nils-herrmann/clean_scirepeval huggingface dataset:
Deployment of Intrusion Prevention System based on Software Defined Networking

The development of the mobile Internet brought about by the thriving mobile intelligent terminals has made it possible to access to the Internet anytime and anywhere. While people enjoy the convenience, they also suffer from a series of security threats caused by cyber-attacks. IPS brings reliability and security in a network system and is regarded as one of the most popular security devices. However, the conventional IPS deployment often has some limitations, and the deployment and maintenance costs are expensive, the utilization rate is low. In order to solve these issues, an SDN-based IPS deployment is presented in this paper, which supports a unified scheduling of security applications in the whole network and load balancing among IPSs. In addition, this paper builds a test-bed and shows evaluation results. As the results, It is confirmed that the proposed scheme can achieve a shorter time for ping after the first ping and that with the load balancing, the network latency is significantly reduced.
A novel Trench Fast Recovery Diode with injection control

A novel Trench Fast Recovery Diode (FRD) structure with injection control is proposed in this paper. The proposed structure achieves improved carrier profile without the need for excess lifetime control. This substantially improves the device performance, especially at extreme temperatures (-40 °C to 175 °C). The device maintains low leakage at high temperatures, and its Qrr and Irm do not degrade as is the usual case in heavily electron radiated devices. A 1600 diode using this structure is proposed in this paper, with a low Vf and good reverse recovery properties. The experimental results show that the structure maintains its performance at high temperatures.
A large array MTJ biosensor chip: Design, manufacturing and evaluation

Since the basic concept of using magnetic sensor to detect biomolecules labelled with magnetic particles (MP) was first disclosed by Baselt, significant development has been made on the material and structure of magnetic transducers . The tri-layer spin-valve GMR device can yield 4 times as much signal as the magnetoresistive device mentioned by Baselt . Magnetic tunnel junction (MTJ) device can produce 100 times as much signal as the magnetoresistive devices . In this work, an active biochip consisting of 12 million magnetic-tunnel-junction (MTJ) transducers was developed for high throughput and high sensitivity detection of biomolecules . This chip can detect 12 million different types of molecules simultaneously .
Cumulant projection method for two-dimentional harmonic retrieval in colored non-gassian noise

The problem of concern here is the two-dimensional (2-D) harmonic retrieval in colored non-Gaussian noise. The 2-D cumulant projection theorem of complex linear non-Gaussian process is presented. The noise auto-correlation estimation is obtained based on this theorem by skillfully constructing a high-order cumulant of the complex noisy harmonic. Then a generalized eigen-value problem is solved to prewhiten the noise space. Finally 2-D MUSIC is employed to retrieve the 2-D harmonic frequencies. This method can effectively extract frequencies from colored non-Gaussian noise based on AR or ARMA model, even though the noise is symmetrically-distributed or there exists quadratic phase coupling. Theory and algorithm are illustrated by simulation examples
Dam Safety Monitoring Forecast Model Based on CPSO

By using steepest descend algorithm to calculate the networks weights, traditional BP networks model in the dam safety monitoring is complex in calculation process and will easily fall into local extreme point. To solve the shortages, the cooperative particle swarm optimization algorithm is proposed to optimize the weights of the neural networks for dam safety monitoring. Firstly, the calculation of the networks weights is converted into the particle swarm optimization. Then the calculation is realized by the cooperative particle swarm optimization. The result of application shows that with easy calculation, fast convergence and high precision, the neural network model based on the cooperative particle swarm optimization algorithm provides a useful new method for the dam safety monitoring. Keywordscooperative particle swarm;neural network;dam safety monitoring
Adaptive link layer security architecture for telecommand communications in space networks

Impressive advances in space technology are enabling complex missions, with potentially significant and long term impacts on human life and activities. In the vision of future space exploration, communication links among planets, satellites, spacecrafts and crewed vehicles will be designed according to a new paradigm, known as the disruption tolerant networking. In this scenario, space channel peculiarities impose a massive reengineering of many of the protocols usually adopted in terrestrial networks; among them, security solutions are to be deeply reviewed, and tailored to the specific space requirements. Security is to be provided not only to the payload data exchanged on the network, but also to the telecommands sent to a spacecraft, along possibly differentiated paths. Starting from the secure space telecommand design developed by the Consultative Committee for Space Data Systems as a response to agency-based requirements, an adaptive link layer security architecture is proposed to address some of the challenges for future space networks. Based on the analysis of the communication environment and the error diffusion properties of the authentication algorithms, a suitable mechanism is proposed to classify frame retransmission requests on the basis of the originating event (error or security attack) and reduce the impact of security operations. An adaptive algorithm to optimize the space control protocol, based on estimates of the time varying space channel, is also presented. The simulation results clearly demonstrate that the proposed architecture is feasible and efficient, especially when facing malicious attacks against frame transmission.
Detection of 3D curve for shoe sole spraying based on laser triangulation measurement

This paper describes the development of laser stripe sensor for automatic 3D Curve detection for Shoe Sole Spraying. It also presents the implementation of a new apparatus for adhesive spray of the shoe upper. Laser stripe sensor which is used to obtain profile of shoe sole upper plays an important part in the production of leather shoes. The sensor consists of a CCD camera and a stripe-type laser diode due to high speed requirement. In on-line program system, a method based on computer vision with structured light including in: median filtering, bi-level thresholding, opening and closing, mid-line of the laser stripe extraction, has been developed. The 3D scan curves and closed contour line could be interpolated as B-spline curves. Finally, the experiments results illustrate its feasibility and effectiveness.
Current-reuse single Miller feedforward compensation for multi-stage amplifiers

Presented is a novel current-reuse single Miller feedforward compensation (CRSMFC) scheme for multi-stage operational amplifiers (opamps). The proposed scheme introduces an implicit feedforward path into an opamp by reusing the current of the input stage, which creates a left-half-plane zero to cancel out the first non-dominant pole in the transfer function, thus improving phase margin and gain bandwidth product (GBW) without extra power consumption. A three-stage opamp prototype with proposed CRSMFC is realised in standard 0.18 µm CMOS process, and measured results show that it achieves a GBW of 4.4 MHz and a phase margin of 73° while driving a large capacitive load of 250 pF with only 34 µW of power consumption, implying a superior FoM over the state-of-the-art.
A Level Set Approach to Image Segmentation With Intensity Inhomogeneity

It is often a difficult task to accurately segment images with intensity inhomogeneity, because most of representative algorithms are region-based that depend on intensity homogeneity of the interested object. In this paper, we present a novel level set method for image segmentation in the presence of intensity inhomogeneity. The inhomogeneous objects are modeled as Gaussian distributions of different means and variances in which a sliding window is used to map the original image into another domain, where the intensity distribution of each object is still Gaussian but better separated. The means of the Gaussian distributions in the transformed domain can be adaptively estimated by multiplying a bias field with the original signal within the window. A maximum likelihood energy functional is then defined on the whole image region, which combines the bias field, the level set function, and the piecewise constant function approximating the true image signal. The proposed level set method can be directly applied to simultaneous segmentation and bias correction for 3 and 7T magnetic resonance images. Extensive evaluation on synthetic and real-images demonstrate the superiority of the proposed method over other representative algorithms.
Complete balance equation and blocked probability for secondary radio limited access scheme in cognitive radio networks

In the secondary radio limited access (SR-LA) scheme, the secondary radio (SR) is limited to access parts of the frequency bins in order to obtain a tradeoff between the SR dropped probability and SR blocked probability. In this paper, we present the complete expressions of the balance equation and the blocked probability for the SR-LA scheme. Simulations and numerical analysis are conducted to verify the correctness of the new expressions.
A three-layer DSS based on multi-objective value analysis for internationalization of software products and services

Today the increasing importance of emerging markets, such as China and Brazil, has pushed many small and medium-sized (SM) software companies to examine the global market expanding their boundaries and capabilities. However, the qualifications of these markets and their influences in the software production process are still not well understood. Cultural development and virtual organizing are required, emphasizing the need for strategic thinking and collaborative work. In this perspective, an appropriate decision support system (DSS) could help the companies to better select a route of alternative actions within limited resources and time. This paper introduces a multi-objective value analysis based DSS to possibly support SM software companies to decide how to participate in the global market, enhancing and localizing their products.
Achieve personalized anonymity through query blocks exchanging

In cyberspace security, the privacy in location-based services (LBSs) becomes more critical. In previous solutions, a trusted third party (TTP) was usually employed to provide disturbance or obfuscation, but it may become the single point of failure or service bottleneck. In order to cope with this drawback, we focus on another important class, establishing anonymous group through short-range communication to achieve k-anonymity with collaborative users. Along with the analysis of existing algorithms, we found users in the group must share the same maximum anonymity degree, and they could not ease the process of preservation in a lower one. To cope with this problem, we proposed a random-QBE algorithm to put up with personalized anonymity in user collaboration algorithms, and this algorithm could preserve both query privacy and location privacy. Then we studied the attacks from passive and active adversaries and used entropy to measure user's privacy level. Finally, experimental evaluations further verify its effectiveness and efficiency.
A 80.4-to-86.5 GHz E-band QVCO with Inductor tuning method and amplifier feedback in 65-nm CMOS Technology

A quadrature voltage-controlled oscillator (QVCO) with low phase noise and low power consumption is proposed. In order to improve the oscillation frequency and reduced the phase noise, a feedback network composed of buffer amplifiers and capacitors is introduced to the QVCO. And a newly structured adjustable inductor is also used in QVCO, which can improve the tuning range without adding extra power consumption. A 1-bit binary-weighted switch-capacitor is also used to widen the tuning range. From the post-layout simulation result we can see that the total power consumption is 6.7mW. The turning range of QVCO is from 80.4-to-86.5 GHz covering the entire high-band of E-band standard, and the phase noise of QVCO is below −91dBc/Hz at 1MHz offset over all 4 sub-bands. The figure-of-merit (FoM) of QVCO is −181.1.
A set of closed form approximate formulae for optimal sensing duration of cognitive radio

In a cognitive radio system, proper configuration of sensing duration enables maximization of the data throughput. However, no existing straightforward formula is available, which is desirable in the mobile context due to tightness of computation time. A set of closed-form approximate formulae of optimal sensing duration for cognitive radio are derived under different signal to noise ratio (SNR). The approximation errors are perfectly small except within a certain SNR range, where the error is still less than 5%.
Benefit evaluation index and methods for road intersection signal control

At present, there is lack of unified benefit evaluation index and methods for road traffic signal control in China. The benefits of isolated intersection signal control are most evaluated by the subjective judgment. Without the quantitative analysis, the conclusions of the evaluating are not scientific and reasonable. Firstly, the existing domestic and foreign research of the benefits evaluation index and methods for intersection signal control were summarized and analyzed, based on which, the indicators such as the queue length of the key lane, the average travel delay, and green time utilization were selected to evaluate the benefit. And the weight of different indicators was determined by the control demand for different time periods, such as peak and a flat peak. Then, a weighted average method was used to evaluating the benefit of the signal control for an isolated intersection, by which the different grades of integrated benefit of signal control were determined. Finally, the field tests were done at the intersection of Kaiyuan Road and Yongtai Street, Luolong District of Luoyang City. The results showed that the method is suitable for evaluating the benefit of signal control for an isolated intersection, which realized the quantitative analysis of the indicators, and evaluation results were in good agreement with the actual situation.
Cost and delay tradeoff in three-stage switch architecture for data center networks

Data center networks (DCNs) generally adopt Clos network with crossbar middle switches to achieve non-blocking data switching among the servers, and the number of middle switches is proportional to the number of ports of the aggregation switches in a fixed manner. Besides, reconfiguration overhead of the switches is generally ignored, which may contradict the engineering practice. In this paper, we consider batch scheduling based packet switching in DCNs with reconfiguration overhead at each middle switch, which inevitably leads to packet delay. With existing state-of-the-art traffic matrix decomposition algorithms, we can generate a set of permutations, each of which stands for the configuration of a middle switch. By reconfiguring each middle switch to fulfill multiple configurations in parallel with others, we reveal that a tradeoff exists between packet delay and switch cost (denoted by the number of middle switches), while performance guaranteed switching with bounded packet delay can be achieved without any packet loss. Based on the tradeoff, we can minimize the number of middle switches (under a given packet delay bound) and an overall cost metric (by translating delay into a comparable cost factor), as well as formulating criterions for choosing a matrix decomposition algorithm. This provides a flexible way to reduce the number of middle switches by slightly enlarging the packet delay bound.
A PSO clustering based RFID middleware

In current, RFID (Radio Frequency Identification) Middleware is widely used in nearly all RFID applications, and provides service for raw data capturing, security data reading/writing as well as sensors controlling. However, as the existing Middlewares were organized with rigorous data comparison and data encryption, it is seriously affecting the usefulness and execution efficiency. Thus, in order to improve the utilization rate of effective data, increase the reading/writing speed as well as preserving the security of RFID, this paper proposed a PSO (Particle swarm optimization) clustering scheme to accelerate the procedure of data operation. Then with the help of PSO cluster, the RFID Middleware can provide better service for both data security and data availability. At last, a comparative experiment is proposed, which is used to further verify the advantage of our proposed scheme.
Distinguishing variables of Boolean function via distinct subBDDs

Boolean matching, checking whether two completely specified Boolean functions are logically equal, is widely used in logic synthesis. It is well known that BDD is a common tool for Boolean matching. The identifiableness of two BDDs' structures means that two Boolean functions are equal. But BDD varies with different variable order. So, before constructing BDDs and checking equivalence, variables must be recognized. Various signatures have been presented to determine the variable mapping relations between two Boolean functions. In this paper, we propose using special parts of BDD to distinguish variables. On the basis of these distinct subBDDs, we take use of a group of signatures to recognize variables efficiently. Experimental results on MCNC benchmark circuits were shown that our algorithm is indeed effective in solving matching problem and runtime for benchmark cordic is greatly shortened.
Certificateless Partially Blind Signatures

Partially blind signature is a variant of blind signature. It allows a signer to explicitly include common information in a blind signature under some agreement with a receiver. It provides anonymity of users in applications such as electronic voting and electronic payment systems. While certificateless public key cryptography eliminates the key escrow problem in identity public key cryptography. In this paper, we introduce the concept of partially blind signature into certificateless public key cryptography. The security model of certificateless partially blind signature scheme is defined. A certificateless partially blind signature scheme is presented as well. The scheme is proven existentially unforgeable against adaptive chosen message attacks under the computational Diffie-Hellman assumption.
A novel hybrid wolf pack algorithm with harmony search for global numerical optimization

Though the wolf pack algorithm (WPA) is a new optimal algorithm with good performance in optimation, its convergence is slow. Aiming at speeding up the convergence of the wolf pack algorithm (WPA), a novel hybrid wolf pack algorithm with harmony search (HS) was proposed, which named WPAHS for short. Harmony search is introduced into the process of the wolves updating to substitute the random update. With the help of the employment of harmony memory, pitch adjusting, and randomization mechanisms of HS, the WPAHS not only obtain a faster convergence, but also keeps a feasible adaptability for a wider range of optimation. Six standard benchmark functions are applied to verify the effects of these improvements. It is shown that the performance of WPAHS is superior to, or at least highly competitive with, the standard WPA and other swarm intelligence optimization algorithm, such as GA, HS, PSO, and ABC in most situations.
3D Surface Detail Enhancement from a Single Normal Map

In 3D reconstruction, the obtained surface details are mainly limited to the visual sensor due to sampling and quantization in the digitalization process. How to get a fine-grained 3D surface with low-cost is still a challenging obstacle in terms of experience, equipment and easyto-obtain. This work introduces a novel framework for enhancing surfaces reconstructed from normal map, where the assumptions on hardware (e.g., photometric stereo setup) and reflection model (e.g., Lambertion reflection) are not necessarily needed. We propose to use a new measure, angle profile, to infer the hidden micro-structure from existing surfaces. In addition, the inferred results are further improved in the domain of discrete geometry processing (DGP) which is able to achieve a stable surface structure under a selectable enhancement setting. Extensive simulation results show that the proposed method obtains significantly improvements over uniform sharpening method in terms of both subjective visual assessment and objective quality metric.
Coherent single range Doppler interferometry algorithm for high-speed spinning targets 2D imaging

This paper focuses on the narrow band radar imaging for high-speed spinning targets. Based on the time-frequency characteristic of the echoed signal, a novel coherent spectrogram redistribution method, coherent single range Doppler interferometry (CSRDI), is proposed, which is of high resolution and robust even in the case of the low signal-to-noise ratio. The spinning rate error is also taken into consideration and an estimating approach based on the focal entropy is proposed. The validity of the approaches is confirmed by Numerical simulations.
Application of Improved LM-BP Neuron Network in stock prediction

The prediction of stock closing quotation is very difficult because the time series seem random but not random completely. So a Back-Propagation Neurons Network based on Improved Levenberg-Marquardt algorithm was presented in this paper. The nonlinear mapping characteristic of the Back-Propagation Neural Network makes it can approach to every function in any precision. The Improved Levenberg-Marquardt algorithm can accelerate the speed of convergence, so it is easy to approach to the global optimal solution, not the local optimal solutions and. The prediction experiment shows that the Improved Levenberg-Marquardt algorithm works better than traditional Auto-regressive Moving-Average model.
Application of artificial neural networks for electromagnetic modeling and computational electromagnetics

This paper presents an overview of emerging artificial neural network (ANN) techniques and applications for electromagnetic (EM) simulation and design. Accurate time domain EM modeling using recurrent neural networks (RNNs) is reviewed. Advanced robust training algorithm combining particle swarm optimization (PSO) and quasi-Newton method is described through frequency domain EM modeling, showing its ability to avoid ANN training being trapped in local minima to obtain accurate models. ANN applications in computational electromagnetics are also discussed. Great efficiency can be achieved by using ANNs to approximate the computationally intensive calculations in solving Maxwell equations using method of moments (MoM). As illustrated in examples, these ANN-based techniques are capable of fast and accurate EM modeling and MoM computation, and useful for efficient EM based design.