nils-herrmann/clean_scirepeval huggingface dataset:
First-Class Phantom Types

Classical phantom types are datatypes in which type constraints are expressed using type variables that do not appear in the datatype cases themselves. They can be used to embed typed languages into Haskell or ML. However, while such encodings guarantee that only wellformed data can be constructed, they do not permit type-safe deconstruction without additional tagging and run-time checks. We introduce first-class phantom types, which make such constraints explicit via type equations. Examples of first-class phantom types include typed type representations and typed higher-order abstract syntax trees. These types can be used to support typed generic functions, dynamic typing, and staged compilation in higher-order, statically typed languages such as Haskell or Standard ML. In our system, type constraints can be equations between type constructors as well as type functions of higher-order kinds. We prove type soundness and decidability for a Haskell-like language extended by first-class phantom types.
The Case for Data Visualization Management Systems

Most visualizations today are produced by retrieving data from a database and using a specialized visualization tool to render it. This decoupled approach results in significant duplication of functionality, such as aggregation and filters, and misses tremendous opportunities for cross-layer optimizations. In this paper, we present the case for an integrated Data Visualization Management System (DVMS) based on a declarative visualization language that fully compiles the end-to-end visualization pipeline into a set of relational algebra queries. Thus the DVMS can be both expressive via the visualization language, and performant by leveraging traditional and visualization-specific optimizations to scale interactive visualizations to massive datasets.
Item categorization in the e-commerce domain

Hierarchical classification is a challenging problem yet bears a broad application in real-world tasks. Item categorization in the ecommerce domain is such an example. In a large-scale industrial setting such as eBay, a vast amount of items need to be categorized into a large number of leaf categories, on top of which a complex topic hierarchy is defined. Other than the scale challenges, item data is extremely sparse and skewed distributed over categories, and exhibits heterogeneous characteristics across categories. A common strategy for hierarchical classification is the "gates-and-experts" methods, where a high-level classification is made first (the gates), followed by a low-level distinction (the experts). In this paper, we propose to leverage domain-specific feature generation and modeling techniques to greatly enhance the classification accuracy of the experts. In particular, we innovatively derive features to encode various rich domain knowledge and linguistic hints, and then adapt a SVM-based model to distinguish several very confusing category groups appeared as the performance bottleneck of a currently deployed live system at eBay. We use illustrative examples and empirical results to demonstrate the effectiveness of our approach, particularly the merit of smartly designed domain-specific features.
Recognizing Overlapped Speech in Meetings: A Multichannel Separation Approach Using Neural Networks

The goal of this work is to develop a meeting transcription system that can recognize speech even when utterances of different speakers are overlapped. While speech overlaps have been regarded as a major obstacle in accurately transcribing meetings, a traditional beamformer with a single output has been exclusively used because previously proposed speech separation techniques have critical constraints for application to real meetings. This paper proposes a new signal processing module, called an unmixing transducer, and describes its implementation using a windowed BLSTM. The unmixing transducer has a fixed number, say J, of output channels, where J may be different from the number of meeting attendees, and transforms an input multi-channel acoustic signal into J time-synchronous audio streams. Each utterance in the meeting is separated and emitted from one of the output channels. Then, each output signal can be simply fed to a speech recognition back-end for segmentation and transcription. Our meeting transcription system using the unmixing transducer outperforms a system based on a stateof-the-art neural mask-based beamformer by 10.8%. Significant improvements are observed in overlapped segments. To the best of our knowledge, this is the first report that applies overlapped speech recognition to unconstrained real meeting audio.
Monitoring of soil parameters for effective irrigation using Wireless Sensor Networks

WSN (Wireless Sensor Network) has attracted the attention of researchers due to its wide applicability to various fields such as disaster management, health and environment monitoring, agriculture, ecology, industrial automation and in military field applications like battlefield surveillance etc. In agricultural ecosystem -situ - continuous smart monitoring of soil parameters like humidity, pH, irrigation control systems etc. can be measured using WSN with high end precision. In this manuscript, we propose to develop a WSN based Dynamic and Automated Irrigation System design and instrumentation. This process maintains soil type and software for real time sensing and control of agricultural irrigation system. The efficiency of irrigation systems can be enhanced by automated remote sensing and continuous analysis of soil parameters. Thus the data acquired is useful to the agricultural sector, namely pest management, irrigation management and soil management. Ultimately, this reduces the fresh water consumption and irrigation costs by maximizing the crop yield.
Mechanisms and consequences of intestinal dysbiosis

The composition of the gut microbiota is in constant flow under the influence of factors such as the diet, ingested drugs, the intestinal mucosa, the immune system, and the microbiota itself. Natural variations in the gut microbiota can deteriorate to a state of dysbiosis when stress conditions rapidly decrease microbial diversity and promote the expansion of specific bacterial taxa. The mechanisms underlying intestinal dysbiosis often remain unclear given that combinations of natural variations and stress factors mediate cascades of destabilizing events. Oxidative stress, bacteriophages induction and the secretion of bacterial toxins can trigger rapid shifts among intestinal microbial groups thereby yielding dysbiosis. A multitude of diseases including inflammatory bowel diseases but also metabolic disorders such as obesity and diabetes type II are associated with intestinal dysbiosis. The characterization of the changes leading to intestinal dysbiosis and the identification of the microbial taxa contributing to pathological effects are essential prerequisites to better understand the impact of the microbiota on health and disease.
EFFECTS OF SODIUM FLUORIDE AND SULFUR DIOXIDE ON SPERM MOTILITY AND SERUM TESTOSTERONE IN MALE RATS

Ninety-six sexually mature male Wistar rats were divided randomly into four groups of twenty-four rats. In experiments to assess effects of sodium fluoride and sulfur dioxide on their sperm motility and serum testosterone (T), one group of rats was left untreated as controls, and the other three groups were administered, respectively, for eight consecutive weeks, 100 mg NaF/L (45 mg F–/L) in their drinking water, sulfur dioxide in ambient air (15 ppm SO2, 4 hr/day), or were exposed to both NaF and SO2 together. In comparison with the control group, the ratio of testis weight to body weight increased in the NaF+SO2 group at week 4, and the mean values of sperm motility were significantly lower in the NaF group and in the SO2 group, and even more so in the NaF+SO2 group over the entire eight-week period. The serum T level in the NaF+SO2 group was significantly increased at week 2 and then markedly decreased at week 4 and 8. Similar changes were also observed in the SO2 group. Sperm motility and serum T in rats were thus affected to some extent by NaF or SO2 but were most drastically affected by NaF and SO2 together. The resulting changes in the testis tissue and serum T concentration may therefore be connected with the low sperm motility in these rats.
Recurrent and Contextual Models for Visual Question Answering

We propose a series of recurrent and contextual neural network models for multiple choice visual question answering on the Visual7W dataset. Motivated by divergent trends in model complexities in the literature, we explore the balance between model expressiveness and simplicity by studying incrementally more complex architectures. We start with LSTM-encoding of input questions and answers; build on this with context generation by LSTM-encodings of neural image and question representations and attention over images; and evaluate the diversity and predictive power of our models and the ensemble thereof. All models are evaluated against a simple baseline inspired by the current state-of-the-art, consisting of involving simple concatenation of bag-of-words and CNN representations for the text and images, respectively. Generally, we observe marked variation in image-reasoning performance between our models not obvious from their overall performance, as well as evidence of dataset bias. Our standalone models achieve accuracies up to 64.6%, while the ensemble of all models achieves the best accuracy of 66.67%, within 0.5% of the current state-of-the-art for Visual7W.
Safe Local Exploration for Replanning in Cluttered Unknown Environments for Microaerial Vehicles

In order to enable microaerial vehicles (MAVs) to assist in complex, unknown, unstructured environments, they must be able to navigate with guaranteed safety, even when faced with a cluttered environment they have no prior knowledge of. While trajectory-optimization-based local planners have been shown to perform well in these cases, prior work either does not address how to deal with local minima in the optimization problem or solves it by using an optimistic global planner. We present a conservative trajectory-optimization-based local planner, coupled with a local exploration strategy that selects intermediate goals. We perform extensive simulations to show that this system performs better than the standard approach of using an optimistic global planner and also outperforms doing a single exploration step when the local planner is stuck. The method is validated through experiments in a variety of highly cluttered environments including a dense forest. These experiments show the complete system running in real time fully onboard an MAV, mapping and replanning at 4 Hz.
To Fake or Not to Fake: Antecedents to Interview Faking, Warning Instructions, and Its Impact on Applicant Reactions

In the present study, we examined the antecedents and processes that impact job interviewees' decisions to engage in deceptive impression management (i.e., interview faking). Willingness and capacity to engage in faking were found to be the processes underlying the decision to use deceptive impression management in the interview. We also examined a personality antecedent to this behavior, Honesty-Humility, which was negatively related to the use of deceptive impression management through increased willingness to engage in these behaviors. We also tested a possible intervention to reduce IM. In particular, we found that warnings against faking - specifically, an identification warning - reduced both the perceived capacity to engage in interview faking, and subsequent use of several faking behaviors. Moreover, this warning reduced faking without adversely impacting applicant reactions.
Properties and Benefits of Calibrated Classifiers

A calibrated classifier provides reliable estimates of the true probability that each test sample is a member of the class of interest. This is crucial in decision making tasks. Procedures for calibration have already been studied in weather forecasting, game theory, and more recently in machine learning, with the latter showing empirically that calibration of classifiers helps not only in decision making, but also improves classification accuracy. In this paper we extend the theoretical foundation of these empirical observations. We prove that (1) a well calibrated classifier provides bounds on the Bayes error (2) calibrating a classifier is guaranteed not to decrease classification accuracy, and (3) the procedure of calibration provides the threshold or thresholds on the decision rule that minimize the classification error. We also draw the parallels and differences between methods that use receiver operating characteristic (ROC) curves and calibration based procedures that are aimed at finding a threshold of minimum error. In particular, calibration leads to improved performance when multiple thresholds exist.
The first public demonstration of machine translation : the Georgetown-IBM system , 7 th January 1954

The public demonstration of a Russian-English machine translation system in New York in January 1954 – a collaboration of IBM and Georgetown University – caused a great deal of public interest and much controversy. Although a small-scale experiment of just 250 words and six ‘grammar’ rules it raised expectations of automatic systems capable of high quality translation in the near future. This paper describes the background motivations, the linguistic methods, and the computational techniques of the system.
The eXtraordinarY Kids Clinic: an interdisciplinary model of care for children and adolescents with sex chromosome aneuploidy

Individuals with sex chromosome aneuploidies (SCAs) are born with an atypical number of X and/or Y chromosomes, and present with a range of medical, developmental, educational, behavioral, and psychological concerns. Rates of SCA diagnoses in infants and children are increasing, and there is a need for specialized interdisciplinary care to address associated risks. The eXtraordinarY Kids Clinic was established to provide comprehensive and experienced care for children and adolescents with SCA, with an interdisciplinary team composed of developmental-behavioral pediatrics, endocrinology, genetic counseling, child psychology, pediatric neuropsychology, speech-language pathology, occupational therapy, nursing, and social work. The clinic model includes an interdisciplinary approach to care, where assessment results by each discipline are integrated to develop unified diagnostic impressions and treatment plans individualized for each patient. Additional objectives of the eXtraordinarY Kids Clinic program include prenatal genetic counseling, research, education, family support, and advocacy.



Satisfaction surveys were distributed to 496 patients, and responses were received from 168 unique patients.



Satisfaction with the overall clinic visit was ranked as "very satisfied" in 85%, and as "satisfied" in another 9.8%. Results further demonstrate specific benefits from the clinic experience, the importance of a knowledgeable clinic coordinator, and support the need for similar clinics across the country. Three case examples of the interdisciplinary approach to assessment and treatment are included.
Automatic recommendation of API methods from feature requests

Developers often receive many feature requests. To implement these features, developers can leverage various methods from third party libraries. In this work, we propose an automated approach that takes as input a textual description of a feature request. It then recommends methods in library APIs that developers can use to implement the feature. Our recommendation approach learns from records of other changes made to software systems, and compares the textual description of the requested feature with the textual descriptions of various API methods. We have evaluated our approach on more than 500 feature requests of Axis2/Java, CXF, Hadoop Common, HBase, and Struts 2. Our experiments show that our approach is able to recommend the right methods from 10 libraries with an average recall-rate@5 of 0.690 and recall-rate@10 of 0.779 respectively. We also show that the state-of-the-art approach by Chan et al., that recommends API methods based on precise text phrases, is unable to handle feature requests.
Summary Analysis of the 2017 GitHub Open Source Survey

"In collaboration with researchers from academia, industry, and the community, GitHub designed a survey to gather high quality and novel data on open source software development practices and communities. We collected responses from 5,500 randomly sampled respondents sourced from over 3,800 open source repositories on GitHub.com, and over 500 responses from a non-random sample of communities that work on other platforms. The results are an open data set about the attitudes, experiences, and backgrounds of those who use, build, and maintain open source software." [Zlotnick et al., 2017b]
Quantum Models of Cognition and Decision

Much of our understanding of human thinking is based on probabilistic models. This innovative book by Jerome R. Busemeyer and Peter D. Bruza argues that, actually, the underlying mathematical structures from quantum theory provide a much better account of human thinking than traditional models. They introduce the foundations for modelling probabilistic-dynamic systems using two aspects of quantum theory. The first, “contextuality,” is a way to understand interference effects found with inferences and decisions under conditions of uncertainty. The second, “quantum entanglement,” allows cognitive phenomena to be modelled in non-reductionist ways. Employing these principles drawn from quantum theory allows us to view human cognition and decision in a totally new light. Introducing the basic principles in an easy-to-follow way, this book does not assume a physics background or a quantum brain and comes complete with a tutorial and fully worked-out applications in important areas of cognition and decision.
Social Media Technologies' Use for the Competitive Information and Knowledge Sharing, and Its Effects on Industrial SMEs' Innovation

The effective use of technologies supporting decision making is essential to companies’ survival. Recent studies analyzed social media technologies (SMT) in the context of smalland mediumsized enterprises (SMEs), contributing to the discussion on SMT benefits from the marketing perspective. This article focuses on the effects of SMT use on innovation. Our findings provide empirical evidence on the positive effects of SMT use for acquiring external information and for sharing knowledge and innovation performance.
Irregular Repeat – Accumulate Codes 1

In this paper we will introduce an ensemble of codes called irregular repeat-accumulate (IRA) codes. IRA codes are a generalization of the repeat-accumluate codes introduced in , and as such have a natural linear-time encoding algorithm. We shall prove that on the binary erasure channel, IRA codes can be decoded reliably in linear time, using iterative sum-product decoding, at rates arbitrarily close to channel capacity. A similar result appears to be true on the AWGN channel, although we have no proof of this. We illustrate our results with numerical and experimental examples.
Analytical magnetic circuit design optimization of electrical machines with air gap winding using a Halbach array

This paper aims to analyze the influence of a Halbach array by using a semi analytical design optimization approach on a novel electrical machine design with slotless air gap winding. The useable magnetic flux density caused by the Halbach array magnetization is studied and compared to conventional radial magnetization systems. First, several discrete magnetic flux densities are analyzed for an infinitesimal wire size in an air gap range from 0.1 mm to 5 mm by the finite element method in Ansys Maxwell. Fourier analysis is used to approximate continuous functions for each magnetic flux density characteristic for each air gap height. Then, using a six-step commutation control, the magnetic flux acting on a certain phase geometry is considered for a parametric machine model. The design optimization approach utilizes the design freedom of the magnetic flux density shape in air gap as well as the heights and depths of all magnetic circuit components, which are stator and rotor cores, permanent magnets, air gap, and air gap winding. Use of a nonlinear optimization formulation, allows for fast and precise analytical calculation of objective function. In this way the influence of both magnetizations on Pareto optimal machine design sets, when mass and efficiency are weighted, are compared. Other design requirements, such as torque, current, air gap and wire height, are considered via constraints on this optimization. Finally, an optimal motor design study for the Halbach array magnetization pattern is compared to the conventional radial magnetization. As a reference design, an existing 15-inch rim wheel-hub motor with air gap winding is used.
FUNCTION AND SURFACE APPROXIMATION BASED ON ENHANCED KERNEL REGRESSION FOR SMALL SAMPLE SETS

The function approximation problem is to find the appropriate relationship between a dependent and independent variable(s). Function approximation algorithms generally require sufficient samples to approximate a function. Insufficient samples may cause any function approximation algorithm to result in unsatisfactory predictions. To solve this problem, a function approximation algorithm called Weighted Kernel Regression (WKR), which is based on Nadaraya-Watson kernel regression (NWKR), is proposed. In the proposed framework, the original NWKR algorithm is enhanced by expressing the observed samples in a square kernel matrix. The WKR is trained to estimate the weight for the testing phase. The weight is estimated iteratively and governed by the error function to find a good approximation model. Four experiments are conducted to show the capability of the WKR. The results show that the proposed WKR model is effective in cases where the target function is non-linear and the given training sample is small. The performance of the WKR is also compared with other existing function approximation algorithms, such as artificial neural networks (ANN).
Functional Characterization of Chitin and Chitosan

Chitin and its deacetylated derivative chitosan are natural polymers composed of randomly distributed -(1-4)linked D-glucosamine (deacetylated unit) and N-acetyl-D-glucosamine (acetylated unit). Chitin is insoluble in aqueous media while chitosan is soluble in acidic conditions due to the free protonable amino groups present in the D-glucosamine units. Due to their natural origin, both chitin and chitosan can not be defined as a unique chemical structure but as a family of polymers which present a high variability in their chemical and physical properties. This variability is related not only to the origin of the samples but also to their method of preparation. Chitin and chitosan are used in fields as different as food, biomedicine and agriculture, among others. The success of chitin and chitosan in each of these specific applications is directly related to deep research into their physicochemical properties. In recent years, several reviews covering different aspects of the applications of chitin and chitosan have been published. However, these reviews have not taken into account the key role of the physicochemical properties of chitin and chitosan in their possible applications. The aim of this review is to highlight the relationship between the physicochemical properties of the polymers and their behaviour. A functional characterization of chitin and chitosan regarding some biological properties and some specific applications (drug delivery, tissue engineering, functional food, food preservative, biocatalyst immobilization, wastewater treatment, molecular imprinting and metal nanocomposites) is presented. The molecular mechanism of the biological properties such as biocompatibility, mucoadhesion, permeation enhancing effect, anticholesterolemic, and antimicrobial has been up-