nils-herrmann/clean_scirepeval huggingface dataset:
An Overview of Sentiment Analysis in Social Media and Its Applications in Disaster Relief

Sentiment analysis refers to the class of computational and natural language processing based techniques used to identify, extract or characterize subjective information, such as opinions, expressed in a given piece of text. The main purpose of sentiment analysis is to classify a writer’s attitude towards various topics into positive, negative or neutral categories. Sentiment analysis has many applications in different domains including, but not limited to, business intelligence, politics, sociology, etc. Recent years, on the other hand, have witnessed the advent of social networking websites, microblogs, wikis and Web applications and consequently, an unprecedented growth in user-generated data is poised for sentiment mining. Data such as web-postings, Tweets, videos, etc., all express opinions on various topics and events, offer immense opportunities to study and analyze human opinions and sentiment. In this chapter, we study the information published by individuals in social media in cases of natural disasters and emergencies and investigate if such information could be used by first responders to improve situational awareness and crisis management. In particular, we explore applications of sentiment analysis and demonstrate how sentiment mining in social media can be exploited to determine how local crowds react during a disaster, and how such information can be used to improve disaster management. Such information can also be used to help assess the extent of the devastation and find people who are in specific need during an emergency situation. We first provide the formal definition of sentiment analysis in social media and cover traditional and the state-of-the-art approaches while highlighting contributions, shortcomings, and pitfalls due to the composition of online media streams. Next we discuss the relationship among social media, disaster relief and situational awareness and explain how social media is used in these contexts with the focus on sentiment analysis. In order to enable quick analysis of real-time geo-distributed data, we will detail applications of visual analytics with an emphasis on sentiment visualization. Finally, we conclude the chapter with a discussion of research challenges in sentiment analysis and its application in disaster relief.
A fingerprint identification algorithm: Combining image enhancement and chaos synchronisation classifier

Fingerprint identification is very popular among the identification in biometric security systems. The identification process comprises of image enhancement, feature extraction and pattern classification. The adjustment of grey scale values improves the intensity values of the image. A region mask is generated which provides a stable sampling window to extract features. Multiple CS based classifier along with maximum likelihood matching is used for pattern identification. Using the samples taken from FVC2002 database the performance of the proposed method is tested.
Evidence on feasibility and effective use of mHealth strategies by frontline health workers in developing countries: systematic review*

Given the large-scale adoption and deployment of mobile phones by health services and frontline health workers (FHW), we aimed to review and synthesise the evidence on the feasibility and effectiveness of mobile-based services for healthcare delivery.



Five databases - MEDLINE, EMBASE, Global Health, Google Scholar and Scopus - were systematically searched for relevant peer-reviewed articles published between 2000 and 2013. Data were extracted and synthesised across three themes as follows: feasibility of use of mobile tools by FHWs, training required for adoption of mobile tools and effectiveness of such interventions.



Forty-two studies were included in this review. With adequate training, FHWs were able to use mobile phones to enhance various aspects of their work activities. Training of FHWs to use mobile phones for healthcare delivery ranged from a few hours to about 1 week. Five key thematic areas for the use of mobile phones by FHWs were identified as follows: data collection and reporting, training and decision support, emergency referrals, work planning through alerts and reminders, and improved supervision of and communication between healthcare workers. Findings suggest that mobile based data collection improves promptness of data collection, reduces error rates and improves data completeness. Two methodologically robust studies suggest that regular access to health information via SMS or mobile-based decision-support systems may improve the adherence of the FHWs to treatment algorithms. The evidence on the effectiveness of the other approaches was largely descriptive and inconclusive.



Use of mHealth strategies by FHWs might offer some promising approaches to improving healthcare delivery; however, the evidence on the effectiveness of such strategies on healthcare outcomes is insufficient.
Promoting Well-Being: The Contribution of Emotional Intelligence

Adopting a primary prevention perspective, this study examines competencies with the potential to enhance well-being and performance among future workers. More specifically, the contributions of ability-based and trait models of emotional intelligence (EI), assessed through well-established measures, to indices of hedonic and eudaimonic well-being were examined for a sample of 157 Italian high school students. The Mayer-Salovey-Caruso Emotional Intelligence Test was used to assess ability-based EI, the Bar-On Emotional Intelligence Inventory and the Trait Emotional Intelligence Questionnaire were used to assess trait EI, the Positive and Negative Affect Scale and the Satisfaction With Life Scale were used to assess hedonic well-being, and the Meaningful Life Measure was used to assess eudaimonic well-being. The results highlight the contributions of trait EI in explaining both hedonic and eudaimonic well-being, after controlling for the effects of fluid intelligence and personality traits. Implications for further research and intervention regarding future workers are discussed.
CompNet: Complementary Segmentation Network for Brain MRI Extraction

Brain extraction is a fundamental step for most brain imaging studies. In this paper, we investigate the problem of skull stripping and propose complementary segmentation networks (CompNets) to accurately extract the brain from T1-weighted MRI scans, for both normal and pathological brain images. The proposed networks are designed in the framework of encoder-decoder networks and have two pathways to learn features from both the brain tissue and its complementary part located outside of the brain. The complementary pathway extracts the features in the non-brain region and leads to a robust solution to brain extraction from MRIs with pathologies, which do not exist in our training dataset. We demonstrate the effectiveness of our networks by evaluating them on the OASIS dataset, resulting in the state of the art performance under the two-fold cross-validation setting. Moreover, the robustness of our networks is verified by testing on images with introduced pathologies and by showing its invariance to unseen brain pathologies. In addition, our complementary network design is general and can be extended to address other image segmentation problems with better generalization.
Lexical Semantics and Pragmatics

In this paper I discuss some general problems one is confronted with when trying to analyze the utterance of words within concrete conceptual and contextual settings and to go beyond the aspects of meaning typically investigated by a contrastive analysis of lexemes within the Katz-Fodor tradition of semantics. After emphasizing some important consequences of the traditional view, several phenomena are collected that seem to conflict with the theoretical settings made by it. Some extensions of the standard theory are outlined that take a broader view of language interpretation and claim to include pragmatic aspects of (utterance) meaning. The models critically considered include Bartsch's indexical theory of polysemy, Bierwisch's two-level semantics and Pustejovsky's generative lexicon. Finally, I argue in favor of a particular account of the division of labor between lexical semantics and pragmatics. This account combines the idea of (radical) semantic underspecification in the lexicon with a theory of pragmatic strengthening (based on conversational implicatures).
Exact and Inexact Subsampled Newton Methods for Optimization

The paper studies the solution of stochastic optimization problems in which approximations to the gradient and Hessian are obtained through subsampling. We first consider Newton-like methods that employ these approximations and discuss how to coordinate the accuracy in the gradient and Hessian to yield a superlinear rate of convergence in expectation. The second part of the paper analyzes an inexact Newton method that solves linear systems approximately using the conjugate gradient (CG) method, and that samples the Hessian and not the gradient (the gradient is assumed to be exact). We provide a complexity analysis for this method based on the properties of the CG iteration and the quality of the Hessian approximation, and compare it with a method that employs a stochastic gradient iteration instead of the CG method. We report preliminary numerical results that illustrate the performance of inexact subsampled Newton methods on machine learning applications based on logistic regression.
Understanding the role of community in crowdfunding work

Crowdfunding provides a new opportunity for entrepre-neurs to launch ventures without having to rely on traditional funding mechanisms, such as banks and angel investing. Despite its rapid growth, we understand little about how crowdfunding users build ad hoc online communities to undertake this new way of performing entrepreneurial work. To better understand this phenomenon, we performed a qualitative study of 47 entrepreneurs who use crowdfunding platforms to raise funds for their projects. We identify community efforts to support crowdfunding work, such as providing mentorship to novices, giving feedback on campaign presentation, and building a repository of example projects to serve as models. We also identify where community efforts and technologies succeed and fail at support-ing the work in order to inform the design of crowdfunding support tools and systems.
Counterfactual thinking.

Counterfactuals are mental representations of alternatives to the past and produce consequences that are both beneficial and aversive to the individual. These apparently contradictory effects are integrated in a functionalist model of counterfactual thinking. The author reviews research in support of the assertions that (a) counterfactual thinking is activated automatically in response to negative affect, (b) the content of counterfactuals targets particularly likely causes of misfortune, (c) counterfactuals produce negative affective consequences through a contrast-effect mechanism and positive inferential consequences through a causal-inference mechanism, and (d) the net effect of counterfactual thinking is beneficial.
Face Generation for Low-Shot Learning Using Generative Adversarial Networks

Recently, low-shot learning has been proposed for handling the lack of training data in machine learning. Despite of the importance of this issue, relatively less efforts have been made to study this problem. In this paper, we aim to increase the size of training dataset in various ways to improve the accuracy and robustness of face recognition. In detail, we adapt a generator from the Generative Adversarial Network (GAN) to increase the size of training dataset, which includes a base set, a widely available dataset, and a novel set, a given limited dataset, while adopting transfer learning as a backend. Based on extensive experimental study, we conduct the analysis on various data augmentation methods, observing how each affects the identification accuracy. Finally, we conclude that the proposed algorithm for generating faces is effective in improving the identification accuracy and coverage at the precision of 99% using both the base and novel set.
Sports-related knee injuries in female athletes: what gives?

Knee injuries occur commonly in sports, limiting field and practice time and performance level. Although injury etiology relates primarily to sports specific activity, female athletes are at higher risk of knee injury than their male counterparts in jumping and cutting sports. Particular pain syndromes such as anterior knee pain and injuries such as noncontact anterior cruciate ligament (ACL) injuries occur at a higher rate in female than male athletes at a similar level of competition. Anterior cruciate ligament injuries can be season or career ending, at times requiring costly surgery and rehabilitation. Beyond real-time pain and functional limitations, previous injury is implicated in knee osteoarthritis occurring later in life. Although anatomical parameters differ between and within the sexes, it is not likely this is the single reason for knee injury rate disparities. Clinicians and researchers have also studied the role of sex hormones and dynamic neuromuscular imbalances in female compared with male athletes in hopes of finding the causes for the increased rate of ACL injury. Understanding gender differences in knee injuries will lead to more effective prevention strategies for women athletes who currently suffer thousands of ACL tears annually. To meet the goal in sports medicine of safely returning an athlete to her sport, our evaluation, assessment, treatments and prevention strategies must reflect not only our knowledge of the structure and innervations of the knee but neuromuscular control in multiple planes and with multiple forces while at play.
Content-Based Image Retrieval : Theory and Applications 2 Basic Concepts

Advances in data storage and image acquisition technologies have enabled the creation of large image datasets. In this scenario, it is necessary to develop appropriate information systems to efficiently manage these collections. The commonest approaches use the so-called Content-Based Image Retrieval (CBIR) systems. Basically, these systems try to retrieve images similar to a user-defined specification or pattern (e.g., shape sketch, image example). Their goal is to support image retrieval based on content properties (e.g., shape, color, texture), usually encoded into feature vectors. One of the main advantages of the CBIR approach is the possibility of an automatic retrieval process, instead of the traditional keyword-based approach, which usually requires very laborious and time-consuming previous annotation of database images. The CBIR technology has been used in several applications such as fingerprint identification, biodiversity information systems, digital libraries, crime prevention, medicine, historical research, among others. This paper aims to introduce the problems and challenges concerned with the creation of CBIR systems, to describe the existing solutions and applications, and to present the state of the art of the existing research in this area.
Enhance word representation for out-of-vocabulary on Ubuntu dialogue corpus

Ubuntu dialogue corpus is the largest public available dialogue corpus to make it feasible to build end-to-end deep neural network models directly from the conversation data. One challenge of Ubuntu dialogue corpus is the large number of out-of-vocabulary words. In this paper we proposed a method which combines the general pre-trained word embedding vectors with those generated on the taskspecific training set to address this issue. We integrated character embedding into Chen et al’s Enhanced LSTM method (ESIM) and used it to evaluate the effectiveness of our proposed method. For the task of next utterance selection, the proposed method has demonstrated a significant performance improvement against original ESIM and the new model has achieved state-of-the-art results on both Ubuntu dialogue corpus and Douban conversation corpus. In addition, we investigated the performance impact of end-of-utterance and end-of-turn token tags.
Light-weight pixel context encoders for image inpainting

In this work we propose Pixel Content Encoders (PCE), a lightweight image inpainting model, capable of generating novel content for large missing regions in images. Unlike previously presented convolutional neural network based models, our PCE model has an order of magnitude fewer trainable parameters. Moreover, by incorporating dilated convolutions we are able to preserve fine grained spatial information, achieving state-of-the-art performance on benchmark datasets of natural images and paintings. Besides image inpainting, we show that without changing the architecture, PCE can be used for image extrapolation, generating novel content beyond existing image boundaries.
Performance effects of using the Balanced Scorecard : a note on the Dutch experience

This article aims to contribute to understanding how to use the Balanced Scorecard (BSC) effectively. The BSC lends itself to various interpretations. This article explores how the way in which the BSC is used affects performance. Empirical evidence from Dutch firms suggests BSC use will not automatically improve company performance, but that the manner of its use matters: BSC use that complements corporate strategy positively influences company performance, while BSC use that is not related to the strategy may decrease it. We discuss the findings and offer managers guidance for optimal use of the BSC. Q 2004
Psychedelic drugs should be legally reclassified so that researchers can investigate their therapeutic potential.

Psychedelic drugs, especially lysergic acid diethylamide (LSD) and psilocybin, which is found in thePsilocybe genus of “magic” mushrooms that grow throughout the United Kingdom, were extensively used and researched in clinical psychiatry before their prohibition in 1967. Hundreds of papers, involving tens of thousands of patients, presented evidence for their use as psychotherapeutic catalysts of mentally beneficial change in many psychiatric disorders, problems of personality development, recidivistic behaviour, and existential anxiety. This research abruptly ended after 1967, when psychedelics were legally classified as schedule 1 drugs under the UKMisuse of Drugs Regulations and as class A drugs under the UKMisuse of Drugs Act 1971. Schedule 1 in the UK broadly mirrors schedule I of the 1971 United Nations Convention on Psychotropic Substances, adoption of which is a requirement of UN membership. This classification denoted psychedelic drugs as having no accepted medical use and the greatest potential for harm, despite the existence of research evidence to the contrary. Indeed, in 1992 John Ehrlichman, former assistant to Richard Nixon—the US president who intensified the “war on drugs” in the 1970s—notoriously admitted that the administration had lied about the harmful effects of drugs and had manipulated media coverage of them for political advantage.Nearly 50 years later psychedelic drugs remain more legally restricted than heroin and cocaine, which are schedule 2, class A in the UK. But no evidence shows that psychedelic drugs are habit forming; little evidence shows that they are harmful in controlled settings; and much historical evidence has shown that they could have use in common psychiatric disorders. A growing number of organisations, most recently in Norway, are questioning the need for such draconian restrictions. Where’s the harm?
The inverted microscope method of estimating algal numbers and the statistical basis of estimations by counting

Various methods for the estimation of populations of algae and other small freshwater organisms are described. A method of counting is described in detail. It is basically that of Utermöhl and uses an inverted microscope. If the organisms are randomly distributed, a single count is sufficient to obtain an estimate of their abundance and confidence limits for this estimate, even if pipetting, dilution or concentration are involved. The errors in the actual counting and in converting colony counts to cell numbers are considered and found to be small relative to the random sampling error. Data are also given for a variant of Utermöhl's method using a normal microscope and for a method of using a haemocytometer for the larger plankton algae.
Internal combustion engine's throttle control as a motivational theme for teaching microprocessors systems lab classes

The increased fuel economy and driveability of modern internal combustion engine vehicles (ICEVs) are the result of the application of advanced digital electronics to control the operation of the internal combustion engine (ICE). Microprocessors (and microcontrollers) play a key role in the engine control, by precisely controlling the amount of both air and fuel admitted into the cylinders. Air intake is controlled by utilizing a throttle valve equipped with a motor and gear mechanism as actuator, and a sensor enabling the measurement of the angular position of the blades. This paperwork presents a lab setup that allows students to control the throttle position using a microcontroller that runs a program developed by them. A commercial throttle body has been employed, whereas a power amplifier and a microcontroller board have been hand assembled to complete the experimental setup. This setup, while based in a high-tech, microprocessor-based solution for a real-world, engine operation optimization problem, has the potential to engage students around a hands-on multidisciplinary lab activity and ignite their interest in learning fundamental and advanced topics of microprocessors systems.
Why the Net is not a Public Sphere :

What is the relation between the idea of the public sphere and computer-mediated interaction? I argue that the notion of the public sphere is not only inapplicable to the Net, but also and more importantly, that it is damaging to practices of democracy under conditions of contemporary technoculture, conditions Manuel Castells theorizes as capitalism in the information mode of development and which I refer to as communicative capitalism. 1 As an alternative to the public sphere, I consider the potential of a political architecture rooted in a notion of networks. To the extent that such an architecture can center democratic practice in conflict and contestation, so can it open up the democratic imagination in the networked societies of communicative capitalism.
Fraud Triangle Theory and Fraud Diamond Theory . Understanding the Convergent and Divergent For Future Research

Many of the world’s most prominent organizations have experienced large-scale frauds. These frauds have had disturbing effects on our world’s economy in addition to contributing unnecessary suffering and increased unemployment for the low and middle class. With the aim of further understanding the fundamental motivations of fraud, this paper takes an in-depth look at the convergent and divergent of two classical fraud theories which are: (i) fraud triangle theory; and (ii) fraud diamond theory. This comparison is important to assist anti-graft bodies and organizations in formulating a practical strategy to prevent and investigate organizational frauds. The paper takes a conceptual approach by first examining the concept of fraud, then discussing the convergence of the two classical theories, and finally differentiating them. By doing so, the similarities and differences between them are highlighted and appreciated for fraud prevention purposes. The study uses secondary sources of information obtained from journal articles, textbooks and the internet. The discussion of the two theories contributes to the understanding of frauds especially by forensic accountants, auditors, fraud examiners and other antifraud bodies. The study also serves as guidance for further fraud related research.