nils-herrmann/clean_scirepeval huggingface dataset:
Analysis of permutation choices for enhanced generalised Feistel structure with SP-type round function

Since the proposition of improved generalised Feistel structure (GFS), many researches and applications have been published. In this study, the authors further enhance the improved GFS with SP-type round function by extending the sub-block-wise permutation to word-wise permutation which can have better diffusion and security effect. Then, they study the security effect of different permutation choices for this kind of enhanced GFS cipher with SP-type round function. By proving several propositions about the equivalent situation, they can eliminate isomorphic permutations so as to narrow down the candidate space notably and propose a method to compute the number of effective permutation candidates. Finally, they take three typical scenes as example, and for each experimental scene, they compute the number of effective permutation candidates and exhaustively evaluate their security results. They also give an optimum permutation as example for each scene.
A teleoperation system for mobile robot with whole viewpoints virtual scene for situation awareness

This paper presents a teleoperation system to control mobile robot remotely. To provide adequate information for the operator to be aware of the robot situation, the whole viewpoints scenes are generated in the first and the third person point of view by virtual tools. These scenes provide the maps and the spatial information to the operators. To complete a complex task, operators can conveniently select different viewpoints to monitor the robot or target for different task process. Multiple input devices such as keyboard, mouse and joystick are adopted to input the commands, and different operational modes are designed to control the robot in different task. Finally, the effectiveness of the teleoperation system is illustrated by experiments that the operator manipulates mobile robot.
A novel method to analyze the mutual coupling of antennas with signal and system concept

The paper presents a novel method to analyze the mutual coupling of antennas by means of digital signal processing on the basis of a signals and systems concept. In this method, the EM characteristics of the antennas are considered as the input signals, the structure of the array as the system or transmission function, while the mutual impedance of the antennas or other EM characteristics can be regarded as the system response of the input signals. Then, the antenna array can be seen as a whole signal and system, and can be processed with DSP. In this manner, the method brings the signal and system concept to EM problem solving. Finally, the mutual impedance between linear dipoles is analyzed. Compared with the literature, the validity of this novel method can be proved.
Cyclic motion generation of multi-link planar robot performing square end-effector trajectory analyzed via gradient-descent and Zhang et al’s neural-dynamic methods

In this paper, a quadratic-programming (QP) based method is employed for cyclic motion generation (CMG) of redundant robot manipulators with joint physical limits considered. To demonstrate the effectiveness of such a QP-based CMG scheme, three types of multi-link planar arms are tested to perform square end-effector trajectories through computer simulations. Moreover, theoretical analysis based on gradient descent method and Zhang et alpsilas neural-dynamic method is conducted to prove the efficacy of the presented scheme.
A new pitch control strategy for wind turbines base on quasi-sliding mode control

Pitch control system is important part in variable speed variable pitch wind power generation systems. Since wind turbine is a strong coupling, high-order nonlinear system, conventional PI controller can not achieve good performance. Sliding mode control (SMC) has the characteristics such as fast response, small over-shoot, strong robustness and anti-disturbances. Sliding mode controller is suitable for the high-order, nonlinear, strong coupling system, however, sliding mode controller has the drawback of chattering. A quasi sliding mode controller is proposed to reduce chattering. The quasi-sliding mode variable structure control is analyzed in this paper. A pitch controller was designed based on quasi-sliding mode variable structure control strategy. Simulation results show that the proposed controller has better performance compared with conventional PI controller. Quasi-sliding mode control is simple and suitable for pitch control.
Beam Forming of Leaky Waves at Fixed Frequency Using Binary Programmable Metasurface

We propose a complete procedure for synthesizing radiation patterns of leaky waves using binary programmable metasurface. As an example, a binary leaky-wave antenna (BLWA) is designed and fabricated using the fast procedure. By optimizing coding states of all radiation elements of the BLWA, beam forming is realized at a fixed frequency. Full-wave simulations and measurements of the proposed BLWA further prove the effectiveness and practicability of the design procedure. The presented method and BLWA may find many significant applications in novel communication and radar systems.
Detecting Microcalcifications in Digital Mammograms using Wavelet Domain Hidden Markov Tree Model

In this paper we investigate the performance of statistical modeling of digital mammograms by means of wavelet domain hidden Markov tree model (WHMT) for its inclusion to a computer-aided diagnostic prompting system for detecting microcalcification (MC) clusters. The system incorporates: (1) gross-segmentation of mammograms for obtaining the breast region; (2) eliminating the pepper-type noise, (3) block-wise wavelet transform of the breast signal and likelihood calculation; (4) image segmentation; (5) postprocessing for retaining MC clusters. FROC curves are obtained for all MC clusters containing mammograms of mini-MIAS database. 100% of true positive cases are detected by the system at 2.9 false positives per case
A New Approach to the Directed Connectivity in Two-Dimensional Lattice Networks

The connectivity of ad hoc networks has been extensively studied in the literature. Most recently, researchers model ad hoc networks with two-dimensional lattices and apply percolation theory for connectivity study. On the lattice, given a message source and the bond probability to connect any two neighbor vertices, percolation theory tries to determine the critical bond probability above which a giant connected component appears. This paper studies a related but different problem, directed connectivity: what is the exact probability of the connection from the source to any vertex following certain directions? The existing studies in math and physics only provide approximation or numerical results. In this paper, by proposing a recursive decomposition approach, we can obtain a closed-form polynomial expression of the directed connectivity of square lattice networks as a function of the bond probability. Based on the exact expression, we have explored the impacts of the bond probability and lattice size and ratio on the lattice connectivity, and determined the complexity of our algorithm. Further, we have studied a realistic ad hoc network scenario, i.e., an urban VANET, where we show the capability of our approach on both homogeneous and heterogeneous lattices and how related applications can benefit from our results.
Cluster and probability competition based MAC scheme in power line communications

Efficiency of MAC (media access control) is one of important issues of power line communication (PLC). There exists efficiency problem in current CSMA/CA (Carrier Sense Multiple Access with Collision Avoidance) mechanism of large-scale PLC network. Aimed to solve this problem, a Cluster and Probability Competition Based MAC (CP-MAC) mechanism is presented to improve the MAC efficiency, in which the network is divided into clusters and competition is conducted among clusters using the probability-function. Finally the simulation results show that the CP-MAC is able to improve the LPLC network performance in term of packet loss rate, throughout and delay.
A Parallel Classification Algorithm Based on Hybrid Genetic Algorithm

In this paper, a parallel classification algorithm based on an improved hybrid genetic algorithm (PC-HGA) is presented. It attempts to solve the problems of lower classification rule quality, more redundancy rules after optimizing generations and classification accuracy using the traditional genetic algorithm in classification mining. A rule extraction approach to improve the classification accuracy and condense the classification rule set is also given. In order to further improve the efficiency of classification mining, the master-slave parallel computing mode is adopted in PC-HGA. Experiments of PC-HGA algorithm are carried out on two benchmark datasets: iris and dermatology from UCI machine-learning repository. The experimental results show that PC-HGA has good speedup performance and can discover a set of the succinct, efficient and understandable classification rules
A Robust Multi-Modal Emotion Recognition Framework for Intelligent Tutoring Systems

This paper presents a multi-modal emotion recognition framework that is capable of estimating the human emotional state through analyzing and fusing a number of non-invasive external cues. The proposed framework consists of a set of data analysis, feature extraction and emotion recognition modules for processing heterogeneous sensory data (e.g., visual appearance and speech) and a novel probabilistic information fusion model to accurately estimate the human emotional state. Experimental results demonstrate that the proposed emotion recognition framework can automatically and robustly recognize human emotional states. Our results also proof that by fusing complementary information such as facial expression analysis and voice intonation analysis results, the emotion recognition performance can be boosted and outperform each individual modal analysis. The proposed emotion recognition framework can be integrated into existing Intelligent Tutoring Systems (ITSs) for improving the effectiveness of the learning systems by providing feedbacks to the ITSs.
Closed-loop fault detection for networked flight vehicle with asynchronous switching

This paper investigates the closed-loop fault detection problem for networked flight control system with asynchronous switching. Firstly, the original flight dynamics is modelled as a linear switched system including multiple operating points. Secondly, observer-based fault detection filters and robust tracking controllers are designed synergistically to detect faults and make the system track a given command signal. Then, an asynchronous H∞ analytical method was proposed to overcome the asynchronous phenomenon between the filters/controllers and flight dynamics which is caused by the updating intervals of the system states. Moreover, the globally uniformly asymptotically stability in the mean (GUAS-M) are guaranteed by combing the Lyapunov function method with average dwell time (ADT) method.
A Modified Matched Filter With Double-Sided Thresholding for Screening Proliferative Diabetic Retinopathy

The early diagnosis of proliferative diabetic retinopathy (PDR), a common complication of diabetes that damages the retina, is crucial to the protection of the vision of diabetes sufferers. The onset of PDR is signaled by the appearance of neovascular net. Such neovascular nets might be identified using retinal vessel extraction techniques. The commonly used matched filter methods often produce false positive detections of neovascular nets due to their proneness to detect nonline edges as well as lines. In this paper, we propose a modified matched filter for retinal vessel extraction that applies a local vessel cross-section analysis using double-sided thresholding to reduce false responses to nonlinear edges. Our proposed modified matched filters demonstrated higher true positive rate and lesser false detection than existing matched-filter-based schemes in vessel extraction.
Algorithm Optimization of Anomaly Detection Based on Data Mining

In this paper, firstly two improved algorithm methods are introduced, namely INFLOF and COF, which are based on LOF, then the motivation of each algorithm, the definition of the algorithm and the specific steps of the algorithm are described respectively. Then through summarizing LOF, INFLOF and COF it can find out the intrinsic link between them: INFLOF can solve the problem of edge misjudgment caused by different density cluster's closing to each other in data set, while COF can solve the problem of outliers, but these kinds of two algorithms are from different steps to solve the outlier factor. Finally, the advantages of the these two algorithms are presented, thus the algorithm of this paper is introduced. Moreover, the definition of the algorithm, as well as the specific steps of the algorithm is respectively introduced, besides it also analyzed the time complexity of algorithm.
Avionics system unified lifecycle model architecting and application

With the increasing complexity of avionics system, a Model-Based Systems Engineering method is needed to improve the reliability, safety and Research & Development (R&D) efficiency of the avionics system. There is no unified lifecycle model to support the entire development process of avionics system now. According to the analysis of avionics system development process, this paper proposes a method concerning the establishment of avionics system unified lifecycle model based on the system dimension and R&D stage dimension. The process of modeling includes four steps, namely the decomposition in system dimension, the decomposition in R&D stage dimension, the integration of the models in R&D stage dimension, the integration of the models in system dimension. A weapon subsystem, which is one of the important parts of avionics system, is taken as an example to describe the application of this modeling method. The effect of the unified lifecycle model in iterative design and system verification is testified by several test cases. The results show that the proposed modeling method can not only help to establish the unified lifecycle model effectively but also supports automated design and verification in the R & D of avionics system.
Adaptive Pattern-driven Compression of Large-Area High-Resolution Terrain Data

This paper presents a novel adaptive pattern-driven approach for compressing large-area high-resolution terrain data. Utilizing a pattern-driven model, the proposed approach achieves efficient terrain data reduction by modeling and encoding disparate visual patterns using a compact set of extracted features. The feasibility and efficiency of the proposed technique were corroborated by experiments using various terrain datasets and comparisons with the state-of-the-art compression techniques. Since different visual patterns are separated and modeled explicitly during the compression process, the proposed technique also holds a great potential for providing a good synergy between compression and compressed-domain analysis.
A Scalable Robust Authentication Protocol for Secure Vehicular Communications

Existing authentication protocols to secure vehicular ad hoc networks (VANETs) raise challenges such as certificate distribution and revocation, avoidance of computation and communication bottlenecks, and reduction of the strong reliance on tamper-proof devices. This paper efficiently copes with these challenges with a decentralized group-authentication protocol in the sense that the group is maintained by each roadside unit (RSU) rather than by a centralized authority, as in most existing protocols that are employing group signatures. In our proposal, we employ each RSU to maintain and manage an on-the-fly group within its communication range. Vehicles entering the group can anonymously broadcast vehicle-to-vehicle (V2V) messages, which can be instantly verified by the vehicles in the same group (and neighboring groups). Later, if the message is found to be false, a third party can be invoked to disclose the identity of the message originator. Our protocol efficiently exploits the specific features of vehicular mobility, physical road limitations, and properly distributed RSUs. Our design leads to a robust VANET since, if some RSUs occasionally collapse, only the vehicles that are driving in those collapsed areas will be affected. Due to the numerous RSUs sharing the load to maintain the system, performance does not significantly degrade when more vehicles join the VANET; hence, the system is scalable.
A Deep-Learning Based Precipitation Forecasting Approach Using Multiple Environmental Factors

Precise precipitation forecasting can better reflect the changing trend of climate and also provide timely and efficient environmental information for management decision, as well as prevent the occurrence of floods or droughts. In the era of big data, this paper proposes a novel approach for precipitation forecasting based on deep belief nets, called DBNPF (Deep Belief Network for Precipitation Forecast). Through simulating neural connecting structure of human brain, Gaussian kernel function for data conversion, and back-propagation network for fine-tuning the entire network, the features of the data in the original space are mapped into the new feature space with semantic feature through the dimensionality reduction. The proposed approach can not only learn the hierarchical representation of raw data using a highly generalized way, fully mining the information hidden in the original data, but also make a more accurate description of the rule underlying the different kind of time series of big data. Seven kinds of environmental factors, which are very closely related to precipitation, are used as input vector, and the next 24 hours precipitation is used as the output vector. A set of dedicated experiments with data from Zunyi area of Guizhou Province is conducted to validate the feasibility and useability of the model. We also compare the deep-learning based approach with other traditional machine learning approaches. The experimental results show that the proposed approach can improve the precision of precipitation forecasting.
A Nonlinear PID Controller Based on an Adaptive Genetic Algorithm

the precision of temperature control is a very important factor to fermentation process. However, because the serious integral and time delay exists in the controlled object, the conventional control algorithm could hardly obtain satisfactory quality. Thus, a nonlinear PID controller is designed based on the nonlinear relationship between PID control parameters and error signals. Furthermore, an adaptive genetic algorithm is proposed in this paper in order to overcome the disadvantages in classical genetic algorithm, such as slow early convergence, local optimum, etc. The new adaptive algorithm adjusts the probabilities of crossover and mutation by construct function of the changes of fitness value. Simulation results show that the nonlinear PID controller with the GA for tuning the parameter is very effective. It has good dynamic and static response and strong robustness.
Adaptive pore model for fingerprint pore extraction

Sweat pores have been recently employed for automated fingerprint recognition, in which the pores are usually extracted by using a computationally expensive skeletonization method or a unitary scale isotropic pore model. In this paper, however, we show that real pores are not always isotropic. To accurately and robustly extract pores, we propose an adaptive anisotropic pore model, whose parameters are adjusted adaptively according to the fingerprint ridge direction and period. The fingerprint image is partitioned into blocks and a local pore model is determined for each block. With the local pore model, a matched filter is used to extract the pores within each block. Experiments on a high resolution (1200dpi) fingerprint dataset are performed and the results demonstrate that the proposed pore model and pore extraction method can locate pores more accurately and robustly in comparison with other state-of-the-art pore extractors.
Certificateless One-Way Authenticated Two-Party Key Agreement Protocol

Key agreement is one of the fundamental cryptographic primitives in public key cryptography. It plays an important role for securing systems in practice. In this paper, we present the first certificateless One-Way authenticated Two-Party key agreement protocol. The security of the proposed protocol is analyzed based on the intractability of the standard discrete logarithm (DL) and bilinear Diffie-Hellman (BDH) problems. For efficiency, our protocol enjoys low complexity in both communication and computation.
A 150Ghz High Gain Amplifier Based on over Neutralization Technique and Marchand Balun Matching Networks in 65nm CMOS

This paper presents a 150GHz fully differential amplifier with gain boosting and optimized Marchand balun matching networks in a 65nm CMOS technology. The optimized Marchand balun overcomes the self- resonance problem that the conventional balun suffers, and reduces the size as well. The over- neutralization technique is proposed and utilized in order to boost the gain of a differential pair without extra penalty on power consumption. A four- stage amplifier achieves a maximum gain of 19.2dB at 150GHz with a 3dB bandwidth of 14GHz, while consuming 48mW of power from a 1.2V supply.
A perceptual quality based rate distortion model

Rate and distortion models (R-D models) are important for current image/video encoders. They can help encoders to find the best encoding parameters for improving encoding performance quickly and effectively. However, most of the existing R-D models are based on mean square error (MSE). For the purpose of achieving better perceptual quality in encoding, the R-D models based on perceptual distortion measurement are involuntarily needed. In this paper, we proposed a perceptual quality based R-D model for lossy image/video encoding. The experimental results show that, the proposed model can accurately describe the R-D characteristics of the sources in the context of perceptual distortion, both for whole frames and the areas inside frame.
Chaos Synchronization of Bullwhip Effect in a Supply Chain

Supply chain is a complex and nonlinear system in which chaos is in general prevalent. This article models the supply chain management with a proposed tri-echelon theoretical framework, showing that it can be transformed into Lorenz equation of state under certain conditions and chaos arises. This article moves one step deeper into the causes and negative impacts of bullwhip effect in a supply chain chaos system. The radial basis function (RBF) neural network parameter - training approach is utilized to dampen bullwhip effect and the methodology is presented for the design of chaos synchronization controller in this article. Simulation test demonstrates that this methodology is effective in that it helps reduce and mitigate bullwhip effect and provide additional insights into theory and practice
Authenticated Asymmetric Group Key Agreement Protocol and Its Application

A recent primitive known as asymmetric group key agreement allows a group of users to negotiate a common encryption key which is accessible to any entities while each user holds her respective secret decryption key. This concept not only enables confidential communications among group users but also permits any outsider to send encrypted messages to the group. The existing instantiation is only secure against passive adversaries. In this paper, we first propose an authenticated asymmetric group key agreement protocol which captures the practical security properties against active attacks. Based on our protocol, we then propose a broadcast encryption system without relying on a trusted dealer to distribute the secret keys to the users. Our system has also short ciphertexts. Furthermore, the proposal is equipped with the perfect forward security property.