nils-herrmann/clean_scirepeval huggingface dataset:
COMMIT-P1WP3: A Co-occurrence Based Approach to Aspect-Level Sentiment Analysis

In this paper, the crucial ingredients for our submission to SemEval-2014 Task 4 “Aspect Level Sentiment Analysis” are discussed. We present a simple aspect detection algorithm, a co-occurrence based method for category detection and a dictionary based sentiment classification algorithm. The dictionary for the latter is based on co-occurrences as well. The failure analysis and related work section focus mainly on the category detection method as it is most distinctive for our work.
Storylines for structuring massive streams of news

Stories are the most natural ways for people to deal with information about the changing world. They provide an efficient schematic structure to order and relate events according to some explanation. We describe (1) a formal model for representing storylines to handle streams of news and (2) a first implementation of a system that automatically extracts the ingredients of a storyline from news articles according to the model. Our model mimics the basic notions from narratology by adding bridging relations to timelines of events in relation to a climax point. We provide a method for defining the climax score of each event and the bridging relations between them. We generate a JSON structure for any set of news articles to represent the different stories they contain and visualize these stories on a timeline with climax and bridging relations. This visualization helps inspecting the validity of the generated structures.
Clinical Natural Language Processing with Deep Learning

The emergence and proliferation of electronic health record (EHR) systems has incrementally resulted in large volumes of clinical free text documents available across healthcare networks. The huge amount of data inspires research and development focused on novel clinical natural language processing (NLP) solutions to optimize clinical care and improve patient outcomes. In recent years, deep learning techniques have demonstrated superior performance over traditional machine learning (ML) techniques for various general-domain NLP tasks e.g. language modeling, parts-of-speech (POS) tagging, named entity recognition, paraphrase identification, sentiment analysis etc. Clinical documents pose unique challenges compared to general-domain text due to widespread use of acronyms and non-standard clinical jargons by healthcare providers, inconsistent document structure and organization, and requirement for rigorous de-identification and anonymization to ensure patient data privacy. This tutorial chapter will present an overview of how deep learning techniques can be applied to solve NLP tasks in general, followed by a literature survey of existing deep learning algorithms applied to clinical NLP problems. Finally, we include a description of various deep learning-driven clinical NLP applications developed at the Artificial Intelligence (AI) lab in Philips Research in recent years such as diagnostic inferencing from unstructured clinical narratives, relevant biomedical article retrieval based on clinical case scenarios, clinical paraphrase generation, adverse drug event (ADE) detection from social media, and medical image caption generation. Sadid A. Hasan Artificial Intelligence Lab, Philips Research North America, Cambridge, MA, USA.  sadid.hasan@philips.com Oladimeji Farri Artificial Intelligence Lab, Philips Research North America, Cambridge, MA, USA.
Forecasting Using Elman Recurrent Neural Network

Forecasting is an important data analysis technique that aims to study historical data in order to explore and predict its future values. In fact, to forecast, different methods have been tested and applied from regression to neural network models. In this research, we proposed Elman Recurrent Neural Network (ERNN) to forecast the Mackey-Glass time series elements. Experimental results show that our scheme outperforms other state-of-art studies.
A Universal Music Translation Network

We present a method for translating music across musical instruments and styles. This method is based on unsupervised training of a multi-domain wavenet autoencoder, with a shared encoder and a domain-independent latent space that is trained end-to-end on waveforms. Employing a diverse training dataset and large net capacity, the single encoder allows us to translate also from musical domains that were not seen during training. We evaluate our method on a dataset collected from professional musicians, and achieve convincing translations. We also study the properties of the obtained translation and demonstrate translating even from a whistle, potentially enabling the creation of instrumental music by untrained humans.
Structured light and stereo vision for underwater 3D reconstruction

Stereo vision and structured light are compared in a common underwater environment with known dimensions and objects. Two different sensors are mounted on top of a Cartesian robot that moves with a known and programmed trajectory. The resulting point clouds from each sensor are compared in terms of distance from point to point, and measurements in the scanned objects, to determine which sensor is best suited depending on the environment and the survey purpose. The conclusions show that a stereo based reconstruction is best suited for long, high altitude surveys, always depending on having enough texture and light, whereas a structured light reconstruction can be better fitted in a short, close distance approach where accurate dimensions of an object or structure are needed.
Hybrid dermoscopy image classification framework based on deep convolutional neural network and Fisher vector

Dermoscopy image is usually used in early diagnosis of malignant melanoma. The diagnosis accuracy by visual inspection is highly relied on the dermatologist's clinical experience. Due to the inaccuracy, subjectivity, and poor reproducibility of human judgement, an automatic recognition algorithm of dermoscopy image is highly desired. In this work, we present a hybrid classification framework for dermoscopy image assessment by combining deep convolutional neural network (CNN), Fisher vector (FV) and support vector machine (SVM). Specifically, the deep representations of subimages at various locations of a rescaled dermoscopy image are first extracted via a natural image dataset pre-trained on CNN. Then we adopt an orderless visual statistics based FV encoding methods to aggregate these features to build more invariant representations. Finally, the FV encoded representations are classified for diagnosis using a linear SVM. Compared with traditional low-level visual features based recognition approaches, our scheme is simpler and requires no complex preprocessing. Furthermore, the orderless representations are less sensitive to geometric deformation. We evaluate our proposed method on the ISBI 2016 Skin lesion challenge dataset and promising results are obtained. Also, we achieve consistent improvement in accuracy even without fine-tuning the CNN.
High-mobility transport anisotropy and linear dichroism in few-layer black phosphorus

Two-dimensional crystals are emerging materials for nanoelectronics. Development of the field requires candidate systems with both a high carrier mobility and, in contrast to graphene, a sufficiently large electronic bandgap. Here we present a detailed theoretical investigation of the atomic and electronic structure of few-layer black phosphorus (BP) to predict its electrical and optical properties. This system has a direct bandgap, tunable from 1.51 eV for a monolayer to 0.59 eV for a five-layer sample. We predict that the mobilities are hole-dominated, rather high and highly anisotropic. The monolayer is exceptional in having an extremely high hole mobility (of order 10,000 cm(2) V(-1) s(-1)) and anomalous elastic properties which reverse the anisotropy. Light absorption spectra indicate linear dichroism between perpendicular in-plane directions, which allows optical determination of the crystalline orientation and optical activation of the anisotropic transport properties. These results make few-layer BP a promising candidate for future electronics.
Venturing Innovation , rule breaking and the ethics of entrepreneurship

This article examines a feature of the ethics of entrepreneurship that is infrequently directly discussed, viz., rule breaking. Entrepreneurs are widely said to engage in rule breaking. Many examples of this appear in popular and academic literature. But howmay this be integrated into an account of the ethics of entrepreneurship? One response would be that when entrepreneurs break legal and moral rules then what they do is wrong and ought to be condemned. There is a great deal to be said for this rule model of entrepreneurial ethics. However, this view is also mistaken. Instead, this article defends a virtue-based account of the ethics of entrepreneurship in which certain instances of rule breaking, even if morally wrong, are nevertheless ethically acceptable and part of the creative destruction that entrepreneurs bring not only to the economy but also to morality.
Controlling Expressiveness ofQuestion Interpretation with a Constrained Tree Transducer Induction

An important problem in Question Answering over Knowledge Bases is to interpret a question into a database query. This problem can be formulated as an instance of semantic parsing where a natural language utterance is analyzed into a (possibly executable) meaning representation. Most semantic parsing strategies for Question Answering use models with limited expressiveness because it is difficult to characterize it and systematically control it. In this work we use tree-to-tree transducers which are very general and solid models to transform the syntactic tree of a question into the executable semantic tree of a database query. When designing these tree transducers, we identify two parameters that influence the construction cost and their expressive capabilities, namely the tree fragment depth and number of variables of the rules. We characterize the search space of tree transducer construction in terms of these parameters and show considerable improvements in accuracy as we increase the expressive power.
Real-Time High Resolution 3D Data on the HoloLens

The recent appearance of augmented reality headsets, such as the Microsoft HoloLens, is a marked move from traditional 2D screen to 3D hologram-like interfaces. Striving to be completely portable, these devices unfortunately suffer multiple limitations, such as the lack of real-time, high quality depth data, which severely restricts their use as research tools. To mitigate this restriction, we provide a simple method to augment a HoloLens headset with much higher resolution depth data. To do so, we calibrate an external depth sensor connected to a computer stick that communicates with the HoloLens headset in real-time. To show how this system could be useful to the research community, we present an implementation of small object detection on HoloLens device.
Time in a Language Without Tense: The Case of Chinese

This paper outlines a framework of the temporal interpretation in Chinese with a special focus on complement and relative clauses. It argues that not only Chinese has no morphological tenses but there is no need to resort to covert semantic features under a tense node in order to interpret time in Chinese. Instead, it utilizes various factors such as the information provided by default aspect, the tense-aspect particles, and pragmatic reasoning to determine the temporal interpretation of sentences. It is shown that aspectual markers in Chinese play the role that tense plays in a tense language. This result implies that the Chinese phrase structure has AspP above VP but no TP is above AspP.
Event-Enhanced Learning for KG Completion

Statistical learning of relations between entities is a popular approach to address the problem of missing data in Knowledge Graphs. In this work we study how relational learning can be enhanced with background of a special kind: event logs, that are sequences of entities that may occur in the graph. Events naturally appear in many important applications as background. We propose various embedding models that combine entities of a Knowledge Graph and event logs. Our evaluation shows that our approach outperforms state-of-the-art baselines on real-world manufacturing and road traffic Knowledge Graphs, as well as in a controlled scenario that mimics manufacturing processes.
The Relationship between Test Anxiety and Academic Achievement

The major aim of this research study was to explore the relationship between test anxiety and academic achievement of students at the post graduate level. A sample of 414 students was randomly selected from seven different science departments in a public sector university in Lahore, Pakistan. Data were collected by using the Test Anxiety Inventory (TAI) developed by Spielberger. Pearson correlation, multivariate statistics and regression analyses were run for data analysis. It was found that a significant negative relationship exists between test anxiety scores and students’ achievement scores. Results showed that a cognitive factor (worry) contributes more in test anxiety than affective factors (emotional). Therefore, it is concluded that test anxiety is one of the factors which are responsible for students’ underachievement and low performance but it can be managed by appropriate training of students in dealing with factors causing test anxiety.
The effect of social network sites on adolescents' social and academic development: Current theories and controversies

Teenagers are among the most prolific users of social network sites (SNS). Emerging studies find that youth spend a considerable portion of their daily life interacting through social media. Subsequently, questions and controversies emerge about the effects SNS have on adolescent development. This review outlines the theoretical frameworks researchers have used to understand adolescents and SNS. It brings together work from disparate fields that examine the relationship between SNS and social capital, privacy, youth safety, psychological well-being, and educational achievement.These research strands speak to high-profile concerns and controversies that surround youth participation in these online communities, and offer ripe areas for future research.
Synthesizing Images of Humans in Unseen Poses

We address the computational problem of novel human pose synthesis. Given an image of a person and a desired pose, we produce a depiction of that person in that pose, retaining the appearance of both the person and background. We present a modular generative neural network that synthesizes unseen poses using training pairs of images and poses taken from human action videos. Our network separates a scene into different body part and background layers, moves body parts to new locations and refines their appearances, and composites the new foreground with a hole-filled background. These subtasks, implemented with separate modules, are trained jointly using only a single target image as a supervised label. We use an adversarial discriminator to force our network to synthesize realistic details conditioned on pose. We demonstrate image synthesis results on three action classes: golf, yoga/workouts and tennis, and show that our method produces accurate results within action classes as well as across action classes. Given a sequence of desired poses, we also produce coherent videos of actions.
QuAC: Question Answering in Context

We present QuAC, a dataset for Question Answering in Context that contains 14K information-seeking QA dialogs (100K questions in total). The dialogs involve two crowd workers: (1) a student who poses a sequence of freeform questions to learn as much as possible about a hidden Wikipedia text, and (2) a teacher who answers the questions by providing short excerpts from the text. QuAC introduces challenges not found in existing machine comprehension datasets: its questions are often more open-ended, unanswerable, or only meaningful within the dialog context, as we show in a detailed qualitative evaluation. We also report results for a number of reference models, including a recently state-ofthe-art reading comprehension architecture extended to model dialog context. Our best model underperforms humans by 20 F1, suggesting that there is significant room for future work on this data. Dataset, baseline, and leaderboard available at http://quac.ai.
Brand Personality , Consumer Satisfaction , and Loyalty : A Perspective from Denim Jeans Brands

This study employed Aaker's brand personality framework to empirically investigate the personality of denim jeans brands and to examine the impact of brand personality on consumer satisfaction and brand loyalty based on data collected from 474 college students. Results revealed that the personality of denim jeans brands can be described in six dimensions with 51 personality traits: attractiveness, practicality, ruggedness, flexibility, friendliness, and honesty. The results indicated that consumers associate particular brand personality dimensions with denim jeans brands. Also, the various dimensions of brand personality have different effects on consumer satisfaction and consumer brand loyalty.
Brain mechanisms linking language and action

For a long time the cortical systems for language and actions were believed to be independent modules. However, as these systems are reciprocally connected with each other, information about language and actions might interact in distributed neuronal assemblies. A critical case is that of action words that are semantically related to different parts of the body (for example, 'lick', 'pick' and 'kick'): does the comprehension of these words specifically, rapidly and automatically activate the motor system in a somatotopic manner, and does their comprehension rely on activity in the action system?
Adversarially Learned One-Class Classifier for Novelty Detection

Novelty detection is the process of identifying the observation(s) that differ in some respect from the training observations (the target class). In reality, the novelty class is often absent during training, poorly sampled or not well defined. Therefore, one-class classifiers can efficiently model such problems. However, due to the unavailability of data from the novelty class, training an end-to-end deep network is a cumbersome task. In this paper, inspired by the success of generative adversarial networks for training deep models in unsupervised and semi-supervised settings, we propose an end-to-end architecture for one-class classification. Our architecture is composed of two deep networks, each of which trained by competing with each other while collaborating to understand the underlying concept in the target class, and then classify the testing samples. One network works as the novelty detector, while the other supports it by enhancing the inlier samples and distorting the outliers. The intuition is that the separability of the enhanced inliers and distorted outliers is much better than deciding on the original samples. The proposed framework applies to different related applications of anomaly and outlier detection in images and videos. The results on MNIST and Caltech-256 image datasets, along with the challenging UCSD Ped2 dataset for video anomaly detection illustrate that our proposed method learns the target class effectively and is superior to the baseline and state-of-the-art methods.
Product-driven supply chain selection using integrated multi-criteria decision-making methodology

Effective supply chain design calls for robust analytical models and design tools. Previous works in this area are mostly Operation Research oriented without considering manufacturing aspects. Recently, researchers have begun to realize that the decision and integration effort in supply chain design should be driven by the manufactured product, specifically, product characteristics and product life cycle. In addition, decision-making processes should be guided by a comprehensive set of performance metrics. In this paper, we relate product characteristics to supply chain strategy and adopt supply chain operations reference (SCOR) model level I performance metrics as the decision criteria. An integrated analytic hierarchy process (AHP) and preemptive goal programming (PGP) based multi-criteria decision-making methodology is then developed to take into account both qualitative and quantitative factors in supplier selection. While the AHP process matches product characteristics with supplier characteristics (using supplier ratings derived from pairwise comparisons) to qualitatively determine supply chain strategy, PGP mathematically determines the optimal order quantity from the chosen suppliers. Since PGP uses AHP ratings as input, the variations of pairwise comparisons in AHP will influence the final order quantity. Therefore, users of this methodology should put greater emphasis on the AHP progress to ensure the accuracy of supplier ratings.
Dancing robots : integrating art , music , and robotics in Singapore ’ s early childhood centers

In recent years, Singapore has increased its national emphasis on technology and engineering in early childhood education. Their newest initiative, the Playmaker Programme, has focused on teaching robotics and coding in preschool settings. Robotics offers a playful and collaborativeway for children to engagewith foundational technology and engineering concepts during their formative early childhood years. This study looks at a sample of preschool children (N = 98) from five early childhood centers in Singapore who completed a 7-week STEAM (Science, Technology, Engineering, Arts, and Mathematics) KIBO robotics curriculum in their classrooms called, ‘‘Dances from Around the World.’’ KIBO is a newly developed robotics kit that teaches both engineering and programming.KIBO’s actions are programmedusing tangible programming blocks—no screen-time required. Children’s knowledge of programming concepts were assessed upon completion of the curriculum using the Solve-Its assessment. Results indicate that children were highly successful at mastering foundational programming concepts. Additionally, teachers were successful at promoting a collaborative and creative environment, but less successful at finding ways to engage with the greater school community through robotics. This research study was part of a large country-wide initiative to increase the use of developmentally appropriate engineering tools in early childhood settings. Implications for the design of technology, curriculum, and other resources are addressed.
Confidence Intervals Permit, but Do Not Guarantee, Better Inference than Statistical Significance Testing

A statistically significant result, and a non-significant result may differ little, although significance status may tempt an interpretation of difference. Two studies are reported that compared interpretation of such results presented using null hypothesis significance testing (NHST), or confidence intervals (CIs). Authors of articles published in psychology, behavioral neuroscience, and medical journals were asked, via email, to interpret two fictitious studies that found similar results, one statistically significant, and the other non-significant. Responses from 330 authors varied greatly, but interpretation was generally poor, whether results were presented as CIs or using NHST. However, when interpreting CIs respondents who mentioned NHST were 60% likely to conclude, unjustifiably, the two results conflicted, whereas those who interpreted CIs without reference to NHST were 95% likely to conclude, justifiably, the two results were consistent. Findings were generally similar for all three disciplines. An email survey of academic psychologists confirmed that CIs elicit better interpretations if NHST is not invoked. Improved statistical inference can result from encouragement of meta-analytic thinking and use of CIs but, for full benefit, such highly desirable statistical reform requires also that researchers interpret CIs without recourse to NHST.