nils-herrmann/clean_scirepeval huggingface dataset:
Mapping Genes to Pathways Using Ontological Fuzzy Rule Systems

In this paper we present a novel algorithm for mapping genes to pathways. The approach is based on the concept of ontological fuzzy rule system (OFRS) that, we believe, represents a step closer toward Zadeh's "computing with words" paradigm. An OFRS is a fuzzy rule system that uses ontological mapping between objects according to their representation as sets of terms from an ontology. In our mapping approach the left-hand-side contains genes objects represented using the gene ontology (GO), and the right-hand-side consists in pathways described using a pathway ontology (KEGG). The question of mapping a set of genes to pathways often arises in microarray experiments where one would like to know what are the pathways that can explain the observed gene expression patterns. To compare various mapping approaches, we use a pilot dataset extracted from KEGG that consisted of 10 sets of 15 genes taken from 3 pathways (5 gene/pathway). We conclude that the best matching strategy consists in two steps: in the first step the crisp approach is used to find the pathways involved, and in the second step the fuzzy (ontological) approach is employed to map the genes that could not be found in KEGG.
An Initialization Method for Fuzzy C-means Algorithm Using Subtractive Clustering

In clustering methods, the estimation of the optimal number of clusters is significant for subsequent analysis. As a simple clustering method, the fuzzy c-means algorithm (FCM) has been widely discussed and applied in pattern recognition and machine learning. However, the FCM could not guarantee unique clustering result because initial cluster number is chosen randomly. As the number of clusters is randomly chosen, the iterative amount is large and the result of the classification is unstable. An initialization method for FCM algorithm using subtractive clustering is presented in this paper. The experiments show that the modified algorithm can improve the speed, and reduce the iterative amount. At the same time, this method can make the results of the classification more stable and have higher precision.
A novel priority weight-based explicit rate allocation scheme for ATM ABR services

A priority weight-based rate allocation scheme is proposed for ATM ABR services. Compared with existing ATM weight-based rate allocation algorithms, the proposed scheme is able to assure quality of service (QoS) guarantee based on its cell loss ratio (CLR) specification and actual cell rate of the application. The priority weight (PW) factor allows prioritization of application services for efficient flow control using minimum control variables and only less than 6% RM cell overheads.
Dimensionality-Dependent Generalization Bounds for k-Dimensional Coding Schemes

The k-dimensional coding schemes refer to a collection of methods that attempt to represent data using a set of representative k-dimensional vectors and include nonnegative matrix factorization, dictionary learning, sparse coding, k-means clustering, and vector quantization as special cases. Previous generalization bounds for the reconstruction error of the k-dimensional coding schemes are mainly dimensionality-independent. A major advantage of these bounds is that they can be used to analyze the generalization error when data are mapped into an infinite- or high-dimensional feature space. However, many applications use finite-dimensional data features. Can we obtain dimensionality-dependent generalization bounds for k-dimensional coding schemes that are tighter than dimensionality-independent bounds when data are in a finite-dimensional feature space? Yes. In this letter, we address this problem and derive a dimensionality-dependent generalization bound for k-dimensional coding schemes by bounding the covering number of the loss function class induced by the reconstruction error. The bound is of order , where m is the dimension of features, k is the number of the columns in the linear implementation of coding schemes, and n is the size of sample, when n is finite and when n is infinite. We show that our bound can be tighter than previous results because it avoids inducing the worst-case upper bound on k of the loss function. The proposed generalization bound is also applied to some specific coding schemes to demonstrate that the dimensionality-dependent bound is an indispensable complement to the dimensionality-independent generalization bounds.
A Novel Pectoral Muscle Segmentation Algorithm Based on Polyline Fitting and Elastic Thread Approaching

Mammography is one of the most effective detection methods of breast cancer. A novel algorithm was proposed to segment the pectoral muscle automatically. It firstly carried out a series of ROIs upon the neighborhood, and in each ROI, the optimal threshold was computed with the iterative thresholding. After combining these thresholds into an optimal threshold curve and computing the corresponding local mean square deviation curve, the segmentation threshold of the pectoral muscle could be determined. Then, zonal Hough transform and polyline fitting were applied to fit the boundary of the pectoral muscle, and finally an elastic thread algorithm was used to extract the pectoral muscle accurately. This algorithm overcame the defects of the conventional ones, and could get a generally high precision.
Book Recommendation Based on Joint Multi-relational Model

Recommender system, which is powerful to deal with the issue of information overload, has been widely investigated by many researchers recently. However, one of the biggest challenges needs to face is the cold start problem. To address this problem, the data source from social network is incorporated into our recommender system in this paper. In a social network, users who tightly connected imply some group-specific interests. Consequently, we may exploit social network information to resolve the cold start problem and improve prediction performance. The main motivation of this paper is to exploit social relationships and other extra data sources to adjust the latent factors learning over the target matrix, namely book rating matrix and a group of auxiliary matrices, typically, the social relationship matrix. Our recommender system is based on coupled matrix factorization in major, and utilizes the random walk and genetic algorithm to learn some special parameters. The data for experiments is crawled from one of the Chinese biggest reading-sharing website, Douban. Finally, the results have proved that our book recommender system incorporating auxiliary data sources has much better performance than traditional methods.
Feature Adaptation and Augmentation for Cross-Scene Hyperspectral Image Classification

Cross-scene hyperspectral image (HSI) classification has recently become increasingly popular due to its crucial use in various applications. It poses great challenges to existing domain adaptation methods because of the data set shift, that is, two scenes exhibit huge distribution discrepancy. To tackle this problem, we propose a new domain adaptation method called hyperspectral feature adaptation and augmentation (HFAA) for cross-scene HSI classification. The proposed HFAA method learns a common subspace by introducing two different projection matrices to extract the transferable knowledge from the source domain to the target domain. To further enhance the common subspace representation, we propose to augment it by the feature selection strategy. HFAA can make full use of the original features from both source and target domains, and increase the similarity of the samples with the same label from the two domains. Our proposed HFAA method achieves compact but discriminative feature representations, which make it well suited for data sets with a large number of classes and huge interclass ambiguity. Experimental results on the Earth Observing 1 hyperspectral data set show that HFAA can produce state-of-the-art performance and surpass previous methods.
A modified fast fuzzy C-means algorithm based on the spatial information for underwater image segmentation

a novel fuzzy C-means algorithm based on spatial neighbor information is proposed. With the Euclidean distance and the weighted spatial distance, a novel objective function has been established which contains neighbor information. Experimental results indicate that this algorithm can get the high qualified segmentation, but spends much time. As the mission of the vision system of autonomous underwater vehicle (AUV), it should deal with the information about the object in the complex environment rapidly and exactly for AUV to use the obtained result for the next task. So, aiming at realizing a clustering quickly on the basis of high qualified segmentation, the algorithm mentioned above is modified using the relative information loss of the re-sampled image to the original image. Experimental results show that the novel algorithm can get a better segmentation result and the processing time of each image is reduced and can enhance efficiency and satisfy the request of highly real-time effectiveness of AUV.
Combining Mimo with Network Coding: A Viable Means to Provide Multiplexing and Diversity in Wireless Relay Networks

In this paper, a two-step communication protocol combined with virtual MIMO (Multiple Input Multiple Output) and network coding technique is proposed. The protocol is therefore termed MINEC (MIMO Network Coding). A three nodes network with multi-antennas on relay node is taken as an illustrative example of MINEC. Theoretical and simulative analyses of the MINEC performance under the assumption of a binary symmetric relay channel model are carried out. It is found that MINEC performs equivalent to a 2x2 V- BLAST MIMO and a two 2x1 Alamouti MISO in transmitting phase and forwarding phase, respectively. Theoretical and simulative analyses prove that MINEC protocol facilitates more reliable transmission and higher throughput.
Circular sparse array beam synthesis based on particle swarm optimization with consideration of polarization

Circular sparse array possess many advantages like conformal manifold, Omni-direction, higher resolution and lower cost compared to conventional array. Besides, beam synthesis of circular array should consider the polarization of every element due to curvature and its computational complexity is expensive. Therefore, a new beam synthesis method, for circular sparse array with consideration of polarization via the particle swarm optimization, is given in this paper. The proposed method may obtain the same beam. Finally, numerical experiments are also provided to demonstrate the effectiveness of the proposed method. (4 pages)
A low-cost protocol and application for UWB localization, exploiting cross-layer design and cognitive radio aspects

The EUWB (Coexisting Short Range Radio by Advanced Ultra-Wideband Radio Technology, FP7-ICT-215669, www.euwb.eu) project has been an industry-led initiative of twenty-one major industrial and excellent academic organizations from Europe and Israel targeting innovative improvement, adaptation, integration and application of short-range Ultra-Wideband Radio Technology (UWB-RT). Started in April 2008, the EUWB project has leveraged and enhanced the scientific knowledge base in the advanced UWB-RT and has provided sophisticated new applications enabled by UWB and highly demanded in several European key industrial sectors such as home entertainment consumer electronics, automotive, public transportation, and heterogeneous cellular networks. Within EUWB, major fields of this emerging challenge have been identified, such as the cognitive radio related Detect-And-Avoid (DAA) technology and the localization and tracking (LT) technology. An important aspect is to design an LT protocol. In this manuscript the authors will first illustrate the technical approach of EUWB, giving a brief overview of the project.
Empowering robot with surrounding resources

Execution of complicated tasks all by robot itself within the physical and dynamic environment always shows low efficiency, facing the bottle-necks of machine intelligence. Compared with empowering robot with all kinds of functions and intelligence within its body, it is more natural and achievable to empower it with various kinds of resources as tools within its surroundings. Difficulties in realizing this idea are summarized, with emphasized discussion on some key technologies in 5 aspects, inspired from human living habit. As a primary realization of this idea, Web service is introduced in order to build a framework, in which part of the functions and intelligence is transferred from robot to various resources. By this transfer, a low cost single robot is successfully empowered with surrounding resources to accomplish a complicated task.
Interval Evaluation of Voltage Sag Magnitude and Frequency in Distribution System

Aiming at the uncertainties of voltage sag evaluation and integrating the interval algorithm to existing stochastic evaluation methods, a interval-based method and approaches for the evaluation of voltage magnitude and sag duration were proposed. In this method, the interval characteristics of reliability parameters of components in distribution system and the variation of customer loading were considered and a interval evaluation method is proposed in order to assess the voltage magnitude and sag frequency in the distribution system based on existing fault location method. For the typical configuration of distribution system, the mathematical model and approaches were researched in detail on the basis of the algorithm of interval power flow. The proposed method was performed on RBTS-2 standard testing distribution system and compared with existing method with certain parameters. It is proved that it needs only one time of interval iteration process. The simulation results indicate that this method is reasonable and effective.
Design and realization of three-level measure and control network based on distributed system

To monitor building equipments, a method of design and realization for distributed measure and control system is proposed, and the configuration of three-level network based on field bus, Ethernet and Internet and their communication techniques are described respectively. The concentrate monitoring and control for building equipments can be realized with B/S model and ADO technique. The data integration and management technique by using Visual C++ multithreading programming and data-base Web accessing technique is also developed. A micro-computer monitoring software system has the functions such as operation analyzing, data searching, running curve and data statistics, report forms printing, etc. Because the running data stream is great and uneven, statistics database and historical database are used to store and manage data efficiently. The fact shows that the system operates conveniently and efficiently, and the design method is practicality.
Learning fuzzy rules for modeling complex classification systems using genetic algorithms

Genetic algorithms, as general purpose learning techniques, have been widely applied in the modeling of fuzzy rules-based classification system. However, the algorithms are more vulnerable to local convergence as a result of the increasing complexity and dimensionality of classification problems, which reduces the performance of the algorithms. To prevent the algorithms only learning rules from small subset of the search space, a fitness sharing method based on the similarity level of one rule from its neighbours rules is proposed. The similarity level is calculated by the similarity values of different antecedent fuzzy sets, which are cached for reducing the additional computing load. The proposed method is studied for two complex data, the sonar signals classification and the hand movement recognition problems. And the experimental results demonstrate that the proposed method is able to efficiently achieve accurate performance.
A monolithically integrated platform for high throughput cell characterization and drug screening

Real-time physiological properties' characterization of living cells is important for bio-engineering applications such as drug screening. In this paper, we propose an integrated platform to automatically characterize muscle cell mechanics under a variety of culture conditions and drug stimuli in parallel. A Post-CMOS process is developed to fabricate the integrated chip, and then living muscle cells self-assembled on the surface directed by the gold pattern. Preliminary experiment validates feasibility of the procedure. Finally, chip-level packaging is performed using a PDMS cap and a plastic tube.
Design and fabrication of double modulation doped InAlAs/lnGaAs/InAs heterojunction FETs for high-speed and millimeter-wave applications

We report the design, fabrication, and characterization of InP-based double-sided-doped (DSD) MODFETs with InAs-layer-inserted channels. Devices based on optimized structures show a significant improvement in the effective saturation velocity, from 2.4/spl times/10/sup 7/ cm/s for lattice-matched MODFETs to 3.1/spl times/10/sup 7/ cm/s for InAs MODFETs. This leads to a maximum extrinsic transconductance of 1.95 S/mm and excellent high-speed performance of f/sub T/=265 GHz for 0.13-/spl mu/m T-gates. A f/sub max/ higher than 300 GHz can be achieved by fabricating a wide lateral recess groove, which simultaneously results in an improved breakdown voltage of 6.7 V. The excellent RF performance is primarily due to the reduction of Coulomb scattering from donor layers, especially under the channel, and to the reduction of scattering caused by the interface roughness. This improvement is achieved by inserting a 4-nm InAs layer, which better confines the two-dimensional electron gas (2DEG) at the center of the channel of MODFET's.
Characteristics and evolution of microstructure in Cu-based bulk amorphous alloys

Bulk Cu50Zr42Al8 amorphous coniform shape samples (10mm in diameter at bottom and 80mm in length) were successfully prepared by the arc-melting master alloy in the water-cooled copper crucible and then suck cast in the copper mold. Using the techniques of X-ray diffraction, Optical microscope, SEM and TEM analysis. The experimental investigation of the samples research of axial and radial characteristics and evolution of microstructure were conducted. Results indicated that along the temperature gradual direction, the cooling rate varied and the microstructure of coniform shape samples was composed of three portions as follows: the amorphous external region, which in the surface of the samples contact copper mold and the central crystal region, which in center of sample and the transition region, which locate between the amorphous region and the crystal region, which was composed of amorphous structure and crystal structure. The crystal size and volume fraction increase gradually inward from the surface
A novel constant modulus array for multiuser detection

A novel multitarget constant modulus array structure is proposed for CDMA systems in this paper. The new algorithm for the structure is called predespreading and wavelet denoising constant modulus algorithm (D-WD-CMA). In the new algorithm, the predespreading and wavelet denoising are applied for multitarget arrays to remove some multiple access. Simulation results illustrate the good performance for the proposed algorithm, compared with the traditional CMA algorithm.
Fuzzy c-medoids method based on JS-divergence for uncertain data clustering

Uncertain data clustering is one significant research in data mining. Many similarity measurements of uncertain objects are proposed. Traditional clustering methods can be extended with these new similarity measurements. In this paper, we propose a new fuzzy c-medoids method for uncertain data clustering, named UFC-medoids. The JS-divergence is used as the similarity measurement between uncertain objects in this algorithm. In the experiments on synthetic datasets, the presented algorithm has shown a good performance.
Influence of using conditions on the performance of PM linear synchronous motor for ropeless elevator

No-lapping concentrated windings permanent magnet linear synchronous motor (PMLSM) which possesses notable advantages such as big thrust force density, short winding head and small magnetic resistance is the preferred drive motor on Linear motor cordless upgrade system. In the paper 15 slots 16 poles PMLSM has been taken as example. On the basis of d-q dual-response theory, key parameters such as the motor resistance of q-axis (quadrature) and d-axis (direct) have been calculated by Finite Element Method. Thus mathematic model of PMLSM steady-state analysis has been built. PMLSM performances such as current, thrust force, power factor, efficiency, and electric power brake force affecting by supply voltage and ambient temperature change have been studied respectively both on load condition and no-load condition. The experiment results agree with theoretical analysis, with good prospects of permanent magnet (PM) linear synchronous motor for design and application of ropeless elevator.
Joint Downlink Power and Time-Slot Allocation for Distributed Satellite Cluster Network Based on Pareto Optimization

In this paper, we design a novel architecture of distributed satellite cluster network (DSCN). In order to achieve a good trade-off between the energy consumption and the total capacity, we investigate the joint downlink power and time-slot allocation problem, taking into account the limitation of resource, collaborative coverage of multi-satellite, and dynamism, which is proved to be a Pareto optimization and NP-hard problem. Different from the existing 1-D multi-objective optimization algorithm (1D-MOA) based on meta-heuristics, such as immune clonal algorithm (ICA), we propose an improved 2-D dynamic immune clonal algorithm (TDICA) to search the solution space for approaching the Pareto front. From simulation results, several important concluding remarks are obtained as follows: 1) the proposed TDICA can obtain more non-dominated solutions in each iteration with better accuracy than existing algorithms; 2) with inter-satellite resource optimization, the total capacity can be improved; 3) compared with 1D-MOAs, the 2D-MOA can save more energy and achieve higher total capacity; d) MOAs can be transferred into multiple single-objective optimization algorithms (SOAs) under certain conditions.
A novel coding algorithm for video surveillance

In this paper, a new coding algorithm designed for video surveillance is proposed. By use of the techniques such as context switch, inertia-based motion estimation, early-out DCT transform, region-based quantization and so on, this algorithm provides some attractive features for video surveillance. The developed algorithm supports multiple video channels, has error-resilience and alarming functions, and leads to high compression ratio. It is also sensitive to moving objects and feasible for browsing. Simulation results show that the proposed algorithm is more suitable than MPEG-1 for the application of video surveillance.
A Study of Autonomous Parking for a 4-Wheel Driven Mobile Robot

This paper deals with the autonomous parallel parking problem of a car-like 4-wheel driven mobile robot. The parking problem corresponds to the point-stabilization problem of nonholonomic system. In this paper, we propose an efficient algorithm for the problem with stability analysis. Also we show parking problem simulation which proves the effectiveness and stability of the algorithm. In the simulation, MFC is utilized with various parking conditions. Using this algorithm, a car-like mobile robot could be controlled to move to a desired posture within a prescribed boundary. The developed system which integrated the control algorithm for parking tested with car-like mobile robot through simulations and experiments.