nils-herrmann/clean_scirepeval huggingface dataset:
Toward Unsupervised Adaptation of LDA for Brain–Computer Interfaces

There is a step of significant difficulty experienced by brain-computer interface (BCI) users when going from the calibration recording to the feedback application. This effect has been previously studied and a supervised adaptation solution has been proposed. In this paper, we suggest a simple unsupervised adaptation method of the linear discriminant analysis (LDA) classifier that effectively solves this problem by counteracting the harmful effect of nonclass-related nonstationarities in electroencephalography (EEG) during BCI sessions performed with motor imagery tasks. For this, we first introduce three types of adaptation procedures and investigate them in an offline study with 19 datasets. Then, we select one of the proposed methods and analyze it further. The chosen classifier is offline tested in data from 80 healthy users and four high spinal cord injury patients. Finally, for the first time in BCI literature, we apply this unsupervised classifier in online experiments. Additionally, we show that its performance is significantly better than the state-of-the-art supervised approach.
Proving the Correctness of Multiprocess Programs

The inductive assertion method is generalized to permit formal, machine-verifiable proofs of correctness for multiprocess programs. Individual processes are represented by ordinary flowcharts, and no special synchronization mechanisms are assumed, so the method can be applied to a large class of multiprocess programs. A correctness proof can be designed together with the program by a hierarchical process of stepwise refinement, making the method practical for larger programs. The resulting proofs tend to be natural formalizations of the informal proofs that are now used.
Factors affecting pass-along email intentions (PAEIs): Integrating the social capital and social cognition theories

1567-4223/$34.00 Crown  Corresponding author. Tel.: +886 7 5254713; fax: E-mail address: tw_cchuang@hotmail.com (C.-C. H While eWOM advertising has recently emerged as an effective marketing strategy among marketing practitioners, comparatively few studies have been conducted to examine the eWOM from the perspective of pass-along emails. Based on social capital theory and social cognitive theory, this paper develops a model involving social enablers and personal cognition factors to explore the eWOM behavior and its efficacy. Data collected from 347 email users have lent credit to the model proposed. Tested by LISREL 8.70, the results indicate that the factors such as message involvement, social interaction tie, affection outcome expectations and message passing self-efficacy exert significant influences on pass-along email intentions (PAEIs). The study result may well be useful to marketing practitioners who are considering email marketing, especially to those who are in the process of selecting key email users and/or designing product advertisements to heighten the eWOM effect.
Generalized Autoencoder: A Neural Network Framework for Dimensionality Reduction

The autoencoder algorithm and its deep version as traditional dimensionality reduction methods have achieved great success via the powerful representability of neural networks. However, they just use each instance to reconstruct itself and ignore to explicitly model the data relation so as to discover the underlying effective manifold structure. In this paper, we propose a dimensionality reduction method by manifold learning, which iteratively explores data relation and use the relation to pursue the manifold structure. The method is realized by a so called "generalized autoencoder" (GAE), which extends the traditional autoencoder in two aspects: (1) each instance xi is used to reconstruct a set of instances {xj} rather than itself. (2) The reconstruction error of each instance (||xj -- x'i||2) is weighted by a relational function of xi and xj defined on the learned manifold. Hence, the GAE captures the structure of the data space through minimizing the weighted distances between reconstructed instances and the original ones. The generalized autoencoder provides a general neural network framework for dimensionality reduction. In addition, we propose a multilayer architecture of the generalized autoencoder called deep generalized autoencoder to handle highly complex datasets. Finally, to evaluate the proposed methods, we perform extensive experiments on three datasets. The experiments demonstrate that the proposed methods achieve promising performance.
Image-based visual hulls

In this paper, we describe an efficient image-based approach to computing and shading visual hulls from silhouette image data. Our algorithm takes advantage of epipolar geometry and incremental computation to achieve a constant rendering cost per rendered pixel. It does not suffer from the computation complexity, limited resolution, or quantization artifacts of previous volumetric approaches. We demonstrate the use of this algorithm in a real-time virtualized reality application running off a small number of video streams.
Monte Carlo localization in dense multipath environments using UWB ranging

For most outdoor applications, systems such as GPS provide users with accurate position estimates. However, reliable range-based localization using radio signals in indoor or urban environments can be a problem due to multipath fading and line-of-sight (LOS) blockage. The measurement bias introduced by these delays causes significant localization error, even when using additional sensors such as an inertial measurement unit (IMU) to perform outlier rejection. We describe an algorithm for accurate indoor localization of a sensor in a network of known beacons. The sensor measures the range to the beacons using an Ultra-Wideband (UWB) signal and uses statistical inference to infer and correct for the bias due to LOS blockage in the range measurements. We show that a particle filter can be used to estimate the joint distribution over both pose and beacon biases. We use the particle filter estimation technique specifically to capture the non-linearity of transitions in the beacon bias as the sensor moves. Results using real-world and simulated data are presented.
Experimental Design and Data Analysis for Biologists

An essential textbook for any student or researcher in biology needing to design experiments, sampling programs or analyze the resulting data. The text begins with a revision of estimation and hypothesis testing methods, covering both classical and Bayesian philosophies, before advancing to the analysis of linear and generalized linear models. Topics covered include linear and logistic regression, simple and complex ANOVA models (for factorial, nested, block, split-plot and repeated measures and covariance designs), and log-linear models. Multivariate techniques, including classification and ordination, are then introduced. Special emphasis is placed on checking assumptions, exploratory data analysis and presentation of results. The main analyses are illustrated with many examples from published papers and there is an extensive reference list to both the statistical and biological literature. The book is supported by a website that provides all data sets, questions for each chapter and links to software.
Linguistic Models for Analyzing and Detecting Biased Language

Unbiased language is a requirement for reference sources like encyclopedias and scientific texts. Bias is, nonetheless, ubiquitous, making it crucial to understand its nature and linguistic realization and hence detect bias automatically. To this end we analyze real instances of human edits designed to remove bias from Wikipedia articles. The analysis uncovers two classes of bias: framing bias, such as praising or perspective-specific words, which we link to the literature on subjectivity; and epistemological bias, related to whether propositions that are presupposed or entailed in the text are uncontroversially accepted as true. We identify common linguistic cues for these classes, including factive verbs, implicatives, hedges, and subjective intensifiers. These insights help us develop features for a model to solve a new prediction task of practical importance: given a biased sentence, identify the bias-inducing word. Our linguistically-informed model performs almost as well as humans tested on the same task.
Autonomous Modular Optical Underwater Robot (AMOUR) Design, Prototype and Feasibility Study

We propose a novel modular underwater robot which can self-reconfigure by stacking and unstacking its component modules. Applications for this robot include underwater monitoring, exploration, and surveillance. Our current prototype is a single module which contains several subsystems that later will be segregated into different modules. This robot functions as a testbed for the subsystems which are needed in the modular implementation. We describe the module design and discuss the propulsion, docking, and optical ranging subsystems in detail. Experimental results demonstrate depth control, linear motion, target module detection, and docking capabilities.
Finding Top-k Min-Cost Connected Trees in Databases

It is widely realized that the integration of database and information retrieval techniques will provide users with a wide range of high quality services. In this paper, we study processing an l-keyword query, p1, p1, ..., pl, against a relational database which can be modeled as a weighted graph, G(V, E). Here V is a set of nodes (tuples) and E is a set of edges representing foreign key references between tuples. Let Vi ⊆ V be a set of nodes that contain the keyword pi. We study finding top-k minimum cost connected trees that contain at least one node in every subset Vi, and denote our problem as GST-k When k = 1, it is known as a minimum cost group Steiner tree problem which is NP-complete. We observe that the number of keywords, l, is small, and propose a novel parameterized solution, with l as a parameter, to find the optimal GST-1, in time complexity O(3ln + 2l ((l + logn)n + m)), where n and m are the numbers of nodes and edges in graph G. Our solution can handle graphs with a large number of nodes. Our GST-1 solution can be easily extended to support GST-k, which outperforms the existing GST-k solutions over both weighted undirected/directed graphs. We conducted extensive experimental studies, and report our finding.
The German Version of the Humor Styles Questionnaire: Psychometric Properties and Overlap With Other Styles of Humor

The Humor Styles Questionnaire (HSQ; Martin et al., 2003) is one of the most frequently used questionnaires in humor research and has been adapted to several languages. The HSQ measures four humor styles (affiliative, self-enhancing, aggressive, and self-defeating), which should be adaptive or potentially maladaptive to psychosocial well-being. The present study analyzes the internal consistency, factorial validity, and factorial invariance of the HSQ on the basis of several German-speaking samples combined (total N = 1,101). Separate analyses were conducted for gender (male/female), age groups (16-24, 25-35, >36 years old), and countries (Germany/Switzerland). Internal consistencies were good for the overall sample and the demographic subgroups (.80-.89), with lower values obtained for the aggressive scale (.66-.73). Principal components and confirmatory factor analyses mostly supported the four-factor structure of the HSQ. Weak factorial invariance was found across gender and age groups, while strong factorial invariance was supported across countries. Two subsamples also provided self-ratings on ten styles of humorous conduct (n = 344) and of eight comic styles (n = 285). The four HSQ scales showed small to large correlations to the styles of humorous conduct (-.54 to .65) and small to medium correlations to the comic styles (-.27 to .42). The HSQ shared on average 27.5-35.0% of the variance with the styles of humorous conduct and 13.0-15.0% of the variance with the comic styles. Thus-despite similar labels-these styles of humorous conduct and comic styles differed from the HSQ humor styles.
Feature Selection: Evaluation, Application, and Small Sample Performance

A large number of algorithms have been proposed for feature subset selection. Our experimental results show that the sequential forward oating selection (SFFS) algorithm, proposed by Pudil et al., dominates the other algorithms tested. We study the problem of choosing an optimal feature set for land use classi cation based on SAR satellite images using four di erent texture models. Pooling features derived from di erent texture models, followed by a feature selection results in a substantial improvement in the classi cation accuracy. We also illustrate the dangers of using feature selection in small sample size situations.
Creativity and Artificial Intelligence: A Digital Art Perspective

This paper describes the application of artificial intelligence (AI) to the creation of digital art. AI is a computational paradigm that codifies intelligence into machines. There are generally three types of AI and these are machine learning, evolutionary programming and soft computing. Machine learning is the statistical approach to building intelligent systems. Evolutionary programming is the use of natural evolutionary systems to design intelligent machines. Some of the evolutionary programming systems include genetic algorithm which is inspired by the principles of evolution and swarm optimization which is inspired by the swarming of birds, fish, ants etc. Soft computing includes techniques such as agent based modelling and fuzzy logic. Opportunities on the applications of these to digital art are explored.
Image segmentation for large-scale subcategory flower recognition

We propose a segmentation algorithm for the purposes of large-scale flower species recognition. Our approach is based on identifying potential object regions at the time of detection. We then apply a Laplacian-based segmentation, which is guided by these initially detected regions. More specifically, we show that 1) recognizing parts of the potential object helps the segmentation and makes it more robust to variabilities in both the background and the object appearances, 2) segmenting the object of interest at test time is beneficial for the subsequent recognition. Here we consider a large-scale dataset containing 578 flower species and 250,000 images. This dataset is developed by our team for the purposes of providing a flower recognition application for general use and is the largest in its scale and scope. We tested the proposed segmentation algorithm on the well-known 102 Oxford flowers benchmark [11] and on the new challenging large-scale 578 flower dataset, that we have collected. We observed about 4% improvements in the recognition performance on both datasets compared to the baseline. The algorithm also improves all other known results on the Oxford 102 flower benchmark dataset. Furthermore, our method is both simpler and faster than other related approaches, e.g. [3, 14], and can be potentially applicable to other subcategory recognition datasets.
Statistical Parsing with a Context-Free Grammar and Word Statistics

We describe a parsing system based upon a language model for English that is, in turn, based upon assigning probabilities to possible parses for a sentence. This model is used in a parsing system by nding the parse for the sentence with the highest probability. This system outperforms previous schemes. As this is the third in a series of parsers by di erent authors that are similar enough to invite detailed comparisons but di erent enough to give rise to di erent levels of performance, we also report on some experiments designed to identify what aspects of these systems best explain their relative performance.
Enhancing the Physical Layer Security of Non-Orthogonal Multiple Access in Large-Scale Networks

This paper investigates the physical layer security of non-orthogonal multiple access (NOMA) in large-scale networks with invoking stochastic geometry. Both single-antenna and multiple-antenna aided transmission scenarios are considered, where the base station (BS) communicates with randomly distributed NOMA users. In the single-antenna scenario, we adopt a protected zone around the BS to establish an eavesdropper-exclusion area with the aid of careful channel ordering of the NOMA users. In the multiple-antenna scenario, artificial noise is generated at the BS for further improving the security of a beamforming-aided system. In order to characterize the secrecy performance, we derive new exact expressions of the security outage probability for both single-antenna and multiple-antenna aided scenarios. For the single-antenna scenario, we perform secrecy diversity order analysis of the selected user pair. The analytical results derived demonstrate that the secrecy diversity order is determined by the specific user having the worse channel condition among the selected user pair. For the multiple-antenna scenario, we derive the asymptotic secrecy outage probability, when the number of transmit antennas tends to infinity. Monte Carlo simulations are provided for verifying the analytical results derived and to show that: 1) the security performance of the NOMA networks can be improved by invoking the protected zone and by generating artificial noise at the BS and 2) the asymptotic secrecy outage probability is close to the exact secrecy outage probability.
Orientation Histograms for Hand Gesture Recognition

We present a method to recognize hand gestures, based on a pattern recognition technique developed by McConnell [16] employing histograms of local orientation. We use the orientation histogram as a feature vector for gesture classification and interpolation. For moving or d̈ynamic gestures̈ , the histogram of the spatio-temporal gradients of image intensity form the analogous feature vector and may be useful for dynamic gesture recognition.  Zurich, June, 1995 This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved.
Automatic Soccer Video Event Detection Based on a Deep Neural Network Combined CNN and RNN

Soccer video semantic analysis has attracted a lot of researchers in the last few years. Many methods of machine learning have been applied to this task and have achieved some positive results, but the neural network method has not yet been used to this task from now. Taking into account the advantages of Convolution Neural Network(CNN) in fully exploiting features and the ability of Recurrent Neural Network(RNN) in dealing with the temporal relation, we construct a deep neural network to detect soccer video event in this paper. First we determine the soccer video event boundary which we used Play-Break(PB) segment by the traditional method. Then we extract the semantic features of key frames from PB segment by pre-trained CNN, and at last use RNN to map the semantic features of PB to soccer event types, including goal, goal attempt, card and corner. Because there is no suitable and effective dataset, we classify soccer frame images into nine categories according to their different semantic views and then construct a dataset called Soccer Semantic Image Dataset(SSID) for training CNN. The sufficient experiments evaluated on 30 soccer match videos demonstrate the effectiveness of our method than state-of-art methods.
The Comfort Assessment of Wearable Computers

This paper presents a tool to measure the comfort of wearable computers. The comfort rating scales (CRS) measure wearable comfort across 6 dimensions. These dimensions are Emotion, Attachment, Harm, Perceived change, Movement and Anxiety. This paper also presents two studies in which the CRS have been used to assess the comfort of two types of wearable technology currently being developed at the University of Birmingham, these are the SensVest and the x3. The results of the studies show that the CRS can be used to aid designers and manufactures focus on what modifications are needed to wearable computer design to make them more comfortable. They aZso show that assessments of wearable computer comfort must be made in situations and environments to which the computer will uZtimateZy be
'It's Reducing a Human Being to a Percentage': Perceptions of Justice in Algorithmic Decisions

Data-driven decision-making consequential to individuals raises important questions of accountability and justice. Indeed, European law provides individuals limited rights to 'meaningful information about the logic' behind significant, autonomous decisions such as loan approvals, insurance quotes, and CV filtering. We undertake three experimental studies examining people's perceptions of justice in algorithmic decision-making under different scenarios and explanation styles. Dimensions of justice previously observed in response to human decision-making appear similarly engaged in response to algorithmic decisions. Qualitative analysis identified several concerns and heuristics involved in justice perceptions including arbitrariness, generalisation, and (in)dignity. Quantitative analysis indicates that explanation styles primarily matter to justice perceptions only when subjects are exposed to multiple different styles---under repeated exposure of one style, scenario effects obscure any explanation effects. Our results suggests there may be no 'best' approach to explaining algorithmic decisions, and that reflection on their automated nature both implicates and mitigates justice dimensions.
Digital Ecosystems: Principles and Semantics

Digital ecosystems transcend the traditional, rigorously defined, collaborative environments from centralised, distributed or hybrid models into an open, flexible, domain cluster, demand-driven, interactive environment. A digital ecosystem is a newly networked architecture and collaborative environment that addresses the weakness of client-server, peer-to-peer, grid, and Web services. In this paper we provide an explanation of digital ecosystems, their analogy to ecological systems, architecture, swarm intelligence, and comparison to existing networked architecture. We then describe how digital ecosystems can benefit from semantic Web ontologies and rules. Finally, we discuss issues in the collaboration between semantically neighbouring digital ecosystems.
Dual-polarized Vivaldi antenna with quarter-wave balun feeding

An ultra-wideband Vivaldi antenna with dual-polarization capability is presented. A two-section quarter-wave balun feedline is developed to feed the tapered slot antenna, which improves the impedance matching performance especially in the low frequency regions. The dual-polarization is realized by orthogonally combining two identical Vivaldi antennas without a galvanic contact. Measured results have been presented with a fractional bandwidth of 172% from 0.56 GHz to 7.36 GHz for S11 < −10 dB and a good port isolation of S21 < −22 dB. The measured antenna gain of up to 9.4 dBi and cross-polarization discrimination (XPD) of more than 18 dB is achieved, making the antenna suitable for mobile communication testing in chambers or open-site facilities.