nils-herrmann/clean_scirepeval huggingface dataset:
Identifying spatial relations in images using convolutional neural networks

Traditional approaches to building a large scale knowledge graph have usually relied on extracting information (entities, their properties, and relations between them) from unstructured text (e.g. Dbpedia). Recent advances in Convolutional Neural Networks (CNN) allow us to shift our focus to learning entities and relations from images, as they build robust models that require little or no pre-processing of the images. In this paper, we present an approach to identify and extract spatial relations (e.g., The girl is standing behind the table) from images using CNNs. Our research addresses two specific challenges: providing insight into how spatial relations are learned by the network and which parts of the image are used to predict these relations. We use the pre-trained network VGGNet to extract features from an image and train a Multi-layer Perceptron (MLP) on a set of synthetic images and the sun09 dataset to extract spatial relations. The MLP predicts spatial relations without a bounding box around the objects or the space in the image depicting the relation. To understand how the spatial relations are represented in the network, a heatmap is overlayed on the image to show the regions that are deemed important by the network. Also, we analyze the MLP to show the relationship between the activation of consistent groups of nodes and the prediction of a spatial relation. We show how the loss of these groups affects the network's ability to identify relations.
Bidirectional Echo State Network-based Reservoir Computing for Cross-domain Authorship Attribution: Notebook for PAN at CLEF 2018

This paper describes and evaluates a model for cross-domain authorship attribution using Bidirectional Echo State Network-based (ESN) Reservoir Computing. We applied this model to the cross-domain authorship attribution task of the PAN18 challenge and show that it can be applied to this task. This BD-ESN based on a word embedding layer of dimension 300 reaches an averaged F-1 score of 0.408 on the development corpus and 0.3870 on the test corpus. The evaluation is based on a collections of Fanfiction gathered online, covering different original work of art.
Compound Fuzzy PID Level Control System Based on WinCC and MATLAB

The simulation, verification and realization of complex control arithmetic are introduced in detail. A compound Fuzzy PID level control system is realized on Siemens PROFINET platform. The communication method between supervision configuration software WinCC 6.0 and engineering calculating software MATLAB through OPC technology is introduced in this paper. Hardware configuration and programming are completed by STEP7 provided by Siemens. Compound Fuzzy PID arithmetic is realized by MATLAB. The programming and calculating of complex arithmetic in control system is simplified consumedly. The method could be broadly applied in practice systems for its simple programming, easy realization and better performance criteria.
Analog tuning of compact varactor-loaded combline filters in substrate integrated waveguide

The design, fabrication and measurement of continuously tunable filters in single-layer SIW technology is presented in this paper. The filters are based on coupled coaxial resonators embedded into the dielectric and conveniently loaded with GaAs varactors. Firstly, a 2-pole filter demonstrator has been fabricated on a low-cost substrate showing a tunable centre frequency between 2.64 - 2.88 GHz with 1.27 - 3.63 dB insertion losses across the tuning range. Then, the application of this approach to post-fabrication fine tuning of narrow-band multi-pole filters is studied. Thus, a 2.8% FBW 3-pole Chebyshev filter is designed showing a good trade-off between tuning range, additional inband losses and fractional bandwidth variation.
A Report on the 2017 Native Language Identification Shared Task

Native Language Identification (NLI) is the task of automatically identifying the native language (L1) of an individual based on their language production in a learned language. It is typically framed as a classification task where the set of L1s is known a priori. Two previous shared tasks on NLI have been organized where the aim was to identify the L1 of learners of English based on essays (2013) and spoken responses (2016) they provided during a standardized assessment of academic English proficiency. The 2017 shared task combines the inputs from the two prior tasks for the first time. There are three tracks: NLI on the essay only, NLI on the spoken response only (based on a transcription of the response and i-vector acoustic features), and NLI using both responses. We believe this makes for a more interesting shared task while building on the methods and results from the previous two shared tasks. In this paper, we report the results of the shared task. A total of 19 teams competed across the three different sub-tasks. The fusion track showed that combining the written and spoken responses provides a large boost in prediction accuracy. Multiple classifier systems (e.g. ensembles and meta-classifiers) were the most effective in all tasks, with most based on traditional classifiers (e.g. SVMs) with lexical/syntactic features. Visit the website for more info about the task: https://sites.google.com/site/nlisharedtask/
MyMediaLite: a free recommender system library

MyMediaLite is a fast and scalable, multi-purpose library of recommender system algorithms, aimed both at recommender system researchers and practitioners. It addresses two common scenarios in collaborative filtering: rating prediction (e.g. on a scale of 1 to 5 stars) and item prediction from positive-only implicit feedback (e.g. from clicks or purchase actions). The library offers state-of-the-art algorithms for those two tasks. Programs that expose most of the library's functionality, plus a GUI demo, are included in the package. Efficient data structures and a common API are used by the implemented algorithms, and may be used to implement further algorithms. The API also contains methods for real-time updates and loading/storing of already trained recommender models.
 MyMediaLite is free/open source software, distributed under the terms of the GNU General Public License (GPL). Its methods have been used in four different industrial field trials of the MyMedia project, including one trial involving over 50,000 households.
Data augmentation of wearable sensor data for parkinson's disease monitoring using convolutional neural networks

While convolutional neural networks (CNNs) have been successfully applied to many challenging classification applications, they typically require large datasets for training. When the availability of labeled data is limited, data augmentation is a critical preprocessing step for CNNs. However, data augmentation for wearable sensor data has not been deeply investigated yet. 
In this paper, various data augmentation methods for wearable sensor data are proposed. The proposed methods and CNNs are applied to the classification of the motor state of Parkinson’s Disease patients, which is challenging due to small dataset size, noisy labels, and large intra-class variability. Appropriate augmentation improves the classification performance from 77.54% to 86.88%.
Compressed Channel Sensing: A New Approach to Estimating Sparse Multipath Channels

High-rate data communication over a multipath wireless channel often requires that the channel response be known at the receiver. Training-based methods, which probe the channel in time, frequency, and space with known signals and reconstruct the channel response from the output signals, are most commonly used to accomplish this task. Traditional training-based channel estimation methods, typically comprising linear reconstruction techniques, are known to be optimal for rich multipath channels. However, physical arguments and growing experimental evidence suggest that many wireless channels encountered in practice tend to exhibit a sparse multipath structure that gets pronounced as the signal space dimension gets large (e.g., due to large bandwidth or large number of antennas). In this paper, we formalize the notion of multipath sparsity and present a new approach to estimating sparse (or effectively sparse) multipath channels that is based on some of the recent advances in the theory of compressed sensing. In particular, it is shown in the paper that the proposed approach, which is termed as compressed channel sensing (CCS), can potentially achieve a target reconstruction error using far less energy and, in many instances, latency and bandwidth than that dictated by the traditional least-squares-based training methods.
The impact of debris on marine life.

Marine debris is listed among the major perceived threats to biodiversity, and is cause for particular concern due to its abundance, durability and persistence in the marine environment. An extensive literature search reviewed the current state of knowledge on the effects of marine debris on marine organisms. 340 original publications reported encounters between organisms and marine debris and 693 species. Plastic debris accounted for 92% of encounters between debris and individuals. Numerous direct and indirect consequences were recorded, with the potential for sublethal effects of ingestion an area of considerable uncertainty and concern. Comparison to the IUCN Red List highlighted that at least 17% of species affected by entanglement and ingestion were listed as threatened or near threatened. Hence where marine debris combines with other anthropogenic stressors it may affect populations, trophic interactions and assemblages.
Opportunities and challenges for a sustainable energy future

Access to clean, affordable and reliable energy has been a cornerstone of the world's increasing prosperity and economic growth since the beginning of the industrial revolution. Our use of energy in the twenty–first century must also be sustainable. Solar and water–based energy generation, and engineering of microbes to produce biofuels are a few examples of the alternatives. This Perspective puts these opportunities into a larger context by relating them to a number of aspects in the transportation and electricity generation sectors. It also provides a snapshot of the current energy landscape and discusses several research and development opportunities and pathways that could lead to a prosperous, sustainable and secure energy future for the world.
A Novel Focal Tversky loss function with improved Attention U-Net for lesion segmentation

We propose a generalized focal loss function based on the Tversky index to address the issue of data imbalance in medical image segmentation. Compared to the commonly used Dice loss, our loss function achieves a better trade off between precision and recall when training on small structures such as lesions. To evaluate our loss function, we improve the attention U-Net model by incorporating an image pyramid to preserve contextual features. We experiment on the BUS 2017 dataset and ISIC 2018 dataset where lesions occupy 4.84% and 21.4% of the images area and improve segmentation accuracy when compared to the standard U-Net by 25.7% and 3.6%, respectively.
Virtual Laboratories: Comparability of Real and Virtual Environments for Environmental Psychology

Virtual environments have the potential to become important new research tools in environment behavior research. They could even become the future (virtual) laboratories, if reactions of people to virtual environments are similar to those in real environments. The present study is an exploration of the comparability of research findings in real and virtual environments. In the study, 101 participants explored an identical space, either in reality or in a computer-simulated environment. Additionally, the presence of plants in the space was manipulated, resulting in a 2 (environment) 2 (plants) between-subjects design. Employing a broad set of measurements, we found mixed results. Performances on size estimations and a cognitive mapping task were significantly better in the real environment. Factor analyses of bipolar adjectives indicated that, although four dimensions were similar for both environments, a fifth dimension of environmental assessmenttermedarousalwas absent in the virtual environment. In addition, we found significant differences on the scores of four of the scales. However, no significant interactions appeared between environment and plants. Experience of and behavior in virtual environments have similarities to that in real environments, but there are important differences as well. We conclude that this is not only a necessary, but also a very interesting research subject for environmental psychology.
TweetFit: Fusing Multiple Social Media and Sensor Data for Wellness Profile Learning

Wellness is a widely popular concept that is commonly applied to fitness and self-help products or services. Inference of personal wellness-related attributes, such as body mass index or diseases tendency, as well as understanding of global dependencies between wellness attributes and users’ behavior is of crucial importance to various applications in personal and public wellness domains. Meanwhile, the emergence of social media platforms and wearable sensors makes it feasible to perform wellness profiling for users from multiple perspectives. However, research efforts on wellness profiling and integration of social media and sensor data are relatively sparse, and this study represents one of the first attempts in this direction. Specifically, to infer personal wellness attributes, we proposed multi-source individual user profile learning framework named “TweetFit”. “TweetFit” can handle data incompleteness and perform wellness attributes inference from sensor and social media data simultaneously. Our experimental results show that the integration of the data from sensors and multiple social media sources can substantially boost the wellness profiling performance.
A spelling device for the paralysed

When Jean-Dominique Bauby suffered from a cortico-subcortical stroke that led to complete paralysis with totally intact sensory and cognitive functions, he described his experience in The Diving-Bell and the Butterfly as “something like a giant invisible diving-bell holds my whole body prisoner”. This horrifying condition also occurs as a consequence of a progressive neurological disease, amyotrophic lateral sclerosis, which involves progressive degeneration of all the motor neurons of the somatic motor system. These ‘locked-in’ patients ultimately become unable to express themselves and to communicate even their most basic wishes or desires, as they can no longer control their muscles to activate communication devices. We have developed a new means of communication for the completely paralysed that uses slow cortical potentials (SCPs) of the electro-encephalogram to drive an electronic spelling device.
Low-Complexity SSOR-Based Precoding for Massive MIMO Systems

With the increase of the number of base station (BS) antennas in massive multiple-input multiple-output (MIMO) systems, linear precoding schemes are able to achieve the near-optimal performance, and thus are more attractive than nonlinear precoding techniques. However, conventional linear precoding schemes such as zero-forcing (ZF) precoding involve the matrix inversion of large size with high computational complexity, especially in massive MIMO systems. To reduce the complexity, in this letter, we propose a low-complexity linear precoding scheme based on the symmetric successive over relaxation (SSOR) method. Moreover, we propose a simple way to approximate the optimal relaxation parameter of the SSOR-based precoding by exploiting the channel property of asymptotical orthogonality in massive MIMO systems. We show that the proposed SSOR-based precoding can reduce the complexity of the classical ZF precoding by about one order of magnitude without performance loss, and it also outperforms the recently proposed linear approximate precoding schemes in typical fading channels.
Can Machine Generate Traditional Chinese Poetry? A Feigenbaum Test

Recent progress in neural learning demonstrated that machines can do well in regularized tasks, e.g., the game of Go. However, artistic activities such as poem generation are still widely regarded as human’s special capability. In this paper, we demonstrate that a simple neural model can imitate human in some tasks of art generation. We particularly focus on traditional Chinese poetry, and show that machines can do as well as many contemporary poets and weakly pass the Feigenbaum Test, a variant of Turing test in professional domains. Our method is based on an attention-based recurrent neural network, which accepts a set of keywords as the theme and generates poems by looking at each keyword during the generation. A number of techniques are proposed to improve the model, including character vector initialization, attention to input and hybrid-style training. Compared to existing poetry generation methods, our model can generate much more theme-consistent and semantic-rich poems.
A system architecture for context-aware mobile computing

A System Architecture for Context-Aware Mobile Computing William Noah Schilit Computer applications traditionally expect a static execution environment. However, this precondition is generally not possible for mobile systems, where the world around an application is constantly changing. This thesis explores how to support and also exploit the dynamic configurations and social settings characteristic of mobile systems. More specifically, it advances the following goals: (1) enabling seamless interaction across devices; (2) creating physical spaces that are responsive to users; and (3) and building applications that are aware of the context of their use. Examples of these goals are: continuing in your office a program started at home; using a PDA to control someone else’s windowing UI; automatically canceling phone forwarding upon return to your office; having an airport overheaddisplay highlight the flight information viewers are likely to be interested in; easily locating and using the nearest printer or fax machine; and automatically turning off a PDA’s audible e-mail notification when in a meeting. The contribution of this thesis is an architecture to support context-aware computing; that is, application adaptation triggered by such things as the location of use, the collection of nearby people, the presence of accessible devices and other kinds of objects, as well as changes to all these things over time. Three key issues are addressed: (1) the information needs of applications, (2) where applications get various pieces of information and (3) how information can be efficiently distributed. A dynamic environment communication model is introduced as a general mechanism for quickly and efficiently learning about changes occurring in the environment in a fault tolerant manner. For purposes of scalability, multiple dynamic environment servers store user, device, and, for each geographic region, context information. In order to efficiently disseminate information from these components to applications, a dynamic collection of multicast groups is employed. The thesis also describes a demonstration system based on the Xerox PARCTAB, a wireless palmtop computer.
Classification of Passes in Football Matches Using Spatiotemporal Data

A knowledgeable observer of a game of football (soccer) can make a subjective evaluation of the quality of passes made between players during the game, such as rating them as Good, OK, or Bad. In this article, we consider the problem of producing an automated system to make the same evaluation of passes and present a model to solve this problem.
 Recently, many professional football leagues have installed object tracking systems in their stadiums that generate high-resolution and high-frequency spatiotemporal trajectories of the players and the ball. Beginning with the thesis that much of the information required to make the pass ratings is available in the trajectory signal, we further postulated that using complex data structures derived from computational geometry would enable domain football knowledge to be included in the model by computing metric variables in a principled and efficient manner. We designed a model that computes a vector of predictor variables for each pass made and uses machine learning techniques to determine a classification function that can accurately rate passes based only on the predictor variable vector.
 Experimental results show that the learned classification functions can rate passes with 90.2% accuracy. The agreement between the classifier ratings and the ratings made by a human observer is comparable to the agreement between the ratings made by human observers, and suggests that significantly higher accuracy is unlikely to be achieved. Furthermore, we show that the predictor variables computed using methods from computational geometry are among the most important to the learned classifiers.
DC-to-DC Converters With Continuous Input and Output Power

This paper presents three topological classes of dc-to-dc converters, totaling nine converters (each class with three buck, boost, and buck-boost voltage transfer function topologies), which offer continuous input and output energy flow, applicable and mandatory for renewable energy source, maximum power point tracking and maximum source energy extraction. A current sourcing output caters for converter module output parallel connection. The first class of three topologies employs both series input and output inductance, while anomalously the other two classes of six related topologies employ only either series input (three topologies) or series output (three topologies) inductance. All nine converter topologies employ the same elements, while additional load shunting capacitance creates a voltage sourcing output. Converter time-domain simulations and experimental results for the converters support and extol the concepts and analysis presented.
A Model of Individual Keyword Performance in Paid Search Advertising

In paid search advertising on Internet search engines, advertisers bid for specific keywords, e.g. “Rental Cars LAX,” to display a text ad in the sponsored section of the search results page. The advertiser is charged when a user clicks on the ad. Many of the keywords in paid search campaigns generate few, if any, sales conversions – even over several months. This sparseness makes it difficult to assess the profit performance of individual keywords and has led to the practice of managing large groups of keywords together or relying on easy-to-calculate heuristics such as click-through rate (CTR). The authors develop a model of individual keyword conversion that addresses the sparseness problem. Conversion rates are estimated using a hierarchical Bayes binary choice model. This enables conversion to be based on both word-level covariates and shrinkage across keywords. The model is applied to keyword-level paid search data containing daily information on impressions, clicks and reservations for a major lodging chain. The results show that including keyword-level covariates and heterogeneity significantly improves conversion estimates. A holdout comparison suggests that campaign management based on the model, i.e., estimated costper-sale on a keyword level, would outperform existing managerial strategies.
Using Markov models for web site link prediction

Markov models have been extensively used to model Web users' navigation behaviors on Web sites. The link structure of a Web site can be seen as a citation network. By applying bibliographic co-citation and coupling analysis to a Markov model constructed from a Web log file on a Web site, we propose a clustering algorithm called CitationCluster to cluster conceptually related pages. The clustering results are used to construct a conceptual hierarchy of the Web site. Markov model based link prediction is integrated with the hierarchy to assist users' navigation on the Web site.