nils-herrmann/clean_scirepeval huggingface dataset:
Narcissism and Sensitivity to Criticism : A Preliminary Investigation

Two studies investigated the connection between narcissism and sensitivity to criticism. In study 1, participants completed the Narcissistic Personality Inventory (NPI) and the Sensitivity to Criticism Scale (SCS) and were asked to construct and deliver speeches to be rated by performance judges. They were then asked whether they would like to receive evaluative feedback. Narcissism and sensitivity to criticism were mildly, but not significantly, negatively correlated and had contrasting relationships with choices regarding feedback. Highly narcissistic participants tended to seek (rather than avoid) feedback, whereas highly sensitive participants tended to reject feedback opportunities. Study 2 examined the relationship between sensitivity to criticism and both overt and covert narcissism. Those scoring high on the trait narcissism, as measured by the NPI, tended to be less sensitive to criticism, sought (rather than avoided) feedback opportunities, experienced little internalized negative emotions in response to “extreme” feedback conditions, and did not expect to ruminate over their performance. By contrast, participants scoring high on a measure of “covert narcissism” were high in sensitivity to criticism, tended to avoid feedback opportunities, experienced high levels of internalized negative emotions, and showed high levels of expected rumination. These findings suggest that the relationship between narcissism and sensitivity to criticism is highly dependent upon the definition or “form” of narcissism considered.
Efficient and Robust Question Answering from Minimal Context over Documents

Robin Jia, Percy Liang. “Adversarial examples for evaluating reading comprehension systems.” In EMNLP 2017.  Caiming Xiong, Victor Zhong, Richard Socher. “DCN+ Mixed objective and deep residual coattention for question answering.” In ICLR 2018.  Danqi Chen, Adam Fisch, Jason Weston, Antoine Bordes. “Reading wikipedia to answer open-domain questions.” In ACL 2017. Check out more of our work at https://einstein.ai/research
Modelling Irony in Twitter

Computational creativity is one of the central research topics of Artificial Intelligence and Natural Language Processing today. Irony, a creative use of language, has received very little attention from the computational linguistics research point of view. In this study we investigate the automatic detection of irony casting it as a classification problem. We propose a model capable of detecting irony in the social network Twitter. In cross-domain classification experiments our model based on lexical features outperforms a word-based baseline previously used in opinion mining and achieves state-of-the-art performance. Our features are simple to implement making the approach easily replicable.
Virtual Scrum: A teaching aid to introduce undergraduate software engineering students to scrum

Teaching agile practices is becoming a priority in Software engineering curricula as a result of the increasing use of agile methods (AMs) such as Scrum in the software industry. Limitations in time, scope, and facilities within academic contexts hinder students’ hands-on experience in the use of professional AMs. To enhance students’ exposure to Scrum, we have developed Virtual Scrum, an educational virtual world that simulates a Scrum-based team room through virtual elements such as blackboards, a Web browser, document viewers, charts, and a calendar. A preliminary version of Virtual Scrum was tested with a group of 45 students running a capstone project with and without Virtual Scrum support. Students’ feedback showed that Virtual Scrum is a viable and effective tool to implement the different elements in a Scrum team room and to perform activities throughout the Scrum process. 2013 Wiley Periodicals, Inc. Comput Appl Eng Educ 23:147–156, 2015; View this article online at wileyonlinelibrary.com/journal/cae; DOI 10.1002/cae.21588
IGCV3: Interleaved Low-Rank Group Convolutions for Efficient Deep Neural Networks

In this paper, we are interested in building lightweight and efficient convolutional neural networks. Inspired by the success of two design patterns, composition of structured sparse kernels, e.g., interleaved group convolutions (IGC), and composition of lowrank kernels, e.g., bottle-neck modules, we study the combination of such two design patterns, using the composition of structured sparse low-rank kernels, to form a convolutional kernel. Rather than introducing a complementary condition over channels, we introduce a loose complementary condition, which is formulated by imposing the complementary condition over super-channels, to guide the design for generating a dense convolutional kernel. The resulting network is called IGCV3. We empirically demonstrate that the combination of low-rank and sparse kernels boosts the performance and the superiority of our proposed approach to the state-of-the-arts, IGCV2 and MobileNetV2 over image classification on CIFAR and ImageNet and object detection on COCO. Code and models are available at https://github.com/homles11/IGCV3.
Comparative Evaluation and Combination of Audio Tempo Estimation Approaches

The automatic analysis of musical tempo from audio is still an open research task in the Music Information Retrieval (MIR) community. The goal of this paper is to provide an updated comparative evaluation of different methods for audio tempo estimation. We overview, following the same block diagram, 23 documented methods. We then analyze their accuracy, error distribution and statistical differences, and we discuss which strategies can provide better performance for different input material. We then take advantage of their complementarity to improve the results by combining different methods, and we finally analyze the limitations of current approaches and give some ideas for future work on the task.
Prototypicality in a Linguistic Context : Effects on Sentence Structure

Three studies explored relationships between prototypicahty and the structure of sentences in recall, preference ratings, and natural dictionary definitions. The first experiment showed that sentences were systematically changed in retail to allow prototypical instances of categories to be mentioned before nonprototypical instances. In the second experiment, sentences in which the prototype preceded the nonprototype were judged more natural than sentences with the opposite order. Finally, an examination of dictionary definitions of categories found that prototypes tended to occur before nonprototypes. These results can be explained in terms of the sensitivity of sentence production processes to the lexical or conceptual accessibility of prototypes. Such processes appear to adjust serial positions and, to a lesser extent, grammatical roles in order to allow lexical items to be produced soon after they are retrieved.
SOCIAL MEDIA MARKETING PLAN FOR A SME

Tampereen ammattikorkeakoulu Tampere University of Applied Sciences Degree Programme in International Business JÄRVENSIVU, VEERA: Social Media Marketing Plan for a SME Bachelor's thesis 53 pages, of which appendices 31 pages October 2017 The aim of this bachelor’s thesis was to create an efficient, low-cost social media marketing plan for a small clothing company called Nikitrade. The data gathered for establishing the marketing plan were mainly secondary data consisting of multiple books and articles related to the topic. For qualitative data gathering, interviews and discussions with the company owners were used. Because of the competitive sensitiveness of the subject, the social marketing plan itself is not published. The thesis report includes the important factors for establishing a marketing plan for a small or medium sized enterprise. The marketing plan explains the importance of a thorough analysis of the current situation, both internal and external. It also introduces strategies that should be established in order to create an efficient marketing plan. Lastly, it explains the importance of metrics and measuring the success of reaching the objectives. For discussion, the author of the thesis has gathered the key points of the social media marketing plan she has created. The most important issues of social media marketing are staying consistent in activity, quality and visuals.
The Linear Programming Approach to Approximate Dynamic Programming

The curse of dimensionality gives rise to prohibitive computational requirements that render infeasible the exact solution of large-scale stochastic control problems. We study an efficient method based on linear programming for approximating solutions to such problems. The approach “fits” a linear combination of pre-selected basis functions to the dynamic programming cost-to-go function. We develop error bounds that offer performance guarantees and also guide the selection of both basis functions and “state-relevance weights” that influence quality of the approximation. Experimental results in the domain of queueing network control provide empirical support for the methodology.
Two faces of narcissism on SNS: The distinct effects of vulnerable and grandiose narcissism on SNS privacy control

This study suggests narcissism as an important psychological factor that predicts one’s behavioral intention to control information privacy on SNS. Particularly, we approach narcissism as a two-dimensional construct—vulnerable and grandiose narcissism—to provide a better understanding of the role of narcissism in SNS usage. As one of the first studies to apply a two-dimensional approach to narcissism in computer-mediated communication, our results show that vulnerable narcissism has a significant positive effect on behavioral intention to control privacy on SNS, while grandiose narcissism has no effect. This effect was found when considering other personality traits, including self-esteem, computer anxiety, and concern for information privacy. The results indicate that unidimensional approaches to narcissism cannot solely predict SNS behaviors, and the construct of narcissism should be broken down into two orthogonal constructs.
Who Uses Social Networking Sites ? Exploring Associations among Personality and the Relationship with Facebook , Twitter , and Instagram

Social network sites are attracting the attention of numerous researchers. Recently, several studies have examined how people are forming identities on social networking sites. Therefore, greater levels of variable specification are required to further the research on this topic. Testing the relationships among key variables is important when trying to understand these online social forums. Although there has been a rapid growth in the use of Facebook, Twitter, and Instagram, psychological theory explaining the use of social networks lags. Therefore, the purpose of this research is 1) to examine how personality traits influence which type of social network site individuals will use, and 2) to investigate whether specific social network sites are eliciting the display of particular personality characteristics. Fifty-five participants were recruited through social media (i.e., Facebook) and 239 participants were obtained from a Western University to participate in a survey. Researchers found no associations between overt narcissism and having social network accounts. Surprisingly, there was significant correlations between having personality characteristics of both conscientiousness and honesty and using Instagram. Finally, there was a significant correlation amongst covert narcissism and using Facebook. By recognizing relationships between variables such as personality and social network site usage, psychologists can work towards understanding how the online web is changing our social lives and social behaviors.
Performance Evaluation of IoT Result for Machine Learning

The vision of Future Internet based on standard communication protocols considers the merging of computer networks, Internet of Things (IoT), Internet of People (IoP), Internet of Energy (IoE), Internet of Media (IoM), and Internet of Services (IoS), into a common global IT platform of seamless networks and networked “smart things/objects”. However, with the widespread deployment of networked, intelligent sensor technologies, an Internet of Things (IoT) is steadily evolving, much like the Internet decades ago. In the future, hundreds of billions of smart sensors and devices will interact with one another without human intervention, on a Machine-to-Machine (M2M) basis. They will generate an enormous amount of data at an unprecedented scale and resolution, providing humans with information and control of events and objects even in remote physical environments. This paper will provide an overview of performance evaluation, challenges and opportunities of IOT results for machine learning presented by this new paradigm.
Aspect Term and Opinion Target Extraction from Web Product Reviews using Semi-markov Conditional Random Fields with Word Embeddings as Features

Descriptions and reviews for products abound on the web and characterise the corresponding products through their aspects. Extracting these aspects is essential to better understand these descriptions, e.g., for comparing or recommending products. Current pattern-based aspect extraction approaches focus on flat patterns extracting flat sets of adjective-noun pairs. Aspects also have crucial importance on sentiment classification in which sentiments are matched with aspect-level expressions. A preliminary step in both aspect extraction and aspect based sentiment analysis is to detect aspect terms and opinion targets. In this paper, we propose a sequential learning approach to extract aspect terms and opinion targets from opinionated documents. For the first time, we use semi-markov conditional random fields for this task and we incorporate word embeddings as features into the learning process. We get comparative results on the benchmark datasets for the subtask of aspect term extraction in SemEval-2014 Task 4 and the subtask of opinion target extraction in SemEval-2015 Task 12. Our results show that word embeddings improve the detection accuracy for aspect terms and opinion targets.
An alternative "description of personality": the big-five factor structure.

In the 45 years since Cattell used English trait terms to begin the formulation of his "description of personality," a number of investigators have proposed an alternative structure based on 5 orthogonal factors. The generality of this 5-factor model is here demonstrated across unusually comprehensive sets of trait terms. In the first of 3 studies, 1,431 trait adjectives grouped into 75 clusters were analyzed; virtually identical structures emerged in 10 replications, each based on a different factor-analytic procedure. A 2nd study of 479 common terms grouped into 133 synonym clusters revealed the same structure in 2 samples of self-ratings and in 2 samples of peer ratings. None of the factors beyond the 5th generalized across the samples. In the 3rd study, analyses of 100 clusters derived from 339 trait terms suggest their potential utility as Big-Five markers in future studies.
A Joint Sequential and Relational Model for Frame-Semantic Parsing

We introduce a new method for framesemantic parsing that significantly improves the prior state of the art. Our model leverages the advantages of a deep bidirectional LSTM network which predicts semantic role labels word by word and a relational network which predicts semantic roles for individual text expressions in relation to a predicate. The two networks are integrated into a single model via knowledge distillation, and a unified graphical model is employed to jointly decode frames and semantic roles during inference. Experiments on the standard FrameNet data show that our model significantly outperforms existing neural and non-neural approaches, achieving a 5.7 F1 gain over the current state of the art, for full frame structure extraction.
Adoptive cell transfer as personalized immunotherapy for human cancer

Adoptive cell therapy (ACT) is a highly personalized cancer therapy that involves administration to the cancer-bearing host of immune cells with direct anticancer activity. ACT using naturally occurring tumor-reactive lymphocytes has mediated durable, complete regressions in patients with melanoma, probably by targeting somatic mutations exclusive to each cancer. These results have expanded the reach of ACT to the treatment of common epithelial cancers. In addition, the ability to genetically engineer lymphocytes to express conventional T cell receptors or chimeric antigen receptors has further extended the successful application of ACT for cancer treatment.
The Impact of Brands on Consumer Purchase Intentions

The purpose of this study is to incorporate the core brand image, brand attitude and brand attachment with environmental consequences to testify the impact on the consumer purchase intentions. Does environmental consequences has some role while formatting purchase intention of the customer or people do not think about it. Either customers want to attach themselves with brand only or they also keep into account the corporate social responsibility index as well. Results show that core brand image and brand attitude has positive impact whereas environmental consequences have negative effect on the purchasing intention of customers (smokers).
Top-Down Tree Structured Text Generation

Text generation is a fundamental building block in natural language processing tasks. Existing sequential models performs autoregression directly over the text sequence and have difficulty generating long sentences of complex structures. This paper advocates a simple approach that treats sentence generation as a tree-generation task. By explicitly modelling syntactic structures in a constituent syntactic tree and performing topdown, breadth-first tree generation, our model fixes dependencies appropriately and performs implicit global planning. This is in contrast to transition-based depth-first generation process, which has difficulty dealing with incomplete texts when parsing and also does not incorporate future contexts in planning. Our preliminary results on two generation tasks and one parsing task demonstrate that this is an effective strategy.
Application-Aware Traffic Redirection: A Mobile Edge Computing Implementation Toward Future 5G Networks

With the development of network technology, there are billions of devices accessing resources and services on the cloud through mobile telecommunication network. A great number of connections and data packets must be handled by the mobile network. It not only consumes the limited spectrum resources and network bandwidth, but also reduces the service quality of applications. To alleviate the problem, the concept of Mobile Edge Computing (MEC) has been proposed by European Telecommunications Standard Institute (ETSI) in 2014. MEC suggests to provide IT and cloud computing capabilities at the network edge to offer low-latency and high-bandwidth service. The architecture and the benefits of MEC have been discussed in many recent literature. But the implementation of underlying network is rarely discussed or evaluated in practice. In this paper, we present our prototype implementation of a MEC platform by developing an application-aware traffic redirection mechanism at edge network to reduce service latency and network bandwidth consumption. Our implementation is based on OAI, an open source project of 5G SoftRAN cellular system. To the best of our knowledge, it is also one of the few MEC solutions that have been built for 5G networks in practice.
3D bioprinting of tissues and organs

Additive manufacturing, otherwise known as three-dimensional (3D) printing, is driving major innovations in many areas, such as engineering, manufacturing, art, education and medicine. Recent advances have enabled 3D printing of biocompatible materials, cells and supporting components into complex 3D functional living tissues. 3D bioprinting is being applied to regenerative medicine to address the need for tissues and organs suitable for transplantation. Compared with non-biological printing, 3D bioprinting involves additional complexities, such as the choice of materials, cell types, growth and differentiation factors, and technical challenges related to the sensitivities of living cells and the construction of tissues. Addressing these complexities requires the integration of technologies from the fields of engineering, biomaterials science, cell biology, physics and medicine. 3D bioprinting has already been used for the generation and transplantation of several tissues, including multilayered skin, bone, vascular grafts, tracheal splints, heart tissue and cartilaginous structures. Other applications include developing high-throughput 3D-bioprinted tissue models for research, drug discovery and toxicology.
The Uncanny Valley : does it exist ?

The ‘Uncanny Valley’ refers to a sense of unease and discomfort when people look at increasingly realistic virtual humans. Despite growing academic interest in the Uncanny Valley our understanding is limited and there has been little rigorous questioning to determine if the phenomenon actually exists. The Uncanny Valley questions widely held assumptions about the correlation between realism and believability within a virtual world. There is considerable anecdotal evidence for the uncanny from film, CGI and sculpture, but this does not in itself support the valley model. Four hypothesises are proposed; considering the role of presence, mismatch of cue realism, the contribution of the eyes and cultural habituation. Future research aims are then described in order to experimentally test the Uncanny Valley.
Author Identification for E-mail Forensic

E-mail communication has become the need of the hour, with the advent of Internet. However, it is being abused for various illegitimate purposes, such as, spamming, drug trafficking, cyber bullying, phishing, racial vilification, child pornography, and sexual harassment, etc. Several cyber crimes such as identity theft, plagiarism, internet fraud stipulate that the true identity of the e-mail&apos;s author be revealed, so that the culprits can be punished in the court of law, by gathering credible evidence against them. Forensic analysis can play a crucial role here, by letting the forensic investigator to gather evidence by examining suspected e-mail accounts. In this context, automated authorship identification can assist the forensic investigator in cyber crime investigation. In this paper we discuss how existing state-of-the-art techniques have been employed for author identification of e-mails and we propose our model for identifying most plausible author of e-mails.
Social media analytics - Challenges in topic discovery, data collection, and data preparation

Since an ever-increasing part of the population makes use of social media in their day-to-day lives, social media data is being analysed in many different disciplines. The social media analytics process involves four distinct steps, data discovery, collection, preparation, and analysis. While there is a great deal of literature on the challenges and difficulties involving specific data analysis methods, there hardly exists research on the stages of data discovery, collection, and preparation. To address this gap, we conducted an extended and structured literature analysis through which we identified challenges addressed and solutions proposed. The literature search revealed that the volume of data was most often cited as a challenge by researchers. In contrast, other categories have received less attention. Based on the results of the literature search, we discuss the most important challenges for researchers and present potential solutions. The findings are used to extend an existing framework on social media analytics. The article provides benefits for researchers and practitioners who wish to collect and analyse social media data.