nils-herrmann/clean_scirepeval huggingface dataset:
Design and analysis of kinematically redundant parallel manipulators with configurable platforms

Redundancy can, in general, improve the ability and performance of parallel manipulators by implementing the redundant degrees of freedom to optimize a secondary objective function. Almost all published researches in the area of parallel manipulators redundancy were focused on the design and analysis of redundant parallel manipulators with rigid (nonconfigurable) platforms and on grasping hands to be attached to the platforms. Conventional grippers usually are not appropriate to grasp irregular or large objects. Very few studies focused on the idea of using a configurable platform as a grasping device. This paper highlights the idea of using configurable platforms in both planar and spatial redundant parallel manipulators, and generalizes their analysis. The configurable platform is actually a closed kinematic chain of mobility equal to the degree of redundancy of the manipulator. The additional redundant degrees of freedom are used in reconfiguring the shape of the platform itself. Several designs of kinematically redundant planar and spatial parallel manipulators with configurable platform are presented. Such designs can be used as a grasping device especially for irregular or large objects or even as a micro-positioning device after grasping the object. Screw algebra is used to develop a general framework that can be adapted to analyze the kinematics of any general-geometry planar or spatial kinematically redundant parallel manipulator with configurable platform.
Three-dimensional storm motion detection by conventional weather radar

KNOWLEDGE of the kinematic structure of storms is important for understanding the internal physical processes. Radar has long provided information on the three-dimensional structure of storms from measurements of the radar reflectivity factor alone. Early users of radar gave total storm movement only, whereas later radar data were used to reveal internal motions based on information related to cloud physics such as the three-dimensional morphology of the storm volume. Such approaches have continued by using the increasingly finer scale details provided by more modern radar systems. Both Barge and Bergwall2 and Browning and Foote3 have used fine scale reflectivity structure to determine airflow in hailstorms. Doppler radar added a new dimension to our capabilities through its ability to measure directly the radial component of motion of an ensemble of hydrometeor particles. Two4 or three5 Doppler radars collecting data in conjunction, the equation of mass continuity, and an empirical radar reflectivity–terminal velocity relationship have enabled the estimation of the full three-dimensional airflow fields in parts of storms. Because of the inherent advantage of Doppler radar in motion detection, little effort has been directed toward developing objective schemes of determining internal storm motions with conventional meteorological radars. Pattern recognition schemes using correlation coefficient techniques6, Fourier analysis7, and gaussian curve fitting8 have been used with radar and satellite data, but primarily for detecting overall storm motions, echo merging and echo splitting. Here we describe an objective use of radar reflectivity factor data from a single conventional weather radar to give information related to the three-dimensional motions within a storm.
Consumer acceptance of online banking: an extension of the technology acceptance model

Advances in electronic banking technology have created novel ways of handling daily banking affairs, especially via the online banking channel. The acceptance of online banking services has been rapid in many parts of the world, and in the leading ebanking countries the number of e-banking contracts has exceeded 50 percent. Investigates online banking acceptance in the light of the traditional technology acceptance model (TAM), which is leveraged into the online environment. On the basis of a focus group interview with banking professionals, TAM literature and e-banking studies, we develop a model indicating onlinebanking acceptance among private banking customers in Finland. The model was tested with a survey sample (n 1⁄4 268). The findings of the study indicate that perceived usefulness and information on online banking on the Web site were the main factors influencing online-banking acceptance.
Converter Volt-Ampere Requirements of the Switched Reluctance Motor Drive

An algebraic nonlinear analysis of the switched reluctance drive system is described. The analysis is intended to provide an understanding of the factors that determine the kVA requirements of the electronic power converter and to determine the fundamental nature of the torque/speed characteristics. The effect of saturation is given special attention. It is shown that saturation has the two main effects of increasing the motor size required for a given torque, and at the same time decreasing the kVA per horsepower (i.e., increasing the effective power factor by analogy with an ac machine). The kVA per horsepower is lower than predicted by simple linear analysis that neglects saturation. Necessary conditions are also developed for a flat-topped current waveform by correctly determining the motor back-EMF. The reason why it is desirable to allow the phase current to continue (though with much reduced magnitude) even after the poles have passed the aligned position is explained. The theory provides a formula for determining the required commutation angle for the phase current. The basis is provided for an estimation of the kVA requirements of the switched reluctance (SR) drive. These requirements have been measured and also calculated by a computer simulation program.
Cross-Domain Collaborative Filtering with Factorization Machines

Factorization machines offer an advantage over other existing collaborative filtering approaches to recommendation. They make it possible to work with any auxiliary information that can be encoded as a real-valued feature vector as a supplement to the information in the user-item matrix. We build on the assumption that different patterns characterize the way that users interact with (i.e., rate or download) items of a certain type (e.g., movies or books). We view interactions with a specific type of item as constituting a particular domain and allow interaction information from an auxiliary domain to inform recommendation in a target domain. Our proposed approach is tested on a data set from Amazon and compared with a state-of-the-art approach that has been proposed for Cross-Domain Collaborative Filtering. Experimental results demonstrate that our approach, which has a lower computational complexity, is able to achieve performance improvements.
Comprehensive Topological Analysis of Conductive and Inductive Charging Solutions for Plug-In Electric Vehicles

The impending global energy crisis has opened up new opportunities for the automotive industry to meet the ever-increasing demand for cleaner and fuel-efficient vehicles. This has necessitated the development of drivetrains that are either fully or partially electrified in the form of electric and plug-in hybrid electric vehicles (EVs and HEVs), respectively, which are collectively addressed as plug-in EVs (PEVs). PEVs in general are equipped with larger on-board storage and power electronics for charging or discharging the battery, in comparison with HEVs. The extent to which PEVs are adopted significantly depends on the nature of the charging solution utilized. In this paper, a comprehensive topological survey of the currently available PEV charging solutions is presented. PEV chargers based on the nature of charging (conductive or inductive), stages of conversion (integrated single stage or two stages), power level (level 1, 2, or 3), and type of semiconductor devices utilized (silicon, silicon carbide, or gallium nitride) are thoroughly reviewed in this paper.
Selecting green suppliers based on GSCM practices: Using fuzzy TOPSIS applied to a Brazilian electronics company

Due to an increased awareness and significant environmental pressures from various stakeholders, companies have begun to realize the significance of incorporating green practices into their daily activities. This paper proposes a framework using Fuzzy TOPSIS to select green suppliers for a Brazilian electronics company; our framework is built on the criteria of green supply chain management (GSCM) practices. An empirical analysis is made, and the data are collected from a set of 12 available suppliers. We use a fuzzy TOPSIS approach to rank the suppliers, and the results of the proposed framework are compared with the ranks obtained by both the geometric mean and the graded mean methods of fuzzy TOPSIS methodology. Then a Spearman rank correlation coefficient is used to find the statistical difference between the ranks obtained by the three methods. Finally, a sensitivity analysis has been performed to examine the influence of the preferences given by the decision makers for the chosen GSCM practices on the selection of green suppliers. Results indicate that the four dominant criteria are Commitment of senior management to GSCM; Product designs that reduce, reuse, recycle, or reclaim materials, components, or energy; Compliance with legal environmental requirements and auditing programs; and Product designs that avoid or reduce toxic or hazardous material use.
MaLoc: a practical magnetic fingerprinting approach to indoor localization using smartphones

Using magnetic field data as fingerprints for localization in indoor environment has become popular in recent years. Particle filter is often used to improve accuracy. However, most of existing particle filter based approaches either are heavily affected by motion estimation errors, which makes the system unreliable, or impose strong restrictions on smartphone such as fixed phone orientation, which is not practical for real-life use. In this paper, we present an indoor localization system named MaLoc, built on our proposed augmented particle filter. We create several innovations on the motion model, the measurement model and the resampling model to enhance the traditional particle filter. To minimize errors in motion estimation and improve the robustness of particle filter, we augment the particle filter with a dynamic step length estimation algorithm and a heuristic particle resampling algorithm. We use a hybrid measurement model which combines a new magnetic fingerprinting model and the existing magnitude fingerprinting model to improve the system performance and avoid calibrating different smartphone magnetometers. In addition, we present a novel localization quality estimation method and a localization failure detection method to address the "Kidnapped Robot Problem" and improve the overall usability. Our experimental studies show that MaLoc achieves a localization accuracy of 1~2.8m on average in a large building.
Video Action Detection with Relational Dynamic-Poselets

1. Cao, L., Liu, Z., Huang,  Cross-dataset action detection. In: CVPR (2010). 2. Yang, Y., Ramanan, D.: Articulated pose estimation with flexible mixtures-of-parts. In: CVPR (2011) 3. Lan, T., etc. Discriminative figure-centric models for joint action localization and recognition.  (2011). 4. Tian, Y., Sukthankar, R., Shah, M.: Spatiotemporal deformable part models for action detection. In: CVPR (2013). 5. Wang, H., Schmid, C.: Action recognition with improved trajectories.  (2013).
Telemedicine Adoption by Different Groups of Physicians

This study addresses the factors that could affect the intention of physicians to adopt telemedicine technology. Based on the theoretical foundations of technology adoption models, a revised model is proposed and tested via a questionnaire with two groups of physicians that were, at the time of the survey, just about to use telemedicine technology. Group A is composed of physicians from a large urban healthcare provider institution involved in clinical, teaching, and research activities. An Intranet solution for teleradiology and teleconferencing based on ATM technology is being implemented. Physicians received no training regarding this technology. Group B is composed of physicians from rural areas who received training just before using the telemedicine network that links 43 sites in the same area. Results analyzed with PLS indicate that in both cases, physicians' perception of usefulness of telemedicine is positively related to their intention to adopt this technology. This is the only common result between the two groups. Physicians from group A have the intention to adopt telemedicine because its ease of use makes it sound useful. Physicians from group B perceive the ease of use of telemedecine as being associated with its usefulness, which is then related to their intention to adopt telemedicine. Specialists and researchers from group A indicated that their perceived effort and persistence is related to their perceived ease of use, which is linked with perceived usefulness. Both perceived ease of use and usefulness have an impact on their intention to adopt telemedecine, whereas the image they project by using it has no bearing on their intention to adopt telemedicine. Finally their perceived voluntariness of use has a negative and significant impact on their behavioral intention to adopt telemedicine. This result may be explained by the fact that these physicians, due to their heavy load of research and clinical practice, have less time to assess new technologies related to their work and they probably prefer that someone else do the legwork for them.
Ontology-Based Workflow Generation for Intelligent Big Data Analytics

Big Data analytics provide support for decision making by discovering patterns and other useful information from large set of data. Organizations utilizing advanced analytics techniques to gain real value from Big Data will grow faster than their competitors and seize new opportunities. Cross-Industry Standard Process for Data Mining (CRISP-DM) is an industry-proven way to build predictive analytics models across the enterprise. However, the manual process in CRISP-DM hinders faster decision making on real-time application for efficient data analysis. In this paper, we present an approach to automate the process using Automatic Service Composition (ASC). Focusing on the planning stage of ASC, we propose an ontology-based workflow generation method to automate the CRISP-DM process. Ontology and rules are designed to infer workflow for data analytics process according to the properties of the datasets as well as user needs. Empirical study of our prototyping system has proved the efficiency of our workflow generation method.
What Can Be Predicted from Six Seconds of Driver Glances?

We consider a large dataset of real-world, on-road driving from a 100-car naturalistic study to explore the predictive power of driver glances and, specifically, to answer the following question: what can be predicted about the state of the driver and the state of the driving environment from a 6-second sequence of macro-glances? The context-based nature of such glances allows for application of supervised learning to the problem of vision-based gaze estimation, making it robust, accurate, and reliable in messy, real-world conditions. So, it's valuable to ask whether such macro-glances can be used to infer behavioral, environmental, and demographic variables? We analyze 27 binary classification problems based on these variables. The takeaway is that glance can be used as part of a multi-sensor real-time system to predict radio-tuning, fatigue state, failure to signal, talking, and several environment variables.
Parameterizable Fonts Based on Shape Components

designed shapes incorporating typedesign tradition, the rules related to visual appearance, and the design ideas of a skilled character designer. The typographic design process is structured and systematic: letterforms are visually related in weight, contrast, space, alignment, and style. To create a new typeface family, type designers generally start by designing a few key characters—such as o, h, p, and v— incorporating the most important structure elements such as vertical stems, round parts, diagonal bars, arches, and serifs . They can then use the design features embedded into these structure elements (stem width, behavior of curved parts, contrast between thick and thin shape parts, and so on) to design the font’s remaining characters. Today’s industrial font description standards such as Adobe Type 1 or TrueType represent typographic characters by their shape outlines, because of the simplicity of digitizing the contours of well-designed, large-size master characters. However, outline characters only implicitly incorporate the designer’s intentions. Because their structure elements aren’t explicit, creating aesthetically appealing derived designs requiring coherent changes in character width, weight (boldness), and contrast is difficult. Outline characters aren’t suitable for optical scaling, which requires relatively fatter letter shapes at small sizes. Existing approaches for creating derived designs from outline fonts require either specifying constraints to maintain the coherence of structure elements across different characters or creating multiple master designs for the interpolation of derived designs. We present a new approach for describing and synthesizing typographic character shapes. Instead of describing characters by their outlines, we conceive each character as an assembly of structure elements (stems, bars, serifs, round parts, and arches) implemented by one or several shape components. We define the shape components by typeface-category-dependent global parameters such as the serif and junction types, by global font-dependent metrics such as the location of reference lines and the width of stems and curved parts, and by group and local parameters.
3D point cloud matching based on principal component analysis and iterative closest point algorithm

Point cloud matching is one of the key technologies of optical three-dimensional contour measurement. Most of the point cloud matching without landmark used the iterative closest point algorithm. In order to improve the performance of the iterative closest point algorithm, the two-step iterative closest point algorithm was proposed. The improved algorithm is divided into a rough matching step and accurate matching step. Rough matching used the principal component analysis algorithm, while the fine matching used the improved iterative closest point algorithm. Compared with the classic iterative closest point algorithm, the improved algorithm can match the partial coincident point cloud. At the same time, the experiment can validate the effectiveness of the proposed algorithm.
Auto-master-slave control technique of parallel inverters in distributed AC power systems and UPS

This work presents a new control scheme: auto-master-slave control technique of parallel connected SPWM inverters. The control scheme ensures a fast dynamic response and precise load current sharing capability. Based on auto-master-slave control technique, the control circuitry employs the real power share bus and reactive power share bus interconnecting all the paralleled inverters. With the power share buses and the power deviation controllers, the paralleled inverter with highest output power becomes the master inverter; other inverters become slave inverters. Master inverter drive the power bus, and the power deviations in slave inverter module are calculated by the power sharing controller. Hereafter, the inverters compensate for the frequency and amplitude of output voltage, and then the unbalance power was eliminated. An experiment system was built to verify this control scheme, and the results indicate the precise power balance performance for parallel operation.
Semantic Image Inpainting with Deep Generative Models

Semantic image inpainting is a challenging task where large missing regions have to be filled based on the available visual data. Existing methods which extract information from only a single image generally produce unsatisfactory results due to the lack of high level context. In this paper, we propose a novel method for semantic image inpainting, which generates the missing content by conditioning on the available data. Given a trained generative model, we search for the closest encoding of the corrupted image in the latent image manifold using our context and prior losses. This encoding is then passed through the generative model to infer the missing content. In our method, inference is possible irrespective of how the missing content is structured, while the state-of-the-art learning based method requires specific information about the holes in the training phase. Experiments on three datasets show that our method successfully predicts information in large missing regions and achieves pixel-level photorealism, significantly outperforming the state-of-the-art methods.
Study of MANET routing protocols by GloMoSim simulator

This paper compares Ad hoc On-demand Distance Vector (AODV), Dynamic Source Routing (DSR), and Wireless Routing Protocol (WRP) for MANETs to Distance Vector protocol to better understand the major characteristics of the three routing protocols, using a parallel discrete event-driven simulator, GloMoSim. MANET (mobile ad hoc network) is a multi-hop wireless network without a fixed infrastructure. There has not been much work that compares the performance of the MANET routing protocols, especially to Distance Vector protocol, which is a general routing protocol developed for legacy wired networks. The results of our experiments brought us nine key findings. Followings are some of our key findings: (1) AODV is most sensitive to changes in traffic load in the messaging overhead for routing. The number of control packets generated by AODV became 36 times larger when the traffic load was increased. For Distance Vector, WRP and DSR, their increase was approximately 1.3 times, 1.1 times and 7.6 times, respectively. (2) Two advantages common in the three MANET routing protocols compared to classical Distance Vector protocol were identified to be scalability for node mobility in end-to-end delay and scalability for node density in messaging overhead. (3) WRP resulted in the shortest delay and highest packet delivery rate, implying that WRP will be the best for real-time applications in the four protocols compared. WRP demonstrated the best traffic-scalability; control overhead will not increase much when traffic load increases.
Recurrent neural network based language model personalization by social network crowdsourcing

Speech recognition has become an important feature in smartphones in recent years. Different from traditional automatic speech recognition, the speech recognition on smartphones can take advantage of personalized language models to model the linguistic patterns and wording habits of a particular smartphone owner better. Owing to the popularity of social networks in recent years, personal texts and messages are no longer inaccessible. However, data sparseness is still an unsolved problem. In this paper, we propose a three-step adaptation approach to personalize recurrent neural network language models (RNNLMs). We believe that its capability to model word histories as distributed representations of arbitrary length can help mitigate the data sparseness problem. Furthermore, we also propose additional user-oriented features to empower the RNNLMs with stronger capabilities for personalization. The experiments on a Facebook dataset showed that the proposed method not only drastically reduced the model perplexity in preliminary experiments, but also moderately reduced the word error rate in n-best rescoring tests.