nils-herrmann/clean_scirepeval huggingface dataset:
Bristle-sensors--low-cost flexible passive dry EEG electrodes for neurofeedback and BCI applications.

In this paper, we present a new, low-cost dry electrode for EEG that is made of flexible metal-coated polymer bristles. We examine various standard EEG paradigms, such as capturing occipital alpha rhythms, testing for event-related potentials in an auditory oddball paradigm and performing a sensory motor rhythm-based event-related (de-) synchronization paradigm to validate the performance of the novel electrodes in terms of signal quality. Our findings suggest that the dry electrodes that we developed result in high-quality EEG recordings and are thus suitable for a wide range of EEG studies and BCI applications. Furthermore, due to the flexibility of the novel electrodes, greater comfort is achieved in some subjects, this being essential for long-term use.
About Face 3: the essentials of interaction design

Some people may be laughing when looking at you reading in your spare time. Some may be admired of you. And some may want be like you who have reading hobby. What about your own feel? Have you felt right? Reading is a need and a hobby at once. This condition is the on that will make you feel that you must read. If you know are looking for the book enPDFd about face 3 the essentials of interaction design alan cooper as the choice of reading, you can find here.
Ecosystem Services in Urban Landscapes: Practical Applications and Governance Implications

Urban landscapes are the everyday environment for the majority of the global population, and almost 80 % of the Europeans live in urban areas. The continuous growth in the number and size of urban areas along with an increasing demand on resources and energy poses great challenges for ensuring human welfare in cities while preventing an increasing loss of biodiversity. The understanding of how urban ecosystems function, provide goods and services for urban dwellers; and how they change and what allows and limits their performance can add to the understanding of ecosystem change and governance in general in an ever more human-dominated world. This Special Issue aims at bridging the knowledge gap among urbanization, demand creation, and provisioning of ecosystem services in urban regions on the one hand and schemes of urban governance and planning on the other.
A Taxonomy of Modeling Techniques using Sketch-Based Interfaces

Traditional user interfaces in modeling have followed the WIMP (Window, Icon, Menu, Pointer) paradigm. While functional and powerful, they can also be cumbersome and daunting to a novice user; creating a complex model requires much expertise and effort. A recent trend is toward more accessible and natural interfaces, which has lead to sketch-based interfaces for modeling (SBIM). The goal is to allow hand-drawn sketches to be used in the modeling process, from rough model creation through to fine detail construction. Mapping 2D sketches to a 3D modeling operation is a difficult and ambiguous task, so our categorization is based on how an SBIM application chooses to interpret a sketch, of which there are three primary methods: to create a 3D model, to add details to an existing model, or to deform and manipulate a model. In this STAR, we present a taxonomy of sketchbased interfaces focused on geometric modeling applications. The canonical and recent works are presented and classified, including techniques for sketch acquisition, filtering, and interpretation. The report also includes a discussion of important challenges and open problems for researchers to tackle in the coming years.
Power Electronic Transformer-Based Railway Traction Systems: Challenges and Opportunities

In this paper, power electronic transformer (PET)-based railway traction systems are comprehensively reviewed according to the unique application features and requirements. By comparing PET and conventional line frequency transformer (LFT)-based systems, their pros and cons are summarized. By further reviewing all kinds of PET-based designs from the early concepts to the latest ones in the order of their publication dates, the developing trends are highlighted. By synthetically considering the requirements and the state of the art, the key challenges and opportunities are identified and discussed. It shows that although PET-based systems are still developing and far from mature, they are already superior to LFT-based systems in terms of system weight, efficiency, and functionalities especially for 15-kV/16.7-Hz applications. With the advancements on wide bandgap power devices, medium frequency transformers, and converters, PET systems will be even more promising and available for all types of railway tractions in the near future.
The spatio-temporal generalized additive model for criminal incidents

Law enforcement agencies need to model spatio-temporal patterns of criminal incidents. With well developed models, they can study the causality of crimes and predict future criminal incidents, and they can use the results to help prevent crimes. In this paper, we described our newly developed spatio-temporal generalized additive model (S-T GAM) to discover underlying factors related to crimes and predict future incidents. The model can fully utilize many different types of data, such as spatial, temporal, geographic, and demographic data, to make predictions. We efficiently estimated the parameters for S-T GAM using iteratively re-weighted least squares and maximum likelihood and the resulting estimates provided for model interpretability. In this paper we showed the evaluation of S-T GAM with the actual criminal incident data from Charlottesville, Virginia. The evaluation results showed that S-T GAM outperformed the previous spatial prediction models in predicting future criminal incidents.
Learning to Detect Anomalies in Surveillance Video

Detecting anomalies in surveillance videos, that is, finding events or objects with low probability of occurrence, is a practical and challenging research topic in computer vision community. In this paper, we put forward a novel unsupervised learning framework for anomaly detection. At feature level, we propose a Sparse Semi-nonnegative Matrix Factorization (SSMF) to learn local patterns at each pixel, and a Histogram of Nonnegative Coefficients (HNC) can be constructed as local feature which is more expressive than previously used features like Histogram of Oriented Gradients (HOG). At model level, we learn a probability model which takes the spatial and temporal contextual information into consideration. Our framework is totally unsupervised requiring no human-labeled training data. With more expressive features and more complicated model, our framework can accurately detect and localize anomalies in surveillance video. We carried out extensive experiments on several benchmark video datasets for anomaly detection, and the results demonstrate the superiority of our framework to state-of-the-art approaches, validating the effectiveness of our framework.
Credit Card Borrowing , Delinquency , and Personal Bankruptcy

C redit card delinquencies and personal bankruptcy rates increased during the mid 1990s, despite the strength of the U.S. economy. Even though per capita income rose during that period, household borrowing grew at an even faster pace. The ratio of consumer debt (excluding real estate) to disposable income increased, and it has remained above 20 percent, despite rising incomes. The rise in revolving debt—mainly credit card loans 1 —was especially noticeable, resulting in an increase in the share of revolving debt in total consumer debt . The increase in personal bankruptcy rates was also substantial. From 1994 to 1998, the number of nonbusiness bankruptcy filings in the United States increased faster than during the four-year period that included the 1991 recession , reaching more than one filing per 100 households in 1998. The good news is that the number of filings dropped in 1999, and in the first quarter of 2000 was 5.8 percent lower than a year earlier. 2 The high rates of credit card delinquency and bankruptcy have generated much discussion about their causes. Some blame credit card default rates on lenders, whose more lenient standards have allowed consumers to borrow more than they can repay; others blame borrowers. The discussion has extended to the Congress, where a bankruptcy reform bill has been debated for the past few years. This article examines the relationship between consumer credit card borrowing, delinquency rates, and personal bankruptcies. It looks at developments involving borrowers , the demand side, and lenders, the supply side. Section I of the article presents background information and summarizes previous literature showing that credit card loans have been extended to higher-risk consumers over time. Section II analyzes borrow-ers' or the demand side of the credit card market. Using data collected in the 1998 Survey of Consumer Finances, we examine the effect of credit card borrowing on consumer payments delinquency. We also examine
AIRBUS A320/A330/A340 electrical flight controls - A family of fault-tolerant systems

The digital electrical flight control system of the Airbus A320/A330/A340 is discussed. The A320 was the first civil aircraft equipped with such a system. It was certified and entered into service in the first quarter of 1988. The A330 and A340 have identical systems, closely related to the A320 system. These systems are built to very stringent dependability requirements both in terms of safety (the systems must not output erroneous signals) and availability. The basic building blocks are fail-safe control and monitoring computers. The control channel performs the function allocated to the computer (control of a control surface for example). The monitoring channel ensures that the control channel operates correctly. A high level of redundancy is built into the system. Special attention has been paid to possible external aggressions. The system is built to tolerate both hardware and software design faults. The A320 system is described together with the significant differences between the A320 and the A330/A340, and A320 in service experience.
Hair Segmentation Using Heuristically-Trained Neural Networks

We present a method for binary classification using neural networks (NNs) that performs training and classification on the same data using the help of a pretraining heuristic classifier. The heuristic classifier is initially used to segment data into three clusters of high-confidence positives, high-confidence negatives, and low-confidence sets. The high-confidence sets are used to train an NN, which is then used to classify the low-confidence set. Applying this method to the binary classification of hair versus nonhair patches, we obtain a 2.2% performance increase using the heuristically trained NN over the current state-of-the-art hair segmentation method.
Double layered recommendation algorithm based on fast density clustering: Case study on Yelp social networks dataset

With the wider applications of mobile devices, network-based recommendation has attracted more attentions than ever. At present, most research of recommender system is on basis of either graph theory or algebraic methods. However almost all of these recommendation algorithms are aiming at static history data, which cannot meet the demand of precise online recommendation for dynamic real-time emerging data. Besides, lots of traditional recommendation algorithms are suffered from high time complexity and parameter tuning difficulty. Here, we propose a personalized recommendation algorithm based on density-distance dynamic clustering model (PRA-DCM). A two layered recommendation algorithm based on fast density clustering is first time put forward. Users and items are clustered respectively into user clusters and item clusters by fast density clustering algorithm. For solving parameter self-adaptive tuning, a novel density-distance clustering center automatic determination method (DCC-ADM) is proposed. Cluster centers could be automatically determined in DCC-ADM for users and items clustering respectively. Moreover, double layered bipartite networks are designed to support precise recommendation. In the first layer bipartite network, user cluster and item cluster are treated as projection nodes. Top n items are recommended for each user node to construct the second layered bipartite network, where edges between user node and item node are calculated on basis of review rates. DCC-ACM algorithm is applied to the second layered bipartite network to realize personalized recommendation for each individual user. Finally a case study on Yelp social network dataset is applied. We compare this algorithm with popular ones, such as non-negative matrix factorization (NMF) algorithm and collaborative filtering algorithm (CF), and find that our algorithm has the advantages of high precision and low complexity.
MobileFaceNets: Efficient CNNs for Accurate Real-time Face Verification on Mobile Devices

We present a class of extremely efficient CNN models, MobileFaceNets, which use less than 1 million parameters and are specifically tailored for high-accuracy real-time face verification on mobile and embedded devices. We first make a simple analysis on the weakness of common mobile networks for face verification. The weakness has been well overcome by our specifically designed MobileFaceNets. Under the same experimental conditions, our MobileFaceNets achieve significantly superior accuracy as well as more than 2 times actual speedup over MobileNetV2. After trained by ArcFace loss on the refined MS-Celeb-1M, our single MobileFaceNet of 4.0MB size achieves 99.55% accuracy on LFW and 92.59% TAR@FAR1e-6 on MegaFace, which is even comparable to state-of-the-art big CNN models of hundreds MB size. The fastest one of MobileFaceNets has an actual inference time of 18 milliseconds on a mobile phone. For face verification, MobileFaceNets achieve significantly improved efficiency over previous state-of-the-art mobile CNNs.
Analysis of Ultra Wideband Antipodal Vivaldi Antenna Design

The characteristics of antipodal Vivaldi antennas with different structures and the key factors that affect the performance of the antipodal antennas have been investigated. The return loss, radiation pattern and current distribution with various elliptical loading sizes, opening rates and ground plane sizes have been analyzed and compared using full wave simulation tool. Based on the parameter study, a design that is capable of achieving a bandwidth from 2.1 GHz to 20 GHz has been identified.
A new methodology to support group decision-making for IoT-based emergency response systems

An emergency response system (ERS) can assist a municipality or government in improving its capabilities to respond urgent and severe events. The responsiveness and effectiveness of an ERS relies greatly on its data acquisition and processing system, which has been evolved with information technology (IT). With the rapid development of sensor networks and cloud computing, the emerging Internet of things (IoT) tends to play an increasing role in ERSs; the networks of sensors, public services, and experts are able to interact with each other and make scientific decisions to the emergencies based on real-time data. When group decision making is required in an ERS, one critical challenge is to obtain the good understanding of massive and diversified data and make consensus group decisions under a high-level stress and strict time constraint. Due to the nature of unorganized data and system complexity, an ERS depends on the perceptions and judgments of experts from different domains; it is challenging to assess the consensus of understanding on the collected data and response plans before appropriate decisions can be reached for emergencies. In this paper, the group decision-making to emergency situations is formulated as a multiple attribute group decision making (MAGDM) problem, the consensus among experts is modeled, and a new methodology is proposed to reach the understanding of emergency response plans with the maximized consensus in course of decision-making. In the implementation, the proposed methodology in integrated with computer programs and encapsulated as a service on the server. The objectives of the new methodology are (i) to enhance the comprehensive group cognizance on emergent scenarios and response plans and (ii) to accelerate the consensus for decision making with an intelligent clustering algorithm, (iii) to adjust the experts’ opinions without affecting the reliability of the decision when the consensus cannot be reached from the preliminary decision-making steps. Partitioning Around Medoids (PAM) has been applied as the clustering algorithm, Particle Swarm Optimization (PSO) is deployed to adjust evaluation values automatically. The methodology is applied in a case study to illustrate its effectiveness in converging group opinions and promoting the consensus of understanding on emergencies.
Ascertaining the validity of individual protocols from Web-based personality inventories

The research described in this article estimated the relative incidence of protocols invalidated by linguistic incompetence, inattentiveness, and intentional misrepresentation in Webbased versus paper-and-pencil personality measures. Estimates of protocol invalidity were derived from a sample of 23,994 protocols produced by individuals who completed an online version of the 300-item IPIP representation of the NEO-PI-R (Goldberg, 1999). Approximately 3.8% of the protocols were judged to be products of repeat participants, many of whom apparently resubmitted after changing some of their answers. Among nonduplicate protocols, about 3.5% came from individuals who apparently selected a response option repeatedly without reading the item, compared to .9% in a sample of paper-and-pencil protocols. The missing response rate was 1.2%, which is 2–10 times higher than the rate found in several samples of paper-and-pencil inventories of comparable length. Two measures of response consistency indicated that perhaps 1% of the protocols were invalid due to linguistic incompetence or inattentive responding, but that Web participants were as consistent as individuals responding to a paper-and-pencil inventory. Inconsistency did not aVect factorial structure and was found to be related positively to neuroticism and negatively to openness to experience. Intentional misrepresentation was not studied directly, but arguments for a low incidence of misrepresentation are presented. Methods for preventing, detecting, and handling invalid response patterns are discussed. Suggested for future research are studies that assess the moderating eVects of linguistic incompetence, inattenPrepared for the special issue of the Journal of Research in Personality 39 (1), February 2005, containing the proceedings of the 2004 meeting of the Association for Research in Personality. E-mail address: j5j@psu.edu. 0092-6566/$ see front matter  2004 Elsevier Inc. All rights reserved. doi:10.1016/j.jrp.2004.09.009 104 J.A. Johnson / Journal of Research in Personality 39 (2005) 103–129 tiveness, and intentional misrepresentation on agreement between self-report and acquaintance judgments about personality.
FOREX Rate prediction using Chaos and Quantile Regression Random Forest

This paper presents a hybrid of chaos modeling and Quantile Regression Random Forest (QRRF) for Foreign Exchange (FOREX) Rate prediction. The exchange rates data of US Dollar (USD) versus Japanese Yen (JPY), British Pound (GBP), and Euro (EUR) are used to test the efficacy of proposed model. Based on the experiments conducted, we conclude that the proposed model yielded accurate predictions compared to Chaos + Quantile Regression (QR), Chaos+Random Forest (RF) and that of Pradeepkumar and Ravi  in terms of both Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE).
Evidences for the Anti-panic Actions of Cannabidiol

Panic disorder (PD) is a disabling psychiatry condition that affects approximately 5% of the worldwide population. Currently, long-term selective serotonin reuptake inhibitors (SSRIs) are the first-line treatment for PD; however, the common side-effect profiles and drug interactions may provoke patients to abandon the treatment, leading to PD symptoms relapse. Cannabidiol (CBD) is the major non-psychotomimetic constituent of the Cannabis sativa plant with antianxiety properties that has been suggested as an alternative for treating anxiety disorders. The aim of the present review was to discuss the effects and mechanisms involved in the putative anti-panic effects of CBD.



electronic database was used as source of the studies selected selected based on the studies found by crossing the following keywords: cannabidiol and panic disorder; canabidiol and anxiety, cannabidiol and 5-HT1A receptor).



In the present review, we included both experimental laboratory animal and human studies that have investigated the putative anti-panic properties of CBD. Taken together, the studies assessed clearly suggest an anxiolytic-like effect of CBD in both animal models and healthy volunteers.



CBD seems to be a promising drug for the treatment of PD. However, novel clinical trials involving patients with the PD diagnosis are clearly needed to clarify the specific mechanism of action of CBD and the safe and ideal therapeutic doses of this compound.
Textile Materials for the Design of Wearable Antennas: A Survey

In the broad context of Wireless Body Sensor Networks for healthcare and pervasive applications, the design of wearable antennas offers the possibility of ubiquitous monitoring, communication and energy harvesting and storage. Specific requirements for wearable antennas are a planar structure and flexible construction materials. Several properties of the materials influence the behaviour of the antenna. For instance, the bandwidth and the efficiency of a planar microstrip antenna are mainly determined by the permittivity and the thickness of the substrate. The use of textiles in wearable antennas requires the characterization of their properties. Specific electrical conductive textiles are available on the market and have been successfully used. Ordinary textile fabrics have been used as substrates. However, little information can be found on the electromagnetic properties of regular textiles. Therefore this paper is mainly focused on the analysis of the dielectric properties of normal fabrics. In general, textiles present a very low dielectric constant that reduces the surface wave losses and increases the impedance bandwidth of the antenna. However, textile materials are constantly exchanging water molecules with the surroundings, which affects their electromagnetic properties. In addition, textile fabrics are porous, anisotropic and compressible materials whose thickness and density might change with low pressures. Therefore it is important to know how these characteristics influence the behaviour of the antenna in order to minimize unwanted effects. This paper presents a survey of the key points for the design and development of textile antennas, from the choice of the textile materials to the framing of the antenna. An analysis of the textile materials that have been used is also presented.
STATE OF THE ART OF DV / DT AND DI / DT CONTROL OF INSULATED GATE POWER SWITCHES

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. STATE OF THE ART OF DV/DT AND DI/DT CONTROL OF INSULATED GATE POWER SWITCHES Pierre Lefranc, Dominique Bergogne