nils-herrmann/clean_scirepeval huggingface dataset:
Big Data Frameworks: A Comparative Study

Recently, increasingly large amounts of data are generated from a variety of sources. Existing data processing technologies are not suitable to cope with the huge amounts of generated data. Yet, many research works focus on Big Data, a buzzword referring to the processing of massive volumes of (unstructured) data. Recently proposed frameworks for Big Data applications help to store, analyze and process the data. In this paper, we discuss the challenges of Big Data and we survey existing Big Data frameworks. We also present an experimental evaluation and a comparative study of the most popular Big Data frameworks. This survey is concluded with a presentation of best practices related to the use of studied frameworks in several application domains such as machine learning, graph processing and real-world applications.
Thesis report

II Preface The final section of the Masters study program at Delft University of Technology consists of an individual thesis on a subject related to the student's specialization. This report is an overview of the results of an investigation on the thesis subject: New concept LNG Marine terminal; Hydrodynamics of a LNG carrier behind a detached breakwater. The research objective is the development of a simulation model that predicts the response of a moored LNG carrier in an exposed LNG marine terminal, as a function of the breakwater configuration and coast. During this investigation a graduation committee, consisting of the following persons, supervised all stages of the thesis work, for which I am very grateful; Prof.ir. H. Ligteringen (head of the committee), Dr.ir. Especially Wim van der Molen I would like to thank for his daily supervision and support during the past months. Due to his clear explanations on the subject and our coorporation, this thesis has been a very constructive and educational process. I also would like to thank Prof.dr.ir. J.A. Pinkster for the DELFRAC simulation results. Besides the graduation committee I would like to thank all those who have donated their time, energy and have given me advice, my family, and friends.  As the worldwide gas market continues to grow and environmental concerns with respect to in-port unloading of gas have increased, there has been a boom of interest in new liquefied natural gas (LNG) import terminals in the past five years. For these terminals, which are more and more located in areas with hostile sea conditions, dedicated provisions are required to create sufficient shelter for the carriers. Proposals have been made to construct a marginal low crested breakwater parallel to the coast protecting a ship moored at a jetty close to the shore. For an optimal economic design of such an LNG marine terminal, the dimensions and orientation of the detached breakwater have to be optimized as a function of the weather related downtime of the moored LNG carrier. Doing so requires adequate simulation tools. However, for the combination of wave and ship motion, a link between an efficient wave simulation tool and a program for ship response calculations is not available at present. The research on ship behaviour has resulted in the development of various so-called six degrees of freedom (SDF) computer programs. These programs solve the equations of motion of a moored vessel for …
Automated essay scoring with string kernels and word embeddings

•We propose to combine string kernels (low-level character n-gram features) and word embeddings (high-level semantic features) for automated essay scoring (AED) •TOK, string kernels have never been used for AED •TOK, this is the first successful attempt to combine string kernels and word embeddings •Using a shallow approach, we surpass recent deep learning approaches [Dong et al, EMNLP 2016; Dong et al, CONLL 2017;Tay et al, AAAI 2018]
Technological pedagogical content knowledge - a review of the literature

Technological Pedagogical Content Knowledge (TPACK) has been introduced as a conceptual framework for the knowledge base teachers need to effectively teach with technology. The framework stems from the notion that technology integration in a specific educational context benefits from a careful alignment of content, pedagogy and the potential of technology, and that teachers who want to integrate technology in their teaching practice therefore need to be competent in all three domains. This study is a systematic literature review about TPACK of 55 peer-reviewed journal articles (and one book chapter), published between 2005 and 2011. The purpose of the review was to investigate the theoretical basis and the practical use of TPACK. Findings showed different understandings of TPACK and of technological knowledge. Implications of these different views impacted the way TPACK was measured. Notions about TPACK in subject domains were hardly found in the studies selected for this review. Teacher knowledge (TPACK) and beliefs about pedagogy and technology are intertwined. Both determine whether a teacher decides to teach with technology. Active involvement in (re)design and enactment of technology-enhanced lessons was found as a promising strategy for the development of TPACK in (student-)teachers. Future directions for research are discussed.
Classification of Vehicle Collision Patterns in Road Accidents using Data Mining Algorithms

This paper emphasizes the importance of Data Mining classification algorithms in predicting the vehicle collision patterns occurred in training accident data set. This paper is aimed at deriving classification rules which can be used for the prediction of manner of collision. The classification algorithms viz. C4.5, C-RT, CS-MC4, Decision List, ID3, Naïve Bayes and RndTree have been applied in predicting vehicle collision patterns. The road accident training data set obtained from the Fatality Analysis Reporting System (FARS) which is available in the University of Alabama’s Critical Analysis Reporting Environment (CARE) system. The experimental results indicate that RndTree classification algorithm achieved better accuracy than other algorithms in classifying the manner of collision which increases fatality rate in road accidents. Also the feature selection algorithms including CFS, FCBF, Feature Ranking, MIFS and MODTree have been explored to improve the classifier accuracy. The result shows that the Feature Ranking method significantly improved the accuracy of the classifiers.
Discovering Knowledge in Data, an Introduction to Data Mining

The field of data mining lies at the confluence of predictive analytics, statistical analysis, and business intelligence. Due to the ever–increasing complexity and size of data sets and the wide range of applications in computer science, business, and health care, the process of discovering knowledge in data is more relevant than ever before. This book provides the tools needed to thrive in today s big data world. The author demonstrates how to leverage a company s existing databases to increase profits and market share, and carefully explains the most current data science methods and techniques. The reader will learn data mining by doing data mining . By adding chapters on data modelling preparation, imputation of missing data, and multivariate statistical analysis, Discovering Knowledge in Data, Second Edition remains the eminent reference on data mining .
Active Detection and Localization of Textureless Objects in Cluttered Environments

This paper introduces an active object detection and localization framework that combines a robust untextured object detection and 3D pose estimation algorithm with a novel next-best-view selection strategy. We address the detection and localization problems by proposing an edge-based registration algorithm that refines the object position by minimizing a cost directly extracted from a 3D image tensor that encodes the minimum distance to an edge point in a joint direction/location space. We face the next-best-view problem by exploiting a sequential decision process that, for each step, selects the next camera position which maximizes the mutual information between the state and the next observations. We solve the intrinsic intractability of this solution by generating observations that represent scene realizations, i.e. combination samples of object hypothesis provided by the object detector, while modeling the state by means of a set of constantly resampled particles. Experiments performed on different real world, challenging datasets confirm the effectiveness of the proposed methods.
Bioactive treatments in bone grafts for implant-based rehabilitation: Systematic review and meta-analysis.

The use of bioactive proteins, such as rhBMP-2, may improve bone regeneration in oral and maxillofacial surgery.



Analyze the effect of using bioactive proteins for bone regeneration in implant-based rehabilitation.



Seven databases were screened. Only clinical trials that evaluated the use of heterologous sources of bioactive proteins for bone formation prior to implant-based rehabilitation were included. Statistical analyses were carried out using a random-effects model by comparing the standardized mean difference between groups for bone formation, and risk ratio for implant survival (P ≤ .05).



Seventeen studies were included in the qualitative analysis, and 16 in the meta-analysis. For sinus floor augmentation, bone grafts showed higher amounts of residual bone graft particles than bioactive treatments (P ≤ .05). While for alveolar ridge augmentation bioactive treatments showed a higher level of bone formation than control groups (P ≤ .05). At 3 years of follow-up, no statistically significant differences were observed for implant survival (P > .05).



Bioactive proteins may improve bone formation in alveolar ridge augmentation, and reduce residual bone grafts in sinus floor augmentation. Further studies are needed to evaluate the long-term effect of using bioactive treatments for implant-based rehabilitation.
Blink detection robust to various facial poses

Applications based on eye-blink detection have increased, as a result of which it is essential for eye-blink detection to be robust and non-intrusive irrespective of the changes in the user's facial pose. However, most previous studies on camera-based blink detection have the disadvantage that their performances were affected by the facial pose. They also focused on blink detection using only frontal facial images. To overcome these disadvantages, we developed a new method for blink detection, which maintains its accuracy despite changes in the facial pose of the subject. This research is novel in the following four ways. First, the face and eye regions are detected by using both the AdaBoost face detector and a Lucas-Kanade-Tomasi (LKT)-based method, in order to achieve robustness to facial pose. Secondly, the determination of the state of the eye (being open or closed), needed for blink detection, is based on two features: the ratio of height to width of the eye region in a still image, and the cumulative difference of the number of black pixels of the eye region using an adaptive threshold in successive images. These two features are robustly extracted irrespective of the lighting variations by using illumination normalization. Thirdly, the accuracy of determining the eye state - open or closed - is increased by combining the above two features on the basis of the support vector machine (SVM). Finally, the SVM classifier for determining the eye state is adaptively selected according to the facial rotation. Experimental results using various databases showed that the blink detection by the proposed method is robust to various facial poses.
Tabulator: Exploring and Analyzing linked data on the Semantic Web

A web of linked RDF data may be enabled by standards specifying how links should be made in RDF and under what conditions they should be followed as well as powerful generic RDF browsers that can traverse an open web of RDF resources. The Tabulator is an RDF browser, which is designed both for new users to provoke interest in the Semantic Web and give them a means to access and interact with the entire web of RDF data, and for developers of RDF content to provide incentive for them to post their data in RDF, to refine and promote RDF linking standards, and to let providers see how their data interacts with the rest of the Semantic Web. A challenge for Semantic Web browsers is to bring the power of domain-specific applications to a generic program when new unexpected domains can be encountered in real time. The Tabulator project is an attempt to demonstrate and utilize the power of linked RDF data with a user-friendly Semantic Web browser that is able to recognize and follow RDF links to other RDF resources based on the user’s exploration and analysis.
Yoga for depression: the research evidence.

Yoga-based interventions may prove to be an attractive option for the treatment of depression. The aim of this study is to systematically review the research evidence on the effectiveness of yoga for this indication.



Searches of the major biomedical databases including MEDLINE, EMBASE, ClNAHL, PsycINFO and the Cochrane Library were conducted. Specialist complementary and alternative medicine (CAM) and the IndMED databases were also searched and efforts made to identify unpublished and ongoing research. Searches were conducted between January and June 2004. Relevant research was categorised by study type and appraised. Clinical commentaries were obtained for studies reporting clinical outcomes.



Five randomised controlled trials were located, each of which utilised different forms of yoga interventions and in which the severity of the condition ranged from mild to severe. All trials reported positive findings but methodological details such as method of randomisation, compliance and attrition rates were missing. No adverse effects were reported with the exception of fatigue and breathlessness in participants in one study.



No language restrictions were imposed on the searches conducted but no searches of databases in languages other than English were included.



Overall, the initial indications are of potentially beneficial effects of yoga interventions on depressive disorders. Variation in interventions, severity and reporting of trial methodology suggests that the findings must be interpreted with caution. Several of the interventions may not be feasible in those with reduced or impaired mobility. Nevertheless, further investigation of yoga as a therapeutic intervention is warranted.
HFT-CNN: Learning Hierarchical Category Structure for Multi-label Short Text Categorization

We focus on the multi-label categorization task for short texts and explore the use of a hierarchical structure (HS) of categories. In contrast to the existing work using non-hierarchical flat model, the method leverages the hierarchical relations between the categories to tackle the data sparsity problem. The lower the HS level, the worse the categorization performance. Because lower categories are fine-grained and the amount of training data per category is much smaller than that in an upper level. We propose an approach which can effectively utilize the data in the upper levels to contribute categorization in the lower levels by applying a Convolutional Neural Network (CNN) with a finetuning technique. The results using two benchmark datasets show that the proposed method, Hierarchical Fine-Tuning based CNN (HFTCNN) is competitive with the state-of-the-art CNN based methods.
Enhancement of SSD by concatenating feature maps for object detection

We propose an object detection method that improves the accuracy of the conventional SSD (Single Shot Multibox Detector), which is one of the top object detection algorithms in both aspects of accuracy and speed. The performance of a deep network is known to be improved as the number of feature maps increases. However, it is difficult to improve the performance by simply raising the number of feature maps. In this paper, we propose and analyze how to use feature maps effectively to improve the performance of the conventional SSD. The enhanced performance was obtained by changing the structure close to the classifier network, rather than growing layers close to the input data, e.g., by replacing VGGNet with ResNet. The proposed network is suitable for sharing the weights in the classifier networks, by which property, the training can be faster with better generalization power. For the Pascal VOC 2007 test set trained with VOC 2007 and VOC 2012 training sets, the proposed network with the input size of 300×300 achieved 78.5% mAP (mean average precision) at the speed of 35.0 FPS (frame per second), while the network with a 512×512 sized input achieved 80.8% mAP at 16.6 FPS using Nvidia Titan X GPU. The proposed network shows state-of-the-art mAP, which is better than those of the conventional SSD, YOLO, Faster-RCNN and RFCN. Also, it is faster than Faster-RCNN and RFCN.
A taxonomy of grid monitoring systems

Monitoring is the act of collecting information concerning the characteristics and status of resources of interest. Monitoring grid resources is a lively research area given the challenges and manifold applications. The aim of this paper is to advance the understanding of grid monitoring by introducing the involved concepts, requirements, phases, and related standardisation activities, including Global Grid Forum’s Grid Monitoring Architecture. Based on a refinement of the latter, the paper proposes a taxonomy of grid monitoring systems, which is employed to classify a wide range of projects and frameworks. The value of the offered taxonomy lies in that it captures a given system’s scope, scalability, generality and flexibility. The paper concludes with, among others, a discussion of the considered systems, as well as directions for future research.
The Behavior Markup Language: Recent Developments and Challenges

Since the beginning of the SAIBA effort to unify key interfaces in the multi-modal behavior generation process, the Behavior Markup Language (BML) has both gained ground as an important component in many projects worldwide, and continues to undergo further refinement. This paper reports on the progress made in the last year in further developing BML. It discusses some of the key challenges identified that the effort is facing, and reviews a number of projects that already are making use of BML or support its use.
Secure CoAP Using Enhanced DTLS forInternet of Things

The Internet of Things (IoT) is next generation technology that is intended to improve and optimize daily life by operating intelligent sensors and smart objects together. At application layer, communication of resourceconstrained devices is expected to use constrained application protocol (CoAP).Communication security is an important aspect of IoT environment. However closed source security solutions do not help in formulating security in IoT so that devices can communicate securely with each other. To protect the transmission of confidential information secure CoAP uses datagram transport layer security (DTLS) as the security protocol for communication and authentication of communicating devices. DTLS was initially designed for powerful devices that are connected through reliable and high bandwidth link. This paper proposes a collaboration of DTLS and CoAP for IoT. Additionally proposed DTLS header compression scheme that helps to reduce packet size, energy consumption and avoids fragmentation by complying the 6LoWPAN standards. Also proposed DTLS header compression scheme does not compromises the point-to-point security provided by DTLS. Since DTLS has chosen as security protocol underneath the CoAP, enhancement to the existing DTLS also provided by introducing the use of raw public key in DTLS.
Firefly algorithm with neighborhood attraction

Firefly algorithm (FA) is a new optimization technique based on swarm intelligence. It simulates the social behavior of fireflies. The search pattern of FA is determined by the attractions among fireflies, whereby a less bright firefly moves toward a brighter firefly. In FA, each firefly can be attracted by all other brighter fireflies in the population. However, too many attractions may result in oscillations during the search process and high computational time complexity. To overcome these problems, we propose a new FA variant called FA with neighborhood attraction (NaFA). In NaFA, each firefly is attracted by other brighter fireflies selected from a predefined neighborhood rather than those from the entire population. Experiments are conducted using several well-known benchmark functions. The results show that the proposed strategy can efficiently improve the accuracy of solutions and reduce the computational time complexity.
Towards THz Communications-Status in Research , Standardization and Regulation

In the most recent years, wireless communication networks have been facing a rapidly increasing demand for mobile traffic along with the evolvement of applications that require data rates of several 10s of Gbit/s. In order to enable the transmission of such high data rates, two approaches are possible in principle. The first one is aiming at systems operating with moderate bandwidths at 60 GHz, for example, where 7 GHz spectrum is dedicated to mobile services worldwide. However, in order to reach the targeted date rates, systems with high spectral efficiencies beyond 10 bit/s/Hz have to be developed, which will be very challenging. A second approach adopts moderate spectral efficiencies and requires ultra high bandwidths beyond 20 GHz. Such an amount of unregulated spectrum can be identified only in the THz frequency range, i.e. beyond 300 GHz. Systems operated at those frequencies are referred to as THz communication systems. The technology enabling small integrated transceivers with highly directive, steerable antennas becomes the key challenges at THz frequencies in face of the very high path losses. This paper gives an overview over THz communications, summarizing current research projects, spectrum regulations and ongoing standardization activities.
Parameter Free Hierarchical Graph-Based Clustering for Analyzing Continuous Word Embeddings

Word embeddings are high-dimensional vector representations of words and are thus difficult to interpret. In order to deal with this, we introduce an unsupervised parameter free method for creating a hierarchical graphical clustering of the full ensemble of word vectors and show that this structure is a geometrically meaningful representation of the original relations between the words. This newly obtained representation can be used for better understanding and thus improving the embedding algorithm and exhibits semantic meaning, so it can also be utilized in a variety of language processing tasks like categorization or measuring similarity.