nils-herrmann/clean_scirepeval huggingface dataset:
Semi-automatic model integration using matching transformations and weaving models

Model transformations are at the heart of model driven engineering (MDE) and can be used in many different application scenarios. For instance, model transformations are used to integrate very large models. As a consequence, they are becoming more and more complex. However, these transformations are still developed manually. Several code patterns are implemented repetitively, increasing the probability of programming errors and reducing code reusability. There is not yet a complete solution that automates the development of model transformations. In this paper we propose a novel approach that uses matching transformations and weaving models to semi-automate the development of transformations. Matching transformations are a special kind of transformations that implement heuristics and algorithms to create weaving models. Weaving models are models that capture different kinds of relationships between models. Our solution enables to rapidly implement and to customize these heuristics. We combine different heuristics, and we propose a new metamodel-based heuristic that exploits metamodel data to automatically produce weaving models. The weaving models are derived into model integration transformations.
Life history data of the rosy apple aphid Dysaphis plantaginea (Pass.) (Homopt., Aphididae) on plantain and as migrant to apple

The rosy apple aphid Dysaphis plantaginea (Passerini) is a key pest in western European apple orchards; the economic damage threshold is so low that outbreaks cannot be forecasted. A mass rearing of the species on plantain (Plantago lanceolata L.) was initiated, with the aim to infest apple trees with either the autumn migrants, gynoparae and males, or the egg-laying females (oviparae). Here, data are presented about the propagation of the species on plantain, on the production of autumn migrants under laboratory conditions, and on the duration of juvenile development and reproductive capacities of both gynoparae and oviparae. Under long-day conditions (18 h light/day) on plantain, the thermal constant for the duration of juvenile development was 166 dd (day-degrees) above the lower development threshold of 5.1 °C, and daily larviposition amounted to 1.87 times the temperature (°C)minus 0.8, above a lower threshold of 4.3 °C. Between 32 and 36 larvae were produced by the young female before the first larvae become adult and their reproduction started to overshadow their mother’s. A plant freshly infested with 12 reproducing females and transferred to short-day conditions (12 h light/day) yielded up to 5,000 autumn migrants, with males in the majority. The first gynoparae appeared after about 25 days at both 16 and 20 °C, and males appeared after 40 and 33 days, respectively. Young adult gynoparae produced most of their about ten offspring right after landing on apples, unless temperature was well below 15 °C. The duration of juvenile development of these oviparae appeared to be rather variable and their egg-laying so protracted that each of these females needs to survive several weeks to produce a handful of winter eggs. With reproductive capacities of up to 14 progeny/female for gynoparae and 7.4 for oviparae, release of one gynopara in the field could theoretically lead to the deposition of 100 winter eggs.
Collaborative expression representation using peak expression and intra class variation face images for practical subject-independent emotion recognition in videos

This paper proposes a facial expression recognition (FER) method in videos. The proposed method automatically selects the peak expression face from a video sequence using closeness of the face to the neutral expression. The severely non-frontal faces and poorly aligned faces are discarded in advance to eliminate their negative effects on the peak expression face selection and FER. To reduce the effect of the facial identity in the feature extraction, we compute difference information between the peak expression face and its intra class variation (ICV) face. An ICV face is generated by combining the training faces of an expression class and looks similar to the peak expression face in identity. Because the difference information is defined as the distances of locally pooled texture features between the two faces, the feature extraction is robust to face rotation and mis-alignment. Results show that the proposed method is practical with videos containing spontaneous facial expressions and pose variations. & 2016
A study on classification techniques in data mining

Data mining is a process of inferring knowledge from such huge data.  Mining has three major components Clustering or Classification, Association Rules and Sequence Analysis. By simple definition, in classification/clustering analyze a set of data and generate a set of grouping rules which can be used to classify future data. Data mining is the process is to extract information from a data set and transform it into an understandable structure. It is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The actual data mining task is the automatic or semi-automatic analysis of large quantities of data to extract previously unknown interesting patterns. Data mining involves six common classes of tasks. Anomaly detection, Association rule learning, Clustering, Classification, Regression, Summarization. Classification is a major technique in data mining and widely used in various fields. Classification is a data mining (machine learning) technique used to predict group membership for data instances. In this paper, we present the basic classification techniques. Several major kinds of classification method including decision tree induction, Bayesian networks, k-nearest neighbor classifier, the goal of this study is to provide a comprehensive review of different classification techniques in data mining.
The true story and advantages of the famous Hepatitis B virus core particles: Outlook 2016

This review article is a continuation of the paper “Hepatitis B core particles as a universal display model: a structure-function basis for development” written by Pumpens P. and Grens E., ordered by Professor Lev Kisselev and published in FEBS Letters, 1999, 442, 1–6. The past 17 years have strengthened the paper’s finding that the human hepatitis B virus core protein, along with other Hepadnaviridae family member core proteins, is a mysterious, multifunctional protein. The core gene of the Hepadnaviridae genome encodes five partially collinear proteins. The most important of these is the HBV core protein p21, or HBc. It can self-assemble by forming viral HBc particles, but also plays a crucial role in the regulation of viral replication. Since 1986, the HBc protein has been one of the first and the most successful tools of the virus-like particle (VLP) technology. Later, the woodchuck hepatitis virus core protein (WHc) was also used as a VLP carrier. The Hepadnaviridae core proteins remain favourite VLP candidates for the knowledge-based design of future vaccines, gene therapy vectors, specifically targeted nanocontainers, and other modern nanotechnological tools for prospective medical use.
Phased array ghost elimination.

Parallel imaging may be applied to cancel ghosts caused by a variety of distortion mechanisms, including distortions such as off-resonance or local flow, which are space variant. Phased array combining coefficients may be calculated that null ghost artifacts at known locations based on a constrained optimization, which optimizes SNR subject to the nulling constraint. The resultant phased array ghost elimination (PAGE) technique is similar to the method known as sensitivity encoding (SENSE) used for accelerated imaging; however, in this formulation is applied to full field-of-view (FOV) images. The phased array method for ghost elimination may result in greater flexibility in designing acquisition strategies. For example, in multi-shot EPI applications ghosts are typically mitigated by the use of an interleaved phase encode acquisition order. An alternative strategy is to use a sequential, non-interleaved phase encode order and cancel the resultant ghosts using PAGE parallel imaging. Cancellation of ghosts by means of phased array processing makes sequential, non-interleaved phase encode acquisition order practical, and permits a reduction in repetition time, TR, by eliminating the need for echo-shifting. Sequential, non-interleaved phase encode order has benefits of reduced distortion due to off-resonance, in-plane flow and EPI delay misalignment. Furthermore, the use of EPI with PAGE has inherent fat-water separation and has been used to provide off-resonance correction using a technique referred to as lipid elimination with an echo-shifting N/2-ghost acquisition (LEENA), and may further generalized using the multi-point Dixon method. Other applications of PAGE include cancelling ghosts which arise due to amplitude or phase variation during the approach to steady state. Parallel imaging requires estimates of the complex coil sensitivities. In vivo estimates may be derived by temporally varying the phase encode ordering to obtain a full k-space dataset in a scheme similar to the autocalibrating TSENSE method. This scheme is a generalization of the UNFOLD method used for removing aliasing in undersampled acquisitions. The more general scheme may be used to modulate each EPI ghost image to a separate temporal frequency as described in this paper.
Placenta weight percentile curves for singleton and twins deliveries.

To establish updated placental percentile nomograms in a large North American population for singleton and twin gestations for the use of researchers and clinicians.



Data was extracted from our computerized registry; McGill Obstetrics and Neonatal Database (MOND). The registry includes all the obstetrical data on all deliveries at the McGill University, including placental weight, placental pathologies, maternal and perinatal complications. 20,635 singleton deliveries and 527 twin deliveries were included. Placental weight, gestational age at delivery, birth weight and gender were retrieved. Tables and figures for the 3rd,10th,25th, 50th, 75th 90th, and 97th percentile of placental weight by gestational age, placental weight by birth weight and placental to birth weight ratio by gestational age were produced.



Tables and figures are presented for placental percentiles curves according to gestational age, gestational weight and gender for singleton and twin deliveries. In addition, tables and figures are presented for the ratio of placental weight to birth weight.



Population percentile curves have been produced for placental weight and for the ratio of placental weight to birth weight to for singleton and twin deliveries.
Space-Time Graph Modeling of Ride Requests Based on Real-World Data

This paper focuses on modeling ride requests and their variations over location and time, based on analyzing extensive real-world data from a ride-sharing service. We introduce a graph model that captures the spatial and temporal variability of ride requests and the potentials for ride pooling. We discover these ride request graphs exhibit a well known property called “densification power law” often found in real graphs modelling human behaviors. We show the pattern of ride requests and the potential of ride pooling for a city can be characterized by the densification factor of the ride request graphs. Previous works have shown that it is possible to automatically generate synthetic versions of these graphs that exhibit a given densification factor. We present an algorithm for automatic generation of synthetic ride request graphs that match quite well the densification factor of ride request graphs from actual ride request data.
Readability of Texts: State of the Art

In TEFL, it is often stated that communication presupposes comprehension. The main purpose of readability studies is thus to measure the comprehensibility of a piece of writing. In this regard, different readability measures were initially devised to help educators select passages suitable for both children and adults. However, readability formulas can certainly be extremely helpful in the realm of EFL reading. They were originally designed to assess the suitability of books for students at particular grade levels or ages. Nevertheless, they can be used as basic tools in determining certain crucial EFL text-characteristics instrumental in the skill of reading and its related issues. The aim of the present paper is to familiarize the readers with the most frequently used readability formulas as well as the pros and cons views toward the use of such formulas. Of course, this part mostly illustrates studies done on readability formulas with the results obtained. The main objective of this part is to help readers to become familiar with the background of the formulas, the theory on which they stand, what they are good for and what they are not with regard to a number of studies cited in this section.
Negative index of refraction in optical metamaterials.

A double-periodic array of pairs of parallel gold nanorods is shown to have a negative refractive index in the optical range. Such behavior results from the plasmon resonance in the pairs of nanorods for both the electric and the magnetic components of light. The refractive index is retrieved from direct phase and amplitude measurements for transmission and reflection, which are all in excellent agreement with simulations. Both experiments and simulations demonstrate that a negative refractive index n' approximately -0.3 is achieved at the optical communication wavelength of 1.5 microm using the array of nanorods. The retrieved refractive index critically depends on the phase of the transmitted wave, which emphasizes the importance of phase measurements in finding n'.
Overview of Problem-Based Learning: Definitions and Distinctions.

Problem-based learning (PBL) is an instructional approach that has been used successfully for over 30 years and continues to gain acceptance in multiple disciplines. It is an instructional (and curricular) learner-centered approach that empowers learners to conduct research, integrate theory and practice, and apply knowledge and skills to develop a viable solution to a defined problem. This overview presents a brief history, followed by a discussion of the similarities and differences between PBL and other experiential approaches to teaching, and identifies some of the challenges that lie ahead for PBL.
Scalable Performance Tuning of Hadoop MapReduce: A Noisy Gradient Approach

Hadoop MapReduce is a popular framework for distributed storage and processing of large datasets and is used for big data analytics. It has various configuration parameters which play an important role in deciding the performance i.e., the execution time of a given big data processing job. Default values of these parameters do not result in good performance and therefore it is important to tune them. However, there is inherent difficulty in tuning the parameters due to two important reasons - first, the parameter search space is large and second, there are cross-parameter interactions. Hence, there is a need for a dimensionality-free method which can automatically tune the configuration parameters by taking into account the cross-parameter dependencies. In this paper, we propose a novel Hadoop parameter tuning methodology, based on a noisy gradient algorithm known as the simultaneous perturbation stochastic approximation (SPSA). The SPSA algorithm tunes the selected parameters by directly observing the performance of the Hadoop MapReduce system. The approach followed is independent of parameter dimensions and requires only 2 observations per iteration while tuning. We demonstrate the effectiveness of our methodology in achieving good performance on popular Hadoop benchmarks namely Grep, Bigram, Inverted Index, Word Co-occurrence and Terasort. Our method, when tested on a 25 node Hadoop cluster shows 45-66% decrease in execution time of Hadoop jobs on an average, when compared to prior methods. Further, our experiments also indicate that the parameters tuned by our method are resilient to changes in number of cluster nodes, which makes our method suitable to optimize Hadoop when it is provided as a service on the cloud.
The Biomechanical Effect of the Sensomotor Insole on a Pediatric Intoeing Gait

The sensomotor insole (SMI) has clinically been shown to be successful in treating an intoeing gait. We investigated the biomechanical effect of SMI on a pediatric intoeing gait by using three-dimensional gait analysis.  Six patients with congenital clubfeet and four patients with idiopathic intoeing gait were included. There were five boys and five girls with the average age at testing of 5.6 years. The torsional profile of the lower limb was assessed clinically. Three-dimensional gait analysis was performed in the same shoes with and without SMI.  All clubfeet patients exhibited metatarsal adductus, while excessive femoral anteversion and/or internal tibial torsion was found in patients with idiopathic intoeing gait. SMI showed significant decreased internal rotation of the proximal femur in terminal swing phase and loading response phase. The internal rotation of the tibia was significantly smaller in mid stance phase and terminal stance phase by SMI. In addition, SMI significantly increased the walking speed and the step length.  SMI improved abnormal gait patterns of pediatric intoeing gait by decreasing femoral internal rotation through the end of the swing phase and the beginning of the stance phase and by decreasing tibial internal rotation during the stance phase.
Scalable Coordinate Descent Approaches to Parallel Matrix Factorization for Recommender Systems

Matrix factorization, when the matrix has missing values, has become one of the leading techniques for recommender systems. To handle web-scale datasets with millions of users and billions of ratings, scalability becomes an important issue. Alternating Least Squares (ALS) and Stochastic Gradient Descent (SGD) are two popular approaches to compute matrix factorization. There has been a recent flurry of activity to parallelize these algorithms. However, due to the cubic time complexity in the target rank, ALS is not scalable to large-scale datasets. On the other hand, SGD conducts efficient updates but usually suffers from slow convergence that is sensitive to the parameters. Coordinate descent, a classical optimization approach, has been used for many other large-scale problems, but its application to matrix factorization for recommender systems has not been explored thoroughly. In this paper, we show that coordinate descent based methods have a more efficient update rule compared to ALS, and are faster and have more stable convergence than SGD. We study different update sequences and propose the CCD++ algorithm, which updatesrank-one factors one by one. In addition, CCD++ can be easily parallelized on both multi-core and distributed systems. We empirically show that CCD++ is much faster than ALS and SGD in both settings. As an example, on a synthetic dataset with 2 billion ratings, CCD++ is 4 times faster than both SGD and ALS using a distributed system with 20 machines.
A Warning System for Obstacle Detection at Vehicle Lateral Blind Spot Area

For a real driver assistance system, the weather, driving speed, and background could affect the accuracy of obstacle detection. In the past, only a few studies covered all the different weather conditions and almost none of them had paid attention to the safety at vehicle lateral blind spot area. So, this paper proposes a hybrid scheme for pedestrian and vehicle detection, and develop a warning system dedicated for lateral blind spot area under different weather conditions and driving speeds. More specifically, the HOG and SVM methods are used for pedestrian detection. The image subtraction, edge detection and tire detection are applied for vehicle detection. Experimental results also show that the proposed system can efficiently detect pedestrian and vehicle under several scenarios.
Towards Conversational Search and Recommendation: System Ask, User Respond

Conversational search and recommendation based on user-system dialogs exhibit major differences from conventional search and recommendation tasks in that 1) the user and system can interact for multiple semantically coherent rounds on a task through natural language dialog, and 2) it becomes possible for the system to understand the user needs or to help users clarify their needs by asking appropriate questions from the users directly. We believe the ability to ask questions so as to actively clarify the user needs is one of the most important advantages of conversational search and recommendation. In this paper, we propose and evaluate a unified conversational search/recommendation framework, in an attempt to make the research problem doable under a standard formalization. Specifically, we propose a System Ask -- User Respond (SAUR) paradigm for conversational search, define the major components of the paradigm, and design a unified implementation of the framework for product search and recommendation in e-commerce. To accomplish this, we propose the Multi-Memory Network (MMN) architecture, which can be trained based on large-scale collections of user reviews in e-commerce. The system is capable of asking aspect-based questions in the right order so as to understand the user needs, while (personalized) search is conducted during the conversation, and results are provided when the system feels confident. Experiments on real-world user purchasing data verified the advantages of conversational search and recommendation against conventional search and recommendation algorithms in terms of standard evaluation measures such as NDCG.
Dynamic and scalable audio classification by collective network of binary classifiers framework: An evolutionary approach

In this paper, we propose a novel framework based on a collective network of evolutionary binary classifiers (CNBC) to address the problems of feature and class scalability. The main goal of the proposed framework is to achieve a high classification performance over dynamic audio and video repositories. The proposed framework adopts a "Divide and Conquer" approach in which an individual network of binary classifiers (NBC) is allocated to discriminate each audio class. An evolutionary search is applied to find the best binary classifier in each NBC with respect to a given criterion. Through the incremental evolution sessions, the CNBC framework can dynamically adapt to each new incoming class or feature set without resorting to a full-scale re-training or re-configuration. Therefore, the CNBC framework is particularly designed for dynamically varying databases where no conventional static classifiers can adapt to such changes. In short, it is entirely a novel topology, an unprecedented approach for dynamic, content/data adaptive and scalable audio classification. A large set of audio features can be effectively used in the framework, where the CNBCs make appropriate selections and combinations so as to achieve the highest discrimination among individual audio classes. Experiments demonstrate a high classification accuracy (above 90%) and efficiency of the proposed framework over large and dynamic audio databases.