nils-herrmann/clean_scirepeval huggingface dataset:
The Business Perspective of Cloud Computing: Actors, Roles and Value Networks

With the rise of a ubiquitous provision of computin g resources over the past years, cloud computing has been established as a prominent research topic. Many researchers, however, focus exclusively on the technical aspects of cloud computing, thereby n eglecting the business opportunities and potentials cloud computing can offer. Enabled through this tec hnology, new market players and business value networks arise and break up the traditional value c hain of service provision. The focus of this paper lies on the business aspects of cloud computing. Be sides elaborating on a comprehensive definition of cloud computing its main contribution is a conceptu al compilation of cloud actors and their roles within a new cloud computing value network and a co nceptual development of a generic value network of different actors in cloud computing. Ext ending the prevailing technical perspective of clou d computing, this paper shifts the focus from an excl usive technological perspective to a broader understanding of business opportunities and busines value.
ACL 2018 Relevance of Linguistic Structure in Neural Architectures for NLP

This work introduces a linguistically motivated architecture, which we label STAFFNET, for composing morphemes to derive word embeddings. The principal novelty in the work is to treat stems as vectors and affixes as functions over vectors. In this way, our model’s architecture more closely resembles the compositionality of morphemes in natural language. Such a model stands in opposition to models which treat morphemes uniformly, making no distinction between stem and affix. We run this new architecture on a dependency parsing task in Korean—a language rich in derivational morphology— and compare it against a lexical baseline, along with other sub-word architectures. STAFFNET shows competitive performance with the state-of-the-art on this task.
Churn Prediction in Social Networks

With the high speed development of online social networks, mobile devices and wireless technologies in the social network systems are increasingly available. Many people are integrated with social networking sites such as Facebook, Twitter and LinkedIn in their daily lives. Social networks have become major source of news, along with the traditional information propagation mediums such as television and newspapers. In social networks influence takes place in the form of “word-ofmouth”. Customer churn take place due to the termination of contract or the customer move to the other service providers. It is fundamental issue to find a subset of most influential nodes (i.e. the customers) that are going to churn. By concentrating on these influential nodes initially we can restrict the churning of customers early. These influential nodes are nothing but existing customers which are using the service provided by the company. The customers are terminating the contract due to unsatisfactory service or they are moving to other service providers due to influence from others.  Churn prediction, influence, Social Networks, Customer Churn, and Rumor International Journal of Engineering Research & Technology (IJERT)
Smart Cities

This chapter provides an overview of modeling techniques for large-scale systems in the Internet of Things (IoT) environment with a special accent on smart self-sustainable cities. The authors present a framework for modeling Large-Scale Multi-Agent Systems (LSMASs) including a graphical modeling language, and a tool, that aim to facilitate development of such systems in a recursive fashion. Smart self-sustainable cities in this chapter are modeled using this language that forms the basis for the Smart Self-Sustainable Human Settlements (SSSHS) framework developed by the authors. The SSSHS framework consists of several sustainability mechanisms which attempt to facilitate the self-sustainability of a human settlement by managing resources such as water, electricity, and heating, based on the current needs, production, and storage using a detailed agent-based methodology. By integrating these two frameworks (LSMAS and SSSHS), the authors show a recursive and layered approach that is able to model large-scale resource management systems in a hierarchical manner by using IoT technologies.
Autonomous image-based exploration for mobile robot navigation

Image-based navigation paradigms have recently emerged as an interesting alternative to conventional model- based methods in mobile robotics. In this paper, we augment the existing image-based navigation approaches by presenting a novel image-based exploration algorithm. The algorithm facilitates a mobile robot equipped only with a monocular pan-tilt camera to autonomously explore a typical indoor environment. The algorithm infers frontier information directly from the images and displaces the robot towards regions that are informative for navigation. The frontiers are detected using a geometric context-based segmentation scheme that exploits the natural scene structure in indoor environments. In the due process, a topological graph of the workspace is built in terms of images which can be subsequently utilised for the tasks of localisation, path planning and navigation. Experimental results on a mobile robot in an unmodified laboratory and corridor environments demonstrate the validity of the approach.
The Interplay of Bayesian and Frequentist Analysis ∗

Statistics has struggled for nearly a century over the issue of whether the Bayesian or frequentist paradigm is superior. This debate is far from over and, indeed, should continue, since there are fundamental philosophical and pedagogical issues at stake. At the methodological level, however, the debate has become considerably muted, with the recognition that each approach has a great deal to contribute to statistical practice and each is actually essential for full development of the other approach. In this article, we embark upon a rather idiosyncratic walk through some of these issues.
Analysis of Traveling Ionospheric Disturbances ( TIDs ) in GPS TEC Launched by the 2011 Tohoku Earthquake

TIDs have been detected using various measurement techniques, including HF sounders, incoherent scatter radars, in-situ measurements, and optical techniques. However, there is still much we do not yet know or understand about TIDs. Observations of TIDs have tended to be sparse, and there is a need for additional observations to provide new scientific insight into the geophysical source phenomenology and wave propagation physics. The dense network of GPS receivers around the globe offers a relatively new data source to observe and monitor TIDs. In this paper, we use Total Electron Content (TEC) measurements from 4000 GPS receivers throughout the continental United States to observe TIDs associated with the 11 March 2011 Tohoku tsunami. The tsunami propagated across the Pacific to the US west coast over several hours, and corresponding TIDs were observed over Hawaii, and via the network of GPS receivers in the US. The network of GPS receivers in effect provides a 2D spatial map of TEC perturbations, which can be used to calculate TID parameters, including horizontal wavelength, speed, and period. Well-formed, planar traveling ionospheric disturbances were detected over the west coast of the US ten hours after the earthquake. Fast Fourier transform analysis of the observed waveforms revealed that the period of the wave was 15.1 minutes with a horizontal wavelength of 194.8 km, phase velocity of 233.0 m/s, and an azimuth of 105.2 (propagating nearly due east in the direction of the tsunami wave). These results are consistent with TID observations in airglow measurements from Hawaii earlier in the day, and with other GPS TEC observations. The vertical wavelength of the TID was found to be 43.5 km. The TIDs moved at the same velocity as the tsunami itself. Much work is still needed in order to fully understand the ocean-atmosphere coupling mechanisms, which could lead to the development of effective tsunami detection/warning systems. The work presented in this paper demonstrates a technique for the study of ionospheric perturbations that can affect navigation, communications and surveillance systems.
Predicting football scores using machine learning techniques

Predicting the results of football matches poses an interesting challenge due to the fact that the sport is so popular and widespread. However, predicting the outcomes is also a difficult problem because of the number of factors which must be taken into account that cannot be quantitatively valued or modeled. As part of this work, a software solution has been developed in order to try and solve this problem. During the development of the system, a number of tests have been carried out in order to determine the optimal combination of features and classifiers. The results of the presented system show a satisfactory capability of prediction which is superior to the one of the reference method (most likely a priori outcome).
Spark based distributed Deep Learning framework for Big Data applications

Deep Learning architectures, such as deep neural networks, are currently the hottest emerging areas of data science, especially in Big Data. Deep Learning could be effectively exploited to address some major issues of Big Data, including withdrawing complex patterns from huge volumes of data, fast information retrieval, data classification, semantic indexing and so on. In this work, we designed and implemented a framework to train deep neural networks using Spark, fast and general data flow engine for large scale data processing. The design is similar to Google software framework called DistBelief which can utilize computing clusters with thousands of machines to train large scale deep networks. Training Deep Learning models requires extensive data and computation. Our proposed framework can accelerate the training time by distributing the model replicas, via stochastic gradient descent, among cluster nodes for data resided on HDFS.
Syntactic Contexts for Finding Semantically Related Words

Finding semantically related words is a first step in the dire ct on of automatic ontology building. Guided by the view that similar words occur in simi lar contexts, we looked at the syntactic context of words to measure their semantic sim ilarity. Words that occur in a direct object relation with the verb drink, for instance, have something in common ( liquidity, ...). Co-occurrence data for common nouns and proper names , for several syntactic relations, was collected from an automatically parsed corp us of 78 million words of newspaper text. We used several vector-based methods to compute the distributional similarity between words. Using Dutch EuroWordNet as evaluation stand ard, we investigated which vector-based method and which combination of syntactic rel ations is the strongest predictor of semantic similarity.
A framework to predict the quality of answers with non-textual features

New types of document collections are being developed by various web services. The service providers keep track of non-textual features such as click counts. In this paper, we present a framework to use non-textual features to predict the quality of documents. We also show our quality measure can be successfully incorporated into the language modeling-based retrieval model. We test our approach on a collection of question and answer pairs gathered from a community based question answering service where people ask and answer questions. Experimental results using our quality measure show a significant improvement over our baseline.
2D-3D Fully Convolutional Neural Networks for Cardiac MR Segmentation

In this paper, we develop a 2D and 3D segmentation pipelines for fully automated cardiac MR image segmentation using Deep Convolutional Neural Networks (CNN). Our models are trained end-to-end from scratch using the ACD Challenge 2017 dataset comprising of 100 studies, each containing Cardiac MR images in End Diastole and End Systole phase. We show that both our segmentation models achieve near state-of-the-art performance scores in terms of distance metrics and have convincing accuracy in terms of clinical parameters. A comparative analysis is provided by introducing a novel dice loss function and its combination with cross entropy loss. By exploring different network structures and comprehensive experiments, we discuss several key insights to obtain optimal model performance, which also is central to the theme of this challenge.
No Dutch Book can be built against the TBM even though update is not obtained by Bayes rule of conditioning

Synchronic and Diachronic Dutch Books are used to justify the use of probability measures to quantify the beliefs held by a rational agent. The argument has been used to reject any non-Bayesian representation of degrees of beliefs. We show that the transferable belief model resists the criticism even though it is not a Bayesian model. We analyze the ‘Peter, Paul and Mary’ example and show how it resists to Dutch Books.
Simultaneous storage of medical images in the spatial and frequency domain: A comparative study

Digital watermarking is a technique of hiding specific identification data for copyright authentication. This technique is adapted here for interleaving patient information with medical images, to reduce storage and transmission overheads.



The patient information is encrypted before interleaving with images to ensure greater security. The bio-signals are compressed and subsequently interleaved with the image. This interleaving is carried out in the spatial domain and Frequency domain. The performance of interleaving in the spatial, Discrete Fourier Transform (DFT), Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) coefficients is studied. Differential pulse code modulation (DPCM) is employed for data compression as well as encryption and results are tabulated for a specific example.



It can be seen from results, the process does not affect the picture quality. This is attributed to the fact that the change in LSB of a pixel changes its brightness by 1 part in 256. Spatial and DFT domain interleaving gave very less %NRMSE as compared to DCT and DWT domain.



The Results show that spatial domain the interleaving, the %NRMSE was less than 0.25% for 8-bit encoded pixel intensity. Among the frequency domain interleaving methods, DFT was found to be very efficient.
Optimal Operator Replication and Placement for Distributed Stream Processing Systems

Exploiting on-the-fly computation, Data Stream Processing (DSP) applications are widely used to process unbounded streams of data and extract valuable information in a near real-time fashion. As such, they enable the development of new intelligent and pervasive services that can improve our everyday life. To keep up with the high volume of daily produced data, the operators that compose a DSP application can be replicated and placed on multiple, possibly distributed, computing nodes, so to process the incoming data flow in parallel. Moreover, to better exploit the abundance of diffused computational resources (e.g., Fog computing), recent trends investigate the possibility of decentralizing the DSP application placement.
 In this paper, we present and evaluate a general formulation of the optimal DSP replication and placement (ODRP) as an integer linear programming problem, which takes into account the heterogeneity of application requirements and infrastructural resources. We integrate ODRP as prototype scheduler in the Apache Storm DSP framework. By leveraging on the DEBS 2015 Grand Challenge as benchmark application, we show the benefits of a joint optimization of operator replication and placement and how ODRP can optimize different QoS metrics, namely response time, internode traffic, cost, availability, and a combination thereof.
Perceived discrimination as worldview threat or worldview confirmation: implications for self-esteem.

In 3 studies, the authors tested the hypothesis that discrimination targets' worldview moderates the impact of perceived discrimination on self-esteem among devalued groups. In Study 1, perceiving discrimination against the ingroup was negatively associated with self-esteem among Latino Americans who endorsed a meritocracy worldview (e.g., believed that individuals of any group can get ahead in America and that success stems from hard work) but was positively associated with self-esteem among those who rejected this worldview. Study 2 showed that exposure to discrimination against their ingroup (vs. a non-self-relevant group) led to lower self-esteem, greater feelings of personal vulnerability, and ingroup blame among Latino Americans who endorsed a meritocracy worldview but to higher self-esteem and decreased ingroup blame among Latino Americans who rejected it. Study 3 showed that compared with women informed that prejudice against their ingroup is pervasive, women informed that prejudice against their ingroup is rare had higher self-esteem if they endorsed a meritocracy worldview but lower self-esteem if they rejected this worldview. Findings support the idea that perceiving discrimination against one's ingroup threatens the worldview of individuals who believe that status in society is earned but confirms the worldview of individuals who do not.
Generating Natural Language Question-Answer Pairs from a Knowledge Graph Using a RNN Based Question Generation Model

In recent years, knowledge graphs such as Freebase that capture facts about entities and relationships between them have been used actively for answering factoid questions. In this paper, we explore the problem of automatically generating question answer pairs from a given knowledge graph. The generated question answer (QA) pairs can be used in several downstream applications. For example, they could be used for training better QA systems. To generate such QA pairs, we first extract a set of keywords from entities and relationships expressed in a triple stored in the knowledge graph. From each such set, we use a subset of keywords to generate a natural language question that has a unique answer. We treat this subset of keywords as a sequence and propose a sequence to sequence model using RNN to generate a natural language question from it. Our RNN based model generates QA pairs with an accuracy of 33.61 percent and performs 110.47 percent (relative) better than a state-of-the-art template based method for generating natural language question from keywords. We also do an extrinsic evaluation by using the generated QA pairs to train a QA system and observe that the F1-score of the QA system improves by 5.5 percent (relative) when using automatically generated QA pairs in addition to manually generated QA pairs available for training. ∗This work was done while the author was a part of IBM Research India †This work was done while the author was a part of IBM Research India
Security of biometric authentication systems

This overview paper outlines our views of actual security of biometric authentication and encryption systems. The attractiveness of some novel approaches like cryptographic key generation from biometric data is in some respect understandable, yet so far has lead to various shortcuts and compromises on security. Our paper starts with an introductory section that is followed by a section about variability of biometric characteristics, with a particular attention paid to biometrics used in large systems. The following sections then discuss the potential for biometric authentication systems, and for the use of biometrics in support of cryptographic applications as they are typically used in computer systems.
Integrating Web Services with Competitive Strategies: A Balanced Scorecard Approach

The significance of aligning IT with corporate strategy is widely recognized, but the lack of appropriate methodologies prevented practitioners from integrating IT projects with competitive strategies effectively. This article addresses the issue of deploying Web services strategically using the concept of a widely accepted management tool, the balanced scorecard. A framework is developed to match potential benefits of Web services with corporate strategy in four business dimensions: innovation and learning, internal business process, customer, and financial. It is argued that the strategic benefits of implementing Web services can only be realized if the Web services initiatives are planned and implemented within the framework of an IT strategy that is designed to support the business strategy of a firm.
Shape from Shading: A Survey

Since the rst shape-from-shading (SFS) technique was developed by Horn in the early 1970s, many di erent approaches have emerged. In this paper, six well-known SFS algorithms are implemented and compared. The performance of the algorithms was analyzed on synthetic images using mean and standard deviation of depth (Z) error, mean of surface gradient (p, q) error and CPU timing. Each algorithm works well for certain images, but performs poorly for others. In general, minimization approaches are more robust, while the other approaches are faster. The implementation of these algorithms in C, and images used in this paper, are available by anonymous ftp under the pub=tech paper=survey directory at eustis:cs:ucf:edu (132.170.108.42). These are also part of the electronic version of paper.
Going deeper than microscopy: the optical imaging frontier in biology

Optical microscopy has been a fundamental tool of biological discovery for more than three centuries, but its in vivo tissue imaging ability has been restricted by light scattering to superficial investigations, even when confocal or multiphoton methods are used. Recent advances in optical and optoacoustic (photoacoustic) imaging now allow imaging at depths and resolutions unprecedented for optical methods. These abilities are increasingly important to understand the dynamic interactions of cellular processes at different systems levels, a major challenge of postgenome biology. This Review discusses promising photonic methods that have the ability to visualize cellular and subcellular components in tissues across different penetration scales. The methods are classified into microscopic, mesoscopic and macroscopic approaches, according to the tissue depth at which they operate. Key characteristics associated with different imaging implementations are described and the potential of these technologies in biological applications is discussed.
Deep Learning using Robust Interdependent Codes

We investigate a simple yet effective method to introduce inhibitory and excitatory interactions between units in the layers of a deep neural network classifier. The method is based on the greedy layer-wise procedure of deep learning algorithms and extends the denoising autoencoder (Vincent et al., 2008) by adding asymmetric lateral connections between its hidden coding units, in a manner that is much simpler and computationally more efficient than previously proposed approaches. We present experiments on two character recognition problems which show for the first time that lateral connections can significantly improve the classification performance of deep networks.
Hydrogen Gas Sensors Based on Semiconductor Oxide Nanostructures

Recently, the hydrogen gas sensing properties of semiconductor oxide (SMO) nanostructures have been widely investigated. In this article, we provide a comprehensive review of the research progress in the last five years concerning hydrogen gas sensors based on SMO thin film and one-dimensional (1D) nanostructures. The hydrogen sensing mechanism of SMO nanostructures and some critical issues are discussed. Doping, noble metal-decoration, heterojunctions and size reduction have been investigated and proved to be effective methods for improving the sensing performance of SMO thin films and 1D nanostructures. The effect on the hydrogen response of SMO thin films and 1D nanostructures of grain boundary and crystal orientation, as well as the sensor architecture, including electrode size and nanojunctions have also been studied. Finally, we also discuss some challenges for the future applications of SMO nanostructured hydrogen sensors.