expansive Educational databases with the utilization of procedures and apparatuses. Educational Data Mining grows new techniques to find information from Educational database and it is utilized for basic decision making in Educational framework. The knowledge is hidden among the Educational informational Sets and it is extractable through data mining techniques. It is essential to think about and dissect Educational information particularly understudies execution. Educational Data Mining (EDM) is the field of study relates about mining Educational information to discover intriguing examples and learning in Educational associations. This investigation is similarly worried about this subject, particularly, the understudies execution. This study investigates numerous components theoretically expected to influence student's performance in higher education, and finds a subjective model which best classifies and predicts the student's performance in light of related individual and phenomenal elements.
A lorentz force magnetometer based on a piezoelectric-on-silicon radial-contour mode disk

We report a unique MEMS magnetometer based on a disk shaped radial contour mode thin-film piezoelectric on silicon (TPoS) CMOS-compatible resonator. This is the first device of its kind that targets operation under atmospheric pressure conditions as opposed that existing Lorentz force MEMS magnetometers that depend on vacuum. We exploit the chosen vibration mode to enhance coupling to deliver a field sensitivity of 10.92 mV/T while operating at a resonant frequency of 6.27 MHz, despite of a sub-optimal mechanical quality (Q) factor of 697 under ambient conditions in air.
A PROPOSED ADDITION TO THE STANDARD PROTOCOL FOR THE VISAGRAPH II TM EYE MOVEMENT RECORDING SYSTEM

The Visagraph IITM Eye Movement Recording System is an instrument that assesses reading eye movement efficiency and related parameters objectively. It also incorporates automated data analysis. In the standard protocol, the patient reads selections only at the level of their current school grade, or at the level that has been determined by a standardized reading test. In either case, deficient reading eye movements may be the consequence of a language-based reading disability, an oculomotor-based reading inefficiency, or both. We propose an addition to the standard protocol: the patient’s eye movements are recorded a second time with text that is significantly below the grade level of the initial reading. The goal is to determine which factor is primarily contributing to the patient’s reading problem, oculomotor or language. This concept is discussed in the context of two representative cases.
Using Spreadsheets and VBA for Teaching Civil Engineering Concepts

Spreadsheets are becoming increasingly popular in solving engineering related problems. Among the strong features of spreadsheets are their instinctive cell-based structure and easy to use capabilities. Excel, for example, is a powerful spreadsheet with VBA robust programming capabilities that can be a powerful tool for teaching civil engineering concepts. Spreadsheets can do basic calculations such as cost estimates, schedule and cost control, and markup estimation, as well as structural calculations of reactions, stresses, strains, deflections, and slopes. Spreadsheets can solve complex problems, create charts and graphs, and generate useful reports. This paper highlights the use of Excel spreadsheet and VBA in teaching civil engineering concepts and creating useful applications. The focus is on concepts related to construction management and structural engineering ranging from a simple cost estimating problem to advanced applications like the simulation using PERT and the analysis of structural members. Several spreadsheet were developed for time-cost tradeoff analysis, optimum markup estimation, simulating activities with uncertain durations, scheduling repetitive projects, schedule and cost control, and optimization of construction operations, and structural calculations of reactions, internal forces, stresses, strains, deflections, and slopes. Seven illustrative examples are presented to demonstrate the use of spreadsheets as a powerful tool for teaching civil engineering concepts.
Canonical Correlation Analysis for Data Fusion and Group Inferences

We have presented two CCA-based approaches for data fusion and group analysis of biomedical imaging data and demonstrated their utility on fMRI, sMRI, and EEG data. The results show that CCA and M-CCA are powerful tools that naturally allow the analysis of multiple data sets. The data fusion and group analysis methods presented are completely data driven, and use simple linear mixing models to decompose the data into their latent components. Since CCA and M-CCA are based on second-order statistics they provide a relatively lessstrained solution as compared to methods based on higherorder statistics such as ICA. While this can be advantageous, the flexibility also tends to lead to solutions that are less sparse than those obtained using assumptions of non-Gaussianity-in particular superGaussianity-at times making the results more difficult to interpret. Thus, it is important to note that both approaches provide complementary perspectives, and hence it is beneficial to study the data using different analysis techniques.
Computational Photography: Epsilon to Coded Photography

Computational photography combines plentiful computing, digital sensors, modern optics, actuators, and smart lights to escape the limitations of traditional cameras, enables novel imaging applications and simplifies many computer vision tasks. However, a majority of current Computational photography methods involves taking multiple sequential photos by changing scene parameters and fusing the photos to create a richer representation. Epsilon photography is concerned with synthesizing omnipictures and proceeds by multiple capture single image paradigm (MCSI).The goal of Coded computational photography is to modify the optics, illumination or sensors at the time of capture so that the scene properties are encoded in a single (or a few) photographs. We describe several applications of coding exposure, aperture, illumination and sensing and describe emerging techniques to recover scene parameters from coded photographs.
Modeling Social Action for AI Agents

AI is a science, not merely technology, engineering. It cannot find an identity (ubi consistam) in a technology, or set of technologies, and we know that such an identification is quite dangerous. AI is the science of possible forms of intelligence, both individual and collective. To rephrase Doyle's claim, AI is the discipline aimed at understanding intelligent beings by constructing intelligent systems. Since intelligence is mainly a social phenomenon and is due to the necessity of social life, we have to construct socially intelligent systems to understand it, and we have to build social entities to have intelligent systems. If we want that the computer is not "just a glorified pencil"  resources, with hybrid architectures, interfering -either cooperatively or competitivelywith other systems. The new password is interaction [Bobrow, 1991): interaction with an evolving environment; among several, distributed and heterogeneous artificial systems in a network; with human users; among humans through computers. Important work has been done in AI (in several domains from D A I to HCI , from Agents to logic for action, knowledge, and speech acts) for modeling social intelligence and behavior. In my talk I wi l l just attempt a principled systematization. On the one side, 1 wil l illustrate what I believe to be the basic ontological categories for social action, structure, and mind; letting, first, sociality (social action, social structure) emerge bottom-up from the action and intelligence of individual agents in a common world, and, second, examine some aspects of the way-down: how emergent collective phenomena shape the individual mind. In this paper I wil l focus on the bottom-up perspective. On the other side, I wi l l propose some critical reflections on current approaches and future directions. Doing this I wi l l in particular stress five points.
Active Regression by Stratification

We propose a new active learning algorithm for parametric linear regression with random design. We provide finite sample convergence guarantees for general distributions in the misspecified model. This is the first active learner for this setting that provably can improve over passive learning. Unlike other learning settings (such as classification), in regression the passive learning rate of O(1/ ) cannot in general be improved upon. Nonetheless, the so-called ‘constant’ in the rate of convergence, which is characterized by a distribution-dependent risk, can be improved in many cases. For a given distribution, achieving the optimal risk requires prior knowledge of the distribution. Following the stratification technique advocated in Monte-Carlo function integration, our active learner approaches the optimal risk using piecewise constant approximations.
Ocr -optical Character Recognition Ocr -optical Character Recognition Ocr -optical Character Recognition Ocr -optical Character Recognition Ocr -optical Character Recognition the Future of Ocr 6.2 Future Needs Ocr -optical Character Recognition Ocr -optical Character Recognition 5.4 Ocr Performance 

Character recognition techniques associate a symbolic identity with the image of character. Character recognition is commonly referred to as optical character recognition (OCR), as it deals with the recognition of optically processed characters. The modern version of OCR appeared in the middle of the 1940's with the development of the digital computers. OCR machines have been commercially available since the middle of the 1950's. Today OCR-systems are available both as hardware devices and software packages, and a few thousand systems are sold every week. In a typical OCR systems input characters are digitized by an optical scanner. Each character is then located and segmented, and the resulting character image is fed intoa preproc-essor for noise reduction and normalization. Certain characteristics are the extracted from the character for classification. The feature extraction is critical and many different techniques exist, each having its strengths and weaknesses. After classification the identified characters are grouped to reconstruct the original symbol strings, and context may then be applied to detect and correct errors. Optical character recognition has many different practical applications. The main areas where OCR has been of importance, are text entry (office automation), data entry (banking environment) and process automation (mail sorting). The present state of the art in OCR has moved from primitive schemes for limited character sets, to the application of more sophisticated techniques for omnifont and handprint recognition. The main problems in OCR usually lie in the segmentation of degraded symbols which are joined or fragmented. Generally, the accuracy of an OCR system is directly dependent upon the quality of the input document. Three figures are used in ratings of OCR systems; correct classification rate, rejection rate and error rate. The performance should be rated from the systems error rate, as these errors go by undetected by the system and must be manually located for correction. In spite of the great number of algorithms that have been developed for character recognition , the problem is not yet solved satisfactory, especially not in the cases when there are no strict limitations on the handwriting or quality of print. Up to now, no recognition algorithm may compete with man in quality. However, as the OCR machine is able to read much faster, it is still attractive. In the future the area of recognition of constrained print is expected to decrease. Emphasis will then be on the recognition of unconstrained writing, like omnifont …
Verification of a novel 5-axis active control type bearingless canned motor pump utilizing passive magnetic bearing function for high power

A centrifugal pump that employs a bearingless motor with 5-axis active control has been developed. In this paper, a novel bearingless canned motor pump is proposed, and differences from the conventional structure are explained. A key difference between the proposed and conventional bearingless canned motor pumps is the use of passive magnetic bearings; in the proposed pump, the amount of permanent magnets (PMs) is reduced by 30% and the length of the rotor is shortened. Despite the decrease in the total volume of PMs, the proposed structure can generate large suspension forces and high torque compared with the conventional design by the use of the passive magnetic bearings. In addition, levitation and rotation experiments demonstrated that the proposed motor is suitable for use as a bearingless canned motor pump.
Exploring Latent Preferences for Context-Aware Personalized Recommendation Systems

Context-aware recommendations offer the potential of exploiting social contents and utilize related tags and rating information to personalize the search for content considering a given context. Recommendation systems tackle the problem of trying to identify relevant resources from the vast number of choices available online. In this study, we propose a new recommendation model that personalizes recommendations and improves the user experience by analyzing the context when a user wishes to access multimedia content. We conducted empirical analysis on a dataset from last.fm to demonstrate the use of latent preferences for ranking items under a given context. Additionally, we use an optimization function to maximize the mean average precision measure of the resulted recommendation. Experimental results show a potential improvement to the quality of the recommendation in terms of accuracy when compared with state-of-the-art algorithms.