nils-herrmann/clean_scirepeval huggingface dataset:
Building a Student At-Risk Model: An End-to-End Perspective From User to Data Scientist

Poor graduation and retention rates are widespread in higher education, with significant economic and social ramifications for individuals and our society in general. Early intervention with students most at risk of attrition can be effective in improving college student retention. Our research aim was to create a firstyear at-risk model using educational data mining and to apply that model at New York Institute of Technology (NYIT). Building the model creates new challenges: (1)the model must be welcomed by counseling staff and the outputs need to be user friendly, and (2)the model needs to work automatically from data collection to processing and prediction in order to eliminate the bottleneck of a human operator which can slow down the process. The result of our effort was an end-to-end solution, including a cost-effective infrastructure, that could be used by student support personnel for early identification and early intervention. The Student At-Risk Model (STAR) provides retention risk ratings for each new freshman at NYIT before the start of the fall semester and identifies the key factors that place a student at risk of not returning the following year. The model was built using historical data for the 2011 and 2012 Fall Class and the STAR system went into production at NYIT in Fall 2013.
BLENDER: Enabling Local Search with a Hybrid Differential Privacy Model

We propose a hybrid model of differential privacy that considers a combination of regular and opt-in users who desire the differential privacy guarantees of the local privacy model and the trusted curator model, respectively. We demonstrate that within this model, it is possible to design a new type of blended algorithm for the task of privately computing the most popular records of a web search log. This blended approach provides significant improvements in the utility of obtained data compared to related work while providing users with their desired privacy guarantees. Specifically, on two large search click data sets comprising 4.8 million and 13.2 million unique queries respectively, our approach attains NDCG values exceeding 95% across a range of commonly used privacy budget values.
Voltammetric Studies of Aspirin in Pharmaceutical Sample Using Ctab-Pencil Graphite Electrode

Acetyl salicylic acid (ASA) is commonly known as aspirin. It is one of the most important anti-inflammatory drugs in the world [1-3]. Aspirin is a white powder stable in a dry environment but that is hydrolyzed to Salicylic acid and acetic acid under humid or moist conditions. Hydrolysis also can occur when aspirin is combined with alkaline salts or with salts containing water of hydration . In 1960’s Felix Hofmann of the Bayer Company in Germany prepared aspirin . This was found to good medicinal properties, low membrane irritation and a reasonable taste. He called the new medicine as aspirin (‘a’ for acetylthe systematic name for the compound at the time of was acetyl salicylic acid, ‘spir’ for spirea, the meadow sweat plant). The active ingredient of aspirin was first discovered from the bark of the willow tree in 1763 by Edward stone. Aspirin is parts of a group of medications called as non steroidal-inflammatory drugs (NSAIDs), but differ from most of other NSAIDs in the mechanism of action [3-5].
Modular Architecture for StarCraft II with Deep Reinforcement Learning

We present a novel modular architecture for StarCraft II AI. The architecture splits responsibilities between multiple modules that each control one aspect of the game, such as build-order selection or tactics. A centralized scheduler reviews macros suggested by all modules and decides their order of execution. An updater keeps track of environment changes and instantiates macros into series of executable actions. Modules in this framework can be optimized independently or jointly via human design, planning, or reinforcement learning. We apply deep reinforcement learning techniques to training two out of six modules of a modular agent with self-play, achieving 94% or 87% win rates against the ”Harder” (level 5) built-in Blizzard bot in Zerg vs. Zerg matches, with or without fog-of-war.
Facebook and Google Usage in Taiwan's College Students

This study proposes and tests a research model which was developed based on the uses-and-gratifications theory. The aim of this study was to investigate if selected factors have differential predicting power on the use of Facebook and Google service in Taiwan. This study employed seven constructs: purposive value, hedonic value, social identity, social support, interpersonal relationship, personality traits, and intimacy as the factors predicting Facebook and Google usage. An electronic survey technique was used to collect data from Internet. The results showed that hedonic value and social identity constructs can significantly predict Facebook usage and purposive value has significant predicting power on Google usage. The construct intimacy is the most significant factor for both Google and Facebook usages. Our findings make suggestions for social network sites (SNSs) providers that to differentiate their SNSs quality from others’, both functional aspects and emotional factors need to be taken into consideration.
RUSBoost: A Hybrid Approach to Alleviating Class Imbalance

Class imbalance is a problem that is common to many application domains. When examples of one class in a training data set vastly outnumber examples of the other class(es), traditional data mining algorithms tend to create suboptimal classification models. Several techniques have been used to alleviate the problem of class imbalance, including data sampling and boosting. In this paper, we present a new hybrid sampling/boosting algorithm, called RUSBoost, for learning from skewed training data. This algorithm provides a simpler and faster alternative to SMOTEBoost, which is another algorithm that combines boosting and data sampling. This paper evaluates the performances of RUSBoost and SMOTEBoost, as well as their individual components (random undersampling, synthetic minority oversampling technique, and AdaBoost). We conduct experiments using 15 data sets from various application domains, four base learners, and four evaluation metrics. RUSBoost and SMOTEBoost both outperform the other procedures, and RUSBoost performs comparably to (and often better than) SMOTEBoost while being a simpler and faster technique. Given these experimental results, we highly recommend RUSBoost as an attractive alternative for improving the classification performance of learners built using imbalanced data.
Metagenomics and Bioinformatics in Microbial Ecology: Current Status and Beyond

Metagenomic approaches are now commonly used in microbial ecology to study microbial communities in more detail, including many strains that cannot be cultivated in the laboratory. Bioinformatic analyses make it possible to mine huge metagenomic datasets and discover general patterns that govern microbial ecosystems. However, the findings of typical metagenomic and bioinformatic analyses still do not completely describe the ecology and evolution of microbes in their environments. Most analyses still depend on straightforward sequence similarity searches against reference databases. We herein review the current state of metagenomics and bioinformatics in microbial ecology and discuss future directions for the field. New techniques will allow us to go beyond routine analyses and broaden our knowledge of microbial ecosystems. We need to enrich reference databases, promote platforms that enable meta- or comprehensive analyses of diverse metagenomic datasets, devise methods that utilize long-read sequence information, and develop more powerful bioinformatic methods to analyze data from diverse perspectives.
Gender identification from E-mails

In this paper, we investigate the topic of gender identification for short length, multi-genre, content-free e-mails. We introduce for the first time (to our knowledge), psycholinguistic and gender-linked cues for this problem, along with traditional stylometric features. Decision tree and Support Vector Machines learning algorithms are used to identify the gender of the author of a given e-mail. The experiment results show that our approach is promising with an average accuracy of 82.2%.
Offline Printed Urdu Nastaleeq Script Recognition with Bidirectional LSTM Networks

Recurrent neural networks (RNN) have been successfully applied for recognition of cursive handwritten documents, both in English and Arabic scripts. Ability of RNNs to model context in sequence data like speech and text makes them a suitable candidate to develop OCR systems for printed Nabataean scripts (including Nastaleeq for which no OCR system is available to date). In this work, we have presented the results of applying RNN to printed Urdu text in Nastaleeq script. Bidirectional Long Short Term Memory (BLSTM) architecture with Connectionist Temporal Classification (CTC) output layer was employed to recognize printed Urdu text. We evaluated BLSTM networks for two cases: one ignoring the character's shape variations and the second is considering them. The recognition error rate at character level for first case is 5.15% and for the second is 13.6%. These results were obtained on synthetically generated UPTI dataset containing artificially degraded images to reflect some real-world scanning artifacts along with clean images. Comparison with shape-matching based method is also presented.
Design preserving garment transfer

We present a fully automatic method for design-preserving transfer of garments between characters with different body shapes. For real-life garments, such transfer is performed through a knowledge intensive and time consuming process, known as pattern grading. Our first contribution is to reformulate the criteria used in professional pattern-grading as a set of geometric requirements, respectively expressing shape or design preservation, proportionality, and fit. We then propose a fully automatic garment transfer algorithm which satisfies all of these criteria while ensuring the physical plausibility of the result. Specifically, we formulate garment transfer as a constrained optimization problem and solve it efficiently through iterative quadratic minimization. As demonstrated by our results, our method is able to automatically generate design-preserving versions of existing garments for target characters whose proportions and body shape significantly differ from those of the source. The method correctly handles the transfer of multiple layers of garment. Lastly, when source 2D patterns are available, we output graded patterns suitable for manufacturing the transferred garments. Our fully automatic design-preserving transfer method leads to significant time savings for both computer artists and fashion designers.
Adoption of Telemedicine - Challenges and Opportunities

U.S. health care system is plagued by rising cost and limited access. While the cost of care is increasing faster than the rate of inflation, people living in rural areas have very limited access to quality health care due to a shortage of physicians and facilities in these areas. Information and communication technologies in general and telemedicine in particular offer great promise to extend quality care to underserved rural communities at an affordable cost. However, adoption of telemedicine among the various stakeholders of the health care system has not been very encouraging. Based on an analysis of the extant research literature, this study identifies critical factors that impede the adoption of telemedicine, and offers suggestions to mitigate these challenges.
Functional Verification of Large ASICs

This paper describes the functional verification effort during a specific hardware development program that included three of the largest ASICs designed at Nortel. These devices marked a transition point in methodology as verification took front and centre on the critical path of the ASIC schedule. Both the simulation and emulation strategies are presented. The simulation methodology introduced new techniques such as ASIC sub-system level behavioural modeling, large multi-chip simulations, and random pattern simulations. The emulation strategy was based on a plan that consisted of integrating parts of the real software on the emulated system. This paper describes how these technologies were deployed, analyzes the bugs that were found and highlights the bottlenecks in functional verification as systems become more complex.
A vision for computer vision: emerging technologies

Computer vision is a rapidly evolving discipline. It includes methods for acquiring, processing, and understanding still images and video to model, replicate, and sometimes, exceed human vision and perform useful tasks.
 Computer vision will be commonly used for a broad range of services in upcoming devices, and implemented in everything from movies, smartphones, cameras, drones and more. Demand for CV is driving the evolution of image sensors, mobile processors, operating systems, application software, and device form factors in order to meet the needs of upcoming applications and services that benefit from computer vision. The resulting impetus means rapid advancements in:
 • visual computing performance
 • object recognition effectiveness
 • speed and responsiveness
 • power efficiency
 • video image quality improvement
 • real-time 3D reconstruction
 • pre-scanning for movie animation
 • image stabilization
 • immersive experiences
 • and more...
 Comprised of innovation leaders of computer vision, this panel will cover recent developments, as well as how CV will be enabled and used in 2016 and beyond.
Team Learning

Team learning is a complex social phenomenon that develops and changes over time. Hence, to promote understanding of the fine-grained dynamics of team learning, research should account for the temporal patterns of team learning behavior. Taking important steps in this direction, this special issue offers novel insights into the dynamics of team learning by advocating a temporal perspective. Based on a symposium presented at the 2016 Interdisciplinary Network for Group Research (INGRoup) Conference in Helsinki, the four empirical articles in this special issue showcase four different and innovative approaches to implementing a temporal perspective in team learning research. Specifically, the contributions highlight team learning dynamics in student teams, self-managing teams, teacher teams, and command and control teams. The articles cover a broad range of methods and designs, including both qualitative and quantitative methodologies, and longitudinal as well as micro-temporal approaches. The contributors represent four countries and five different disciplines in group research.
Overexpression of a soybean salicylic acid methyltransferase gene confers resistance to soybean cyst nematode.

Salicylic acid plays a critical role in activating plant defence responses after pathogen attack. Salicylic acid methyltransferase (SAMT) modulates the level of salicylic acid by converting salicylic acid to methyl salicylate. Here, we report that a SAMT gene from soybean (GmSAMT1) plays a role in soybean defence against soybean cyst nematode (Heterodera glycines Ichinohe, SCN). GmSAMT1 was identified as a candidate SCN defence-related gene in our previous analysis of soybean defence against SCN using GeneChip microarray experiments. The current study started with the isolation of the full-length cDNAs of GmSAMT1 from a SCN-resistant soybean line and from a SCN-susceptible soybean line. The two cDNAs encode proteins of identical sequences. The GmSAMT1 cDNA was expressed in Escherichia coli. Using in vitro enzyme assays, E. coli-expressed GmSAMT1 was confirmed to function as salicylic acid methyltransferase. The apparent Km value of GmSAMT1 for salicylic acid was approximately 46 μM. To determine the role of GmSAMT1 in soybean defence against SCN, transgenic hairy roots overexpressing GmSAMT1 were produced and tested for SCN resistance. Overexpression of GmSAMT1 in SCN-susceptible backgrounds significantly reduced the development of SCN, indicating that overexpression of GmSAMT1 in the transgenic hairy root system could confer resistance to SCN. Overexpression of GmSAMT1 in transgenic hairy roots was also found to affect the expression of selected genes involved in salicylic acid biosynthesis and salicylic acid signal transduction.
New rocker-bogie and terramechanics-based wheel/soil interaction models for planetary rovers

Mars rovers have to this point been almost completely reliant on the solar panel/rechargeable battery combination as a source of power. Curiosity, currently en route, relies on radio isotope decay as its source of electrical power. Given the limited amount of space available for solar panels and that the wattage available from radioisotope decay is limited; power is clearly a critical resource for any rover. The goal of this work is to estimate the energy cost of traversing terrains of various properties. Knowledge of energy costs for terrain traversal will allow for more efficient path planning enabling rovers to have longer periods of activity. Our system accepts grid-based terrain elevation data in the form of a Digital Elevation Model (DEM) along with rover and soil parameters, and uses a newly developed model of the most common rover suspension design (rocker-bogie) along with a terramechanics-based wheel-soil interaction model to build a map of the estimated torque required by each wheel to move the rover to each adjacent terrain square. Future work will involve real world testing and verification of our model.
A New Method of Correcting Uneven Illumination Problem in Fundus Image

Recent advancements in signal and image processing have reduced the time of diagnoses, effort and pressure on the screeners by providing auto diagnostic tools for different diseases. The success rate of these tools greatly depend on the quality of acquired images. Bad image quality can significantly reduce the specificity and the sensitivity which in turn forces screeners back to their tedious job of manual diagnoses. In acquired fundus images, some areas appear to be brighter than the other, that is areas close to the center of the image are always well illuminated, hence appear very bright while areas far from the center are poorly illuminated hence appears to be very dark. Several techniques including the simple thresholding, Naka Rushton (NR) filtering technique and histogram equalization (HE) method have been suggested by various researchers to overcome this problem. However, each of these methods has limitations at their own and hence the need to develop a more robust technique that will provide better performance with greater flexibility. A new method of compensating uneven (irregular) illumination in fundus images termed global-local adaptive histogram equalization using partially-overlapped windows (GLAPOW) is proposed in this paper. The developed algorithm has been tested and the results obtained show superior performance when compared to other known techniques for uneven illumination correction.
The OWL API: A Java API for OWL ontologies

We present the OWL API, a high level Application Programming Interface (API) for working with OWL ontologies. The OWL API is closely aligned with the OWL 2 structural specification. It supports parsing and rendering in the syntaxes defined in the W3C specification (Functional Syntax, RDF/XML, OWL/XML and the Manchester OWL Syntax); manipulation of ontological structures; and the use of reasoning engines. The reference implementation of the OWL API, written in Java, includes validators for the various OWL 2 profiles OWL 2 QL, OWL 2 EL and OWL 2 RL. The OWL API has widespread usage in a variety of tools and applications.
Dermoscopic features of pearly penile papules.

Histopathological examination of the biopsy disclosed the following findings ( fig. 1 c). The epidermis showed orthokeratosis and mild acanthosis with focally elongated rete ridges. Collagen fibers in the dermis were only slightly increased. Ectatic capillaries and venules were a prominent feature of the hyperplastic papillary dermis. A sparse infiltrate of lymphocytes and histiocytes was present in the dermis. These findings conformed to the histopathological features of angiofibroma. There was no pathological finding of condyloma acuminatum.
Transparent Development of the WHO Rapid Advice Guidelines

Emerging health problems require rapid advice. We describe the development and pilot testing of a systematic, transparent approach used by the World Health Organization (WHO) to develop rapid advice guidelines in response to requests from member states confronted with uncertainty about the pharmacological management of avian influenza A (H5N1) virus infection. We first searched for systematic reviews of randomized trials of treatment and prevention of seasonal influenza and for non-trial evidence on H5N1 infection, including case reports and animal and in vitro studies. A panel of clinical experts, clinicians with experience in treating patients with H5N1, influenza researchers, and methodologists was convened for a two-day meeting. Panel members reviewed the evidence prior to the meeting and agreed on the process. It took one month to put together a team to prepare the evidence profiles (i.e., summaries of the evidence on important clinical and policy questions), and it took the team only five weeks to prepare and revise the evidence profiles and to prepare draft guidelines prior to the panel meeting. A draft manuscript for publication was prepared within 10 days following the panel meeting. Strengths of the process include its transparency and the short amount of time used to prepare these WHO guidelines. The process could be improved by shortening the time required to commission evidence profiles. Further development is needed to facilitate stakeholder involvement, and evaluate and ensure the guideline's usefulness.
Overview on last advances of feature selection

In the context of learning from high-dimensional datasets, the performance of machine learning algorithms decreases and can even deteriorate in the presence of data noisiness. Feature selection is a dimension reduction process for encountering the curse of dimensionality, which can also solve the noise issue. This paper aims to describe the latest advances in feature selection. The contributions are investigated as attempts for overcoming challenges related to the feasibility, computational complexity, accuracy and reliability. This paper may allow the researcher to take a broad view of developments in feature selection.
Rotor Angle Instability Prediction Using Post-Disturbance Voltage Trajectories

A new method for predicting the rotor angle stability status of a power system immediately after a large disturbance is presented. The proposed two-stage method involves estimation of the similarity of post-fault voltage trajectories of the generator buses after the disturbance to some pre-identified templates and then prediction of the stability status using a classifier which takes the similarity values calculated at the different generator buses as inputs. The typical bus voltage variation patterns after a disturbance for both stable and unstable situations are identified from a database of simulations using fuzzy C-means clustering algorithm. The same database is used to train a support vector machine classifier which takes proximity of the actual voltage variations to the identified templates as features. Development of the system and its performance were demonstrated using a case study carried out on the IEEE 39-bus system. Investigations showed that the proposed method can accurately predict the stability status six cycles after the clearance of a fault. Further, the robustness of the proposed method was examined by analyzing its performance in predicting the instability when the network configuration is altered.