nils-herrmann/clean_scirepeval huggingface dataset:
Multiview Label Sharing for Visual Representations and Classifications

Different views represent different aspects of images. It is more effective to combine them for visual classifications. This paper proposes a novel multiview label sharing method to combine the discriminative power of different views for classifications. Especially, we linearly transfer different views into a shared space for representations. The inter-view similarities are kept in the shared space for each view. We also ensure the intra-view similarities of the same class between different views are preserved in the shared space. We jointly learn the classifiers and transformation matrices by minimizing the summed classification loss along with the inter-view and intra-view similarity constraints. In this paper, the inter-view constraints refer to the similarities between images of the corresponding view, whereas the intra-view constraints refer to the similarities between different views of images with the same semantics. Experimental results and analysis on several public datasets show the effectiveness of the proposed multiview label sharing method for visual classifications.
Semantic-Aware Co-indexing for Image Retrieval

Inverted indexes in image retrieval not only allow fast access to database images but also summarize all knowledge about the database, so that their discriminative capacity largely determines the retrieval performance. In this paper, for vocabulary tree based image retrieval, we propose a semantic-aware co-indexing algorithm to jointly embed two strong cues into the inverted indexes: 1) local invariant features that are robust to delineate low-level image contents, and 2) semantic attributes from large-scale object recognition that may reveal image semantic meanings. For an initial set of inverted indexes of local features, we utilize 1000 semantic attributes to filter out isolated images and insert semantically similar images to the initial set. Encoding these two distinct cues together effectively enhances the discriminative capability of inverted indexes. Such co-indexing operations are totally off-line and introduce small computation overhead to online query cause only local features but no semantic attributes are used for query. Experiments and comparisons with recent retrieval methods on 3 datasets, i.e., UKbench, Holidays, Oxford5K, and 1.3 million images from Flickr as distractors, manifest the competitive performance of our method.
What are the high-level concepts with small semantic gaps?

Concept-based multimedia search has become more and more popular in multimedia information retrieval (MIR). However, which semantic concepts should be used for data collection and model construction is still an open question. , there is very little research found on automatically choosing multimedia concepts with small semantic gaps. In this paper, we propose a novel framework to develop a lexicon of high-level concepts with small semantic gaps (LCSS) from a large-scale Web image dataset. By defining a confidence map and content-context similarity matrix, images with small semantic gaps are selected and clustered. The final concept lexicon is mined from the surrounding descriptions (titles, categories and comments) of these images. This lexicon offers a set of high-level concepts with small semantic gaps, which is very helpful for people to focus for data collection, annotation and modeling. It also shows a promising application potential for image annotation refinement and rejection. The experimental results demonstrate the validity of the developed concepts lexicon.
From hemorrhage to midline shift: A new method of tracing the deformed midline in traumatic brain injury ct images

In intracranial pathological examinations using CT scan, brain midline shift (MLS) is an important diagnostic feature indicating the pathological severity and patient's survival possibility. In this paper, we develop a new method of tracing the brain midline shift in traumatic brain injury (TBI) CT images using its original cause - the hemorrhage. Firstly, we model the relationship between the hemorrhage and the midline deformation caused by it using a linear regression model (H-MLS model). Secondly, using the H-MLS model, the deformed midline is predicted from the hemorrhage detected in CT images. Finally, the predicted deformed midline is adjusted according to the visual symmetry information. Preliminary experiments show that the proposed method is effective and time-efficient.
HVDC and STATCOM Coordinate Modulation Damping Low Frequency Oscillation

The high-voltage direct current (HVDC) transmission active power modulation and static reactive compensation device (STATCOM) voltage regulation on power system of low frequency oscillation suppression principle is analyzed. During DC power modulation process, the consumption of reactive power will also change with it. The converter voltage changes and it causes parallel AC lines power changes, and it's disadvantageous to DC power modulation effect. Reactive power and voltage control ability of STATCOM can be used to restrain the negative effects to the dc modulation. Using Simplex optimization algorithm to coordinate their control to suppress low frequency oscillation. The simulation results show that the coordination control, has good effect to suppress the area low frequency oscillation.
News sports video shot classification with sports play field and motion features

In this paper a novel sports news video shot classification method has been proposed. First two features based on motion and color are constructed and extracted from video shots: play field color ratio for specific types of sports, background motion and consistency ratio, then they are combined to generate an 11-dimension shot feature to feed into a C4.5 decision tree for shot classification. Based on our video data sets-the sports news video from the CNN Headline News video used in the TRECVID 2003, 7 predefined video shot classes were defined: 4 types of sports field video (basketball, baseball, ice hockey and golf) and sports news lead-in/lead-out, text and others. Sports news video segments from 15 half-hour CNN News video were used for the training and testing. A performance of average precision and recall 88%, 82% has been achieved, respectively. The proposed method can be further developed and used to search news video for individual sports news and sports highlights.
Improved concept similarity measuring in the visual domain

Exploring semantic similarity between concepts in visual domain has a wide range of applications such as natural language processing and multimedia retrieval, which in general requires both a large pool of sample images for each concept and a model to capture its visual characteristics. Instead of relying on high quality and large quantity sample data which is very difficult to obtain, in this paper, a novel method is proposed to achieve improvement in measuring concept similarity by incorporating concept modeling technique into data pruning process. At first, a number of sampling concept models are obtained by sampling a subset from the sample dataset of each concept. Then noisy samples are discarded in terms of their probabilities to the sampling concept models. Experimental results on 31,275 web images of 38 concepts defined in LSCOM indicate that the concept similarity obtained through our proposed approach is more coherent to human cognition. A concept hierarchy tree built from the 38 concepts with their similarity further demonstrates the effectiveness of our proposed method.
Generation of Personalized Music Sports Video Using Multimodal Cues

In this paper, we propose a novel automatic approach for personalized music sports video generation. Two research challenges are addressed, specifically the semantic sports video content extraction and the automatic music video composition. For the first challenge, we propose to use multimodal (audio, video, and text) feature analysis and alignment to detect the semantics of events in broadcast sports video. For the second challenge, we introduce the video-centric and music-centric music video composition schemes and proposed a dynamic-programming based algorithm to perform fully or semi-automatic generation of personalized music sports video. The experimental results and user evaluations are promising and show that our systems generated music sports video is comparable to professionally generated ones. Our proposed system greatly facilitates the music sports video editing task for both professionals and amateurs
Lightning Current Estimating and Protective Design of Smart Water Meter's Data Acquisition Card

There are two ways to transmit signals which are RS-485 signal and a kind of special signal in the new type of Smart water meter's data acquisition card. The work on design of the Surge Protection Device (SPD) is made by estimating the lightning current which travels in signal wires, comparing the capacity of restraining interfering signal between balanced signal and unbalanced signal, in according to JJG162-2009. A lot of experiments were tried with LST-6kV/3kA and ICGS, which could approve the feasibility of this design, after these approaches, some suggestions are made which enjoy great practical engineering significance.
Packing and Padding: Coupled Multi-index for Accurate Image Retrieval

In Bag-of-Words (BoW) based image retrieval, the SIFT visual word has a low discriminative power, so false positive matches occur prevalently. Apart from the information loss during quantization, another cause is that the SIFT feature only describes the local gradient distribution. To address this problem, this paper proposes a coupled Multi-Index (c-MI) framework to perform feature fusion at indexing level. Basically, complementary features are coupled into a multi-dimensional inverted index. Each dimension of c-MI corresponds to one kind of feature, and the retrieval process votes for images similar in both SIFT and other feature spaces. Specifically, we exploit the fusion of local color feature into c-MI. While the precision of visual match is greatly enhanced, we adopt Multiple Assignment to improve recall. The joint cooperation of SIFT and color features significantly reduces the impact of false positive matches. Extensive experiments on several benchmark datasets demonstrate that c-MI improves the retrieval accuracy significantly, while consuming only half of the query time compared to the baseline. Importantly, we show that c-MI is well complementary to many prior techniques. Assembling these methods, we have obtained an mAP of 85.8% and N-S score of 3.85 on Holidays and Ukbench datasets, respectively, which compare favorably with the state-of-the-arts.
Scalable Person Re-identification: A Benchmark

This paper contributes a new high quality dataset for person re-identification, named "Market-1501". Generally, current datasets: 1) are limited in scale, 2) consist of hand-drawn bboxes, which are unavailable under realistic settings, 3) have only one ground truth and one query image for each identity (close environment). To tackle these problems, the proposed Market-1501 dataset is featured in three aspects. First, it contains over 32,000 annotated bboxes, plus a distractor set of over 500K images, making it the largest person re-id dataset to date. Second, images in Market-1501 dataset are produced using the Deformable Part Model (DPM) as pedestrian detector. Third, our dataset is collected in an open system, where each identity has multiple images under each camera. As a minor contribution, inspired by recent advances in large-scale image search, this paper proposes an unsupervised Bag-of-Words descriptor. We view person re-identification as a special task of image search. In experiment, we show that the proposed descriptor yields competitive accuracy on VIPeR, CUHK03, and Market-1501 datasets, and is scalable on the large-scale 500k dataset.
Time-varying full-order state-space modeling of variable-switching-frequency control for single-phase single-stage dual-active-bridge based AC/DC converter

Full-bridge power-factor-correction (PFC) front-end + dual-active-bridge (DAB) AC/DC topology is widely used and its small-signal model has been studied in depth. Compared to the two-stage design, the single-stage design, unfolding bridge + DAB, is attracting more attention nowadays. By eliminating the bulky DC link bus capacitor and operating the front-end with only 60Hz switching frequency, the DAB based single-stage AC/DC converter has the potential to increase the system power density and efficiency. Different from the conventional constant-switching-frequency (CSF) DAB control, a novel variable-switching-frequency (VSF) DAB control is more suitable for the single-stage design due to the 0 to peak input voltage. A full-order state-space model is proposed in this paper to describing the VSF control. The influence of DC blocking capacitor is stressed because there is always a low frequency excitation of VSF control which is not existed with CSF control. The resonance of DC blocking capacitor and transformer leakage inductor is well described by the full-order state-space model. Simulation and experimental results verified the correctness of proposed DAB full-order state-space model.
Multiview Face Recognition: From TensorFace to V-TensorFace and K-TensorFace

Face images under uncontrolled environments suffer from the changes of multiple factors such as camera view, illumination, expression, etc. Tensor analysis provides a way of analyzing the influence of different factors on facial variation. However, the TensorFace model creates a difficulty in representing the nonlinearity of view subspace. In this paper, to break this limitation, we present a view-manifold-based TensorFace (V-TensorFace), in which the latent view manifold preserves the local distances in the multiview face space. Moreover, a kernelized TensorFace (K-TensorFace) for multiview face recognition is proposed to preserve the structure of the latent manifold in the image space. Both methods provide a generative model that involves a continuous view manifold for unseen view representation. Most importantly, we propose a unified framework to generalize TensorFace, V-TensorFace, and K-TensorFace. Finally, an expectation-maximization like algorithm is developed to estimate the identity and view parameters iteratively for a face image of an unknown/unseen view. The experiment on the PIE database shows the effectiveness of the manifold construction method. Extensive comparison experiments on Weizmann and Oriental Face databases for multiview face recognition demonstrate the superiority of the proposed V- and K-TensorFace methods over the view-based principal component analysis and other state-of-the-art approaches for such purpose.
Multi-feature metric learning with knowledge transfer among semantics and social tagging

Previous metric learning approaches learn a unified metric for all the classes on single feature representation, thus cannot be directly transplanted to applications involving multiple features, hundreds to thousands of hierarchical structured semantics and abundant social tagging. In this paper, we propose a novel multi-task multi-feature metric learning method which models the information sharing mechanism among different learning tasks. We decompose the real world multi-class problems such as semantic categorization or automatic tagging into a set of tasks where each task corresponds to several classes with strong visual correlation. We conduct metric learning to learn a set of (hyper)category-specific metrics for all the tasks. By encouraging model sharing among tasks, more generalization power is acquired. Another advantage is the capability of simultaneous learning with semantic information and social tagging based on the multi-task learning framework, and thus they both benefit from the information provided by each other. Experiments demonstrate the advantages on applications including semantic categorization and automatic tagging compared with other popular metric learning approaches.
Fast and robust search method for short video clips from large video collection

In this paper a fast and robust method is proposed to search a large video collection for given short clips. Compared with existing video searching methods which use visual features only, our scheme performs a two-phase hierarchical matching technique using visual and audio features successively. Considering that video sampling rate (25 or 30 fps) is much lower than that of audio (8 to 48 kHz), a coarse search is implemented with sub-sampled video frames first, and then potential matches are verified and accurately located using fine audio features. Both features are extracted directly from MPEG compressed video for computational efficiency. Experiments have been conducted on over 10.5 hours of video to search for re-occurrences of 83 TV commercials and one news lead-out clip. All the 220 instances are correctly detected with no false alarm. Our experiments also show that the proposed method is robust to variations of video bit rate, frame rate, frame size and color shifting.
A study on the evaluation of water conservancy for well-off rural area building in China—The evaluation of water resource management

To build a well-off society in an all-around way is the target of China. It is a long term goal that may take a century to accomplish. The rural population is the majority of the nation's. In order to reach a well-off society at a quick pace, the comprehensive improvement of economy, culture, technology and social level in the rural areas is the key, and, water conservancy is vital to the national economy and the people's livelihood. In reference to relevant materials and research results, the authors sum up the evaluating indicators of water conservancy for a well-off rural area. In addition they make evaluation of the indexes of water resources based on DEA and arrive at some conclusions. This article can be used as a referential material for the state, government and society.
Automatically Discovering Unknown Short Video Repeats

In this paper we propose an efficient and robust method to automatically discover unknown short video repeats with arbitrary lengths, from a few seconds to a few minutes, from large video databases or streams. The proposed method consists of non-uniform video segmentation, self-similarity analysis, locality sensitive hashing, and video repeat boundary refinement. In order to achieve efficient and accurate processing feature extraction and similarity measure are performed at two levels: video frame level and video segment level. Experiments are conducted on 12 hour CNN/ABC news, and 12 hour documentaries (Discovery and National Geography), high recall and precision of 98% - 99% have been achieved. Video repeats' boundaries can be located within several frames. Applying the proposed method for video structure analysis is also briefly discussed.
Discriminant Subspace Analysis: An Adaptive Approach for Image Classification

Linear discriminant analysis (LDA) and biased discriminant analysis (BDA) are two effective techniques for dimension reduction, which pay attention to different roles of the positive and negative samples in finding discriminating subspace. However, the drawbacks of these two methods are obvious: LDA has limited efficiency in classifying sample data from subclasses with different distributions, and BDA does not account for the underlying distribution of negative samples. In order to effectively exploit favorable attributes of both BDA and LDA and avoid their unfavorable ones, we propose a novel adaptive discriminant analysis (ADA) for image classification. ADA can find an optimal discriminative subspace with adaptation to different sample distributions. In addition, three novel variants and extensions of ADA are further proposed: 1) integrated boosting (i.Boosting), which enhances and combines a set of ADA classifiers into a more powerful one. i.Boosting integrates feature re-weighting, relevance feedback, and AdaBoost into one framework. With affordable computational cost, i.Boosting can provide a unified and stable solution to ADA prediction result. 2) Fast adaptive discriminant analysis (FADA). Instead of searching parameters, FADA can directly find a close-to-optimal projection very fast based on different sample distributions. 3) Two-dimensional adaptive discriminant analysis (2DADA). As opposed to ADA, 2DADA is based on 2-D image matrix representation rather than 1-D vector. So it is simpler, more straightforward, and has lower time complexity to use for image feature extraction. Extensive experiments on synthetic data, UCI benchmark data sets, hand-digit data set, four facial image data sets, and COREL color image data sets show the superior performance of our proposed approaches.
Learning cascaded shared-boost classifiers for part-based object detection.

This paper focuses on the problem of detecting a number of different class objects in images. We present a novel part-based model for object detection with cascaded classifiers. The coarse root and fine part classifiers are combined into the model. Different from the existing methods which learn root and part classifiers independently, we propose a shared-Boost algorithm to jointly train multiple classifiers. This paper is distinguished by two key contributions. The first is to introduce a new definition of shared features for similar pattern representation among multiple classifiers. Based on this, a shared-Boost algorithm which jointly learns multiple classifiers by reusing the shared feature information is proposed. The second contribution is a method for constructing a discriminatively trained part-based model, which fuses the outputs of cascaded shared-Boost classifiers as high-level features. The proposed shared-Boost-based part model is applied for both rigid and deformable object detection experiments. Compared with the state-of-the-art method, the proposed model can achieve higher or comparable performance. In particular, it can lift up the detection rates in low-resolution images. Also the proposed procedure provides a systematic framework for information reusing among multiple classifiers for part-based object detection.
A wavelet based denoising method for weak target detection of pulse compression radar

Hough transform is usually used for target detection as an incoherent integration method. However, it is not suitable for weak target detection under low signal-to-noise ratio (SNR) background. To solve this problem, a wavelet based de-noising method is proposed, which can be used for weak target detection of pulse compression radar. In this method, the pulse compressed echo signals are first denoised by a modified wavelet threshold filtering method and then the denoised echo signals are incoherently integrated by Hough transform. This method can effectively eliminate the noise in pulse compressed echo, resolving the frequency aliasing problem of the echo signal and noise. Finally, numerical simulation results verify the effectiveness of the proposed method.