nils-herrmann/clean_scirepeval huggingface dataset:
Blockchain Voting and its effects on Election Transparency and Voter Confidence

Public elections are one of the basis upon which representative democracy is built. Thus, it is of the utmost importance that governments and organizations are able to successfully hold non-fraudulent representative elections. Several methods are employed in order to allow citizens to cast their votes, such as Ballot-based voting, purely electronic methods, and Electronic Voting Machines, among others. However, we argue that current methods, specially those based on electronic platforms, provide unsatisfactory levels of transparency to voters, thus harming the trust voters have that the vote they cast was the same one counted by election officials, a problem known as voter confidence. Instead of stepping back to traditional and inefficient offline strategies, we suggest the modernization of State structures by the use of emerging technologies. In this research, we explore the possibility of using Blockchain technology to help in solving those transparency and confidence problems. First, we give an overview of Blockchain itself and other uses focused on societal problems and their respective analysis. We then analyze how the adoption of Blockchain into a digital government repertoire can contribute to common e-voting issues and also promote elections transparency, increase auditability, enhance voter confidence and strengthen democracy. By attending to this poster presentation, visitors will have a clear understanding of what is Blockchain and its basic concepts, why are market and researchers so excited about it, how it can help to solve common voting systems issues, who is already using it and the benefits and potential risks of its adoption.
An Approach to Forecasting QoS Attributes of Web Services Based on ARIMA and GARCH Models

Availability of several web services having a similar functionality has led to using quality of service (QoS) attributes to support services selection and management. To improve these operations and be performed proactively, time series ARIMA models have been used to forecast the future QoS values. However, the problem is that in this extremely dynamic context the observed QoS measures are characterized by a high volatility and time-varying variation to the extent that existing ARIMA models cannot guarantee accurate QoS forecasting where these models are based on a homogeneity (constant variation over time) assumption, which can introduce critical problems such as proactively selecting a wrong service and triggering unrequired adaptations and thus leading to follow-up failures and increased costs. To address this limitation, we propose a forecasting approach that integrates ARIMA and GARCH models to be able to capture the QoS attributes' volatility and provide accurate forecasts. Using QoS datasets of real-world web services we evaluate the accuracy and performance aspects of the proposed approach. Results show that the proposed approach outperforms the popular existing ARIMA models and improves the forecasting accuracy of QoS measures and violations by on average 28.7% and 15.3% respectively.
The Neural Hawkes Process: A Neurally Self-Modulating Multivariate Point Process

Many events occur in the world. Some event types are stochastically excited or inhibited—in the sense of having their probabilities elevated or decreased—by patterns in the sequence of previous events. Discovering such patterns can help us predict which type of event will happen next and when. We model streams of discrete events in continuous time, by constructing a neurally self-modulating multivariate point process in which the intensities of multiple event types evolve according to a novel continuous-time LSTM. This generative model allows past events to influence the future in complex and realistic ways, by conditioning future event intensities on the hidden state of a recurrent neural network that has consumed the stream of past events. Our model has desirable qualitative properties. It achieves competitive likelihood and predictive accuracy on real and synthetic datasets, including under missing-data conditions.
Interpretable to Whom? A Role-based Model for Analyzing Interpretable Machine Learning Systems

Several researchers have argued that a machine learning system’s interpretability should be defined in relation to a specific agent or task: we should not ask if the system is interpretable, but to whom is it interpretable. We describe a model intended to help answer this question, by identifying different roles that agents can fulfill in relation to the machine learning system. We illustrate the use of our model in a variety of scenarios, exploring how an agent’s role influences its goals, and the implications for defining interpretability. Finally, we make suggestions for how our model could be useful to interpretability researchers, system developers, and regulatory bodies auditing machine learning systems.
Text-Enhanced Representation Learning for Knowledge Graph

Learning the representations of a knowledge graph has attracted significant research interest in the field of intelligent Web. By regarding each relation as one translation from head entity to tail entity, translation-based methods including TransE, TransH and TransR are simple, effective and achieving the state-of-the-art performance. However, they still suffer the following issues: (i) low performance when modeling 1-to-N, N-to-1 and Nto-N relations. (ii) limited performance due to the structure sparseness of the knowledge graph. In this paper, we propose a novel knowledge graph representation learning method by taking advantage of the rich context information in a text corpus. The rich textual context information is incorporated to expand the semantic structure of the knowledge graph and each relation is enabled to own different representations for different head and tail entities to better handle 1-to-N, N-to-1 and N-to-N relations. Experiments on multiple benchmark datasets show that our proposed method successfully addresses the above issues and significantly outperforms the state-of-the-art methods.
A Discourse-Driven Content Model for Summarising Scientific Articles Evaluated in a Complex Question Answering Task

We present a method which exploits automatically generated scientific discourse annotations to create a content model for the summarisation of scientific articles. Full papers are first automatically annotated using the CoreSC scheme, which captures 11 contentbased concepts such as Hypothesis, Result, Conclusion etc at the sentence level. A content model which follows the sequence of CoreSC categories observed in abstracts is used to provide the skeleton of the summary, making a distinction between dependent and independent categories. Summary creation is also guided by the distribution of CoreSC categories found in the full articles, in order to adequately represent the article content. Finally, we demonstrate the usefulness of the summaries by evaluating them in a complex question answering task. Results are very encouraging as summaries of papers from automatically obtained CoreSCs enable experts to answer 66% of complex content-related questions designed on the basis of paper abstracts. The questions were answered with a precision of 75%, where the upper bound for human summaries (abstracts) was 95%.
Termite: visualization techniques for assessing textual topic models

Topic models aid analysis of text corpora by identifying latent topics based on co-occurring words. Real-world deployments of topic models, however, often require intensive expert verification and model refinement. In this paper we present Termite, a visual analysis tool for assessing topic model quality. Termite uses a tabular layout to promote comparison of terms both within and across latent topics. We contribute a novel saliency measure for selecting relevant terms and a seriation algorithm that both reveals clustering structure and promotes the legibility of related terms. In a series of examples, we demonstrate how Termite allows analysts to identify coherent and significant themes.
Fingertip Tactile Devices for Virtual Object Manipulation and Exploration

One of the main barriers to immersivity during object manipulation in virtual reality is the lack of realistic haptic feedback. Our goal is to convey compelling interactions with virtual objects, such as grasping, squeezing, pressing, lifting, and stroking, without requiring a bulky, world-grounded kinesthetic feedback device (traditional haptics) or the use of predetermined passive objects (haptic retargeting). To achieve this, we use a pair of finger-mounted haptic feedback devices that deform the skin on the fingertips to convey cutaneous force information from object manipulation. We show that users can perceive differences in virtual object weight and that they apply increasing grasp forces when lifting virtual objects as rendered mass is increased. Moreover, we show how naive users perceive changes of a virtual object's physical properties when we use skin deformation to render objects with varying mass, friction, and stiffness. These studies demonstrate that fingertip skin deformation devices can provide a compelling haptic experience appropriate for virtual reality scenarios involving object manipulation.
A Review of Power Decoupling Techniques for Microinverters With Three Different Decoupling Capacitor Locations in PV Systems

The reliability of the microinverter is a very important feature that will determine the reliability of the ac-module photovoltaic (PV) system. Recently, many topologies and techniques have been proposed to improve its reliability. This paper presents a thorough study for different power decoupling techniques in single-phase microinverters for grid-tie PV applications. These power decoupling techniques are categorized into three groups in terms of the decoupling capacitor locations: 1) PV-side decoupling; 2) dc-link decoupling; and 3) ac-side decoupling. Various techniques and topologies are presented, compared, and scrutinized in scope of the size of decoupling capacitor, efficiency, and control complexity. Also, a systematic performance comparison is presented for potential power decoupling topologies and techniques.
Automatic 3D ear reconstruction based on binocular stereo vision

This paper presents an automatic 3D ear reconstruction method based on binocular stereo vision. At first, we calibrate the stereo vision system by Zhang's method. Then the quasi-dense matching method is performed. We use SIFT feature based matching approach and the coarse to fine strategy to compute the seed matches. The adapted match propagation algorithm with known epipolar geometry constraint is used for obtain quasi-dense correspondence points. Finally the 3D ear model can be reconstructed by triangulation and the results are optimized using the bundle adjustment technique. Extensive experimental results have shown that our proposed method can obtain denser 3D ear model than Liu's multi-view based method. It can get sufficient 3D ear points with lower cost and higher efficiency.
Theory of quantum annealing of an Ising spin glass.

Probing the lowest energy configuration of a complex system by quantum annealing was recently found to be more effective than its classical, thermal counterpart. By comparing classical and quantum Monte Carlo annealing protocols on the two-dimensional random Ising model (a prototype spin glass), we confirm the superiority of quantum annealing relative to classical annealing. We also propose a theory of quantum annealing based on a cascade of Landau-Zener tunneling events. For both classical and quantum annealing, the residual energy after annealing is inversely proportional to a power of the logarithm of the annealing time, but the quantum case has a larger power that makes it faster.
MOOC Leaner Motivation and Learning Pattern Discovery

The landscape of online learning has evolved in a synchronous fashion with the development of the every-growing repertoire of technologies, especially with the recent addition of Massive Online Open Courses (MOOCs). Since MOOC platforms allow thousands of students to participate at the same time, MOOC participants can have fairly varied motivation. Meanwhile, a low course completion rate has been observed across different MOOC platforms. The first and initiated stage of the proposed research here is a preliminary attempt to study how different motivational aspects of MOOC learners correlate with course participation and completion, with motivation measured using a survey and participation measured using log analytics. The exploratory stage of the study has been conducted within the context of an educational data mining MOOC, within Coursera. In the long run, research results can be expected to inform future interventions, and the design of MOOCs, as well as increasing understanding of the emergent needs of MOOC learners as data collection extends beyond the current scope by incorporating wider disciplinary areas.
Creating personalized systems that people can scrutinize and control: Drivers, principles and experience

Widespread personalized computing systems play an already important and fast-growing role in diverse contexts, such as location-based services, recommenders, commercial Web-based services, and teaching systems. The personalization in these systems is driven by information about the user, a user model. Moreover, as computers become both ubiquitous and pervasive, personalization operates across the many devices and information stores that constitute the user's personal digital ecosystem. This enables personalization, and the user models driving it, to play an increasing role in people's everyday lives. This makes it critical to establish ways to address key problems of personalization related to privacy, invisibility of personalization, errors in user models, wasted user models, and the broad issue of enabling people to control their user models and associated personalization. We offer scrutable user models as a foundation for tackling these problems.
 This article argues the importance of scrutable user modeling and personalization, illustrating key elements in case studies from our work. We then identify the broad roles for scrutable user models. The article describes how to tackle the technical and interface challenges of designing and building scrutable user modeling systems, presenting design principles and showing how they were established over our twenty years of work on the Personis software framework. Our contributions are the set of principles for scrutable personalization linked to our experience from creating and evaluating frameworks and associated applications built upon them. These constitute a general approach to tackling problems of personalization by enabling users to scrutinize their user models as a basis for understanding and controlling personalization.
A reduced QWERTY keyboard for mobile text entry

In this paper we describe a specialized keyboard for text entry that maps four rows of a standard keyboard onto the home row, with different characters encoded via modifier keys and multi-tap input. Use of the keyboard also relies on lexicon-based disambiguation. This design has two motivations: limiting physical space requirements and capitalizing on user knowledge of the standard QWERTY keyboard layout. The resulting "stick" keyboard is between 15% and 25% of the size of a standard keyboard. In a preliminary empirical study, users reached half of their normal typing speed using lexicon-based disambiguation (22.5 wpm) and a reasonable but lower speed with multi-tap input (10.4 wpm) with only a few minutes of practice.
Copyright compliance and infringement in ResearchGate full-text journal articles

ResearchGate is increasingly used by scholars to upload the full-text of their articles and make them freely available for everyone. This study aims to investigate the extent to which ResearchGate members as authors of journal articles comply with publishers’ copyright policies when they self-archive full-text of their articles on ResearchGate. A random sample of 500 English journal articles available as full-text on ResearchGate were investigated. 108 articles (21.6%) were open access (OA) published in OA journals or hybrid journals. Of the remaining 392 articles, 61 (15.6%) were preprint, 24 (6.1%) were post-print and 307 (78.3%) were published (publisher) PDF. The key finding was that 201 (51.3%) out of 392 non-OA articles infringed the copyright and were non-compliant with publishers’ policy. While 88.3% of journals allowed some form of self-archiving (SHERPA/RoMEO green, blue or yellow journals), the majority of non-compliant cases (97.5%) occurred when authors self-archived publishers’ PDF files . This indicates that authors infringe copyright most of the time not because they are not allowed to self-archive, but because they use the wrong version, which might imply their lack of understanding of copyright policies and/or complexity and diversity of policies.
Extracting and Ranking Travel Tips from User-Generated Reviews

User-generated reviews are a key driving force behind some of the leading websites, such as Amazon, TripAdvisor, and Yelp. Yet, the proliferation of user reviews in such sites also poses an information overload challenge: many items, especially popular ones, have a large number of reviews, which cannot all be read by the user. In this work, we propose to extract short practical tips from user reviews. We focus on tips for travel attractions extracted from user reviews on TripAdvisor. Our method infers a list of templates from a small gold set of tips and applies them to user reviews to extract tip candidates. For each attraction, the associated candidates are then ranked according to their predicted usefulness. Evaluation based on labeling by professional annotators shows that our method produces high-quality tips, with good coverage of cities and attractions.
Context Contrasted Feature and Gated Multi-scale Aggregation for Scene Segmentation

Scene segmentation is a challenging task as it need label every pixel in the image. It is crucial to exploit discriminative context and aggregate multi-scale features to achieve better segmentation. In this paper, we first propose a novel context contrasted local feature that not only leverages the informative context but also spotlights the local information in contrast to the context. The proposed context contrasted local feature greatly improves the parsing performance, especially for inconspicuous objects and background stuff. Furthermore, we propose a scheme of gated sum to selectively aggregate multi-scale features for each spatial position. The gates in this scheme control the information flow of different scale features. Their values are generated from the testing image by the proposed network learnt from the training data so that they are adaptive not only to the training data, but also to the specific testing image. Without bells and whistles, the proposed approach achieves the state-of-the-arts consistently on the three popular scene segmentation datasets, Pascal Context, SUN-RGBD and COCO Stuff.
Challenges for Brain­Computer Interface Research for Human­Computer Interaction Applications

Electro-encephalography (EEG) is unique among functional brain imaging methods in that it promises a means of providing a cost-e cient, safe, portable and easy to use braincomputer interface (BCI) for both healthy users and the disabled. While an already extensive corpus of EEG-BCI experimental work has demonstrated that, to a degree, a person's mental states can be detected in single-trials using sophisticated mathematical tools, clear challenges for the use in human-computer interaction applications have also been outlined. He we discuss challenges, which are relevant for the use of BCIs in wider range of applications beyond rehabilitation.
Brain Tumor Extraction in MRI images using Clustering and Morphological Operations Techniques

In this paper, Magnetic Resonance Images,T2 weighte d modality , have been pre-processed by bilateral filter to reduce th e noise and maintaining edges among the different tissues. Four different t echniques with morphological operations have been applied to extra c the tumor region. These were: Gray level stretching and Sobel edge de tection, K-Means Clustering technique based on location and intensit y, Fuzzy C-Means Clustering, and An Adapted K-Means clustering techn ique and Fuzzy CMeans technique. The area of the extracted tumor re gions has been calculated. The present work showed that the four i mplemented techniques can successfully detect and extract the brain tumor and thereby help doctors in identifying tumor's size and region.
ADAPTIVE LEAST SQUARES CORRELATION : A POWERFUL IMAGE MATCHING TECHNIQUE

The Adaptive Least Squares Correlation is a very potent and flexible technique for all kinds of data matching problems. Here its application to image matching is outlined. It allows for simultaneous radiometric corrections and local geometrical image shaping, whereby the system parameters are automatically assessed, corrected, and thus optimized during the least squares iterations. The various tools of least squares estimation can be favourably utilized for the assessment of the correlation quality. Furthermore, the system allows for stabilization and improvement of the correlation procedure through the simultaneous consideration of geometrical constraints, e.g. the collinearity condition. Some exciting new perspectives are emphasized, as for example multiphoto correlation, multitemporal and multisensor correlation, multipoint correlation, and simultaneous correlation/triangulation.
Development of a high-voltage closed-loop power supply for ozone generation

This paper deals with the design of an AC-220-volt-mains-fed power supply for ozone generation. A power stage consisting of a buck converter to regulate the output power plus a current-fed parallel-resonant push-pull inverter to supply an ozone generator (OG) is proposed and analysed. A closed-loop operation is presented as a method to compensate variations in the AC source voltage. Inverter's step-up transformer issues and their effect on the performance of the overall circuit are also studied. The use of a UC3872 integrated circuit is proposed to control both the push-pull inverter and the buck converter, as well as to provide the possibility to protect the power supply in case a short circuit, an open-lamp operation or any other circumstance might occur. Implementation of a 100 W prototype and experimental results are shown and discussed.
On automated source selection for transfer learning in convolutional neural networks

Transfer learning, or inductive transfer, refers to the transfer of knowledge from a source task to a target task. In the context of convolutional neural networks (CNNs), transfer learning can be implemented by transplanting the learned feature layers from one CNN (derived from the source task) to initialize another (for the target task). Previous research has shown that the choice of the source CNN impacts the performance of the target task. In the current literature, there is no principled way for selecting a source CNN for a given target task despite the increasing availability of pre-trained source CNNs. In this paper we investigate the possibility of automatically ranking source CNNs prior to utilizing them for a target task. In particular, we present an information theoretic framework to understand the source-target relationship and use this as a basis to derive an approach to automatically rank source CNNs in an efficient, zero-shot manner. The practical utility of the approach is thoroughly evaluated using the PlacesMIT dataset, MNIST dataset and a real-world MRI database. Experimental results demonstrate the efficacy of the proposed ranking method for transfer learning.