nils-herrmann/clean_scirepeval huggingface dataset:
MediConceptNet: An Affinity Score Based Medical Concept Network

In healthcare, information extraction is essential in building automatic domain-specific applications. Medical concepts and their semantic identification take an important role to develop a network for visualizing medical concepts and their relations. The challenge appears while available medical corpora are only in either unstructured or semi-structured forms. In the present paper, to overcome the challenge and consequently to construct a structured corpus, we apply a domain-specific lexicon, namely WordNet of Medical Event. Medical concepts assigned by this lexicon and their affinity score, polarity score, sense, and semantic features assist in identifying conceptual and sentiment relations from the corpus. The lexicon and all these features provide an essential support to analyze an unstructured corpus and represent it in a structured corpus which we term MediConceptNet: the medical concepts are connected with each other through the concerned features. A previously suggested network for the same purpose, e.g., SemNet, is only based on the semantic and affinity features. The semantic relations of the concepts can be successfully determined in three distinct ranges, e.g., 0 for no relation, 0-1 for partial relations, and 1 corresponding a full relation. To evaluate the data of MediConceptNet, we apply an agreement analysis provided by the Cohen’s kappa coefficient and achieve 0.66 agreement score, evaluating the comparative statistics of two medical practitioners working as manual
Research on data stream clustering algorithms

Data stream is a potentially massive, continuous, rapid sequence of data information. It has aroused great concern and research upsurge in the field of data mining. Clustering is an effective tool of data mining, so data stream clustering will undoubtedly become the focus of the study in data stream mining. In view of the characteristic of the high dimension, dynamic, real-time, many effective data stream clustering algorithms have been proposed. In addition, data stream information are not deterministic and always exist outliers and contain noises, so developing effective data stream clustering algorithm is crucial. This paper reviews the development and trend of data stream clustering and analyzes typical data stream clustering algorithms proposed in recent years, such as Birch algorithm, Local Search algorithm, Stream algorithm and CluStream algorithm. We also summarize the latest research achievements in this field and introduce some new strategies to deal with outliers and noise data. At last, we put forward the focal points and difficulties of future research for data stream clustering.
Fabric defect detection using local homogeneity and morphological image processing

In this paper, a new fabric detect detection algorithm based on local homogeneity and mathematical morphology is presented. In a first step, the local homogeneity of each pixel is computed to construct a new homogeneity image denoted as (H-image). Then a classical histogram is computed for the H-image to choose an optimal thresholding value to produce a corresponding binary image, which will be used to extract the optimal size and the shape of the Structuring Element (SE) for mathematical morphology. In a second step, the image is subjected to a series of morphological operations with this SE to detect the possible existing fabric defect. Simulation results exhibit accurate defect detection with low false alarms.
The CLEAR 2006 Evaluation

This paper is a summary of the first CLEAR evaluation on CLassification of Events, Activities and Relationships which took place in early 2006 and concluded with a two day evaluation workshop in April 2006. CLEAR is an international effort to evaluate systems for the multimodal perception of people, their activities and interactions. It provides a new international evaluation framework for such technologies. It aims to support the definition of common evaluation tasks and metrics, to coordinate and leverage the production of necessary multimodal corpora and to provide a possibility for comparing different algorithms and approaches on common benchmarks, which will result in faster progress in the research community. This paper describes the evaluation tasks, including metrics and databases used, that were conducted in CLEAR 2006, and provides an overview of the results. The evaluation tasks in CLEAR 2006 included person tracking, face detection and tracking, person identification, head pose estimation, vehicle tracking as well as acoustic scene analysis. Overall, more than 20 subtasks were conducted, which included acoustic, visual and audio-visual analysis for many of the main tasks, as well as different data domains and evaluation conditions.
SVO: Fast semi-direct monocular visual odometry

We propose a semi-direct monocular visual odometry algorithm that is precise, robust, and faster than current state-of-the-art methods. The semi-direct approach eliminates the need of costly feature extraction and robust matching techniques for motion estimation. Our algorithm operates directly on pixel intensities, which results in subpixel precision at high frame-rates. A probabilistic mapping method that explicitly models outlier measurements is used to estimate 3D points, which results in fewer outliers and more reliable points. Precise and high frame-rate motion estimation brings increased robustness in scenes of little, repetitive, and high-frequency texture. The algorithm is applied to micro-aerial-vehicle state-estimation in GPS-denied environments and runs at 55 frames per second on the onboard embedded computer and at more than 300 frames per second on a consumer laptop. We call our approach SVO (Semi-direct Visual Odometry) and release our implementation as open-source software.
Lack of combinatorial productivity in language processing with simple recurrent networks

An astronomical set of sentences can be produced in natural language by combining relatively simple sentence structures with a human-size lexicon. These sentences are within the range of human language performance. Here, we investigate the ability of simple recurrent networks (SRNs) to handle such combinatorial productivity. We successfully trained SRNs to process sentences formed by combining sentence structures with different groups of words. Then, we tested the networks with test sentences in which words from different training sentences were combined. The networks failed to process these sentences, even though the sentence structures remained the same and all words appeared on the same syntactic positions as in the training sentences. In these combination cases, the networks produced work–word associations, similar to the condition in which words are presented in the context of a random word sequence. The results show that SRNs have serious difficulties in handling the combinatorial productivity that underlies human language performance. We discuss implications of this result for a potential neural architecture of human language processing.
Nonlinear model predictive control of glucose concentration in subjects with type 1 diabetes.

A nonlinear model predictive controller has been developed to maintain normoglycemia in subjects with type 1 diabetes during fasting conditions such as during overnight fast. The controller employs a compartment model, which represents the glucoregulatory system and includes submodels representing absorption of subcutaneously administered short-acting insulin Lispro and gut absorption. The controller uses Bayesian parameter estimation to determine time-varying model parameters. Moving target trajectory facilitates slow, controlled normalization of elevated glucose levels and faster normalization of low glucose values. The predictive capabilities of the model have been evaluated using data from 15 clinical experiments in subjects with type 1 diabetes. The experiments employed intravenous glucose sampling (every 15 min) and subcutaneous infusion of insulin Lispro by insulin pump (modified also every 15 min). The model gave glucose predictions with a mean square error proportionally related to the prediction horizon with the value of 0.2 mmol L(-1) per 15 min. The assessment of clinical utility of model-based glucose predictions using Clarke error grid analysis gave 95% of values in zone A and the remaining 5% of values in zone B for glucose predictions up to 60 min (n = 1674). In conclusion, adaptive nonlinear model predictive control is promising for the control of glucose concentration during fasting conditions in subjects with type 1 diabetes.
Evolving Inversion Methods in Geophysics with Cloud Computing - A Case Study of an eScience Collaboration

Magnetotellurics is a geophysics technique for characterisation of geothermal reservoirs, mineral exploration, and other geoscience endeavours that need to sound deeply into the earth -- many kilometres or tens of kilometres. Central to its data processing is an inversion problem which currently takes several weeks on a desktop machine. In our new eScience lab, enabled by cloud computing, we parallelised an existing FORTAN program and embedded the parallel version in a cloud-based web application to improve its usability. A factor-of-five speedup has taken the time for some inversions from weeks down to days and is in use in a pre-fracturing and post-fracturing study of a new geothermal site in South Australia, an area with a high occurrence of hot dry rocks. We report on our experience with Amazon Web Services cloud services and our migration to Microsoft Azure, the collaboration between computer scientists and geophysicists, and the foundation it has laid for future work exploiting cloud data-parallel programming models.
On deep generative models with applications to recognition

The most popular way to use probabilistic models in vision is first to extract some descriptors of small image patches or object parts using well-engineered features, and then to use statistical learning tools to model the dependencies among these features and eventual labels. Learning probabilistic models directly on the raw pixel values has proved to be much more difficult and is typically only used for regularizing discriminative methods. In this work, we use one of the best, pixel-level, generative models of natural images–a gated MRF–as the lowest level of a deep belief network (DBN) that has several hidden layers. We show that the resulting DBN is very good at coping with occlusion when predicting expression categories from face images, and it can produce features that perform comparably to SIFT descriptors for discriminating different types of scene. The generative ability of the model also makes it easy to see what information is captured and what is lost at each level of representation.
Website characteristics , Trust and purchase intention in online stores :-An Empirical study in the Indian context

Lack of trust in online transactions has been cited, by past scholars, as the main reason for the abhorrence of online shopping. In this paper we proposed a model and provided empirical evidence on the impact of the website characteristics on trust in online transactions in Indian context. In the first phase, we identified and empirically verified the relative importance of the website factors that develop online trust in India. In the next phase, we have tested the mediator effect of trust in the relationship between the website factors and purchase intention (and perceived risk). The present study for the first time provided empirical evidence on the mediating role of trust in online shopping among Indian customers.
A general theory of addictions: A new theoretical model

A general theory of addictions is proposed, using the compulsive gambler as the prototype. Addiction is defined as a dependent state acquired over time to relieve stress. Two interrelated sets of factors predispose persons to addictions: an abnormal physiological resting state, and childhood experiences producing a deep sense of inadequacy. All addictions are hypothesized to follow a similar three-stage course. A matrix strategy is outlined to collect similar information from different kinds of addicts and normals. The ultimate objective is to identify high risk youth and prevent the development of addictions.
Localization algorithms for multilateration (MLAT) systems in airport surface surveillance

We present a general scheme for analyzing the performance of a generic localization algorithm for multilateration (MLAT) systems (or for other distributed sensor, passive localization technology). MLAT systems are used for airport surface surveillance and are based on time difference of arrival measurements of Mode S signals (replies and 1,090 MHz extended squitter, or 1090ES). In the paper, we propose to consider a localization algorithm as composed of two components: a data model and a numerical method, both being properly defined and described. In this way, the performance of the localization algorithm can be related to the proper combination of statistical and numerical performances. We present and review a set of data models and numerical methods that can describe most localization algorithms. We also select a set of existing localization algorithms that can be considered as the most relevant, and we describe them under the proposed classification. We show that the performance of any localization algorithm has two components, i.e., a statistical one and a numerical one. The statistical performance is related to providing unbiased and minimum variance solutions, while the numerical one is related to ensuring the convergence of the solution. Furthermore, we show that a robust localization (i.e., statistically and numerI. A. Mantilla-Gaviria · J. V. Balbastre-Tejedor Instituto ITACA, Universidad Politécnica de Valencia, Camino de Vera S/N, 46022 Edificio 8G, Acceso B, Valencia, Spain e-mail: iamantillagaviria@gmail.com J. V. Balbastre-Tejedor e-mail: jbalbast@itaca.upv.es M. Leonardi · G. Galati (B) DIE, Tor Vergata University, Via del Politecnico 1, 00133 Rome, Italy e-mail: gaspare.galati@uniroma2.it; gaspare.galati@gmail.com M. Leonardi e-mail: mauro.leonardi@uniroma2.it ically efficient) strategy, for airport surface surveillance, has to be composed of two specific kind of algorithms. Finally, an accuracy analysis, by using real data, is performed for the analyzed algorithms; some general guidelines are drawn and conclusions are provided.
Question classification for Arabic Question Answering Systems

Due to very fast growth of information in the last few decades, getting precise information in real time is becoming increasingly difficult. Search engines such as Google and Yahoo are helping in finding the information but the information provided by them are in the form of documents which consumes a lot of time of the user.  Answering Systems have emerged as a good alternative to search engines where they produce the desired information in a very precise way in the real time. This saves a lot of time for the user. There has been a lot of research in the field of English and some European language Question Answering Systems. However, Arabic Question Answering Systems could not match the pace due to some inherent difficulties with the language itself as well as due to lack of tools available to assist the researchers. Question classification is a very important module of Question Answering Systems. In this paper, we are presenting a method to accurately classify the Arabic questions in order to retrieve precise answers. The proposed method gives promising results.
A Learning-based Variable Size Part Extraction Architecture for 6D Object Pose Recovery in Depth

State-of-the-art techniques for 6D object pose recovery depend on occlusion-free point clouds to accurately register objects in 3D space. To deal with this shortcoming, we introduce a novel architecture called Iterative Hough Forest with Histogram of Control Points that is capable of estimating the 6D pose of an occluded and cluttered object, given a candidate 2D bounding box. Our Iterative Hough Forest (IHF) is learnt using parts extracted only from the positive samples. These parts are represented with Histogram of Control Points (HoCP), a “scale-variant” implicit volumetric description, which we derive from recently introduced Implicit B-Splines (IBS). The rich discriminative information provided by the scale-variant HoCP features is leveraged during inference. An automatic variable size part extraction framework iteratively refines the object’s roughly aligned initial pose due to the extraction of coarsest parts, the ones occupying the largest area in image pixels. The iterative refinement is accomplished based on finer (smaller) parts, which are represented with more discriminative control point descriptors by using our Iterative Hough Forest. Experiments conducted on a publicly available dataset report that our approach shows better registration performance than the state-of-the-art methods.
3D Bounding Box Estimation Using Deep Learning and Geometry

We present a method for 3D object detection and pose estimation from a single image. In contrast to current techniques that only regress the 3D orientation of an object, our method first regresses relatively stable 3D object properties using a deep convolutional neural network and then combines these estimates with geometric constraints provided by a 2D object bounding box to produce a complete 3D bounding box. The first network output estimates the 3D object orientation using a novel hybrid discrete-continuous loss, which significantly outperforms the L2 loss. The second output regresses the 3D object dimensions, which have relatively little variance compared to alternatives and can often be predicted for many object types. These estimates, combined with the geometric constraints on translation imposed by the 2D bounding box, enable us to recover a stable and accurate 3D object pose. We evaluate our method on the challenging KITTI object detection benchmark [2] both on the official metric of 3D orientation estimation and also on the accuracy of the obtained 3D bounding boxes. Although conceptually simple, our method outperforms more complex and computationally expensive approaches that leverage semantic segmentation, instance level segmentation and flat ground priors  and sub-category detection [23][24]. Our discrete-continuous loss also produces state of the art results for 3D viewpoint estimation on the Pascal 3D+ dataset[26].
Travel history and malaria infection risk in a low-transmission setting in Ethiopia: a case control study

Malaria remains the leading communicable disease in Ethiopia, with around one million clinical cases of malaria reported annually. The country currently has plans for elimination for specific geographic areas of the country. Human movement may lead to the maintenance of reservoirs of infection, complicating attempts to eliminate malaria. An unmatched case–control study was conducted with 560 adult patients at a Health Centre in central Ethiopia. Patients who received a malaria test were interviewed regarding their recent travel histories. Bivariate and multivariate analyses were conducted to determine if reported travel outside of the home village within the last month was related to malaria infection status. After adjusting for several known confounding factors, travel away from the home village in the last 30 days was a statistically significant risk factor for infection with Plasmodium falciparum (AOR 1.76; p=0.03) but not for infection with Plasmodium vivax (AOR 1.17; p=0.62). Male sex was strongly associated with any malaria infection (AOR 2.00; p=0.001). Given the importance of identifying reservoir infections, consideration of human movement patterns should factor into decisions regarding elimination and disease prevention, especially when targeted areas are limited to regions within a country.
Use and Impact of ICT on SMEs in Oman

This paper presents the results of an exploratory study carried out to learn about the use and impact of Information and Communication Technologies (ICT) on Small and Medium Sized Enterprises (SMEs) in Oman. The study investigates ICT infrastructure, software used, driver for ICT investment, perceptions about business benefits of ICT and outsourcing trends of SMEs. The study provides an insight on the barriers for the adoption of ICT. Data on these aspects of ICT was collected from 51 SMEs through a survey instrument. The results of the study show that only a small number of SMEs in Oman are aware of the benefits of ICT adoption. The main driving forces for ICT investment are to provide better and faster customer service and to stay ahead of the competition. A majority of surveyed SMEs have reported a positive performance and other benefits by utilizing ICT in their businesses. Majority of SMEs outsource most of their ICT activities. Lack of internal capabilities, high cost of ICT and lack of information about suitable ICT solutions and implementation were some of the major barriers in adopting ICT. These findings are consistent with other studies e.g. (Harindranath et al 2008). There is a need for more focus and concerted efforts on increasing awareness among SMEs on the benefits of ICT adoption. The results of the study recognize the need for more training facilities in ICT for SMEs, measures to provide ICT products and services at an affordable cost, and availability of free professional advice and consulting at reasonable cost to SMEs. Our findings therefore have important implication for policy aimed at ICT adoption and use by SMEs. The findings of this research will provide a foundation for future research and will help policy makers in understanding the current state of affairs of the usage and impact of ICT on SMEs in Oman.
Steered crowdsensing: incentive design towards quality-oriented place-centric crowdsensing

Crowdsensing technologies are rapidly evolving and are expected to be utilized on commercial applications such as location-based services. Crowdsensing collects sensory data from daily activities of users without burdening users, and the data size is expected to grow into a population scale. However, quality of service is difficult to ensure for commercial use. Incentive design in crowdsensing with monetary rewards or gamifications is, therefore, attracting attention for motivating participants to collect data to increase data quantity. In contrast, we propose Steered Crowdsensing, which controls the incentives of users by using the game elements on location-based services for directly improving the quality of service rather than data size. For a feasibility study of steered crowdsensing, we deployed a crowdsensing system focusing on application scenarios of building processes on wireless indoor localization systems. In the results, steered crowdsensing realized deployments faster than non-steered crowdsensing while having half as many data.
Rectilinear Leaky-Wave Antennas With Broad Beam Patterns Using Hybrid Printed-Circuit Waveguides

A theoretical study on the design of broadbeam leaky-wave antennas (LWAs) of uniform type and rectilinear geometry is presented. A new broadbeam LWA structure based on the hybrid printed-circuit waveguide is proposed, which allows for the necessary flexible and independent control of the leaky-wave phase and leakage constants. The study shows that both the real and virtual focus LWAs can be synthesized in a simple manner by tapering the printed-slot along the LWA properly, but the real focus LWA is preferred in practice. Practical issues concerning the tapering of these LWA are investigated, including the tuning of the radiation pattern asymmetry level and beamwidth, the control of the ripple level inside the broad radiated main beam, and the frequency response of the broadbeam LWA. The paper provides new insight and guidance for the design of this type of LWAs.