nils-herrmann/clean_scirepeval huggingface dataset:
Kernel-Induced Label Propagation by Mapping for Semi-Supervised Classification

Kernel methods have been successfully applied to the areas of pattern recognition and data mining. In this paper, we mainly discuss the issue of propagating labels in kernel space. A Kernel-Induced Label Propagation (Kernel-LP) framework by mapping is proposed for high-dimensional data classification using the most informative patterns of data in kernel space. The essence of Kernel-LP is to perform joint label propagation and adaptive weight learning in a transformed kernel space. That is, our Kernel-LP changes the task of label propagation from the commonly-used Euclidean space in most existing work to kernel space. The motivation of our Kernel-LP to propagate labels and learn the adaptive weights jointly by the assumption of an inner product space of inputs, i.e., the original linearly inseparable inputs may be mapped to be separable in kernel space. Kernel-LP is based on existing positive and negative LP model, i.e., the effects of negative label information are integrated to improve the label prediction power. Also, Kernel-LP performs adaptive weight construction over the same kernel space, so it can avoid the tricky process of choosing the optimal neighborhood size suffered in traditional criteria. Two novel and efficient out-of-sample approaches for our Kernel-LP to involve new test data are also presented, i.e., (1) direct kernel mapping and (2) kernel mapping-induced label reconstruction, both of which purely depend on the kernel matrix between training set and testing set. Owing to the kernel trick, our algorithms will be applicable to handle the high-dimensional real data. Extensive results on real datasets demonstrate the effectiveness of our approach.
Optimal Task Scheduling by Removing Inter-Core Communication Overhead for Streaming Applications on MPSoC

In this paper, we jointly optimize computation and communication task scheduling for streaming applications on MPSoC. The objective is to minimize schedule length by totally removing inter-core communication overhead. By minimizing schedule length, the system performance can be improved by adopting a smaller period or exploring the slacks generated for energy reduction with DVS. To guarantee the schedulability of communication tasks, we perform the schedulability analysis, and theoretically obtain the upper bound of the times needed to reschedule each computation task. Based on the analysis, we formulate the scheduling problem as an ILP (Integer Linear Programming) formulation and obtain an optimal solution. We evaluate our technique with a set of benchmarks from both real-life streaming applications and synthetic task graphs. The simulation results show that our technique can achieve a 27.72% reduction in schedule length and a 13.38% reduction in energy consumption on average compared with the previous work.
Forecasting of dissolved gas in transformer oil by optimized nonlinear Grey bernoulli Markov model

Accurate forecasting of concentration of the dissolved gas with fault characteristic in power transformer oil is of great significance to detect the incipient fault in transformer and to achieve safe and economic operation of power system. This paper proposes a novel comprehensively optimized nonlinear Grey Bernoulli Markov model which suitable for forecasting fault characteristic gas in oil. In order to improve the precision, this paper use Metabolic model to modify the original data and we also smoothing the gas data by Exponential smoothing operation. In addition, the optimized model is improved by the modification of the background value. ) can be used to forcecast Weishan Dali and Jinghong Xishuangbanna 110kV Substation faulty with real measured dissolved gas data. Predictive analytics use the GM(1,1) and ), respectively. The results show that the optimized prediction accuracy is greatly improved.
Fuzzy logic based robot path planning in unknown environment

This paper proposes a new method, minimum risk approach, to address the local path planning to escape from local minimum during goal-oriented robot navigation in unknown environments. This approach is theoretically proved to guarantee global convergence even in the long-wall, unstructured, cluttered, maze-like, and modified environments. The approach adopts a strategy of multi-behavior coordination, in which a novel path-searching behavior is developed to recommend the regional direction with minimum risk. The paper provides a fuzzy logic framework to implement the behavior design and coordination. It is verified by the simulated and real world tests.
High-performance p-channel Schottky-barrier SOI FinFET featuring self-aligned PtSi source/drain and electrical junctions

A simplified and improved Schottky-barrier metal-oxide-semiconductor device featuring a self-aligned offset channel length, PtSi Schottky junction, and reduced oxide thickness underneath the sub-gate was proposed and demonstrated. To alleviate the drawbacks related to the nonself-aligned offset channel length in the original version, a self-aligned offset channel length is achieved in the new device by forming the silicide source/drain junction self-aligning to the sidewall spacers abutting the gate. This results in not only one mask count saving but also better device performance, as facilitated by the reduced offset channel length of the self-aligned sidewall spacers. Moreover, the adoption of PtSi for the Schottky junction further improves the on-state current of p-channel operation, while a thinner oxide employed underneath the sub-gate effectively reduces the sub-gate bias needed to form the electrical junction to below 5 V. Significant improvement in on-current as well as leakage current reduction is achieved in the new improved device.
The electrochemical performance of yttrium oxide coated LiNi1/3Mn1/3Co1/3O2 as cathode material for lithium-ion batteries

The surface of LiNi1/3Mn1/3Co1/3O2 was coated with 1.0 wt.% Y2O3 to improve the cycling performance for lithium-ion batteries. X-ray diffraction (XRD) of the coated material indicates Y2O3-coating does not destroy the crystal structure of LiNi1/3Mn1/3Co1/3O2, only forming a compact coating layer on the surface of it. Differential scanning calorimetry-thermal gravimetric analyses (DSC-TGA) results show the coating layer could suppress the heating release. The Y2O3-coated LiNi1/3Mn1/3Co1/3O2 possesses higher discharge capacity and better cyclability than that of the bared material. The discharge capacity of coated sample is 100.7 mAh g-1 at 2.0 C while that of bared one is only 80.0 mAh g-1. The rate of capacity retention after 12 cycles for the coated sample is 100% at 333 K, which is much better than that of the bared one (94.9 %). The chemical diffusion coefficient of Li+ ion (DLi ) in the electrodes were measured by potentiostatic intermittent titration technique (PITT). The result shows that Li+ ion can move faster after coating.
Urban Traffic Signal Learning Control Using Fuzzy Actor-Critic Methods

Urban traffic control is very complicated, so it is very difficult to build a precise mathematical model. In this paper, we propose a fuzzy Actor-Critic reinforcement leaning algorithm to control the traffic signal, thus the decision can be made dynamically according to real-time traffic state information, and the change of environment can be adapted automatically; In order to solve the curse of the dimensionality problem, we applied fuzzy radial basis function (FRBF) neural network to approximate the state value function. By training self-adapted non-linear processing unit, and realizing online and adaptive constructing of state space, the approximation is improved, thus the control of traffic signal at single intersections is solved. The simulation results show that the effectiveness of the new control algorithm is obviously better than traditional sliced time allocation methods.
In-Fiber M-Z Interferometer Based on Cascaded Long Period Gratings in Embedded-Core Fiber

A novel Mach-Zehnder interferometer (MZI) in an embedded-core hollow optical fiber (ECOF) was demonstrated, which was composed of a pair of long period gratings (LPGs). In order to form an in-fiber MZI in an ECOF, a high-frequency CO2 laser was used to fabricate two similar LPGs with their peak attenuation of approximate 3 dB. The responses of the MZI to the temperature, the axial strain, and the refractive index were measured and discussed. The temperature sensitivity of the MZI is about 48.8 pm/°C, and the sensitivity of the axial strain is 0.5 pm/ $\mu \varepsilon $ . The ECOF has a big air hole, which is easy to form a microfluidic channel. The sensitivity of the internal refractive index can be as high as 1437.4 nm/RIU in the refractive index range of 1.4248~1.4569. The proposed MZI has a high resolution and can be a potential candidate for sensing, especially for on-line microfluidics sensors.
Notice of Retraction Study on IPO's short-term overpricing in China: Evidence from Chinese listed companies

IPO's short-term overpricing is hot issues of its research in recent years, which is important to improve the efficiency of China's securities market. But few scholars specially study IPO short-term (3 months) overpricing problem and its influencing factors. Using 264 new-issued A-shares of all circulated shares issue from 2006 to 2008, this paper empirically analyses the CAR and BHAR of shares within three months is significantly positive, namely short-term overpricing, and takes related variables of market condition, including investor sentiment, and company characteristics into multiple regression model in order to study the factors influencing adjusted IPO return volatility. Conductivity is found that marketing factors affect the adjusted IPO return volatility directly, and corporate features have an indirect effect by influencing investor sentiment, one of market factors.
A cell counting method for BEVS based on nonlinear Transformed Sliding Band Filter

Insect cell is the host of baculovirus in Baculovirus Expression Vector System (BEVS). However, the insect cell counting is an obstacle that constrains the efficiency in BEVS. In this paper, an insect cell counting method based on Transformed Sliding Band Filter (TSBF) was proposed according to insect cell cultivation manner. The proposed method was then applied to insect cell image datasets, and results exhibited that the average relative error rate was 2.21% compared with manual counting. Growth curve evaluation showed that this method was suitable to the protocol of cell cultivation. These exciting results proved that the proposed method was an ideal automatic counting tool for inset cells in BEVS.
Security Analysis of Simple Network Management Protocol Based IEEE P21451 Internet of Things

In recent years, a lot of attentions have been paid to Internet of Things (IoT) while IoT is more widely used in life. To ensure the security and stability of the IoT and integrate heterogeneous networks in the IoT, IEEE establishes IEEE P21451 protocol family. And IEEE P21451-1-5 is designed to help IoT to manage network through Simple Network Management Protocol (SNMP) efficiently, but the SNMP based IEEE P21451-1-5 has some security problem. This paper analyses the main structure and security mechanisms of SNMP, and summarize the existing security threats, which can help researchers to improve the security of the IEEE P21451-1-5.
Research on Service-Oriented Reconfigurable Business Collaboration Platform for Cluster Supply Chain

Cluster supply chain is a kind of supply chain coupling with the industrial cluster, and its effective operation plays an important role in the promotion of industrial clusters. This paper presents a service-oriented solution to support the business cooperation among the enterprises via analyzing the complex business relationships and the features of the cluster supply chain. Furthermore, a reconfigurable cluster supply chain service platform is proposed with the details including the architecture. The platform realizes the service components reuse and the process reconfiguration by constructing the service components in different granularities with multiple forms and configuring on demand dynamically. Meanwhile, various services, applications and resources of the cluster supply chain are integrated into platform by using the web-service technology based on ESB. Finally, the platform is developed based on the proposed system architecture and the experimental results prove its effectiveness.
A Novel Hyperspectral Classification Method Based on C5.0 Decision Tree of Multiple Combined Classifiers

It is difficult for a single classifier to resolve the problem of high dimension in the hyperspectral image classification applications. Combination of multiple classifiers can make full use of the complementary of the existing classifiers, thus owns better classification performance. A novel multiple classifiers based on C5.0 decision tree is proposed. It reduces the hyperspectral dimension through wavelet-PCA transform algorithm firstly. Then three supervised classifiers, namely Minimum Distance, Maximum Likelihood and SVM, combined by C5.0 decision tree, are used to realize hyperspectral classification. Experiments based on AVIRIS hyperspectral image data show that higher classification accuracy may be achieved via the multiple combined classifiers than a single sub-classifier. The proposed method can reduce the dimension of features and improve the classification performance efficiently.
Underfill selection for reducing Cu/low-K delamination risk of flip-chip assembly

Experimental and numerical studies were conducted to investigate Cu/low-k delamination potentials of a flip-chip package assembly implemented with different underfills under an accelerated thermal cycling test condition. A total of six underfills were considered and compared. Their temperature-dependent Young's moduli, coefficients of thermal expansion, and glass transition temperatures were measured and incorporated in the finite element model. The numerical predictions were benchmarked by actual thermal cycling test results. Results indicate that the first principal stress at the die edge shows a good correlation with Cu/low-k delamination potentials observed from experiments.
Toward the Optimization of Normalized Graph Laplacian

Normalized graph Laplacian has been widely used in many practical machine learning algorithms, e.g., spectral clustering and semisupervised learning. However, all of them use the Euclidean distance to construct the graph Laplacian, which does not necessarily reflect the inherent distribution of the data. In this brief, we propose a method to directly optimize the normalized graph Laplacian by using pairwise constraints. The learned graph is consistent with equivalence and nonequivalence pairwise relationships, and thus it can better represent similarity between samples. Meanwhile, our approach, unlike metric learning, automatically determines the scale factor during the optimization. The learned normalized Laplacian matrix can be directly applied in spectral clustering and semisupervised learning algorithms. Comprehensive experiments demonstrate the effectiveness of the proposed approach.
An analysis of a case of plastic packaging SiP module delamination failure

In the analysis of plastic packaging SiP product failure, we should not only consider the plastic material and techniques inherent problems, but also the problem that SiP products have a lot of materials, complex structure, and small surface bonding. A type of plastic packaging SiP module function failure after reflow soldering, through appearance inspection, pin electrical characteristic test, X-ray analysis, Scanning Acoustic Microscope(SAM) examination and longitudinal section inspection and so on. We found that due to the compact structure, the design refers to the bonding finger with a small distance from the bare die, resulting in a small amount of adhesive film and forming a hole. In addition to improper selection of adhesive, the conductive adhesive after curing is relatively brittle and hard. It is not conducive to large chip resistance stress, resulting in plastic devices after moisture absorption during reflow soldering. The internal Vapor thermal expansion leads to delamination and failure.
Overview of Pulsed Power Research at CAEP

Pulsed power research for military and civil applications has been conducted at the China Academy of Engineering Physics (CAEP) for more than 50 years. The pulsed power research activities include development of pulsed power components, such as different kinds of high-voltage switches, series of pulsed power sources and pulsed X-ray machines, high-current accelerators for Z-pinch and flash X-ray radiography, as well as medical application and electromagnetic launch. The most recent progress of pulsed power research at CAEP will be presented.
Ambipolar Schottky barrier SOI MOSFETs

Recently, we proposed and demonstrated a novel Schottky barrier (SB) poly-Si thin-film transistor (TFT) that exhibits excellent ambipolar operation performance. In this work, we extend the idea to the fabrication of SB SOI devices. The new device employs a field-plate (or sub-gate) to induce an electrical drain extension in the active layer. The unique field-induced drain (FID) feature reduces effectively the off-state leakage current, while maintaining a reasonable on-current, resulting in significant improvement in the device performance.
Compensation method for distorted planar array antennas based on structural–electromagnetic coupling and fast Fourier transform

Complex operating environment could introduce serious degradation to the electromagnetic performance of active phased array antenna in both the main lobe and sidelobe areas. The effective compensation techniques become the key for antenna to perform in reliable service condition. Therefore, a method combined coupled structural-electromagnetic model with two-dimensional fast Fourier transform for compensation is presented. A compensation calculation model of the excitation current for planar array is established accordingly, quantitatively expressing the relationship between the excitation current compensation and the structure error. The adjustment quantities of excitation amplitude and phase can be quickly obtained and implemented to recover a high-quality pattern from a distorted antenna in both the main lobe and sidelobe areas. Lastly, the simulation of the space-based array antenna is illustrated to compensate its property under the impact of space environment and heat power from its electric devices. Furthermore, an experiment platform of an X-band active phased array antenna with 32 × 24 elements is built and tested for the electromagnetic performance compensation. The simulation and experimental results show that the proposed method can guarantee the performance of the service antenna quickly and effectively in the whole observation area.
An Effective Bottleneck Detection Method for Job Shop

A new bottleneck definition from the perspective of the manufacturing system’s objective is proposed. A distinct bottleneck detection method based on orthogonal experiment (BD-OE) for job shop is proposed according to the definition. The method takes the objective of the manufacturing system as estimated index, carries out typical and finite number of experiments by combining different dispatching rules, to detect bottleneck machine which has the greatest effect on the estimated index. The method can detect bottleneck before the system running, and to guide the following production planning and preparation of necessity in advance for the improvement of the system’s performance.
Missing Data Recovery for High-Dimensional Signals With Nonlinear Low-Dimensional Structures

Motivated by missing data recovery in power system monitoring, we study the problem of recovering missing entries of high-dimensional signals that exhibit low-dimensional nonlinear structures. We propose a novel model, termed as “union and sums of subspaces,” to characterize practical nonlinear datasets. In this model, each data point belongs to either one of a few low-dimensional subspaces or the sum of a subset of subspaces. We propose convex-optimization-based methods to recover missing entries under this model. We theoretically analyze the recovery guarantee of our proposed methods with both noiseless and noisy measurements. Numerical experiments on synthetic data and simulated power system data are conducted to verify the effectiveness of the proposed methods.
Modeling the individuality of iris pattern and the effectiveness of inconsistent bit masking strategy

Iris recognition is one of the most accurate biometric technologies. The uniqueness of iris, also known as iris individuality, has been widely accepted as one foundation for iris recognition. Although a few iris individuality models have been proposed, they are either incomplete or less accurate. In this paper, we investigate the iris individuality problem using Daugman's iris code method. We divide the bits in an iriscode into two groups, i.e., consistent and inconsistent bits, and provide the individuality analysis by both FAR and FRR modeling. Numeric evaluation using real iris data shows its usefulness in predicting the empirical performance. Furthermore, till now it is just experimentally confirmed that the recognition accuracy could be improved by masking out inconsistent bits. In order to formally e- valuate the effectiveness of this strategy, we derive the iris individuality model after masking out the inconsistent bits. Comparison of the two models has demonstrated the improved accuracy of the masking strategy, and the drop of EER is up to about 80%.
Towards architecture model based deployment for dynamic grid services

The deployment of grid services should make the services, including those to be deployed and those already deployed, operate with desired functionalities and qualities. The critical challenge in the deployment is that many technical and nontechnical factors have to be taken into account, such as performance, reliability, utilization, operating cost, incomes, and so on. Since the factors change continuously, some deployed services may have to be redeployed for guaranteeing their functionalities and qualities. This position paper presents an approach to the deployment and redeployment of grid services based on software architecture models. In this approach, all services in a grid consist in a software architecture, which represents the services, their relationships and other factors in a global, understandable and easy-to-use way. To demonstrate the approach, a visual tool for deploying services onto a set of popular grid infrastructures, including J2EE application servers and BPEL engines, with the help of software architectures is developed
Sparse Recovery With Graph Constraints

Sparse recovery can recover sparse signals from a set of underdetermined linear measurements. Motivated by the need to monitor the key characteristics of large-scale networks from a limited number of measurements, this paper addresses the problem of recovering sparse signals in the presence of network topological constraints. Unlike conventional sparse recovery where a measurement can contain any subset of the unknown variables, we use a graph to characterize the topological constraints and allow an additive measurement over nodes (unknown variables) only if they induce a connected subgraph. We provide explicit measurement constructions for several special graphs, and the number of measurements by our construction is less than that needed by existing random constructions. Moreover, our construction for a line network is provably optimal in the sense that it requires the minimum number of measurements. A measurement construction algorithm for general graphs is also proposed and evaluated. For any given graph G with n nodes, we derive bounds of the minimum number of measurements needed to recover any k-sparse vector over G (Mk,nG). Using the Erdõs-Rényi random graph as an example, we characterize the dependence of Mk,nG on the graph structure. This paper suggests that Mk,nG may serve as a graph connectivity metric.