nils-herrmann/clean_scirepeval huggingface dataset:
A REAL – TIME IRRIGATION CONTROL SYSTEM FOR PRECISION AGRICULTURE USING WSN IN INDIAN AGRICULTURAL SECTORS

Agricultural sector is playing vital role in Indian economy, in which irrigation mechanism is of key concern. Our paper aims to find the exact field condition and to control the wastage of water in the field and to provide exact controlling of field by using the drip irrigation, atomizing the agricultural environment by using the components and building the necessary hardware. For the precisely monitoring and controlling of the agriculture filed, different types of sensors were used. To implement the proposed system ARM LPC2148 Microcontroller is used. The irrigation mechanism is monitored and controlled more efficiently by the proposed system, which is a real time feedback control system. GSM technology is used to inform the end user about the exact field condition. Actually this method of irrigation system has been proposed primarily to save resources, yield of crops and farm profitability.
Exploiting Social Network to Enhance Human-to-Human Infection Analysis without Privacy Leakage

Human-to-human infection, as a type of fatal public health threats, can rapidly spread, resulting in a large amount of labor and health cost for treatment, control and prevention. To slow down the spread of infection, social network is envisioned to provide detailed contact statistics to isolate susceptive people who has frequent contacts with infected patients. In this paper, we propose a novel human-to-human infection analysis approach by exploiting social network data and health data that are collected by social network and e-healthcare technologies. We enable the social cloud server and health cloud server to exchange social contact information of infected patients and user's health condition in a privacy-preserving way. Specifically, we propose a privacy-preserving data query method based on conditional oblivious transfer to guarantee that only the authorized entities can query users’ social data and the social cloud server cannot infer anything during the query. In addition, we propose a privacy-preserving classification-based infection analysis method that can be performed by untrusted cloud servers without accessing the users’ health data. The performance evaluation shows that the proposed approach achieves higher infection analysis accuracy with the acceptable computational overhead.
A Method for Fast, High-Precision Characterization of Synthetic Biology Devices

Engineering biological systems with predictable behavior is a foundational goal of synthetic biology. To accomplish this, it is important to accurately characterize the behavior of biological devices. Prior characterization efforts, however, have generally not yielded enough high-quality information to enable compositional design. In the TASBE (A Tool-Chain to Accelerate Synthetic Biological Engineering) project we have developed a new characterization technique capable of producing such data. This document describes the techniques we have developed, along with examples of their application, so that the techniques can be accurately used by others.  IFP MEFL O FP M EF L/ pl as m id Normalized Tal1 transfer curve, colored by plasmid count Work partially sponsored by DARPA; the views and conclusions contained in this document are those of the authors and not DARPA or the U.S. Government.
Machine-vision-based control of zinc flotation — A case study

It is widely accepted that mineral flotation is a very challenging control problem due to chaotic nature of process. This paper introduces a novel approach of combining multi-camera system and expert controllers to improve flotation performance. The system has been installed into the zinc circuit of Pyhäsalmi Mine (Finland). Long-term data analysis in fact shows that the new approach has improved considerably the recovery of the zinc circuit, resulting in a substantial increase in the mill’s annual profit.
Wideband Radar for Ballistic Missile Defense and Range-Doppler Imaging of Satellites

■ Lincoln Laboratory led the nation in the development of high-power wideband radar with a unique capability for resolving target scattering centers and producing three-dimensional images of individual targets. The Laboratory fielded the first wideband radar, called ALCOR, in 1970 at Kwajalein Atoll. Since 1970 the Laboratory has developed and fielded several other wideband radars for use in ballistic-missile-defense research and space-object identification. In parallel with these radar systems, the Laboratory has developed high-capacity, high-speed signal and data processing techniques and algorithms that permit generation of target images and derivation of other target features in near real time. It has also pioneered new ways to realize improved resolution and scatterer-feature identification in wideband radars by the development and application of advanced signal processing techniques. Through the analysis of dynamic target images and other wideband observables, we can acquire knowledge of target form, structure, materials, motion, mass distribution, identifying features, and function. Such capability is of great benefit in ballistic missile decoy discrimination and in space-object identification.
Learning Fashion Compatibility with Bidirectional LSTMs

The ubiquity of online fashion shopping demands effective recommendation services for customers. In this paper, we study two types of fashion recommendation: (i) suggesting an item that matches existing components in a set to form a stylish outfit (a collection of fashion items), and (ii) generating an outfit with multimodal (images/text) specifications from a user. To this end, we propose to jointly learn a visual-semantic embedding and the compatibility relationships among fashion items in an end-to-end fashion. More specifically, we consider a fashion outfit to be a sequence (usually from top to bottom and then accessories) and each item in the outfit as a time step. Given the fashion items in an outfit, we train a bidirectional LSTM (Bi-LSTM) model to sequentially predict the next item conditioned on previous ones to learn their compatibility relationships. Further, we learn a visual-semantic space by regressing image features to their semantic representations aiming to inject attribute and category information as a regularization for training the LSTM. The trained network can not only perform the aforementioned recommendations effectively but also predict the compatibility of a given outfit. We conduct extensive experiments on our newly collected Polyvore dataset, and the results provide strong qualitative and quantitative evidence that our framework outperforms alternative methods.
Dynamic pricing models for electronic business

Dynamic pricing is the dynamic adjustment of prices to consumers depending upon the value these customers attribute to a product or service. Today’s digital economy is ready for dynamic pricing; however recent research has shown that the prices will have to be adjusted in fairly sophisticated ways, based on sound mathematical models, to derive the benefits of dynamic pricing. This article attempts to survey different models that have been used in dynamic pricing. We first motivate dynamic pricing and present underlying concepts, with several examples, and explain conditions under which dynamic pricing is likely to succeed. We then bring out the role of models in computing dynamic prices. The models surveyed include inventory-based models, data-driven models, auctions, and machine learning. We present a detailed example of an e-business market to show the use of reinforcement learning in dynamic pricing.
Educational data mining with Python and Apache spark: a hands-on tutorial

Enormous amount of educational data has been accumulated through Massive Open Online Courses (MOOCs), as well as commercial and non-commercial learning platforms. This is in addition to the educational data released by US government since 2012 to facilitate disruption in education by making data freely available. The high volume, variety and velocity of collected data necessitate use of big data tools and storage systems such as distributed databases for storage and Apache Spark for analysis.
 This tutorial will introduce researchers and faculty to real-world applications involving data mining and predictive analytics in learning sciences. In addition, the tutorial will introduce statistics required to validate and accurately report results. Topics will cover how big data is being used to transform education. Specifically, we will demonstrate how exploratory data analysis, data mining, predictive analytics, machine learning, and visualization techniques are being applied to educational big data to improve learning and scale insights driven from millions of student's records.
 The tutorial will be held over a half day and will be hands on with pre-posted material. Due to the interdisciplinary nature of work, the tutorial appeals to researchers from a wide range of backgrounds including big data, predictive analytics, learning sciences, educational data mining, and in general, those interested in how big data analytics can transform learning. As a prerequisite, attendees are required to have familiarity with at least one programming language.
Employee Retention Through Employee Engagement-A Study At An Indian International Airport

Employee engagement leads to commitment and psychological attachment and reflects in the form of high retention (low attrition) of employees. The level of engagement in employees can be enhanced by identifying its drivers (influential factors) and work on them.For the purpose of study, the drivers of the employee engagement are identified and hypotheses have been formulated. The relationship between employee engagement and employee retention is examined from the response to separate questionnaires from 185 employees who are chosen based on random sampling. The study finds that the employee retention can be improved by increasing the level of employee engagement and focusing on few non-financial drivers.Practical implication of this study is the retention can be improved without financial expenditure when there are economic constraints. Organizations can design good practices in the light of findings to retain their besttalent (highly skilled and specialized human resources) without much financial burden.
Graphene wrapped ordered LiNi0.5Mn1.5O4 nanorods as promising cathode material for lithium-ion batteries

LiNi0.5Mn1.5O4 nanorods wrapped with graphene nanosheets have been prepared and investigated as high energy and high power cathode material for lithium-ion batteries. The structural characterization by X-ray diffraction, Raman spectroscopy, and Fourier transform infrared spectroscopy indicates the LiNi0.5Mn1.5O4 nanorods prepared from β-MnO2 nanowires have ordered spinel structure with P4332 space group. The morphological characterization by scanning electron microscopy and transmission electron microscopy reveals that the LiNi0.5Mn1.5O4 nanorods of 100-200 nm in diameter are well dispersed and wrapped in the graphene nanosheets for the composite. Benefiting from the highly conductive matrix provided by graphene nanosheets and one-dimensional nanostructure of the ordered spinel, the composite electrode exhibits superior rate capability and cycling stability. As a result, the LiNi0.5Mn1.5O4-graphene composite electrode delivers reversible capacities of 127.6 and 80.8 mAh g(-1) at 0.1 and 10 C, respectively, and shows 94% capacity retention after 200 cycles at 1 C, greatly outperforming the bare LiNi0.5Mn1.5O4 nanorod cathode. The outstanding performance of the LiNi0.5Mn1.5O4-graphene composite makes it promising as cathode material for developing high energy and high power lithium-ion batteries.
Bilingual experience and executive functioning in young children.

Advanced inhibitory control skills have been found in bilingual speakers as compared to monolingual controls (Bialystok, 1999). We examined whether this effect is generalized to an unstudied language group (Spanish-English bilingual) and multiple measures of executive function by administering a battery of tasks to 50 kindergarten children drawn from three language groups: native bilinguals, monolinguals (English), and English speakers enrolled in second-language immersion kindergarten. Despite having significantly lower verbal scores and parent education/income level, Spanish-English bilingual children's raw scores did not differ from their peers. After statistically controlling for these factors and age, native bilingual children performed significantly better on the executive function battery than both other groups. Importantly, the relative advantage was significant for tasks that appear to call for managing conflicting attentional demands (Conflict tasks); there was no advantage on impulse-control (Delay tasks). These results advance our understanding of both the generalizability and specificity of the compensatory effects of bilingual experience for children's cognitive development.
The Combinatorial Multi-Armed Bandit Problem and Its Application to Real-Time Strategy Games

Game tree search in games with large branching factors is a notoriously hard problem. In this paper, we address this problem with a new sampling strategy for Monte Carlo Tree Search (MCTS) algorithms, called Naı̈ve Sampling, based on a variant of the Multi-armed Bandit problem called the Combinatorial Multi-armed Bandit (CMAB) problem. We present a new MCTS algorithm based on Naı̈ve Sampling called Naı̈veMCTS, and evaluate it in the context of real-time strategy (RTS) games. Our results show that as the branching factor grows, Naı̈veMCTS performs significantly better than other algorithms.
Selfie posting behaviors are associated with narcissism among men

Although many studies have investigated individual differences in online social networking, few have examined the recent and rapidly popularized social phenomenon of the ‘‘selfie’’ (a selfportrait photograph of oneself). In two studies with a pooled sample of 1296 men and women, we tested the prediction that individuals who score high on four narcissism sub-scales (Self-sufficiency, Vanity, Leadership, and Admiration Demand) will be more likely to post selfies to social media sites than will individuals who exhibit low narcissism. We examined three categories of selfies: own selfies; selfies with a romantic partner; and group selfies, controlling for non-selfie photographs. Women posted more selfies of all types than did men. However, women’s selfie-posting behavior was generally unrelated to their narcissism scores. In contrast, men’s overall narcissism scores positively predicted posting own selfies, selfies with a partner, and group selfies. Moreover, men’s Vanity, Leadership, and Admiration Demand scores each independently predicted the posting of one or more types of selfies. Our findings provide the first evidence that the link between narcissism and selfie-posting behavior is comparatively weak among women than men, and provide novel insight into the social motivations and functions of online social networking. 2015
PREDICTION OF HUMAN PERSONALITY BY HANDWRITING ANALYSIS BASED ON SEGMENTATION METHOD USING SUPPORT VECTOR MACHINE

Earlier personality of person is predicted by spending lot of time with the person. As we know spending time with person is very difficult task. Referring to this problem, in the present study a method has been proposed for the behavioral prediction of a person through automated handwriting analysis. Handwriting analysis is a method to predict personality of a Person .This is done by Image Processing in MATLAB. In order to predict the personality we are going to take the writing sample and from it we are going to extract different features i.e. slant of letters and words, pen pressure, spacing between letter, spacing between word, size of letters, baseline Segmentation method is used to extract the feature of handwriting which are given to the SVM which shows the behavior of the writer sample. This gives optimum accuracy with the use of Redial Kernel function.
Illustrative Parallel Coordinates

Illustrative parallel coordinates (IPC) is a suite of artistic rendering techniques for augmenting and improving parallel coordinate (PC) visualizations. IPC techniques can be used to convey a large amount of information about a multidimensional dataset in a small area of the screen through the following approaches: (a) edge-bundling through splines; (b) visualization of “branched ” clusters to reveal the distribution of the data; (c) opacity-based hints to show cluster density; (d) opacity and shading effects to illustrate local line density on the parallel axes; and (e) silhouettes, shadows and halos to help the eye distinguish between overlapping clusters. Thus, the primary goal of this work is to convey as much information as possible in a manner that is aesthetically pleasing and easy to understand for non-experts.
Variables associated with achievement in higher education: A systematic review of meta-analyses.

The last 2 decades witnessed a surge in empirical studies on the variables associated with achievement in higher education. A number of meta-analyses synthesized these findings. In our systematic literature review, we included 38 meta-analyses investigating 105 correlates of achievement, based on 3,330 effect sizes from almost 2 million students. We provide a list of the 105 variables, ordered by the effect size, and summary statistics for central research topics. The results highlight the close relation between social interaction in courses and achievement. Achievement is also strongly associated with the stimulation of meaningful learning by presenting information in a clear way, relating it to the students, and using conceptually demanding learning tasks. Instruction and communication technology has comparably weak effect sizes, which did not increase over time. Strong moderator effects are found for almost all instructional methods, indicating that how a method is implemented in detail strongly affects achievement. Teachers with high-achieving students invest time and effort in designing the microstructure of their courses, establish clear learning goals, and employ feedback practices. This emphasizes the importance of teacher training in higher education. Students with high achievement are characterized by high self-efficacy, high prior achievement and intelligence, conscientiousness, and the goal-directed use of learning strategies. Barring the paucity of controlled experiments and the lack of meta-analyses on recent educational innovations, the variables associated with achievement in higher education are generally well investigated and well understood. By using these findings, teachers, university administrators, and policymakers can increase the effectivity of higher education.
Deep Clustering Based on a Mixture of Autoencoders

In this paper we propose a Deep Autoencoder Mixture Clustering (DAMIC) algorithm based on a mixture of deep autoencoders where each cluster is represented by an autoencoder. A clustering network transforms the data into another space and then selects one of the clusters. Next, the autoencoder associated with this cluster is used to reconstruct the data-point. The clustering algorithm jointly learns the nonlinear data representation and the set of autoencoders. The optimal clustering is found by minimizing the reconstruction loss of the mixture of autoencoder network. Unlike other deep clustering algorithms, no regularization term is needed to avoid data collapsing to a single point. Our experimental evaluations on image and text corpora show significant improvement over state-of-the-art methods.
Big data and sentiment analysis using KNIME: Online reviews vs. social media

Text analytics and sentiment analysis can help an organization derive potentially valuable business insights from text-based content such as word documents, email and postings on social media streams like Facebook, Twitter and LinkedIn. The system described here analyses opinions about various gadgets collected from two different sources and in two different forms; online reviews and Twitter posts (tweets). Sentiment analysis can be applied to online reviews in easier and more detailed way than to the tweets. Namely, online reviews are written in clear and grammatically more accurate form, while in tweets, internet slang, sarcasm and allegory are often used. System described here explains methods of data collection, sentiment analysis process for online reviews and tweets using KNIME, gives an overview of differences and analysis possibilities in sentiment analysis for both data sources.
A Survey on Artificial Intelligence and Data Mining for MOOCs

Massive Open Online Courses (MOOCs) have gained tremendous popularity in the last few years. Thanks to MOOCs, millions of learners from all over the world have taken thousands of high-quality courses for free. Putting together an excellent MOOC ecosystem is a multidisciplinary endeavour that requires contributions from many different fields. Artificial intelligence (AI) and data mining (DM) are two such fields that have played a significant role in making MOOCs what they are today. By exploiting the vast amount of data generated by learners engaging in MOOCs, DM improves our understanding of the MOOC ecosystem and enables MOOC practitioners to deliver better courses. Similarly, AI, supported by DM, can greatly improve student experience and learning outcomes. In this survey paper, we first review the state-of-the-art artificial intelligence and data mining research applied to MOOCs, emphasising the use of AI and DM tools and techniques to improve student engagement, learning outcomes, and our understanding of the MOOC ecosystem. We then offer an overview of key trends and important research to carry out in the fields of AI and DM so that MOOCs can reach their full potential.
Corporate Social and Financial Performance : A Meta-analysis

Most theorizing on the relationship between corporate social/environmental performance (CSP) and corporate financial performance (CFP) assumes that the current evidence is too fractured or too variable to draw any generalizable conclusions. With this integrative, quantitative study, we intend to show that the mainstream claim that we have little generalizable knowledge about CSP and CFP is built on shaky grounds. Providing a methodologically more rigorous review than previous efforts, we conduct a meta-analysis of 52 studies (which represent the population of prior quantitative inquiry) yielding a total sample size of 33,878 observations. The metaanalytic findings suggest that corporate virtue in the form of social responsibility and, to a lesser extent, environmental responsibility is likely to pay off, although the operationalizations of CSP and CFP also moderate the positive association. For example, CSP appears to be more highly correlated with accounting-based measures of CFP than with market-based indicators, and CSP reputation indices are more highly correlated with CFP than are other indicators of CSP. This meta-analysis establishes a greater degree of certainty with respect to the CSP–CFP relationship than is currently assumed to exist by many business scholars.