nils-herrmann/clean_scirepeval huggingface dataset:
Side-Lobe Reduction for Radio Frequency Interference Suppression via Clipping of Strong Scatterers

In this letter, we present a normalized least mean square filter with clipping of strong scatterers to suppress radio frequency interference (RFI) in low-frequency synthetic aperture radar (SAR) systems. In this filter, the impulse responses of bright scatterers are clipped after range compression to improve the performance of narrow-band RFI estimation for adaptive line enhancer. Then, the estimated RFI is subtracted from the original echo for SAR imaging; thus, the range side-lobes of bright scatterers are reduced successfully. This method is particularly applicable to situations where there are bright scatterers in the image scene. The performance for RFI suppression is investigated by using the simulated data and the raw data as well.
Video-Based Noncooperative Iris Image Segmentation

In this paper, we propose a video-based noncooperative iris image segmentation scheme that incorporates a quality filter to quickly eliminate images without an eye, employs a coarse-to-fine segmentation scheme to improve the overall efficiency, uses a direct least squares fitting of ellipses method to model the deformed pupil and limbic boundaries, and develops a window gradient-based method to remove noise in the iris region. A remote iris acquisition system is set up to collect noncooperative iris video images. An objective method is used to quantitatively evaluate the accuracy of the segmentation results. The experimental results demonstrate the effectiveness of this method. The proposed method would make noncooperative iris recognition or iris surveillance possible.
Survey on multicast data origin authentication

Multicast is an efficient and natural way of communicating information via Internet, but its security is identified as one of the most important problems to solve for the successful deployment of group communication applications. First of all, in secure multicast group members must be able to verify that the data received is indeed sent by an authorized sender, and this is called dasiadata origin authenticationpsila. This paper introduces the main schemes for authenticating multicast data origin, and evaluates their performances with computation overhead, communication overhead and some other parameters. The result shows there are no ideal solutions to satisfy all multicast applications.
Research on lightning trips and protection measures of overhead transmission lines

This paper presents some statistical analysis of lightning trips of overhead transmission lines in Jiangsu power grid and lightning activities across the province from 2005 to 2013 and analyzes the effects of lightning activities, topography, tower structure and insulation configuration and other factors along line corridors on lightning trips. Based on existing lightning protection measures such as reducing the lightning protection angle of transmission lines, increasing the length of composite insulators and long bar porcelain insulator with parallel gaps, this paper proposes lightning protection measures for new and existing overhead transmission lines to provide references for operation and maintenance.
Anonymizing geographic ad hoc routing for preserving location privacy

Due to the utilization of location information, geographic ad hoc routing presents superiority in scalability compared with traditional topology-based routing in mobile ad hoc networks. However, the consequent solicitation for location presence incurs severe concerns of location privacy, which has not been properly studied. In this paper, we attempt to preserve location privacy based on the idea of dissociating user's location information with its identity. We propose an anonymous geographic routing algorithm which includes three components to avoid the explicit exposure of identity and location in communication without compromising the efficiency guaranteed by geographic routing.
A modified Hausdorff distance using edge gradient for robust object matching

Conventional object matching algorithms based on Hausdorff distance mostly use edge position information to compute distances. In this paper we represent an edge point using its position and the strength of its gradient to define a 3D distance function. We propose a modified Hausdorff distance based on this 3D distance function, and investigate its application in object matching problems. Finally, experimental comparisons are performed with conventional methods. At this stage, we tested different methods on the images with different levels of noise. Their sensitivities to geometric distortion are also evaluated against image rotation and scale change. Experimental results show that the proposed algorithm is more robust and reliable.
Cocktail Ensemble for Regression

This paper is motivated to improve the performance of individual ensembles using a hybrid mechanism in the regression setting. Based on an error-ambiguity decomposition, we formally analyze the optimal linear combination of two base ensembles, which is then extended to multiple individual ensembles via pairwise combinations. The Cocktail ensemble approach is proposed based on this analysis. Experiments over a broad range of data sets show that the proposed approach outperforms the individual ensembles, two other methods of ensemble combination, and two state-of-the-art regression approaches.
Efficient adaptive MRF-MAP error concealment of video sequences

A new adaptive error concealment method based on Markov random field (MRF)-maximum a posteriori (MAP) is proposed as a post-processing tool at the decoder side to recover the lost information of video sequences after transmitting over the communication channels. In order to protect the edges, discrimination analysis is used to detect edges. So, edge threshold T for the Huber function of MRF is adaptively obtained, according to the edge or smooth area that the pixel belongs to. In order to eliminate the blocking artifacts, a slope k is also introduced to the linear part of the Huber function. Simulation results show that the proposed method can recover image with the higher quality, comparing to the existing error concealment methods.
Metamorphic Testing: Applications and Integration with Other Methods: Tutorial Synopsis

In software testing, an oracle refers to a mechanism against which testers can decide whether or not outcomes of test case executions are correct. The oracle problem refers to situations when either an oracle is not available, or it is too expensive to apply. Metamorphic testing has emerged as an effective and efficient approach to alleviating the oracle problem. This article introduces the basic concepts and procedures of metamorphic testing, and gives examples to show its applications, and integration with other methods.
Ultra-wideband synthetic aperture radar time-frequency representation image formation

In ultra-wideband synthetic aperture radar (UWB SAR) imaging, scatterers often perform the frequency- and aspect angle-dependent reflectivity characteristic, which is usually neglected in traditional imaging processing. A novel UWB SAR time-frequency representation image formation (TFRIF) is proposed to retrieve the frequency- and aspect angle-dependent information, which is valuable in target detection. Based on the TFRIF, two fusion schemes are studied: pixel level fusion (PLF) and decision level fusion (DLF). Based on the analytical expressions, it is concluded that TFRIF can extract the reflectivity pattern information with high precision and little sacrifice of the spatial resolutions. Quantitative analyses of spatial resolutions and detectability improvement of the proposed fusion schemes are tested on airborne UWB SAR data. The real data results validate the proposed TFRIF and its associated PLF and DLF schemes.
Selective Convolutional Descriptor Aggregation for Fine-Grained Image Retrieval

Deep convolutional neural network models pre-trained for the ImageNet classification task have been successfully adopted to tasks in other domains, such as texture description and object proposal generation, but these tasks require annotations for images in the new domain. In this paper, we focus on a novel and challenging task in the pure unsupervised setting: fine-grained image retrieval. Even with image labels, fine-grained images are difficult to classify, letting alone the unsupervised retrieval task. We propose the selective convolutional descriptor aggregation (SCDA) method. The SCDA first localizes the main object in fine-grained images, a step that discards the noisy background and keeps useful deep descriptors. The selected descriptors are then aggregated and the dimensionality is reduced into a short feature vector using the best practices we found. The SCDA is unsupervised, using no image label or bounding box annotation. Experiments on six fine-grained data sets confirm the effectiveness of the SCDA for fine-grained image retrieval. Besides, visualization of the SCDA features shows that they correspond to visual attributes (even subtle ones), which might explain SCDA’s high-mean average precision in fine-grained retrieval. Moreover, on general image retrieval data sets, the SCDA achieves comparable retrieval results with the state-of-the-art general image retrieval approaches.
Dynamic Modeling of Political Districting Problem

Our model is a dynamic network model to the Political Districting Problem. Firstly, we map counties in the state to points in the County-based Network. Using depth first search (DFS) algorithm and breadth-first search (BFS) algorithm, we guarantee the continuity and compactness of each district. Also we introduce and use grid-based method to divide counties. Finally, we illustrate this model by districting New York State and get such results: the total number of counties to be divided is 17. The average times dividing each county is 0.69. Statistical properties of the model are also studied.
Enhanced Pictorial Structures for precise eye localization under incontrolled conditions

In this paper, we present an enhanced pictorial structure (PS) model for precise eye localization, a fundamental problem involved in many face processing tasks. PS is a computationally efficient framework for part-based object modelling. For face images taken under uncontrolled conditions, however, the traditional PS model is not flexible enough for handling the complicated appearance and structural variations. To extend PS, we 1) propose a discriminative PS model for a more accurate part localization when appearance changes seriously, 2) introduce a series of global constraints to improve the robustness against scale, rotation and translation, and 3) adopt a heuristic prediction method to address the difficulty of eye localization with partial occlusion. Experimental results on the challenging LFW (Labeled Face in the Wild) database show that our model can locate eyes accurately and efficiently under a broad range of uncontrolled variations involving poses, expressions, lightings, camera qualities, occlusions, etc.
A New Human Identification Method: Sclera Recognition

The blood vessel structure of the sclera is unique to each person, and it can be remotely obtained nonintrusively in the visible wavelengths. Therefore, it is well suited for human identification (ID). In this paper, we propose a new concept for human ID: sclera recognition. This is a challenging research problem because images of sclera vessel patterns are often defocused and/or saturated and, most importantly, the vessel structure in the sclera is multilayered and has complex nonlinear deformations. This paper has several contributions. First, we proposed the new approach for human ID: sclera recognition. Second, we developed a new method for sclera segmentation which works for both color and grayscale images. Third, we designed a Gabor wavelet-based sclera pattern enhancement method to emphasize and binarize the sclera vessel patterns. Finally, we proposed a line-descriptor-based feature extraction, registration, and matching method that is illumination, scale, orientation, and deformation invariant and can mitigate the multilayered deformation effects and tolerate segmentation error. The experimental results show that sclera recognition is a promising new biometrics for positive human ID.
Human Hair Segmentation and Length Detection for Human Appearance Model

This paper presents a new approach for human hair length detection. In contrast to others, the proposed method is able to segment hairs from different views of human heads even with low resolution. Faces are not necessary to be visible in the images, as no face detection is needed in our method. Firstly, it conducts background subtraction to detect foreground objects and then detects human heads with a trained human head detector. Next, histogram analysis is carried out on the detected head region to segment hair region with K-mean clustering. Finally, hair length is determined by performing line scanning on the segmented hair region. The main advantages of the proposed method are able to handle the cases of (1) view variation and (2) low resolution or small human body appearance in the image. It is specially designed for surveillance system. The detected human hair length can be used as an invariant feature to be embedded into a human appearance model which can be employed for human detection, indexing and searching in multi cameras network system. This method aims at the fast processing speed and is able to achieve 7ms for hair length identification in a head patch detected. The experiments show the improvements in terms of speed and accuracy.
Nonlinearity estimation and correction of VCO based on temperature-varied tuning characteristic model

As nonlinearity in voltage controlled oscillator (VCO) degrades the range resolution of frequency modulated continuous wave (FMCW) radar, a novel method to estimate and correct the nonlinearity is presented in this paper. Firstly, in order to provide a more accurate description of the nonlinear term, a temperature-varied tuning characteristic model of VCO (VCO-TT) is built. Afterwards, based on the VCO-TT model, a contrast of range profile optimization algorithm is adopted, which achieves the optimal estimation and correction of the nonlinearity. Without changing hardware system, this method which operates directly on the deramped data can refocus the range profile as well as nonlinearity removal simultaneously. Finally, experimental results from a commercial VCO convincingly validate the feasibility and effectiveness of the proposed method.
Fast Power Allocation Algorithm for Cognitive Radio Networks

A fast algorithm is proposed to tackle the optimal power allocation problem for orthogonal frequency division multiplexing (OFDM)-based cognitive radio networks, where the key is to replace the Newton step with O(N^3) complexity in the barrier method by a procedure with approximate linear complexity developed based on the structure of the optimization problem. Simulation results validate that our method can always work out the optimal solution, with complexity even lower than heuristic methods that can only produce suboptimal solutions.
LMCM: Layered Multiple Chaining Model for Authenticating Multicast Streams

Multicast authentication is a vital component in secure group communications, but authenticating real-time multicast streams over lossy channels or unreliable networks is very difficult due to the packet losses, expensive overheads and high delays. So this paper introduces a novel approach to multicast authentication based on LMCM (Layered Multiple Chaining Model). By the theoretical analyses, it is robust enough against packet losses, has low delays on the senderpsilas side and the receiverpsilas side. Both computation and communication overheads are in the reasonable scopes if its parameter values are determined systematically. And the high authentication probability can be achieved by increasing the number of packets that contain the hash of a previous packet and the number of hashes that are appended to the signature packets. Hence LMCM is secure, efficient and practical to more multicast applications than many previously proposed approaches.
Similarity-Based Search for Model Checking: A Pilot Study with Java PathFinder

When a model checker cannot explore the entire state space because of limited resources, model checking becomes a kind of testing with an attempt to find a failure (violation of properties) quickly. We consider two state sequences in model checking: (i) the sequence in which new states are generated, and (ii) the sequence in which the states generated in sequence (i) are checked for property violation. We observe that neighboring states in sequence (i) often have similarities in certain ways. Based on this observation we propose a search strategy, which generates sequence (ii) in such a way that similar states are evenly spread over the sequence. As a result, neighboring states in sequence (ii) can have a higher diversity. A pilot empirical study with Java Path Finder suggests that the proposed strategy can outperform random search in terms of creating equal or smaller number of states to detect a failure.
Combining convolutional neural network and self-adaptive algorithm to defeat synthetic multi-digit text-based CAPTCHA

We always use CAPTCHA(Completely Automated Public Turing test to Tell Computers and Humans Apart) to prevent automated bot for data entry. Although there are various kinds of CAPTCHAs, text-based scheme is still applied most widely, because it is one of the most convenient and user-friendly way for daily user . The fact is that segmentations of different types of CAPTCHAs are not always the same, which means one of CAPTCHA's bottleneck is the segmentation. Once we could accurately split the character, the problem could be solved much easier. Unfortunately, the best way to divide them is still case by case, which is to say there is no universal way to achieve it. In this paper, we present a novel algorithm to achieve state-of-the-art performance, what was more, we also constructed a new convolutional neural network as an add-on recognition part to stabilize our state-of-the-art performance of the whole CAPTCHA system. The CAPTCHA datasets we are using is from the State Administration for Industry& Commerce of the People's Republic of China. In this datasets, there are totally 33 entrances of CAPTCHAs. In this experiments, we assume that each of the entrance is known. Results are provided showing how our algorithms work well towards these CAPTCHAs.
Novel formulations for evaluating hypersingular volume integrals over cuboid domains

Volume integral equations (VIEs) are indispensable to formulate inhomogeneous or anisotropic electromagnetic (EM) problems and cuboid meshes are preferred to discretize volume domains in some applications. In solving the VIEs, accurate evaluation for singular integrals is very essential but there is no direct method to evaluate hypersingular integrals over a cuboid domain so far. In this work, we propose some closed-form formulas for calculating those hypersingular volume integrals and they are simpler and more efficient to implement. A numerical example is presented to demonstrate the formulas and their effectiveness has been verified.
Genome-Wide Protein Function Prediction through Multi-Instance Multi-Label Learning

Automated annotation of protein function is challenging. As the number of sequenced genomes rapidly grows, the vast majority of proteins can only be annotated computationally. Nature often brings several domains together to form multi-domain and multi-functional proteins with a vast number of possibilities, and each domain may fulfill its own function independently or in a concerted manner with its neighbors. Thus, it is evident that the protein function prediction problem is naturally and inherently Multi-Instance Multi-Label (MIML) learning tasks. Based on the state-of-the-art MIML algorithm MIMLNN, we propose a novel ensemble MIML learning framework EnMIMLNN and design three algorithms for this task by combining the advantage of three kinds of Hausdorff distance metrics. Experiments on seven real-world organisms covering the biological three-domain system, i.e., archaea, bacteria, and eukaryote, show that the EnMIMLNN algorithms are superior to most state-of-the-art MIML and Multi-Label learning algorithms.
Research on Safe and Reliable Remote Data Transmission Technology Based on SMS

To realize remote data collection and transmission by using SMS, the operating principle and characteristics of SMS are researched for higher reliability and security in this article. Enhanced design is implemented for its safety and reliability based on the SMS protocol. A new remote data transmission technology is proposed, which is based on SMS with low cost, high safety and high reliability, and a data exchange protocol is designed by using the hierarchical structure. In this article, the said protocol is developed and realized by using the JAVA language. The experimental test for additional expense of delay from the above-mentioned technology and the application in remote monitoring has primarily verified the validity, safety and reliability of the technology.
Sequential square root filtering for measuring tractor driving wheel slip rate

Adaptive sequential square root Kalman filtering (ASSRKF) algorithm is purposed to measure slip rate of wheel tractor online. The filtering process is formulated as a process of recursive the Kalman state model, where signals from wheel speed sensors, angular acceleration, GPS and accelerometer are fused. The principal advantages of combining sequential processing with square root algorithm are enhancing numerical accuracy and lowering storage requirements, thus removing the limitation of the computing capabilities of the embedded control system on the Kalman filter algorithm. On the basis of the sequential square root algorithm, the paper further propose formulas for the parallel fusion of data and adaptive filtering, so that the phenomenon of covariance matrix being unable to be inversed is avoided and real-time wheel slip rate can be obtained without the statistical law of the prior error. Both the simulation and the experimental results indicate that those presented in this paper are efficient.
Performance Analysis of Multi-Priority Buffer Queue and Its Application of Data Transmission System in Remote Monitoring System

To achieve flexible support for multi-channels supplied by remote monitor system and to implement hybrid transmission mode, this paper designs and introduces a mechanism of data transmitting buffer queue, which is with multiple priorities processing. The mechanism is composed of data input queue facing to upper layer and data output queue facing to diverse communication channels and it contains retransmission mechanism of communication channel exchange and data packets reuse mechanism in order to fulfill the efficiency and reliability of remote monitor. By analyzing the performances of queue model and time-delay with the aid of parameter hypothesis based on traditional queue and example tests of hybrid communication model, the paper demonstrates the feasibility and reliability of the designed queue model.