nils-herrmann/clean_scirepeval huggingface dataset:
Automatic summarization for personal digital photos

With the rapid development and wide spread of digital cameras, sizable personal digital image libraries would become mainstay of our digital assets for recording pictorial memories in our lives. As a matter of fact, most consumers cannot afford the time to annotate and organize their personal digital photos, they would face genuine problem of accessing them effectively and efficiently. More often than not, consumers wish to share a selective summary of their images with their relatives and friends, increasingly using remote repository services and messaging services (emails, multimedia message service). In this paper, we address this issue with automatic organization based on analysis of time stamps and image content to facilitate browsing and summarization of personal digital photos. In particular, both photo creation time and image content are exploited to efficiently partition related images in two stages. From the partitions, key photos are selected to represent the partition based on content analysis and then collated to generate a summary, which can be enjoyed as a slide show for visual communication. We demonstrate our approach on genuine heterogeneous personal digital photos with encouraging results. We also develop a photo summarization application with multimedia message service on Nokia 7650 camera phone emulator.
Understanding Blooming Human Groups in Social Networks

Human group, which indicates the people who share similar characteristics, is used to categorize humans into distinct populations or groups. In recent years, with the explosive growth of image, new concepts of human group are blooming in social networks . People in the same human group can be categorized by their facial and clothes appearance characteristics. In this work, we propose an approach to understanding the new concepts of human group with few positive samples. To this end, we construct visual models crossing two modalities related to human images and surrounding texts. Two convolutional neural networks based on face and upper body are constructed separately. Two different convolutional neural networks (CNNs) architectures are explored for visual pre-traing. To assist the human group recognition, we also merge global convolutional feature of the image. The surrounding texts are represented by semantical vectors and utilized as image labels. We transform words in the text into fixed length vectors by the skip-gram model. Then the texts corresponding to each image are converted into one feature vector by sparse coding and max pooling. Given a few positive samples of new concepts of human group, the visual model can be improved to understand the semantical meaning of the new label. The experimental results demonstrate the effectiveness of the proposed visual model and show the excellent learning capacity with few samples.
Towards Codebook-Free: Scalable Cascaded Hashing for Mobile Image Search

State-of-the-art image retrieval algorithms using local invariant features mostly rely on a large visual codebook to accelerate the feature quantization and matching. This codebook typically contains millions of visual words, which not only demands for considerable resources to train offline but also consumes large amount of memory at the online retrieval stage. This is hardly affordable in resource limited scenarios such as mobile image search applications. To address this issue, we propose a codebook-free algorithm for large scale mobile image search. In our method, we first employ a novel scalable cascaded hashing scheme to ensure the recall rate of local feature matching. Afterwards, we enhance the matching precision by an efficient verification with the binary signatures of these local features. Consequently, our method achieves fast and accurate feature matching free of a huge visual codebook. Moreover, the quantization and binarizing functions in the proposed scheme are independent of small collections of training images and generalize well for diverse image datasets. Evaluated on two public datasets with a million distractor images, the proposed algorithm demonstrates competitive retrieval accuracy and scalability against four recent retrieval methods in literature.
Analysis on PN code acquisition performance in DS-SS over multipath environment

In this paper, an acquisition algorithm is proposed to solve the problem of long PN code acquisition in direct-sequence spread-speactrum (DS-SS) communications over multipath environment. The algorithm is the combination of partial correlation, cumulation and average of power spectrum density (PSD). Through partial correlation, we can get a sinusoidal signal which reflects the Doppler shift, and then by averaging the cumulative PSD, we can detect the multipath signal at lower SNR. This paper gives the multipath DS-SS signal model, and analyzes the acquisition performance of the proposed algorithm over multipath environment, and compares the acquisition effect over multipath and single-path environment by simulation.
A simplified advantage ACE and PSO algorithm for PAR reduction in STBC MC-CDMA systems

As to the high Peak-to-average ratio (PAR) of Space Time Block Coding Multicarrier Code Division Multiple Access (STBC MC-CDMA) systems, a simplified advantage Active Constellation Extension (ACE) and Particle Swam Optimization (PSO) algorithm is proposed. The algorithm first according to the baseband physical model of STBC MC-CDMA system, signal structure and matrix, the signal peak to average ratio of existing problems go to analyze. Then the signals are processed by the improved ACE algorithm, that QAM symbol on MC-CDMA subcarriers was be as complex signal, constellation points correspond to the constellation, the constellation points of modulation symbol are added offset ( time offset vector called trial cut peak vector ). And then using PSO algorithm to find the optimal test cut peak threshold, thus PAP of each subcarrier respectively as the trial cut peak threshold. Finally, through iterative search get PAP optimal trial minimum cut peak threshold, to meet the preset peak to average index. The simulation results and theoretical analysis indicates that the PAR of STBC MC-CDMA system can be efficiently reduced using the new algorithm, these methods can work well on the lower SNR input signals.
Third-octave power amplifier using ring based matching network with high efficiency

A power amplifier (PA) output matching network (OMN) based on the ring structure network is proposed to design multi-octave wide band PA. The characteristics of ring structure and micro-strip are analysed. For verification, validate the proposed structure, a multi-octave wide band PA operating ranges 0.5−4.4 GHz over three octaves is fabricated and measured, with the minimum power added efficiency over 48%, output power (Pout) over about 40 dBm and 9.5−11.5 dB power gain are achieved during the operating frequency band. Comparison with reported over octave PAs are given.
Multiple target detection using split spectrum processing and group delay moving entropy

The split spectrum processing technique obtains a frequency-diverse ensemble of narrow-band signals through a filterbank then recombines them nonlinearly to improve target visibility. Although split spectrum processing is an effective method for suppressing grain noise in ultrasonic nondestructive testing, its application was mainly limited to the detection of single targets or multiple targets having similar spectral characteristics. In this paper, the group delay moving entropy technique is introduced primarily to enhance the performance of split spectrum processing in detecting multiple targets which exhibit different spectral characteristics (i.e., variations in target signal center frequency and bandwidth). This is likely to occur in complex, dispersive, and nonhomogeneous media such as composites, layered, and clad materials, etc. The analysis shows that the group delay moving entropy method can be used effectively to select the optimal frequency region for split spectrum processing when detecting such targets. Based on an iterative procedure that combines group delay moving entropy and split spectrum processing, multiple targets can be identified one at a time, and subsequently eliminated by using time domain windows. The removal of the dominant target improves the detection of the remaining weaker targets. Simulation results are presented which demonstrate the feasibility of the multistep split spectrum processing technique for detecting multiple targets in such materials.
Image classification by non-negative sparse coding, low-rank and sparse decomposition

We propose an image classification framework by leveraging the non-negative sparse coding, low-rank and sparse matrix decomposition techniques (LR-Sc+ SPM). First, we propose a new non-negative sparse coding along with max pooling and spatial pyramid matching method (Sc+ SPM) to extract local features' information in order to represent images, where non-negative sparse coding is used to encode local features. Max pooling along with spatial pyramid matching (SPM) is then utilized to get the feature vectors to represent images. Second, motivated by the observation that images of the same class often contain correlated (or common) items and specific (or noisy) items, we propose to leverage the low-rank and sparse matrix recovery technique to decompose the feature vectors of images per class into a low-rank matrix and a sparse error matrix. To incorporate the common and specific attributes into the image representation, we still adopt the idea of sparse coding to recode the Sc+ SPM representation of each image. In particular, we collect the columns of the both matrixes as the bases and use the coding parameters as the updated image representation by learning them through the locality-constrained linear coding (LLC). Finally, linear SVM classifier is leveraged for the final classification. Experimental results show that the proposed method achieves or outperforms the state-of-the-art results on several benchmarks.
Automatic Information Extraction from E-Commerce Web Sites

With the rapid development of e-commerce, online transactions has become an important part in people's lives, in order to support the smooth development of e-commerce activities, how to provide users with efficient and practical product information has become an urgent and critical problem. This paper presents a set of novel techniques based on page similarity measure, page clustering and wrapper generation to automatically extract data from E-Commerce web sites. Experiments on real web sources show the effectiveness of the proposed technique.
Study on Blind Estimation of Weak DS-UWB Signals

In this paper, the signal model and expression in time domain of DS-UWB signal are introduced, and an approach using eigen-analysis of correlation matrix for DS-UWB signal is proposed to blindly estimate the PN sequence and several parameters in low SNR DS-UWB signal. Compared with the previous methods, both synchronization and PN sequence estimation can be realized simultaneously by the approach in this paper. Based on the theoretic analysis, experimental results show that this approach is very effective in lower SNR.
Gait recognition robust to dress and carrying using multi-link gravity center track

There has been increasing interest in applying gait recognition systems to real lives. Currently, the main obstacle hindering the appearance of gait recognition system is high complexity and suffering from dress and carrying variance. In this paper, it proposes a gait recognition method based on multi-link gravity center track (ML-GCT) that has low computation cost but robust to dress and carrying. It divides human silhouette into multiple links and calculates the GCT of each link. In order to improve the recognition accuracy, it takes Fisher's ratio as the importance score (IS) to rank body regions, then completes matching on the ranked regions. Test shows that the method is capable of achieving excellent recognition under different dress and carrying conditions.
Adaptive Feature Extraction of Lower SNR DS-CDMA signals

This paper introduces an on-line unsupervised learning neural network (NN) for adaptive feature extraction via principal component analysis (LEAP) of lower signal to noise ratios (SNR) direct sequence code-division multiple-access (DS-CDMA) signals. The proposed method is based on eigen-analysis of DS-CDMA signals. The received signal is firstly sampled and divided into non-overlapping signal vectors according to a temporal window, which duration is a period of signature waveform. Then an autocorrelation matrix is computed and accumulated by these signal vectors one by one. Since we have assumed that the synchronous point between the symbol waveform and observation window have been known, the signal vectors may be sampled and divided at the beginning of this synchronous point, therefore, each vector must contain all information of signature waveforms. In the end, each signature waveform and its strength can be extracted by the principal eigenvector and its associated eigenvalue of autocorrelation matrix blindly. However, the eigenanalysis method belongs to a batch processing method, it becomes inefficiency when it uses in real-time signal process and input signal tracking. In this case, we can use the LEAP NN method to realize on-line adaptive principal feature extraction of the lower SNR input DS-CDMA signals effectively
Error criteria analysis and robust data fusion

Except the well-known least mean square error criterion which results in the arithmetic mean operation and the mean absolute value criterion which results in the median operation, other nonlinear operations have not been extensively studied in terms of error criteria in the existing literature. In this paper, the error criteria of the harmonic mean, geometric mean, as well as some other nonlinear operations have been derived. Both simulation and experimental data presented in this paper indicate that these nonlinear operations are more robust than that of the linear arithmetic mean. Therefore, the error criteria analysis method can be used as a general guide line for designing the most appropriate operations for the robust fusion problems.<<ETX>>
Gabor wavelet associative memory for face recognition

This letter describes a high-performance face recognition system by combining two recently proposed neural network models, namely Gabor wavelet network (GWN) and kernel associative memory (KAM), into a unified structure called Gabor wavelet associative memory (GWAM). GWAM has superior representation capability inherited from GWN and consequently demonstrates a much better recognition performance than KAM. Extensive experiments have been conducted to evaluate a GWAM-based recognition scheme using three popular face databases, i.e., FERET database, Olivetti-Oracle Research Lab (ORL) database and AR face database. The experimental results consistently show our scheme's superiority and demonstrate its very high-performance comparing favorably to some recent face recognition methods, achieving 99.3% and 100% accuracy, respectively, on the former two databases, exhibiting very robust performance on the last database against varying illumination conditions.
BSIFT: Toward Data-Independent Codebook for Large Scale Image Search

Bag-of-Words (BoWs) model based on Scale Invariant Feature Transform (SIFT) has been widely used in large-scale image retrieval applications. Feature quantization by vector quantization plays a crucial role in BoW model, which generates visual words from the high- dimensional SIFT features, so as to adapt to the inverted file structure for the scalable retrieval. Traditional feature quantization approaches suffer several issues, such as necessity of visual codebook training, limited reliability, and update inefficiency. To avoid the above problems, in this paper, a novel feature quantization scheme is proposed to efficiently quantize each SIFT descriptor to a descriptive and discriminative bit-vector, which is called binary SIFT (BSIFT). Our quantizer is independent of image collections. In addition, by taking the first 32 bits out from BSIFT as code word, the generated BSIFT naturally lends itself to adapt to the classic inverted file structure for image indexing. Moreover, the quantization error is reduced by feature filtering, code word expansion, and query sensitive mask shielding. Without any explicit codebook for quantization, our approach can be readily applied in image search in some resource-limited scenarios. We evaluate the proposed algorithm for large scale image search on two public image data sets. Experimental results demonstrate the index efficiency and retrieval accuracy of our approach.
SIFT Meets CNN: A Decade Survey of Instance Retrieval

In the early days, content-based image retrieval (CBIR) was studied with global features. Since 2003, image retrieval based on local descriptors (de facto SIFT) has been extensively studied for over a decade due to the advantage of SIFT in dealing with image transformations. Recently, image representations based on the convolutional neural network (CNN) have attracted increasing interest in the community and demonstrated impressive performance. Given this time of rapid evolution, this article provides a comprehensive survey of instance retrieval over the last decade. Two broad categories, SIFT-based and CNN-based methods, are presented. For the former, according to the codebook size, we organize the literature into using large/medium-sized/small codebooks. For the latter, we discuss three lines of methods, i.e., using pre-trained or fine-tuned CNN models, and hybrid methods. The first two perform a single-pass of an image to the network, while the last category employs a patch-based feature extraction scheme. This survey presents milestones in modern instance retrieval, reviews a broad selection of previous works in different categories, and provides insights on the connection between SIFT and CNN-based methods. After analyzing and comparing retrieval performance of different categories on several datasets, we discuss promising directions towards generic and specialized instance retrieval.
Robust stabilizability of MIMO delay systems: Bounds on delay radii

The delay margin of a time-delay system constitutes the fundamental limit beyond which no single controller may exist to robustly stabilize an unstable delay plant for a range of delay values. For single-input single-output systems with a linear time-invariant controller, this margin is known to be finite, and bounds on the delay margin are available. This paper extends the existing results to multi-input multi-output systems. We derive upper bounds on a generalized notion called delay radius. Our results show that for a delay whose direction is orthogonal to that of an unstable pole, no limit is imposed by the pole, while if the delay direction is parallel to that of a nonminimum phase zero, its allowable range is further restricted.
Research on motion control of mobile robot with fuzzy PID arithmetic

Through analysis the kinematics model of Dongfeng Ilsoccer robot, considering time change, nonlinear and other characteristics of this system, a fuzzy PID. Parameter self-tuning PID control method combining fuzzy control with traditional PID control is present. To contrapose the problems of robot soccer motion system, the methods of dynamically regulate the three PID parameters (kp, ki, kd) based on fuzzy control is present. The improve of control effect is verified by experimental result.
Modulation Recognition Algorithms for Communication Signals Based on Particle Swarm Optimization and Support Vector Machines

To solve the problems of most communication signals modulation recognition methods' computational complexity and classifier training difficulties, a method of modulation recognition is proposed based on particle swarm optimization(PSO) and support vector machine (SVM). Combine wavelet decomposition theory with the modulated signals' instantaneous characteristics, high-order cumulants and fractal theory to obtain a hybrid model of feature vector, and use PSO-SVM classifier to identify ten kinds of modulation signals as 2ASK, 4ASK, 2PSK, 4PSK, 8PSK, 2FSK, 4FSK, 8FSK, 16QAM, MSK. The simulation results show that the recognition rates are all over 98% at SNR 5dB.
LEGO-MM: LEarning Structured Model by Probabilistic loGic Ontology Tree for MultiMedia.

Recent advances in multimedia ontology have resulted in a number of concept models, e.g., large-scale concept for multimedia and Mediamill 101, which are accessible and public to other researchers. However, most current research effort still focuses on building new concepts from scratch, very few work explores the appropriate method to construct new concepts upon the existing models already in the warehouse. To address this issue, we propose a new framework in this paper, termed LEarning Structured Model by Probabilistic loGic Ontology Tree for MultiM edia (LEGO 1 -MM), which can seamlessly integrate both the new target training examples and the existing primitive concept models to infer the more complex concept models. LEGO-MM treats the primitive concept models as the lego toy to potentially construct an unlimited vocabulary of new concepts. Specifically, we first formulate the logic operations to be the lego connectors to combine the existing concept models hierarchically in probabilistic logic ontology trees. Then, we incorporate new target training information simultaneously to efficiently disambiguate the underlying logic tree and correct the error propagation. Extensive experiments are conducted on a large vehicle domain data set from ImageNet. The results demonstrate that LEGO-MM has significantly superior performance over the existing state-of-the-art methods, which build new concept models from scratch.
Feature normalization for part-based image classification

Part-based Bag-of-Features (BoF) models such as Spatial Pyramid Matching (SPM) play an important role in image classification. Before sending the feature vectors into classifiers for training and testing, it is required to normalize them in order to approximately equalize ranges of the attributes and make them have comparable effects in distance computation. Although some works have been focused on general feature normalization, we do not see any discussion on specialized normalization algorithms for part-based BoF models. In this paper, we fill in the blank with extensive experiments and discussions. Based on solid normalization parameters (power and coefficient), we further study two straightforward part-based properties, i.e., the independent assumption and the hierarchical-contribution assumption, to scale the feature super-vectors separately. Finally, we test our algorithm on challenging image sets, i.e., Caltech 101 and CUB-200-2011, for general and fine-grained classification, and show its efficiency, scalability and adaptability in both scenarios.