nils-herrmann/clean_scirepeval huggingface dataset:
Orthogonal graph-regularized matrix factorization and its application for recommendation

As one of the most successful approaches for recommendation, matrix factorization based Collaborative Filtering (CF) technique has received considerable attentions over the past years. In this paper, we propose an orthogonal matrix factorization model with graph regularization to preserve the consistency of the local structure both in user and item spaces, respectively. Instead of traditional alternating optimization method, a greedy sequential one is introduced to optimize a pair of coupled factor vector and its corresponding loading vector simultaneously each time, thus the original optimization problem is converted into the well-studied Multivariate Eigen Problem (MEP). Furthermore, multiple pairs of coupled eigen-vectors can be obtained in sequence. To guarantee nonrecurring of repetition of solutions, a novel dual-deflation technique is developed to incorporate into the sequential optimization. Experimental results on MovieLens and Each Movie data sets demonstrate that the proposed method is much more competitive compared with the state of the art matrix factorization based collaborative filtering methods.
A Suite of Schemes for User-Level Network Diagnosis without Infrastructure

It is highly desirable and important for end users, with no special privileges, identify and pinpoint faults inside the network that degrade the performance of their applications. However, existing tools are inaccurate to infer the link-level loss rates and have large diagnosis granularity (in terms of the number of hops). To address these problems, we propose a suite of user-level diagnosis approaches in two categories: (1) only need to be deployed at the source and (2) deployed at both source and destination. For the former, we propose two fragmentation aided diagnosis approaches (FAD), Algebraic FAD and Opportunistic FAD, which uses IP fragmentation to enable accurate link-level loss rate inference. For the latter category, we propose Striped Probe Analysis (SPA) which significantly improves the diagnosis granularity over those of the source-only approaches. Internet experiments are applied to evaluate each individual schemes (including an improved version of the state-of-the-art tool, Tulip ) and various hybrid approaches. The results indicate that our approaches dramatically outperform existing work (especially for diagnosis granularity) and provide not only the best performance but also smooth tradeoff among deployment requirement, diagnosis accuracy and granularity.
Intra-/inter-View Correlation Based Multiple Description Coding for Multiview Transmission

With the development of 3D video technology, many studies have paid attention to compression efficiency and rate distortion performance. When 3D videos are transmitted over error-prone channels, they may suffer significant quality degradation. In this paper, we combine multiview video coding (MVC) with multiple description coding (MDC) for robust transmission. The proposed scheme can give full consideration of both intra-view and inter-view correlation for better estimation. Furthermore, an adaptive mode decision is designed to generate a label as redundant information. The experiments show that the redundant information occupies just a few bits while the PSNR values of the reconstructed videos demonstrate a significant improvement.
Second order perturbation with BFGS optimization for damage detection of fixed-fixed beam

A generic method is formulated to estimate damage location and extent from the explicit perturbation terms of eigenparameters. Specific permutation numbers and generalized Kronecker delta functions are composed. Stiffness parameters are estimated from these equations, inversely, using BFGS optimization. The algorithm is iterative, and it terminates under certain criteria. A fixed-fixed beam with even elements is used as test structure to investigate the applicability of the developed approach. In six elements beam, second order algorithm converges faster to 1% d-norm for small percentage damage. Meanwhile, first order algorithm converges faster for medium percentage damage. Both first and second order algorithms are nearly the same for large percentage damage. In all percentage damages, second order algorithm converges faster to the minimum percentage error vectors. For eight elements large percentage damage and ten elements small percentage damage, second order algorithm converges faster to their termination criteria.
Steganalysis to adaptive pixel pair matching using two-group subtraction pixel adjacency model of covers

Steganalysis has played a positive role on protection and improvement of information security. The adaptive pixel pair matching (APPM) embedded stego signal which is independent in any notational system by providing more compact neighborhood and with lower distortion. No method is known to be applicable to estimation of the APPM. In this paper, a novel steganalysis scheme is presented to effectively detect the APPM steganography. Based on the subtractive pixel adjacency model of covers (SPAM), two-group modeling differences between adjacent pixels along horizontal, vertical, and diagonal directions are used to enhance changes caused by APPM steganography using a second-order Markov chain. Subsets of sample transition probability matrices are then used as features classified by support vector machines. The experiment results demonstrate that the proposed method is efficient to detect the APPM steganography compared to SPAM.
Secondary control strategy of islanded micro-grid based on multi-agent consistency

The technology of virtual synchronous generator has attracted much attention in the field of distributed generation and micro-grid, as it could simulate the internal mechanism and external characteristics of synchronous generator. Affected by the new energy power generation, line impedance, local load and other factors, inverters using the droop control or VSG control (differential regulation) cannot ensure the stable operation of the micro-grid, so the secondary control of frequency and voltage of islanding micro-grid is essential. Aiming at the limitations of centralized and decentralized control, a control method of distributed hierarchical control method based on dynamic consistency algorithm has proposed. The distributed micro sources are equivalent to the agents, and each agent only use local information and adjacent agent's information interaction through the consistency algorithm, then control the P-f curve adaptively by using the PI controller's function, thus the constant frequency and power allocation have been realized. The physical simulation model of micro-grid has established on the MATLAB/Simulink simulation platform, and its performance in different operating conditions is simulated and analyzed, with the correctness and effectiveness of the proposed secondary control method are verified.
Robust relevance vector machine with noise variance coefficient

Classical relevance vector machine is sensitive to outliers during training and has weak robustness. In order to solve this problem, a novel robust relevance vector machine is presented in this paper. The key idea of the proposed method is to introduce individual noise variance coefficient for each training sample. In the process of model training, the noise variance coefficients of outliers gradually decrease so as to automatically detect and eliminate outliers. In addition, the iterative formulae for the optimization of noise variance coefficients and hyperparameters are derived according to the Bayesian evidence framework. Simulation results on sinc function and some benchmark data sets demonstrate that the proposed robust relevance vector machine can resist the impact of outliers effectively and obtain better robustness in comparison with other methods.
Two-Stage Multiview Image Compression Using Interview SIFT Matching

In this paper, a novel scheme of two-stage multiview image compression is proposed to create two-level reconstructed quality. Differently from the conventional multiview image compression algorithms, SIFT (Scale-Invariant Feature Transform) features matching from interview images are exploited to remove the correlations between multiple views. In the first stage coding, SIFT and RANSAC (RANdom SAmple Consensus) algorithms are combined to calculate the correlation matrix of interview, which then can be developed to obtain the coarse reconstruction of the current view. In the second stage coding, the reconstructed quality can be improved further by using the residual information. The experimental results have shown that at higher compression ratio, the proposed scheme can obtain better rate-distortion performance than intra coding in MVC (Multiview Video Coding). Furthermore, with the change of the compression ratio, the proposed scheme can achieve more stable reconstructed quality.
Kernel Reconstruction ICA for Sparse Representation

Independent component analysis with soft reconstruction cost (RICA) has been recently proposed to linearly learn sparse representation with an overcomplete basis, and this technique exhibits promising performance even on unwhitened data. However, linear RICA may not be effective for the majority of real-world data because nonlinearly separable data structure pervasively exists in original data space. Meanwhile, RICA is essentially an unsupervised method and does not employ class information. Motivated by the success of the kernel trick that maps a nonlinearly separable data structure into a linearly separable case in a high-dimensional feature space, we propose a kernel RICA (kRICA) model to nonlinearly capture sparse representation in feature space. Furthermore, we extend the unsupervised kRICA to a supervised one by introducing a class-driven discrimination constraint, such that the data samples from the same class are well represented on the basis of the corresponding subset of basis vectors. This discrimination constraint minimizes inhomogeneous representation energy and maximizes homogeneous representation energy simultaneously, which is essentially equivalent to maximizing between-class scatter and minimizing within-class scatter at the same time in an implicit manner. Experimental results demonstrate that the proposed algorithm is more effective than other state-of-the-art methods on several datasets.
A new observation of hot-carrier induced interface traps spatial distribution in 0.135 /spl mu/m n-MOSFET by gate-diode method

In this work, the interface traps spatial distribution in 0.135 /spl mu/m n-MOSFET under V/sub G/ = V/sub D//2 stress mode by gated-diode method was studied. Not only interface traps generated in drain region trend to saturate at the beginning of hot carrier stress, but also interface traps generated in channel may trend to saturate. Interface traps generated in source region show no saturation and increase device source-drain parasitic resistance linearly along stress time. This experiment result is tentatively interpreted by MRS model that the distance from mobile hydrogen generating location to interface location determines interface traps generation rate and saturation time at interface location.
On joint hardware-in-the-loop simulation of aircraft control system and propulsion system

A novel hardware-in-the-loop (HIL) simulation system of aircraft is presented in this paper. The simulation aims to evaluate the performance of the aircraft flight control system and propulsion system with the influence of coupling characteristics between the two systems. The simulation system is designed and established based on the on-board devices and simulation equipment, such as flight control computer, engine controller, servo actuators and ground simulation computer. In order to meet the requirement of real-time, the simulation software is developed based on the real-time extension (RTX) operating system. The presented system has been successfully utilized in the joint HIL simulation experiment of an aircraft to test its flight control system and population system. The results show that the simulation system runs stably and reliably, and it can be an effective platform for the design and evaluation of the aircraft before its flight test.
Application of response surface methodology in the optimization of burnishing parameters for surface integrity

Surface treatment is an important aspect of all manufacturing processes to impart special physical, mechanical properties. Burnishing process is a post-machining operation in which the surface irregularities of workpiece are compressed by the application of a ball, which produces a smooth compact surface and simultaneously induces compressive residual stress in. In the present study, a constant-force burnishing tool, with an interchangeable adapter for ball and roller has been newly developed and integrated with a lathe to decrease the surface roughness and increase the surface microhardness of AL6061. Ball burnishing process was carried out under different parameters while Response Surface Methodology (RSM) was used to investigate the influence of turning feed rate, burnishing force and number of tool passes on the surface integrity. The results showed that turning feed rate of 0.14 mm/r, burnishing force of 116.8 N and 2 passes produce the optimum results.
Distributed Information Storage and Retrieval in 3-D Sensor Networks With General Topologies

Distributed in-network data-centric processing aims to reduce energy consumed for communication and establish a self-contained data storage, retrieval, aggregation, and query sensor system that focuses more on the data itself rather than the identities of the individual network nodes. Double-ruling-based schemes support efficient in-network data-centric information storage and retrieval, especially for aggregated data, since all data with different types generated in a network can be conveniently retrieved along any single retrieval curve. Previous double-ruling-based research focuses on two-dimensional (2-D) wireless sensor networks where a 2-D planar setting is assumed. With increasing interests in deploying wireless sensors in three-dimensional (3-D) space for various applications, it is urgent yet fundamentally challenging to design double-ruling-based approach in general 3-D sensor networks because double-ruling-based schemes in general have much harder geometric constraints than other distributed in-network data-centric processing schemes. In this research, we propose a geographic location-free double-ruling-based approach for general 3-D sensor networks with possibly complicated topology and geometric shapes. Without the knowledge of the geographic location and the distance bound, a query simply travels along a simple curve with the guaranteed success to retrieve aggregated data through time and space with one or different types across the network. Extensive simulations and comparisons show the proposed scheme with low cost and a balanced traffic load.
Dynamic Sub-GOP Forward Error Correction Code for Real-Time Video Applications

Reed-Solomon erasure codes are commonly studied as a method to protect the video streams when transmitted over unreliable networks. As a block-based error correcting code, on one hand, enlarging the block size can enhance the performance of the Reed-Solomon codes; on the other hand, large block size leads to long delay which is not tolerable for real-time video applications. In this paper a novel Dynamic Sub-GOP FEC (DSGF) approach is proposed to improve the performance of Reed-Solomon codes for video applications. With the proposed approach, the Sub-GOP, which contains more than one video frame, is dynamically tuned and used as the RS coding block, yet no delay is introduced. For a fixed number of extra introduced packets, for protection, the length of the Sub-GOP and the redundancy devoted to each Sub-GOP becomes a constrained optimization problem. To solve this problem, a fast greedy algorithm is proposed. Experimental results show that the proposed ap proach outperforms other real-time error resilient video coding technologies.
An adaptive tuning strategy on spark based on in-memory computation characteristics

We present an adaptive tuning method to improve Spark performance, especially for its in-memory computation. This manner serves one purpose: making a better use of memory reasonably through adaptively adopting suitable category based on Spark application runtime statistics on different working sets. This solution works in two steps. Firstly, it collects run-time statistics dynamically and stores them in round-robin structures to save memory. Secondly, it can change system storage category based on these statistics. Additionally we focus on serialization strategy optimization. For this purpose we test Spark integrated serialization algorithms: Java and Kryo serialization algorithms, and make a comparison of their performance. In order to gain flexibility we change Spark serialization mechanism by setting the default serialization unit from one RDD to one block. In this way, for the case which RDD has huge amount of blocks our solution can use different serialization algorithms to serialize different blocks in one RDD. We show that our solution is expressive enough to obtain 2x speedup than original Spark when there is inadequate memory resource.
Depth Map Driven Hole Filling Algorithm Exploiting Temporal Correlation Information

The depth-image-based-rendering is a key technique to realize free viewpoint television. However, one critical problem in these systems is filling the disocclusion due to the 3-D warping process. This paper exploits the temporal correlation of texture and depth information to generate a background reference image. This is then used to fill the holes associated with the dynamic parts of the scene, whereas for static parts the traditional inpainting method is used. To generate the background reference image, the Gaussian mixture model is employed on the texture information, whereas, depth maps information are used to detect moving objects so as to enhance the background reference image. The proposed holes filling approach is particularly useful for the single-view-plus-depth format, where, contrary to the multi-view-plus-depth format, only information of one view could be used for this task. The experimental results show that objective and subjective gains can be achieved, and the gain ranges from 1 to 3 dB over the inpainting method.
The Collaborative Configuration of Service-Oriented Product Lines Based on Evolutionary Approach

Service-oriented product lines provide a unified representation of variants of the possible applications of a specific domain. The configuration process is often completed by multi-stakeholders collaboratively. However, current configuration methods do not provide means to coordinate concurrent decision making. In this paper, we present a framework that describes the collaborative configuration process of Service-oriented Product lines based on fuzzy quality requirements. First, the configuration objective is obtained based on an improved fuzzy multi-attribute group decision making method. Then, the corresponding multi-objective evolutionary approaches are presented for product configuration and business process configuration that satisfy both functional and non-functional preferences and constraints, respectively.
Chaotic Ant Colony Algorithm for Preliminary Ship Design

This paper describes the improved chaotic ant colony algorithm (CACA) for the preliminary ship design. An optimization model for ship design has been identified as a problem with multiple local optima representing widely varying sets of designs. The model as developed handles the ship design optimization problem as a multivariable nonlinear optimization process and aims at a global optimum solution of the problem. Ant colony algorithm (ACA) is a multi-agent optimization algorithm, which simulates the foraging behavior of ants for solving various complex combinatorial optimization problems. The CACA is based on the AC A combined with multiple scale optimization and tabu search which can improve efficiently the deficiency of long searching time and sinking into local optima of simple ACA. This algorithm was tested on some standard test functions and satisfying results were obtained. After that an attempt on solving the preliminary ship design problem, it performance was observed to be in effect.
Celerity: A Low-Delay Multi-Party Conferencing Solution

In this paper, we revisit the problem of multi-party conferencing from a practical perspective, and to rethink the design space involved in this problem. We believe that an emphasis on low end-to-end delays between any two parties in the conference is a must, and the source sending rate in a session should adapt to bandwidth availability and congestion. We present Celerity, a multi-party conferencing solution specifically designed to achieve our objectives. It is entirely Peer-to-Peer (P2P), and as such eliminating the cost of maintaining centrally administered servers. It is designed to deliver video with low end-to-end delays, at quality levels commensurate with available network resources over arbitrary network topologies where bottlenecks can be anywhere in the network. This is in contrast to commonly assumed P2P scenarios where bandwidth bottlenecks reside only at the edge of the network. The highlight in our design is a distributed and adaptive rate control protocol, that can discover and adapt to arbitrary topologies and network conditions quickly, converging to efficient link rate allocations allowed by the underlying network. In accordance with adaptive link rate control, source video encoding rates are also dynamically controlled to optimize video quality in arbitrary and unpredictable network conditions. We have implemented Celerity in a prototype system, and demonstrate its superior performance over existing solutions in a local experimental testbed and over the Internet.
Wavelet-Domain Distributed Video Coding with Motion-Compensated Refinement

In this paper, we propose a distributed video coding (DVC) paradigm based on lattice vector quantization in wavelet domain. In this framework, we use a fine and a coarse lattice vector quantizer to wavelet coefficients and the difference of two lattice quantizers is coded by turbo encoder. At decoder, side information is gradually updated by motion-compensated refinement. The refinement is built on partially decoded current frame and we give its matching strategy. Due to the refinement, burden of turbo encoding is cut down, bit rate is saved and the reconstruction is improved to some degree.
Unpowered landing guidance with large initial condition errors

An auto-landing guidance law on the basis of sliding mode control (SMC) is presented in this paper for landing an unpowered reusable launch vehicle (URLV) at the predetermined landing site. The guidance scheme, which takes the full nonlinear point-mass dynamics into account, is developed in three-dimensional space. For the sake of dealing with large initial condition errors, the guidance process is divided into two phases, i.e., the error correction phase and the approach and landing phase. For each phase, a specific sliding mode function is designed according to the terminal constraints. The desired goal can be achieved by employing a backstepping approach which is capable of driving the sliding mode function together with its derivative to zero at the fixed downrange. Combining with the Lyapunov theory, the closed-loop guidance commands can be obtained for online implementation. This guidance approach which is characterized by its simple format and less information demand possesses very high precision at touchdown. Furthermore, strong robustness of the guidance method is guaranteed by the virtue of SMC. Simulation results are given to illustrate the superior performance of the proposed method.