nils-herrmann/clean_scirepeval huggingface dataset:
A TUTORIAL ON PRINCIPAL COMPONENT ANALYSIS Derivation , Discussion and Singular Value Decomposition

Principal component analysis (PCA) is a mainstay of modern data analysis a black box that is widely used but poorly understood. The goal of this paper is to dispel the magic behind this black box. This tutorial focuses on building a solid intuition for how and why principal component analysis works; furthermore, it crystallizes this knowledge by deriving from first principals, the mathematics behind PCA . This tutorial does not shy away from explaining the ideas informally, nor does it shy away from the mathematics. The hope is that by addressing both aspects, readers of all levels will be able to gain a better understanding of the power of PCA as well as the when, the how and the why of applying this technique.
BroncoVote: Secure Voting System using Ethereum's Blockchain

Voting is a fundamental part of democratic systems; it gives individuals in a community the faculty to voice their opinion. In recent years, voter turnout has diminished while concerns regarding integrity, security, and accessibility of current voting systems have escalated. E-voting was introduced to address those concerns; however, it is not cost-effective and still requires full supervision by a central authority. The blockchain is an emerging, decentralized, and distributed technology that promises to enhance different aspects of many industries. Expanding e-voting into blockchain technology could be the solution to alleviate the present concerns in e-voting. In this paper, we propose a blockchain-based voting system, named BroncoVote, that preserves voter privacy and increases accessibility, while keeping the voting system transparent, secure, and cost-effective. BroncoVote implements a university-scaled voting framework that utilizes Ethereum’s blockchain and smart contracts to achieve voter administration and auditable voting records. In addition, BroncoVote utilizes a few cryptographic techniques, including homomorphic encryption, to promote voter privacy. Our implementation was deployed on Ethereum’s Testnet to demonstrate usability, scalability, and efficiency.
Balancing New against Old Information: The Role of Puzzlement Surprise in Learning

Surprise describes a range of phenomena from unexpected events to behavioral responses. We propose a novel measure of surprise and use it for surprise-driven learning. Our surprise measure takes into account data likelihood as well as the degree of commitment to a belief via the entropy of the belief distribution. We find that surprise-minimizing learning dynamically adjusts the balance between new and old information without the need of knowledge about the temporal statistics of the environment. We apply our framework to a dynamic decision-making task and a maze exploration task. Our surprise-minimizing framework is suitable for learning in complex environments, even if the environment undergoes gradual or sudden changes, and it could eventually provide a framework to study the behavior of humans and animals as they encounter surprising events.
Fully-Convolutional Siamese Networks for Object Tracking

The problem of arbitrary object tracking has traditionally been tackled by learning a model of the object’s appearance exclusively online, using as sole training data the video itself. Despite the success of these methods, their online-only approach inherently limits the richness of the model they can learn. Recently, several attempts have been made to exploit the expressive power of deep convolutional networks. However, when the object to track is not known beforehand, it is necessary to perform Stochastic Gradient Descent online to adapt the weights of the network, severely compromising the speed of the system. In this paper we equip a basic tracking algorithm with a novel fully-convolutional Siamese network trained end-to-end on the ILSVRC15 dataset for object detection in video. Our tracker operates at frame-rates beyond real-time and, despite its extreme simplicity, achieves state-of-the-art performance in multiple benchmarks.
Numerical Coordinate Regression with Convolutional Neural Networks

We study deep learning approaches to inferring numerical coordinates for points of interest in an input image. Existing convolutional neural network-based solutions to this problem either take a heatmap matching approach or regress to coordinates with a fully connected output layer. Neither of these approaches is ideal, since the former is not entirely differentiable, and the latter lacks inherent spatial generalization. We propose our differentiable spatial to numerical transform (DSNT) to fill this gap. The DSNT layer adds no trainable parameters, is fully differentiable, and exhibits good spatial generalization. Unlike heatmap matching, DSNT works well with low heatmap resolutions, so it can be dropped in as an output layer for a wide range of existing fully convolutional architectures. Consequently, DSNT offers a better trade-off between inference speed and prediction accuracy compared to existing techniques. When used to replace the popular heatmap matching approach used in almost all state-of-the-art methods for pose estimation, DSNT gives better prediction accuracy for all model architectures tested.
Chest X-Ray Analysis of Tuberculosis by Deep Learning with Segmentation and Augmentation

The results of chest X-ray (CXR) analysis of 2D images to get the statistically reliable predictions (availability of tuberculosis) by computer-aided diagnosis (CADx) on the basis of deep learning are presented. They demonstrate the efficiency of lung segmentation, lossless and lossy data augmentation for CADx of tuberculosis by deep convolutional neural network (CNN) applied to the small and not well-balanced dataset even. CNN demonstrates ability to train (despite overfitting) on the pre-processed dataset obtained after lung segmentation in contrast to the original not-segmented dataset. Lossless data augmentation of the segmented dataset leads to the lowest validation loss (without overfitting) and nearly the same accuracy (within the limits of standard deviation) in comparison to the original and other pre-processed datasets after lossy data augmentation. The additional limited lossy data augmentation results in the lower validation loss, but with a decrease of the validation accuracy. In conclusion, besides the more complex deep CNNs and bigger datasets, the better progress of CADx for the small and not well-balanced datasets even could be obtained by better segmentation, data augmentation, dataset stratification, and exclusion of non-evident outliers.
Novel Deep Learning Model for Traffic Sign Detection Using Capsule Networks

convolutional neural networks are the most widely used deep learning algorithms for traffic signal classification till date[1] but they fail to capture pose, view, orientation of the images because of the intrinsic inability of max pooling layer.This paper proposes a novel method for Traffic sign detection using deep learning architecture called capsule networks that achieves outstanding performance on the German traffic sign dataset.Capsule network consists of capsules which are a group of neurons representing the instantiating parameters of an object like the pose and orientation[2] by using the dynamic routing and route by agreement algorithms.unlike the previous approaches of manual feature extraction,multiple deep neural networks with many parameters,our method eliminates the manual effort and provides resistance to the spatial variances.CNNs ́ can be fooled easily using various adversary attacks[3] and capsule networks can overcome such attacks from the intruders and can offer more reliability in traffic sign detection for autonomous vehicles.Capsule network have achieved the state-of-the-art accuracy of 97.6% on German Traffic Sign Recognition Benchmark dataset (GTSRB).
TEACHER AGENCY IN CURRICULUM MAKING : AGENTS OF CHANGE AND SPACES FOR MANOEUVRE

In the wake of new forms of curricular policy in many parts of the world, teachers are increasingly required to act as agents of change. And yet, teacher agency is under-theorised and often misconstrued in the educational change literature, wherein agency and change are seen as synonymous and positive. This paper addresses the issue of teacher agency in the context of an empirical study of curriculum making in schooling. Drawing upon the existing literature, we outline an ecological view of agency as an effect. These insights frame the analysis of a set of empirical data, derived from a research project about curriculum-making in a school and further education college in Scotland. Based upon the evidence, we argue that the extent to which teachers are able to achieve agency varies from context to context based upon certain environmental conditions of possibility and constraint, and that an important factor in this lies in the beliefs, values and attributes that teachers mobilise in relation to particular situations.
Coverage Embedding Models for Neural Machine Translation

In this paper, we enhance the attention-based neural machine translation (NMT) by adding explicit coverage embedding models to alleviate issues of repeating and dropping translations in NMT. For each source word, our model starts with a full coverage embedding vector to track the coverage status, and then keeps updating it with neural networks as the translation goes. Experiments on the large-scale Chinese-to-English task show that our enhanced model improves the translation quality significantly on various test sets over the strong large vocabulary NMT system.
Topological overview of hybrid electric and fuel cell vehicular power system architectures and configurations

This paper discusses the operational characteristics of the topologies for hybrid electric vehicles (HEV), fuel cell vehicles (FCV), and more electric vehicles (MEV). A brief description of series hybrid, parallel hybrid, and fuel cell-based propulsion systems are presented. The paper also presents fuel cell propulsion applications, more specific to light-duty passenger cars as well as heavy-duty buses. Finally, some of the major fundamental issues that currently face these advanced vehicular technologies including the challenges for market penetration are highlighted.
Nursing theory: its importance to practice.

Nursing theory should provide the principles that underpin practice and help to generate further nursing knowledge. However, a lack of agreement in the professional literature on nursing theory confuses nurses and has caused many to dismiss nursing theory as irrelevant to practice. This article aims to identify why nursing theory is important in practice.  By giving nurses a sense of identity, nursing theory can help patients, managers and other healthcare professionals to recognise the unique contribution that nurses make to the healthcare service ( Draper 1990 ). Providing a definition of nursing theory also helps nurses to understand their purpose and role in the healthcare setting.
Health Care Organizations as Complex Adaptive Systems

From its roots in physics, mathematics, and biology, the study of complexity science, or complex adaptive systems, has expanded into the domain of organizations and systems of organizations. Complexity science is useful for studying the evolution of complex organizations -entities with multiple, diverse, interconnected elements. Evolution of complex organizations often is accompanied by feedback effects, nonlinearity, and other conditions that add to the complexity of existing organizations and the unpredictability of the emergence of new entities. Health care organizations are an ideal setting for the application of complexity science due to the diversity of organizational forms and interactions among organizations that are evolving. Too, complexity science can benefit from attention to the world’s most complex human organizations. Organizations within and across the health care sector are increasingly interdependent. Not only are new, highly powerful and diverse organizational forms being created, but also the restructuring has occurred within very short periods of time. In this chapter, we review the basic tenets of complexity science. We identify a series of key differences between the complexity science and established theoretical approaches to studying health organizations, based on the ways in which time, space, and constructs are framed. The contrasting perspectives are demonstrated using two case examples drawn from healthcare innovation and healthcare integrated systems research. Complexity science broadens and deepens the scope of inquiry into health care organizations, expands corresponding methods of research, and increases the ability of theory to generate valid research on complex organizational forms.
Health psychology: psychological factors and physical disease from the perspective of human psychoneuroimmunology.

This review addresses the importance of studies of human psychoneuroimmunology in understanding the role of psychological factors in physical illness. First, it provides psychologically and biologically plausible explanations for how psychological factors might influence immunity and immune system-mediated disease. Second, it covers substantial evidence that factors such as stress, negative affect, clinical depression, social support, and repression/denial can influence both cellular and humoral indicators of immune status and function. Third, at least in the case of the less serious infectious diseases (colds, influenza, herpes), it considers consistent and convincing evidence of links between stress and negative affect and disease onset and progression. Although still early in its development, research also suggests a role of psychological factors in autoimmune diseases. Evidence for effects of stress, depression, and repression/denial on onset and progression of AIDs and cancer is less consistent and inconclusive, possibly owing to methodological limitations inherent in studying these complex illnesses, or because psychological influences on immunity are not of the magnitude or type necessary to alter the body's response in these cases. What is missing in this literature, however, is strong evidence that the associations between psychological factors and disease that do exist are attributable to immune changes.
Applying Web Usage Mining Techniques to Design Effective Web Recommendation Systems: A Case Study

Recommender systems are helpful tools which provide an adaptive Web environment for Web users. Recently, a number of Web page recommender systems have been developed to extract the user behavior from the user’s navigational path and predict the next request as he/she visits Web pages. Web Usage Mining (WUM) is a kind of data mining method that can be used to discover this behavior of user and his/her access patterns from Web log data. This paper first presents an overview of the used concepts and techniques of WUM to design Web recommender systems. Then it is shown that how WUM can be applied to Web server logs for discovering access patterns. Afterward, we analyze some of the problems and challenges in deploying recommender systems. Finally, we propose the solutions which address these problems.
MOTION VECTOR BASED VIDEO STEGANOGRAPHY

Although tremendous progress has been made on steganography in last decade but still there exist problems to detect the steganalysis in motion based video where the content is consistently is in motion behavior which creates hurdles to detect it. The motion value plays a crucial role in analyzing the difference between the rated, which allows us to focus on the difference between the actual SAD and the locally optimal SAD after the adding-orsubtracting-one operation on the motion value. Finally to perform the classification and extraction process based motion vectors two feature sets are been used to complete this process based on the fact that most motion vectors are locally optimal for most video codec’s. The proposed method succeeds to meet the applications requirement and simultaneously succeed in detecting the steganalysis in videos compare to conventional approaches reported in the literature.
Annotating Derivations: A New Evaluation Strategy and Dataset for Algebra Word Problems

We propose a new evaluation for automatic solvers for algebra word problems, which can identify mistakes that existing evaluations overlook. Our proposal is to evaluate such solvers using derivations, which reflect how an equation system was constructed from the word problem. To accomplish this, we develop an algorithm for checking the equivalence between two derivations, and show how derivation annotations can be semi-automatically added to existing datasets. To make our experiments more comprehensive, we include the derivation annotation for DRAW-1K, a new dataset containing 1000 general algebra word problems. In our experiments, we found that the annotated derivations enable a more accurate evaluation of automatic solvers than previously used metrics. We release derivation annotations for over 2300 algebra word problems for future evaluations.
AUTOSAR – the Standardized Software Architecture

Mastering the complexity of Electric/Electronics (E/E) architectures is one of the challenges of the automotive industry. In order to manage the growing system complexity and increasing number of dependencies while keeping the costs feasible, the basic software and the interfaces to applications and bus-systems have to be standardized in a futureproof way. In this context, leading automotive manufacturers and suppliers launched the AUTOSAR (AUTomotive Open System ARchitecture) Development Partnership in 2003 . The objective of this global cooperation was to establish an open industry standard for automotive software architecture for suppliers and manufacturers according to the motto: “Cooperate on standards, compete on implementation” . Accordingly the AUTOSAR standard comprises a set of specifications describing software architecture components and defining their interfaces as well as the definition of a standardized development methodology [7]. The AUTOSAR layered software architecture enables the application of independent software components. It aims to increase the reuse of software components, in particular between different vehicle platforms, and between OEMs and suppliers. It enables the scalability of embedded automotive software to different vehicle and platform variants, the transferability of functions throughout the vehicle network, and the integration of functional modules from multiple suppliers. Therefore, the AUTOSAR standard defines an architecture that separates application software from infrastructure-related basic software, as depicted in Fig. 1. The functional contents of the application software are different and related to the brand identity and the desired characteristics of the car manufacturer, or its system suppliers, whereas the functionality of the basic software is not visible to the customer and thus could be standardized by AUTOSAR in detail.
Application of empirical mode decomposition (EMD) for automated identification of congestive heart failure using heart rate signals

Electrocardiogram is widely used to diagnose the congestive heart failure (CHF). It is the primary noninvasive diagnostic tool that can guide in the management and follow-up of patients with CHF. Heart rate variability (HRV) signals which are nonlinear in nature possess the hidden signatures of various cardiac diseases. Therefore, this paper proposes a nonlinear methodology, empirical mode decomposition (EMD), for an automated identification and classification of normal and CHF using HRV signals. In this work, HRV signals are subjected to EMD to obtain intrinsic mode functions (IMFs). From these IMFs, thirteen nonlinear features such as approximate entropy $$ (E_{\text{ap}}^{x} ) $$ ( E ap x ) , sample entropy $$ (E_{\text{s}}^{x} ) $$ ( E s x ) , Tsallis entropy $$ (E_{\text{ts}}^{x} ) $$ ( E ts x ) , fuzzy entropy $$ (E_{\text{f}}^{x} ) $$ ( E f x ) , Kolmogorov Sinai entropy $$ (E_{\text{ks}}^{x} ) $$ ( E ks x ) , modified multiscale entropy $$ (E_{{{\text{mms}}_{y} }}^{x} ) $$ ( E mms y x ) , permutation entropy $$ (E_{\text{p}}^{x} ) $$ ( E p x ) , Renyi entropy $$ (E_{\text{r}}^{x} ) $$ ( E r x ) , Shannon entropy $$ (E_{\text{sh}}^{x} ) $$ ( E sh x ) , wavelet entropy $$ (E_{\text{w}}^{x} ) $$ ( E w x ) , signal activity $$ (S_{\text{a}}^{x} ) $$ ( S a x ) , Hjorth mobility $$ (H_{\text{m}}^{x} ) $$ ( H m x ) , and Hjorth complexity $$ (H_{\text{c}}^{x} ) $$ ( H c x ) are extracted. Then, different ranking methods are used to rank these extracted features, and later, probabilistic neural network and support vector machine are used for differentiating the highly ranked nonlinear features into normal and CHF classes. We have obtained an accuracy, sensitivity, and specificity of 97.64, 97.01, and 98.24 %, respectively, in identifying the CHF. The proposed automated technique is able to identify the person having CHF alarming (alerting) the clinicians to respond quickly with proper treatment action. Thus, this method may act as a valuable tool for increasing the survival rate of many cardiac patients.
RASA: A New Task Scheduling Algorithm in Grid Environment

In this paper, a new task scheduling algorithm called RASA, considering the distribution and scalability characteristics of grid resources, is proposed. The algorithm is built through a comprehensive study and analysis of two well known task scheduling algorithms, Min-min and Max-min. RASA uses the advantages of the both algorithms and covers their disadvantages. To achieve this, RASA firstly estimates the completion time of the tasks on each of the available grid resources and then applies the Max-min and Min-min algorithms, alternatively. In this respect, RASA uses the Min-min strategy to execute small tasks before the large ones and applies the Max-min strategy to avoid delays in the execution of large tasks and to support concurrency in the execution of large and small tasks. Our experimental results of applying RASA on scheduling independent tasks within grid environments demonstrate the applicability of RASA in achieving schedules with comparatively lower makespan.
Read Markov Decision Processes Discrete Stochastic Dynamic Programming Markov Decision Processes Discrete Stochastic Dynamic Programming

No wonder you activities are, reading will be always needed. It is not only to fulfil the duties that you need to finish in deadline time. Reading will encourage your mind and thoughts. Of course, reading will greatly develop your experiences about everything. Reading markov decision processes discrete stochastic dynamic programming is also a way as one of the collective books that gives many advantages. The advantages are not only for you, but for the other peoples with those meaningful benefits.
Alexa, Ask Wikidata! Voice Interaction with Knowledge Graphs using Amazon Alexa

Voice-enabled user interfaces have become a popular means of interaction with various kinds of applications and services. In addition to more traditional interaction paradigms such as keyword search, voice interaction can be a convenient means of communication for many groups of users. Amazon Alexa has become a valuable tool for building custom voice-enabled applications. In this demo paper we describe how we use Amazon Alexa technologies to build a Semantic Web applications able to answer factual questions using the Wikidata knowledge graph. We describe how the Amazon Alexa voice interface allows the user to communicate with the metaphactory knowledge graph management platform and a reusable procedure for producing the Alexa application configuration from semantic data in an automated way.