nils-herrmann/clean_scirepeval huggingface dataset:
On microstructures and properties of laser modified roller materials

Laser treatment, using a continuous wave CO2 crosscurrent laser with generated beam power of 1 kW, was performed on the roller samples of the adamite steel and the indefinite chilled cast iron, two of which are in commercial use. Optical microscopy, scanning electron microscopy (SEM) and micro-Vickers hardness tester were used to reveal the microstructural details and mechanical properties of the laser treated zones. The results indicate that, laser treated zones of the two roll samples consist of melted zone, phase transformation zone, heat affected zone and original microstructural zone. After laser treatment, the melting zone appears to have lower hardness compared to the phase transformation zone, but after tempering of the two samples at 540°C for 1 h, the hardness of the melting zones increases differently due to the formation of the fresh martensite from the retained austenite. In contrast, the hardness of the phase transformation zone decreases sharply as fresh martensite has changed to tempered martensite with lower carbon super-saturation. Several hot tears formed in the melting zone of the indefinite chilled cast iron sample, but no in the adamite steel.
Linearity Comparison of Silicon Carrier-Depletion-Based Single, Dual-Parallel, and Dual-Series Mach–Zehnder Modulators

In this paper, a model is built to investigate the linearity of silicon carrier depletion based Mach–Zehnder modulators (MZMs). By approximating the nonlinear electro-optical response of a PN junction with a fourth-order polynomial, the model quantitatively calculates the modulation linearity of three different MZM configurations that have been widely used in analog optical links, i.e., single, dual-parallel, and dual-series MZMs, and qualitatively explains their respective operation mechanisms to suppress the third-order intermodulation distortion. The optimal operation condition of each configuration is derived to enhance the first-order harmonic component and suppress the nonlinear distortion. A fair comparison is performed between the three configurations in terms of their robustness, footprint, strength of the first-order harmonic component, stability, and tolerance against variations of driving RF power and control error, and so on. The result indicates that the dual-series MZM represents a reasonable tradeoff between these performance characteristics of interests.
Salient region detection and its application to video retargeting

In spite of extensive studies on visual saliency, e.g., generating a saliency map from an image, less work has been addressed how to crop salient regions from saliency maps. We present a new approach to detect salient regions with maximum saliency density from videos. A branch-and-bound search algorithm is developed to find the global optimal solution efficiently. The proposed detection approach can automatically adapt to the shapes and motions of salient objects regardless of cluttered backgrounds. Moreover, by introducing an intermediate cropping window, video retargeting as an application of salient region detection gets optimized saliency coverage. Extensive experimental results validate the advantages of the proposed method.
A new algorithm for line detection based on the Randomized Hough Transform

The Hough transform is an elegant way of extracting global features like line segments from binary edge images. However, long computation time and large memory requirements prevents it from being used for practical computer vision tasks. In this paper, we introduce a new randomized Hough transform to improve line detection accuracy and robustness, as well as computational efficiency. The method is based on the fact that choosing all feature points as seed points, and random picking a part of feature points for pairing with seed points, and corresponding accumulator cells are incremented in the space. The experimentation have proved that the new algorithm is more effective and robust than others.
Trip Outfits Advisor: Location-Oriented Clothing Recommendation

When packing for a journey, have you ever asked “what clothes should I take with me?” Wearing appropriate and aesthetically pleasing clothing when traveling is a concern for many of us. Our data observation of photos from several popular travel websites reveals that people's choice of clothing items and their color combinations have strong correlations with the weather, the season, and the main type of attraction at the destination. This leads to an interesting and novel problem: can the correlation between clothing and locations be automatically learned from social photos and leveraged for location-oriented clothing recommendations? In this paper, we systematically study this problem and propose a hybrid multilabel convolutional neural network combined with the support vector machine (mCNN-SVM) approach to capture the intrinsic and complex correlations between clothing attributes and location attributes. Specifically, we adapt the CNN architecture to multilabel learning and fine-tune it using each fine-grained clothing item. Then, the recognized items are fed to the SVM to learn the correlations. Experiments on three fashion datasets and a benchmark journey outfit dataset show that our proposed approach outperforms several baselines by over 10.52–16.38% in terms of the mAP for clothing item recognition and outperforms several alternative methods by over 9.59–29.41% in terms of the mAP when ranking clothing by appropriateness for travel destinations. Finally, an interesting case study demonstrates the effectiveness of our method by answering what items to wear, how to match them, and how to dress in an aesthetically pleasing manner for a journey.
Optimal tracking design for a linear system with a quantized control input

This paper studies the optimal tracking performance of a unstable linear system with a quantized control signal. The plant under consideration is discrete-time linear time-invariant (LTI) unstable and the reference signal in the tracking problem is a step signal. The tracking performance is measured by the energy of the error between the output of the plant and the reference. Two degree of freedom(2DOF) is adopted. To achieve asymptotical tracking, the quantization scheme includes two parts: one is quantized steady-state control signal transmitted to the plant at initial time and the other is a logarithmic quantizer which quantizes the error between the control signal and its steady-state value. The logarithmic quantization error is assumed to be a product of the original signal and a white noise with a uniform distribution over a given range. By using dynamic programming approach, discrete-time algebraic Riccati equation (ARE) is obtained. The best attainable tracking performance is obtained, in terms of the space equation of given system and the unique solution of the discrete-time ARE. At last, the minimum quantization density which guarantees the closed-loop system in tracking problem via a two degree of freedom controller is quadratic stable and has optimal tracking performance is obtained.
A hybrid integration method for uniformly accelerated target

In radar signal processing, across range unit (ARU) and Doppler frequency migration (DFM) effect are important problems, which results in the degradation of integration performance. Especially for uniformly accelerated targets, both ARU and DFM will occur in long time integration. In order to deal with it, a hybrid integration algorithm is proposed in this paper. The keystone transform is applied in each frame for coherent integration. Then the trajectory feature of target is used for incoherent integration by parabola fitting. The experiment compares the performance of different methods and the results verify the validity of the proposed method.
Multimedia LEGO: Learning Structured Model by Probabilistic Logic Ontology Tree

Recent advances in Multimedia research have generated a large collection of concept models, e.g., LSCOM and Media mill 101, which become accessible to other researchers. While most current research effort still focuses on building new concepts from scratch, little effort has been made on constructing new concepts upon the existing models already in the warehouse. To address this issue, we develop a new framework in this paper, termed LEGO, to seamlessly integrate both the new target training examples and the existing primitive concept models. LEGO treats the primitive concept models as a lego toy to potentially construct an unlimited vocabulary of new concepts. Specifically, LEGO first formulates the logic operations to be the lego connectors to combine existing concept models hierarchically in probabilistic logic ontology trees. LEGO then simultaneously incorporates new target training information to efficiently disambiguate the underlying logic tree and correct the error propagation. We present extensive experimental results on a large vehicle domain data set from Image Net, and demonstrate significantly superior performance over existing state-of-the-art approaches which build new concept models from scratch.
Estimation of the ball size in broadcast soccer video using salient objects

This paper presents an algorithm for estimating the ball size for each frame in broadcast soccer video by using various types of salient objects. Four types of salient objects, which are the center line, the ellipse, the goalmouth, and the people, are used to estimate the ball size. Before estimating the ball size, we have obtained all the video segments with the field by a video segmentor. For each such video segment, the proposed algorithm takes three steps to obtain the ball size for each frame. Experimental results show that the proposed algorithm achieves above 97% recall in detecting the center line, above 82% recall in detecting the ellipse, and above 92% recall in detecting goalmouth. Moreover, we achieve 100% precision for all detections. With the accurate results of salient object detections, we can achieve the accurate estimates of the ball size.
Fine-grained visual categorization with fine-tuned segmentation

Fine-grained visual categorization (FGVC) refers to the task of classifying objects that belong to the same basic-level class (e.g., different bird species). Since the subtle inter-class variation often exists on small parts (e.g., beak, belly, etc.), it is reasonable to localize semantic parts of an object before describing it. However, unsupervised part-segmentation methods often suffer from over-segmentation which harms the quality of image representation. In this paper, we present a fine-tuning approach to tackle this problem. To this end, we perform a greedy algorithm to optimize an intuitive objective function, preserving principal parts meanwhile filtering noises, and further construct mid-level parts beyond the refined parts toward a more descriptive representation. Experiments demonstrate that our approach achieves competitive classification accuracy on the CUB-200-2011 dataset with both Fisher vectors and deep conv-net features.
Exploring feature space with semantic attributes

Indexing is a critical step for searching digital images in a large database. To date, how to design discriminative and compact indexing strategy still remains a challenging issue, partly due to the well-known semantic gap between user queries and rich semantics in the large scale dataset. In this paper, we propose to construct a novel joint semantic-visual space by leveraging visual descriptors and semantic attributes, which aims to narrow down the semantic gap by taking both attribute and indexing into one framework. Such a joint space embraces the flexibility of conducting Coherent Semantic-visual Indexing, which employs binary codes to boost the retrieval speed with satisfying accuracy. To solve the proposed model effectively, three contributions are made in this submission. First, we propose an interactive optimization method to find the joint space of semantic and visual descriptors. Second, we prove the convergence property of our optimization algorithm, which guarantees our system will find a good solution in certain rounds. At last, we integrate the semantic-visual joint space system with spectral hashing, which can find an efficient solution to search up to million scale datasets. Experiments on two standard retrieval datasets i.e., Holidays1M and Oxford5K, show that the proposed method presents promising performance compared with the state-of-the-arts.
Image search reranking with multi-latent topical graph

Image search reranking has attracted extensive attention. However, existing image reranking approaches deal with different features independently while ignoring the latent topics among them. It is important to mine multi-latent topic from the features to solve the image search reranking problem. In this paper, we propose a new image reranking model, named reranking with multi-latent topical graph (RMTG), which not only exploits the explicit information of local and global features, but also mines multi-latent topic from these features. We evaluate RMTG over the MSRA-MM dataset and show that RMTG outperforms several existing reranking methods.
Cross-media hashing with kernel regression

Cross-media retrieval is a challenging problem in multimedia retrieval area. In the real-world, many applications involve multi-modal data, e.g., web pages containing both images and texts. How to utilize the intrinsic intra-modality and inter-modality similarity to learn the appropriate relationships of the data objects and provide efficient search across different modalities is the core of cross-media retrieval. Inspired by the fact that hashing methods well address the fast retrieval problem in the large-scale data settings, designing a cross-media hashing approach which can perform efficient retrieval over heterogenous high-dimensional feature spaces is highly desirable. In this paper, we propose a cross-media hashing approach based on kernel regression (abbreviated as KRCMH) to obtain the hash codes for the data objects across different modalities. The experiments on two real-world data sets show that KRCMH achieves superior cross-media retrieval performance comparing with the state-of-the-art methods.
Object Categorization Using Class-Specific Representations

Object categorization refers to the task of automatically classifying objects based on the visual content. Existing approaches simply represent each image with the visual features without considering the specific characters of images within the same class. However, objects of the same class may exhibit unique characters, which should be represented accordingly. In this brief, we propose a novel class-specific representation strategy for object categorization. For each class, we first model the characters of images within the same class using Gaussian mixture model (GMM). We then represent each image by calculating the Euclidean distance and relative Euclidean distance between the image and the GMM model for each class. We concatenate the representations of each class for joint representation. In this way, we can represent an image by not only considering the visual contents but also combining the class-specific characters. Experiments on several public available data sets validate the superiority of the proposed class-specific representation method over well-established algorithms for object category predictions.
Contextual Hashing for Large-Scale Image Search

With the explosive growth of the multimedia data on the Web, content-based image search has attracted considerable attentions in the multimedia and the computer vision community. The most popular approach is based on the bag-of-visual-words model with invariant local features. Since the spatial context information among local features is critical for visual content identification, many methods exploit the geometric clues of local features, including the location, the scale, and the orientation, for explicitly post-geometric verification. However, usually only a few initially top-ranked results are geometrically verified, considering the high computational cost in full geometric verification. In this paper, we propose to represent the spatial context of local features into binary codes, and implicitly achieve geometric verification by efficient comparison of the binary codes. Besides, we explore the multimode property of local features to further boost the retrieval performance. Experiments on holidays, Paris, and Oxford building benchmark data sets demonstrate the effectiveness of the proposed algorithm.
Direction-of-Arrival Estimation Using a Sparse Representation of Array Covariance Vectors

A new direction-of-arrival (DOA) estimation method is proposed based on a novel data model using the concept of a sparse representation of array covariance vectors (SRACV), in which DOA estimation is achieved by jointly finding the sparsest coefficients of the array covariance vectors in an overcomplete basis. The proposed method not only has high resolution and the capability of estimating coherent signals based on an arbitrary array, but also gives an explicit error-suppression criterion that makes it statistically robust even in low signal-to-noise-ratio (SNR) cases. Simulation experiments are conducted to validate the effectiveness of the proposed method. The performance is compared with several existing DOA estimation methods and the Cramér-Rao lower bound (CRLB).
Visual Synset: Towards a higher-level visual representation

We present a higher-level visual representation, visual synset, for object categorization. The visual synset improves the traditional bag of words representation with better discrimination and invariance power. First, the approach strengthens the inter-class discrimination power by constructing an intermediate visual descriptor, delta visual phrase, from frequently co-occurring visual word-set with similar spatial context. Second, the approach achieves better intra-class invariance power, by clustering delta visual phrases into visual synset, based their probabilistic dasiasemanticspsila, i.e. class probability distribution. Hence, the resulting visual synset can partially bridge the visual differences of images of same class. The tests on Caltech-101 and Pascal-VOC 05 dataset demonstrated that the proposed image representation can achieve good accuracies.
Sports highlight detection from keyword sequences using HMM

Sports video highlight detection is a popular topic. A multi-layer sport event detection framework is described. In the mid-level of this framework, visual and audio keywords are created from low-level features and the original video is converted into a keyword sequence. In the high-level, the temporal pattern of keyword sequences is analyzed by an HMM classifier. The creation of visual and audio keywords can help to bridge the gap between low-level features and high-level semantics. The use of the HMM classifier can automatically find the temporal change character of the event instead of rule based heuristic modeling to map certain keyword sequences into events. Experiments using our model on soccer games produced some promising results
Decreasing the frequency of HVDC commutation failures caused by harmonics

Commutation failure, primarily caused by voltage drops and distortion, may lead to high-voltage direct current (HVDC) power interruption and AC system oscillation. To date, there have been few studies on the mechanism and prevention of commutation failure based on voltage distortion caused by harmonics. This study analyses the influence of harmonic voltage on commutation failure based on voltage time area and proposes a method for quantitative analysis of harmonic effects. The analysis shows that limiting the DC current is an effective method to mitigate commutation failure caused by harmonics. A voltage distortion-dependent commutation failure prevention strategy is proposed, and the controller is applied on the inverter side. The selection of parameters is based on the influence of various harmonic orders. The validity and effectiveness of the proposed methods are verified by simulations, which show that voltage distortion-dependent commutation failure prevention strategy can improve the DC system recovery characteristics and suppress subsequent commutation failure caused by harmonics.
Integrating Discriminant and Descriptive Information for Dimension Reduction and Classification

In this paper, a novel hybrid dimension reduction technique for classification is proposed based on the hybrid analysis of principal component analysis (PCA) and linear discriminant analysis (LDA). LDA is known for capturing the most discriminant features of the data in the projected space while PCA is known for preserving the most descriptive ones after projection. Our hybrid technique integrates discriminant and descriptive information and finds a richer set of alternatives beyond LDA and PCA in a 2-D parametric space, which fits a specific classification task and data distribution better. Theoretical study shows that our technique also alleviates the singularity problem of scatter matrix, which is caused by small training set, and increases the effective dimension of the projected subspace. In order to find the hybrid features adaptively and avoid exhaustive parameter searching, we further propose a boosted hybrid analysis method that incorporates a nonlinear boosting process to enhance a set of hybrid classifiers and combine them into a more accurate one. Compared with the other techniques that aim at combining PCA and LDA, our approaches are novel because our method finds alternatives to LDA and PCA in a 2-D parameter space and the boosting process provides enhancement and robust combination of the classifiers. Extensive experiments are conducted on benchmark and real image databases to compare our proposed methods with the state-of-the-art linear and nonlinear discriminant analysis techniques. The results show the superior performance of our hybrid analysis methods
An Observation Dimension Weight-Based U-Tree Algorithm

Instance-based methods are a class of effective algorithms for solving reinforcement learning problems. U-Tree algorithm presents the state space from instance chains effectively, which is very beneficial to solve the reasonable Q-value of actions. However, the complexity of the construction of suffix tree in U-Tree algorithm is exponential. A new observation dimension weight-based algorithm ODWU-Tree for optimizing the expansion of fringe of suffix tree is presented in the paper. ODWUTree algorithm obtains the heuristic information of environments by simple heuristic exploration, evaluates the weight coefficient of observation dimensions, and expands the fringes by the most important observation dimensions for reasonable leaf state. Experiment results of New York Driving show that both the efficiency and the effect have been improved by ODWU-Tree algorithm compared to U-Tree algorithm.
Multiple Kernel Learning with High Order Kernels

Previous Multiple Kernel Learning approaches (MKL) employ different kernels by their linear combination. Though some improvements have been achieved over methods using single kernel, the advantages of employing multiple kernels for machine learning are far from being fully developed. In this paper, we propose to use “high order kernels” to enhance the learning of MKL when a set of original kernels are given. High order kernels are generated by the products of real power of the original kernels. We incorporate the original kernels and high order kernels into a unified localized kernel logistic regression model. To avoid over-fitting, we apply group LASSO regularization to the kernel coefficients of each training sample. Experiments on image classification prove that our approach outperforms many of the existing MKL approaches.