nils-herrmann/clean_scirepeval huggingface dataset:
PCA-based feature selection scheme for machine defect classification

The sensitivity of various features that are characteristic of a machine defect may vary considerably under different operating conditions. Hence it is critical to devise a systematic feature selection scheme that provides guidance on choosing the most representative features for defect classification. This paper presents a feature selection scheme based on the principal component analysis (PCA) method. The effectiveness of the scheme was verified experimentally on a bearing test bed, using both supervised and unsupervised defect classification approaches. The objective of the study was to identify the severity level of bearing defects, where no a priori knowledge on the defect conditions was available. The proposed scheme has shown to provide more accurate defect classification with fewer feature inputs than using all features initially considered relevant. The result confirms its utility as an effective tool for machine health assessment.
Two-factor authentication for the Bitcoin protocol

We show how to realize two-factor authentication for a Bitcoin wallet. To do so, we explain how to employ an ECDSA adaption of the two-party signature protocol by MacKenzie and Reiter ( 2004.  in the context of Bitcoin and present a prototypic implementation of a Bitcoin wallet that offers both: two-factor authentication and verification over a separate channel. Since we use a smart phone as the second authentication factor, our solution can be used with hardware already available to most users and the user experience is quite similar to the existing online banking authentication methods.
Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals

An encephalogram (EEG) is a commonly used ancillary test to aide in the diagnosis of epilepsy. The EEG signal contains information about the electrical activity of the brain. Traditionally, neurologists employ direct visual inspection to identify epileptiform abnormalities. This technique can be time-consuming, limited by technical artifact, provides variable results secondary to reader expertise level, and is limited in identifying abnormalities. Therefore, it is essential to develop a computer-aided diagnosis (CAD) system to automatically distinguish the class of these EEG signals using machine learning techniques. This is the first study to employ the convolutional neural network (CNN) for analysis of EEG signals. In this work, a 13-layer deep convolutional neural network (CNN) algorithm is implemented to detect normal, preictal, and seizure classes. The proposed technique achieved an accuracy, specificity, and sensitivity of 88.67%, 90.00% and 95.00%, respectively.
Gaze estimation using local features and non-linear regression

In this paper, we present an appearance-based gaze estimation method for a head-mounted eye tracker. The idea is to extract discriminative image descriptors with respect to gaze before applying a regression scheme. We employ multilevel Histograms of Oriented Gradients (HOG) features as our appearance descriptor. To learn the mapping between eye appearance and gaze coordinates, two learning-based approaches are evaluated : Support Vector Regression (SVR) and Relevance Vector Regression (RVR). Experimental results demonstrate that, despite the high dimensionality, our method works well and RVR provides a more efficient and generalized solution than SVR by retaining a low number of basis functions.
Multimodal depression recognition with dynamic visual and audio cues

In this paper, we present our system design for audio visual multi-modal depression recognition. To improve the estimation accuracy of the Beck Depression Inventory (BDI) score, besides the Low Level Descriptors (LLD) features and the Local Gabor Binary Pattern-Three Orthogonal Planes (LGBP-TOP) features provided by the 2014 Audio/Visual Emotion Challenge and Workshop (AVEC2014), we extract extra features to capture key behavioural changes associated with depression. From audio we extract the speaking rate, and from video, the head pose features, the Space-Temporal Interesting Point (STIP) features, and local kinematic features via the Divergence-Curl-Shear descriptors. These features describe body movements, and spatio-temporal changes within the image sequence. We also consider global dynamic features, obtained using motion history histogram (MHH), bag of words (BOW) features and vector of local aggregated descriptors (VLAD). To capture the complementary information within the used features, we evaluate two fusion systems - the feature fusion scheme, and the model fusion scheme via local linear regression (LLR). Experiments are carried out on the training set and development set of the Depression Recognition Sub-Challenge (DSC) of AVEC2014, we obtain root mean square error (RMSE) of 7.6697, and mean absolute error (MAE) of 6.1683 on the development set, which are better or comparable with the state of the art results of the AVEC2014 challenge.
Predictive and corrective text input for desktop editor using n-grams and suffix trees

Even though desktop computers have existed since a long time, the method of typing and feeding input has not changed much. Versions after versions of popular text editors have come, and yet no editor yet has addressed the difficulty that smaller user groups such as senior citizens and handicapped people have while entering input. Also, predictive editors cease to exist in desktop computers even today. This paper explores the use of a new software for input on desktops, which relies on a dynamic predictive algorithm using n-grams and suffix trees to significantly reduce the effort of typing. This paper also compares the newly developed Smart Text Editor to traditional popular editors.
Automatic labeling of multinomial topic models

Multinomial distributions over words are frequently used to model topics in text collections. A common, major challenge in applying all such topic models to any text mining problem is to label a multinomial topic model accurately so that a user can interpret the discovered topic. So far, such labels have been generated manually in a subjective way. In this paper, we propose probabilistic approaches to automatically labeling multinomial topic models in an objective way. We cast this labeling problem as an optimization problem involving minimizing Kullback-Leibler divergence between word distributions and maximizing mutual information between a label and a topic model. Experiments with user study have been done on two text data sets with different genres.The results show that the proposed labeling methods are quite effective to generate labels that are meaningful and useful for interpreting the discovered topic models. Our methods are general and can be applied to labeling topics learned through all kinds of topic models such as PLSA, LDA, and their variations.
Depth-of-field-based alpha-matte extraction

In compositing applications, objects depicted in images frequently have to be separated from their background, so that they can be placed in a new environment. Alpha mattes are important tools aiding the selection of objects, but cannot normally be created in a fully automatic way. We present an algorithm that requires as input two images---one where the object is in focus, and one where the background is in focus---and then automatically produces an alpha matte indicating which pixels belong to the object. This algorithm is inspired by human visual processing and involves nonlinear response compression, center-surround mechanisms as well as a filling-in stage. The output can then be refined with standard computer vision techniques.
Alzheimer's disease: genes, proteins, and therapy.

Rapid progress in deciphering the biological mechanism of Alzheimer's disease (AD) has arisen from the application of molecular and cell biology to this complex disorder of the limbic and association cortices. In turn, new insights into fundamental aspects of protein biology have resulted from research on the disease. This beneficial interplay between basic and applied cell biology is well illustrated by advances in understanding the genotype-to-phenotype relationships of familial Alzheimer's disease. All four genes definitively linked to inherited forms of the disease to date have been shown to increase the production and/or deposition of amyloid beta-protein in the brain. In particular, evidence that the presenilin proteins, mutations in which cause the most aggressive form of inherited AD, lead to altered intramembranous cleavage of the beta-amyloid precursor protein by the protease called gamma-secretase has spurred progress toward novel therapeutics. The finding that presenilin itself may be the long-sought gamma-secretase, coupled with the recent identification of beta-secretase, has provided discrete biochemical targets for drug screening and development. Alternate and novel strategies for inhibiting the early mechanism of the disease are also emerging. The progress reviewed here, coupled with better ability to diagnose the disease early, bode well for the successful development of therapeutic and preventative drugs for this major public health problem.
Coping with autism: a journey toward adaptation.

As the number of individuals with autism grows, it is critical for nurses in all settings to understand how autism influences the family unit, as they will likely interact with these children, the adults, and their families. The intent of this descriptive narrative study was to explore the experiences of families of individuals with autism as perceived by the mother. Through personal interviews, 16 mothers' perceptions of the impact of autism on the family unit during different stages of the life cycle were revealed through a constructivist lens. Pediatric nurses employed in acute care settings, community, and schools are poised to assess and support these families following diagnosis and throughout the child's life.
Emotion recognition from text using semantic labels and separable mixture models

This study presents a novel approach to automatic emotion recognition from text. First, emotion generation rules (EGRs) are manually deduced from psychology to represent the conditions for generating emotion. Based on the EGRs, the emotional state of each sentence can be represented as a sequence of semantic labels (SLs) and attributes (ATTs); SLs are defined as the domain-independent features, while ATTs are domain-dependent. The emotion association rules (EARs) represented by SLs and ATTs for each emotion are automatically derived from the sentences in an emotional text corpus using the a priori algorithm. Finally, a separable mixture model (SMM) is adopted to estimate the similarity between an input sentence and the EARs of each emotional state. Since some features defined in this approach are domain-dependent, a dialog system focusing on the students' daily expressions is constructed, and only three emotional states, happy, unhappy, and neutral, are considered for performance evaluation. According to the results of the experiments, given the domain corpus, the proposed approach is promising, and easily ported into other domains.
An Ensemble-of-Classifiers Based Approach for Early Diagnosis of Alzheimer's Disease: Classification Using Structural Features of Brain Images

Structural brain imaging is playing a vital role in identification of changes that occur in brain associated with Alzheimer's disease. This paper proposes an automated image processing based approach for the identification of AD from MRI of the brain. The proposed approach is novel in a sense that it has higher specificity/accuracy values despite the use of smaller feature set as compared to existing approaches. Moreover, the proposed approach is capable of identifying AD patients in early stages. The dataset selected consists of 85 age and gender matched individuals from OASIS database. The features selected are volume of GM, WM, and CSF and size of hippocampus. Three different classification models (SVM, MLP, and J48) are used for identification of patients and controls. In addition, an ensemble of classifiers, based on majority voting, is adopted to overcome the error caused by an independent base classifier. Ten-fold cross validation strategy is applied for the evaluation of our scheme. Moreover, to evaluate the performance of proposed approach, individual features and combination of features are fed to individual classifiers and ensemble based classifier. Using size of left hippocampus as feature, the accuracy achieved with ensemble of classifiers is 93.75%, with 100% specificity and 87.5% sensitivity.
PSIM-based modeling of automotive power systems: conventional, electric, and hybrid electric vehicles

Automotive manufacturers have been taking advantage of simulation tools for modeling and analyzing various types of vehicles, such as conventional, electric, and hybrid electric vehicles. These simulation tools are of great assistance to engineers and researchers to reduce product-development cycle time, improve the quality of the design, and simplify the analysis without costly and time-consuming experiments. In this paper, a modeling tool that has been developed to study automotive systems using the power electronics simulator (PSIM) software is presented. PSIM was originally made for simulating power electronic converters and motor drives. This user-friendly simulation package is able to simulate electric/electronic circuits; however, it has no capability for simulating the entire system of an automobile. This paper discusses the PSIM validity as an automotive simulation tool by creating module boxes for not only the electrical systems, but also the mechanical, energy-storage, and thermal systems of the vehicles. These modules include internal combustion engines, fuel converters, transmissions, torque couplers, and batteries. Once these modules are made and stored in the library, the user can make the car model either a conventional, an electric, or a hybrid vehicle at will, just by dragging and dropping onto a schematic blank page.
SeVR+: Secure and privacy-aware cloud-assisted video reporting service for 5G vehicular networks

Recently, Eiza et al. proposed a secure and privacy-aware scheme for video reporting service in 5G enabled Vehicular Ad hoc Networks (VANET). They employ heterogeneous network and cloud platform to obtain more availability with low latency platform for an urgent accident video reporting service. In their study, for the first time the security issues of 5G enabled vehicular networks have been addressed. Eiza et al. claimed that their scheme guarantees user's privacy, confidentiality, non-repudiation, message integrity and availability for participant vehicles. In this paper, we show that Eiza et al. scheme is vulnerable to replay, message fabrication and DoS attacks. Regarding the sensibility of video reporting services in VANET, then, we propose an efficient protocol to overcome security weaknesses of Eiza et.al. scheme and show that the proposed protocol resists against commonplace attacks in VANET with acceptable communication and computation overhead.
Users of the world , unite ! The challenges and opportunities of Social Media

As of January 2009, the online social networking application Facebook registered more than 175 million active users. To put that number in perspective, this is only slightly less than the population of Brazil (190 million) and over twice the population of Germany (80 million)! At the same time, every minute, 10 hours of content were uploaded to the video sharing platform YouTube. And, the image hosting site Flickr provided access to over 3 billion photographs, making the world-famous Louvre Museum’s collection of 300,000 objects seem tiny in comparison. According to Forrester Research, 75% of Internet surfers used ‘‘Social Media’’ in the second quarter of 2008 by joining social networks, reading blogs, or contributing reviews to shopping sites; this represents a significant rise from 56% in 2007. The growth is not limited to teenagers, either; members of Generation X, now 35—44 years old, increasingly populate the ranks of joiners, spectators, and critics. It is therefore reasonable to say that Social Media represent a revolutionary new trend that should be of interest to companies operating in online space–—or any space, for that matter. Yet, not overly many firms seem to act comfortably in a world where consumers can speak so freely Business Horizons (2010) 53, 59—68
Global Hypothesis Generation for 6D Object Pose Estimation

This paper addresses the task of estimating the 6D-pose of a known 3D object from a single RGB-D image. Most modern approaches solve this task in three steps: i) compute local features, ii) generate a pool of pose-hypotheses, iii) select and refine a pose from the pool. This work focuses on the second step. While all existing approaches generate the hypotheses pool via local reasoning, e.g. RANSAC or Hough-Voting, we are the first to show that global reasoning is beneficial at this stage. In particular, we formulate a novel fully-connected Conditional Random Field (CRF) that outputs a very small number of pose-hypotheses. Despite the potential functions of the CRF being non-Gaussian, we give a new, efficient two-step optimization procedure, with some guarantees for optimality. We utilize our global hypotheses generation procedure to produce results that exceed state-of-the-art for the challenging Occluded Object Dataset.
Online Footsteps to Purchase: Exploring Consumer Behaviors on Online Shopping Sites

As an important part of the Internet economy, online markets have gained much interest in research community as well as industry. Researchers have studied various aspects of online markets including motivations of consumer behaviors on online markets. However, due to the lack of log data of consumers' online behaviors including their purchase, it has not been thoroughly investigated or validated on what drives consumers to purchase products on online markets. Our research moves forward from prior studies by analyzing consumers' actual online behaviors that lead to actual purchases, and using datasets from multiple online shopping sites that can provide comparisons across different types of online shopping sites. We analyzed consumers' buying process and constructed consumers' behavior trajectory to gain deeper understanding of consumer behaviors on online markets. We find that a substantial portion (24%) of consumers in a general-purpose marketplace (like eBay) discover items from external sources (e.g., price comparison sites), while most (>95%) of consumers in a special-purpose shopping site directly access items from the site itself. We also reveal that item browsing patterns and cart usage patterns are the important predictors of the actual purchases. Using behavioral features identified by our analysis, we developed a prediction model to infer whether a consumer purchases item(s). Our prediction model of purchases achieved over 80% accuracy across four different online shopping sites.
A Survey on Network Embedding

Network embedding assigns nodes in a network to lowdimensional representations and effectively preserves the network structure. Recently, a significant amount of progresses have been made toward this emerging network analysis paradigm. In this survey, we focus on categorizing and then reviewing the current development on network embedding methods, and point out its future research directions. We first summarize the motivation of network embedding. We discuss the classical graph embedding algorithms and their relationship with network embedding. Afterwards and primarily, we provide a comprehensive overview of a large number of network embedding methods in a systematic manner, covering the structureand property-preserving network embedding methods, the network embedding methods with side information and the advanced information preserving network embedding methods. Moreover, several evaluation approaches for network embedding and some useful online resources, including the network data sets and softwares, are reviewed, too. Finally, we discuss the framework of exploiting these network embedding methods to build an effective system and point out some potential future directions.
Gender Identity and Adjustment in Middle Childhood

Gender identity is a central construct in many accounts of psychosocial development, yet it has been defined in diverse ways. Kohlberg (1966) and Zucker et al. (1993) viewed gender identity as knowing that one is a member of one sex rather than the other; Kagan (1964) regarded gender identity as the degree to which one perceives the self as conforming to cultural stereotypes for one's gender; Bem (1981) saw gender identity as the degree to which one internalizes societal pressures for gender conformity; Green (1974) and Spence (1985) viewed gender identity as a fundamental sense of acceptance of, and of belonging to, one's gender. It is conceivable that all of the foregoing (and still other) conceptualizations of gender identity have merit but that different varieties or facets of gender identity serve different psychological functions or affect adjustment in different ways. Thus, it may be fruitful to regard gender identity as a multidimensional construct and to define gender identity as the collection of thoughts and feelings one has about one's gender category and one's membership in it. A recent study by Egan and Perry (2001) was built on this premise. Egan and Perry proposed that gender identity is composed of five major components: (a) membership knowledge (knowledge of membership in a gender category); (b) gender typicality (the degree to which one feels one is a typical member of one's gender category); (c) gender contentedness (the degree to which one is happy with one's gender assignment); (d) felt pressure for gender conformity (the degree to which one feels pressure from parents, peers, and self for conformity to gender stereotypes); and (e) intergroup bias (the extent to which one believes one's own sex is superior to the other). Egan and Perry (2001) measured the last four of these components of gender identity in preadolescent children and found the components to be relatively independent, to be fairly stable over a school year, and to relate to adjustment (i.e., self-esteem and peer acceptance) in different ways. Gender typicality and gender contentedness were favorably related to adjustment, whereas felt pressure and intergroup bias were negatively associated with adjustment. Links between the gender identity constructs and the adjustment indexes remained significant when children's perceptions of self-efficacy for a wide variety of sex-typed activities were statistically controlled. This suggests that the gender identity constructs carry implications for adjustment beyond self-perceptions of specific sex-linked competencies. The purposes of the …
Spatio-spectral filters for improving the classification of single trial EEG

Data recorded in electroencephalogram (EEG)-based brain-computer interface experiments is generally very noisy, nonstationary, and contaminated with artifacts that can deteriorate discrimination/classification methods. In this paper, we extend the common spatial pattern (CSP) algorithm with the aim to alleviate these adverse effects. In particular, we suggest an extension of CSP to the state space, which utilizes the method of time delay embedding. As we will show, this allows for individually tuned frequency filters at each electrode position and, thus, yields an improved and more robust machine learning procedure. The advantages of the proposed method over the original CSP method are verified in terms of an improved information transfer rate (bits per trial) on a set of EEG-recordings from experiments of imagined limb movements.