nils-herrmann/clean_scirepeval huggingface dataset:
Vector field design on surfaces

Vector field design on surfaces is necessary for many graphics applications: example-based texture synthesis, nonphotorealistic rendering, and fluid simulation. For these applications, singularities contained in the input vector field often cause visual artifacts. In this article, we present a vector field design system that allows the user to create a wide variety of vector fields with control over vector field topology, such as the number and location of singularities. Our system combines basis vector fields to make an initial vector field that meets user specifications.The initial vector field often contains unwanted singularities. Such singularities cannot always be eliminated due to the Poincaré-Hopf index theorem. To reduce the visual artifacts caused by these singularities, our system allows the user to move a singularity to a more favorable location or to cancel a pair of singularities. These operations offer topological guarantees for the vector field in that they only affect user-specified singularities. We develop efficient implementations of these operations based on Conley index theory. Our system also provides other editing operations so that the user may change the topological and geometric characteristics of the vector field.To create continuous vector fields on curved surfaces represented as meshes, we make use of the ideas of geodesic polar maps and parallel transport to interpolate vector values defined at the vertices of the mesh. We also use geodesic polar maps and parallel transport to create basis vector fields on surfaces that meet the user specifications. These techniques enable our vector field design system to work for both planar domains and curved surfaces.We demonstrate our vector field design system for several applications: example-based texture synthesis, painterly rendering of images, and pencil sketch illustrations of smooth surfaces.
A fast all nearest neighbor algorithm for applications involving large point-clouds

Algorithms that use point-cloud models make heavy use of the neighborhoods of the points. These neighborhoods are used to compute the surface normals for each point, mollification, and noise removal. All of these primitive operations require the seemingly repetitive process of finding the k nearest neighbors (kNNs) of each point. These algorithms are primarily designed to run in main memory. However, rapid advances in scanning technologies have made available point-cloud models that are too large to fit in the main memory of a computer. This calls for more efficient methods of computing the kNNs of a large collection of points many of which are already in close proximity. A fast kNN algorithm is presented that makes use of the locality of successive points whose k nearest neighbors are sought to reduce significantly the time needed to compute the neighborhood needed for the primitive operation as well as enable it to operate in an environment where the data is on disk. Results of experiments demonstrate an order of magnitude improvement in the time to perform the algorithm and several orders of magnitude improvement in work efficiency when compared with several prominent existing methods.
Text Simplification as Tree Transduction

Lexical and syntactic simplification aim to make texts more accessible to certain audiences. Syntactic simplification uses either hand-crafted linguistic rules for deep syntactic transformations, or machine learning techniques to model simpler transformations. Lexical simplification performs a lookup for synonyms followed by context and/or frequency-based models. In this paper we investigate modelling both syntactic and lexical simplification through the learning of general tree transduction rules. Experiments with the Simple English Wikipedia corpus show promising results but highlight the need for clever filtering strategies to remove noisy transformations.  A simplificação em nı́vel lexical e sintático objetiva tornar textos mais acessı́veis a certos públicos-alvo. Simplificação em nı́vel sintático usa regras confeccionadas manualmente para empregar transformações sintáticas, ou técnicas de aprendizado de máquina para modelar transformações mais simples. Simplificação em nı́vel lexical emprega busca por sinônimos para palavras complexas seguida por análise de contexto e/ou busca em modelos de frequência de palavras. Neste trabalho investiga-se a modelagem de ambas estratégias de simplificação em nı́vel sintático e lexical pelo aprendizado de regras através da transdução de árvores. Experimentos com dados da Simple English Wikipedia mostram resultados promissores, porém destacam a necessidade de estratégias inteligentes de filtragem para remover transformações ruidosas.
Energy-aware resource allocation heuristics for efficient management of data centers for Cloud computing

Cloud computing offers utility-oriented IT services to users worldwide. Based on a pay-as-you-go model, it enables hosting of pervasive applications from consumer, scientific, and business domains. However, data centers hosting Cloud applications consume huge amounts of electrical energy, contributing to high operational costs and carbon footprints to the environment. Therefore, we need Green Cloud computing solutions that can not only save energy for the environment but also reduce operational costs. In this paper, we define an architectural framework and principles for energy-efficient Cloud computing. Based on this architecture, we present a vision, challenges, and resource provisioning and allocation algorithms for energy-efficient management of Cloud computing environments. The proposed energy-aware allocation heuristics provision data center resources to client applications in a way that improves energy efficiency of the data center, while delivering the negotiated Quality of Service (QoS). In particular, in this paper we propose: (a) architectural principles for energy-efficient management of Clouds; (b) energy-efficient resource allocation policies and scheduling algorithms considering QoS expectations, and devices power usage characteristics; and (c) a number of open research challenges, addressing which can bring substantial benefits to both resource providers and consumers. We have validated our approach by conducting a rigorous performance evaluation study using the CloudSim toolkit. The results demonstrate that Cloud computing model has immense potential as it offers significant cost savings and demonstrates high potential for the improvement of energy efficiency under dynamic workload scenarios.
Fast Iterative Graph Computation: A Path Centric Approach

Large scale graph processing represents an interesting challenge due to the lack of locality. This paper presents PathGraph for improving iterative graph computation on graphs with billions of edges. Our system design has three unique features: First, we model a large graph using a collection of tree-based partitions and use an path-centric computation rather than vertex-centric or edge-centric computation. Our parallel computation model significantly improves the memory and disk locality for performing iterative computation algorithms. Second, we design a compact storage that further maximize sequential access and minimize random access on storage media. Third, we implement the path-centric computation model by using a scatter/gather programming model, which parallels the iterative computation at partition tree level and performs sequential updates for vertices in each partition tree. The experimental results show that the path-centric approach outperforms vertex-centric and edge-centric systems on a number of graph algorithms for both in-memory and out-of-core graphs.
A Classification Framework for Software Component Models

In the last decade, a large number of different software component models have been developed, with different aims and using different principles and technologies. This has resulted in a number of models which have many similarities, but also principal differences, and in many cases unclear concepts. Component-based development has not succeeded in providing standard principles, as has, for example, object-oriented development. In order to increase the understanding of the concepts and to differentiate component models more easily, this paper identifies, discusses, and characterizes fundamental principles of component models and provides a Component Model Classification Framework based on these principles. Further, the paper classifies a large number of component models using this framework.
On the Optimality of Classifier Chain for Multi-label Classification

To capture the interdependencies between labels in multi-label classification problems, classifier chain (CC) tries to take the multiple labels of each instance into account under a deterministic high-order Markov Chain model. Since its performance is sensitive to the choice of label order, the key issue is how to determine the optimal label order for CC. In this work, we first generalize the CC model over a random label order. Then, we present a theoretical analysis of the generalization error for the proposed generalized model. Based on our results, we propose a dynamic programming based classifier chain (CC-DP) algorithm to search the globally optimal label order for CC and a greedy classifier chain (CC-Greedy) algorithm to find a locally optimal CC. Comprehensive experiments on a number of real-world multi-label data sets from various domains demonstrate that our proposed CC-DP algorithm outperforms state-of-the-art approaches and the CCGreedy algorithm achieves comparable prediction performance with CC-DP.
The Nature of the Quaternion

Some of the confusions concerning quaternions as they are employed in spacecraft attitude work are discussed. The order of quaternion multiplication is discussed in terms of its historical development and its consequences for the quaternion imaginaries. The di erent formulations for the quaternions are also contrasted. It is shown that the three Hamilton imaginaries cannot be interpreted as the basis of the vector space of physical vectors but only as constant numerical column vectors, the autorepresentation of a physical basis.
Providing Robust and Ubiquitous Security Support for Mobile Ad Hoc Networks

Providing security support for mobile ad-hoc networks is challenging for several reasons: (a) wireless networks are susceptible to attacks ranging from passive eavesdropping to active interfering, occasional break-ins by adversaries may be inevitable in a large time window; (b) mobile users demand “anywhere, anytime” services; (c) a scalable solution is needed for a large-scale mobile network. In this paper, we describe a solution that supports ubiquitous security services for mobile hosts, scales to network size, and is robust against break-ins. In our design, we distribute the certification authority functions through a threshold secret sharing mechanism, in which each entity holds a secret share and multiple entities in a local neighborhood jointly provide complete services. We employ localized certification schemes to enable ubiquitous services. We also update the secret shares to further enhance robustness against break-ins. Both simulations and implementation confirm the effectiveness of our design.
Boosting and Maximum Likelihood for Exponential Models

We derive an equivalence between AdaBoost and the dual of a co nvex optimization problem, showing that the only difference bet ween minimizing the exponential loss used by AdaBoost and maximum lik elihood for exponential models is that the latter requires the model to be normalized to form a conditional probability distribution over la bels. In addition to establishing a simple and easily understood connect ion between the two methods, this framework enables us to derive new regu larization procedures for boosting that directly correspond to penali zed maximum likelihood. Experiments on UCI datasets support our theore tical analysis and give additional insight into the relationship betwe en boosting and logistic regression.
A novel hybridization of artificial neural networks and ARIMA models for time series forecasting

Improving forecasting especially time series forecasting accuracy is an important yet often difficult task facing decision makers in many areas. Both theoretical and empirical findings have indicated that integration of different models can be an effective way of improving upon their predictive performance, especially when the models in combination are quite different. Artificial neural networks (ANNs) are flexible computing frameworks and universal approximators that can be applied to a wide range of forecasting problems with a high degree of accuracy. However, using ANNs to model linear problems have yielded mixed results, and hence; it is not wise to apply ANNs blindly to any type of data. Autoregressive integrated moving average (ARIMA) models are one of the most popular linear models in time series forecasting, which have been widely applied in order to construct more accurate hybrid models during the past decade. Although, hybrid techniques, which decompose a time series into its linear and nonlinear components, have recently been shown to be successful for single models, these models have some disadvantages. In this paper, a novel hybridization of artificial neural networks and ARIMA model is proposed in order to overcome mentioned limitation of ANNs and yield more general and more accurate forecasting model than traditional hybrid ARIMA-ANNs models. In our proposed model, the unique advantages of ARIMA models in linear modeling are used in order to identify and magnify the existing linear structure in data, and then a neural network is used in order to determine a model to capture the underlying data generating process and predict, using preprocessed data. Empirical results with three well-known real data sets indicate that the proposed model can be an effective way to improve forecasting accuracy ybrid achieved by traditional h
Power Control of Series-Parallel Resonant Inverter For Induction Heating Using Buck Converter

Dr. Turki K. Hassan* & Enaam A. Ali*  Abstract The purpose of this work is to study, analyze, and design a half-bridge seriesparallel resonant inverter for induction heating applications. A pulse width modulation (PWM)-based double integral sliding mode voltage controlled buck converter is proposed for control the induction heating power. This type of controller is used in order to obtain very small steady state error, stable and fast dynamic response, and robustness against variations in the line voltage and converter parameters. A small induction heating coil is designed and constructed. A carbon steel (C45) cylindrical billet is used as a load. The induction heating load parameters (RL and LL) are measured at the resonant frequency of 85 kHz. The parameters of the resonant circuit are chosen for operation at resonant. The inverter is operated at unity power factor by phased locked loop (PLL) control irrespective of load variations, with maximum current gain, and practically no voltage spikes in the switching devices at turn-off, therefore no snubber circuit is used for operation at unity power factor. A power MOSFET transistor is used as a switching device for buck converter and the IGBT transistor is used as a switching device for the inverter. A complete designed system is simulated using Matlab/Simulink. All the electronic control circuits are designed and implemented. The practical results are compared with simulation results to verify the proposed induction heating system. A close agreement between simulation and practical results is noticed and a good performance is achieved.
Computer aided clothing pattern design with 3D editing and pattern alteration

The traditional apparel product development process is a typical iterative ‘optimization’ process that involves trial-and-error. In order to confirm the design and achieve a satisfactory fit, a number of repeated cycles of sample preparation, trial fitting and pattern alteration must be conducted. The process itself is time-consuming, costly, and dependent on the designer’s skills and experience. In this paper, a novel computer aided design (CAD) solution for virtual try-on, fitting evaluation and style editing is proposed to speed up the clothing design process. A series of new techniques from cross parameterization, geometrical and physical integrated deformation, to novel editing methods are proposed. First, a cross parameterization technique is employed to map clothing pattern pieces on a model surface. The pattern can be precisely positioned to form the initial shapewith low distortion. Next, a new deformationmethod called hybrid pop-up is proposed to approximate the virtual try-on shape. This method is an integration of geometrical reconstruction and physical based simulation. In addition, user interactive operations are introduced for style editing and pattern alteration in both 2D and 3D manners. The standard rules regulating pattern editing in the fashion industry can be incorporated in the system, so that the resulting clothing patterns are suitable for everyday production.
CCCF: Improving Collaborative Filtering via Scalable User-Item Co-Clustering

Collaborative Filtering (CF) is the most popular method for recommender systems. The principal idea of CF is that users might be interested in items that are favorited by similar users, and most of the existing CF methods measure users' preferences by their behaviours over all the items. However, users might have different interests over different topics, thus might share similar preferences with different groups of users over different sets of items. In this paper, we propose a novel and scalable method CCCF which improves the performance of CF methods via user-item co-clustering. CCCF first clusters users and items into several subgroups, where each subgroup includes a set of like-minded users and a set of items in which these users share their interests. Then, traditional CF methods can be easily applied to each subgroup, and the recommendation results from all the subgroups can be easily aggregated. Compared with previous works, CCCF has several advantages including scalability, flexibility, interpretability and extensibility. Experimental results on four real world data sets demonstrate that the proposed method significantly improves the performance of several state-of-the-art recommendation algorithms.
Benchmarking Distributed Stream Processing Platforms for IoT Applications

Internet of Things (IoT) is a technology paradigm where millions of sensors monitor, and help inform or manage, physical, environmental and human systems in real-time. The inherent closed-loop responsiveness and decision making of IoT applications makes them ideal candidates for using low latency and scalable stream processing platforms. Distributed Stream Processing Systems (DSPS) are becoming essential components of any IoT stack, but the efficacy and performance of contemporary DSPS have not been rigorously studied for IoT data streams and applications. Here, we develop a benchmark suite and performance metrics to evaluate DSPS for streaming IoT applications. The benchmark includes 13 common IoT tasks classified across various functional categories and forming micro-benchmarks, and two IoT applications for statistical summarization and predictive analytics that leverage various dataflow compositional features of DSPS. These are coupled with stream workloads sourced from real IoT observations from smart cities. We validate the IoT benchmark for the popular Apache Storm DSPS, and present empirical results.
A Large-Displacement 3-DOF Flexure Parallel Mechanism with Decoupled Kinematics Structure

This paper proposes an XYZ-flexure parallel mechanism (FPM) with large displacement and decoupled kinematics structure. The large-displacement FPM has large motion range more than 1 mm. Moreover, the decoupled XYZ-stage has small cross-axis error and small parasitic rotation. In this study, the typical prismatic joints are investigated, and a new large-displacement prismatic joint using notch hinges is designed. The conceptual design of the FPM is proposed by assembling these modular prismatic joints, and then the optimal design of the FPM is conducted. The analytical models of linear stiffness and dynamics are derived using pseudo-rigid-body (PRB) method. Finally, the numerical simulation using ANSYS is conducted for modal analysis to verify the analytical dynamics equation. Experiments are conducted to verify the proposed design for linear stiffness, cross-axis error and parasitic rotation
Content-based Recommender Systems: State of the Art and Trends

Recommender systems have the effect of guiding users in a personalized way to interesting objects in a large space of possible options. Content-based recommendation systems try to recommend items similar to those a given user has liked in the past. Indeed, the basic process performed by a content-based recommender consists in matching up the attributes of a user profile in which preferences and interests are stored, with the attributes of a content object (item), in order to recommend to the user new interesting items. This chapter provides an overview of content-based recommender systems, with the aim of imposing a degree of order on the diversity of the different aspects involved in their design and implementation. The first part of the chapter presents the basic concepts and terminology of contentbased recommender systems, a high level architecture, and their main advantages and drawbacks. The second part of the chapter provides a review of the state of the art of systems adopted in several application domains, by thoroughly describing both classical and advanced techniques for representing items and user profiles. The most widely adopted techniques for learning user profiles are also presented. The last part of the chapter discusses trends and future research which might lead towards the next generation of systems, by describing the role of User Generated Content as a way for taking into account evolving vocabularies, and the challenge of feeding users with serendipitous recommendations, that is to say surprisingly interesting items that they might not have otherwise discovered. Pasquale Lops Department of Computer Science, University of Bari “Aldo Moro”, Via E. Orabona, 4, Bari (Italy) e-mail: lops@di.uniba.it Marco de Gemmis Department of Computer Science, University of Bari “Aldo Moro”, Via E. Orabona, 4, Bari (Italy) e-mail: degemmis@di.uniba.it Giovanni Semeraro Department of Computer Science, University of Bari “Aldo Moro”, Via E. Orabona, 4, Bari (Italy) e-mail: semeraro@di.uniba.it
Can't see the forest for the trees?: a citation recommendation system

Scientists continue to find challenges in the ever increasing amount of information that has been produced on a world wide scale, during the last decades. When writing a paper, an author searches for the most relevant citations that started or were the foundation of a particular topic, which would very likely explain the thinking or algorithms that are employed. The search is usually done using specific keywords submitted to literature search engines such as Google Scholar and CiteSeer. However, finding relevant citations is distinctive from producing articles that are only topically similar to an author's proposal. In this paper, we address the problem of citation recommendation using a singular value decomposition approach. The models are trained and evaluated on the Citeseer digital library. The results of our experiments show that the proposed approach achieves significant success when compared with collaborative filtering methods on the citation recommendation task.