nils-herrmann/clean_scirepeval huggingface dataset:
Part-Based Deep Hashing for Large-Scale Person Re-Identification

Large-scale is a trend in person re-identi- fication (re-id). It is important that real-time search be performed in a large gallery. While previous methods mostly focus on discriminative learning, this paper makes the attempt in integrating deep learning and hashing into one framework to evaluate the efficiency and accuracy for large-scale person re-id. We integrate spatial information for discriminative visual representation by partitioning the pedestrian image into horizontal parts. Specifically, Part-based Deep Hashing (PDH) is proposed, in which batches of triplet samples are employed as the input of the deep hashing architecture. Each triplet sample contains two pedestrian images (or parts) with the same identity and one pedestrian image (or part) of the different identity. A triplet loss function is employed with a constraint that the Hamming distance of pedestrian images (or parts) with the same identity is smaller than ones with the different identity. In the experiment, we show that the proposed PDH method yields very competitive re-id accuracy on the large-scale Market-1501 and Market-1501+500K datasets.
Convergence analysis of implicit Z-bus power flow method for general distribution networks with distributed generators

Implicit Z-bus distribution power flow method may encounter divergence problems when applied to distribution systems with distributed generators modelled as P-V nodes. In this study, analysis of divergence problems associated with the implicit Z-bus method is performed. Physical insights toward the divergence problem are also provided. To this end, the iterative map of the implicit Z-bus method is first derived. Then the fix-point theorem and the non-linear discrete stability theorem are applied to perform convergence/divergence analysis of the implicit Z-bus power flow method. The authors apply the derived analytical results to conduct divergence analysis of the IEEE 13-bus, and a practical 1101-node distribution networks.
Rapid Service Creation Environment for service delivery platform based on service templates

The requirement of quickly creating new value-added telecom services is increasingly becoming a business imperative today. Telecom operators and service providers are facing the challenge how to reduce cost and time-to-market of creating new services. The reduction is expected to be 1 or 2 orders of magnitude from months or weeks to days, even hours. In traditional Service Creation Environment, a full lifecycle of requirements analyzing, design, development, testing and deployment always has to be undergone. Such way of services creation always results in poor reuse of software assets and involves mass resources. Some technologies have been developed to speed up steps in this lifecycle, such as BPEL or SCXML. These orchestration techniques and tools are already used to reduce cost in design and development stages but the creation procedure still rely on attendance of IT specialists. This paper presents a way to shorten and simplify the whole service creation lifecycle and to reach the reduction target by building a template based Service Creation Environment. A model is used to separate service work flow definition and service parameters configuration and to achieve rapid development for different roles. A prototype of template based service creation environment is also introduced in this paper.
SNR estimation for OFDM signals in frequency selective fading channels

Owing to the frequency-selective fading channel with a carrier frequency of OFDM (orthogonal frequency division multiplexing) signal to noise ratio (SNR) estimation problem, we proposed SNR blind estimation method based on air carrier OFDM system. Due to the inconsistency between the oscillator and the received signal, the received signal will be with a certain deviation. The presence of frequency offset destroyed the orthogonality among subcarriers and increased the interference noise, and then reduced the SNR. This paper analyzed the effects of frequency offset on the signal to noise ratio, followed by characteristics of air carrier to estimate the SNR where there is no frequency offset, and then based on the above premise to estimate SNR where the signal has the frequency offset. Finally, this paper proved the correctness of theoretical analysis by simulation.
Extend the Class-B to Class-J Continuum Mode by Adding Arbitrary Harmonic Voltage Elements

An extended Class-B to Class-J continuum mode is presented and a power amplifier(PA) based on this theory is realized in this paper. The enhanced voltage waveform formulation, which gives multiple impedances solutions of the fundamental and harmonics for the target performance by adding the harmonic elements, has been described mathematically. Unlike conventional continuous Class-J mode, the optimal impedance solutions of the new mode can contain real and imaginary parts of arbitrary even harmonics. To verify this theory, a high-efficiency PA from 2.85-4.25 GHz is designed at the package plane. The measurement results illustrate that the fabricated PA outputs 11.7-15.8 W saturation power with drain efficiency(DE) of 58% to 78% and PAE of 54.5%-74% across the entire band. For a 5 MHz WCDMA signal with a peak-to-average ratio (PAR) of 8.6 dB, an adjacent channel leakage ratio (ACLR) of -31.5 to -38 dBc is measured at an average DE of 38%-48% when the output power (Pout) is 36 dBm from 2.85-4.25 GHz.
Building pair-wise visual word tree for efficent image re-ranking

Bag-of-visual Words (BoW) image representation is getting popular in computer vision and multimedia communities. However, experiments show that the traditional BoW representation is not as effective as it is desired. One of the most important reasons for its ineffectiveness is that, the traditional BoW representation lost the spatial information in images. To overcome this problem, we propose the pair-wise visual word tree, within which each visual word keeps both the appearance and spatial information between two interest points in image. Thus, the corresponding novel BoW representation preserves the spatial structure in image. Based on the pair-wise visual word tree, we propose an efficient topic word selection algorithm, which utilizes the Latent Semantic Analysis to discover the most expressive visual words for different image categories. An efficient strategy is then utilized to combine the selected topic words for image re-ranking. Massive experiments show that the novel BoW representation shows promising performance. Meanwhile, the proposed image re-ranking strategy shows the state-of-the-art precision and promising efficiency.
Context aware topic model for scene recognition

We present a discriminative latent topic model for scene recognition. The capacity of our model is originated from the modeling of two types of visual contexts, i.e., the category specific global spatial layout of different scene elements, and the reinforcement of the visual coherence in uniform local regions. In contrast, most previous methods for scene recognition either only modeled one of these two visual contexts, or just totally ignored both of them. We cast these two coupled visual contexts in a discriminative Latent Dirichlet Allocation framework, namely context aware topic model. Then scene recognition is achieved by Bayesian inference given a target image. Our experiments on several scene recognition benchmarks clearly demonstrated the advantages of the proposed model.
A New Searching Method for Intentional Islanding of Distribution Network

According to the distribution network with wind power formulated offline island plan and implementation with online island search. In the formulation of offline island plan only to ensure that important load power supply, alternative power supply path; when online search according to the real-time output power of wind turbines , under the premise of the power constraints, with the new online search algorithm to generate the most divided island trees, to maximize power supply area. As a case study, PG&E69-bus test system is used to divide island, the result proves that this proposed method is feasible and effective.
Multi-index fusion via similarity matrix pooling for image retrieval

Different kinds of features hold some distinct merits, making them complementary to each other. Inspired by this idea an index level multiple feature fusion scheme via similarity matrix pooling is proposed in this paper. We first compute the similarity matrix of each index, and then a novel scheme is used to pool on these similarity matrices for updating the original indices. Compared with the existing fusion schemes, the proposed scheme performs feature fusion at index level to save memory and reduce computational complexity. On the other hand, the proposed scheme treats different kinds of features adaptively based on its importance, thus improves retrieval accuracy. The performance of the proposed approach is evaluated using two public datasets, which significantly outperforms the baseline methods in retrieval accuracy with low memory consumption and computational complexity.
Hamming embedding with fragile bits for image search

Recently, several binary descriptors are proposed, which represent interest points in image using binary codes. In these binary feature schemes, two descriptors are considered as a match, if the Hamming distance between them is below a threshold. Applying Hamming distance to measure the similarity between binary descriptors can extremely promote the computational efficiency. However, our experimental results presents that there exists a large number of bits in the binary feature vector cannot maintain the robustness while image conditions change. Rather than ignore the impacts of those unstable bits, we take into account the difference of robustness among the feature bits and propose a novel similarity measurement, which called the Fragile Bit Ratio (FBR). FBR is used in binary feature matching to measure how two features differ. High FBRs are associated with genuine matches between two binary features and low FBRs are associated with impostor ones. Based on this metric, we propose a new binary feature matching scheme to fuse the Hamming distance and Fragile Bit Ratio. In our approach, we match the descriptors using the Hamming distance threshold roughly, and then filtered by the Fragile Bits Ratio to refine the candidate set. In experiments, using Fragile Bits Radio can effectively remove the false matches and highly improve the accuracy of image search. Furthermore, our method can easily be integrated into the other well-established binary features schemes.
A method of automatic web information extraction based on page clustering

Dynamic web page has a large amount of pages, high-value data and high- modularity structure. According to these feature, this paper developed an automatic web information extraction system based on page clustering. On the basis of DOM extraction technique, it used page clustering to find the high similarity clusters, and improved the accuracy of clustering results by using the column similarity measure and global auto-similarity measure. Extraction template applied the optional nodes to modify and adjust the template in order to improve the identification of the content nodes. Experimental result shows this method automatically locates and extracts the main information of pages and achieves high precision and recall.
Face detection using SVM trained in independent space

The classical face representation method, such as eigenface, extracts covariance based on low-order statistics feature of image. However, high-order information represents image details, which are necessary for pattern recognition. Hence, PCA is first used to reduce its dimension; then the independent component analysis (ICA) is applied to further obtain independent feature vector instead of low-order statistics; finally support vector machine is used as a classifier that has demonstrated high generalization capabilities for face detection. The feasibility and correctness of this new face detection method are shown in CBCL Face Dataset.
Path planning and simulation of the circumferential placement process on conical shell surface

Automatic placement technology is an advanced molding technology for composite materials developed in recent decades, which have been widely used in manufacturing process of high-performance composite materials. In this paper, we proposed a new placement method, i.e., the circumferential placement, which is appropriate for conical shell surface. The model of placement linetype was designed, which build the mathematical foundation for the movement locus of placement equipment. Using three-dimensional software, the placement path was simulated, and the results verified that this method is feasible for manufacturing process of high-performance composite materials.
Towards temporal adaptive representation for video action recognition

Action recognition has been one of the challenging problems in the computer vision community. Most of the recent research work in this area exploits the motion features captured by dense trajectory descriptors. On the other hand, static image classification has seen the rise of deep learning architectures, with evidence that the output of intermediate layers could be successfully employed as a low level descriptor for new learning tasks. However, the same level of classification success has not yet translated to the video domain. In this paper, we investigate to jointly combine dynamic trajectory features and static deep features that enhance the distinctiveness of the classifiers. We also propose a Temporal-Pyramid-Pooling strategy with intermediate layer deep features for improving action classification performance. Extensive empirical evaluations are provided to corroborate the effectiveness of the proposed framework on real-world untrimmed video datasets.
Question classification for e-learning by artificial neural network

Text categorization is the classification of unstructured text documents with respect to a set of one or more predefined categories. This paper describes our work in exploring automatic question classification tests which can be used in e-learning system. Such tests can take the form of multiple-choice tests, as well as fill-in-the-blank and short-answer tests. We acquired 20 texts used for high school students and each text is followed by several multiple choice questions from e-learning Webpage. We propose a text categorization model using an artificial neural network trained by the backpropagation learning algorithm as the text classifier. Our test results show that the system achieved the performance in terms of F/sub 1/ value of nearly 78%.
A variable step-size EASI algorithm based on PI for DS-CDMA system blind estimation

To solve the problem of the direct sequence code division multiple access (DS-CDMA) system multiuser detection, this paper proposes a variable step-size based on performance index (PI) EASI algorithm for information code and chip sequence blind estimation. Contrasting blind source separation (BSS) and DS-CDMA system model, the algorithm uses adaptive method to estimate the mixing matrix for estimating the chip sequence, and uses the demixing matrix to separate the observation signal for estimating the information code. In addition, the algorithm uses evaluation performance index (PI) to adjust the step-size, this makes the convergence speed and stability of the algorithm reach an ideal balance. The experimental results show that the algorithm has good resistance to multi-access interference (MAI). The bit error rate (BER) of information code is below 10-2 in 0 dB.
Guest Editorial: Large-Scale Multimedia Data Retrieval, Classification, and Understanding

The papers in this special section focus on multimedia data retrieval and classification via large-scale systems. Today, large collections of multimedia data are explosively created in different fields and have attracted increasing interest in the multimedia research area. Large-scale multimedia data provide great unprecedented opportunities to address many challenging research problems, e.g., enabling generic visual classification to bridge the well-known semantic gap by exploring large-scale data, offering a promising possibility for in-depth multimedia understanding, as well as discerning patterns and making better decisions by analyzing the large pool of data. Therefore, the techniques for large-scale multimedia retrieval, classification, and understanding are highly desired. Simultaneously, the explosion of multimedia data puts urgent needs for more sophisticated and robust models and algorithms to retrieve, classify, and understand these data. Another interesting challenge is, how can the traditional machine learning algorithms be scaled up to millions and even billions of items with thousands of dimensionalities? This motivated the community to design parallel and distributed machine learning platforms, exploiting GPUs as well as developing practical algorithms. Besides, it is also important to exploit the commonalities and differences between different tasks, e.g., image retrieval and classification have much in common while different indexing methods evolve in a mutually supporting way.
SiC Trench MOSFET With Integrated Self-Assembled Three-Level Protection Schottky Barrier Diode

A silicon carbide (SiC) trench MOSFET (TMOS) with integrated three-level protection (TLP) Schottky barrier diode (SBD), named ITS-TMOS, is proposed and investigated by simulation. The device features the integrated TLP-SBD that remarkably improves body diode characteristics while guarantees excellent fundamental performance of TMOS. In the blocking state, the P-base region, the trench gate, and the P+ shield at the trench bottom serve as the TLP of the Schottky contact. Each protection assists in depleting the drift region beneath Schottky contact. Benefiting from the self-assembled TLP, the leakage current of the integrated body diode of the ITS-TMOS is significantly reduced. Moreover, the reverse turn-on voltage (<inline-formula> <tex-math notation="LaTeX">${V} _{ \mathrm{\scriptscriptstyle ON}}$ </tex-math></inline-formula>) and the gate charge (<inline-formula> <tex-math notation="LaTeX">${Q} _{g}$ </tex-math></inline-formula>) of the ITS-TMOS are 65% and 18% lower than those of the conventional TMOS, respectively. The improved overall performances make the SiC ITS-TMOS a competitive candidate for high-efficiency and high power density applications.
Optimal control of linear discrete-time systems with quantization effects

This paper studies optimal control designs for networked linear discrete-time systems with quantization effects and/or fading channel. The quantization errors and/or fading channels are modeled as multiplicative noises. The H2 optimal control in mean-square sense is formulated. The necessary and sufficient condition to the existence of the mean-square stabilizing solution to a modified algebraic Riccati equation (MARE) is presented. The optimal H2 control via state feedback for the systems is designed by using the solution to the MARE. It is a nature extension for the result in standard optimal discrete-time H2 state feedback design. It is shown that this optimal state feedback design problem is eigenvalue problem (EVP) and the optimal design algorithm is developed.
Design and implementation of test paper auto-generation strategy for electric energy measurement

With the establishment of the standard for “strong and smart electric grid”, various kinds of intelligent metering equipments are gradually produced and adopted in practice. A set of training and examination systems is thus urgently needed in the electric power industry for the staffs to measure their level of measuring electric energy. In this paper, based on the practical requirement of a training system for the electric energy measurement in some electric company, combining the thoughts of both the random selection method and the backtracking search method, a test paper auto-generation strategy is designed. The presented strategy has two main advantages. One is that this strategy can automatically calculate the difficulty proportion for the test papers by utilizing the normal distribution function according to the expectation distribution of the test scores. In this way, the efficiency of generating paper can be greatly improved. The other is that the importance of different knowledge in electric energy measurement is different. Therefore, the knowledge required in the test papers is classified into different importance level to meet the requirement of the electric energy measurement test.
Optimal tracking design and performance analysis for LTI systems with quantization effects

This paper studies the tracking problem for linear time-invariant multi-input single-output (MISO) discrete-time systems with quantization effects. Logarithmic quantization laws are adopted in the systems. The tracking performance is measured by the energy of the error response between the output of the plant and the reference signal. Our goals are to design an optimal controller for the tracking problem and to find an explicit formula of the minimum tracking cost. It turns out that the optimal state feedback law can be obtained by solving a modified discrete-time Riccati equation associated with the state space model of the plant and the features of the quantization law. Furthermore, from the unique positive solution of the modified Riccati equation, we obtain an analytic expression for the minimum tracking cost in terms of the nonminimum phase zeros and the bound of quantization error. When the quantization error approaches zero, the minimum tracking cost degrades to the minimum tracking cost of the system without quantization effects, which is presented some existing works. The results obtained in this work explicitly show how is the optimal tracking performance limited by the quantization error.
Video clip representation and recognition using composite shot models

This paper proposes using composite shot models to represent and recognize short video clips, such as advertisement clips. The temporal constraints of the shots constituting the clip are represented by a directed graph, which takes the shots as the nodes. Because each shot is represented by a hidden Markov model, the whole clip representation becomes a composite HMM. The test sequence is recognized by computing its likelihood belonging to the composite HMM. The experiment results show that composite HMM is not only robust to feature distortion induced by transcoding, but also demonstrates quite good performance on recognizing shorter versions. This representation scheme is also a general and flexible framework, which allows the choice of shot representation, and different shot representations should have different matching methods.
Low-complexity global motion estimation from P-frame motion vectors for MPEG-7 applications

We present an algorithm for low-complexity global motion estimation, that works with block-coded video (e.g. MPEG-2). A superimposed global motion model is fitted to decoded P-frame motion vector fields, providing real-time performance. Therefore a robust M-estimator is applied since a lot of outliers have to be expected in the motion vector fields. The algorithm is compared to a state-of-the-art global motion estimator. The main characteristics of the global motion are captured accurately in most cases, although estimation may fail. We also report the integration of the estimator into a complete MPEG-7 system for search-and-retrieval of video based on motion characteristics.