nils-herrmann/clean_scirepeval huggingface dataset:
The relationship between energy, phase charge, impedance, and perceived sensation in electrotactile stimulation

Electrotactile stimulation is a common method of sensory substitution and haptic feedback. One problem with this method has been the large variability in perceived sensation that derives from changes in the impedance of the electrode-skin interface. One way to reduce this variability is to modulate stimulation parameters (current amplitude and pulse duration) in response to impedance changes, which are reflected in the time domain by changes in measured peak resistance, Rp. To work well, this approach requires knowing precisely the relationship between stimulation parameters, peak resistance, and perceived sensation. In this paper, experimental results show that at a constant level of perceived sensation there are linear relationships between Rp and both peak pulse energy, Ep, and phase charge, Q, from which stimulation parameters are easily computed. These linear relationships held across different subjects, sessions, magnitudes of sensation, stimulation locations, and electrode sizes. The average R2 values for these linear relationships were 0.957 for Ep vs. Rp and 0.960 for Q vs. Rp, indicating a nearly perfect fit.
Game Theory with Learning for Cyber Security Monitoring

Recent attacks show that threats to cyber infrastructure are not only increasing in volume, but are getting more sophisticated. The attacks may comprise multiple actions that are hard to differentiate from benign activity, and therefore common detection techniques have to deal with high false positive rates. Because of the imperfect performance of automated detection techniques, responses to such attacks are highly dependent on human-driven decision-making processes. While game theory has been applied to many problems that require rational decisionmaking, we find limitation on applying such method on security games when the defender has limited information about the opponent's strategies and payoffs. In this work, we propose Q-Learning to react automatically to the adversarial behavior of a suspicious user to secure the system. This work compares variations of Q-Learning with a traditional stochastic game. Simulation results show the possibility of Naive Q-Learning, despite restricted information on opponents.
How Can Memory-Augmented Neural Networks Pass a False-Belief Task?

A question-answering system needs to be able to reason about unobserved causes in order to answer questions of the sort that people face in everyday conversations. Recent neural network models that incorporate explicit memory and attention mechanisms have taken steps towards this capability. However, these models have not been tested in scenarios for which reasoning about the unobservable mental states of other agents is necessary to answer a question. We propose a new set of tasks inspired by the well-known false-belief test to examine how a recent question-answering model performs in situations that require reasoning about latent mental states. We find that the model is only successful when the training and test data bear substantial similarity, as it memorizes how to answer specific questions and cannot reason about the causal relationship between actions and latent mental states. We introduce an extension to the model that explicitly simulates the mental representations of different participants in a reasoning task, and show that this capacity increases the model’s performance on our theory of mind test.
A survey on distribution system feeder reconfiguration: Objectives and solutions

Feeder reconfiguration (FRC) is an important function of distribution automation system. It modifies the topology of distribution network through changing the open/close statuses of tie switches and sectionalizing switches. The change of topology redirects the power flow within the distribution network, in order to obtain a better performance of the system. Various methods have been explored to solve FRC problems. This paper presents a literature survey on distribution system FRC. Among many aspects to be reviewed for a comprehensive study, this paper focuses on FRC objectives and solution methods. The problem definition of FRC is first discussed, the objectives are summarized, and various solution methods are categorized and evaluated.
Signed Distance Fields : A Natural Representation for Both Mapping and Planning

How to represent a map of the environment is a key question of robotics. In this paper, we focus on suggesting a representation well-suited for online map building from vision-based data and online planning in 3D. We propose to combine a commonly-used representation in computer graphics and surface reconstruction, projective Truncated Signed Distance Field (TSDF), with a representation frequently used for collision checking and collision costs in planning, Euclidean Signed Distance Field (ESDF), and validate this combined approach in simulation. We argue that this type of map is better-suited for robotic applications than existing representations.
CD-REST: a system for extracting chemical-induced disease relation in literature

Mining chemical-induced disease relations embedded in the vast biomedical literature could facilitate a wide range of computational biomedical applications, such as pharmacovigilance. The BioCreative V organized a Chemical Disease Relation (CDR) Track regarding chemical-induced disease relation extraction from biomedical literature in 2015. We participated in all subtasks of this challenge. In this article, we present our participation system Chemical Disease Relation Extraction SysTem (CD-REST), an end-to-end system for extracting chemical-induced disease relations in biomedical literature. CD-REST consists of two main components: (1) a chemical and disease named entity recognition and normalization module, which employs the Conditional Random Fields algorithm for entity recognition and a Vector Space Model-based approach for normalization; and (2) a relation extraction module that classifies both sentence-level and document-level candidate drug-disease pairs by support vector machines. Our system achieved the best performance on the chemical-induced disease relation extraction subtask in the BioCreative V CDR Track, demonstrating the effectiveness of our proposed machine learning-based approaches for automatic extraction of chemical-induced disease relations in biomedical literature. The CD-REST system provides web services using HTTP POST request. The web services can be accessed fromhttp://clinicalnlptool.com/cdr The online CD-REST demonstration system is available athttp://clinicalnlptool.com/cdr/cdr.html. Database URL:http://clinicalnlptool.com/cdr;http://clinicalnlptool.com/cdr/cdr.html.
Modelling of traffic situations at urban intersections with probabilistic non-parametric regression

Driving intention recognition and trajectory prediction of moving vehicles are two important requirements of future advanced driver assistance systems (ADAS) for urban intersections. In this paper, we present a consistent framework for solving these two problems. The key idea is to model the spatio-temporal dependencies of traffic situations with a two-dimensional Gaussian process regression. With this representation the driving intention can be recognized by evaluating the data likelihood for each individual regression model. For the trajectory prediction purpose, we transform these regression models into the corresponding dynamical models and combine them with Unscented Kalman Filters (UKF) to overcome the non-linear issue. We evaluate our framework with data collected from real traffic scenarios and show that our approach can be used for recognition of different driving intentions and for long-term trajectory prediction of traffic situations occurring at urban intersections.
Multiagent cooperation and competition with deep reinforcement learning

Evolution of cooperation and competition can appear when multiple adaptive agents share a biological, social, or technological niche. In the present work we study how cooperation and competition emerge between autonomous agents that learn by reinforcement while using only their raw visual input as the state representation. In particular, we extend the Deep Q-Learning framework to multiagent environments to investigate the interaction between two learning agents in the well-known video game Pong. By manipulating the classical rewarding scheme of Pong we show how competitive and collaborative behaviors emerge. We also describe the progression from competitive to collaborative behavior when the incentive to cooperate is increased. Finally we show how learning by playing against another adaptive agent, instead of against a hard-wired algorithm, results in more robust strategies. The present work shows that Deep Q-Networks can become a useful tool for studying decentralized learning of multiagent systems coping with high-dimensional environments.
Analogy Events: How Examples are Used During Problem Solving

In attempting to fit a model of analogical problem solving to protocol data of students solving physics problems, several unexpected observations were made. Analogies between examples and exercises (a form of case-based reasoning) consisted of two distinct types of events. During an initialization event, the solver retrieved an example, set up a mapping between it and the problem, and decided whether the example was useful. During a transfer event, the solver inferred something about the problem’s solution. Many different types of initialization and transfer events were observed. Poor solvers tended to follow the example verbatim, copying each solution line over to the problem. Good solvers tried to solve the problem themselves, but referred to the example when they got stuck, or wanted to check a step, or wanted to avoid a detailed calculation. Rather than learn from analogies, both Good and Poor solvers tended to repeat analogies at subsequent similar situations. A revised version of the model is proposed (but not yet implemented) that appears to be consistent with all the findings observed in this and other studies of the same subjects.
User experience - a research agenda

s (New York: ACM), pp. 1617 – 20. MASLOW, A.H., 1954,Motivation and personality (New York: Harper). MCDONAGH, D., HEKKERT, P., VAN ERP, J. and GYI, D. (Eds), 2003,  The Experience of Everyday Things (. MILLARD, N., HOLE, L. and CROWLE, S., 1999, Smiling through: motivation at the user interface. In Proceedings of the HCI International’99, Volume 2 (pp. 824 – 8) (Mahwah, NJ, London: Lawrence Erlbaum Associates). NORMAN, D., 2004a,  Why we love (or hate) everyday things (New York: Basic Books). NORMAN, D., 2004b, Introduction to this special section on beauty, goodness, and usability. Human Computer Interaction, 19, pp. 311 – 18. OVERBEEKE, C.J., DJAJADININGRAT, J.P., HUMMELS, C.C.M. and WENSVEEN, S.A.G., 2002, Beauty in Usability: Forget about ease of use! In Pleasure with products: Beyond usability, W. Green and P. Jordan (Eds), pp. 9 – 18 (. 96 M. Hassenzahl and N. Tractinsky D ow nl oa de d by [ M as se y U ni ve rs ity L ib ra ry ] at 2 1: 34 2 3 Ju ly 2 01 1 PICARD, R., 1997, Affective computing (Cambridge, MA: MIT Press). PICARD, R. and KLEIN,  2002, Computers that recognise and respond to user emotion: theoretical and practical implications. Interacting with Computers, 14, pp. 141 – 69. POSTREL, V., 2002, The substance of style (New York: Harper Collins). SELIGMAN, M.E.P. and CSIKSZENTMIHALYI, M., 2000, Positive Psychology: An Introduction. American Psychologist, 55, pp. 5 – 14. SHELDON, K.M., ELLIOT, A.J., KIM, Y. and KASSER, T., 2001, What is satisfying about satisfying events? Testing 10 candidate psychological needs. Journal of Personality and Social Psychology, 80, pp. 325 – 39. SINGH, S.N. and DALAL, N.P., 1999, Web home pages as advertisements. Communications of the ACM, 42, pp. 91 – 8. SUH, E., DIENER, E. and FUJITA, F., 1996, Events and subjective well-being: Only recent events matter. Journal of Personality and Social Psychology,
ContikiSec: A Secure Network Layer for Wireless Sensor Networks under the Contiki Operating System

In this paper we introduce ContikiSec, a secure network layer for wireless sensor networks, designed for the Contiki Operating System. ContikiSec has a configurable design, providing three security modes st arting from confidentiality and integrity, and expanding to confidentiality, authentication, and integrity. ContikiSec has been designed to balanc e low energy consumption and security while conforming to a small memory footprint. Our design was based on performance evaluation of existing se curity primitives and is part of the contribution of this paper. Our evaluat ion was performed in the Modular Sensor Board hardware platform for wireles s s nsor networks, running Contiki.  that is widely us ed in WSNs.
Computationally Manageable Combinational Auctions

There is interest in designing simultaneous auctions for situations such as the recent FCC radio spectrum auctions, in which the value of assets to a bidder depends on which other assets he or she wins. In such auctions, bidders may wish to submit bids for combinations of assets. When this is allowed, the problem of determining the revenue maximizing set of nonconflicting bids can be difficult. We analyze this problem, identifying several different structures of permitted combinational bids for which computational tractability is constructively demonstrated and some structures for which computational tractability cannot be guaranteed.  Bidding with Synergies; Computational Complexity)
Comprehending figurative referential descriptions.

A common way of referring to people is with figurative language. People can be referred to metaphorically, as in calling a terrible boxer "a creampuff," or metonymically, as in calling a naval admiral "the brass." The present studies investigated the anaphoric inferences that occur during comprehension of figurative referential descriptions. Subjects read short narratives, each ending in either a literal or figurative description of another person. Immediately after the last line of each text, the anaphoric antecedent for the description of another person. Immediately after the last line of each text, the anaphoric antecedent for the description was presented in a probe recognition task. The results of three experiments indicated that metaphoric and metonymic referential descriptions reinstate their antecedents in the course of comprehension. Subjects were faster at reinstating the antecedents for literal referential descriptions than at reinstating metaphoric and metonymic descriptions. Moreover, people understand metaphoric referential descriptions more easily than they do metonymic ones. The implications of these findings for theories of anaphora resolution and figurative language comprehension are discussed.
Quantitative geography : perspectives on spatial data analysis by

The authors of this text are at the vanguard of progress in quantitative human geography. Their work provides a welcome and refreshing insight into current research. The authors, and their publishers, are to be congratulated on the high quality of this publication: both in terms of textual layout, and the quality of the figures. The book is organised into 10 chapters, of which the first and last are contextual rather than technical. Chapters 2, 3, and 4 cover basic concepts of spatial data, GIS, and visual representation. There are then major contributions on local analysis techniques, point pattern analysis, spatial regression and geostatistical models, statistical inference of spatial data, and the relationship between spatial modelling and the evolution of spatial theory. In the preface and the first chapter, the authors provide a powerful defence of the importance of quantitative geography and lose no time (page 2!) in suggesting that many of the critics of quantitative approaches are not writing from an informed perspective. Other critics may have moved away from the limitations of quantitative approaches that are now several decades old, and the authors hope to persuade them that substantial developments in theory, technique, and application are worthy of a new evaluation. Having injected some considerable vitality into the work, chapters 2 and 3 are, although perfectly competent, a little pedestrian and revisit some themes that a cynical reader would immediately identify as`traditional'. I think I might have been tempted to hide map projections , for example, somewhere in an appendix. However, it is reassuring to see a pragmatic approach to GIS. One of the unfortunate spin-offs of the widespread availability of desktop systems to the untrained user is a growth in inappropriate application, and in fundamental errors. Chapter 4 eventually gathers pace, inducing guilt in all those, like myself, who are often content to use a basic choropleth map. There was scope here to stimulate the reader a little more with some colourful multimedia dynamic approaches. Chapters 5 ^ 9 are comprehensive coverages of the bodies of work produced by the authors over the last few years. Geographically weighted regression is now well developed, and the comparison between this and the spatial expansion method is particularly instructive. It is encouraging to note the maintenance of a website on this work, and perhaps more such references could have been included (for example, to the work at the Centre for Computational …
Text information extraction in images and video: a survey

Text data present in images and video contain useful information for automatic annotation, indexing, and structuring of images. Extraction of this information involves detection, localization, tracking, extraction, enhancement, and recognition of the text from a given image. However, variations of text due to differences in size, style, orientation, and alignment, as well as low image contrast and complex background make the problem of automatic text extraction extremely challenging. While comprehensive surveys of related problems such as face detection, document analysis, and image & video indexing can be found, the problem of text information extraction is not well surveyed. A large number of techniques have been proposed to address this problem, and the purpose of this paper is to classify and review these algorithms, discuss benchmark data and performance evaluation, and to point out promising directions for future research.
Learning in the Presence of Concept Drift and Hidden Contexts

On-line learning in domains where the target concept depends on some hidden context poses serious problems. A changing context can induce changes in the target concepts, producing what is known as concept drift. We describe a family of learning algorithms that flexibly react to concept drift and can take advantage of situations where contexts reappear. The general approach underlying all these algorithms consists of (1) keeping only a window of currently trusted examples and hypotheses; (2) storing concept descriptions and re-using them when a previous context re-appears; and (3) controlling both of these functions by a heuristic that constantly monitors the system's behavior. The paper reports on experiments that test the systems' performance under various conditions such as different levels of noise and different extent and rate of concept drift.
Label Efficient Learning of Transferable Representations across Domains and Tasks

We propose a framework that learns a representation transferable across different domains and tasks in a label efficient manner. Our approach battles domain shift with a domain adversarial loss, and generalizes the embedding to novel task using a metric learning-based approach. Our model is simultaneously optimized on labeled source data and unlabeled or sparsely labeled data in the target domain. Our method shows compelling results on novel classes within a new domain even when only a few labeled examples per class are available, outperforming the prevalent fine-tuning approach. In addition, we demonstrate the effectiveness of our framework on the transfer learning task from image object recognition to video action recognition.
Communicating emotions and mental states to robots in a real time parallel framework using Laban movement analysis

This paper presents a parallel real time framework for emotions and mental states extraction and recognition from video fragments of human movements. In the experimental setup human hands are tracked by evaluation of moving skin-colored objects. The tracking analysis demonstrates that acceleration and frequency characteristics of the traced objects are relevant for classification of the emotional expressiveness of human movements. The outcomes of the emotional and mental states recognition are cross-validatedwith the analysis of two independent certifiedmovement analysts (CMA’s) who use the Laban movement analysis (LMA) method. We argue that LMA based computer analysis can serve as a common language for expressing and interpreting emotional movements between robots and humans, and in that way it resembles the common coding principle between action and perception by humans and primates that is embodied by themirror neuron system. The solution is part of a larger project on interaction between a human and a humanoid robot with the aim of training social behavioral skills to autistic children with robots acting in a natural environment.
V3: Unsupervised Generation of Domain Aspect Terms for Aspect Based Sentiment Analysis

This paper presents V3, an unsupervised system for aspect-based Sentiment Analysis when evaluated on the SemEval 2014 Task 4. V3 focuses on generating a list of aspect terms for a new domain using a collection of raw texts from the domain. We also implement a very basic approach to classify the aspect terms into categories and assign polarities to them.
Parametric Image Alignment Using Enhanced Correlation Coefficient Maximization

In this work we propose the use of a modified version of the correlation coefficient as a performance criterion for the image alignment problem. The proposed modification has the desirable characteristic of being invariant with respect to photometric distortions. Since the resulting similarity measure is a nonlinear function of the warp parameters, we develop two iterative schemes for its maximization, one based on the forward additive approach and the second on the inverse compositional method. As it is customary in iterative optimization, in each iteration the nonlinear objective function is approximated by an alternative expression for which the corresponding optimization is simple. In our case we propose an efficient approximation that leads to a closed form solution (per iteration) which is of low computational complexity, the latter property being particularly strong in our inverse version. The proposed schemes are tested against the forward additive Lucas-Kanade and the simultaneous inverse compositional algorithm through simulations. Under noisy conditions and photometric distortions our forward version achieves more accurate alignments and exhibits faster convergence whereas our inverse version has similar performance as the simultaneous inverse compositional algorithm but at a lower computational complexity.