nils-herrmann/clean_scirepeval huggingface dataset:
Interactive genetic algorithm with implicit uncertainty evaluation for application in personalized search

Interactive evolutionary algorithms have been used successfully for search and optimization problems. However, user evaluation fatigue and uncertainties greatly restrict their application in complex optimization scenarios. Implicit evaluation techniques can reduce user fatigue effectively, but methods for handling evaluation uncertainties have not yet been explored thoroughly. We study an interactive evolutionary algorithm with uncertain preferences using an implicit evaluation model. First, we design a Gaussian function to define evaluation uncertainties based on interactions. Then, by combining the evaluation uncertainties and the interactions, we calculate the preference weights for each variable, with which a weighted Conditional Preference Net is constructed to provide quantitative depiction of the user's evaluation preference. Using the weighted Net, we estimate the fitness of an individual for the achievement of the evolutionary process. In the iteration, the Net is updated according to newly performed interactions to ensure reliable tracking of the user's preference. We apply a genetic algorithm-based interactive evolutionary algorithm to a personalized search for books. The proposed algorithm accelerates search speed, alleviates user fatigue, and achieves higher rates of satisfying solutions.
Single image super-resolution via 2D sparse representation

Image super-resolution with sparsity prior provides promising performance. However, traditional sparse-based super resolution methods transform a two dimensional (2D) image into a one dimensional (1D) vector, which ignores the intrinsic 2D structure as well as spatial correlation inherent in images. In this paper, we propose the first image super-resolution method which reconstructs a high resolution image from its low resolution counterpart via a two dimensional sparse model. Correspondingly, we present a new dictionary learning algorithm to fully make use of the corresponding relationship of two pairs of 2D dictionaries of low and high resolution images, respectively. Experimental results demonstrate that our proposed image super-resolution with 2D sparse model outperforms state-of-the-art 1D sparse model based super resolution methods in terms of both reconstruction ability and memory usage.
Photo Album Compression for Cloud Storage Using Local Features

This paper proposes a novel feature-based photo album compression scheme for cloud storage. The main contribution of this paper is the use of local features rather than pixel values for analyzing and exploring the correlation between images. Unlike previously established image set compression schemes, we adopt content-based feature matching, which is invariant to scale, rotation, and robust to illumination changes, in both correlation estimation and redundancy reduction. Based on feature-based correlation analysis, we organize correlated images as a pseudo sequence by minimizing the predictive cost. Since pseudo sequences have much more complicated correlations than natural videos, we further propose a three-step prediction to reduce inter-image redundancy based on local features. We first propose a feature-based multi-model approach to reduce local geometric deformations between images, resulting in multiple deformed predictions. We then decrease the illumination difference between each deformed prediction and the target image with a photometric transformation. Finally, we reduce redundancy between images using a block-based motion compensation, similar to video compression. Experimental results show that our proposed feature-based coding scheme can be 10 times more efficient than individual JPEG compression.
Cloud-Based Image Coding for Mobile Devices—Toward Thousands to One Compression

Current image coding schemes make it hard to utilize external images for compression even if highly correlated images can be found in the cloud. To solve this problem, we propose a method of cloud-based image coding that is different from current image coding even on the ground. It no longer compresses images pixel by pixel and instead tries to describe images and reconstruct them from a large-scale image database via the descriptions. First, we describe an input image based on its down-sampled version and local feature descriptors. The descriptors are used to retrieve highly correlated images in the cloud and identify corresponding patches. The down-sampled image serves as a target to stitch retrieved image patches together. Second, the down-sampled image is compressed using current image coding. The feature vectors of local descriptors are predicted by the corresponding vectors extracted in the decoded down-sampled image. The predicted residual vectors are compressed by transform, quantization, and entropy coding. The experimental results show that the visual quality of reconstructed images is significantly better than that of intra-frame coding in HEVC and JPEG at thousands to one compression .
Video coding with spatio-temporal texture synthesis and edge-based inpainting

This paper proposes a video coding scheme, in which textural and structural regions are selectively removed in the encoder, and restored in the decoder by spatio-temporal texture synthesis and edge-based inpainting. In the proposed scheme, two types of regions are classified based on two motion models: local motion and global motion. In local motion regions, conventional block-based motion estimation is employed for region removal and spatio-temporal texture synthesis is applied for recovery of the removed regions. In global motion regions, edge-based image inpainting is utilized to recover removed regions, and sprite generation is used as an auxiliary tool to keep temporal consistency. In the proposed scheme, both structures and textures are handled and some kinds of assistant information which can guide restoration are extracted and coded. This approach is blockbased and thus is flexible and generic to be implemented into standard-compliant video coding schemes. It has been implemented into H.264/AVC and achieves up to 35% bitrate saving at similar visual quality levels compared with H.264/AVC without our approach.
Evolutionary optimization of hybrid indices for fashion design

The color harmony and style novelty of fashion are both considered for fashion design problems. The characteristics of the problem are firstly analyzed. It drew the conclusion that this kind of problems contains explicit and implicit objectives simultaneously and the two objectives are not conflicting as typical multi-objective optimization problems. Secondly, the explicit expression of color harmony is presented and genetic algorithm is adopted to perform the optimization. Thirdly, interactive genetic algorithm is applied to optimize the style novelty and to obtain the evaluation of implicit index. The results obtained by the two methods respectively are integrated and the two indices are co-evolved. Lastly, the interactive interface of human-machine is given and the results compared with the optimization using merely interactive genetic algorithm show the validity of the presented algorithm.
Performance Analysis of Active Target Localization Using TDOA and FDOA Measurements in WSN

Target localization is one of the basic functions of wireless sensor networks. In the target localization algorithms, the problem of how many sensors are needed and how they are deployed is what we are concerned about in this paper. The authors of (K.C. Ho et al., 2007) present the mathematical expression of Cramer-Rao lower bound for the localization error, which is the smallest lower bound of any unbiased estimation. However it is difficult to be used to find out the relation between sensor number and Cramer-Rao lower bound directly. In this paper the relation between sensor number and Cramer- Rao lower bound is revealed by simulation. And how the topology affects the Cramer-Rao lower bound is also presented.
Applying knowledge of users with similar preference to construct surrogate models of IGAs

Interactive genetic algorithms (IGAs) are effective methods of solving optimization problems with qualitative indices. The problem of user fatigue resulting from his/her evaluations, however, restricts their applications in complex optimization problems. Employing various surrogate models to evaluate (a part of) individuals instead of a user is a feasible approach to solving the above problem. Previous studies, however, have not fully utilized knowledge provided by users with similar preference when constructing these models. The problem of constructing surrogate models by using knowledge of users with similar preference was focused in this study. First, users with similar preference participating the evolution were identified based on the matrix formed by the relationship between users and the “fitness” of allele meaning units and the users' interests in allele meaning units by using the collaborative filtering algorithm based on nearest-neighbor; and then the individuals evaluated by users with similar preference and chosen according to the users' preference similarities and confidence, along with their fitness, were as a part of samples for training the surrogate model of the current user's cognition. The proposed method was applied to an evolutionary fashion design system, and the experimental results show that the proposed method can improve the capability in exploration on the premise of greatly alleviating user fatigue.
Don’t Be Surprised: I See Your Mobile App Stealing Your Data

To detect the potential malicious mobile applications that may cause data leakages, this paper1 proposes to take advantage of the attack-neutral and hard-to-avoid system calls of the mobile system, reconstructing an activity graph for each application to reflect its interactions with the system. This paper makes efforts to automate the activity graph generation in Android, with the main contribution being an open source tool that can be of great assistance for the test team of application stores. The tool’s effectiveness has been validated by our evaluation against some existing or home-brewed mobile applications that leak data.1This paper is a report of research outcomes from open source master projects [4]–[6], and the resultant code can be shared upon request.
Using Bayesian Networks for Probabilistic Identification of Zero-Day Attack Paths

Enforcing a variety of security measures (such as intrusion detection systems, and so on) can provide a certain level of protection to computer networks. However, such security practices often fall short in face of zero-day attacks. Due to the information asymmetry between attackers and defenders, detecting zero-day attacks remains a challenge. Instead of targeting individual zero-day exploits, revealing them on an attack path is a substantially more feasible strategy. Such attack paths that go through one or more zero-day exploits are called zero-day attack paths. In this paper, we propose a probabilistic approach and implement a prototype system ZePro for zero-day attack path identification. In our approach, a zero-day attack path is essentially a graph. To capture the zero-day attack, a dependency graph named object instance graph is first built as a supergraph by analyzing system calls. To further reveal the zero-day attack paths hidden in the supergraph, our system builds a Bayesian network based upon the instance graph. By taking intrusion evidence as input, the Bayesian network is able to compute the probabilities of object instances being infected. Connecting the high-probability-instances through dependency relations forms a path, which is the zero-day attack path. The experiment results demonstrate the effectiveness of ZePro for zero-day attack path identification.
Modulated Infrared Electroluminescence From Organic Light-Emitting Diodes

Modulated infrared electroluminescence from organic light-emitting diodes (OLEDs) based on rare earth complexes (tris-dibenzoylmethanato-mono-bathophenanthroline) erbium or neodymium were fabricated. The modulations were realized by varying the device configuration and by varying an applied forward bias at room temperature. We found that the intensity of EL peak at 1.54 mum was reduced by a factor of almost 20 times as the Nd complex layer thickness in the OLEDs was increased from 1 nm to 5 nm. The intensity ratio of the 1.54 mum (Nd) to 1.06 mum (Er) decreases from 0.82 to 0.53 as the forward bias increases from 6.5 V to 18 V. The effects of the modulation of these IR emissions were also discussed.
A framework to evaluate the Power Application Software of the dispatching automatic system

The power application software (PAS) of the dispatching automatic system (DAS) is an important support for the development of the smart distribution grid, but it is difficult to evaluate the operational effects of PAS. To find the potential problems in operation, the PSA should be tested before interconnecting into the grid. Here a framework is proposed to evaluate the PAS of the DSA, using middleware technologies for the communication between the simulation system and PAS. The relative index system is established to evaluate the PAS. Test results show that the evaluation framework possess good operational robustness for various applications.
Large scale image retrieval with visual groups

Bag-of-visual words (BoW) representation has been widely used in the large scale image retrieval. Though efficient, it ignores the geometric correlation among visual words, whereas the geometric verification has demonstrated its effectiveness in image retrieval. In this paper, we propose a new representation - visual group, to improve the retrieval precision by grouping the geometrically related features based on the inclusion relationship between features at different scales. A visual group consists of a master feature and several member features covered by the master feature. The geometric constraint inside each group is introduced into visual group matching for efficient geometric verification. Experimental evaluation on the dataset Oxford5K+Flickr1M shows that our visual group based image search approach outperforms BoW and the state-of-the-art visual phrase based schemes.
Reduction of malicious behavior patterns based on attribute order

In the field of malware study, the automatic generation of malicious behavior pattern based on system call trace is important to malware detection. This paper studied the generation of malicious behavior distinguishing pattern, and used attributes order reduction method to reduce the patterns. It gave the ordering rules based on mutual information and the size of pattern. After studying the quick reduction algorithm based on divide and conquer method, it improved the algorithm according to the characteristic of datasets. Finally, test results verifies that the improved algorithm can decrease the reduction time, the ordering method can obtain relatively good reduction results, and the classification result is relatively good.
Multi-point detection for polarization-sensitive optical time domain reflectometry and its applications in electric power industry

In this paper, we propose a novel method of disturbance detection by using image morphology to solve the challenging multi-point detection problems in polarization-sensitive optical time domain reflectometry (P-OTDR), which restrict its practical applications greatly. The new methodology is processed by accumulating the temporally differentiated OTDR traces and building up a two-dimensional temporal-spatial SOP evolving graph, and then using edge detection and automatic-clustering, adopted from imaging processing and statistical classification techniques, to distinguish different disturbance points and pinpoint their locations. The proposed method proved better performance than the conventional direct differentiation method and the Fast Fourier Transform (FFT) spectrum analysis. The location accuracy in particular, has improved significantly. Also, we present two typical field applications of this representative Distributed Optical Fiber Sensor (DOFS) in the electric power industry, for on-line safety monitoring of both the long-distance power transmission cables buried underground and those overhead. It demonstrated that P-OTDR is a promising technology for on-line safety monitoring of long-distance power transmission lines and optical fiber communication cables, due to its outstanding advantages, such as fully passive operation, electro-magnetic interference immunity (EMI), corrosion resistance, good reliability, and more especially its long-distance single-end detection ability, quantitative measurement capability and moderate sensibility for disturbance detection.
Separable Kernel for Image Deblurring

In this paper, we deal with the image deblurring problem in a completely new perspective by proposing separable kernel to represent the inherent properties of the camera and scene system. Specifically, we decompose a blur kernel into three individual descriptors (trajectory, intensity and point spread function) so that they can be optimized separately. To demonstrate the advantages, we extract one-pixel-width trajectories of blur kernels and propose a random perturbation algorithm to optimize them but still keeping their continuity. For many cases, where current deblurring approaches fall into local minimum, excellent deblurred results and correct blur kernels can be obtained by individually optimizing the kernel trajectories. Our work strongly suggests that more constraints and priors should be introduced to blur kernels in solving the deblurring problem because blur kernels have lower dimensions than images.
Geometric Hashing Classifier Based on Modified D-S Theory in SAR Target Recognition

Geometric hashing technology can effectively recognize targets with partially changed shape. But when the known targets in training data set don't satisfy with the condition of 360 azimuths, the effect of recognition degrades. The reason is that the imperfect training set and partially changed shape make the uncertain information increase. Shafer theory can deal with the uncertainty successfully. However, Dempster-Shafer theory does not model well evidences with a high degree of conflict. In order to overcome the aforementioned problems, in this paper, a modified D-S theory combined with geometric hashing is proposed and used in SAR images recognition. Experimental results with MSTAR dataset show that this fusion method is feasible, and it can correctly recognize the targets with partially changed shape
The Bovine Serum Albumin role's in analyzing the its of soil fungus

In order to analyze the Bovine Serum Albumin role's in ITS-PCR technique. We observed the improvement of ITS-PCR application through added BSA in PCR systems. The result shown: In fungi ITS-PCR analysis process, adding BSA should improve the ITS-PCR amplification effect notably and reduce the amount of Taq enzymes, the best use concentration of BSA of 2 μ g/μ l. This suggested: the BSA adding to improve soil fungus ITS-PCR analysis method was feasible; The study provided the basis for applying BSA in soil fungus ITS-PCR analysis technology.
DAC-Mobi: Data-Assisted Communications of Mobile Images with Cloud Computing Support

This research proposes a novel data assisted image transmission scheme, which utilizes a large amount of correlated images stored in the cloud to improve the spectrum efficiency and visual quality. First, a two-layer Coset coding is proposed for the DCT coefficients transmission. The most significant bits (MSB) of the coefficients are generated by the first layer Coset and together with a few low frequency coefficients are transmitted through the most reliable channel coding and digital modulation. The middle bits generated by the second layer Coset are discarded by the sender and the residual bits are transmitted through amplitude modulation. Based on the MSB and the residual bits, an approximation of the original image is reconstructed. With this approximation, a lot of correlated images can be retrieved from the cloud, which are used to recover the discarded middle bits. The two layer Coset coding can significantly decrease the data energy so as to improve the transmission power efficiency. Hence, the end to end distortion of amplitude modulation can be reduced. Second, the image quality can be further improved by joint internal and external denoising with the retrieved images. Simulations show that the proposed scheme outperforms conventional digital schemes about 4 dB in peak signal to noise power ratio (PSNR) and achieves 2 dB gain over the state-of-the-art uncoded transmission. At low signal to noise power ratio (SNR), an additional 2-3 dB gain is achieved. The visual quality comparison also validates the objective image assessment result.
Learning adaptive filter banks for hierarchical image representation

Conventional hierarchical image representation methods, e.g. Wavelet transform, use pre-determined filter banks which lack in adaption to the variant statistical characteristics of images. In this paper, we propose learning adaptive filter banks for hierarchical sparse image representation with a wavelet-like compact form using a deconvolutional network. The proposed scheme is verified by evaluating its sparsity in image representation. Experimental results demonstrate that the proposed scheme outperforms 9/7 and 5/3 wavelets transform in terms of both objective and subjective qualities under the same sparsity.
Subjective-Driven Complexity Control Approach for HEVC

The latest High Efficiency Video Coding (HEVC) standard significantly increases the encoding complexity for improving its coding efficiency, compared with the preceding H.264/Advanced Video Coding (AVC) standard. In this paper, we present a novel subjective-driven complexity control (SCC) approach to reduce and control the encoding complexity of HEVC. Through reasonably adjusting the maximum depth of each largest coding unit (LCU), the encoding complexity can be reduced to a target level with minimal visual distortion. Specifically, the maximum depths of different LCUs can be varied through solving the proposed optimization formulation of complexity control, based on two explored relationships: 1) the relationship between the maximum depth and encoding complexity and 2) the relationship between the maximum depth and visual distortion. Besides, the subjective visual quality is favored with a novel subjective-driven constraint imposed in the formulation, on the basis of a visual attention model. Finally, the experimental results show that our approach can achieve a wide range of encoding complexity control (as low as 20%) for HEVC, with the smallest complexity bias being 0.2%. Meanwhile, our SCC approach outperforms other two state-of-the-art complexity control approaches, in terms of both control accuracy and visual quality.
Spatially Scalable Video Coding for HEVC

Spatially scalable video coding (SSVC in short) provides an efficient way to deliver one video at different resolutions. Based on the development of emerging High Efficiency Video Coding (HEVC), we propose a SSVC scheme to provide both single-loop and multi-loop solutions by enabling different inter-layer prediction mechanisms. Specifically, there are three basic inter-layer prediction modes, inter-layer intra, motion and residual prediction, which are first investigated for enhancement layer coding. These three modes provide a basic single-loop SSVC solution for low complexity applications. Beside the correlations explored in single-loop solution, we employ an extra patch learning-based prediction mode named P-mode to further improve coding performance. In P-mode, the temporal and spatial correlations of the reference frames are explored simultaneously by the visual patch-based learning and mapping at pixel level. These correlations help enhance the accuracy of the inter-layer frame prediction from the base-layer reconstruction within a multi-loop structure. The experimental results show the effectiveness of the proposed SSVC scheme compared with simulcast case.