nils-herrmann/clean_scirepeval huggingface dataset:
Multi-model prediction for image set compression

The key task in image set compression is how to efficiently remove set redundancy among images and within a single image. In this paper, we propose the first multi-model prediction (MoP) method for image set compression to significantly reduce inter image redundancy. Unlike the previous prediction methods, our MoP enhances the correlation between images using feature-based geometric multi-model fitting. Based on estimated geometric models, multiple deformed prediction images are generated to reduce geometric distortions in different image regions. The block-based adaptive motion compensation is then adopted to further eliminate local variances. Experimental results demonstrate the advantage of our approach, especially for images with complicated scenes and geometric relationships.
Classified patch learning for spatially scalable video coding

This paper proposes an advanced spatially scalable video coding approach that exploits the inter layer correlation between different resolution layers by classified patch learning. The novelty of our proposed scheme is twofold. First, the correlation between low and high resolution frames is explored at patch level with regard to image features. Patches extracted from the previous coded frame are classified into structural and textural sets according to the gradient information. Then the inter layer correlation is separately studied for the two sets, resulting in two databases containing pairs of patches at different resolutions. Second, our proposed patch-based compensation manages to simultaneously exploit the spatial and temporal redundancies without overhead bit for motion. Based on the two databases, a high resolution prediction is derived from the current low resolution reconstruction at structural and textural regions, respectively. Experimental results show that our proposed approach improves the performance of H.264/MPEG spatially scalable coding up to 1.9dB and significantly enhances the subjective quality, especially at low bit rates.
SIFT-based image super-resolution

This paper presents a new exemplar-based image super-resolution (SR) method in which we propose making use of scale invariant image features for high frequency (HF) approximation. We introduce the scale invariant feature transform (SIFT) descriptors in both building an exemplar dataset adaptively and producing the HF details with respect to the features of an input low resolution image. Given a large image database, we propose using the highly correlated images retrieved by SIFT descriptors for exemplar training rather than using a general set of images to increase the matching accuracy. Through building the training set of high resolution/low resolution exemplar pairs, the HF details for SR are retrieved from the training set by matching the SIFT features in a dense way. The flexibility as well as effectiveness of our SR approach is demonstrated at different magnification factors, e.g. 3 and 4. Experimental results show that our SIFT-based SR approach achieves enhanced high resolution images in terms of both objective and subjective qualities in comparison with the state-of-the-art exemplar-based methods.
Spatio-temporal video error concealment using priority-ranked region-matching

When transmitted over error-prone networks, compressed video sequences may be received with errors. In this paper, we propose a priority-ranked region-matching algorithm to recover the "lost" area of the decoded frames, in which both temporal and spatial correlations of the video sequence are exploited. In the proposed scheme, we first calculate the priorities of all edge pixels of the "lost" area and generate a priority-ranked region group. Then according to their priorities, the regions in the group will search their best matching regions temporally and spatially. Finally, the "lost" area is recovered progressively by the corresponding pixels in the matching regions. Experimental results show that the proposed scheme achieves higher PSNR as well as better video quality in comparison with the method adopted in H.264.
Rate-distortion optimization for fast hierarchical B-picture transcoding

An efficient rate-distortion (R-D) optimal method for transcoding hierarchical B-pictures is proposed in this paper. A new R-D model is presented for fast transcoding hierarchical B-pictures in DCT domain, in which the total R-D optimization problem is adjusted to motion R-D optimization and texture R-D optimization separately. Accordingly, a mechanism for fast mode decision is proposed to enable the mode and motion adjustment in hierarchical B-picture DCT-domain transcoding. Experimental results show that the proposed transcoding scheme with the new R-D optimization can achieve 4-dB average PNSR improvement at low bit-rate, and similar performance at high bit-rate, compared to the transcoding by reusing the input motion information. Moreover, as the transcoding is performed in DCT domain, it is fast and simple enough for many real applications
Microcavity Mach–Zehnder Interferometer Sensors for Refractive Index Sensing

Two types of robust Mach-Zehnder interferometer (MZI) sensors based on microcavity in a single mode optical fibers (SMFs) are proposed, which are fabricated by femtosecond laser inscription and chemical etching. The SMFs are modified by an improved point-by-point inscription method (called a transversal-scanning method) or a line-by-line scanning inscription method, which lead to form a rectangular-shaped or a V-shaped MZI after etching. The MZIs show high refractive index (RI) sensitivity above 105 nm/RIU with good linearity. Especially, the rectangular-shaped MZI exhibits a ultra-high RI sensitivity of -17503.73 nm/RIU with a linearity of 0.999 in the range of 1.3371-1.3407. The results of which are coincident with theoretical calculation. In addition, the MZI structures have good mechanical strength and temperature sensitivities in water are also studied.
Landmark Image Super-Resolution by Retrieving Web Images

This paper proposes a new super-resolution (SR) scheme for landmark images by retrieving correlated web images. Using correlated web images significantly improves the exemplar-based SR. Given a low-resolution (LR) image, we extract local descriptors from its up-sampled version and bundle the descriptors according to their spatial relationship to retrieve correlated high-resolution (HR) images from the web. Though similar in content, the retrieved images are usually taken with different illumination, focal lengths, and shot perspectives, resulting in uncertainty for the HR detail approximation. To solve this problem, we first propose aligning these images to the up-sampled LR image through a global registration, which identifies the corresponding regions in these images and reduces the mismatching. Second, we propose a structure-aware matching criterion and adaptive block sizes to improve the mapping accuracy between LR and HR patches. Finally, these matched HR patches are blended together by solving an energy minimization problem to recover the desired HR image. Experimental results demonstrate that our SR scheme achieves significant improvement compared with four state-of-the-art schemes in terms of both subjective and objective qualities.
The implementation of the travel cloud interpretation system

With the development of tourism, a large number of electronic guide systems pour into the market, but the existing electronic tour guide system needs specialized hardware devices with high cost and bulky, which can not meet growing users' demand for a wide range of travel. This paper put forward a tourism cloud interpretation system based on the 3G smart phone and cloud computing technology, so client can use 3G smart phones directly. This server adopts JavaEE technologies and cloud computing technologies, so the client program can automatically download the related scenic spots information about the customer's location by latitude and longitude and then to play it, in order to improve the quality of tourism and the interests of tourists. The system has a good market prospect, good scalability, portability and reliability.
The improved SP frame coding technique for the JVT standard

An efficient and flexible coding technique is proposed in this paper inspired by the SP frame in the H.26L standard, which can achieve a drift-free bitstream switching at the predicted frame. The proposed scheme improves the coding efficiency of the SP frames in the H.26L standard by limiting the mismatch between the references for the prediction and reconstruction with two DCT coefficient coding modes and the rate-distortion optimization. Furthermore, the proposed scheme allows independent quantization parameters for up-switching and down-switching bitstreams. It further reduces the switching bitstream size while keeping the coding efficiency of the normal bitstreams. More rapid and frequent down-switching than up-switching and much smaller size of down-switching bitstream can be achieved with the proposed SP technique. These are very desirable features for any TCP-friendly protocols. Compared with the original SP method for H.26L, the proposed SP method improves the coding efficiency up to 1.0 dB. This SP technique has been officially accepted by the JVT standard.
A Study on Performance of Inter-Vehicle Communications in Bidirectional Traffic Streams

Inter-vehicle communication (IVC) network has become an important topic in research for its potential advantages in enhancing traffic security and efficiency. This paper investigates the communication performance of IVC networks in two lane bidirectional traffic streams. By changing the transmission range of communication units and penetration rate of equipped vehicles, we studied several performance measures, such as average throughput, end to end delay and number of hops. The simulations were conducted in network simulator 2 (ns-2) with Monte Carlo method and the equipped vehicles were generated randomly according to the penetration rate. From the simulation results we found that the bidirectional traffic streams indeed affect each other’s communication performance. In addition, the average throughput and end to end delay can be improved by increasing the transmission range and penetration rate. The study could help to fix some network parameters such as transmission range to advance the communication performance of IVC networks.
H.264-compatible spatially scalable video coding with in-band prediction

In this paper, a H.264 compatible spatially scalable video coding method with in-band prediction is proposed which taking advantages from both the high coding efficiency of H.264 coding scheme and the attractive performance of in-band overcomplete discrete wavelet transform (ODWT) in wavelet-domain motion estimation and motion compensation. Four MV prediction modes are proposed for INTER prediction of high frequency subbands. The intra prediction modes of H.264 are also simplified for each high band according to the directional features inherited inside. Finally, a H.264 compatible scheme based on one of the MV prediction modes is presented to provide better tradeoff among standard compatibility, low complexity and high performance.
Fast directional image interpolation with difference projection

This paper presents a new directional image interpolator, aiming to increase image resolution with high perceptual quality and low computational complexity. In our method, missing pixels in a magnified image are generated through linear interpolation on certain fixed supports to facilitate fast implementation, while local directional features are imposed on the adaptive interpolation weights which are determined by the gradients diffused from the low resolution image. Afterwards, a novel difference projection strategy is proposed to enforce the continuity of the magnified image by reusing the directional interpolator. Experimental results show that our method outperforms conventional bicubic and some existing adaptive interpolators, in terms of both the perceptual and quantitative quality.
Collecting Internet Malware Based on Client-side Honeypot

With the improvement of software security, attacks based on RPC vulnerabilities declined, however, attacks based on client application software vulnerabilities have increased. Such client application software includes Web browsers, email client and office. The spread of malware using these software vulnerabilities has become a severe threat to todaypsilas Internet. In allusion to this kind of threat, this paper designed an Internet malware collecting system based on client-side honeypot. This system can not only collect malware but also detect malicious Web site. It uses a unique network crawler based on client-side attack techniques to collect source of URL, and it collects URL and attachments from emails, then it creates software processes to open URL or files, and uses a device-drive monitor to detect malicious behaviors. It gives an alarm and locates the malicious file, and sends the malware coming through the Internet to the collecting server. We introduce the design and implement of this system and give the results.
Sets evolution-based particle swarm optimization for many-objective problems

Optimization problems with more than three objectives, i.e., many-objective problems (MaOPs), have gained more and more attentions in the field of evolutionary multi-objective optimization (EMO) in that the powerful Pareto comparisons and evolutionary strategies are very scarce. Particle swarm optimization (PSO) is an effective method for multi-objective problems, however, it has not been well scaled for solving those MaOPs. In this paper, a set evolution guided PSO for MaOPs (S-MOPSO for short) is presented by regarding the sets of solutions together with the solutions themselves as “particles” in different evolutionary processes. The framework of the algorithm is first presented, and then the optimization objectives of the MaOPs are converted according to the commonly used metrics of EMOs to provide the search space of sets. Accordingly, the method for evolving the sets of solutions in the PSO framework is given along with finely selecting the global and local best sets particles. The merits of the proposed algorithm are experimentally demonstrated by applying it to scalable benchmark many-objective functions.
A New Surrogate-Assisted Interactive Genetic Algorithm With Weighted Semisupervised Learning

Surrogate-assisted interactive genetic algorithms (IGAs) are found to be very effective in reducing human fatigue. Different from models used in most surrogate-assisted evolutionary algorithms, surrogates in IGA must be able to handle the inherent uncertainties in fitness assignment by human users, where, e.g., interval-based fitness values are assigned to individuals. This poses another challenge to using surrogates for fitness approximation in evolutionary optimization, in addition to the lack of training data. In this paper, a new surrogate-assisted IGA has been proposed, where the uncertainty in subjective fitness evaluations is exploited both in training the surrogates and in managing surrogates. To enhance the approximation accuracy of the surrogates, an improved cotraining algorithm for semisupervised learning has been suggested, where the uncertainty in interval-based fitness values is taken into account in training and weighting the two cotrained models. Moreover, uncertainty in the interval-based fitness values is also considered in model management so that not only the best individuals but also the most uncertain individuals will be chosen to be re-evaluated by the human user. The effectiveness of the proposed algorithm is verified on two test problems as well as in fashion design, a typical application of IGA. Our results indicate that the new surrogate-assisted IGA can effectively alleviate user fatigue and is more likely to find acceptable solutions in solving complex design problems.
A synthesized ranking-assisted NSGA-II for interval multi-objective optimization

Multi-objective optimization problems with interval (MOPs-I) uncertainties parameters are common in practice. Evolutionary multi-objective (EMO) algorithms are popularly employed to solve these problems due to their powerful explorations. The comparison strategies among interval objectives of MOPs-I are crucially important for obtaining a superior Pareto front when applying EMOs. By effectively combining two different intervals ranking methods together, i.e., μ and P metrics, we present an improved NSGA-II with a synthesized intervals ranking strategy for optimizing MOPs-I. The characteristics of μ and P in ranking intervals are first analyzed, and then the synthesized ranking method termed as μ ⊕ P is developed to compare and select individuals within the NSGA-II framework. The proposed algorithm is experimentally validated by four MOPs-I functions and a practical problem, and the results empirically demonstrate its merits in obtaining Pareto front with outstanding convergence and spread.
Surrogate enhanced interactive genetic algorithm with weighted Gaussian process

Interactive genetic algorithm (IGA), combining a user's intelligent evaluation with the traditional operators of genetic algorithms, are developed to optimize those problems with aesthetic indicators. The evaluation uncertainties and burden, however, greatly restrict the applications of IGA in complicated situations. Surrogate model approximating to the evaluation of the user has been generally applied to alleviate the evaluation burden of the user. The evaluation uncertainties, however, are not taken into account in existing research, therefore, a weighted multi-output gaussian process is here proposed to build the surrogate model by incorporating the uncertainty so as to enhance the performance of IGA. First, an IGA with interval fitness evaluation is adopted to depict the evaluation uncertainty, and the evaluation noise is defined based on the assignment. With the evaluation noise, the weight of each training sample is calculated and used to train a gaussian process which has two outputs to approximate the upper and lower values of the interval fitness, respectively. The trained gaussian process is treated as a fitness function and used to estimate the fitness of individuals generated in the subsequent evolutions. The proposed algorithm is applied to a benchmark function and a real-world fashion design to experimentally demonstrate its strength in searching.
Performance evaluation of inter-vehicle communication in a unidirectional dynamic traffic flow with shockwave

The rapid development of wireless communication and information technologies has increased research interests in Inter-vehicle communication system. In this paper, we study the throughput and end-to-end delay of an inter-vehicle communication system along a unidirectional dynamic traffic flow with shockwave for different transmission ranges of wireless units. We briefly introduce the traffic flow theories and our simulation platform based on Network Simulator 2. We emphasize the application of dynamic traffic flow theories in wireless communication networks. We then present Monte Carlo simulation results of information delivered between source and destination vehicles through communication performance measures for traffic flow with shockwave. We compare our results with uniform traffic scenario and explain the similarity and difference between these two traffic scenarios.
Directed fuzzy graph-based surrogate model-assisted interactive genetic algorithms with uncertain individual's fitness

In order to alleviate user fatigue of interactive genetic algorithms with an individual's fuzzy and stochastic fitness, we propose a surrogate model-assisted algorithm by using a directed fuzzy graph to extract user cognition. According to cut-set level and interval dominance probability, we present approaches to construct a directed fuzzy graph of an evolutionary population and calculate an individual's precise fitness based on it. By applying the fuzzy entropy, the chance of data sampling is achieved to obtain reliable samples for training the surrogate model. We adopt a support vector regression machine as the surrogate model, train it using the sampled individuals and their precise fitness, and apply a traditional genetic algorithm to optimize the surrogate model for some generations, providing guided individuals to the user to accelerate the evolution. We quantitatively analyze the performance of the presented algorithm in alleviating user fatigue and increasing more opportunities to look for the satisfactory individuals. Finally, we apply our algorithm to a fashion evolutionary design system to demonstrate its efficiency.
Evolutionary algorithms for multi-objective optimization problems with interval parameters

Multi-objective optimization problems with interval parameters are a kind of complicated optimization problems that are popular in real-world applications and hard to be effectively solved. We present an evolutionary optimization algorithm to solve the problems above in this paper. First, dominance relation among optimal solutions is defined suitable for interval objectives to reflect the quality of an optimal solution; then, crowding distance of an optimal solution is defined suitable for interval objectives to reflect the distribution of optimal solutions; finally, the method of selecting optimal solutions is given based on the rank and the crowding distance. We apply the proposed algorithm in four benchmark optimization problems, and compare it with IP-MOEA, a typical and effective method of solving the problems above. The experimental results show that our algorithm obtains optimal solutions with high quality, small uncertainty as well as uniform distribution. Our achievement provides a novel and feasible way to solve multi-objective optimization problems with uncertainties.
Continuous-Variable Quantum Key Distribution Scheme with Odd Coherent States

The Quantum key distribution (QKD) has been entering the practical application era. The odd coherent state which follows sub Poisson distributed has been confirmed to extend transmission distance and improve secret key rate in Discrete-variable quantum key distribution (DVQKD) protocol. We propose Entanglement-based (EB) scheme of Continuous-variable quantum key distribution (CVQKD) with odd coherent state. This scheme allows distributing Gaussian modulated CVQKD with laser light which could be odd coherent state. We analyze the logarithmic negativity between entangled odd coherent states and the security. The results show that renewed scheme can extend secure distance by 10.6% and enhance the tolerance excess noise in same parameter, and the renewed scheme could be applied to prepare-and-measure (P&M) model in CVQKD.
Two dimensional analysis sparse model

An analysis sparse model represents an image signal by multiplying it using an analysis dictionary, leading to a sparse outcome. It transforms an image (two dimensional signal) into a one-dimensional (1D) vector. However, this 1D model ignores the two dimensional property and breaks the local spatial correlation inside images. In this paper, we propose a two dimensional (2D) analysis sparse model. Our 2D model uses two analysis dictionaries to efficiently exploit the horizontal and vertical features simultaneously. The corresponding sparse coding and dictionary learning algorithm are also presented in this paper. The 2D sparse model is further evaluated for image denoising. Experimental results demonstrate our 2D analysis sparse model outperforms a state-of-the-art 1D analysis model in terms of both denoising ability and memory usage.