nils-herrmann/clean_scirepeval huggingface dataset:
MRS-MIL: Minimum reference set based multiple instance learning for automatic image annotation

Automatic image annotation (AIA) is a promising way to improve the performance of image retrieval. In this paper, we propose a novel AIA scheme based on multiple-instance learning (MIL). By introducing the minimum reference set (MRS) into MIL (denoted by MRS-MIL), the positive instances (i.e. regions in images) embedded in the positive bags (i.e. images) can be picked out via reliable inferring for a concept. Generated through the 1-NN classifier, MRS denotes the set of minimum number of bags that correctly classify all the labeled bags. Following the principle of structure risk minimum, MRS shows good generalization ability and is particularly suitable for the problem of being short of labeled training bags, i.e. problem of small samples. Compared with the previous annotation approaches, the experimental results demonstrate that the proposed MRS-MIL based annotation scheme achieves better performance of AIA even with a small set of labeled bags.
Multiple Description Video Coding Based on Lattice Vector Quantization

Multiple description coding (MDC) is a promising alternative for robust transmission of information over non-prioritized and unpredictable networks. Especially, MDC has emerged as an attractive approach for video applications where retransmission is unacceptable or infeasible. In this paper, an effective multiple description video coding scheme is introduced. This scheme is mainly constructed using motion-compensated prediction in temporal domain and multiple description lattice vector quantization (MDLVQ) for wavelet coefficients. The experimental results are presented to demonstrate the effectiveness of the proposed scheme
Adaptive Total Variation Regularization Based SAR Image Despeckling and Despeckling Evaluation Index

We introduce a total variation (TV) regularization model for synthetic aperture radar (SAR) image despeckling. A dual-formulation-based adaptive TV (ATV) regularization method is applied to solve the TV regularization. The parameter adaptation of the TV regularization is performed based on the noise level estimated via wavelets. The TV-regularization-based image restoration model has a good performance in preserving image sharpness and edges while removing noises, and it is therefore effective for edge preserve SAR image despeckling. Experiments have been carried out using optical images contaminated with artificial speckles first and then SAR images. A despeckling evaluation index (DEI) is designed to assess the effectiveness of edge preserve despeckling on SAR images, which is based on the ratio of the standard deviations of two neighborhood areas of different sizes of a pixel. Experimental results show that the proposed ATV method can effectively suppress SAR image speckles without compromising the edge sharpness of image features according to both subjective visual assessment of image quality and objective evaluation using DEI.
Evaluation on the soil pollution degree on two sides of highway with the Fuzzy Mathematics Method

Analysis of soil samples collected from either side of highway in HuRong showed that the soil has much greater pollution with higher concentration of heavy metals such as Zn, Ni, Pb, Cd, and the concentration of heavy metals tendency to reduce with the increase of horizontal distance from 10m to 40m to highway centerline. The degree of pollution was evaluated of the synthetic by using the Fuzzy Mathematics  The result showed that the soil has slight pollution (belongs three grade soil level) of heavy metals within distance of 20m to the highway centre line, still clean (belongs two grade soil level) range 20m from 120m which was the warning level of pollution in the soil. This result was further provided the basis for its controlling measurements.
A Novel Reversible Watermarking Based on an Integer Transform

A novel reversible data hiding scheme based on an integer transform is presented in this paper. The invertible integer transform exploits the correlations among four pixels in a quad. Data embedding is carried out by expanding the differences between one pixel and each of its three neighboring pixels. However, the high hiding capacity can not be achieved only by difference expansion, so the companding technique is introduced into the embedding process so as to further increase hiding capacity. A series of experiments are conducted to verify the feasibility and effectiveness of the proposed approach.
A Novel High-Capacity Reversiblewatermarking Scheme

A novel reversible data hiding scheme is proposed in this article. Each pixel is predicted by its right neighboring pixel in scan order to get its prediction-error. Then, a companding technique is introduced so as to largely increase the number of prediction-errors available for embedding. Accordingly, a location map recording available positions can be compressed into a short bitstream. By largely decreasing the capacity consumed by the compressed location map, the high hiding capacity is achieved. A series of experiments are conducted to verify the feasibility and effectiveness of the proposed approach.
Sequential Architecture for Efficient Car Detection

Based on multi-cue integration and hierarchical SVM, we present a sequential architecture for efficient car detection under complex outdoor scene in this paper. On the low level, two novel area templates based on edge and interest-point cues respectively are first constructed, which can be applied to forming the identities of visual perception to some extent and thus utilized to reject rapidly most of the negative non-car objects at the cost of missing few of the true ones. Moreover on the high level, both global structure and local texture cues are exploited to characterize the car objects precisely. To improve the computational efficiency of general SVM, a solution approximating based two-level hierarchical SVM is proposed. The experimental results show that the integration of global structure and local texture properties provides more powerful ability in discrimination of car objects from non-car ones. The final high detection performance also contributes to the utilizing of two novel low level visual cues and the hierarchical SVM.
A Deadbeat Control Constrained Control Input in IMC System

In the paper, a new deadbeat control system which employed the IMC structure. Based on traditional deadbeat control and linear algebra theory, we find out the general form of the non-minimum time deadbeat controller and the exact numerical relationship between the prolonged settling time and the reduction of the control input. Then we designed the feed-forword controller of the IMC structure, which can achieve the minimum time deadbeat control in the case of constrained control input. Simulation results show the dynamic performance, robustness and disturbance-rejection in the case of constrained control input are greatly improved.
Position-Sensorless Control of  $\text{DC} + \text{AC}$ Stator Fed Doubly Salient Electromagnetic Motor Covered Full Speed Range

The position-sensorless control methods of dc+ac stator fed doubly salient electromagnetic motor (DSFDSEM) covered full speed range are presented in this paper. To implement these methods, the following procedures are done. First, the series self-inductance area difference method, in consideration of cogging torque produced by field current, is adopted for identifying the initial position. Second, the current pulse injection technique in the commutation point is proposed to detect the commutation moment at low speed. Finally, the terminal voltages are considered as vectors with a fixed phase difference, and the composed vector is rotated with varying rotor positions; the commutation point can be obtained by coordinate transformation of three terminal voltages at high speed. The filter with a low cutoff frequency is not required in this method. Moreover, the smooth-switch technology between startup and high-speed operations is researched to prevent the out-of-step fault. The sensorless control of a DSFDSEM from startup at standstill to high-speed operation can be easily implemented by the aforementioned methods. No additional hardware or complex computation is required. A 12/8 DSFDSEM prototype is built and tested to verify the feasibility of the proposed sensorless schemes.
Research on suspension system based on fuzzy logic control

In this paper, a six degree of freedom half body vehicle suspension system is developed and the road roughness intensity is modeled as a filtered white noise stochastic process. Based on control theory, the fuzzy control system of the active suspension is established. With the aid of software Matlab/simulink, simulation process is done. Simulation results indicate that the proposed active suspension system proves to be effective in the vibration isolation of the suspension system. A mechanical dynamic model of the two degree of freedom quarter body of vehicle suspension system is
Repair Policies of Coverage Holes Based Dynamic Node Activation in Wireless Sensor Networks

The failed nodes lead to the phenomenon of coverage holes in Wireless Sensor Networks (WSN), which is due to the exhausted energy or destroyed environment. In this paper, the Best Fit Node Policy (BFNP) is proposed to repair coverage holes of WSN. The main idea of Best Fit Node Policy (BFNP) is that WSN detects the coverage holes when the base station has found the failed nodes of network, and selects the closest inactive node to the center of the minimal coverage circle of the polygon that surrounds the hole and then replaces the failed nodes, meanwhile activates the closest inactive node to repair the coverage holes. The simulation results show that Best Fit Node Policy can maintain better quality of coverage, make higher utilization of energy resources, and extend lifetime of networks. In conclusion, BFNP performs superior to the Coverage Hole Patching Algorithm (CHPA).
Detecting the operation chain in digital images by type and order fingerprint

Nowadays, digital images are widespread and also easily tampered, which promotes the development of digital image forensics techniques rapidly. A series of forensics techniques have been developed to reveal sophisticated forgeries and intentional manipulations. However, few methods consider multiple operations together and provide a complete description of the processing history of a digital image. Multiple operations detection is a challenging problem in the research field of forensics. In this paper, for tracing such processing history, we propose a two-step approach to estimate the topology and order of the operation chain applied to a digital image. We propose an operation chain topology fingerprint (OCTF) to identify the operation chain which an image has undergone. The operation chain order fingerprint (OCOF) is proposed to discriminate the order of the operations. Simulation results verify the effectiveness of our proposed scheme in two sequential operations scenarios.
3D video super-resolution using fully convolutional neural networks

Large amount of redundant information and huge data size have been a serious problem for multiview video systems. To address this problem, one popular solution is mixed-resolution, where only few viewpoints are kept with full resolution and other views are kept with lower resolution. In this paper, we propose a super-resolution (SR) method, where the low-resolution viewpoints in the 3D video are up-sampled using a fully convolutional neural network. By simply projecting the neighboring high resolution image to the position of the low resolution image, we learn the relationship of high and low resolution patches, and reconstruct the low resolution images into high resolution ones using the projected image information. We propose to use a fully convolutional neural network to establish a mapping between those images. The network is barely trained on 17 pairs of multiview images, and tested on other multiview images and video sequences. It is observed that our proposed method outperforms existing methods objectively and subjectively, with more than 1 dB average gain achieved. Meanwhile, our network training procedure is efficient, with less than 3 hours using one Titan X GPU.
Normalization-based multi-bit image watermarking with FEC resistant to affine transformation

A modified image normalization method and a new multi-bit watermarking embedding algorithm with FEC are proposed in this paper based on the scheme resistant to affine transformation attacks proposed by Ping Dong (June 2002). Image normalization process is simplified and the disadvantage of low imperceptibility in Ping Dong (June 2002) is overcome. Experiments indicate that the modified scheme proposed in tins paper is more robust to affine transformation, and has larger embedding capacity and better image quality.
Random Triangle Sampling Path Planning of Assembly/Disassembly in Environment with Dangerzones

To solve the assembly/disassembly path planning in the environment with danger zones, a RRT-based path planning algorithm was presented. The algorithm addressed a random sampling function which obtains random state from the triangles that describe the dangerzones. The local planner connecting the samples with RRT tree is also introduced. Experimental results show that the algorithm much more effectively searches the paths between dangerzones and obstacles.
A Complex Network Based Virtual Computing Environment Topology Generating Method

The topologies of Internet and Internet-based information systems have complex network properties. Designing Internet-based virtual computing environment topology with appropriate properties is significant for both the resource sharing and system performance. We analyses the topology properties of the typical P2P systems, and proposes a new topology generating method, which includes three phases, birth, growth and maturity, and supports multi-node concurrent joining in. The iVCE topology generation method can produce stable structure, with load balancing capability. Analysis of the generated topologies shows that the degree of their super-node obeys normal distribution law, the average path length between nodes shows small-world properties.
An Investigation of Cooperative Slotted ALOHA with the Capture Effect

It has been shown that cooperative transmissions improve the throughput of slotted ALOHA. However, the impact of the capture effect on cooperative slotted ALOHA, which has practical significance for radio channels with fading, shadowing and the near-far effect, remains unknown. This paper proposes and analyzes a random access protocol which incorporates cooperative transmissions and the capture effect in slotted ALOHA. Numerical results illustrate that this scheme improves the throughput by 56% in Rayleigh fading channels when the capture ratio is 3 dB as compared to cooperative slotted ALOHA without the capture effect.
Just Noticeable Difference Based Fast Coding Unit Partition in 3D-HEVC Intra Coding

This paper mainly studies currently developing 3D video coding based on HEVC. HEVC-based 3D video coding mainly focuses on 3DTV and auto-stereoscopic video compression system. A variety of new encoding tools, such as inter-view motion prediction and depth modeling modes, have been added in 3D-HEVC. Although 3D-HEVC provides greater bit rate saving, it also brings the enormous encoding complexity increase. The coding time is increased correspondingly. It is necessary to reduce the encoding time. In this paper, a fast CU-sized partition algorithm is proposed for 3D-HEVC intra coding. The key point of this algorithm is to find the relationship between the texture characteristic and the sub-partition in each CU. It needs to determine whether the LCU can be subdivided to smaller CU according to the relationship. In order to reduce the redundancy of the human eye, just noticeable difference (JND) is a high efficiency model in the base of psychology and physiology. Instead of the time-consuming rate distortion optimization for coding mode decision, the variance of JND in each CU can be exploited to partition the coding unit according to human visual system characteristics. In other words, the larger blocks with higher JND variance will be subdivided to smaller blocks with lower JND variance. Consequently, the rules of CU preliminary partition are decided as follows: (a) For a 64×64 CU, if the variance of JND is larger than 0.25, the CU will be sub-divided into four 32×32 sub-blocks. (b) For a 32×32 CU, if the variance of JND is larger than 0.15, the CU will be sub-divided into four 16×16 sub-blocks. (c) For a 16×16 CU, if the variance of JND is larger than 0.10, the CU will be sub-divided into four 8×8 sub-blocks. The proposed algorithm is implemented based on HTM-13.1 reference software. The experiment condition is set up as "All Intra-Main" (AI-Main) configuration . The quantization parameter (QP) values of texture are set to 25, 30, 35 and 40, respectively and the corresponding QPs of depth can be set to 34,39,42,45. The experimental results show that the fast intra mode decision algorithm provides over 29.25% encoding time saving on average with comparable rate distortion performance.
Numerical Simulation of Metal Powder Compaction Considering Material & Geometrical Nonlinearity

Metal powder compaction is a quite important process in powder metallurgy (PM) industry and it is widely applied in the manufacturing of key components in different fields. The numerical simulation based on the finite element method (FEM) provides a flexible and efficient approach for the researches of this process and its complicated mechanical behaviors. Numerical simulation plays an important role in the intelligent manufacturing technology (IMT) and computer integrated manufacturing system (CIMS). By the aid of FEM, intricate mechanical models can be introduced into a calculation and many parameters can be determined. The work in this paper was based on the assumption that the metal powder system is a kind of compressible continuous material. The ellipsoidal yield criterion and elasto-plastic constitutive relationship correspondent to the mechanical behaviors of powder compaction were analyzed. Considering the geometrical nonlinearity conditions such as large displacement and large strain, the incremental-form constitutive relationship based on the updated Lagrange (U.L.) formulation was derived. And the correlative calculating codes was implemented into the user-subroutine in the MSC.Marc platform. With these codes, three-dimensional elasto-plastic Finite Element (FE) simulations of the compaction processes of a cylindrical sample were performed. The compact force and distribution of the relative density of this sample were analyzed. Numerical simulations provide effective references for the optimization of the tools, processes and its control schemes as well as for the IMT and the development of the devices.
Status evaluation of secondary system in intelligent substation

With the rapid development of intelligent substation, the continuous progress of the condition-based maintenance of intelligent substation has already become very urgent. Status evaluation is the main stage of condition-based maintenance. Its purpose is to determine the health status of the equipment, and be able to timely maintenance before the failure occurred. The paper focuses on the research on the status evaluation methods. This paper makes clear the scope of the evaluation and establishes a evaluation system of secondary system in intelligent substation on condition of the existing standards and research. It uses fuzzy mathematical algorithm to establish the evaluation algorithm model. Meanwhile, it introduces two new fuzzy sets to evaluate its status more accurately. This paper also uses the method of BP artificial neural network to determine the weight of each kind of evaluation index, and finally it completes the evaluation of the secondary system in intelligent substation.
A gradient-based pixel-domain attack against SVM detection of global image manipulations

We present a gradient-based attack against SVM-based forensic techniques relying on high-dimensional SPAM features. As opposed to prior work, the attack works directly in the pixel domain even if the relationship between pixel values and SPAM features can not be inverted. The proposed method relies on the estimation of the gradient of the SVM output with respect to pixel values, however it departs from gradient descent methodology due to the necessity of preserving the integer nature of pixels and to reduce the effect of the attack on image quality. A fast algorithm to estimate the gradient is also introduced to reduce the complexity of the attack. We tested the proposed attack against SVM detection of histogram stretching, adaptive histogram equalization and median filtering. In all cases the attack succeeded in inducing a decision error with a very limited distortion, the PSNR between the original and the attacked images ranging from 50 to 70 dBs. The attack is also effective in the case of attacks with Limited Knowledge (LK) when the SVM used by the attacker is trained on a different dataset with respect to that used by the analyst.
An Enhanced Location Service with Social Feature

Location based service can easily locate mobile station's position, which becomes a tool for connecting the virtual world and real environment. But current location service can notify user only bases on the location or time period. In real world, when people use location service, they always need some kind of help more than just know the position. So a traditional location based service can't fit user's demand very well. As it is well known, people are in some kind of relationships, which can help people when they are in difficulty. So in this study, the author will add some social features in the location based service. Some efforts will be taken to do some social functions in pure location service. An enhance location service with Web2.0 feature in it will be proposed. The architecture, key function principle and the privacy issue will also be presented. Moreover, a demo of the idea will be presented in the paper, using an ISG Parlay Simulator to implement a enhance location based service.
A Web Service Composition Algorithmic Method Based on TOPSIS Supporting Multiple Decision-Makers

This paper presents a novel Web service composition algorithm based on TOPSIS (WSC_TOPSIS) to solve the service composition difficulties with multiple decision-makers and heterogeneous QoS for the first time. It includes three main steps: normalizing decision matrix, evaluating alternatives synthetically and evaluating group alternatives synthetically. Experimental results show that the proposed algorithm can better support Web service composition with heterogeneous QoS data and multiple decision-makers.
Critical Nodes Detecting in Virtual Networking Environment

As cloud computing goes, How to provide a security cloud becomes an important problem. Virtual networking plays an important role in cloud computing infrastructure. To identify which node is a critical node become an important research question. In this study, our team analyzed the fat tree network and small-world network, and proposed a network modeling method for the virtual networking. On this basis, we analyze time performance and detecting accuracy of the two critical nodes detecting algorithms. One is based on depth-first search, while the other concentricity analysis.