nils-herrmann/clean_scirepeval huggingface dataset:
Classification of Four-Class Motor Imagery Employing Single-Channel Electroencephalography

With advances in brain-computer interface (BCI) research, a portable few- or single-channel BCI system has become necessary. Most recent BCI studies have demonstrated that the common spatial pattern (CSP) algorithm is a powerful tool in extracting features for multiple-class motor imagery. However, since the CSP algorithm requires multi-channel information, it is not suitable for a few- or single-channel system. In this study, we applied a short-time Fourier transform to decompose a single-channel electroencephalography signal into the time-frequency domain and construct multi-channel information. Using the reconstructed data, the CSP was combined with a support vector machine to obtain high classification accuracies from channels of both the sensorimotor and forehead areas. These results suggest that motor imagery can be detected with a single channel not only from the traditional sensorimotor area but also from the forehead area.
Computational Creativity Research: Towards Creative Machines

The notion that software could be independently and usefully creative is becoming more commonplace in scientific, cultural, business and public circles. It is not fanciful to imagine creative software embedded in society in the short to medium term, acting as collaborators and autonomous creative agents for much societal benefit. Technologically, there is still some way to go to enable Artificial Intelligence methods to create artefacts and ideas of value, and to get software to do so in interesting and engaging ways. There are also a number of sociological hurdles to overcome in getting society to accept software as being truly creative, and we concentrate on those here. We discuss the various communities that can be considered stakeholders in the perception of computers being creative or not. In particular, we look in detail at three sets of stakeholders, namely the general public, Computational Creativity researchers and fellow creatives. We put forward various philosophical points which we argue will shape the way in which society accepts creative software. We make various claims along the way about how people perceive software as being creative or not,whichwebelieve should be addressedwith scientific experimentation, and we call on the Computational Creativity research community to do just that. S. Colton (B) · J. Corneli · M. Cook · R. Hepworth Computational Creativity Group, Department of Computing, Goldsmiths College, University of London, London, UK e-mail: s.colton@gold.ac.uk http://ccg.doc.gold.ac.uk A. Pease School of Computing, University of Dundee, Dundee, UK D. Ventura Computer Science Department, Brigham Young University, Provo, USA © Atlantis Press and the authors 2015 T.R. Besold et al. ( Towards Creative Machines, Atlantis Thinking Machines 7, DOI 10.2991/978-94-6239-085-0_1 3
AI tools for software development effort estimation

Software development involves a number of interrelated factors which affect development effort and productivity. Since many of these relationships are not well understood, accurate estimation of so&are development time and effort is a dificult problem. Most estimation models in use or proposed in the literature are based on regression techniques. This paper examines the potential of two artijcial intelligence approaches i.e. artificial neural network and case-based reasoning for creating development effort estimation models. Artijcial neural network can provide accurate estimates when there are complex relationships between variables and where the input data is distorted by high noise levels Case-based reasoning solves problems by adapting solutions from old problems similar to the current problem. This research examines both the performance of back-propagation artificial neural networks in estimating software development effort and the potential of case-based reasoning for development estimation using the same dataset.
Reliable Patch Trackers: Robust visual tracking by exploiting reliable patches

Most modern trackers typically employ a bounding box given in the first frame to track visual objects, where their tracking results are often sensitive to the initialization. In this paper, we propose a new tracking method, Reliable Patch Trackers (RPT), which attempts to identify and exploit the reliable patches that can be tracked effectively through the whole tracking process. Specifically, we present a tracking reliability metric to measure how reliably a patch can be tracked, where a probability model is proposed to estimate the distribution of reliable patches under a sequential Monte Carlo framework. As the reliable patches distributed over the image, we exploit the motion trajectories to distinguish them from the background. Therefore, the visual object can be defined as the clustering of homo-trajectory patches, where a Hough voting-like scheme is employed to estimate the target state. Encouraging experimental results on a large set of sequences showed that the proposed approach is very effective and in comparison to the state-of-the-art trackers. The full source code of our implementation will be publicly available.
Exploiting a Semantic Annotation Tool for Metaphor Analysis

In this paper, we describe the development and exploitation of a corpus-based tool for the identification of metaphorical patterns in large datasets. The analysis of metaphor as a cognitive and cultural, rather than solely linguistic, phenomenon has become central as metaphor researchers working within ‘Cognitive Metaphor Theory’ have drawn attention to the presence of systematic and pervasive conventional metaphorical patterns in ‘ordinary’ language (e.g. I’m at a crossroads in my life).  Theory suggests that these linguistic patterns reflect the existence of conventional conceptual metaphors, namely systematic cross-domain correspondences in conceptual structure (e.g. LIFE IS A JOURNEY). This theoretical approach, described further in section 2, has led to considerable advances in our understanding of metaphor both as a linguistic device and a cognitive model, and to our awareness of its role in many different genres and discourses. Although some recent research has incorporated corpus linguistic techniques into this framework for the analysis of metaphor, to date, such analyses have primarily involved the concordancing of pre-selected search strings (e.g. Deignan 2005). The method described in this paper represents an attempt to extend the limits of this form of analysis. In our approach, we have applied an existing semantic field annotation tool (USAS) developed at Lancaster University to aid metaphor researchers in searching corpora. We are able to filter all possible candidate semantic fields proposed by USAS to assist in finding possible ‘source’ (e.g. JOURNEY) and ‘target’ (e.g. LIFE) domains, and we can then go on to consider the potential metaphoricity of the expressions included under each possible source domain. This method thus enables us to identify open-ended sets of metaphorical expressions, which are not limited to predetermined search strings. In section 3, we present this emerging methodology for the computer-assisted analysis of metaphorical patterns in discourse. The semantic fields automatically annotated by USAS can be seen as roughly corresponding to the domains of metaphor theory. We have used USAS in combination with key word and domain techniques in Wmatrix (Rayson, 2003) to replicate earlier manual analyses, e.g. machine metaphors in Ken Kesey’s One Flew Over the Cuckoo’s Nest (Semino and Swindlehurst, 1996) and war, machine and organism metaphors in business magazines (Koller, 2004a). These studies are described in section 4.
Sever: A Robust Meta-Algorithm for Stochastic Optimization

In high dimensions, most machine learning methods are brittle to even a small fraction of structured outliers. To address this, we introduce a new meta-algorithm that can take in a base learner such as least squares or stochastic gradient descent, and harden the learner to be resistant to outliers. Our method, Sever, possesses strong theoretical guarantees yet is also highly scalable—beyond running the base learner itself, it only requires computing the top singular vector of a certain n×d matrix. We apply Sever on a drug design dataset and a spam classification dataset, and find that in both cases it has substantially greater robustness than several baselines. On the spam dataset, with 1% corruptions, we achieved 7.4% test error, compared to 13.4%− 20.5% for the baselines, and 3% error on the uncorrupted dataset. Similarly, on the drug design dataset, with 10% corruptions, we achieved 1.42 mean-squared error test error, compared to 1.51-2.33 for the baselines, and 1.23 error on the uncorrupted dataset.
Model-Free Episodic Control

State of the art deep reinforcement learning algorithms take many millions of interactions to attain human-level performance. Humans, on the other hand, can very quickly exploit highly rewarding nuances of an environment upon first discovery. In the brain, such rapid learning is thought to depend on the hippocampus and its capacity for episodic memory. Here we investigate whether a simple model of hippocampal episodic control can learn to solve difficult sequential decisionmaking tasks. We demonstrate that it not only attains a highly rewarding strategy significantly faster than state-of-the-art deep reinforcement learning algorithms, but also achieves a higher overall reward on some of the more challenging domains.
code_swarm: A Design Study in Organic Software Visualization

In May of 2008, we published online a series of software visualization videos using a method called code_swarm. Shortly thereafter, we made the code open source and its popularity took off. This paper is a study of our code swarm application, comprising its design, results and public response. We share our design methodology, including why we chose the organic information visualization technique, how we designed for both developers and a casual audience, and what lessons we learned from our experiment. We validate the results produced by code_swarm through a qualitative analysis and by gathering online user comments. Furthermore, we successfully released the code as open source, and the software community used it to visualize their own projects and shared their results as well. In the end, we believe code_swarm has positive implications for the future of organic information design and open source information visualization practice.
Xpipes: a network-on-chip architecture for gigascale systems-on-chip

The growing complexity of embedded multiprocessor architectures for digital media processing will soon require highly scalable communication infrastructures. Packet switched networks-on-chip (NoC) have been proposed to support the trend for systems-on-chip integration. In this paper, an advanced NoC architecture, called Xpipes, targeting high performance and reliable communication for on-chip multi-processors is introduced. It consists of a library of soft macros (switches, network interfaces and links) that are design-time composable and tunable so that domain-specific heterogeneous architectures can be instantiated and synthesized. Links can be pipelined with a flexible number of stages to decouple link throughput from its length and to get arbitrary topologies. Moreover, a tool called XpipesCompiler, which automatically instantiates a customized NoC from the library of soft network components, is used in this paper to test the Xpipes-based synthesis flow for domain-specific communication architectures.
Knowledge exchange using Web 2.0 technologies in NGOs

Members’ knowledge is the most important resource in NGOs and it is important to stimulate it’s exchange. Knowledge that is needed to act in complex situations is hard to exchange, but Web 2.0 technologies provide a toolbox to develop a knowledge management strategy. The paper aims to specify optimal conditions and necessary measures that NGOs should take in order to successfully use Web 2.0 technologies for knowledge exchange.  NGOs and Web 2.0 technologies have specific characteristics in common. It is suggested that these characteristics require certain prerequisites in order to make the use of Web 2.0 technologies successful. Using an iterative and participative design method, a case study is presented that investigated prior boundary conditions and implemented a Web 2.0 based platform as knowledge management strategy in a large NGO.  NGOs and Web 2.0 are based on strong voluntariness, they are democratic, contribution and personality are strongly tied to each other, and education is rarely formal. Therefore, there is a strong need for contact between members, internal motivation and organizational identification, for quality criteria, for protected spaces and patternstructures, if Web 2.0 technologies shall lead to successful knowledge management. The case study demonstrates that considering these circumstances enabled the successful implementation in a large NGO.  The current research helps NGOs to successfully implement Web 2.0 technologies as a means to support knowledge management. A figure with the crucial questions and the means that have to be taken in dependence of the answers is provided.  NGOs do not have the same means to develop formal management strategies as other organizations. Therefore, a thorough understanding of the circumstances that enable knowledge exchange with Web 2.0 technologies is needed. The current research provides information about optimal circumstances and about possibilities how to improve less promising circumstances.
Fully Parsing the Penn Treebank

We present a two stage parser that recovers Penn Treebank style syntactic analyses of new sentences including skeletal syntactic structure, and, for the first time, both function tags and empty categories. The accuracy of the first-stage parser on the standard Parseval metric matches that of the (Collins, 2003) parser on which it is based, despite the data fragmentation caused by the greatly enriched space of possible node labels. This first stage simultaneously achieves near state-of-theart performance on recovering function tags with minimal modifications to the underlying parser, modifying less than ten lines of code. The second stage achieves state-of-the-art performance on the recovery of empty categories by combining a linguistically-informed architecture and a rich feature set with the power of modern machine learning methods.
Verification and Validation of Agent-based Scientific Simulation Models

Most of the existing model verification and validation techniques are largely used in the industrial and system engineering fields. The agent-based modeling approach is different from traditional equation-based modeling approach in many aspects. As the agent-based modeling approach has recently become an attractive and efficient way for modeling large-scale complex systems, there are few formalized validation methodologies existing for model validation. In our proposed work, we design, develop, adapt, and apply various verification and validation techniques to an agent-based scientific model and investigate the sufficiency of these techniques for the validation of agent-based mod-
Accelerated, Parallel and Proximal Coordinate Descent

We propose a new stochastic coordinate descent method for minimizing the sum of convex functions each of which depends on a small number of coordinates only. Our method (APPROX) is simultaneously Accelerated, Parallel and PROXimal; this is the first time such a method is proposed. In the special case when the number of processors is equal to the number of coordinates, the method converges at the rate 2ω̄L̄R/(k+2), where k is the iteration counter, ω̄ is an average degree of separability of the loss function, L̄ is the average of Lipschitz constants associated with the coordinates and individual functions in the sum, and R is the distance of the initial point from the minimizer. We show that the method can be implemented without the need to perform full-dimensional vector operations, which is considered to be the major bottleneck of accelerated coordinate descent. The fact that the method depends on the average degree of separability, and not on the maximum degree of separability, can be attributed to the use of new safe large stepsizes, leading to improved expected separable overapproximation (ESO). These are of independent interest and can be utilized in all existing parallel stochastic coordinate descent algorithms based on the concept of ESO.
Minnesota Satisfaction Questionnaire-Psychometric Properties and Validation in a Population of Portuguese Hospital Workers

Job Satisfaction is usually treated as a collection of feelings or affective responses associated with the job situation, or “simply how people feel about different aspects of their jobs” (Spector, 1997: 2). In 1967, Weiss et al. developed the short version of the MSQ Minnesota Satisfaction Questionnaire, a 5-point Likert-type scale with 20 items. This scale has been widely used in the literature being a well-known and stable over the time instrument with previous researches yielding excellent coefficient alpha. In this study we developed an exploratory factor analysis to assess the factor structure of this scale in a Hospital Workers sample, with N=140, recurring to IBM – PASW and encountered a 2-factor structure. Following this procedure, we conducted a Confirmatory Factor Analysis using AMOS, where our exploratory structure was compared with other structures found in previous studies. This work aims at improving our understanding of the nature and assessment of Job Satisfaction in the Portuguese healthcare context, providing a more stable ground for future research in this area.
Development of "Souryu-IV" and "Souryu-V: " Serially connected crawler vehicles for in-rubble searching operations

The authors have developed Souryu-I, Souryu-II and Souryu-III, connected crawler vehicles that can travel in rubble. These machines were developed for the purpose of finding survivors trapped inside collapsed buildings. However, when conducting experiments in post-disaster environments with Souryu-III, mechanical and control limitations have been identified. This led the authors to develop novel crawler units using crawler tracks strengthened with metal, and develop two improved models, called Souryu-IV composed of three double-sided crawler bodies, a joint driving unit, a blade-spring joint mechanism, and cameras and Souryu-V composed of mono-tread-crawler bodies, elastic-rod-joint mechanisms, and cameras . The authors then conducted basic motion experiments and teleoperated control experiments on off-road fields with Souryu-IV and Souryu-V. Their high performance in experiments of urban rescue operations was confirmed. However, several problems were identified during the driving experiments, and • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • •
An Initial Study of Automatic Curb Ramp Detection with Crowdsourced Verification Using Google Street View Images

In our previous research, we examined whether minimally trained crowd workers could find, categorize, and assess sidewalk accessibility problems using Google Street View (GSV) images. This poster paper presents a first step towards combining automated methods (e.g., machine visionbased curb ramp detectors) in concert with human computation to improve the overall scalability of our approach.
Shannon Entropy based Randomness Measurement and Test for Image Encryption

The quality of image encryption is commonly measured by the Shannon entropy over the ciphertext image. However, this measurement does not consider to the randomness of local image blocks and is inappropriate for scrambling based image encryption methods. In this paper, a new information entropy-based randomness measurement for image encryption is introduced which, for the first time, answers the question of whether a given ciphertext image is sufficiently random-like. It measures the randomness over the ciphertext in a fairer way by calculating the averaged entropy of a series of small image blocks within the entire test image. In order to fulfill both quantitative and qualitative measurement, the expectation and the variance of this averaged block entropy for a true-random image are strictly derived and corresponding numerical reference tables are also provided. Moreover, a hypothesis test at significance α-level is given to help accept or reject the hypothesis that the test image is ideally encrypted/random-like. Simulation results show that the proposed test is able to give both effectively quantitative and qualitative results for image encryption. The same idea can also be applied to measure other digital data, like audio and video.
BELMiner: adapting a rule-based relation extraction system to extract biological expression language statements from bio-medical literature evidence sentences

Extracting meaningful relationships with semantic significance from biomedical literature is often a challenging task. BioCreative V track4 challenge for the first time has organized a comprehensive shared task to test the robustness of the text-mining algorithms in extracting semantically meaningful assertions from the evidence statement in biomedical text. In this work, we tested the ability of a rule-based semantic parser to extract Biological Expression Language (BEL) statements from evidence sentences culled out of biomedical literature as part of BioCreative V Track4 challenge. The system achieved an overall best F-measure of 21.29% in extracting the complete BEL statement. For relation extraction, the system achieved an F-measure of 65.13% on test data set. Our system achieved the best performance in five of the six criteria that was adopted for evaluation by the task organizers. Lack of ability to derive semantic inferences, limitation in the rule sets to map the textual extractions to BEL function were some of the reasons for low performance in extracting the complete BEL statement. Post shared task we also evaluated the impact of differential NER components on the ability to extract BEL statements on the test data sets besides making a single change in the rule sets that translate relation extractions into a BEL statement. There is a marked improvement by over 20% in the overall performance of the BELMiner's capability to extract BEL statement on the test set. The system is available as a REST-API at http://54.146.11.205:8484/BELXtractor/finder/.


Database URL
Towards Real World Human Parsing: Multiple-Human Parsing in the Wild

The recent progress of human parsing techniques has been largely driven by the availability of rich data resources. In this work, we demonstrate some critical discrepancies between the current benchmark datasets and the real world human parsing scenarios. For instance, all the human parsing datasets only contain one person per image, while usually multiple persons appear simultaneously in a realistic scene. It is more practically demanded to simultaneously parse multiple persons, which presents a greater challenge to modern human parsing methods. Unfortunately, absence of relevant data resources severely impedes the development of multiple-human parsing methods. To facilitate future human parsing research, we introduce the Multiple-Human Parsing (MHP) dataset, which contains multiple persons in a real world scene per single image. The MHP dataset contains various numbers of persons (from 2 to 16) per image with 18 semantic classes for each parsing annotation. Persons appearing in the MHP images present sufficient variations in pose, occlusion and interaction. To tackle the multiple-human parsing problem, we also propose a novel Multiple-Human Parser (MH-Parser), which considers both the global context and local cues for each person in the parsing process. The model is demonstrated to outperform the naive “detect-and-parse” approach by a large margin, which will serve as a solid baseline and help drive the future research in real world human parsing.