nils-herrmann/clean_scirepeval huggingface dataset:
Experiments in multimodality image classification and data fusion

In this paper, we report the results of some experiments on image classification and data fusion of remote sensing images, as part of ongoing efforts at the CVIP to develop a general strategy for the analysis of multimodality imaging. Statistical and Fuzzy logic approaches have been employed in these experiments. In all, six different algorithms for image classification, and an image fusion algorithm have been implemented and evaluated on common data sets. These algorithms are: (1) Supervised Parametric Bayes Classifier; (2) Non-parametric Bayesian Classifier using the Parzen density estimate; (3) Maximum a posteriori classification using the k-nearest neighbors (k-NN) approach; (4) MAP Estimation using Markov random field modeling; (5) a Fuzzy logic approach; and (6) a novel discriminate function classifier. The AMP segmentation of the regions in the image has been implemented using the Iterated Conditional Mode (ICM) optimization method. This approach provided the best results, in terms of the minimum probability of error and best reliability. A novel decision fusion algorithm, based on the a priori class conditional probability, has been applied to the classifiers' output.
A New Speed Sensorless Controller Based on Double Observers for Permanent-Magnet Synchronous Motor

A new sensorless speed control method which is based on new sliding-mode observer (SMO) is proposed for permanent-magnet synchronous motor (PMSM). The main basis of the observer is the back electromotive force (EMF). The rotor speed is estimated by the back EMF observer (BEMFO). The method can be better than low-pass filter in accuracy. In the observer, the stator resistance also is as an estimator to improve the robustness of the system. The stability of the system is proved by the Lyapunov theory. And numerical simulations based on M-file in Matlab is put into practice. The simulation results testify that the novel sensorless speed control method based on sliding-mode observer is fast and pinpoint for speed tracking control of PMSM.
The development of the PEM fuel cell power system for the electric bike

The purpose of the paper is to develop a PEM fuel cell power system with application for the electric bike. The output voltage of the fuel cell subjects to change due to load varying and polarization loss. By applying the energy conversion method with pulse-width modulation and MOSFET switching with proper frequency and duty cycle, the issues of power balance and load regulation have been improved. By using the ultra capacitor for buffering of the changing power flow, steady power can be provided for the hub motor and the driving performance can be improved. Moreover, the status parameters of the fuel cell, including the voltage, current, and temperature, are displayed in the LCD Scheme, so that the operation of the fuel cell power system can be real timely monitoring.
Constructing a Novel Chinese Readability Classification Model Using Principal Component Analysis and Genetic Programming

The studies of readability aim to measure the level of text difficulty. Although traditional formulae such as the Flesch-Kincaid formula can properly predict text readability, they are only effective for English text. Other formulae with very few features may result in inaccurate text classification. The study takes into account multiple linguistic features, and attempts to increase the level of accuracy in text classification by adopting a new model which integrates Principal Component Analysis (PCA) with Genetic Programming (GP). Empirical data are utilized to demonstrate the performance of the proposed model.
Emergency operation analysis of electric power system for Kaohsiung MRT network

This paper is mainly to investigate the emergency operation strategies of the Kaohsiung mass rapid transit (KMRT) system. First of all, the power system configuration and data was described in detailed. Load transfer strategies were performed to ensure the continuous operation of the KMRT system when one of the bulk supply substations is unavailable. KMRT system will be separated into many isolated subsystems when more than two bulk supply substations are out of service. To simulate the dynamic response of isolated subsystem powered by the emergency diesel generator, the proper mathematical models and parameters of the diesel generator, excitation system, governor system, load and power cable were investigated and applied in the computer software. In addition, a load shedding scheme and a shunt reactor was both well designed and applied to the subsystem to avoid emergency generator from tripping. Transient stability analysis was then executed at various vital load scenarios to ensure the isolated subsystem can keep operating continuously by examining the dynamic responses of system voltage, frequency and generator.
Learning of context-aware single image super-resolution

We propose a novel learning-based method for single image super-resolution (SR). Given a low-resolution input image and its image pyramid, we advance a context-constrained image segmentation to construct a super-pixel database with different context categories for learning purposes. By utilizing context-specific image sparse representation, our method aims at modeling the relationship between the interpolated image patches and their ground truth pixels from different context categories via support vector regression (SVR). To produce the final SR output, we upsample the low-resolution input, followed by the refinement of each image patch using the SVR models observed from the associated context categories. Unlike prior learning-based SR methods, our approach advances a self-learning technique and does not assume the reoccurrence of image patches (within or across image scales). We do not need to collect training low/high-resolution image data in advance either. Empirical results verify the effectiveness of our SR approach, which quantitatively and qualitatively outperforms existing interpolation or learning-based SR methods in most cases.
Secure dynamic source routing protocol

Mobile ad hoc networks (MANETs) are a collection of wireless hosts that can be rapidly deployed as a multi-hop packet radio network without the aid of any established infrastructure or centralized administration. The routing security is a critical problem for MANETs. However, existing protocols are not sufficient for security requirements. In this paper, we analyze the weakness of some existing popular protocols. Then, a secure routing protocol SDSR is implemented based on end-to-end integrity check and hop-by-hop discovery request authentication. Unlike traditional technique, our neighbor monitor scheme does not work in promiscuous mode and we demonstrate that the existing overhearing scheme can not perform effectively without the support of authentication. Security and performance evaluation indicate that our scheme achieves a good security at the cost of acceptable overhead.
Learning Query and Image Similarities with Ranking Canonical Correlation Analysis

One of the fundamental problems in image search is to learn the ranking functions, i.e., similarity between the query and image. The research on this topic has evolved through two paradigms: feature-based vector model and image ranker learning. The former relies on the image surrounding texts, while the latter learns a ranker based on human labeled query-image pairs. Each of the paradigms has its own limitation. The vector model is sensitive to the quality of text descriptions, and the learning paradigm is difficult to be scaled up as human labeling is always too expensive to obtain. We demonstrate in this paper that the above two limitations can be well mitigated by jointly exploring subspace learning and the use of click-through data. Specifically, we propose a novel Ranking Canonical Correlation Analysis (RCCA) for learning query and image similarities. RCCA initially finds a common subspace between query and image views by maximizing their correlations, and further simultaneously learns a bilinear query-image similarity function and adjusts the subspace to preserve the preference relations implicit in the click-through data. Once the subspace is finalized, query-image similarity can be computed by the bilinear similarity function on their mappings in this subspace. On a large-scale click-based image dataset with 11.7 million queries and one million images, RCCA is shown to be powerful for image search with superior performance over several state-of-the-art methods on both keyword-based and query-by-example tasks.
Using Weblogs to Promote Self-Directed Learning in Elementary Students Independent Science Study

This paper provides an overview of implementing self-directed learning by utilizing weblogs in elementary studentspsila independent study. The key feature of studentspsila independent study in Taiwanese elementary settings was used to teacher-directed learning. By utilizing weblogs in studentspsila science study, it is found that students seem to behave more self-directed learning behaviors and more active in pursuing their study goals because of the feedbacks and participation by the peer community.
A non-dominated sorting firefly algorithm for multi-objective optimization

The so-called multi-objective optimization problem (MOP) has become a critical research area because many MOPs exist in our daily life and solutions to these problems may strongly impact the performance of systems we use. Unlike solving a single-objective problem, solving a MOP requires that many conflicting objectives be optimized altogether at the same time. Since most MOPs are NP-hard, how to find an approximate solution using a limited computation resource has become an active research topic in recent years. In this paper, we present a high-performance algorithm for solving the MOP that leverages the strengths of firefly algorithm and non-dominated sorting genetic algorithm II (NSGA-II). To evaluate the performance of the proposed algorithm, we apply it to several MOPs. Simulation results show that the proposed algorithm can essentially provide a better result than all the state-of-the-art multi-objective optimization algorithms compared in this paper in most cases.
A self-stabilizing distributed multicast algorithm for mobile ad-hoc networks

Self-stabilization is an attractive approach to tolerate transient faults such as link failures or new link creations due to nodes mobility in mobile ad hoc networks. Multicasting is an important communication service that allows the source node to efficiently transmit copies of data packet to a set of destination nodes. In this paper, a self-stabilizing distributed multicast algorithm is presented based on the computation of all-pairs shortest paths. Furthermore, the correctness proof is given. The cost of multicast tree constructed by the proposed approach is lower than that by the existing approach.
A wavelength assignment and traffic control scheme in reservation slotted OBS rings

In this paper, we design a slotted OBS ring with a proposed reservation signaling protocol. Also, the slotted OBS ring is designed with a wavelength assignment and traffic control (WATOR) scheme to achieve high bandwidth utilization, fairness, and stability. The WATOR scheme adopts a reservation mechanism to avoid burst collisions in the slotted OBS ring system which employs no transit buffers in each node for the reason of bypassing the traffic directly. It is sophisticatedly composed of a wavelength assigner to effectively allocate the ingress data bursts (DBs) with a wavelength and a traffic controller to determine a proper advertised fair rate. The TC further contains a local fair rate generator (LFRG) and an advertised fair rate generator (AFRG). The former generates a local fair rate not only to satisfy the ring ingress aggregated with spatial reuse (RIAS) fairness but also to diminish the effect of the propagation delay; and the latter decides the advertised fair rate referring to the local fair rate generated by the former. Simulation results show that the WA achieves system utilization higher than a group scheduling (GS) scheme [11] by about 5%, and the TC can stabilize all flows much better than but DBA .
The Influence of using Augmented Reality on Textbook Support for Learners of Different Learning Styles

It has been shown in numerous studies that the application of Augmented Reality (AR) to teaching and learning is beneficial, but determining the reasons behind its effectiveness, and in particular the characteristics of students for whom an AR is best suited, can bring forth new opportunities to integrate adaptive instruction and AR in the future. Through a quasi-experimental research design, our study recruited 66 participants in an 8-week long AR-assisted learning activity, and lag sequential analysis was used to analyze participants' behavior in an AR learning environment. We found that AR was more effective in enhancing the learning gains in elementary school science of learners who prefer a Kinesthetic approach to learning. We hypothesize that these effects are due to the increase in opportunity for hands-on activities, effectively increasing learners' concentration and passion for learning.
MSR-VTT: A Large Video Description Dataset for Bridging Video and Language

While there has been increasing interest in the task of describing video with natural language, current computer vision algorithms are still severely limited in terms of the variability and complexity of the videos and their associated language that they can recognize. This is in part due to the simplicity of current benchmarks, which mostly focus on specific fine-grained domains with limited videos and simple descriptions. While researchers have provided several benchmark datasets for image captioning, we are not aware of any large-scale video description dataset with comprehensive categories yet diverse video content. In this paper we present MSR-VTT (standing for "MSRVideo to Text") which is a new large-scale video benchmark for video understanding, especially the emerging task of translating video to text. This is achieved by collecting 257 popular queries from a commercial video search engine, with 118 videos for each query. In its current version, MSR-VTT provides 10K web video clips with 41.2 hours and 200K clip-sentence pairs in total, covering the most comprehensive categories and diverse visual content, and representing the largest dataset in terms of sentence and vocabulary. Each clip is annotated with about 20 natural sentences by 1,327 AMT workers. We present a detailed analysis of MSR-VTT in comparison to a complete set of existing datasets, together with a summarization of different state-of-the-art video-to-text approaches. We also provide an extensive evaluation of these approaches on this dataset, showing that the hybrid Recurrent Neural Networkbased approach, which combines single-frame and motion representations with soft-attention pooling strategy, yields the best generalization capability on MSR-VTT.
Circular Reranking for Visual Search

Search reranking is regarded as a common way to boost retrieval precision. The problem nevertheless is not trivial especially when there are multiple features or modalities to be considered for search, which often happens in image and video retrieval. This paper proposes a new reranking algorithm, named circular reranking, that reinforces the mutual exchange of information across multiple modalities for improving search performance, following the philosophy that strong performing modality could learn from weaker ones, while weak modality does benefit from interacting with stronger ones. Technically, circular reranking conducts multiple runs of random walks through exchanging the ranking scores among different features in a cyclic manner. Unlike the existing techniques, the reranking procedure encourages interaction among modalities to seek a consensus that are useful for reranking. In this paper, we study several properties of circular reranking, including how and which order of information propagation should be configured to fully exploit the potential of modalities for reranking. Encouraging results are reported for both image and video retrieval on Microsoft Research Asia Multimedia image dataset and TREC Video Retrieval Evaluation 2007-2008 datasets, respectively.
Real-time simulation of a 3-leg 6-DOF parallel manipulator based on RT-Linux operation system

The real-time multi-task simulation of a 3-leg 6-DOF high performance platform system based on RT-Linux operation system is presented in this paper. The new architecture is setup by three extensible legs sliding on three linear slide-rail each actuated by a synchronous linear servo motor. The extensible legs are actuated by inductive AC servo motors. In the meantime, the inverse and forward kinematical equations describing the motion of the platform system are derived. This kind of architecture using the hybrid (linear and AC) motors yields high performance of motions, especially in speed and working space. The novel result of maximal working angles is the significant contribution of this architecture. The platform system is operated in the RT-Linux real-time multi-task executive environment, which is superior to windows, including: (a) RT-Linux real-time is hard real-time, (b) RT-Linux real-time operation system is free, (c) Multi-task can be scheduled by the optimum preemptive static scheduling algorithm. We have demonstrated the dynamic behavior of platform in the RT-Linux window and the OpenGL 3D/VR multi-medium.
Boosting Image Captioning with Attributes

Automatically describing an image with a natural language has been an emerging challenge in both fields of computer vision and natural language processing. In this paper, we present Long Short-Term Memory with Attributes (LSTM-A) - a novel architecture that integrates attributes into the successful Convolutional Neural Networks (CNNs) plus Recurrent Neural Networks (RNNs) image captioning framework, by training them in an end-to-end manner. Particularly, the learning of attributes is strengthened by integrating inter-attribute correlations into Multiple Instance Learning (MIL). To incorporate attributes into captioning, we construct variants of architectures by feeding image representations and attributes into RNNs in different ways to explore the mutual but also fuzzy relationship between them. Extensive experiments are conducted on COCO image captioning dataset and our framework shows clear improvements when compared to state-of-the-art deep models. More remarkably, we obtain METEOR/CIDEr-D of 25.5%/100.2% on testing data of widely used and publicly available splits in  when extracting image representations by GoogleNet and achieve superior performance on COCO captioning Leaderboard.
The cost effective structure for designing hybrid cloud based enterprise E-learning platform

The total initial cost of enterprise learning management system is very high on the infrastructure and software applications, so that enterprises start to adopt cloud computing to reduce expenditure on infrastructure, software and IT human resource. The paper provides a cost effective structure of hybrid cloud architecture that has been implemented the multi-tenant model for enterprise to support customization sharing among different virtualized applications in a tenant area. The schema-sharing and multi-tenant data storage architecture also supports the learning content delivery. The important basis for the design method of software development shall embed with the cost effective structure, such as using multithreads to compute background processing and unzip the content files to reduce brand width usage. Even the file sizes of learning content shall be zipped to the less small size and duplicate to different servers for cutting down the traffic flow of brand width. The detailed calculation of the all I/O computer processing and brand width become the crucial implementation way.
UBM-based incremental speaker adaptation

This paper addresses a novel algorithm of incremental speaker adaptation (ISA) based on universal background model (UBM) for saving storage and real-time processing. This algorithm can be seen as an extension of traditional speaker adaptation. It consists of two steps, adaptation and combination. It not only considers the speaker's characteristics in limited training data, but also prohibits over-fitting of the updated model. The incremental adaptation algorithm needs little storage and meets the requirement of real-time processing. In order to evaluate the efficiency and effectivity of the proposed approach, a real-time speaker segmentation system for broadcasting news is built. Experiment results demonstrate that our approach yields real time operation and achieves satisfactory performance.
A validity study for Yes/No Angoff standard setting method using cluster analysis

Test validity is a property of the interpretation assigned to test scores. To provide an objective validating evidence for a standard-referenced assessment is especially important. In this study we utilize a statistical technique, cluster analysis, to explore the validity of one of the expert judgement technique- Yes/No Angoff standard setting method. We first segregated each examinee ability cluster using the hierarchical clustering (HC). Assume that each ability cluster is a Gaussian distribution and that the distribution of each test subject data can be modeled by mixture of Gaussians (MoG), where the mean, variance and the proportion of each cluster were initialized by the HC results. Finally, the ability clustering was implemented by the expectation maximization (EM) method. The results from the traditional standard-setting procedure and cluster analysis were compared. The study suggested that cluster analysis could be applied as a support tool to provide validating information in the process of standard setting for high-stakes achievement tests.
Mechanical design of 7-DOF manipulator for water jet cutting

Discussed in this paper are the issues underlying the mechanical design of 7-DOF manipulator. A comprehensive solution for the structural type selecting, and the detail design of the links, the joints and the parts is introduced. First the design requirements including the workspace, manipulability, loading capacity, weight, precision, motor power etc are studied. After the preliminary design and calculation, the parameters of the D-H model are determined. We put forward a method to resolve the dynamic problems by software. It is accomplished by blending the unique capabilities of three engineering CAD packages named PRO/E, ADAMS and ANSYS. The statics problems and the multi-body dynamic problems met during the design process will be easily solved by using the software. The parametric geometric model of the manipulator built with PRO/E was transferred to ADAMS. Uniting ANSYS and ADAMS, we began the stress-strain and modality analysis. At last, the complicated flexible multi-body model of the manipulator was set up, and then given the dynamics simulation results which are important for the further design. The finished manipulator has the compact structure and high manipulability. Based on the experiments, the manipulator steers clear of the obstacle in a certain area included in the workspace.