nils-herrmann/clean_scirepeval huggingface dataset:
User Interaction Sequences for Search Satisfaction Prediction

Detecting and understanding implicit measures of user satisfaction are essential for meaningful experimentation aimed at enhancing web search quality. While most existing studies on satisfaction prediction rely on users' click activity and query reformulation behavior, often such signals are not available for all search sessions and as a result, not useful in predicting satisfaction. On the other hand, user interaction data (such as mouse cursor movement) is far richer than just click data and can provide useful signals for predicting user satisfaction. In this work, we focus on considering holistic view of user interaction with the search engine result page (SERP) and construct detailed universal interaction sequences of their activity. We propose novel ways of leveraging the universal interaction sequences to automatically extract informative, interpretable subsequences. In addition to extracting frequent, discriminatory and interleaved subsequences, we propose a Hawkes process model to incorporate temporal aspects of user interaction. Through extensive experimentation we show that encoding the extracted subsequences as features enables us to achieve significant improvements in predicting user satisfaction. We additionally present an analysis of the correlation between various subsequences and user satisfaction. Finally, we demonstrate the usefulness of the proposed approach in covering abandonment cases. Our findings provide a valuable tool for fine-grained analysis of user interaction behavior for metric development.
Hindi CCGbank: A CCG treebank from the Hindi dependency treebank

In this paper, we present an approach for automatically creating a Combinatory Categorial Grammar (CCG) treebank from a dependency treebank for the Subject-Object-Verb language Hindi. Rather than a direct conversion from dependency trees to CCG trees, we propose a two stage approach: a language independent generic algorithm first extracts a CCG lexicon from the dependency treebank. A deterministic CCG parser then creates a treebank of CCG derivations. We also discuss special cases of this generic algorithm to handle linguistic phenomena specific to Hindi. In doing so we extract different constructions with long-range dependencies like coordinate constructions and non-projective dependencies resulting from constructions like relative clauses, noun elaboration and verbal modifiers.
Appendix A The Psychology of Prejudice : Ingroup Love or Outgroup Hate ?

Allport (1954) recognized that attachment to one’s ingroups does not necessarily require hostility toward outgroups. Yet the prevailing approach to the study of ethnocentrism, ingroup bias, and prejudice presumes that ingroup love and outgroup hate are reciprocally related. Findings from both cross-cultural research and laboratory experiments support the alternative view that ingroup identification is independent of negative attitudes toward outgoups and that much ingroup bias and intergroup discrimination is motivated by preferential treatment of ingroup members rather than direct hostility toward outgroup members. Thus to understand the roots of prejudice and discrimination requires first of all a better understanding of the functions that ingroup formation and identification serve for human beings. This article reviews research and theory on the motivations for maintenance of ingroup boundaries and the implications of ingroup boundary protection for intergroup relations, conflict, and conflict prevention.
Human Daily Activity Recognition With Sparse Representation Using Wearable Sensors

Human daily activity recognition using mobile personal sensing technology plays a central role in the field of pervasive healthcare. One major challenge lies in the inherent complexity of human body movements and the variety of styles when people perform a certain activity. To tackle this problem, in this paper, we present a novel human activity recognition framework based on recently developed compressed sensing and sparse representation theory using wearable inertial sensors. Our approach represents human activity signals as a sparse linear combination of activity signals from all activity classes in the training set. The class membership of the activity signal is determined by solving a l1 minimization problem. We experimentally validate the effectiveness of our sparse representation-based approach by recognizing nine most common human daily activities performed by 14 subjects. Our approach achieves a maximum recognition rate of 96.1%, which beats conventional methods based on nearest neighbor, naive Bayes, and support vector machine by as much as 6.7%. Furthermore, we demonstrate that by using random projection, the task of looking for “optimal features” to achieve the best activity recognition performance is less important within our framework.
P-ETL: Parallel-ETL based on the MapReduce paradigm

Big data is an opportunity in the emergence of novel business applications such as “Big Data Analytics” (BDA). However, these data with non-traditional volumes create a real problem given the capacity constraints of traditional systems. The aim of this paper is to deal with the impact of big data in a decision-support environment and more particularly in the data integration phase. In this context, we developed a platform, called P-ETL (Parallel-ETL) for extracting (E), transforming (T) and loading (L) very large data in a data warehouse (DW). To cope with very large data, ETL processes under our P-ETL platform run on a cluster of computers in parallel way with MapReduce paradigm. The conducted experiment shows mainly that increasing tasks dealing with large data speeds-up the ETL process.
Replacing the non-differentiable sampling operations with efficient gradient approximators

Neural text generation models are often autoregressive language models or seq2seq models. These models generate text by sampling words sequentially, with each word conditioned on the previous word, and are state-of-the-art for several machine translation and summarization benchmarks. These benchmarks are often defined by validation perplexity even though this is not a direct measure of the quality of the generated text. Additionally, these models are typically trained via maximum likelihood and teacher forcing. These methods are well-suited to optimizing perplexity but can result in poor sample quality since generating text requires conditioning on sequences of words that may have never been observed at training time. We propose to improve sample quality using Generative Adversarial Networks (GANs), which explicitly train the generator to produce high quality samples and have shown a lot of success in image generation. GANs were originally designed to output differentiable values, so discrete language generation is challenging for them. We claim that validation perplexity alone is not indicative of the quality of text generated by a model. We introduce an actor-critic conditional GAN that fills in missing text conditioned on the surrounding context. We show qualitatively and quantitatively, evidence that this produces more realistic conditional and unconditional text samples compared to a maximum likelihood trained model.
MODELING THE PROBABILITY OF MORTGAGE DEFAULT VIA LOGISTIC REGRESSION AND SURVIVAL ANALYSIS

The goal of this thesis is to model and predict the probability of default (PD) for a mortgage portfolio. In order to achieve this goal, logistic regression and survival analysis methods are applied to a large dataset of mortgage portfolios recorded by one of the national banks. While logistic regression has been commonly used for modeling PD in the banking industry, survival analysis has not been explored extensively in the area. Here, survival analysis is offered as a competitive alternative to logistic regression. The results of the final modeling for both methods show very similar fit in terms of the ROC with the survival model having slightly better performance than logistic regression in the training dataset and almost the same performance in the testing dataset. In term of prediction of defaulted and non-defaulted mortgage portfolios, the logistic regression model outperforms survival analysis in the training dataset, while survival model outperforms logistic regression in the testing dataset. Overall, the results support that the survival analysis approach is competitive with the logistic regression approach traditionally used in the banking industry. In addition, the survival methodology offers a number of advantages useful for both credit risk management and capital management.
The Effect Of Colors Of E-commerce Websites On Mood, Memorization And Buying Intention

This paper aims at studying the impact of the colors of e-commerce websites on consumer memorization and buying intention. Based on a literature review we wish to introduce the theoretical and methodological bases addressing this issue. A conceptual model is proposed, showing the effects of the color of the e-commerce website and of its components Hue, Brightness and Saturation, on the behavioral responses of the consumer memorization and buying intention. These responses are conveyed by mood. Data collection was carried out during a laboratory experiment in order to control for the measurement of the colored appearance of e-commerce websites. Participants visited one of the 8 versions of a website designed for the research, selling music CDs. Data analysis using ANOVA, regressions and general linear models (GLM), show a significant effect of color on memorization, conveyed by mood. The interaction of hue and brightness, using chromatic colors for the dominant (background) and dynamic (foreground) supports memorization and buying intention, when contrast is based on low brightness. A negative mood infers better memorization but a decreasing buying intention. The managerial, methodological and theoretical implications, as well as the future ways of research were put in prospect.
Long-Range Wireless Interrogation of Passive Humidity

This paper presents a method for the remote interrogation of batteryless humidity sensors based on statistical estimations and three-dimensional beam scanning techniques. As a proof-of-concept demonstration, a Van-Atta reflectarray sensor is interrogated with a 24 GHz Frequency-Modulated ContinuousWave transceiver at various reading ranges. The extension of the proposed approach to passive or moving sensors is reported and measurement results are discussed. The relative humidity is derived from the statistical analysis of multidimensional radar echoes, while method is proposed to detect the sensor among clutter without knowing its exact position. Moreover it is demonstrated that the cross-polarization effect of the Van-Atta reflectarray combined with the 3D beam scanning technique allows the indoor measurement of the relative humidity up to 58 meters.
An economic analysis of online streaming music services

Streaming music services represent the music industry’s greatest prospective source of revenue and are well established among consumers. This paper presents a theory of a streaming music business model consisting of two types of services provided by a monopolist. The first service, which offers access free of charge, is of low quality and financed by advertising. The second service charges its users and is of high quality. The analysis demonstrates that if users are highly tolerant of commercials, the monopolist benefits from advertising funding and hence charges a high price to users of the fee-based service to boost demand for the advertising supported service. The analysis addresses the welfare consequences of such a business model and shows it is an effective policy for combating digital piracy.
Hierarchical Random Walk Inference in Knowledge Graphs

Relational inference is a crucial technique for knowledge base population. The central problem in the study of relational inference is to infer unknown relations between entities from the facts given in the knowledge bases. Two popular models have been put forth recently to solve this problem, which are the latent factor models and the random-walk models, respectively. However, each of them has their pros and cons, depending on their computational efficiency and inference accuracy. In this paper, we propose a hierarchical random-walk inference algorithm for relational learning in large scale graph-structured knowledge bases, which not only maintains the computational simplicity of the random-walk models, but also provides better inference accuracy than related works. The improvements come from two basic assumptions we proposed in this paper. Firstly, we assume that although a relation between two entities is syntactically directional, the information conveyed by this relation is equally shared between the connected entities, thus all of the relations are semantically bidirectional. Secondly, we assume that the topology structures of the relation-specific subgraphs in knowledge bases can be exploited to improve the performance of the random-walk based relational inference algorithms. The proposed algorithm and ideas are validated with numerical results on experimental data sampled from practical knowledge bases, and the results are compared to state-of-the-art approaches.
Character Recognition in Natural Images

This paper tackles the problem of recognizing characters in images of natural scenes. In particular, we focus on recognizing characters in situations that would traditionally not be handled well by OCR techniques. We present an annotated database of images containing English and Kannada characters. The database comprises of images of street scenes taken in Bangalore, India using a standard camera. The problem is addressed in an object cateogorization framework based on a bag-of-visual-words representation. We assess the performance of various features based on nearest neighbour and SVM classification. It is demonstrated that the performance of the proposed method, using as few as 15 training images, can be far superior to that of commercial OCR systems. Furthermore, the method can benefit from synthetically generated training data obviating the need for expensive data collection and annotation.
Communication systems for building automation and control

Building automation systems (BAS) provide automatic control of the conditions of indoor environments. The historical root and still core domain of BAS is the automation of heating, ventilation and air-conditioning systems in large functional buildings. Their primary goal is to realize significant savings in energy and reduce cost. Yet the reach of BAS has extended to include information from all kinds of building systems, working toward the goal of "intelligent buildings". Since these systems are diverse by tradition, integration issues are of particular importance. When compared with the field of industrial automation, building automation exhibits specific, differing characteristics. The present paper introduces the task of building automation and the systems and communications infrastructure necessary to address it. Basic requirements are covered as well as standard application models and typical services. An overview of relevant standards is given, including BACnet, LonWorks and EIB/KNX as open systems of key significance in the building automation domain.
Like It or Not: A Survey of Twitter Sentiment Analysis Methods

Sentiment analysis in Twitter is a field that has recently attracted research interest. Twitter is one of the most popular microblog platforms on which users can publish their thoughts and opinions. Sentiment analysis in Twitter tackles the problem of analyzing the tweets in terms of the opinion they express. This survey provides an overview of the topic by investigating and briefly describing the algorithms that have been proposed for sentiment analysis in Twitter. The presented studies are categorized according to the approach they follow. In addition, we discuss fields related to sentiment analysis in Twitter including Twitter opinion retrieval, tracking sentiments over time, irony detection, emotion detection, and tweet sentiment quantification, tasks that have recently attracted increasing attention. Resources that have been used in the Twitter sentiment analysis literature are also briefly presented. The main contributions of this survey include the presentation of the proposed approaches for sentiment analysis in Twitter, their categorization according to the technique they use, and the discussion of recent research trends of the topic and its related fields.
Jalangi: a selective record-replay and dynamic analysis framework for JavaScript

JavaScript is widely used for writing client-side web applications and is getting increasingly popular for writing mobile applications. However, unlike C, C++, and Java, there are not that many tools available for analysis and testing of JavaScript applications. In this paper, we present a simple yet powerful framework, called Jalangi, for writing heavy-weight dynamic analyses. Our framework incorporates two key techniques: 1) selective record-replay, a technique which enables to record and to faithfully replay a user-selected part of the program, and 2) shadow values and shadow execution, which enables easy implementation of heavy-weight dynamic analyses. Our implementation makes no special assumption about JavaScript, which makes it applicable to real-world JavaScript programs running on multiple platforms. We have implemented concolic testing, an analysis to track origins of nulls and undefined, a simple form of taint analysis, an analysis to detect likely type inconsistencies, and an object allocation profiler in Jalangi. Our evaluation of Jalangi on the SunSpider benchmark suite and on five web applications shows that Jalangi has an average slowdown of 26X during recording and 30X slowdown during replay and analysis. The slowdowns are comparable with slowdowns reported for similar tools, such as PIN and Valgrind for x86 binaries. We believe that the techniques proposed in this paper are applicable to other dynamic languages.
Towards a Relation Extraction Framework for Cyber-Security Concepts

In order to assist security analysts in obtaining information pertaining to their network, such as novel vulnerabilities, exploits, or patches, information retrieval methods tailored to the security domain are needed. As labeled text data is scarce and expensive, we follow developments in semi-supervised Natural Language Processing and implement a bootstrapping algorithm for extracting security entities and their relationships from text. The algorithm requires little input data, specifically, a few relations or patterns (heuristics for identifying relations), and incorporates an active learning component which queries the user on the most important decisions to prevent drifting from the desired relations. Preliminary testing on a small corpus shows promising results, obtaining precision of .82.
Models for count data with many zeros

Poisson regression models provide a standard framework for the analysis of count data. In practice, however, count data are often overdispersed relative to the Poisson distribution. One frequent manifestation of overdispersion is that the incidence of zero counts is greater than expected for the Poisson distribution and this is of interest because zero counts frequently have special status. For example, in counting disease lesions on plants, a plant may have no lesions either because it is resistant to the disease, or simply because no disease spores have landed on it. This is the distinction between structural zeros, which are inevitable, and sampling zeros, which occur by chance. In recent years there has been considerable interest in models for count data that allow for excess zeros, particularly in the econometric literature. These models complement more conventional models for overdispersion that concentrate on modelling the variance-mean relationship correctly. Application areas are diverse and have included manufacturing defects (Lambert, 1992), patent applications (Crepon & Duguet, 1997), road safety (Miaou, 1994), species abundance (Welsh et al., 1996; Faddy, 1998), medical consultations
End-to-End Optimization of Task-Oriented Dialogue Model with Deep Reinforcement Learning

In this paper, we present a neural network based task-oriented dialogue system that can be optimized end-to-end with deep reinforcement learning (RL). The system is able to track dialogue state, interface with knowledge bases, and incorporate query results into agent’s responses to successfully complete task-oriented dialogues. Dialogue policy learning is conducted with a hybrid supervised and deep RL methods. We first train the dialogue agent in a supervised manner by learning directly from task-oriented dialogue corpora, and further optimize it with deep RL during its interaction with users. In the experiments on two different dialogue task domains, our model demonstrates robust performance in tracking dialogue state and producing reasonable system responses. We show that deep RL based optimization leads to significant improvement on task success rate and reduction in dialogue length comparing to supervised training model. We further show benefits of training task-oriented dialogue model end-to-end comparing to componentwise optimization with experiment results on dialogue simulations and human evaluations.
The relationship between organization strategy, total quality management (TQM), and organization performance--the mediating role of TQM

The study presented in this paper examines the fit of total quality management (TQM) practices in mediating the relationship between organization strategy and organization performance. By examining TQM in relation to organization strategy, the study seeks to advance the understanding of TQM in a broader context. It also resolves some controversies that appear in the literature concerning the relationship between TQM and differentiation and cost leadership strategies as well as quality and innovation performance. The empirical data for this study was drawn from a survey of 194 middle/senior managers from Australian firms. The analysis was conducted using structural equation modeling (SEM) technique by examining two competing models that represent full and partial mediation. The findings indicate that TQM is positively and significantly related to differentiation strategy, and it only partially mediates the relationship between differentiation strategy and three performance measures (product quality, product innovation, and process innovation). The implication is that TQM needs to be complemented by other resources to more effectively realize the strategy in achieving a high level of performance, particularly innovation.
Improving Protein Gamma-Turn Prediction Using Inception Capsule Networks

Protein gamma-turn prediction is useful in protein function studies and experimental design. Several methods for gamma-turn prediction have been developed, but the results were unsatisfactory with Matthew correlation coefficients (MCC) around 0.2–0.4. Hence, it is worthwhile exploring new methods for the prediction. A cutting-edge deep neural network, named Capsule Network (CapsuleNet), provides a new opportunity for gamma-turn prediction. Even when the number of input samples is relatively small, the capsules from CapsuleNet are effective to extract high-level features for classification tasks. Here, we propose a deep inception capsule network for gamma-turn prediction. Its performance on the gamma-turn benchmark GT320 achieved an MCC of 0.45, which significantly outperformed the previous best method with an MCC of 0.38. This is the first gamma-turn prediction method utilizing deep neural networks. Also, to our knowledge, it is the first published bioinformatics application utilizing capsule network, which will provide a useful example for the community. Executable and source code can be download at http://dslsrv8.cs.missouri.edu/~cf797/MUFoldGammaTurn/download.html.