nils-herrmann/clean_scirepeval huggingface dataset:
Dialogue State Tracking using Long Short Term Memory Neural Networks

We propose a dialogue state tracker based on long short term memory (LSTM) neural networks. LSTM is an extension of a recurrent neural network (RNN), which can better consider distant dependencies in sequential input. We construct a LSTM network that receives utterances of dialogue participants as input, and outputs the dialogue state of the current utterance. The input utterances are separated into vectors of words with their orders, which are further converted to word embeddings to avoid sparsity problems. In experiments, we combined this system with the baseline system of the dialogue state tracking challenge (DSTC), and achieved improved dialogue state tracking accuracy.
Improving BCI-based emotion recognition by combining EEG feature selection and kernel classifiers

Current emotion recognition computational techniques have been successful on associating the emotional changes with the EEG signals, and so they can be identified and classified from EEG signals if appropriate stimuli are applied. However, automatic recognition is usually restricted to a small number of emotions classes mainly due to signal’s features and noise, EEG constraints and subject-dependent issues. In order to address these issues, in this paper a novel feature-based emotion recognition model is proposed for EEGbased Brain–Computer Interfaces. Unlike other approaches, our method explores a wider set of emotion types and incorporates additional features which are relevant for signal pre-processing and recognition classification tasks, based on a dimensional model of emotions: Valence and Arousal. It aims to improve the accuracy of the emotion classification task by combining mutual information based feature selection methods and kernel classifiers. Experiments using our approach for emotion classification which combines efficient feature selection methods and efficient kernel-based classifiers on standard EEG datasets show the promise of the approach when compared with state-of-the-art computational methods.
Mining customer product ratings for personalized marketing

With the increasing popularity of Internet commerce, a wealth of information about the customers can now be readily acquired on-line. An important example is the customers’ preference ratings for the various products offered by the company. Successful mining of these ratings can thus allow the company’s direct marketing campaigns to provide automatic product recommendations. In general, these recommender systems are based on two complementary techniques. Content-based systems match customer interests with information about the products, while collaborative systems utilize preference ratings from the other customers. In this paper, we address some issues faced by these systems, and study how recent machine learning algorithms, namely the support vector machine and the latent class model, can be used to alleviate these problems. D 2002 Elsevier Science B.V. All rights reserved.
Superintelligence cannot be contained: Lessons from Computability Theory

Manuel Alfonseca,1 Manuel Cebrian,2 Antonio Fernandez Anta,3 Lorenzo Coviello,4 Andres Abeliuk,5 and Iyad Rahwan6 Escuela Politécnica Superior, Universidad Autónoma de Madrid, Madrid, Spain Data61 Unit, Commonwealth Scientific and Industrial Research Organisation, Melbourne, Victoria, Australia IMDEA Networks Institute, Madrid, Spain Google, USA Melbourne School of Engineering, University of Melbourne, Melbourne, Australia The Media Lab, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
Robust Poisson Surface Reconstruction

We propose a method to reconstruct surfaces from oriented point clouds with non-uniform sampling and noise by formulating the problem as a convex minimization that reconstructs the indicator function of the surface’s interior. Compared to previous models, our reconstruction is robust to noise and outliers because it substitutes the leastsquares fidelity term by a robust Huber penalty; this allows to recover sharp corners and avoids the shrinking bias of least squares. We choose an implicit parametrization to reconstruct surfaces of unknown topology and close large gaps in the point cloud. For an efficient representation, we approximate the implicit function by a hierarchy of locally supported basis elements adapted to the geometry of the surface. Unlike ad-hoc bases over an octree, our hierarchical B-splines from isogeometric analysis locally adapt the mesh and degree of the splines during reconstruction. The hierarchical structure of the basis speeds-up the minimization and efficiently represents clustered data. We also advocate for convex optimization, instead isogeometric finite-element techniques, to efficiently solve the minimization and allow for non-differentiable functionals. Experiments show state-of-the-art performance within a more flexible framework.
Enabling Agile Testing through Continuous Integration

A Continuous Integration system is often considered one of the key elements involved in supporting an agile software development and testing environment. As a traditional software tester transitioning to an agile development environment it became clear to me that I would need to put this essential infrastructure in place and promote improved development practices in order to make the transition to agile testing possible. This experience report discusses a continuous integration implementation I led last year. The initial motivations for implementing continuous integration are discussed and a pre and post-assessment using Martin Fowler's" Practices of Continuous Integration" is provided along with the technical specifics of the implementation. The report concludes with a retrospective of my experiences implementing and promoting continuous integration within the context of agile testing.
A 2-kW Single-Phase Seven-Level Flying Capacitor Multilevel Inverter With an Active Energy Buffer

High-efficiency and compact single-phase inverters are desirable in many applications such as solar energy harvesting and electric vehicle chargers. This paper presents a 2-kW, 60-Hz, 450-V<inline-formula> <tex-math notation="LaTeX">$ _{\text{DC}}$</tex-math></inline-formula>-to-240-V<inline-formula> <tex-math notation="LaTeX">$_{\text{AC}}$</tex-math></inline-formula> power inverter, designed and tested subject to the specifications of the Google/IEEE Little Box Challenge. The inverter features a seven-level flying capacitor multilevel converter, with low-voltage GaN switches operating at 120 kHz. The inverter also includes an active buffer for twice-line-frequency power pulsation decoupling, which reduces the required capacitance by a factor of 8 compared to conventional passive decoupling capacitors, while maintaining an efficiency above 99%. The inverter prototype is a self-contained box that achieves a high power density of 216 W/in<inline-formula> <tex-math notation="LaTeX">$^3$</tex-math></inline-formula> and a peak overall efficiency of 97.6%, while meeting the constraints including input current ripple, load transient, thermal, and FCC Class B EMC specifications.
Comparison of open-source cloud management platforms: OpenStack and OpenNebula

Cloud management platforms may manage the resources provided by the infrastructure as a service (IaaS) cloud. With the rapid development of open-source cloud platforms, they have been widely used due to open and free, some of them can substitute commercial clouds. Some existed related works only concisely compare the basic features of open-source platforms, and not including some new released features. In this paper, we firstly present the function of OpenStack and OpenNebula briefly, and then compare them from provenance, architecture, hypervisors, security and other angles in detail. Moreover, we provide some deployment recommendations according to different user demands and platform characteristics.
Handwriting Recognition in Indian Regional Scripts: A Survey of Offline Techniques

Offline handwriting recognition in Indian regional scripts is an interesting area of research as almost 460 million people in India use regional scripts. The nine major Indian regional scripts are Bangla (for Bengali and Assamese languages), Gujarati, Kannada, Malayalam, Oriya, Gurumukhi (for Punjabi language), Tamil, Telugu, and Nastaliq (for Urdu language). A state-of-the-art survey about the techniques available in the area of offline handwriting recognition (OHR) in Indian regional scripts will be of a great aid to the researchers in the subcontinent and hence a sincere attempt is made in this article to discuss the advancements reported in this regard during the last few decades. The survey is organized into different sections. A brief introduction is given initially about automatic recognition of handwriting and official regional scripts in India. The nine regional scripts are then categorized into four subgroups based on their similarity and evolution information. The first group contains Bangla, Oriya, Gujarati and Gurumukhi scripts. The second group contains Kannada and Telugu scripts and the third group contains Tamil and Malayalam scripts. The fourth group contains only Nastaliq script (Perso-Arabic script for Urdu), which is not an Indo-Aryan script. Various feature extraction and classification techniques associated with the offline handwriting recognition of the regional scripts are discussed in this survey. As it is important to identify the script before the recognition step, a section is dedicated to handwritten script identification techniques. A benchmarking database is very important for any pattern recognition related research. The details of the datasets available in different Indian regional scripts are also mentioned in the article. A separate section is dedicated to the observations made, future scope, and existing difficulties related to handwriting recognition in Indian regional scripts. We hope that this survey will serve as a compendium not only for researchers in India, but also for policymakers and practitioners in India. It will also help to accomplish a target of bringing the researchers working on different Indian scripts together. Looking at the recent developments in OHR of Indian regional scripts, this article will provide a better platform for future research activities.
Development of expressive robotic head for bipedal humanoid robot

This paper describes the development of a new expressive robotic head for the bipedal humanoid robot. Facial expressions of our old robotic head have low facial expression recognition rate and in order to improve it we asked amateur cartoonists to create computer graphics (CG) images. To realize such expressions found in the CGs, the new head was provided with 24-DoFs and facial color. We designed compact mechanisms that fit into the head, which dimensions are based on average adult Japanese female size. We conducted a survey with pictures and videos to evaluate the expression ability. Results showed that facial expression recognition rates for the 6 basic emotions are increased compared to the old KOBIAN's head.
Online Ensemble Learning of Data Streams with Gradually Evolved Classes

Class evolution, the phenomenon of class emergence and disappearance, is an important research topic for data stream mining. All previous studies implicitly regard class evolution as a transient change, which is not true for many real-world problems. This paper concerns the scenario where classes emerge or disappear gradually. A class-based ensemble approach, namely Class-Based ensemble for Class Evolution (CBCE), is proposed. By maintaining a base learner for each class and dynamically updating the base learners with new data, CBCE can rapidly adjust to class evolution. A novel under-sampling method for the base learners is also proposed to handle the dynamic class-imbalance problem caused by the gradual evolution of classes. Empirical studies demonstrate the effectiveness of CBCE in various class evolution scenarios in comparison to existing class evolution adaptation methods.
MojiTalk: Generating Emotional Responses at Scale

Generating emotional language is a key step towards building empathetic natural language processing agents. However, a major challenge for this line of research is the lack of large-scale labeled training data, and previous studies are limited to only small sets of human annotated sentiment labels. Additionally, explicitly controlling the emotion and sentiment of generated text is also difficult. In this paper, we take a more radical approach: we exploit the idea of leveraging Twitter data that are naturally labeled with emojis. We collect a large corpus of Twitter conversations that include emojis in the response and assume the emojis convey the underlying emotions of the sentence. We investigate several conditional variational autoencoders training on these conversations, which allow us to use emojis to control the emotion of the generated text. Experimentally, we show in our quantitative and qualitative analyses that the proposed models can successfully generate highquality abstractive conversation responses in accordance with designated emotions.
Improvement in cancer-related knowledge following use of a psychoeducational video game for adolescents and young adults with cancer.

Adolescents with chronic illnesses have been shown to have management and treatment issues resulting in poor outcomes. These issues may arise from non-interest in self care and illness knowledge. A video game, "Re-Mission," was developed to actively involve young people with cancer in their own treatment. Re-Mission provides opportunities to learn about cancer and its treatment.



The efficacy of Re-Mission was investigated in a multi-site, randomized, controlled study with 375 adolescent and young adult cancer patients. Participants received either a regular commercial game (control) or both the regular game plus Re-Mission (Re-Mission group). Participants were given a mini-PC with the games installed and requested to play for an hour each week for 3 months. A test on cancer-related knowledge was given prior to game play (baseline) and again after 1 and 3 months. At 3 months the Re-Mission group also rated the acceptability and credibility of Re-Mission.



Analyses of the knowledge test scores showed that whereas scores of both groups improved significantly over the follow-up periods, the scores of the Re-Mission group improved significantly more. The size of this effect was related to usage of Re-Mission. Credibility scores were negatively correlated with level of knowledge but not with change in knowledge level at 1 month or 3 months.



The results indicate a specific effect of Re-Mission play on cancer knowledge that is not attributable to patients' expectations. It is concluded that video games can be an effective vehicle for health education in adolescents and young adults with chronic illnesses.
Ontology-Based Interpretation of Natural Language

A book aiming to build a bridge between two fields that share the subject of research but do not share the same views necessarily puts itself in a difficult position: The authors have either to strike a fair balance at peril of dissatisfying both sides or nail their colors to the mast and cater mainly to one of two communities. For semantic processing of natural language with either NLP methods or Semantic Web approaches, the authors clearly favor the latter and propose a strictly ontology-driven interpretation of natural language. The main contribution of the book, driving semantic processing from the ground up by a formal domain-specific ontology, is elaborated in ten well-structured chapters spanning 143 pages of content.
Negative emotional and cognitive responses to being unfriended on Facebook: An exploratory study

0747-5632/$ see front matter 2012  A http://dx.doi.org/10.1016/j.chb.2012.03.008 ⇑ Corresponding author.  We consider Facebook unfriending as a form of relationship termination with negative emotional and cognitive consequences. Specifically, ruminative and negative emotional responses are examined via an online survey of adult Facebook users who were unfriended. These responses were positively related to each other and to Facebook intensity. Rumination was positively predicted by using Facebook to connect with existing contacts and was more likely when the unfriender was a close partner. Participants also responded with greater rumination and negative emotion when they knew who unfriended them, when they thought they were unfriended for Facebook-related reasons, and when participants initiated the Facebook friend request. The contribution of these exploratory findings to our growing understanding of negative relational behaviors on Facebook are discussed.
Complex Graphs and Networks

Contents Preface vii Chapter 1. Graph Theory in the Information Age 1 1.1.  1 1.2. Basic definitions 3 1.3. Degree sequences and the power law 6 1.4. History of the power law 8 1.5. Examples of power law graphs 10 1.6. An outline of the book 17 Chapter 2. Old and New Concentration Inequalities 21 2.1. The binomial distribution and its asymptotic behavior 21 2.2. General Chernoff inequalities 25 2.3. More concentration inequalities 30 2.4. A concentration inequality with a large error estimate 33 2.5. Martingales and Azuma's inequality 35 2.6. General martingale inequalities 38 2.7. Supermartingales and Submartingales 41 2.8. The decision tree and relaxed concentration inequalities 46 Chapter 3. A Generative Model — the Preferential Attachment Scheme 55 3.1. Basic steps of the preferential attachment scheme 55 3.2. Analyzing the preferential attachment model 56 3.3. A useful lemma for rigorous proofs 59 3.4. The peril of heuristics via an example of balls-and-bins 60 3.5. Scale-free networks 62 3.6. The sharp concentration of preferential attachment scheme 64 3.7. Models for directed graphs 70 Chapter 4. Duplication Models for Biological Networks 75 4.1. Biological networks 75 4.2. The duplication model 76 4.3. Expected degrees of a random graph in the duplication model 77 4.4. The convergence of the expected degrees 79 4.5. The generating functions for the expected degrees 83 4.6. Two concentration results for the duplication model 84 4.7. Power law distribution of generalized duplication models 89 Chapter 5. Random Graphs with Given Expected Degrees 91 5.1. The Erd˝ os-Rényi model 91 5.2. The diameter of G n,p 95 iii iv CONTENTS 5.3. A general random graph model 97 5.4. Size, volume and higher order volumes 97 5.5. Basic properties of G(w) 100 5.6. Neighborhood expansion in random graphs 103 5.7. A random power law graph model 107 5.8. Actual versus expected degree sequence 109 Chapter 6. The Rise of the Giant Component 113 6.1. No giant component if w < 1? 114 6.2. Is there a giant component if˜w > 1? 115 6.3. No giant component if˜w < 1? 116 6.4. Existence and uniqueness of the giant component 117 6.5. A lemma on neighborhood growth 126 6.6. The volume of the giant component 129 6.7. Proving the volume estimate of the giant component 131 6.8. Lower bounds for the volume of the giant component 136 6.9. The complement of the giant component and its size 138 6.10. …
A genetic algorithm-based method for feature subset selection

As a commonly used technique in data preprocessing, feature selection selects a subset of informative attributes or variables to build models describing data. By removing redundant and irrelevant or noise features, feature selection can improve the predictive accuracy and the comprehensibility of the predictors or classifiers. Many feature selection algorithms with different selection criteria has been introduced by researchers. However, it is discovered that no single criterion is best for all applications. In this paper, we propose a framework based on a genetic algorithm (GA) for feature subset selection that combines various existing feature selection methods. The advantages of this approach include the ability to accommodate multiple feature selection criteria and find small subsets of features that perform well for a particular inductive learning algorithm of interest to build the classifier. We conducted experiments using three data sets and three existing feature selection methods. The experimental results demonstrate that our approach is a robust and effective approach to find subsets of features with higher classification accuracy and/or smaller size compared to each individual feature selection algorithm. F. Tan (B) · X. Fu · Y. Zhang · A. G. Bourgeois Department of Computer Science, Georgia State University, Atlanta, GA 30302, USA e-mail: ftan@student.gsu.edu X. Fu e-mail: xfu1@gsu.edu Y. Zhang e-mail: yzhang@cs.gsu.edu A. G. Bourgeois e-mail: anu@cs.gsu.edu
Graph-Based Keyword Extraction for Single-Document Summarization

In this paper, we introduce and compare between two novel approaches, supervised and unsupervised, for identifying the keywords to be used in extractive summarization of text documents. Both our approaches are based on the graph-based syntactic representation of text and web documents, which enhances the traditional vector-space model by taking into account some structural document features. In the supervised approach, we train classification algorithms on a summarized collection of documents with the purpose of inducing a keyword identification model. In the unsupervised approach, we run the HITS algorithm on document graphs under the assumption that the top-ranked nodes should represent the document keywords. Our experiments on a collection of benchmark summaries show that given a set of summarized training documents, the supervised classification provides the highest keyword identification accuracy, while the highest F-measure is reached with a simple degree-based ranking. In addition, it is sufficient to perform only the first iteration of HITS rather than running it to its convergence.
A Survey of Data Mining Techniques for Social Media Analysis

Social network has gained remarkable attention in t he last decade. Accessing social network sites such as Twitter, Fac ebook LinkedIn and Google+ through the internet and the web 2.0 technologies h a become more affordable. People are becoming more interested in and relying o social network for information, news and opinion of other users on div erse subject matters. The heavy reliance on social network sites causes them to gen erate massive data characterised by three computational issues namely; size, noise a nd dynamism. These issues often make social network data very complex to analyse ma nually, resulting in the pertinent use of computational means of analysing t hem. Data mining provides a wide range of techniques for detecting useful knowl edge from massive datasets like trends, patterns and rules [44]. Data mining techni ques are used for information retrieval, statistical modelling and machine learni ng. These techniques employ data pre-processing, data analysis, and data interpretat ion processes in the course of data analysis. This survey discusses different dat a mining techniques used in mining diverse aspects of the social network over decades going from the historical techniques to the up-to-date models, including our novel technique named TRCM. All the techniques covered in this survey are liste d in the Table.1 including the tools employed as well as names of their authors.
Molecular signatures database (MSigDB) 3.0

Well-annotated gene sets representing the universe of the biological processes are critical for meaningful and insightful interpretation of large-scale genomic data. The Molecular Signatures Database (MSigDB) is one of the most widely used repositories of such sets.



We report the availability of a new version of the database, MSigDB 3.0, with over 6700 gene sets, a complete revision of the collection of canonical pathways and experimental signatures from publications, enhanced annotations and upgrades to the web site.



MSigDB is freely available for non-commercial use at http://www.broadinstitute.org/msigdb.