nils-herrmann/clean_scirepeval huggingface dataset:
from entailment meshes via semantic nets to learning webs

The symbol-based, correspondence epistemology used in AI is contrasted with the constructivist, coherence epistemology promoted by cybernetics. The latter leads to bootstrapping knowledge representations, in which different parts of the cognitive system mutually support each other. Gordon Pask's entailment meshes and their implementation in the THOUGHTSTICKER program are reviewed as a basic application of this approach. Entailment meshes are then extended to entailment nets: directed graph representations governed by the “bootstrapping axiom”, determining which concepts are to be distinguished or merged. This allows a constant restructuring and elicitation of the conceptual network. Semantic networks and frame-like representations with inheritance can be expressed in this very general scheme by introducing a basic ontology of node and link types. Entailment nets are then generalized to associative nets characterized by weighted links. Learning algorithms are presented which can adapt the link strengths, based on the frequency with which links are selected by hypertext browsers. It is argued that these different bootstrapping methods could be applied to make the World-Wide Web more intelligent, by allowing it to self-organize and support inferences through spreading activation.
Combining Deep Learning and Survival Analysis for Asset Health Management

We propose a method to integrate feature extraction and prediction as a single optimization task by stacking a three-layer model as a deep learning structure. The first layer of the deep structure is a Long Short Term Memory (LSTM) model which deals with the sequential input data from a group of assets. The output of the LSTM model is followed by meanpooling, and the result is fed to the second layer. The second layer is a neural network layer, which further learns the feature representation. The output of the second layer is connected to a survival model as the third layer for predicting asset health condition. The parameters of the three-layer model are optimized together via stochastic gradient decent. The proposed method was tested on a small dataset collected from a fleet of mining haul trucks. The model resulted in the “individualized” failure probability representation for assessing the health condition of each individual asset, which well separates the in-service and failed trucks. The proposed method was also tested on a large open source hard drive dataset, and it showed promising result.
ContextNet: Exploring Context and Detail for Semantic Segmentation in Real-time

Modern deep learning architectures produce highly accurate results on many challenging semantic segmentation datasets. State-of-the-art methods are, however, not directly transferable to real-time applications or embedded devices, since naïve adaptation of such systems to reduce computational cost (speed, memory and energy) causes a significant drop in accuracy. We propose ContextNet, a new deep neural network architecture which builds on factorized convolution, network compression and pyramid representation to produce competitive semantic segmentation in real-time with low memory requirement. ContextNet combines a deep network branch at low resolution that captures global context information efficiently with a shallow branch that focuses on highresolution segmentation details. We analyse our network in a thorough ablation study and present results on the Cityscapes dataset, achieving 66.1% accuracy at 18.3 frames per second at full (1024× 2048) resolution (23.2 fps with pipelined computations for streamed data).
Dual-Band Circularly Polarized Microstrip RFID Reader Antenna Using Metamaterial Branch-Line Coupler

A dual-band circularly polarized aperture coupled microstrip RFID reader antenna using a metamaterial (MTM) branch-line coupler has been designed, fabricated, and measured. The proposed antenna is fabricated on a FR-4 substrate with relative permittivity of 4.6 and thickness of 1.6 mm. The MTM coupler is designed employing the provided explicit closed-form formulas. The dual-band (UHF and ISM) circularly-polarized RFID reader antenna with separate Tx and Rx ports is connected to the designed metamaterial (MTM) branch-line coupler. The maximum measured LHCP antenna gain is 6.6 dBic at 920 MHz (UHF) and RHCP gain is 7.9 dBic at 2.45 GHz (ISM). The cross-polar CP gains near broadside of the RFID reader antenna are approximately less than - 20 dB compared with the mentioned co-polar CP gains in both bands. The isolations between the two ports are about 25 dB and 38 dB, at 920 MHz and 2.45 GHz, respectively. The measured axial ratios are less than 0.7 dB in the UHF band (917-923 MHz) and 1.5 dB in the ISM band (2.4-2.48 GHz).
Integral Channel Features

We study the performance of ‘integral channel features’ for image classification tasks, focusing in particular on pedestrian detection. The general idea behind integral channel features is that multiple registered image channels are computed using linear and non-linear transformations of the input image, and then features such as local sums, histograms, and Haar features and their various generalizations are efficiently computed using integral images. Such features have been used in recent literature for a variety of tasks – indeed, variations appear to have been invented independently multiple times. Although integral channel features have proven effective, little effort has been devoted to analyzing or optimizing the features themselves. In this work we present a unified view of the relevant work in this area and perform a detailed experimental evaluation. We demonstrate that when designed properly, integral channel features not only outperform other features including histogram of oriented gradient (HOG), they also (1) naturally integrate heterogeneous sources of information, (2) have few parameters and are insensitive to exact parameter settings, (3) allow for more accurate spatial localization during detection, and (4) result in fast detectors when coupled with cascade classifiers.
Efficient planar heterojunction perovskite solar cells by vapour deposition

Many different photovoltaic technologies are being developed for large-scale solar energy conversion. The wafer-based first-generation photovoltaic devices have been followed by thin-film solid semiconductor absorber layers sandwiched between two charge-selective contacts and nanostructured (or mesostructured) solar cells that rely on a distributed heterojunction to generate charge and to transport positive and negative charges in spatially separated phases. Although many materials have been used in nanostructured devices, the goal of attaining high-efficiency thin-film solar cells in such a way has yet to be achieved. Organometal halide perovskites have recently emerged as a promising material for high-efficiency nanostructured devices. Here we show that nanostructuring is not necessary to achieve high efficiencies with this material: a simple planar heterojunction solar cell incorporating vapour-deposited perovskite as the absorbing layer can have solar-to-electrical power conversion efficiencies of over 15 per cent (as measured under simulated full sunlight). This demonstrates that perovskite absorbers can function at the highest efficiencies in simplified device architectures, without the need for complex nanostructures.
Hybrid Excitation Topologies and Control Strategies of Stator Permanent Magnet Machines for DC Power System

The stator permanent magnet (PM) machines have simple and robust rotor structure as well as high torque density. The hybrid excitation topology can realize flux regulation and wide constant power operating capability of the stator PM machines when used in dc power systems. This paper compares and analyzes the electromagnetic performance of different hybrid excitation stator PM machines according to different combination modes of PMs, excitation winding, and iron flux bridge. Then, the control strategies for voltage regulation of dc power systems are discussed based on different critical control variables including the excitation current, the armature current, and the electromagnetic torque. Furthermore, an improved direct torque control (DTC) strategy is investigated to improve system performance. A parallel hybrid excitation flux-switching generator employing the improved DTC which shows excellent dynamic and steady-state performance has been achieved experimentally.
Forging high-quality User Stories: Towards a discipline for Agile Requirements

User stories are a widely used notation for formulating requirements in agile development projects. Despite their popularity in industry, little to no academic work is available on assessing their quality. The few existing approaches are too generic or employ highly qualitative metrics. We propose the Quality User Story Framework, consisting of 14 quality criteria that user story writers should strive to conform to. Additionally, we introduce the conceptual model of a user story, which we rely on to design the AQUSA software tool. AQUSA aids requirements engineers in turning raw user stories into higher-quality ones by exposing defects and deviations from good practice in user stories. We evaluate our work by applying the framework and a prototype implementation to three user story sets from industry.
The role of Internet addiction in online game loyalty: an exploratory study

The paper’s aim is to explore the factors that affect the online game addiction and the role that online game addiction plays in the relationship between online satisfaction and loyalty.  A web survey of online game players was conducted, with 1,186 valid responses collected. Structure equation modeling – specifically partial least squares – was used to assess the relationships in the proposed research framework.  The results indicate that perceived playfulness and descriptive norms influence online game addiction. Furthermore, descriptive norms indirectly affect online game addiction through perceived playfulness. Addiction also directly contributes to loyalty and attenuates the relationship between satisfaction and loyalty. This finding partially explains why people remain loyal to an online game despite being dissatisfied.  Online gaming vendors should strive to create amusing game content and to maintain their online game communities in order to enhance players’ perceptions of playfulness and the effects of social influences. Also, because satisfaction is the most significant indicator of loyalty, vendors can enhance loyalty by providing better services, such as fraud prevention and the detection of cheating behaviors.  The value of this study is that it reveals the moderating influences of addiction on the satisfaction-loyalty relationship and factors that contribute to the online game addiction. Moreover, while many past studies focused on addiction’s negative effects and on groups considered particularly vulnerable to Internet addiction, this paper extends previous work by investigating the relationship of addiction to other marketing variables and by using a more general population, mostly young adults, as research subjects.
A Voltage-Controlled PFC Cuk Converter-Based PMBLDCM Drive for Air-Conditioners

This paper deals with a Cuk dc-dc converter as a single-stage power-factor-correction converter for a permanent-magnet (PM) brushless dc motor (PMBLDCM) fed through a diode bridge rectifier from a single-phase ac mains. A three-phase voltage-source inverter is used as an electronic commutator to operate the PMBLDCM driving an air-conditioner compressor. The speed of the compressor is controlled to achieve optimum air-conditioning using a concept of the voltage control at dc link proportional to the desired speed of the PMBLDCM. The stator currents of the PMBLDCM during step change in the reference speed are controlled within the specified limits by an addition of a rate limiter in the reference dc link voltage. The proposed PMBLDCM drive (PMBLDCMD) is designed and modeled, and its performance is evaluated in Matlab-Simulink environment. Simulated results are presented to demonstrate an improved power quality at ac mains of the PMBLDCMD system in a wide range of speed and input ac voltage. Test results of a developed controller are also presented to validate the design and model of the drive.
Texture Mapping Using Surface Flattening via Multidimensional Scaling

We present a novel technique for texture mapping on arbitrar y su faces with minimal distortions, by preserving the local and globa l structure of the texture. The recent introduction of the fast marching metho d on triangulated surfaces [9], made it possible to compute geodesic distance s i O(~ n lg ~ n) where~ n is the number of triangles that represent the surface. We use this method to design a surface flattening approach based on multi -dimensional scaling (MDS). MDS is a family of methods that map a set of poin ts to a finite dimensional flat (Euclidean) domain, where the only gi ven data is the corresponding distances between every pair of points. The M DS mapping yields minimal changes of the distances between the corresp onding points. We then solve an ‘inverse’ problem and map a flat texture patch onto the curved surface while preserving the structure of the textur .
LSRN: A Parallel Iterative Solver for Strongly Over- or Underdetermined Systems

We describe a parallel iterative least squares solver named LSRN that is based on random normal projection. LSRN computes the min-length solution to min x∈ℝ n ‖Ax - b‖2, where A ∈ ℝ m × n with m ≫ n or m ≪ n, and where A may be rank-deficient. Tikhonov regularization may also be included. Since A is involved only in matrix-matrix and matrix-vector multiplications, it can be a dense or sparse matrix or a linear operator, and LSRN automatically speeds up when A is sparse or a fast linear operator. The preconditioning phase consists of a random normal projection, which is embarrassingly parallel, and a singular value decomposition of size ⌈γ min(m, n)⌉ × min(m, n), where γ is moderately larger than 1, e.g., γ = 2. We prove that the preconditioned system is well-conditioned, with a strong concentration result on the extreme singular values, and hence that the number of iterations is fully predictable when we apply LSQR or the Chebyshev semi-iterative method. As we demonstrate, the Chebyshev method is particularly efficient for solving large problems on clusters with high communication cost. Numerical results show that on a shared-memory machine, LSRN is very competitive with LAPACK's DGELSD and a fast randomized least squares solver called Blendenpik on large dense problems, and it outperforms the least squares solver from SuiteSparseQR on sparse problems without sparsity patterns that can be exploited to reduce fill-in. Further experiments show that LSRN scales well on an Amazon Elastic Compute Cloud cluster.
Dense versus Sparse Approaches for Estimating the Fundamental Matrix

There are two main strategies for solving correspondence problems in computer vision: sparse local feature based approaches and dense global energy based methods. While sparse feature based methods are often used for estimating the fundamental matrix by matching a small set of sophistically optimised interest points, dense energy based methods mark the state of the art in optical flow computation. The goal of our paper is to show that this separation into different application domains is unnecessary and can be bridged in a natural way. As a first contribution we present a new application of dense optical flow for estimating the fundamental matrix. Comparing our results with those obtained by feature based techniques we identify cases in which dense methods have advantages over sparse approaches. Motivated by these promising results we propose, as a second contribution, a new variational model that recovers the fundamental matrix and the optical flow simultaneously as the minimisers of a single energy functional. In experiments we show that our coupled approach is able to further improve the estimates of both the fundamental matrix and the optical flow. Our results prove that dense variational methods can be a serious alternative even in classical application domains of sparse feature based approaches.
Event Detection in Twitter using Aggressive Filtering and Hierarchical Tweet Clustering

Twitter has become as much of a news media as a social network, and much research has turned to analyzing its content for tracking real-world events, from politics to sports and natural disasters. This paper describes the techniques we employed for the SNOW Data Challenge 2014, described in . We show that aggressive filtering of tweets based on length and structure, combined with hierarchical clustering of tweets and ranking of the resulting clusters, achieves encouraging results. We present empirical results and discussion for two different Twitter streams focusing on the US presidential elections in 2012 and the recent events about Ukraine, Syria and the Bitcoin, in February 2014.
A Fast Algorithm for Computing Longest Subsequences

Previously published algorithms for finding the longest common subsequence of two sequences of length n have had a best-case running time of O(n2). An algorithm for this problem is presented which has a running time of O((r + n) log n), where r is the total number of ordered pairs of positions at which the two sequences match. Thus in the worst case the algorithm has a running time of O(n2 log n). However, for those applications where most positions of one sequence match relatively few positions in the other sequence, a running time of O(n log n) can be expected.
Blog site search using resource selection

A blog site consists of many individual blog postings. Current blog search services focus on retrieving postings but there is also a need to identify relevant blog sites. Blog site search is similar to resource selection in distributed information retrieval, in that the target is to find relevant collections of documents. We introduce resource selection techniques for blog site search and evaluate their performance. Further, we propose a "diversity factor" that measures the topic diversity of each blog site. Our results show that the appropriate combination of the resource selection techniques and the diversity factor can achieve significant improvements in retrieval performance compared to baselines. We also report results using these techniques on the TREC blog distillation task.
Clustering More than Two Million Biomedical Publications: Comparing the Accuracies of Nine Text-Based Similarity Approaches

We investigate the accuracy of different similarity approaches for clustering over two million biomedical documents. Clustering large sets of text documents is important for a variety of information needs and applications such as collection management and navigation, summary and analysis. The few comparisons of clustering results from different similarity approaches have focused on small literature sets and have given conflicting results. Our study was designed to seek a robust answer to the question of which similarity approach would generate the most coherent clusters of a biomedical literature set of over two million documents.



We used a corpus of 2.15 million recent (2004-2008) records from MEDLINE, and generated nine different document-document similarity matrices from information extracted from their bibliographic records, including titles, abstracts and subject headings. The nine approaches were comprised of five different analytical techniques with two data sources. The five analytical techniques are cosine similarity using term frequency-inverse document frequency vectors (tf-idf cosine), latent semantic analysis (LSA), topic modeling, and two Poisson-based language models--BM25 and PMRA (PubMed Related Articles). The two data sources were a) MeSH subject headings, and b) words from titles and abstracts. Each similarity matrix was filtered to keep the top-n highest similarities per document and then clustered using a combination of graph layout and average-link clustering. Cluster results from the nine similarity approaches were compared using (1) within-cluster textual coherence based on the Jensen-Shannon divergence, and (2) two concentration measures based on grant-to-article linkages indexed in MEDLINE.



PubMed's own related article approach (PMRA) generated the most coherent and most concentrated cluster solution of the nine text-based similarity approaches tested, followed closely by the BM25 approach using titles and abstracts. Approaches using only MeSH subject headings were not competitive with those based on titles and abstracts.
Knowledge management in software engineering - describing the process

The management of knowledge and experience are key means by which systematic software development and process improvement occur. Within the domain of software engineering (SE), quality continues to remain an issue of concern. Although remedies such as fourth generation programming languages, structured techniques and object-oriented technology have been promoted, a "silver bullet" has yet to be found. Knowledge management (KM) gives organisations the opportunity to appreciate the challenges and complexities inherent in software development. We report on two case studies that investigate KM in SE at two IT organisations. Structured interviews were conducted, with the assistance of a qualitative questionnaire. The results were used to describe current practices for KM in SE, to investigate the nature of KM activities in these organisations, and to explain the impact of leadership, technology, culture and measurement as enablers of the KM process for SE.
Automatic Mutation Test Case Generation via Dynamic Symbolic Execution

The automatic test case generation is the principal issue of the software testing activity. Dynamic symbolic execution appears to be a promising approach to this matter as it has been shown to be quite powerful in producing the sought tests. Despite its power, it has only been effectively applied to the entry level criteria of the structural criteria hierarchy such as branch testing. In this paper an extension of this technique is proposed in order to effectively generate test data based on mutation testing. The proposed approach conjoins program transformation and dynamic symbolic execution techniques in order to successfully automate the test generation process. The propositions made in this paper have been incorporated into an automated framework for producing mutation based test cases. Its evaluation on a set of benchmark programs suggests that it is able to produce tests capable of killing most of the non equivalent introduced mutants. The same study also provides some evidence that by employing efficient heuristics it can be possible to perform mutation with reasonable resources.
An RNN Model of Text Normalization

We present a recurrent neural net (RNN) model of text normalization — defined as the mapping of written text to its spoken form, and a description of the open-source dataset that we used in our experiments. We show that while the RNN model achieves very high overall accuracies, there remain errors that would be unacceptable in a speech application like TTS.We then show that a simple FST-based filter can help mitigate those errors. Even with that mitigation challenges remain, and we end the paper outlining some possible solutions. In releasing our data we are thereby inviting others to help solve this problem.