nils-herrmann/clean_scirepeval huggingface dataset:
Structure-based classification and ontology in chemistry

Recent years have seen an explosion in the availability of data in the chemistry domain. With this information explosion, however, retrieving relevant results from the available information, and organising those results, become even harder problems. Computational processing is essential to filter and organise the available resources so as to better facilitate the work of scientists. Ontologies encode expert domain knowledge in a hierarchically organised machine-processable format. One such ontology for the chemical domain is ChEBI. ChEBI provides a classification of chemicals based on their structural features and a role or activity-based classification. An example of a structure-based class is 'pentacyclic compound' (compounds containing five-ring structures), while an example of a role-based class is 'analgesic', since many different chemicals can act as analgesics without sharing structural features. Structure-based classification in chemistry exploits elegant regularities and symmetries in the underlying chemical domain. As yet, there has been neither a systematic analysis of the types of structural classification in use in chemistry nor a comparison to the capabilities of available technologies.



We analyze the different categories of structural classes in chemistry, presenting a list of patterns for features found in class definitions. We compare these patterns of class definition to tools which allow for automation of hierarchy construction within cheminformatics and within logic-based ontology technology, going into detail in the latter case with respect to the expressive capabilities of the Web Ontology Language and recent extensions for modelling structured objects. Finally we discuss the relationships and interactions between cheminformatics approaches and logic-based approaches.



Systems that perform intelligent reasoning tasks on chemistry data require a diverse set of underlying computational utilities including algorithmic, statistical and logic-based tools. For the task of automatic structure-based classification of chemical entities, essential to managing the vast swathes of chemical data being brought online, systems which are capable of hybrid reasoning combining several different approaches are crucial. We provide a thorough review of the available tools and methodologies, and identify areas of open research.
LKL: The Linux kernel library

The Linux kernel is a repository of high-quality, extensively reviewed and tested, but highly interconnected code. There have been several attempts to free this code and employ it to other uses, and, in each case, great amounts of resources were dedicated to decoupling the subsets that were needed from the rest of the kernel. Successful attempts faced significant maintenance issues, as small projects find themselves unable to keep pace with upstream kernel development. In time, differences between the extracted code and its mainline counterpart get bigger and it becomes very difficult to benefit from bug fixes, new features and improvements implemented in newer versions of the Linux kernel. The Linux kernel library (LKL) enables other projects to use code from the Linux kernel, without requiring them to isolate the needed pieces of code, separate them from other tightly knit components they may interact with, and extract them from the kernel. At the same time it allows LKL based applications to transparently make use of new features and bug fixes developed in the upstream version of the Linux kernel. Several proof-of-concept applications indicate that complete subsystems such as file system drivers or the networking stack can be easily used in applications running in environments as diverse as the Linux and Windows user space, or the Windows kernel space.
A millimeter-wave wide-band transition from a differential microstrip to a rectangular waveguide for 60 GHz applications

In this paper an integrated wide-band transition from a differential micro-strip line to a rectangular WR-15 waveguide is presented. The transition makes use of a cavity that is entirely integrated into the multilayer printed circuit board (PCB), which offers three layers (RF signal layer, ground plane and DC signal layer) for signal routing. The transition including the 18 mm long micro-strip feed lines provides a bandwidth of 20 GHz from 50 GHz to 70 GHz and an insertion loss of less than 2.3 dB. This makes the transition perfectly suited for differential wide-band transceivers operating in the 60 GHz band.
Competence and impact of tools for BPR

Software tools for Business Process Reengineering (BPR) promise to reduce cost and improve quality of projects. This paper discusses the contribution of BPR tools in BPR projects and identi®es critical factors for their success. A model was built based on previous research on tool success. The analysis of empirical data shows that BPR tools are related to effectiveness rather than ef®ciency of the projects. Process visualization and process analysis features are key to BPR tool competence. Also success factors for BPR tools are different from those for CASE tools.
100+ Times Faster Weighted Median Filter (WMF)

Weighted median, in the form of either solver or filter, has been employed in a wide range of computer vision solutions for its beneficial properties in sparsity representation. But it is hard to be accelerated due to the spatially varying weight and the median property. We propose a few efficient schemes to reduce computation complexity from O(r2) to O(r) where r is the kernel size. Our contribution is on a new joint-histogram representation, median tracking, and a new data structure that enables fast data access. The effectiveness of these schemes is demonstrated on optical flow estimation, stereo matching, structure-texture separation, image filtering, to name a few. The running time is largely shortened from several minutes to less than 1 second. The source code is provided in the project website.
Stochastic Answer Networks for Machine Reading Comprehension

We propose a simple yet robust stochastic answer network (SAN) that simulates multi-step reasoning in machine reading comprehension. Compared to previous work such as ReasoNet which used reinforcement learning to determine the number of steps, the unique feature is the use of a kind of stochastic prediction dropout on the answer module (final layer) of the neural network during the training. We show that this simple trick improves robustness and achieves results competitive to the state-of-the-art on the Stanford Question Answering Dataset (SQuAD), the Adversarial SQuAD, and the Microsoft MAchine Reading COmprehension Dataset (MS MARCO).
Fast and Regular Algorithms for Scalar Multiplication over Elliptic Curves

Elliptic curve cryptosystems are more and more widespread in everyday-life applications. This trend should still gain momentum in coming years thanks to the exponential security enjoyed by these systems compared to the subexponential security of other systems such as RSA. For this reason, efficient elliptic curve arithmetic is still a hot topic for cryptographers. The core operation of elliptic curve cryptosystems is the scalar multiplication which multiplies some point on an elliptic curve by some (usually secret) scalar. When such an operation is implemented on an embedded system such as a smart card, it is subject to side channel attacks. To withstand such attacks, one must constrain the scalar multiplication algorithm to be regular, namely to have an operation flow independent of the input scalar. A large amount of work has been published that focus on efficient and regular scalar multiplication and the choice leading to the best performances in practice is not clear. In this paper, we look into this question for general-form elliptic curves over large prime fields and we complete the current state-of-the-art. One of the fastest low-memory algorithms in the current literature is the Montgomery ladder using co-Z Jacobian arithmetic with X and Y coordinates only. We detail the regular implementation of this algorithm with various trade-offs and we introduce a new binary algorithm achieving comparable performances. For implementations that are less constrained in memory, windowing techniques and signed exponent recoding enable reaching better timings. We survey regular algorithms based on such techniques and we discuss their security with respect to side-channel attacks. On the whole, our work give a clear view of the currently best time-memory trade-offs for regular implementation of scalar multiplication over prime-field elliptic curves.
Spreading Processes in Multilayer Networks

Several systems can be modeled as sets of interconnected networks or networks with multiple types of connections, here generally called multilayer networks. Spreading processes such as information propagation among users of online social networks, or the diffusion of pathogens among individuals through their contact network, are fundamental phenomena occurring in these networks. However, while information diffusion in single networks has received considerable attention from various disciplines for over a decade, spreading processes in multilayer networks is still a young research area presenting many challenging research issues. In this paper, we review the main models, results and applications of multilayer spreading processes and discuss some promising research directions.
JBits: Java based interface for reconfigurable computing

The JBitsTM software is a set of JavaTM classes which provide an Application Programming Interface (API) to access the Xilinx FPGA bitstream. The interface operates on either bitstreams generated by Xilinx design tools, or on bitstreams read back from actual hardware. This permits all configurable resources like Look-up tables, routing and the flip-flops in the FPGA to be individually configured under software control.
Robust prediction of fault-proneness by random forests

Accurate prediction of fault prone modules (a module is equivalent to a C function or a C+ + method) in software development process enables effective detection and identification of defects. Such prediction models are especially beneficial for large-scale systems, where verification experts need to focus their attention and resources to problem areas in the system under development. This paper presents a novel methodology for predicting fault prone modules, based on random forests. Random forests are an extension of decision tree learning. Instead of generating one decision tree, this methodology generates hundreds or even thousands of trees using subsets of the training data. Classification decision is obtained by voting. We applied random forests in five case studies based on NASA data sets. The prediction accuracy of the proposed methodology is generally higher than that achieved by logistic regression, discriminant analysis and the algorithms in two machine learning software packages, WEKA [I. H. Witten et al. (1999)] and See5. The difference in the performance of the proposed methodology over other methods is statistically significant. Further, the classification accuracy of random forests is more significant over other methods in larger data sets.
Planning Paths for Package Delivery in Heterogeneous Multirobot Teams

This paper addresses the task scheduling and path planning problem for a team of cooperating vehicles performing autonomous deliveries in urban environments. The cooperating team comprises two vehicles with complementary capabilities, a truck restricted to travel along a street network, and a quadrotor micro-aerial vehicle of capacity one that can be deployed from the truck to perform deliveries. The problem is formulated as an optimal path planning problem on a graph and the goal is to find the shortest cooperative route enabling the quadrotor to deliver items at all requested locations. The problem is shown to be NP-hard. A solution is then proposed using a novel reduction to the Generalized Traveling Salesman Problem, for which well-established heuristic solvers exist. The heterogeneous delivery problem contains as a special case the problem of scheduling deliveries from multiple static warehouses. We propose two additional algorithms, based on enumeration and a reduction to the traveling salesman problem, for this special case. Simulation results compare the performance of the presented algorithms and demonstrate examples of delivery route computations over real urban street maps.
Statistics and Causal Inference

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The Wordometer -- Estimating the Number of Words Read Using Document Image Retrieval and Mobile Eye Tracking

We introduce the Wordometer, a novel method to estimate the number of words a user reads using a mobile eye tracker and document image retrieval. We present a reading detection algorithm which works with over 91 % accuracy over 10 test subjects using 10-fold cross validation. We implement two algorithms to estimate the read words using a line break detector. A simple version gives an average error rate of 13,5 % for 9 users over 10 documents. A more sophisticated word count algorithm based on support vector regression with an RBF kernel reaches an average error rate from only 8.2 % (6.5 % if one test subject with abnormal behavior is excluded). The achieved error rates are comparable to pedometers that count our steps in our daily life. Thus, we believe the Wordometer can be used as a step counter for the information we read to make our knowledge life healthier.
THREE PHASE INDUCTION MOTOR DRIVE USING IGBTs AND CONSTANT V/F METHOD

This paper presents design and analysis of a three phase induction motor drive using IGBT‟s at the inverter power stage with volts hertz control (V/F) in closed loop using dsPIC30F2010 as a controller. It is a 16 bit high-performance digital signal controller (DSC). DSC is a single chip embedded controller that integrates the controller attributes of a microcontroller with the computation and throughput capabilities of a DSP in a single core. A 1HP, 3-phase, 415V, 50Hz induction motor is used as load for the inverter. Digital Storage Oscilloscope Textronix TDS2024B is used to record and analyze the various waveforms. The experimental results for V/F control of 3Phase induction motor using dsPIC30F2010 chip clearly shows constant volts per hertz and stable inverter line to line output voltage. DSC, constant volts per hertz, PWM inverter,
Multilevel Inverter Topology for Renewable Energy Grid Integration

In this paper, a novel three-phase parallel grid-connected multilevel inverter topology with a novel switching strategy is proposed. This inverter is intended to feed a microgrid from renewable energy sources (RES) to overcome the problem of the polluted sinusoidal output in classical inverters and to reduce component count, particularly for generating a multilevel waveform with a large number of levels. The proposed power converter consists of <inline-formula><tex-math notation="LaTeX">$n$</tex-math></inline-formula> two-level <inline-formula> <tex-math notation="LaTeX">$(n+1)$</tex-math></inline-formula> phase inverters connected in parallel, where <inline-formula><tex-math notation="LaTeX">$n$</tex-math></inline-formula> is the number of RES. The more the number of RES, the more the number of voltage levels, the more faithful is the output sinusoidal waveform. In the proposed topology, both voltage pulse width and height are modulated and precalculated by using a pulse width and height modulation so as to reduce the number of switching states (i.e., switching losses) and the total harmonic distortion. The topology is investigated through simulations and validated experimentally with a laboratory prototype. Compliance with the <inline-formula><tex-math notation="LaTeX">$\text{IEEE 519-1992}$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$\text{IEC 61000-3-12}$</tex-math></inline-formula> standards is presented and an exhaustive comparison of the proposed topology is made against the classical cascaded H-bridge topology.
Enriching a Formal Ontology with a Thesaurus: an Application in the Cultural Heritage Domain

This paper describes a pattern-based method to automatically enrich a core ontology with the definitions of a domain glossary. We show an application of our methodology to the cultural heritage domain, using the CIDOC CRM core ontology. To enrich the CIDOC, we use available resources such as the AAT art and architecture glossary, WordNet, the Dmoz taxonomy for named entities, and others.
Learning to detect chest radiographs containing lung nodules using visual attention networks

Machine learning approaches hold great potential for the automated detection of lung nodules on chest radiographs, but training algorithms requires very large amounts of manually annotated radiographs, which are difficult to obtain. The increasing availability of PACS (Picture Archiving and Communication System), is laying the technological foundations needed to make available large volumes of clinical data and images from hospital archives. Binary labels indicating whether a radiograph contains a pulmonary lesion can be extracted at scale, using natural language processing algorithms. In this study, we propose two novel neural networks for the detection of chest radiographs containing pulmonary lesions. Both architectures make use of a large number of weakly-labelled images combined with a smaller number of manually annotated x-rays. The annotated lesions are used during training to deliver a type of visual attention feedback informing the networks about their lesion localisation performance. The first architecture extracts saliency maps from high-level convolutional layers and compares the inferred position of a lesion against the true position when this information is available; a localisation error is then back-propagated along with the softmax classification error. The second approach consists of a recurrent attention model that learns to observe a short sequence of smaller image portions through reinforcement learning; the reward function penalises the exploration of areas, within an image, that are unlikely to contain nodules. Using a repository of over 430,000 historical chest radiographs, we present and discuss the proposed methods over related architectures that use either weakly-labelled or annotated images only.
User behavior analysis in web log through comparative study of Eclat and Apriori

As we know World Wide Web plays vital role in serving the needs of the user's on web. The web log files are generated as a result of an interaction between the client and the service provider on web. Web log file contains the massive hidden valuable information pertaining to the visitors, if mined can be used for predicting the navigation behavior of the users. However the task of discovering frequent sequence patterns from the web log is challenging. Sequential pattern mining provides a significant role in serving a promising approach of the access behavior of the user. This paper focuses on adopting an intelligent technique that can provide personalized web service for accessing related web pages more efficiently and effectively, so that it can be determined which web pages are more likely to be accessed by the user in future. This paper uses two intelligent algorithms for predicting the user behavior's namely Apriori and Eclat and also does the performance comparison of the two algorithms in terms of time and space complexity for the filtered data.
YIN, a fundamental frequency estimator for speech and music.

An algorithm is presented for the estimation of the fundamental frequency (F0) of speech or musical sounds. It is based on the well-known autocorrelation method with a number of modifications that combine to prevent errors. The algorithm has several desirable features. Error rates are about three times lower than the best competing methods, as evaluated over a database of speech recorded together with a laryngograph signal. There is no upper limit on the frequency search range, so the algorithm is suited for high-pitched voices and music. The algorithm is relatively simple and may be implemented efficiently and with low latency, and it involves few parameters that must be tuned. It is based on a signal model (periodic signal) that may be extended in several ways to handle various forms of aperiodicity that occur in particular applications. Finally, interesting parallels may be drawn with models of auditory processing.
Fast geometric point labeling using conditional random fields

In this paper we present a new approach for labeling 3D points with different geometric surface primitives using a novel feature descriptor - the Fast Point Feature Histograms, and discriminative graphical models. To build informative and robust 3D feature point representations, our descriptors encode the underlying surface geometry around a point p using multi-value histograms. This highly dimensional feature space copes well with noisy sensor data and is not dependent on pose or sampling density. By defining classes of 3D geometric surfaces and making use of contextual information using Conditional Random Fields (CRFs), our system is able to successfully segment and label 3D point clouds, based on the type of surfaces the points are lying on. We validate and demonstrate the method's efficiency by comparing it against similar initiatives as well as present results for table setting datasets acquired in indoor environments.
NCBI BLAST: a better web interface

Basic Local Alignment Search Tool (BLAST) is a sequence similarity search program. The public interface of BLAST, http://www.ncbi.nlm.nih.gov/blast, at the NCBI website has recently been reengineered to improve usability and performance. Key new features include simplified search forms, improved navigation, a list of recent BLAST results, saved search strategies and a documentation directory. Here, we describe the BLAST web application's new features, explain design decisions and outline plans for future improvement.
Scatteract: Automated Extraction of Data from Scatter Plots

Charts are an excellent way to convey patterns and trends in data, but they do not facilitate further modeling of the data or close inspection of individual data points. We present a fully automated system for extracting the numerical values of data points from images of scatter plots. We use deep learning techniques to identify the key components of the chart, and optical character recognition together with robust regression to map from pixels to the coordinate system of the chart. We focus on scatter plots with linear scales, which already have several interesting challenges. Previous work has done fully automatic extraction for other types of charts, but to our knowledge this is the first approach that is fully automatic for scatter plots. Our method performs well, achieving successful data extraction on 89% of the plots in our test set.