nils-herrmann/clean_scirepeval huggingface dataset:
Brexit, Trump, and 'methodological whiteness': on the misrecognition of race and class.

The rhetoric of both the Brexit and Trump campaigns was grounded in conceptions of the past as the basis for political claims in the present. Both established the past as constituted by nations that were represented as 'white' into which racialized others had insinuated themselves and gained disproportionate advantage. Hence, the resonant claim that was broadcast primarily to white audiences in each place 'to take our country back'. The politics of both campaigns was also echoed in those social scientific analyses that sought to focus on the 'legitimate' claims of the 'left behind' or those who had come to see themselves as 'strangers in their own land'. The skewing of white majority political action as the action of a more narrowly defined white working class served to legitimize analyses that might otherwise have been regarded as racist. In effect, I argue that a pervasive 'methodological whiteness' has distorted social scientific accounts of both Brexit and Trump's election victory and that this needs to be taken account of in our discussion of both phenomena.
A compact stair-climbing wheelchair with two 3-DOF legs and a 1-DOF base

PurposeThe purpose of this paper is to describe a compact wheelchair, which has two 3-DOF legs and a 1DOF base (the total DOF of the leg system is 7) for stair-climbing, and wheels for flat surface driving.  proposed wheelchair climbs stairs using the two 3-DOF legs with boomerang-shaped feet. The leg mechanisms are folded into the compact wheelchair body when the wheelchair moves over flat surfaces. We also proposes a simple estimation method of stair shape using laser distance sensors, and a dual motor driving system to increase joint power.  proposed wheelchair can climb arbitrary height and width stairs by itself, even when they are slightly curved. During climbing, the trajectory of the seat position is linear to guarantee the comfort of rider, and the wheelchair always keeps a stable condition to ensure the stability in an emergency stop. valueThe wheelchair mechanism with foldable legs and driving wheels enables smooth stair climbing, efficient flat surface driving, and additional useful motions such as standing and tilting.
Hybrid rotor position observer for wide speed-range sensorless PM motor drives including zero speed

This paper addresses the problem of wide speed-range sensorless control of a surface-mount permanent-magnet (SMPM) machine including zero-speed operation. A hybrid structure integrating a flux observer and signal-injection techniques is proposed, which results in a rotor position signal independent of motor parameters at low and zero speed. Although the SMPM machine typically has a very low geometric saliency, the injection technique is effective in tracking the saturation-induced saliency produced by the stator flux. Experimental results are presented showing an excellent performance for both the sensorless speed and position control using an off-the-shelf SMPM machine.
Stockyard: A discrete event-based stock market exchange simulator

We describe an agent-based stock market simulator built using an asynchronous discrete event simulation framework. The simulator is unique in that it's driven by real-world financial algorithms and protocols; and it's open source. It utilizes an order book bid and ask matching model, and real-world exchange protocols. Our simulation is based on multiple agents interacting through an exchange agent. This method is distinct from those that utilize historical pricing data. Order book execution supports a more realistic interaction between agents. Pricing in our model arises from the dynamics of matching orders in the order book. Our simulator enables the study of market dynamics, and trading strategies using real-world exchange protocols. We present our design and implementation of a market simulator and discuss our initial results using the message protocols defined by NASDAQ: OUCH and ITCH. Our initial results demonstrate StockYard's capability and efficiency in simulating markets with realistic trade volumes.
Data mining on LinkedIn data to define professional profile via MineraSkill methodology

Social networks are of significant analytical interest. This is because their data are generated in great quantity, and intermittently, besides that, the data are from a wide variety, and it is widely available to users. Through such data, it is desired to extract knowledge or information that can be used in decision-making activities. In this context, we have identified the lack of methods that apply data mining techniques to the task of analyzing the professional profile of employees. The aim of such analyses is to detect competencies that are of greater interest by being more required and also, to identify their associative relations. Thus, this work introduces MineraSkill methodology that deals with methods to infer the desired profile of a candidate for a job vacancy. In order to do so, we use keyword detection via natural language processing techniques; which are related to others by inferring their association rules. The results are presented in the form of a case study, which analyzed data from LinkedIn, demonstrating the potential of the methodology in indicating trending competencies that are required together.
Fingerprinting Based Indoor Positioning System using RSSI Bluetooth

Positioning is basis for providing location information to mobile users, however, with the growth of wireless and mobile communications technologies. Mobile phones are equipped with several radio frequency technologies for driving the positioning information like GSM, Wi-Fi or Bluetooth etc. In this way, the objective of this thesis was to implement an indoor positioning system relying on Bluetooth Received Signal Strength (RSS) technology and it integrates into the Global Positioning Module (GPM) to provide precise information inside the building. In this project, we propose indoor positioning system based on RSS fingerprint and footprint architecture that smart phone users can get their position through the assistance collections of Bluetooth signals, confining RSSs by directions, and filtering burst noises that can overcome the server signal fluctuation problem inside the building. Meanwhile, this scheme can raise more accuracy in finding the position inside the building.
Sparse Bayesian learning-based channel estimation in millimeter wave hybrid MIMO systems

This paper develops a novel sparse Bayesian learning (SBL)-based multiple-input multiple-output (MIMO) channel estimation technique for hybrid millimeter wave (mmWave) wireless systems by exploiting spatial sparsity in the wireless channels arising from the highly directional nature of propagation. The spatially sparse MIMO channel is modeled in terms of the basis array response matrices corresponding to the quantized directional cosines at the transmit and receive antenna arrays followed by the development of an expectation maximization (EM)-based sparse Bayesian learning (SBL) channel estimation approach. Subsequently, an enhanced variant of the SBL scheme is proposed based on hard thresholding the associated hyperparameter estimates, which is observed to significantly improve the accuracy of channel estimation. The Bayesian Cramér-Rao bound (BCRB) is also derived to benchmark the accuracy of the proposed SBL-based channel estimation schemes. Finally, simulation results are presented to illustrate the performance improvement achieved in comparison to the existing state-of-the-art orthogonal matching pursuit (OMP)-based sparse mmWave channel estimation scheme.
Bayesian Matrix Factorization with Side Information and Dirichlet Process Mixtures

Matrix factorization is a fundamental technique in machine learning that is applicable to collaborative filtering, information retrieval and many other areas. In collaborative filtering and many other tasks, the objective is to fill in missing elements of a sparse data matrix. One of the biggest challenges in this case is filling in a column or row of the matrix with very few observations. In this paper we introduce a Bayesian matrix factorization model that performs regression against side information known about the data in addition to the observations. The side information helps by adding observed entries to the factored matrices. We also introduce a nonparametric mixture model for the prior of the rows and columns of the factored matrices that gives a different regularization for each latent class. Besides providing a richer prior, the posterior distribution of mixture assignments reveals the latent classes. Using Gibbs sampling for inference, we apply our model to the Netflix Prize problem of predicting movie ratings given an incomplete user-movie ratings matrix. Incorporating rating information with gathered metadata information, our Bayesian approach outperforms other matrix factorization techniques even when using fewer dimensions.
Android App Store ( Google Play ) Mining and Analysis

The aim of mining and analysis of Apps in Google Play, the largest Android app store, is to provide in-depth insight on the hidden properties of the repository to app developers or app market contributors. This approach can help them to view the current circumstances of the market and make valuable decisions before releasing products. To perform this analysis, all available features (descriptions of the app, app developer information, app version, updating date, category, number of download, app size, user rating, number of participants in rating, price, user reviews and security policies) are collected for the repository and stored in structured profile for each app. This scientific study is mainly divided into two approaches: measuring pair-wise correlations between extracted features and clustering the dataset into number of groups with functionally similar apps. Two distinct datasets are exploited to perform the study, one of which is collected from Google Play (in 2012) and another one from Android Market, the former version of Google Play (in 2011). As soon as experiments and analysis is successfully conducted, significant levels of pair-wise correlations are identified between some features for both datasets, which are further compared to achieve a generalized conclusion. Finally, cluster analysis is done to provide a similarity based recommendation system through probabilistic topic modeling method that can resolve Google Play’s deficiency upon app similarity.
Collyria seals in the Roman Empire.

Roman seals associated with collyria (Latin expression for eye drops/washes and lotions for eye maintenance) provide valuable information about eye care in the antiquity. These small, usually stone-made pieces bore engravings with the names of eye doctors and also the collyria used to treat an eye disease. The collyria seals have been found all over the Roman empire and Celtic territories in particular and were usually associated with military camps. In Hispania (Iberian Peninsula), only three collyria seals have been found. These findings speak about eye care in this ancient Roman province as well as about of the life of the time. This article takes a look at the utility and social significance of the collyria seals and seeks to give an insight in the ophthalmological practice of in the Roman Empire.
A New Insight of Salt Stress Signaling in Plant

Many studies have been conducted to understand plant stress responses to salinity because irrigation-dependent salt accumulation compromises crop productivity and also to understand the mechanism through which some plants thrive under saline conditions. As mechanistic understanding has increased during the last decades, discovery-oriented approaches have begun to identify genetic determinants of salt tolerance. In addition to osmolytes, osmoprotectants, radical detoxification, ion transport systems, and changes in hormone levels and hormone-guided communications, the Salt Overly Sensitive (SOS) pathway has emerged to be a major defense mechanism. However, the mechanism by which the components of the SOS pathway are integrated to ultimately orchestrate plant-wide tolerance to salinity stress remains unclear. A higher-level control mechanism has recently emerged as a result of recognizing the involvement of GIGANTEA (GI), a protein involved in maintaining the plant circadian clock and control switch in flowering. The loss of GI function confers high tolerance to salt stress via its interaction with the components of the SOS pathway. The mechanism underlying this observation indicates the association between GI and the SOS pathway and thus, given the key influence of the circadian clock and the pathway on photoperiodic flowering, the association between GI and SOS can regulate growth and stress tolerance. In this review, we will analyze the components of the SOS pathways, with emphasis on the integration of components recognized as hallmarks of a halophytic lifestyle.
An Image Based Technique for Enhancement of Underwater Images

The underwater images usually suffers from non-uniform lighting, low contrast, blur and diminished colors. In this paper, we proposed an image based preprocessing technique to enhance the quality of the underwater images. The proposed technique comprises a combination of four filters such as homomorphic filtering, wavelet denoising, bilateral filter and contrast equalization. These filters are applied sequentially on degraded underwater images. The literature survey reveals that image based preprocessing algorithms uses standard filter techniques with various combinations. For smoothing the image, the image based preprocessing algorithms uses the anisotropic filter. The main drawback of the anisotropic filter is that iterative in nature and computation time is high compared to bilateral filter. In the proposed technique, in addition to other three filters, we employ a bilateral filter for smoothing the image. The experimentation is carried out in two stages. In the first stage, we have conducted various experiments on captured images and estimated optimal parameters for bilateral filter. Similarly, optimal filter bank and optimal wavelet shrinkage function are estimated for wavelet denoising. In the second stage, we conducted the experiments using estimated optimal parameters, optimal filter bank and optimal wavelet shrinkage function for evaluating the proposed technique. We evaluated the technique using quantitative based criteria such as a gradient magnitude histogram and Peak Signal to Noise Ratio (PSNR). Further, the results are qualitatively evaluated based on edge detection results. The proposed technique enhances the quality of the underwater images and can be employed prior to apply computer vision techniques.
The acoustic emotion gaussians model for emotion-based music annotation and retrieval

One of the most exciting but challenging endeavors in music research is to develop a computational model that comprehends the affective content of music signals and organizes a music collection according to emotion. In this paper, we propose a novel acoustic emotion Gaussians (AEG) model that defines a proper generative process of emotion perception in music. As a generative model, AEG permits easy and straightforward interpretations of the model learning processes. To bridge the acoustic feature space and music emotion space, a set of latent feature classes, which are learned from data, is introduced to perform the end-to-end semantic mappings between the two spaces. Based on the space of latent feature classes, the AEG model is applicable to both automatic music emotion annotation and emotion-based music retrieval. To gain insights into the AEG model, we also provide illustrations of the model learning process. A comprehensive performance study is conducted to demonstrate the superior accuracy of AEG over its predecessors, using two emotion annotated music corpora MER60 and MTurk. Our results show that the AEG model outperforms the state-of-the-art methods in automatic music emotion annotation. Moreover, for the first time a quantitative evaluation of emotion-based music retrieval is reported.
Adult Content Recognition from Images Using a Mixture of Convolutional Neural Networks

With rapid development of the Internet, the web contents become huge. Most of the websites are publicly available and anyone can access the contents everywhere such as workplace, home and even schools. Nevertheless, not all the web contents are appropriate for all users, especially children. An example of these contents is pornography images which should be restricted to certain age group. Besides, these images are not safe for work (NSFW) in which employees should not be seen accessing such contents. Recently, convolutional neural networks have been successfully applied to many computer vision problems. Inspired by these successes, we propose a mixture of convolutional neural networks for adult content recognition. Unlike other works, our method is formulated on a weighted sum of multiple deep neural network models. The weights of each CNN models are expressed as a linear regression problem learnt using Ordinary Least Squares (OLS). Experimental results demonstrate that the proposed model outperforms both single CNN model and the average sum of CNN models in adult content recognition.
High performance drives for hybrid electric vehicles through voltage stabilization

This paper deals with the approach of integrating a bidirectional boost-converter into the drivetrain of a (hybrid) electric vehicle in order to exploit the full potential of the electric drives and the battery. Currently, the automotive norms and standards are defined based on the characteristics of the voltage source. The current technologies of batteries for automotive applications have voltage which depends on the load and the state-of charge. The aim of this paper is to provide better system performance by stabilizing the voltage without the need of redesigning any of the current components in the system. To show the added-value of the proposed electrical topology, loss estimation is developed and proved based on actual components measurements and design. The component and its modelling is then implemented in a global system simulation environment of the electric architecture to show how it contributes enhancing the performance of the system.
Multi-modal & Multi-view & Interactive Benchmark Dataset for Human Action Recognition

Human action recognition is one of the most active research areas in both computer vision and machine learning communities. Several methods for human action recognition have been proposed in the literature and promising results have been achieved on the popular datasets. However, the comparison of existing methods is often limited given the different datasets, experimental settings, feature representations, and so on. In particularly, there are no human action dataset that allow concurrent analysis on three popular scenarios, namely single view, cross view, and cross domain. In this paper, we introduce a Multi-modal & Multi-view & Interactive (M2I) dataset, which is designed for the evaluation of the performances of human action recognition under multi-view scenario. This dataset consists of 1760 action samples, including 9 person-person interaction actions and 13 person-object interaction actions. Moreover, we respectively evaluate three representative methods for the single-view, cross-view, and cross domain human action recognition on this dataset with the proposed evaluation protocol. It is experimentally demonstrated that this dataset is extremely challenging due to large intraclass variation, multiple similar actions, significant view difference. This benchmark can provide solid basis for the evaluation of this task and will benefit advancing related computer vision and machine learning research topics.
Palliative radiation therapy for pulmonary metastases from hepatocellular carcinoma

Although the lung is the most common site of extrahepatic metastases from hepatocellular carcinoma (HCC), the optimal treatment for such metastases has’nt been established. External beam radiotherapy (EBRT) is becoming a useful local control therapy for lung cancer. To evaluated the efficacy of EBRT treatment for such metastases, we retrospectively studied 13 patients (11 men and 2 women; mean age, 52.6 years) with symptomatic pulmonary metastases from HCC who had been treated with EBRT in our institution. The palliative radiation dose delivered to the lung lesions ranged from 47 to 60 Gy (median 50) in conventional fractions, while the intrahepatic lesions were treated with surgery or transarterial chemoembolization, and/or EBRT. Follow-up period from radiotherapy ranged from 3.7 to 49.1 months (median, 16.7). Among the 13 patients, 23 out of a total of 31 pulmonary metastatic lesions received EBRT. In 12/13(92.3%) patients, significant symptoms were completely or partially relieved. An objective response was observed in 10/13(76.9%) of the subjects by computed tomography imaging. The median progression-free survival for all patients was 13.4 months. The 2-year survival rate from pulmonary metastasis was 70.7%. Adverse effects were mild and consisted of bone marrow suppression in three patients and pleural effusion in one patient (all CTCAE Grade II). In conclusion, EBRT with ≤60 Gy appears to be a good palliative therapy with reasonable safety for patients with pulmonary metastases from HCC. However, large-scale randomized clinical trials will be necessary to confirm the therapeutic role of this method.
The appropriate use of proton pump inhibitors (PPIs): Need for a reappraisal.

The advent of powerful acid-suppressive drugs, such as proton pump inhibitors (PPIs), has revolutionized the management of acid-related diseases and has minimized the role of surgery. The major and universally recognized indications for their use are represented by treatment of gastro-esophageal reflux disease, eradication of Helicobacter pylori infection in combination with antibiotics, therapy of H. pylori-negative peptic ulcers, healing and prophylaxis of non-steroidal anti-inflammatory drug-associated gastric ulcers and control of several acid hypersecretory conditions. However, in the last decade, we have witnessed an almost continuous growth of their use and this phenomenon cannot be only explained by the simple substitution of the previous H2-receptor antagonists, but also by an inappropriate prescription of these drugs. This endless increase of PPI utilization has created an important problem for many regulatory authorities in terms of increased costs and greater potential risk of adverse events. The main reasons for this overuse of PPIs are the prevention of gastro-duodenal ulcers in low-risk patients or the stress ulcer prophylaxis in non-intensive care units, steroid therapy alone, anticoagulant treatment without risk factors for gastro-duodenal injury, the overtreatment of functional dyspepsia and a wrong diagnosis of acid-related disorder. The cost for this inappropriate use of PPIs has become alarming and requires to be controlled. We believe that gastroenterologists together with the scientific societies and the regulatory authorities should plan educational initiatives to guide both primary care physicians and specialists to the correct use of PPIs in their daily clinical practice, according to the worldwide published guidelines.
Πάντα ῥεῖ καὶ οὐδὲν μένει1

the content of «technical» realization of three special methods during criminalistic cognition: criminalistic identification, criminalistic diagnostics and criminalistic classification. Criminalistic technics (as a system of knowledge) is a branch of the special part of criminalistic theory describing and explaining regularities of emergence of materially fixed traces during investigation of criminal offences. It’s for finding and examining concrete technical means, knowledge and skills are already worked out and recommended.