nils-herrmann/clean_scirepeval huggingface dataset:
Meta-level sentiment models for big social data analysis

People react to events, topics and entities by expressing their personal opinions and emotions. These reactions can correspond to a wide range of intensities, from very mild to strong. An adequate processing and understanding of these expressions has been the subject of research in several fields, such as business and politics. In this context, Twitter sentiment analysis, which is the task of automatically identifying and extracting subjective information from tweets, has received increasing attention from the Web mining community. Twitter provides an extremely valuable insight into human opinions, as well as new challenging Big Data problems. These problems include the processing of massive volumes of streaming data, as well as the automatic identification of human expressiveness within short text messages. In that area, several methods and lexical resources have been proposed in order to extract sentiment indicators from natural language texts at both syntactic and semantic levels. These approaches address different dimensions of opinions, such as subjectivity, polarity, intensity and emotion. This article is the first study of how these resources, which are focused on different sentiment scopes, complement each other. With this purpose we identify scenarios in which some of these resources are more useful than others. Furthermore, we propose a novel approach for sentiment classification based on meta-level features. This supervised approach boosts existing sentiment classification of subjectivity and polarity detection on Twitter. Our results show that the combination of meta-level features provides significant improvements in performance. However, we observe that there are important differences that rely on the type of lexical resource, the dataset used to build the model, and the learning strategy. Experimental results indicate that manually generated lexicons are focused on emotional words, being very useful for polarity prediction. On the other hand, lexicons generated with automatic methods include neutral words, introducing noise in the detection of subjectivity. Our findings indicate that polarity and subjectivity prediction are different dimensions of the same problem, but they need to be addressed using different subspace features. Lexicon-based approaches are recommendable for polarity, and stylistic part-of-speech based approaches are meaningful for subjectivity. With this research we offer a more global insight of the resource components for the complex task of classifying human emotion and opinion.
Learning to personalize query auto-completion

Query auto-completion (QAC) is one of the most prominent features of modern search engines. The list of query candidates is generated according to the prefix entered by the user in the search box and is updated on each new key stroke. Query prefixes tend to be short and ambiguous, and existing models mostly rely on the past popularity of matching candidates for ranking. However, the popularity of certain queries may vary drastically across different demographics and users. For instance, while instagram and imdb have comparable popularities overall and are both legitimate candidates to show for prefix i, the former is noticeably more popular among young female users, and the latter is more likely to be issued by men.
 In this paper, we present a supervised framework for personalizing auto-completion ranking. We introduce a novel labelling strategy for generating offline training labels that can be used for learning personalized rankers. We compare the effectiveness of several user-specific and demographic-based features and show that among them, the user's long-term search history and location are the most effective for personalizing auto-completion rankers. We perform our experiments on the publicly available AOL query logs, and also on the larger-scale logs of Bing. The results suggest that supervised rankers enhanced by personalization features can significantly outperform the existing popularity-based base-lines, in terms of mean reciprocal rank (MRR) by up to 9%.
Tensor-Train Decomposition

A simple nonrecursive form of the tensor decomposition in d dimensions is presented. It does not inherently suffer from the curse of dimensionality, it has asymptotically the same number of parameters as the canonical decomposition, but it is stable and its computation is based on lowrank approximation of auxiliary unfolding matrices. The new form gives a clear and convenient way to implement all basic operations efficiently. A fast rounding procedure is presented, as well as basic linear algebra operations. Examples showing the benefits of the decomposition are given, and the efficiency is demonstrated by the computation of the smallest eigenvalue of a 19-dimensional operator.
Gait Analysis Methods: An Overview of Wearable and Non-Wearable Systems, Highlighting Clinical Applications

This article presents a review of the methods used in recognition and analysis of the human gait from three different approaches: image processing, floor sensors and sensors placed on the body. Progress in new technologies has led the development of a series of devices and techniques which allow for objective evaluation, making measurements more efficient and effective and providing specialists with reliable information. Firstly, an introduction of the key gait parameters and semi-subjective methods is presented. Secondly, technologies and studies on the different objective methods are reviewed. Finally, based on the latest research, the characteristics of each method are discussed. 40% of the reviewed articles published in late 2012 and 2013 were related to non-wearable systems, 37.5% presented inertial sensor-based systems, and the remaining 22.5% corresponded to other wearable systems. An increasing number of research works demonstrate that various parameters such as precision, conformability, usability or transportability have indicated that the portable systems based on body sensors are promising methods for gait analysis.
On the k-NN performance in a challenging scenario of imbalance and overlapping

A two-class data set is said to be imbalanced when one (minority) class is heavily under-represented with respect to the other (majority) class. In the presence of a significant overlapping, the task of learning from imbalanced data can be a very difficult problem. Additionally, if the overall imbalance ratio is different from local imbalance ratios in overlap regions, the task can become in a major challenge. This paper explains the behaviour of the k-nearest neighbour (k-NN) rule when learning from such a complex scenario. This local model is compared to other machine learning algorithms, attending to how their behaviour depends on a number of data complexity features (global imbalance, size of overlap region, and its local imbalance). As a result, several conclusions useful for classifier design are inferred.
UiLog: Improving Log-Based Fault Diagnosis by Log Analysis

In modern computer systems, system event logs have always been the primary source for checking system status. As computer systems become more and more complex, the interaction between software and hardware increases frequently. The components will generate enormous log information, including running reports and fault information. The sheer quantity of data is a great challenge for analysis relying on the manual method. In this paper, we implement a management and analysis system of log information, which can assist system administrators to understand the real-time status of the entire system, classify logs into different fault types, and determine the root cause of the faults. In addition, we improve the existing fault correlation analysis method based on the results of system log classification. We apply the system in a cloud computing environment for evaluation. The results show that our system can classify fault logs automatically and effectively. With the proposed system, administrators can easily detect the root cause of faults.
3D video surveillance with Augmented Virtual Environments

Recent advances in sensing and computing technologies have inspired a new generation of data analysis and visualization systems for video surveillance applications. We present a novel visualization system for video surveillance based on an Augmented Virtual Environment (AVE) that fuses dynamic imagery with 3D models in a real-time display to help observers comprehend multiple streams of temporal data and imagery from arbitrary views of the scene. This paper focuses on our recent technical extensions to our AVE system, including moving object detection, tracking, and 3D display for effective dynamic event comprehension and situational awareness. Moving objects are detected and tracked in video sequences and visualized as pseudo-3D elements in the AVE scene display in real-time. We show results that illustrate the utility and benefits of these new capabilities.
Querying semantic web data with SPARQL

The Semantic Web is the initiative of the W3C to make information on the Web readable not only by humans but also by machines. RDF is the data model for Semantic Web data, and SPARQL is the standard query language for this data model. In the last ten years, we have witnessed a constant growth in the amount of RDF data available on the Web, which have motivated the theoretical study of some fundamental aspects of SPARQL and the development of efficient mechanisms for implementing this query language.
 Some of the distinctive features of RDF have made the study and implementation of SPARQL challenging. First, as opposed to usual database applications, the semantics of RDF is open world, making RDF databases inherently incomplete. Thus, one usually obtains partial answers when querying RDF with SPARQL, and the possibility of adding optional information if present is a crucial feature of SPARQL. Second, RDF databases have a graph structure and are interlinked, thus making graph navigational capabilities a necessary component of SPARQL. Last, but not least, SPARQL has to work at Web scale!
 RDF and SPARQL have attracted interest from the database community. However, we think that this community has much more to say about these technologies, and, in particular, about the fundamental database problems that need to be solved in order to provide solid foundations for the development of these technologies. In this paper, we survey some of the main results about the theory of RDF and SPARQL putting emphasis on some research opportunities for the database community.
QuERy: A Framework for Integrating Entity Resolution with Query Processing

This paper explores an analysis-aware data cleaning architecture for a large class of SPJ SQL queries. In particular, we propose QuERy, a novel framework for integrating entity resolution (ER) with query processing. The aim of QuERy is to correctly and efficiently answer complex queries issued on top of dirty data. The comprehensive empirical evaluation of the proposed solution demonstrates its significant advantage in terms of efficiency over the traditional techniques for the given problem settings.
Audiovisual speech synthesis: An overview of the state-of-the-art

We live in a world where there are countless interactions with computer systems in every-day situations. In the most ideal case, this interaction feels as familiar and as natural as the communication we experience with other humans. To this end, an ideal means of communication between a user and a computer system consists of audiovisual speech signals. Audiovisual text-to-speech technology allows the computer system to utter any spoken message towards its users. Over the last decades, a wide range of techniques for performing audiovisual speech synthesis has been developed. This paper gives a comprehensive overview on these approaches using a categorization of the systems based on multiple important aspects that determine the properties of the synthesized speech signals. The paper makes a clear distinction between the techniques that are used to model the virtual speaker and the techniques that are used to generate the appropriate speech gestures. In addition, the paper discusses the evaluation of audiovisual speech synthesizers, it elaborates on the hardware requirements for performing visual speech synthesis and it describes some important future directions that should stimulate the use of audiovisual speech synthesis technology in real-life applications.
Opinion Mining and Sentiment Analysis - An Assessment of Peoples' Belief: A Survey

Mining is a process of automatic extraction of knowledge from the opinion of others about some particular topic or problem. The idea of Opinion mining and Sentiment Analysis tool is to “process a set of search results for a given item, generating a list of product attributes (quality, features etc.) and aggregating opinion”. But with the passage of time more interesting applications and developments came into existence in this area and now its main goal is to make computer able to recognize and generate emotions like human. This paper will try to focus on the basic definitions of Opinion Mining, analysis of linguistic resources required for Opinion Mining, few machine learning techniques on the basis of their usage and importance for the analysis, evaluation of Sentiment classifications and its various applications. KeywordsSentiment Mining, Opinion Mining, Text Classification.
Three-Port Bidirectional Converter for Hybrid Fuel Cell Systems

The implementation of a hybrid fuel cell/battery system is proposed to improve the slow transient response of a fuel cell stack. This system can be used for an autonomous device with quick load variations. A suitable three-port, galvanic isolated, bidirectional power converter is proposed to control the power flow. An energy management method for the proposed three-port circuit is analyzed and implemented. Measurements from a 500-W laboratory prototype are presented to demonstrate the validity of the approach
If, When, and How to Perform Lane Change Maneuvers on Highways

Advanced driver assistance systems or highly automated driving systems for lane change maneuvers are expected to enhance highway traffic safety, transport efficiency, and driver comfort. To extend the capability of current advanced driver assistance systems, and eventually progress to highly automated highway driving, the task of automatically determine if, when, and how to perform a lane change maneuver, is essential. This paper thereby presents a low-complexity lane change maneuver algorithm which determines whether a lane change maneuver is desirable, and if so, selects an appropriate inter-vehicle traffic gap and time instance to perform the maneuver, and calculates the corresponding longitudinal and lateral control trajectory. The ability of the proposed lane change maneuver algorithm to make appropriate maneuver decisions and generate smooth and safe lane change trajectories in various traffic situations is demonstrated by simulation and experimental results.
Fatigue characteristics of McKibben artificial muscle actuators

The McKibben artificial muscle is a pneumatic actuator whose properties include a very high force to weight ratio. This characteristic makes it very attractive for a wide range of applications such as mobile robots and prosthetic appliances for the disabled. Typical applications often require a significant number of repeated contractions and extensions or cycles of the actuator. This repeated action leads to fatigue and failure of the actuator, yielding a life span that is often shorter than its more common robotic counterparts such as electric motors or pneumatic cylinders. In this paper, we develop a model that predicts the maximum number of life cycles of the actuator based on available uniaxial tensile properties of the actuator’s inner bladder. Experimental results, which validate the model, reveal McKibben actuators fabricated with natural latex rubber bladders have a fatigue limit 24 times greater than actuators fabricated with synthetic silicone rubber at large contraction ratios.
Defining and assessing professional competence.

Current assessment formats for physicians and trainees reliably test core knowledge and basic skills. However, they may underemphasize some important domains of professional medical practice, including interpersonal skills, lifelong learning, professionalism, and integration of core knowledge into clinical practice.



To propose a definition of professional competence, to review current means for assessing it, and to suggest new approaches to assessment.



We searched the MEDLINE database from 1966 to 2001 and reference lists of relevant articles for English-language studies of reliability or validity of measures of competence of physicians, medical students, and residents.



We excluded articles of a purely descriptive nature, duplicate reports, reviews, and opinions and position statements, which yielded 195 relevant citations.



Data were abstracted by 1 of us (R.M.E.). Quality criteria for inclusion were broad, given the heterogeneity of interventions, complexity of outcome measures, and paucity of randomized or longitudinal study designs.



We generated an inclusive definition of competence: the habitual and judicious use of communication, knowledge, technical skills, clinical reasoning, emotions, values, and reflection in daily practice for the benefit of the individual and the community being served. Aside from protecting the public and limiting access to advanced training, assessments should foster habits of learning and self-reflection and drive institutional change. Subjective, multiple-choice, and standardized patient assessments, although reliable, underemphasize important domains of professional competence: integration of knowledge and skills, context of care, information management, teamwork, health systems, and patient-physician relationships. Few assessments observe trainees in real-life situations, incorporate the perspectives of peers and patients, or use measures that predict clinical outcomes.



In addition to assessments of basic skills, new formats that assess clinical reasoning, expert judgment, management of ambiguity, professionalism, time management, learning strategies, and teamwork promise a multidimensional assessment while maintaining adequate reliability and validity. Institutional support, reflection, and mentoring must accompany the development of assessment programs.
CONSUMER SWITCHING BEHAVIOR FROM ONLINE BANKING TO MOBILE BANKING

Through investigating factors that influence consumers to make a transition from online to mobile banking, this empirical study shows that relative attitude and relative subjective norm positively motivated respondents to switch from Internet to mobile banking while relative perceived behavior control deterred respondents from transitioning. Empirical results also demonstrated that Internet banking is superior to mobile banking in terms of consumer relative compatibility, self-efficacy, resource facilitating conditions, and technology facilitating conditions. Meanwhile, mobile banking emerged as superior to Internet banking for other constructs. By adding a comparative concept into an extended decomposed theory of planned behavior (DTPB) model, this study may expand the applicable domain of current social psychology theories from the adoption of single products or services to the choice between competing products or services that achieve similar purposes and functions.
Measurement of Integrated Circuit Conducted Emissions by Using a Transverse Electromagnetic Mode ( TEM ) Cell

This paper presents a new technique for the measurement of integrated circuit (IC) conducted emissions. In particular, the spectrum of interfering current flowing through an IC port is detected by using a transverse electromagnetic mode (TEM) cell. A structure composed of a matched TEM cell with inside a transmission line is considered. The structure is excited by an interfering source connected to one end of the transmission line. The relationship between the current spectrum of the source and the spectrum of the RF power delivered to the TEM mode of the cell is derived. This relationship is evaluated for one specific structure and the experimental validation is shown. Results of conducted emission measurement performed by using such a technique are shown as well and compared with those derived by using the magnetic probe method.
Kernel principal component analysis and support vector machines for stock price prediction

Financial time series are complex, non-stationary and deterministically chaotic. Technical indicators are used with principal component analysis (PCA) in order to identify the most influential inputs in the context of the forecasting model. Neural networks (NN) and support vector regression (SVR) are used with different inputs. Our assumption is that the future value of a stock price depends on the financial indicators although there is no parametric model to explain this relationship. This relationship comes from technical analysis. Comparison shows that SVR and MLP networks require different inputs. The MLP networks outperform the SVR technique.
Virtual Teams versus Face-to-Face Teams : An Exploratory Study of a Web-based Conference System *

Many organizations are forming “virtual teams” of geographically distributed knowledge workers to collaborate on a variety of workplace tasks. But how effective are these virtual teams compared to traditional face-to-face groups? Do they create similar teamwork and is information exchanged as effectively? An exploratory study of a World Wide Web-based asynchronous computer conference system known as MeetingWebTM is presented and discussed. It was found that teams using this computer-mediated communication system (CMCS) could not outperform traditional (face-to-face) teams under otherwise comparable circumstances. Further, relational links among team members were found to be a significant contributor to the effectiveness of information exchange. Though virtual and face-to-face teams exhibit similar levels of communication effectiveness, face-to-face team members report higher levels of satisfaction. Therefore, the paper presents steps that can be taken to improve the interaction experience of virtual teams. Finally, guidelines for creating and managing virtual teams are suggested, based on the findings of this research and other authoritative sources. The authors wish to thank the Special Focus Editor and the reviewers for their thoughtful critique of the earlier versions of this paper. We also wish to acknowledge the contributions of the Northeastern University College of Business Administration and its staff, which provided the web server and the MeetingWebTM software used in these experiments.