nils-herrmann/clean_scirepeval huggingface dataset:
QoE-aware resource allocation for scalable video transmission over multiuser MIMO-OFDM systems

We investigate in this paper how to maximize multiuser Quality of Experience (QoE) when scalable videos are transmitted over Multiple-Input Multiple-Output (MIMO)-Orthogonal Frequency Division Multiplexing (OFDM) systems. We first study the QoE issues in Scalable Video Coding (SVC) adaptation by constructing a QoE assessment database. We derive the optimal scalability adaptation track for individual video and further summarize common scalability adaptation tracks for grouped videos. A rate-model is developed for SVC adaptation and is employed in designing an efficient resource allocation solution for SVC streaming over multiuser MIMO-OFDM systems. Specifically, time-frequency unit assignment, power allocation, and modulation selection are jointly optimized to maximize users' QoE. Experimental results show that the proposed QoE-aware scalability adaptation scheme significantly outperforms the conventional adaptation schemes, and the proposed QoE-aware resource allocation achieves better QoE performance when compared to existing resource allocation methods.
Content-based sports video analysis and modeling

We propose in this paper some new methods for analyzing and modeling sports based on their low-level visual are first automatically identified by using the color features derived from its video are first automatically identified by using the color features derived from its video shots. To improve the performance on the identification of dominant scenes and reduce the dependency on a proper threshold, a comparison on different forms of shot color features, including shot color histograms, their principal components, and subspace linear discriminant representations, is performed. Second, the content compactness and motion attributes of clustered video shots are analyzed to differentiate dominant scene types. A scene transition diagram is then constructed to form a structural descriptor for sports video contents. Third, the video shots belonging to each dominant scene are processed, using customized schemes and domain specific knowledge, to identify interesting play events in the sports video. Experimental results on identification of dominant scenes, structural desprictors and high-level ball videos, are presented to demonstrate the possible applications of the proposed methods.
Scalable Resource Allocation for SVC Video Streaming Over Multiuser MIMO-OFDM Networks

In this paper, we propose a scalable resource allocation framework for streaming scalable videos over multiuser multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) networks. We exploit the utilities of scalable videos produced by the scalable extension of H.264/AVC (SVC) and investigate the multidimensional diversities of the multiuser MIMO-OFDM wireless networks. First, we study the rate-utility relationship of SVC via a packet prioritization scheme. Based on the rate-utility analysis, a scalable resource-allocation framework is proposed to achieve differentiated service objectives for different scalable video layers. To provide users with fair opportunities to acquire basic viewing experience, a fair scheme is designed to guarantee that each user is entitled to a MAXMIN fairness to have their base layer video packets received. After all users have their base layer packets successfully scheduled, resources are distributed to exploit the network efficiency. The two schemes are integrated into a unified bit loading and power allocation solution to enhance the practicability of the scalable framework. Experiment results confirms that the proposed scheme handles fairness and efficiency better at different scenarios than the conventional schemes.
Robust Co-Optimization Planning of Interdependent Electricity and Natural Gas Systems With a Joint N-1 and Probabilistic Reliability Criterion

As the sharp growth of gas-fired power plants and the new emergence of power-to-gas (PtG) technology intensify the interdependence between electricity and natural gas systems, it is imperative to co-optimize the two systems for improving the overall efficiency. This paper presents a long-term robust co-optimization planning model for interdependent systems, for minimizing total investment and operation costs. Beside generators, transmission lines, gas suppliers, and pipelines, PtGs and gas compressor stations are also considered as investment candidates to effectively handle wind power uncertainties in the power system and compensate pressure losses in the gas network. Furthermore, the proposed model includes a joint N-1 and probabilistic reliability criterion to promote economical and reliable planning solutions. The proposed model is solved via a decomposition approach, by iteratively solving a base-case master problem and two operation subproblems to check N-1 and probabilistic reliability criteria. Numerical case studies illustrate the effectiveness of the proposed robust co-optimization planning approach.
Visual reranking with improved image graph

This paper introduces an improved reranking method for the Bag-of-Words (BoW) based image search. Built on , a directed image graph robust to outlier distraction is proposed. In our approach, the relevance among images is encoded in the image graph, based on which the initial rank list is refined. Moreover, we show that the rank-level feature fusion can be adopted in this reranking method as well. Taking advantage of the complementary nature of various features, the reranking performance is further enhanced. Particularly, we exploit the reranking method combining the BoW and color information. Experiments on two benchmark datasets demonstrate that our method yields significant improvements and the reranking results are competitive to the state-of-the-art methods.
TV Commercial Classification by using Multi-Modal Textual Information

In this paper, we propose an approach for TV commercial video classification by the categories of advertised products or services (e.g. automobiles, healthcare products, etc). Since automatic speech recognition (ASR) and optical character recognition (OCR) can deliver meaningful textual information related to products or services, TV commercial video classification is formulated as the problem of text categorization. However, there exist two challenges. Firstly, the background music of TV commercials makes ASR techniques yield erroneous and deficient output transcripts. Secondly, even if ASR and OCR could work perfectly, the limited textual information from TV commercials do not suffice to train a generic and non-overfitting text categorizer. For the first issue, our approach resorts to the external resources to expand deficient ASR and OCR transcripts. The output transcripts of ASR and OCR are parsed to yield a few keywords, on which a Web searching is executed to retrieve relevant and semantically informative articles from World Wide Web (WWW). The retrieved articles are then utilized to construct textual feature vectors and perform text categorization on behalf of commercials. For the second issue, a topic-wise document corpus is constructed from the public corpora like Reuters-21578 or from the articles manually collected from WWW for the training of text categorizers. Experimental results have shown that the proposed approach alleviates the negative effects from weak ASR/OCR performance and yield a promising classification accuracy of 80.9%
L-GEM based robust learning against poisoning attack

Poisoning attack in which an adversary misleads the learning process by manipulating its training set significantly affect the performance of classifiers in security applications. This paper proposed a robust learning method which reduces the influences of attack samples on learning. The sensitivity, defined as the fluctuation of the output with small perturbation of the input, in Localized Generalization Error Model (L-GEM) is measured for each training sample. The classifier's output on attack samples may be sensitive and inaccurate since these samples are different from other untainted samples. An import score is assigned to each sample according to its localized generalization error bound. The classifier is trained using a new training set obtained by resampling the samples according to their importance scores. RBFNN is applied as the classifier in experimental evaluation. The proposed model outperforms than the traditional one under the well-known label flip poisoning attacks including nearest-first and farthest-first flips attack.
Cross Indexing With Grouplets

Most of the current image indexing systems for retrieval view a database as a set of individual images. It limits the flexibility of the retrieval framework to conduct sophisticated cross-image analysis, resulting in higher memory consumption and sub-optimal retrieval accuracy. To conquer this issue, we propose cross indexing with grouplets, where the core idea is to view the database images as a set of grouplets, each of which is defined as a group of highly relevant images. Because a grouplet groups similar images together, the number of grouplets is smaller than the number of images, thus naturally leading to less memory cost. Moreover, the definition of a grouplet could be based on customized relations, allowing for seamless integration of advanced image features and data mining techniques like the deep convolutional neural network (DCNN) in off-line indexing . To validate the proposed framework, we construct three different types of grouplets , which are respectively based on local similarity , regional relation, and global semantic modeling. Extensive experiments on public benchmark datasets demonstrate the efficiency and superior performance of our approach.
Outlier Detection from Pooled Data for Image Retrieval System Evaluation

Widely used in the evaluation of retrieval systems, the pooling method collects top ranked images from submitted retrieval systems resulting in possibly a very large pool of images. Inevitably, the pool may contain outliers. Human experts then manually annotate the relevance of them to create a ground truth for evaluation. Studies show that this annotation is time-consuming, tedious and inconsistent. To reduce human workload, this paper introduces an automatic method to detect outliers. Different from traditional detection methods using unsupervised techniques only, we utilize both supervised and unsupervised techniques sequentially as both positive and negative examples are (partially) available in this context. Specifically, support vector machines (SVMs) and fuzzy c-means clustering are used to predict data relevance and "outlierness". Performance improvements using our method after outlier removal have been validated on the medical image retrieval task in ImageCLEF 2004.
Bundled Local Features for Image Representation

Local features have been widely used for image representation. Traditional methods often treat each local feature independently or simply model the correlations of local features with spatial partition. However, local features are correlated and should be jointly modeled. Besides, due to the variety of images, predefined partition rules will probably introduce noisy information. To solve these problems, in this paper we propose a novel bundled local features method for efficient image representation and apply it for classification. Specially, we first extract local features and bundle them together with over-complete spatial shapes by viewing each local feature as the central point. Then, the most discriminatively bundling features are selected by reconstruction error minimization. The encoding parameters are then used for image representations in a matrix form. Finally, we train bi-linear classifiers with quadratic hinge loss to predict the classes of images. The proposed method can combine local features appropriately and efficiently for discriminative representations. Experimental results on three image data sets show the effectiveness of the proposed method compared with other local features combination strategies.
Widen the Zero-Voltage-Switching Range and Secure Grid Power Quality for An EV Charger Using Variable-Switching-Frequency Single-Dual-Phase-Shift Control

Compared to conventional electrical-vehicle(EV) on-board chargers utilizing a front-end Power-Factor-Correction(PFC) + an isolated DC/DC converter, which limits the wall-to-battery efficiency to ~94%, a new control strategy using variable switching frequency(VSF) and variable phase shifts frees the PFC stage thereby getting rid of the DC link capacitor and further increasing the system efficiency and power density. The challenge is to secure zero-voltage-switching (ZVS) turn-on for all switches within the full-power range. In this paper a novel VSF single-dual-phase-shift(SDPS) control strategy is proposed, which consists of three control freedoms, i.e., two phase shifts and one variable switching frequency to secure ZVS and achieve PFC simultaneously. ZVS boundaries are pictured and compared among single-phase-shift(SPS), dual-phase-shift(DPS) and the proposed single-dual-phase-shift(SDPS) control. Simulation results and experimental validation through a level-2 EV on-board charger indicate that by using the proposed SDPS control, both ZVS and PFC are secured not only for the heavy load but also for the light load, without sacrificing the system efficiency.
Invariant Salient Region Selection and Scale Normalization of Image

Scale estimation is important in image and vision computing. We propose in this paper an invariant salient region selection and scale normalization method which is robust to rotation, scaling, translation and cropping. This new method is based on the first and second order invariant geometric moments calculated from an intensity difference map. The first-order moments are used to obtain invariant circular regions for different scale hypotheses, while a second-order moment is chosen as region descriptor to select the most salient scale. The image is normalized by scale of the selected salient region. Experiments demonstrate effectiveness of this method
Parameterized discriminant analysis for image classification

Linear and nonlinear (i.e., kernel) discriminant analysis have been proposed to address the difficulties of the small sample problem, the curse of dimensionality, and the multi-modality of image data distribution in content-based image retrieval (CBIR). The existing discriminant analysis is implemented either in a regular way, such as MDA (multiple discriminant analysis), or in a biased way, such as biased discriminant analysis (BDA). A rich set of parameterized discriminant analysis is proposed as an alternative to the regular MDA and BDA when taking regularization into account to avoid the singularity of the scatter matrices. Extensive experiments are carried out for performance evaluation and the results show the superior performance of the parameterized discriminant analysis over regular MDA and BDA for both linear and nonlinear settings.
Robust multi-level video representation using mean shift analysis

A robust method for multi-level video representation based on the mean shift analysis (MSA) of low-level visual features is proposed in this paper. By tuning the bandwidth of MSA, video representation from the coarse level to the fine level can be achieved. This representation form provides a flexible scheme for content-based video analysis such as summarization, classification, and retrieval. Compared with the conventional k-means or fuzzy c-means algorithms, our method can adjust the resolution of representation in a more straightforward way, and is more robust since it does not need to initialize the cluster centers
Efficient Short Video Repeat Identification With Application to News Video Structure Analysis

This paper aims at repeat clip mining and knowledge discovery from video data. A unified approach is proposed to detect both unknown video repeats and known video clips of arbitrary length. Two detectors in a cascade structure are employed to achieve fast and accurate detection, and a reinforcement learning approach is adopted to efficiently maximize detection accuracy. In this approach very short video repeats (<1 s) and long ones can be detected by a single process, while overall accuracy remains high. Since video segmentation is essential for repeat detection, performance analysis is also conducted for several segmentation methods. Furthermore we propose a method to analyze video syntactical structure based on short video repeats detection. Experimental results on news videos demonstrate that identifying short video repeats is an effective way for video structure discovery and syntactical segmentation
Learning Affective Features With a Hybrid Deep Model for Audio–Visual Emotion Recognition

Emotion recognition is challenging due to the emotional gap between emotions and audio–visual features. Motivated by the powerful feature learning ability of deep neural networks, this paper proposes to bridge the emotional gap by using a hybrid deep model, which first produces audio–visual segment features with Convolutional Neural Networks (CNNs) and 3D-CNN, then fuses audio–visual segment features in a Deep Belief Networks (DBNs). The proposed method is trained in two stages. First, CNN and 3D-CNN models pre-trained on corresponding large-scale image and video classification tasks are fine-tuned on emotion recognition tasks to learn audio and visual segment features, respectively. Second, the outputs of CNN and 3D-CNN models are combined into a fusion network built with a DBN model. The fusion network is trained to jointly learn a discriminative audio–visual segment feature representation. After average-pooling segment features learned by DBN to form a fixed-length global video feature, a linear Support Vector Machine is used for video emotion classification. Experimental results on three public audio–visual emotional databases, including the acted RML database, the acted eNTERFACE05 database, and the spontaneous BAUM-1s database, demonstrate the promising performance of the proposed method. To the best of our knowledge, this is an early work fusing audio and visual cues with CNN, 3D-CNN, and DBN for audio–visual emotion recognition.
Hybrid PCA and LDA Analysis of Microarray Gene Expression Data

Microarray technology offers a high throughput means to study expression networks and gene regulatory networks in cells. The intrinsic nature of high dimensionality and small sample size in microarray data calls for the development of effective computational methods. In this paper, we propose a novel hybrid dimension reduction technique for classification - hybrid PCA (principal component analysis) and LDA (linear discriminant analysis) analysis. This technique effectively solves the singular scatter matrix problem caused by small training samples and increases the effective dimension of the projected subspace. It offers more flexibility and a richer set of alternatives to LDA and PCA in the parametric space. In addition, generalization of hybrid analysis of other dimension reduction techniques is also proposed in this paper, such as multiple discriminant analysis (MDA) and biased discriminant analysis (BDA). Extensive experiments on the yeast cell cycle regulation data set show the superior performance of the hybrid analysis over the traditional methods such as SVM.
Research and practice of decoupling control based on the dual-chamber electric furnace

The static decoupling control method is tested on a dual-chamber electric furnace. Experiments have shown that the method enables dual-chamber body electric furnace-the coupling system operate well, and also reduces the changes of the controlled parameter's amplitude to a certain extent. Typically, for multivariable control system, it can basically meet the specific requirements in the project after achieving these indicator requirements.
Spatio-temporal enhanced sparse feature selection for video saliency estimation

Video saliency mechanism is crucial in the human visual system and helpful to object detection and recognition. In this paper we propose a novel video saliency model that video saliency should be both consistently salient among consecutive frames and temporally novel due to motion or appearance changes. Based on the model, temporal coherence, in addition to spatial saliency, is fully considered by introducing temporal consistence and temporal difference into sparse feature selections. Features selected spatio-temporally are enhanced and fused together to generate the proposed video saliency maps. Comparisons with several state-of-th-art methods on two public video datasets further demonstrate the effectiveness of our method.
2D AOA estimation of multiple wide-band FM sources based on spatial time-frequency distributions

A novel 2D angle-of-arrival (AOA) estimation technique for multiple wide-band FM sources is developed. Based on an L-shaped array, the proposed method employs spatial time-frequency distributions (STFD) in conjunction with a coherent signal-subspace (CSS) processing technique to estimate the direction cosines. CSS processing is achieved by using a unitary constraint interpolation technique over multiple time-frequency points. Computer simulations demonstrate the efficacy of the proposed algorithm.
Visually Summarizing Web Pages Through Internal and External Images

Visually summarizing web pages is an attractive approach that provides users an effective and friendly interface to identify desired contents at a first glance for search and re-finding tasks. Using dominant images in web pages is generally reliable for this purpose. However, dominant images are often unavailable in many web pages. To solve this problem, we first propose a new approach to summarize those web pages without any dominant images by retrieving relevant external images from the Internet. However, relevant external images are sometimes unreliable. To take the advantages of these two kinds of images, we further propose a clustering based algorithm to select the best summarization among all of internal and external images. This algorithm leverages relevance and dominance of images as the prior information. Experimental results show that our approach achieves 0.098 and 0.082 NDCG1 gain on a human labeled data set, compared with relevant external image and dominant image, respectively. Our user study also indicates that the images selected by our algorithm are useful as the summarization of web pages.
Color correction in image stitching using histogram specification and global mapping

Color correction is an important problem in image stitching. There is a color inconsistency issue between the images (good quality as a reference image and bad quality as a test image) to be stitched. This paper presents a color correction approach with histogram specification and global mapping. The proposed algorithm can make images share the same color style and obtain color consistency. There are four main steps in this algorithm. Firstly, overlapping regions between a reference image and a test image are obtained. Secondly, an exact histogram specification is conducted for the overlapping region in the test image using the histogram of the overlapping region in the reference image. Thirdly, a global mapping function is obtained by minimizing color differences with an iterative method. Lastly, the global mapping function is applied to the whole test image to produce a color corrected image. Both synthetic dataset and real dataset are tested. The experiments demonstrate that the proposed algorithm outperforms other methods both quantitatively and qualitatively.