nils-herrmann/clean_scirepeval huggingface dataset:
JUMPER: Learning When to Make Classification Decisions in Reading

In early years, text classification is typically accomplished by feature-based machine learning models; recently, deep neural networks, as a powerful learning machine, make it possible to work with raw input as the text stands. However, exiting end-to-end neural networks lack explicit interpretation of the prediction. In this paper, we propose a novel framework, JUMPER, inspired by the cognitive process of text reading, that models text classification as a sequential decision process. Basically, JUMPER is a neural system that scans a piece of text sequentially and makes classification decisions at the time it wishes. Both the classification result and when to make the classification are part of the decision process, which is controlled by a policy network and trained with reinforcement learning. Experimental results show that a properly trained JUMPER has the following properties: (1) It can make decisions whenever the evidence is enough, therefore reducing total text reading by 30–40% and often finding the key rationale of prediction. (2) It achieves classification accuracy better than or comparable to state-of-the-art models in several benchmark and industrial datasets.
An Independent Living Skills Training Program (occupational therapy program, rehabilitation, self-care)

The growing rehabilitation and consumer movement toward independent community living for disabled adults has placed new demands on the health care delivery system. ProgTams must be developed for the disabled adult that provide direct training in adaptive community skills, such as banking, budgeting, consumer advocacy, personal health care, and attendant management. An Independent Living Skills Training Program that uses a psychoeducational model is described. To date, 17 multiply handicapped adults, whose average length of institutionalization was I 1.9 years, have participated in the program. Of these 17, 58.8% returned to community living and 23.5% are waiting for openings m accessible housing units.
Improved Study of Heart Disease Prediction System using Data Mining Classification Techniques

The Healthcare industry is generally &quot;information rich&quot;, but unfortunately not all the data are mined which is required for discovering hidden patterns & effective decision making. Advanced data mining techniques are used to discover knowledge in database and for medical research, particularly in Heart disease prediction. This paper has analysed prediction systems for Heart disease using more number of input attributes. The system uses medical terms such as sex, blood pressure, cholesterol like 13 attributes to predict the likelihood of patient getting a Heart disease. Until now, 13 attributes are used for prediction. This research paper added two more attributes i. e. obesity and smoking. The data mining classification techniques, namely Decision Trees, Naive Bayes, and Neural Networks are analyzed on Heart disease database. The performance of these techniques is compared, based on accuracy. As per our results accuracy of Neural Networks, Decision Trees, and Naive Bayes are 100%, 99. 62%, and 90. 74% respectively. Our analysis shows that out of these three classification models Neural Networks predicts Heart disease with highest accuracy.
Character and Subword-Based Word Representation for Neural Language Modeling Prediction

Most of neural language models use different kinds of embeddings for word prediction. While word embeddings can be associated to each word in the vocabulary or derived from characters as well as factored morphological decomposition, these word representations are mainly used to parametrize the input, i.e. the context of prediction. This work investigates the effect of using subword units (character and factored morphological decomposition) to build output representations for neural language modeling. We present a case study on Czech, a morphologically-rich language, experimenting with different input and output representations. When working with the full training vocabulary, despite unstable training, our experiments show that augmenting the output word representations with character-based embeddings can significantly improve the performance of the model. Moreover, reducing the size of the output look-up table, to let the character-based embeddings represent rare words, brings further improvement.
Integrating technology into K-12 teaching and learning: current knowledge gaps and recommendations for future research

Although research studies in education show that use of technology can help student learning, its use is generally affected by certain barriers. In this paper, we first identify the general barriers typically faced by K-12 schools, both in the United States as well as other countries, when integrating technology into the curriculum for instructional purposes, namely: (a) resources, (b) institution, (c) subject culture, (d) attitudes and beliefs, (e) knowledge and skills, and (f) assessment. We then describe the strategies to overcome such barriers: (a) having a shared vision and technology integration plan, (b) overcoming the scarcity of resources, (c) changing attitudes and beliefs, (d) conducting professional development, and (e) reconsidering assessments. Finally, we identify several current knowledge gaps pertaining to the barriers and strategies of technology integration, and offer pertinent recommendations for future research.
Intrinsically motivated reinforcement learning: A promising framework for procedural content generation

So far, Evolutionary Algorithms (EA) have been the dominant paradigm for Procedural Content Generation (PCG). While we believe the field has achieved a remarkable success, we claim that there is a wide window for improvement. The field of machine learning has an abundance of methods that promise solutions to some aspects of PCG that are still under-researched. In this paper, we advocate the use of Intrinsically motivated reinforcement learning for content generation. A class of methods that thrive for knowledge for its own sake rather than as a step towards finding a solution. We argue that this approach promises solutions to some of the well-known problems in PCG: (1) searching for novelty and diversity can be easily incorporated as an intrinsic reward, (2) improving models of player experience and generation of adapted content can be done simultaneously through combining extrinsic and intrinsic rewards, and (3) mix-initiative design tools can incorporate more knowledge about the designer and her preferences and ultimately provide better assistance. We demonstrate our arguments and discuss the challenges that face the proposed approach.
Competing Paradigms in Qualitative Research

IN this chapter we analyze four paradigms that currently are competing, or have until recently competed, for acceptance as the paradigm of choice in informing and guiding inquiry, especially qualitative inquiry: positivism, postpositivism, critical theory and related ideological positions, and constructivism. We acknowledge at once our own commitment to constructivism (which we earlier called "naturalistic inquiry"; Lincoln & Guba, 1985); the reader may wish to take that fact into account in judging the appropriateness and usefulness of our analysis. Although the title of this volume, Handbook of Qualitative Research, implies that the term qualitative is an umbrella term superior to the term paradigm (and, indeed, that usage is not uncommon), it is our position that it is a term that ought to be reserved for a description of types of methods. From our perspective, both qualitative and quantitative methods may be used appropriately with any research paradigm. Questions of method are secondary to questions of paradigm, which we define as the basic belief system or worldview that guides the investigator, not only in choices of method but in ontologicallyandepistemologicallyfundamentalways. It is certainly the case that interest in alternative paradigms has been stimulated by a growing dissatisfaction with the patent overemphasis on quantitative methods. But as efforts were made to build a case for a renewed interest in qualitative approaches, it became clear that the metaphysical assumptions undergirding the conventional paradigm (the "received view") must be seriously questioned. Thus the emphasis of this chapter is on paradigms, their assumptions, and the implications of those assumptions for a variety of research issues, not on the relative utility of qualitative versus quantitative methods. Nevertheless, as discussions of paradigms/methods over the past decade have often begun with a consideration of problems associated with overquantification, we will also begin there, shifting only later to our predominant interest.
A Paraphrase and Semantic Similarity Detection System for User Generated Short-Text Content on Microblogs

Existing systems deliver high accuracy and F1-scores for detecting paraphrase and semantic similarity on traditional clean-text corpus. For instance, on the clean-text Microsoft Paraphrase benchmark database, the existing systems attain an accuracy as high as 0.8596. However, existing systems for detecting paraphrases and semantic similarity on user-generated short-text content on microblogs such as Twitter, comprising of noisy and ad hoc short-text, needs significant research attention. In this paper, we propose a machine learning based approach towards this. We propose a set of features that, although well-known in the NLP literature for solving other problems, have not been explored for detecting paraphrase or semantic similarity, on noisy user-generated short-text data such as Twitter. We apply support vector machine (SVM) based learning. We use the benchmark Twitter paraphrase data, released as a part of SemEval 2015, for experiments. Our system delivers a paraphrase detection F1-score of 0.717 and semantic similarity detection F1-score of 0.741, thereby significantly outperforming the existing systems, that deliver F1-scores of 0.696 and 0.724 for the two problems respectively. Our features also allow us to obtain a rank among the top-10, when trained on the Microsoft Paraphrase corpus and tested on the corresponding test data, thereby empirically establishing our approach as ubiquitous across the different paraphrase detection databases.
Evaluating React Native and Progressive Web App development using ISO 25010

The vast supply of different smartphone makes and models, along with their accompanying operating systems, increase the demand for an all-in-one development solution. Quite a few approaches to solving this problem have cropped up over the years, ranging from purely web-oriented solutions to something more akin to a native application. React Native and Progressive Web App development are two different approaches, both new and promising, on this spectrum. This thesis evaluates these approaches in a standardized way using the ISO 25010 Product Quality Model to gain insight into these types of cross-platform development as well as how well such an evaluation works in this context. The results show that, while not a perfect fit, a standardized evaluation brings forward less obvious aspects of the development process and contributes with a helpful structure to the evaluation process.
Trajectory Based Autonomous Vehicle Following using a Robotic Driver

An autonomous vehicle following system including control approaches is presented in this paper. An existing robotic driver is used to control a standard passenger vehicle such that no modifications to the car are necessary. Only information about the relative position of the lead vehicle and the motion of the following vehicle is required, and methods are presented to construct a reference trajectory to enable accurate following. A laser scanner is used to detect the lead vehicle and the following vehicle’s ego-motion is sensed using an IMU and wheel encoder. An algorithm was developed and tested to locate the lead vehicle with RMS position and orientation errors of 65mm and 5.8◦ respectively. Several trajectory-based lateral controllers were tested in simulation and then experimentally, with the best controller having an RMS lateral deviation of 37cm from the path of the lead vehicle. A new trajectorybased spacing controller was tested in simulation which allows the following vehicle to reproduce the velocity of the lead vehicle.
Selfie-Presentation in Everyday Life: A Large-Scale Characterization of Selfie Contexts on Instagram

Carefully managing the presentation of self via technology is a core practice on all modern social media platforms. Recently, selfies have emerged as a new, pervasive genre of identity performance. In many ways unique, selfies bring us fullcircle to Goffman—blending the online and offline selves together. In this paper, we take an empirical, Goffman-inspired look at the phenomenon of selfies. We report a large-scale, mixed-method analysis of the categories in which selfies appear on Instagram—an online community comprising over 400M people. Applying computer vision and network analysis techniques to 2.5M selfies, we present a typology of emergent selfie categories which represent emphasized identity statements. To the best of our knowledge, this is the first large-scale, empirical research on selfies. We conclude, contrary to common portrayals in the press, that selfies are really quite ordinary: they project identity signals such as wealth, health and physical attractiveness common to many online media, and to offline life.
Neuropathological stageing of Alzheimer-related changes

Eighty-three brains obtained at autopsy from nondemented and demented individuals were examined for extracellular amyloid deposits and intraneuronal neurofibrillary changes. The distribution pattern and packing density of amyloid deposits turned out to be of limited significance for differentiation of neuropathological stages. Neurofibrillary changes occurred in the form of neuritic plaques, neurofibrillary tangles and neuropil threads. The distribution of neuritic plaques varied widely not only within architectonic units but also from one individual to another. Neurofibrillary tangles and neuropil threads, in contrast, exhibited a characteristic distribution pattern permitting the differentiation of six stages. The first two stages were characterized by an either mild or severe alteration of the transentorhinal layer Pre-α (transentorhinal stages I–II). The two forms of limbic stages (stages III–IV) were marked by a conspicuous affection of layer Pre-α in both transentorhinal region and proper entorhinal cortex. In addition, there was mild involvement of the first Ammon's horn sector. The hallmark of the two isocortical stages (stages V–VI) was the destruction of virtually all isocortical association areas. The investigation showed that recognition of the six stages required qualitative evaluation of only a few key preparations.
Leadership and Knowledge Sharing

Knowledge processes and leadership and management have both been central to discussions within the academic literature. While the number of empirical studies looking at the interaction between these dimensions has been limited. Within this study structural equation modelling using SMART PLS is utilised to identify the implications when determining the most effective leadership styles for achieving knowledge transfer. It was found that a combined leadership approach was the most effective when considering knowledge processes through the SECI model. The study contributes to the literature by allowing us to identify specific leadership style attributes being matched to specific knowledge process stages for achieving maximum impact. The study also allowed us to test empirically the suitability of the leadership and knowledge constructs within the UAE.
Cloud computing with the Azure platform

Software industry is heading towards centralized computin g. Due to this trend data and programs are being taken away from traditional desktop PCs and placed in compute clouds instead. Compute clouds are enormous server farms packed with computing power and storage space accessible through the Internet. Instead of having to manage one’s own infrastructure to run applications, server time and storage space can can be bought from an external service provider. From the customers’ point of view the benefit behind this idea is to be able to dynamically adjust computing power up or down to meet the demand for that power at a particular moment. This kind of flexibility not only ensures that no costs are incurred by excess processing capacity, but also enables hard ware infrastructure to scale up with business growth. Because of growing interest in taking advantage of cloud computing a number of service providers are working on providing cloud services. As stated in , Amazon, Salerforce.co m and Google are examples of firms that already have working solutions on the market. Recently also Microsoft released a preview version of its cloud platform called the Azure. Earl y adopters can test the platform and development tools free of charge.[2, 3, 4] The main purpose of this paper is to shed light on the internals of Microsoft’s Azure platform. In addition to examinin g how Azure platform works, the benefits of Azure platform are explored. The most important benefit in Microsoft’s solu tion is that it resembles existing Windows environment a lot . Developers can use the same application programming interfaces (APIs) and development tools they are already used to. The second benefit is that migrating applications to cloud is easy. This partially stems from the fact that Azure’s servic es can be exploited by an application whether it is run locally or in the cloud.
Group Anomaly Detection Using Deep Generative Models

Unlike conventional anomaly detection research that focuses on point anomalies, our goal is to detect anomalous collections of individual data points. In particular, we perform group anomaly detection (GAD) with an emphasis on irregular group distributions (e.g. irregular mixtures of image pixels). GAD is an important task in detecting unusual and anomalous phenomena in real-world applications such as high energy particle physics, social media and medical imaging. In this paper, we take a generative approach by proposing deep generative models: Adversarial autoencoder (AAE) and variational autoencoder (VAE) for group anomaly detection. Both AAE and VAE detect group anomalies using point-wise input data where group memberships are known a priori. We conduct extensive experiments to evaluate our models on real world datasets. The empirical results demonstrate that our approach is effective and robust in detecting group anomalies.
Explaining Explanations in AI

Recent work on interpretability in machine learning and AI has focused on the building of simplified models that approximate the true criteria used to make decisions. These models are a useful pedagogical device for teaching trained professionals how to predict what decisions will be made by the complex system, and most importantly how the system might break. However, when considering any such model it's important to remember Box's maxim that "All models are wrong but some are useful." We focus on the distinction between these models and explanations in philosophy and sociology. These models can be understood as a "do it yourself kit" for explanations, allowing a practitioner to directly answer "what if questions" or generate contrastive explanations without external assistance. Although a valuable ability, giving these models as explanations appears more difficult than necessary, and other forms of explanation may not have the same trade-offs. We contrast the different schools of thought on what makes an explanation, and suggest that machine learning might benefit from viewing the problem more broadly.
Autonomous Evolution of Topographic Regularities in Artificial Neural Networks

Looking to nature as inspiration, for at least the past 25 years, researchers in the field of neuroevolution (NE) have developed evolutionary algorithms designed specifically to evolve artificial neural networks (ANNs). Yet the ANNs evolved through NE algorithms lack the distinctive characteristics of biological brains, perhaps explaining why NE is not yet a mainstream subject of neural computation. Motivated by this gap, this letter shows that when geometry is introduced to evolved ANNs through the hypercube-based neuroevolution of augmenting topologies algorithm, they begin to acquire characteristics that indeed are reminiscent of biological brains. That is, if the neurons in evolved ANNs are situated at locations in space (i.e., if they are given coordinates), then, as experiments in evolving checkers-playing ANNs in this letter show, topographic maps with symmetries and regularities can evolve spontaneously. The ability to evolve such maps is shown in this letter to provide an important advantage in generalization. In fact, the evolved maps are sufficiently informative that their analysis yields the novel insight that the geometry of the connectivity patterns of more general players is significantly smoother and more contiguous than less general ones. Thus, the results reveal a correlation between generality and smoothness in connectivity patterns. They also hint at the intriguing possibility that as NE matures as a field, its algorithms can evolve ANNs of increasing relevance to those who study neural computation in general.
Review of Physicochemical-Based Diagnostic Techniques for Assessing Insulation Condition in Aged Transformers

A power transformer outage has a dramatic financial consequence not only for electric power systems utilities but also for interconnected customers. The service reliability of this important asset largely depends upon the condition of the oil-paper insulation. Therefore, by keeping the qualities of oil-paper insulation system in pristine condition, the maintenance planners can reduce the decline rate of internal faults. Accurate diagnostic methods for analyzing the condition of transformers are therefore essential. Currently, there are various electrical and physicochemical diagnostic techniques available for insulation condition monitoring of power transformers. This paper is aimed at the description, analysis and interpretation of modern physicochemical diagnostics techniques for assessing insulation condition in aged transformers. Since fields and laboratory experiences have shown that transformer oil contains about 70% of diagnostic information, the physicochemical analyses of oil samples can therefore be extremely useful in monitoring the condition of power transformers.
PM Generational Differences in the Hospitality Industry : An issue of concern ?

Each generation that enters the workforce brings with it its own unique perspectives and values, shaped by the times of their life, about work and the work environment; thus posing atypical human resources management challenges. Following the completion of an extensive quantitative study conducted in Cyprus, and by adopting a qualitative methodology, the researchers aim to further explore the occupational similarities and differences of the two prevailing generations, X and Y, currently active in the workplace. Moreover, the study investigates the effects of the perceptual generational differences on managing the diverse hospitality workplace. Industry implications, recommendations for stakeholders as well as directions for further scholarly research are discussed.
Graph Databases

@oreillymedia facebook.com/oreilly Discover how graph databases can help you manage and query highly connected data. With this practical book, you’ll learn how to design and implement a graph database that brings the power of graphs to bear on a broad range of problem domains. Whether you want to speed up your response to user queries or build a database that can adapt as your business evolves, this book shows you how to apply the schema-free graph model to real-world problems.