nils-herrmann/clean_scirepeval huggingface dataset:
A fused deep learning architecture for viewpoint classification of echocardiography

This study extends the state of the art of deep learning convolutional neural network (CNN) to the classification of video images of echocardiography, aiming at assisting clinicians in diagnosis of heart diseases. Specifically, the architecture of neural networks is established by embracing hand-crafted feature within a data-driven learning framework, incorporating both spatial and temporal information sustained by the video images of the moving heart, and giving rise to two strands of two-dimensional convolutional neural network (CNN). In particular, acceleration measurement along the time direction of each point is calculated using dense optical flow technique to represent temporal motion information. Subsequently, the fusion of both networks is conducted via linear integration of histograms of class scores obtained from the two strands of networks. As a result, this architecture gives the best classification results of eight viewpoint categories of echo videos with 92.1% accuracy rate whereas 89.5% can be achieved using only single spatial network. When concerning only three primary locations, 98% of accuracy rate is realised. In addition, comparisons with a number of wellknown handcrafted approaches are also performed, including 2D KAZE, 2D KAZE with Optical Flow, 3D KAZA, Optical Flow, 2D SIFT and 3D SIFT, which delivers accuracy rate of 89.4%, 84.3%, 87.9%, 79.4%, 83.8% and 73.8% respectively.
Qualitative research and its place in psychological science.

In discussing the place of diverse qualitative research within psychological science, the authors highlight the potential permeability of the quantitative-qualitative boundary and identify different ways of increasing communication between researchers specializing in different methods. Explicating diversity within qualitative research is facilitated, initially, through documenting the range of qualitative data collection and analytic methods available. The authors then consider the notion of paradigmatic frame and review debates on the current and future positioning of qualitative research within psychological science. In so doing, the authors argue that the different ways in which the concept of paradigm can be interpreted allow them to challenge the idea that diverse research paradigms are prima facie incommensurate. Further, reviewing the ways in which proponents of qualitative research are seeking to reconfigure the links between paradigms helps the authors to envisage how communication between research communities can be enhanced. This critical review allows the authors to systematize possible configurations for research practice in psychology on a continuum of paradigm integration and to specify associated criteria for judging intermethod coherence.
Standardised Evaluation of English Noun Compound Interpretation

We present a tagged corpus for English noun compound interpretation and describe the method used to generate them. In order to collect noun compounds, we extracted binary noun compounds (i.e. noun-noun pairs) by looking for sequences of two nouns in the POS tag data of the Wall Street Journal. We then manually filtered out all noun compounds which were incorrectly tagged or included proper nouns. This left us with a data set of 2,169 noun compounds, which we annotated using a set of 20 semantic relations defined by Barker and Szpakowicz (1998) allowing the annotators to assign multiple semantic relations if necessary. The initial agreement was 52.31%. The final data set contains 1,081 test noun compounds and 1,088 training noun compounds.
Fully Convolutional Networks for Surface Defect Inspection in Industrial Environment

In this paper, we propose a reusable and high-efficiency two-stage deep learning based method for surface defect inspection in industrial environment. Aiming to achieve trade-offs between efficiency and accuracy simultaneously, our method makes a novel combination of a segmentation stage (stage1) and a detection stage (stage2), which are consisted of two fully convolutional networks (FCN) separately. In the segmentation stage we use a lightweight FCN to make a spatially dense pixel-wise prediction to inference the area of defect coarsely and quickly. Those predicted defect areas act as the initialization of stage2, guiding the process of detection to refine the segmentation results. We also use an unusual training strategy: training with the patches cropped from the images. Such strategy has greatly utility in industrial inspection where training data may be scarce. We will validate our findings by analyzing the performance obtained on the dataset of DAGM 2007.
A Neural Local Coherence Model for Text Quality Assessment

We propose a local coherence model that captures the flow of what semantically connects adjacent sentences in a text. We represent the semantics of a sentence by a vector and capture its state at each word of the sentence. We model what relates two adjacent sentences based on the two most similar semantic states, each of which is in one of the sentences. We encode the perceived coherence of a text by a vector, which represents patterns of changes in salient information that relates adjacent sentences. Our experiments demonstrate that our approach is beneficial for two downstream tasks: Readability assessment, in which our model achieves new state-of-the-art results; and essay scoring, in which the combination of our coherence vectors and other taskdependent features significantly improves the performance of a strong essay scorer.
Developing a technology roadmapping system

Technology roadmapping is becoming an increasingly important and widespread approach for aligning technology with organizational goals. The popularity of roadmapping is due mainly to the communication and networking benefits that arise from the development and dissemination of roadmaps, particularly in terms of building common understanding across internal and external organizational boundaries. From its origins in Motorola and Corning more than 25 years ago, where it was used to link product and technology plans, the approach has been adapted for many different purposes in a wide variety of sectors and at all levels, from small enterprises to national foresight programs. Building on previous papers presented at PICMET, concerning the rapid initiation of the technique, and how to customize the approach, this paper highlights the evolution and continuing growth of the method and its application to general strategic planning. The issues associated with extending the roadmapping method to form a central element of an integrated strategic planning process are considered.
A Game-Theoretic Approach to Generating Spatial Descriptions

Language is sensitive to both semantic and pragmatic effects. To capture both effects, we model language use as a cooperative game between two players: a speaker, who generates an utterance, and a listener, who responds with an action. Specifically, we consider the task of generating spatial references to objects, wherein the listener must accurately identify an object described by the speaker. We show that a speaker model that acts optimally with respect to an explicit, embedded listener model substantially outperforms one that is trained to directly generate spatial descriptions.
[Personality disorders, psychopathy and serial killers].

To illustrate the basic characteristics of several specific personality disorders, focusing mainly in antisocial personality disorder. The differences between antisocial personality disorder and psychopathy are highlighted. Serial killers and its psychopathic aspects are also discussed.



A bibliographic review was completed in order to outline convergences and divergences among different authors about this controversial issue, especially those concerning the possibility of treatment.



While anti-social personality disorder is a medical diagnosis, the term "psychopathy" (which belongs to the sphere of forensic psychiatry) may be understood as a "legal diagnosis". It is not still possible to identify an effective treatment for serial killers.



Personality disorders, especially of the antisocial type, still represent a formidable challenge to forensic psychiatry today. Questions as yet unanswered include the best and most humane place for patients with this condition and the nature of a standardised treatment recommendation.
How Much is 131 Million Dollars? Putting Numbers in Perspective with Compositional Descriptions

How much is 131 million US dollars? To help readers put such numbers in context, we propose a new task of automatically generating short descriptions known as perspectives, e.g. “$131 million is about the cost to employ everyone in Texas over a lunch period”. First, we collect a dataset of numeric mentions in news articles, where each mention is labeled with a set of rated perspectives. We then propose a system to generate these descriptions consisting of two steps: formula construction and description generation. In construction, we compose formulae from numeric facts in a knowledge base and rank the resulting formulas based on familiarity, numeric proximity and semantic compatibility. In generation, we convert a formula into natural language using a sequence-to-sequence recurrent neural network. Our system obtains a 15.2% F1 improvement over a non-compositional baseline at formula construction and a 12.5 BLEU point improvement over a baseline description generation.
The impact of leadership on student outcomes : An analysis of the differential effects of leadership types

El propósito de este estudio fue examinar el impacto relativo de los diferentes tipos de liderazgo en los resultados académicos y no académicos de los estudiantes. La metodología consistió en el análisis de los resultados de 27 estudios publicados sobre la relación entre liderazgo y resultados de los estudiantes. El primer metaanálisis, que incluyó 22 de los 27 estudios, implicó una comparación de los efectos de la transformación y liderazgo instructivo en los resultados de los estudiantes. Con el segundo meta-análisis se realizó una comparación de los efectos de cinco conjuntos derivados inductivamente de prácticas de liderazgo en los resultados de los estudiantes. Doce de los estudios contribuyeron a este segundo análisis. El primer meta-análisis indicó que el efecto promedio de liderazgo instructivo en los resultados de los estudiantes fue de tres a cuatro veces la de liderazgo transformacional. La inspección de los elementos de la encuesta que se utilizaron para medir el liderazgo escolar reveló cinco conjuntos de prácticas de liderazgo o dimensiones: el establecimiento de metas y expectativas; dotación de recursos estratégicos, la planificación, coordinación y evaluación de la enseñanza y el currículo; promoción y participan en el aprendizaje y desarrollo de los profesores, y la garantía de un ambiente ordenado y de apoyo. El segundo metaanálisis reveló fuertes efectos promedio para la dimensión de liderazgo que implica promover y participar en el aprendizaje docente, el desarrollo y efectos moderados de las dimensiones relacionadas con la fijación de objetivos y la planificación, coordinación y evaluación de la enseñanza y el currículo. Las comparaciones entre el liderazgo transformacional y el instructivo y entre las cinco dimensiones de liderazgo sugirieron que los líderes que focalizan sus relaciones, su trabajo y su aprendizaje en el asunto clave de la enseñanza y el aprendizaje, tendrán una mayor influencia en los resultados de los estudiantiles. El artículo concluye con una discusión sobre la necesidad de que liderazgo, investigación y práctica estén más estrechamente vinculados a la evidencia sobre la enseñanza eficaz y el aprendizaje efectivo del profesorado. Dicha alineación podría aumentar aún más el impacto del liderazgo escolar en los resultados de los estudiantes.
Cyberbullying: another main type of bullying?

Cyberbullying has recently emerged as a new form of bullying and harassment. 360 adolescents (12-20 years), were surveyed to examine the nature and extent of cyberbullying in Swedish schools. Four categories of cyberbullying (by text message, email, phone call and picture/video clip) were examined in relation to age and gender, perceived impact, telling others, and perception of adults becoming aware of such bullying. There was a significant incidence of cyberbullying in lower secondary schools, less in sixth-form colleges. Gender differences were few. The impact of cyberbullying was perceived as highly negative for picture/video clip bullying. Cybervictims most often chose to either tell their friends or no one at all about the cyberbullying, so adults may not be aware of cyberbullying, and (apart from picture/video clip bullying) this is how it was perceived by pupils. Findings are discussed in relation to similarities and differences between cyberbullying and the more traditional forms of bullying.
Is it all about having Fun? - Developing a Taxonomy to gamify Information Systems

Gamification is a well-known approach that refers to the use of game design elements in information systems to make monotonous and tedious tasks more enjoyable. However, research and practice show that game design elements are oftentimes chosen and integrated in information systems randomly, therefore impeding the outcomes of such systems. In this regard, taxonomies can guide system developers, i.e., in selecting and combining game design elements to gamify their information system. Nonetheless, existing taxonomies do not provide such guidance for gamifying information systems. Therefore, the goal of our research is to consolidate the state of the art of gamification research and rigorously develop a gamification taxonomy. To achieve our goal, we conducted a systematic literature review and developed a taxonomy based on a rigorous taxonomy development process. We evaluate our theory by providing evidence of its feasibility with two practical cases: First, we show how the taxonomy helps to analyze existing gamification approaches, and, second, how the taxonomy guides to gamify information systems. Overall, we enrich theory by introducing a new taxonomy to better explain the meaning and characteristics of game design elements. Likewise, practitioners will be guided in selecting and combining game design elements for their gamification approaches.
Towards Automating Social Engineering Using Social Networking Sites

A growing number of people use social networking sites to foster social relationships among each other. While the advantages of the provided services are obvious, drawbacks on a users' privacy and arising implications are often neglected. In this paper we introduce a novel attack called automated social engineering which illustrates how social networking sites can be used for social engineering. Our approach takes classical social engineering one step further by automating tasks which formerly were very time-intensive. In order to evaluate our proposed attack cycle and our prototypical implementation (ASE bot), we conducted two experiments. Within the first experiment we examine the information gathering capabilities of our bot. The second evaluation of our prototype performs a Turing test. The promising results of the evaluation highlight the possibility to efficiently and effectively perform social engineering attacks by applying automated social engineering bots.
IMPROVING THE EFFICIENCY OF SOLAR PHOTOVOLTAIC POWER SYSTEM

As the local and national clamor for foreign energy independent United States continues to grow unabated; renewable energy has been receiving increased focus and it’s widely believed that it’s not only the answer to ever increasing demand for energy in this country, but also the environmentally friendly means of meeting such demand. During the spring of 2010, I was involved with a 5KW solar power system design project; the project involved designing and building solar panels and associated accessories like the solar array mounts and Solar Inverter system. One of the key issues we ran into during the initial stage of the project was how to select efficient solar cells for panel building at a reasonable cost. While we were able to purchase good solar cells within our allocated budget, the issue of design for efficiency was not fully understood , not just in the contest of solar cells performance , but also in the overall system efficiency of the whole solar power system, hence the door was opened for this thesis. My thesis explored and expanded beyond the scope of the aforementioned project to research different avenues for improving the efficiency of solar photovoltaic power system from the solar cell level to the solar array mounting, array tracking and DC-AC inversion system techniques.
A Basic Bivariate Structure of Personality Attributes Evident Across Nine Languages.

Here, two studies seek to characterize a parsimonious common-denominator personality structure with optimal cross-cultural replicability. Personality differences are observed in all human populations and cultures, but lexicons for personality attributes contain so many distinctions that parsimony is lacking. Models stipulating the most important attributes have been formulated by experts or by empirical studies drawing on experience in a very limited range of cultures. Factor analyses of personality lexicons of nine languages of diverse provenance (Chinese, Korean, Filipino, Turkish, Greek, Polish, Hungarian, Maasai, and Senoufo) were examined, and their common structure was compared to that of several prominent models in psychology. A parsimonious bivariate model showed evidence of substantial convergence and ubiquity across cultures. Analyses involving key markers of these dimensions in English indicate that they are broad dimensions involving the overlapping content of the interpersonal circumplex, models of communion and agency, and morality/warmth and competence. These "Big Two" dimensions-Social Self-Regulation and Dynamism-provide a common-denominator model involving the two most crucial axes of personality variation, ubiquitous across cultures. The Big Two might serve as an umbrella model serving to link diverse theoretical models and associated research literatures.
S4Net: Single Stage Salient-Instance Segmentation

In this paper, we consider an interesting vision problem—salient instance segmentation. Other than producing approximate bounding boxes, our network also outputs high-quality instance-level segments. Taking into account the category-independent property of each target, we design a single stage salient instance segmentation framework, with a novel segmentation branch. Our new branch regards not only local context inside each detection window but also its surrounding context, enabling us to distinguish the instances in the same scope even with obstruction. Our network is end-to-end trainable and runs at a fast speed (40 fps when processing an image with resolution 320 × 320). We evaluate our approach on a public available benchmark and show that it outperforms other alternative solutions. In addition, we also provide a thorough analysis of the design choices to help readers better understand the functions of each part in our network. To facilitate the development of this area, our code will be available at https://github.com/RuochenFan/S4Net.
Bilingual Word Representations with Monolingual Quality in Mind

Recent work in learning bilingual representations tend to tailor towards achieving good performance on bilingual tasks, most often the crosslingual document classification (CLDC) evaluation, but to the detriment of preserving clustering structures of word representations monolingually. In this work, we propose a joint model to learn word representations from scratch that utilizes both the context coocurrence information through the monolingual component and the meaning equivalent signals from the bilingual constraint. Specifically, we extend the recently popular skipgram model to learn high quality bilingual representations efficiently. Our learned embeddings achieve a new state-of-the-art accuracy of 80.3 for the German to English CLDC task and a highly competitive performance of 90.7 for the other classification direction. At the same time, our models outperform best embeddings from past bilingual representation work by a large margin in the monolingual word similarity
Progress toward multi-robot reconnaissance and the MAGIC 2010 competition

Tasks like search-and-rescue and urban reconnaissance benefit from large numbers of robots working together, but high levels of autonomy are needed in order to reduce operator requirements to practical levels. Reducing the reliance of such systems on human operators presents a number of technical challenges including automatic task allocation, global state and map estimation, robot perception, path planning, communications, and human-robot interfaces. This paper describes our 14-robot team, designed to perform urban reconnaissance missions, that won the MAGIC 2010 competition. This paper describes a variety of autonomous systems which require minimal human effort to control a large number of autonomously exploring robots. Maintaining a consistent global map, essential for autonomous planning and for giving humans situational awareness, required the development of fast loop-closing, map optimization, and communications algorithms. Key to our approach was a decoupled centralized planning architecture that allowed individual robots to execute tasks myopically, but whose behavior was coordinated centrally. In this paper, we will describe technical contributions throughout our system that played a significant role in the performance of our system. We will also present results from our system both from the competition and from subsequent quantitative evaluations, pointing out areas in which the system performed well and where interesting research problems remain.
Classification of Text Documents

Traditional supervised learning makes the closed-world assumption that the classes appeared in the test data must have appeared in training. This also applies to text learning or text classification. As learning is used increasingly in dynamic open environments where some new/test documents may not belong to any of the training classes, identifying these novel documents during classification presents an important problem. This problem is called openworld classification or open classification. This paper proposes a novel deep learning based approach. It outperforms existing state-of-the-art techniques dramatically.
Self-Supervised Neural Aggregation Networks for Human Parsing

In this paper, we present a Self-Supervised Neural Aggregation Network (SS-NAN) for human parsing. SS-NAN adaptively learns to aggregate the multi-scale features at each pixel "address". In order to further improve the feature discriminative capacity, a self-supervised joint loss is adopted as an auxiliary learning strategy, which imposes human joint structures into parsing results without resorting to extra supervision. The proposed SS-NAN is end-to-end trainable. SS-NAN can be integrated into any advanced neural networks to help aggregate features regarding the importance at different positions and scales and incorporate rich high-level knowledge regarding human joint structures from a global perspective, which in turn improve the parsing results. Comprehensive evaluations on the recent Look into Person (LIP) and the PASCAL-Person-Part benchmark datasets demonstrate the significant superiority of our method over other state-of-the-arts.