nils-herrmann/clean_scirepeval huggingface dataset:
Visual event detection using orientation histograms with feature point trajectory information

Visual event detection in video streams allows easier access to, and better organization of large media collections. This paper presents an event detection framework with a novel feature that incorporates flow, appearance and trajectory information jointly. While previous event detection methods have been designed for understanding human behaviourswhere the camera is either static or with minimal motion, a more general approach is needed as real-life events are always subjected to fast camera motion and involve non-human dynamic objects. Inspired by the success of dense and overlapping orientation histograms in human detection, we build an event descriptor using orientation histograms augmented with feature point trajectory information. We put our system to test on tennis videos which have significant camera motion and multiple dynamic objects, and achieved good classification performance under a comparable setting.
Global and Local Contrast Adaptive Enhancement for Non-uniform Illumination Color Images

Color images captured by digital devices may contain some non-uniform illuminations. Many enhancement methods produce undesirable results in the aspect of contrast improvement or naturalness preservation. A global and local contrast enhancement method is proposed for adaptively enhancing the non-uniform illumination images. Firstly, a novel global contrast adaptive enhancement algorithm obtains the global enhancement image. Secondly, a huepreserving local contrast adaptive enhancement algorithm produces the local enhancement image. Finally, a contrast-brightness-based fusion algorithm obtains the final result, which represents a trade-off between global contrast and local contrast. This method improves the visual quality and preserves the image naturalness. Experiments are conducted on a dataset including different kinds of non-uniform illumination images. Results demonstrate the proposed method outperforms the compared enhancement algorithms both qualitatively and quantitatively.
Cross-Scale Cost Aggregation for Stereo Matching

Human beings process stereoscopic correspondence across multiple scales. However, this bio-inspiration is ignored by state-of-the-art cost aggregation methods for dense stereo correspondence. In this paper, a generic cross-scale cost aggregation framework is proposed to allow multi-scale interaction in cost aggregation. We firstly reformulate cost aggregation from a unified optimization perspective and show that different cost aggregation methods essentially differ in the choices of similarity kernels. Then, an inter-scale regularizer is introduced into optimization and solving this new optimization problem leads to the proposed framework. Since the regularization term is independent of the similarity kernel, various cost aggregation methods can be integrated into the proposed general framework. We show that the cross-scale framework is important as it effectively and efficiently expands state-of-the-art cost aggregation methods and leads to significant improvements, when evaluated on Middlebury, KITTI and New Tsukuba datasets.
Two Modeling Methods for Near Infrared Spectroscopy Nondestructive Quantitative Analysis of the Polysaccharide Contents in Cordyceps Gunnii Mycelia Powder

This paper presents a comparative study between Partial Least Squares (PLS) method and support vector regression (SVR) in modeling the relationship between the near infrared spectra (NIRS) and the polysaccharide contents in Cordyceps gunnii mycelia powder samples. Both of the models were optimized by selecting the suitable spectra preprocessing methods and the best modeling parameters. And then the optimum models were obtained. The results demonstrated that the SVR model was superior to PLS model. The root mean square error of cross-validation (RMSECV), the coefficient relation between actual values and predictive values obtained by cross-validation (Rv) and root mean square error of prediction set (RMSEP) of the optimum SVR model were 4.1157, 0.9846 and 3.7871, which indicated that the stability, the fit and the predictive capability of the model were satisfied.
Medium voltage solid state transformers based on 15 kV SiC MOSFET and JBS diode

This paper discusses the advancements in the development of the medium voltage solid state transformer (SST) based on 15 kV SiC MOSFET and JBS diode. Designed for 7.2 kV single phase distribution grid applications, the medium voltage SST converts high voltage AC to low voltage 240/120V ac. The use of ultra-high voltage SiC devices allows the simplification of the power conversion circuit topology. This paper presents the characteristics of the high voltage SiC MOSFET devices as well as the topology innovations to achieve ultra-efficient SST design. Specifically, three different designs are discussed which utilize three-stage, two-stage and single stage power conversion topologies to achieve the AC to AC conversion.
Application of ontology intelligence search engine based on ant colony algorithm

As the search efficiency is not high and it's difficult to find user's interested information, this paper proposes the combination of ant colony algorithm and ontology to create search engine. By the clear formal specification of body shared concept, the search engine has intelligent search features, and then it can use ontology to provide users' interested search results. In the process of ontology's usage, in order to make different ontology interoperate, the paper introduces similarity computing, and then improves it. Based on the characteristics of ant colony algorithm, it introduces multi-pheromone to change search engine strategy, so it can improve search engine efficiency. While the multi-pheromones apply to different servers, with the increase of search times, it can make the finding of best path more effective.
What can visual content analysis do for text based image search?

Modern image search engines such as Google, Yahoo!, Microsoft Live image search are all text metaword based. To search for images, the users type in a text query and the search engines rank the result images almost sorely based on the text meta-words. The abundant visual information in the images themselves is largely neglected. Recently, we have observed several new features released in the aforementioned image search engines, especially Microsoft Live image search, which are clearly based on the analysis of the visual content. We summarize some of these features, give insights about how they are designed, and motivate new content analysis based features for text based image search engines.
A Stereo-Vision-Assisted model for depth map super-resolution

In this paper, we propose a novel Stereo-Vision-Assisted (SVA) model for depth map super-resolution. Given a low-resolution depth map as input, we investigate to enhance its resolution or quality using the registered and potentially highresolution color stereo image pair. First, based on the mutual benefits between raw depth map and features of highresolution color image, we model the relationship with two constraint terms of local and non-local priors which sufficiently explore their complementary nature. Then by considering reliable disparity pixels calculated from stereo matching algorithm, we formulate a stereo disparity regularization term to further reinforce the preservation of fine depth detail. In addition, we employ an efficient iterative algorithm to optimize the objective function. Experimental results demonstrate that our approach can achieve high-quality depth map in terms of both spatial resolution and depth precision.
A semi-fragile object based video authentication system

This paper presents a semi-fragile object-based authentication solution for MPEG4 video. To protect the integrity of the video objects/sequences, a content-based watermark is embedded into each frame in the discrete Fourier transform (DFT) domain before the MPEG4 encoding. A set of angular radial transformation (ART) coefficients are selected as the robust features of the video objects. Error correction coding (ECC) is employed for watermark generation and embedding. Using this methodology, our semi-fragile authentication solution can robustly tolerate some natural object manipulations and errors (e.g. translation, scaling, rotation, lossy compression, segmentation errors, etc) while securely preventing other malicious modifications. Experimental results further demonstrate that the proposed method can achieve a good tradeoff among system robustness, security and complexity.
Codebook Guided Feature-Preserving for Recognition-Oriented Image Retargeting

Traditional image resizing methods, such as uniform scaling and content-aware image retargeting, are designed to preserve the visually salient contents of an image while resizing it. In this paper, we propose a novel image resizing approach called recognition-oriented image retargeting. Its goal is to preserve the distinctive local features for recognition instead of the traditional visual saliency during resizing. Moreover, we also apply our approach to image matching and image retrieval applications to verify its performance. Meanwhile, using our approach to these applications is able to solve some of the challenging problems in their fields. In image matching application, we find that our approach shows promising preservation of local feature descriptors. In image retrieval task, extensive experiments on Oxford5K, Holidays, Paris, and Flickr100k data sets demonstrate that our approach consistently outperforms other image retargeting methods by large margins in the aspects of retrieval precision and query bits.
DOA estimation of nonstationary sources with time-frequency maximum likelihood method

Direction finding of nonstationary signals using an antenna array is of growing interest in. the signal processing community. We develop a time-frequency maximum likelihood method based on cross pseudo Wigner-Ville distributions (PWVDs) between the array signal vector and the satellite sensor output to obtain the DOA estimates. Compared with methods based on STFD (spatial time-frequency distribution) matrices, the proposed method can make full use of the crossterm TFDs between sources, and signal selective direction finding can be realized with much simpler computation. Moreover, the proposed method can handle signals with the same time-frequency signature. Numerical simulations are provided to demonstrate the validity of the method.
Two dimensional timeline and its application to Conversant Media system

We present a two dimensional timeline to replace the conventional 1D timeline used in video annotation systems. The extra dimension is augmented to represent the text annotations from user input and link them to the corresponding video time. A histogram of user annotations is developed over time. Not only does it highlight the history of prior user interaction with the video annotation system, it also gives a bird's eye view of how annotations are distributed along the video timeline. Users can then quickly find the annotation hotspots. Using this unique 2D timeline, we have developed a video annotation system prototype, Conversant Media (CM). Experimental trials conducted at two educational institutes on presentation skill training yield encouraging feedback, one of which concluded that CM can become a potential alternative to classroom visits for trainee teachers
High-resolution estimation of frequencies, azimuths and elevations of coherent signals

This paper proposes a new method to simultaneously estimate the frequencies, azimuths and elevations of narrowband coherent signals. With spatial smoothing and rotational invariance, we construct a passive stereoscopic array to receive the multiple incident electronic signals. Then, through parallel handling, we obtain the parameters (frequencies, azimuths and elevations) of incident signals. The method not only saves the computing load, but also greatly improves the ability of the ESPRIT-like methods in dealing with coherent signals. The computer simulation results verify the efficiency of the method proposed.
Service Differentiation for Business Process by Value Based Service Scheduling

Service differentiation is a practical approach for service provider to deliver ldquogoodrdquo service quality to different customers or customer segments under limited computing resources. In this paper, we address the problem of differentiating business process services by effectively scheduling tasks inside business processes, where dynamic value of service request, process instance execution status and workload of service components are all taken into consideration. Corresponding framework architecture and a scheduling algorithm are purposed for this purpose.
Event-based home photo retrieval

With rapid advances in sensor, storage, processor, and communication technologies, consumers can now afford to create, store, process, and share large digital photo collections. With more and more digital photos accumulated, consumers need effective and efficient tools to organize and access photos in a semantically meaningful way without too much manual annotation effort. From user studies, we confirm that users prefer to organize and access photos along semantic axes such as event, people, time, and place. In this paper, we propose a computational learning framework to construct event models from sample photos with event labels given by a user and to compute relevance measures of unlabeled photos to the event models. We demonstrate event-based retrieval on 2400 genuine home photos using our proposed approach.
Analysis of Lightning Risk of Huangshan Pines

In order to get the risk character of lightning hazards of Huangshan area, lightning activities associated with geological landform and its effect on Huangshan pine and tourists are analyzed. Some parameters of the risk assessment are listed in the example of Longzhao Pine. According to the research, the risk of personal injury and death for Longzhao pine exceeds the permissible risk value. The main reason that caused damage is the lightning stroke (from trunk or crown to people) and burst wounding of Longzhao pine due to direct strike. Meanwhile, the risk of touch and step voltage should also be taken into account. However, the risk of damage caused by fire and burst is much smaller, with a probability of occurrence once-in-a-century.
An approach to enhance the robustness of audio watermark based on Turbo code

We present an approach to enhance robustness of audio watermark based on Turbo code. Firstly, we encode the decreased dimensional watermark sequence using Turbo code, then embed the watermark sequence into the diagonal matrix which has been decomposed by the singular value using low frequency coefficients after the wavelet transform. Finally, we can get the watermark sequence during the watermark extraction, decoded it with the Turbo iterative decoding algorithm. In this article, a pseudo random interleaver is added to the Turbo encoder to enhance the robustness of anti-burst-error. Simulations results show that the algorithm is with strong robustness on anti-general random errors attack and good robustness on anti-burst errors attack.
Multiple instance learning using visual phrases for object classification

Recently, bag of words (BoW) model has led to many significant results in visual object classification. However, due to the limited descriptive and discriminative ability of visual words, the resulting performance of visual object classification is still incomparable to its analogy in text domain, i.e. document categorization. Furthermore, for weakly labeled image data, where we only know whether an object is present or not, traditional learning based methods may suffer from background clutters and large appearance variations. To address these issues, we propose a novel visual phrase based Multiple Instance Learning (MIL) method. In this method, the visual phrase is first generated from over-segmented image regions of homogeneous appearance and visual words within each region, which may provide enhanced descriptive ability by enforcing the spatial coherency. Then a MIL algorithm is applied to efficiently learn from the weakly labeled image data. The experiments on benchmark datasets show that our proposed method always significantly outperforms several state-of-the-art algorithms, such as Spatial Pyramid Matching (SPM)  and Spatial-LTM .
A High-Precision Time Synchronization Algorithm Based on Selective Software Switch

In this paper, we present a selective time synchronization algorithm (STSA) based-on two selective switches using a hardware timestamp at a low-level layer to reduce time synchronization errors over Ethernet, and give the improved formula to calculate the time offset when considering the frequency offset factor in SNTP. The STSA transmits frame-based time synchronization frame on a truncated constant back-off collision resolution algorithm to reduce the delay jitter. This means the time accessing a shared Ethernet bus is more predictable. Experiment results indicate that the STSA achieves microsecond-level synchronization and a high synchronization success rate
Homotopy-enhanced power flow methods for general distribution networks with distributed generators

Power flow analysis of distribution systems needs special methods to handle the distributed generators (DGs) modeled as P-V nodes. However, many existing power flow methods often fail to solve this problem. This paper presents a homotopy-enhanced numerical method for power flow analysis of distribution networks with DGs modeled as P-V nodes. The method is applicable to general distribution networks and large-scale practical distribution networks. The proposed method is applied to several standard test systems and a practical 1101-node distribution network with promising results.
Study of carrier tracking for high dynamic M-ary spread spectrum signals

High dynamic condition will cause Doppler shift acutely, then digital carrier loop may fail to track signal and wrong demodulation may occur, For the problem of carrier acquisition and tracking of high-dynamic M-ary spread spectrum signals, utilizing the characteristics that the dynamics of the signal can be removed by FLL, and the phase can be tracked precisely by PLL. In this paper, the implementation of the structure adopts the algorithms of cross-product auto frequency tracking (CPAFC) and Four-Quadrant Frequency Discriminator and the digital Costas loop to achieve carrier tracking. Theory analysis and simulation results show that this method can realize the carrier tracking quickly and precisely under high dynamic circumstance.
OpenGL-Based Hybrid GO/PO Computation for RCS of Electrically Large Complex Objects

In this letter, a novel method is proposed to assess the simulation of scattered fields from electrically large complex metallic objects. The basic idea is to employ Open Graphics Library (OpenGL) to accelerate paths tracing for geometrical optics (GO) and physical optics (PO) hybrid method. The procedure of OpenGL-based paths tracing is divided into two steps, which are shadow faces removal and reflected rays searching. In addition, some OpenGL speedup schemes have been proposed to improve the efficiency of this algorithm. Based on this, the GO/PO hybrid method can yield a superior performance for scattering analysis of large complex targets, especially at high frequency. Simulation results obtained from this model are verified by comparison to FEKO-MLFMM results, which proves an excellent accuracy computation, and the last example for an airplane model illustrates that the method we present can be very fast even at electrically large complex objects.
Automatic music summarization based on temporal, spectral and cepstral features

In this paper, we present an algorithm for automatic music summarization. Spectral power, amplitude envelope and mel-cepstral coefficients are calculated as features to characterize the segmented frames of the music. Based on calculated features, a clustering technique is applied to group segmented frames into different clusters to structure the music content. Finally, the music summary is generated based on the clustering results and domain-specific music knowledge. The proposed algorithm is tested on different genres of music samples and the results of summarization are effective to actual expectation after listening evaluation by experienced users.
Road Recognition From Remote Sensing Imagery Using Incremental Learning

Roads, as important artificial objects, are the main body of modern traffic system, providing many conveniences for human civilization. With the development of Intelligent Transportation Systems (ITS), the road structure is changing frequently. Road recognition is to identify the road type from remote sensing imagery, and road types depend largely on the characteristics of roads. Thus, how to extract road features and further making road classification efficient have become a popular and challenging research topic. In this paper, we propose a road recognition method for remote sensing imagery using incremental learning. In principle, our method includes the following steps: 1) the non-road remote sensing imagery is first filtered by using support vector machine; 2) the road network is obtained from the road remote sensing imagery by computing multiple saliency features; 3) the road features are extracted from road network and background environment; and 4) the roads are recognized as three road types according to the classification results of incremental learning algorithm. The experimental results show that our method has higher road recognition rate as well as less recognition time than the other popular algorithms.
Multi-band spectral subtraction method combined with auditory masking properties for speech enhancement

In order to reduce the music noise introduced by conventional spectral subtraction method for speech enhancement, we put forward a speech enhancement algorithm based on the combination of multi-band spectral subtraction and the masking properties of human auditory system. Firstly, we use the weighted recursive averaging method to estimate the noise power spectrum, exert subtraction of multi-band on the noise-corrupted speech signal; then, compute auditory masking threshold using the estimated speech signal, adjust the subtraction factor according to the threshold masking; finally, we obtain the spectrum of enhanced speech through computing the gain faction according to the subtraction factor. The simulation shows that, at low SNR, compared with conventional spectral subtraction, background noise and residual music noise have been effectively suppressed, and the clarity and intelligibility of speech signal have been obviously improved.
A new analysis of the value of unlabeled data in semi-supervised learning for image retrieval

There has been an increasing interest in using unlabeled data in semi-supervised learning for various classification problems. Previous work shows that unlabeled data can improve or degrade the classification performance depending on whether the model assumption matches the ground-truth data distribution, and also on the complexity of the classifier compared with the size of the labeled training set. In this paper, we provide a new analysis on the value of unlabeled data by considering different distributions of the labeled and unlabeled data and showing the migrating effect for semi-supervised learning. Extensive experiments have been performed in the context of image retrieval applications. Our approach evaluates the value of unlabeled data from a new aspect and is aimed to provide a guideline on how unlabeled data should be used