nils-herrmann/clean_scirepeval huggingface dataset:
Recurrent Neural Network for Text Classification with Multi-Task Learning

Neural network based methods have obtained great progress on a variety of natural language processing tasks. However, in most previous works, the models are learned based on single-task supervised objectives, which often suffer from insufficient training data. In this paper, we use the multitask learning framework to jointly learn across multiple related tasks. Based on recurrent neural network, we propose three different mechanisms of sharing information to model text with task-specific and shared layers. The entire network is trained jointly on all these tasks. Experiments on four benchmark text classification tasks show that our proposed models can improve the performance of a task with the help of other related tasks.
Hidden Markov models for churn prediction

Most companies favour the creation and nurturing of long-term relationships with customers because retaining customers is more profitable than acquiring new ones. Churn prediction is a predictive analytics technique to identify churning customers ahead of their departure and enable customer relationship managers to take action to keep them. This work evaluates the development of an expert system for churn prediction and prevention using a Hidden Markov model (HMM). A HMM is implemented on unique data from a mobile application and its predictive performance is compared to other algorithms that are commonly used for churn prediction: Logistic Regression, Neural Network and Support Vector Machine. Predictive performance of the HMM is not outperformed by the other algorithms. HMM has substantial advantages for use in expert systems though due to low storage and computational requirements and output of highly relevant customer motivational states. Generic session data of the mobile app is used to train and test the models which makes the system very easy to deploy and the findings applicable to the whole ecosystem of mobile apps distributed in Apple's App and Google's Play Store.
Realtime 3D eye gaze animation using a single RGB camera

This paper presents the first realtime 3D eye gaze capture method that simultaneously captures the coordinated movement of 3D eye gaze, head poses and facial expression deformation using a single RGB camera. Our key idea is to complement a realtime 3D facial performance capture system with an efficient 3D eye gaze tracker. We start the process by automatically detecting important 2D facial features for each frame. The detected facial features are then used to reconstruct 3D head poses and large-scale facial deformation using multi-linear expression deformation models. Next, we introduce a novel user-independent classification method for extracting iris and pupil pixels in each frame. We formulate the 3D eye gaze tracker in the Maximum A Posterior (MAP) framework, which sequentially infers the most probable state of 3D eye gaze at each frame. The eye gaze tracker could fail when eye blinking occurs. We further introduce an efficient eye close detector to improve the robustness and accuracy of the eye gaze tracker. We have tested our system on both live video streams and the Internet videos, demonstrating its accuracy and robustness under a variety of uncontrolled lighting conditions and overcoming significant differences of races, genders, shapes, poses and expressions across individuals.
Cybersecurity for electric power control and automation systems

Disruption of electric power operations can be catastrophic on the national security and economy. Due to the complexity of widely dispersed assets and the interdependency between computer, communication, and power systems, the requirement to meet security and quality compliance on the operations is a challenging issue. In recent years, NERC's cybersecurity standard was initiated to require utilities compliance on cybersecurity in control systems - NERC CIP 1200. This standard identifies several cyber-related vulnerabilities that exist in control systems and recommends several remedial actions (e.g., best practices). This paper is an overview of the cybersecurity issues for electric power control and automation systems, the control architectures, and the possible methodologies for vulnerability assessment of existing systems.
An Attentional Model for Speech Translation Without Transcription

For many low-resource languages, spoken language resources are more likely to be annotated with translations than transcriptions. This bilingual speech data can be used for word-spotting, spoken document retrieval, and even for documentation of endangered languages. We experiment with the neural, attentional model applied to this data. On phoneto-word alignment and translation reranking tasks, we achieve large improvements relative to several baselines. On the more challenging speech-to-word alignment task, our model nearly matches GIZA++’s performance on gold transcriptions, but without recourse to transcriptions or to a lexicon.
A review of metrics for knowledge management systems and knowledge management initiatives

Metrics are essential for the advancement of research and practice in an area. In knowledge management (KM), the process of measurement and development of metrics is made complex by the intangible nature of the knowledge asset. Further, the lack of standards for KM business metrics and the relative infancy of research on KM metrics points to a need for research in this area. This paper reviews KM metrics for research and practice and identifies areas where there is a gap in our understanding. It classifies existing research based on the units of evaluation such as user of KMS, KMS, project, KM process, KM initiative, and organization as a whole. The paper concludes by suggesting avenues for future research on KM and KMS metrics based on the gaps identified.
Prefrontal–cingulate interactions in action monitoring

We found that medial frontal cortex activity associated with action monitoring (detecting errors and behavioral conflict) depended on activity in the lateral prefrontal cortex. We recorded the error-related negativity (ERN), an event-related brain potential proposed to reflect anterior cingulate action monitoring, from individuals with lateral prefrontal damage or age-matched or young control participants. In controls, error trials generated greater ERN activity than correct trials. In individuals with lateral prefrontal damage, however, correct-trial ERN activity was equal to error-trial ERN activity. Lateral prefrontal damage also affected corrective behavior. Thus the lateral prefrontal cortex seemed to interact with the anterior cingulate cortex in monitoring behavior and in guiding compensatory systems.
Affordances of Augmented Reality in Science Learning: Suggestions for Future Research

Augmented reality (AR) is currently considered as having potential for pedagogical applications. However, in science education, research regarding AR-aided learning is in its infancy. To understand how AR could help science learning, this review paper firstly has identified two major approaches of utilizing AR technology in science education, which are named as image-based AR and locationbased AR. These approaches may result in different affordances for science learning. It is then found that students’ spatial ability, practical skills, and conceptual understanding are often afforded by image-based AR and location-based AR usually supports inquiry-based scientific activities. After examining what has been done in science learning with AR supports, several suggestions for future research are proposed. For example, more research is required to explore learning experience (e.g., motivation or cognitive load) and learner characteristics (e.g., spatial ability or perceived presence) involved in AR. Mixed methods of investigating learning process (e.g., a content analysis and a sequential analysis) and in-depth examination of user experience beyond usability (e.g., affective variables of esthetic pleasure or emotional fulfillment) should be considered. Combining image-based and location-based AR technology may bring new possibility for supporting science learning. Theories including mental models, spatial cognition, situated cognition, and social constructivist learning are suggested for the profitable uses of future AR research in science education.
Autonomic Identity Framework for the Internet of Things

The Internet of Things (IoT) will connect not only computers and mobile devices, but it will also interconnect smart buildings, houses, and cities, as well as electrical grids, gas plants, and water networks, automobiles, airplanes, etc. IoT will lead to the development of a wide range of advanced information services that are pervasive, cost-effective, and can be accessed from anywhere and at any time. However, due to the exponential number of interconnected devices, cyber-security in the IoT is a major challenge. It heavily relies on the digital identity concept to build security mechanisms such as authentication and authorization. Current centralized identity management systems are built around third party identity providers, which raise privacy concerns and present a single point of failure. In addition, IoT unconventional characteristics such as scalability, heterogeneity and mobility require new identity management systems to operate in distributed and trustless environments, and uniquely identify a particular device based on its intrinsic digital properties and its relation to its human owner. In order to deal with these challenges, we present a Blockchain-based Identity Framework for IoT (BIFIT). We show how to apply our BIFIT to IoT smart homes to achieve identity self-management by end users. In the context of smart home, the framework autonomously extracts appliances signatures and creates blockchain-based identifies for their appliance owners. It also correlates appliances signatures (low level identities) and owners identifies in order to use them in authentication credentials and to make sure that any IoT entity is behaving normally.
Sentiment analysis of online news text: a case study of appraisal theory

Sentiment analysis and emotion processing are attracting increasing interest in many fields. Computer and information scientists are developing automated methods for sentiment analysis of online text. Most of the research have focused on identifying sentiment polarity or orientation—whether a document, usually product or movie review, carries a positive or negative sentiment. It is time for researchers to address more sophisticated kinds of sentiment analysis. This paper evaluates a particular linguistic framework called appraisal theory for adoption in manual as well as automatic sentiment analysis of news text.  The appraisal theory is applied to the analysis of a sample of political news articles reporting on Iraq and economic policies of George W. Bush and Mahmoud Ahmadinejad to assess its utility and to identify challenges in adopting this framework.  The framework was useful in uncovering various aspects of sentiment that should be useful to researchers such as the appraisers and object of appraisal, bias of the appraisers and the author, type of attitude and manner of expressing the sentiment. Problems encountered include difficulty in identifying appraisal phrases and attitude categories because of the subtlety of expression in political news articles, lack of treatment of tense and timeframe, lack of a typology of emotions, and need to identify different types of behaviors (political, verbal and material actions) that reflect sentiment.  The study has identified future directions for research in automated sentiment analysis as well as sentiment analysis of online news text. It has also demonstrated how sentiment analysis of news text can be carried out.
Dynamic topic detection and tracking: A comparison of HDP, C-word, and cocitation methods

Cocitation and co-word methods have long been used to detect and track emerging topics in scientific literature, but both have weaknesses. Recently, while many researchers have adopted generative probabilistic models for topic detection and tracking, few have compared generative probabilistic models with traditional cocitation and co-word methods in terms of their overall performance. In this article, we compare the performance of hierarchical Dirichlet process (HDP), a promising generative probabilistic model, with that of the 2 traditional topic detecting and tracking methods— cocitation analysis and co-word analysis. We visualize and explore the relationships between topics identified by the 3 methods in hierarchical edge bundling graphs and time flow graphs. Our result shows that HDP is more sensitive and reliable than the other 2 methods in both detecting and tracking emerging topics. Furthermore, we demonstrate the important topics and topic evolution trends in the literature of terrorism research with the HDP method.
Effects of Prosocial, Neutral, and Violent Video Games on Children's Helpful and Hurtful Behaviors.

Recent research reveals that playing prosocial video games increases prosocial cognitions, positive affect, and helpful behaviors [Gentile et al., 2009; Greitemeyer and Osswald, 2009, 2010, 2011]. These results are consistent with the social-cognitive models of social behavior such as the general learning model [Buckley and Anderson, 2006]. However, no experimental studies have examined such effects on children. Previous research on violent video games suggests that short-term effects of video games are largely based on priming of existing behavioral scripts. Thus, it is unclear whether younger children will show similar effects. This research had 9-14 years olds play a prosocial, neutral, or violent video game, and assessed helpful and hurtful behaviors simultaneously through a new tangram measure. Prosocial games increased helpful and decreased hurtful behavior, whereas violent games had the opposite effects.
Multi-label Classification via Feature-aware Implicit Label Space Encoding

To tackle a multi-label classification problem with many classes, recently label space dimension reduction (LSDR) is proposed. It encodes the original label space to a low-dimensional latent space and uses a decoding process for recovery. In this paper, we propose a novel method termed FaIE to perform LSDR via Feature-aware Implicit label space Encoding. Unlike most previous work, the proposed FaIE makes no assumptions about the encoding process and directly learns a code matrix, i.e. the encoding result of some implicit encoding function, and a linear decoding matrix. To learn both matrices, FaIE jointly maximizes the recoverability of the original label space from the latent space, and the predictability of the latent space from the feature space, thus making itself feature-aware. FaIE can also be specified to learn an explicit encoding function, and extended with kernel tricks to handle non-linear correlations between the feature space and the latent space. Extensive experiments conducted on benchmark datasets well demonstrate its effectiveness.
Toward a Conceptual Framework of Culturally Relevant Pedagogy : An Overview of the Conceptual and Theoretical Literature

The United States is a diverse country with constantly changing demographics. The noticeable shift in demographics is even more phenomenal among the school-aged population. The increase of ethnic-minority student presence is largely credited to the national growth of the Hispanic population, which exceeded the growth of all other ethnic minority group students in public schools. Scholars have pondered over strategies to assist teachers in teaching about diversity (multiculturalism, racism, etc.) as well as interacting with the diversity found within their classrooms in order to ameliorate the effects of cultural discontinuity. One area that has developed in multicultural education literature is culturally relevant pedagogy (CRP). CRP maintains that teachers need to be non-judgmental and inclusive of the cultural backgrounds of their students in order to be effective facilitators of learning in the classroom. The plethora of literature on CRP, however, has not been presented as a testable theoretical model nor has it been systematically viewed through the lens of critical race theory (CRT). By examining the evolution of CRP among some of the leading scholars, the authors broaden this work through a CRT infusion which includes race and indeed racism as normal parts of American society that have been integrated into the educational system and the systematic aspects of school relationships. Their purpose is to infuse the tenets of CRT into an overview of the literature that supports a conceptual framework for understanding and studying culturally relevant pedagogy. They present a conceptual framework of culturally relevant pedagogy that is grounded in over a quarter of a century of research scholarship. By synthesizing the literature into the five areas and infusing it with the tenets of CRT, the authors have developed a collection of principles that represents culturally relevant pedagogy.
Semi-supervised feature extraction for EEG classification

Two semi-supervised feature extraction methods are proposed for electroencephalogram (EEG) classification. They aim to alleviate two important limitations in brain–computer interfaces (BCIs). One is on the requirement of small training sets owing to the need of short calibration sessions. The second is the time-varying property of signals, e.g., EEG signals recorded in the training and test sessions often exhibit different discriminant features. These limitations are common in current practical applications of BCI systems and often degrade the performance of traditional feature extraction algorithms. In this paper, we propose two strategies to obtain semi-supervised feature extractors by improving a previous feature extraction method extreme energy ratio (EER). The two methods are termed semi-supervised temporally smooth EER and semi-supervised importance weighted EER, respectively. The former constructs a regularization term on the preservation of the temporal manifold of test samples and adds this as a constraint to the learning of spatial filters. The latter defines two kinds of weights by exploiting the distribution information of test samples and assigns the weights to training data points and trials to improve the estimation of covariance matrices. Both of these two methods regularize the spatial filters to make them more robust and adaptive to the test sessions. Experimental results on data sets from nine subjects with comparisons to the previous EER demonstrate their better capability for classification.
Torque, Power, Losses, and Heat Calculation of a Transverse Flux Reluctance Machine With Soft Magnetic Composite Materials and Disk-Shaped Rotor

In this paper, the authors introduce a type of transverse flux reluctance machines. These machines work without permanent magnets or electric rotor excitation and hold several advantages, including a high power density, high torque, and compact design. Disadvantages are a high fundamental frequency and a high torque ripple that complicates the control of the motor. The device uses soft magnetic composites (SMCs) for the magnetic circuit, which allows complex stator geometries with 3-D magnetic flux paths. The winding is made from hollow copper tubes, which also form the main heat sink of the machine by using water as a direct copper coolant. Models concerning the design and computation of the magnetic circuit, torque, and the power output are described. A crucial point in this paper is the determination of hysteresis and eddy-current losses in the SMC and the calculation of power losses and current displacement in the copper winding. These are calculated with models utilizing a combination of analytic approaches and finite-element method simulations. Finally, a thermal model based on lumped parameters is introduced, and calculated temperature rises are presented.
Identifying Domain-Dependent Influential Microblog Users: A Post-Feature Based Approach

Users of a social network like to follow the posts published by influential users. Such posts usually are delivered quickly and thus will produce a strong influence on public opinions. In this paper, we focus on the problem of identifying domaindependent influential users(or topic experts). Some of traditional approaches are based on the post contents of users users to identify influential users, which may be biased by spammers who try to make posts related to some topics through a simple copy and paste. Others make use of user authentication information given by a service platform or user self description (introduction or label) in finding influential users. However, what users have published is not necessarily related to what they have registed and described. In addition, if there is no comments from other users, its less objective to assess a users post quality. To improve effectiveness of recognizing influential users in a topic of microblogs, we propose a post-feature based approach which is supplementary to postcontent based approaches. Our experimental results show that the post-feature based approach produces relatively higher precision than that of the content based approach.
Hardware Acceleration of Genetic Sequence Alignment

Next generation DNA sequencing machines have been improving at an exceptional rate; the subsequent analysis of the generated sequenced data has become a bottleneck in current systems. This paper explores the use of reconfigurable hardware to accelerate the short read mapping problem, where the positions of millions of short DNA sequences are located relative to a known reference sequence. The proposed design comprises of an alignment processor based on a backtracking variation of the FM-index algorithm. The design represents a full solution to the short read mapping problem, capable of efficient exact and approximate alignment. We use reconfigurable hardware to accelerate the design and find that an implementation targeting the MaxWorkstation performs considerably faster and more energy efficient than current CPU and GPU based software
Towards an Ontology-Driven Blockchain Design for Supply Chain Provenance

An interesting research problem in our age of Big Data is that of determining provenance. Granular evaluation of provenance of physical goods--e.g. tracking ingredients of a pharmaceutical or demonstrating authenticity of luxury goods--has often not been possible with today's items that are produced and transported in complex, inter-organizational, often internationally-spanning supply chains. Recent adoption of Internet of Things and Blockchain technologies give promise at better supply chain provenance. We are particularly interested in the blockchain as many favoured use cases of blockchain are for provenance tracking. We are also interested in applying ontologies as there has been some work done on knowledge provenance, traceability, and food provenance using ontologies. In this paper, we make a case for why ontologies can contribute to blockchain design. To support this case, we analyze a traceability ontology and translate some of its representations to smart contracts that execute a provenance trace and enforce traceability constraints on the Ethereum blockchain platform.