nils-herrmann/clean_scirepeval huggingface dataset:
Discovering the Academic Situation of Students by Relationship Mining

While the data mining in education field gained more and more popularity in recent years, there have many research endeavors to find association rules in students' academic situation. The current methods normally apply traditional association rules mining technique to identify those rules. However, traditional association rules mining technique can not identify difference between different types of students' academic situation. To solve this problems, we applied a novel contrast target rules mining method in this paper. Real world data set from Computer Science department of a university of China, the empirical results show the difference characteristics of different types of students in their academic situation.
Design and implementation of integrated navigation system based on DSP

In this paper, we adopt switching technology of double satellites navigation mode based on BeiDou-2 (BD2) and Global Position System (GPS), design GNSS/MIMU integrated navigation system based on TMS320C6747. The designed system is not affected by the possible transient invalidation of GNSS. The hardware design of system is completed. The navigation switching technology is analyzed when GNSS signal is disturbed, and the Kalman filter of system is designed, what's more, the precision of system during navigation mode switching period is held through MIMU. Thus we realize seamless switching of navigation mode. We adopt integrated navigation system based on BD2, GPS and low-cost IMU, which increases the stability of system when the signal of satellites navigation system is disturbed. Additionally, we provide results and system details from several experiments to further prove the effectiveness of the integrated navigation system.
Discriminative high-level representations for scene classification

High-level image representations, e.g, Object Bank, have drawn increasing attention in visual recognition. In this paper, we propose a discriminative high-level representation based on object bank for scene classification. By projecting the high-level features from the object bank into discriminative subspaces, which are obtained by clustering the features in a supervised way, the final representations are more compact and discriminative. We have conducted extensive experiments on two benchmark datasets: UIUC-Sports dataset and 15-Scene dataset, which demonstrates that the proposed approach can significantly improve the original object bank and achieves state-of-the-art performances.
Design and Implementation of a BCI System Based on Steady-State Visual Evoked Potentials

In recent years, Brain-computer interface (BCI) based on electroencephalogram (EEG) is an active topic in brain function research. BCI provides a direct communication and control channel for sending messages and instructions from brain to external computers or other electronic devices. However, the EEG signals are very faint and often corrupted by noises such as power line noise, EMG, EOG etc.. EEG signal acquisition is very critical for the BCI system. In the paper, a circuit for EEG signal acquisition is designed. The circuit module is mainly composed of protection circuit, instrumentation amplifier, common mode rejection (CMR) circuit, gain adjustable amplifiers and filters. Finally, a BCI system based on SSVEP sets up by this hardware circuit's platform. The experimental results show that the circuit is effective with good amplification ability and high CMR, and it is very suitable for the BCI system.
An Efficient Lossless Compression Algorithm for fMRI Data Volume

This paper presents an efficient lossless compression scheme designed for the fMRI data volume. An fMRI time series is composed of trend, noise and external stimulus response, which have different characteristics in wavelet domain. Reversible integer wavelet transform is applied to the fMRI time series. The smooth trend signals are extracted from the low frequency wavelet bands and they are coded by a lossy compression scheme. The lossy compression residual, noise, and other components are inversely transformed into time domain followed by adaptive clustering and then lossless coding. The experimental results show that the proposed method outperforms many existing schemes
Common avionics system design for serial unmanned adaption RUAS

Through the technical approach analysis of the Rotorcraft Unmanned Air System (RUAS) in resent ten years, we find that the way of realizing the RUAS development, rapid prototype test platform, and serial RUASs programs are all using the mature manned helicopters to carry out unmanned adaption. Moreover, we think the serial RUAS based on unmanned adaption is the trends in the future. However, there are less products or systems aiming at the serial RUAS development. As the key to reflect the advantages of serial equipments, the architecture and functions of the common avionics system is proposed. It referred the open system architecture has layered architecture based on the common core processor and AFDX network. Through the designs of common helicopter interface unit and the common flight control unit, the isolation between the avionics system and the helicopter platform is realized. So as to permit the serial RUASs can use the common equipments. That's increasing the portability, universal, and scalability of the common avionics system. It has reference value for serial unmanned adaption RUAS development.
A highly efficient external memory interface architecture for AVS HD video encoder

This paper presents a highly efficient external memory interface architecture to improve memory bandwidth utilization for AVS HD video encoder. Both burst and bank interleaved SDRAM accesses are intelligently adopted in the memory interface design. Our proposed architecture is composed of an address mapping layer and an arbitration layer. In the address mapping layer, according to the data request pattern and quantity, the clients in the encoder are divided into four groups which are assigned to different banks of the SDRAM. In each group, efficient address mapping schemes are proposed to minimize inner client overhead. In the arbitration layer, a straightforward group-based interleaved arbitration scheme is proposed to minimize inter client overhead. Experimental results show that the data access overhead cycles of our proposed interface design are reduced significantly and the bandwidth utilization is improved by up to 10% compared to the tile-linear address mapping scheme.
A Framework for Big Data Security Analysis and the Semantic Technology

The emerging security analysis challenge in big data environment is getting more attention of security industry and research institute. The current problems are mainly about the volume of security data and the understanding of the data. To solve the problems, we should employ the big data technology, and meantime, consider the semantic processing methods. In this paper, we combine the big data processing with semantic methods and propose a framework for security analysis in big data era with semantics. In this framework, we can merge multi-sources security data, and process and analyze data using semantic connection knowledge, which is supposed to understand security data semantically and process data with semantic association and inference methods. The proposed framework can be worked with current threat intelligence sharing and exchanging mechanism.
Aware service base on the assembly of multiple weak location sensors

Indoor location information is important for the awareness computing environment. One single location sensor always is not accurate and applicable enough for providing the right service to the user. In this paper, an OSGi based simulation system is introduced firstly, and then we propose a framework to support the combination of several weak location sensors. With this combination model, multiple weak location sensors can work together to provide the better location aware service to the user. The experiment platform which consists of RFID array location sensor, RF sensor, pressure sensor and other sensors can work together in the OSGi based simulation system, experiment results show that the assembly of multiple weak sensors do better than the solo sensor. It can help the designers to achieve the better cost-performance for the awareness applications between various possible combinations.
Active Self-Paced Learning for Cost-Effective and Progressive Face Identification

This paper aims to develop a novel cost-effective framework for face identification, which progressively maintains a batch of classifiers with the increasing face images of different individuals. By naturally combining two recently rising techniques: active learning (AL) and self-paced learning (SPL), our framework is capable of automatically annotating new instances and incorporating them into training under weak expert recertification. We first initialize the classifier using a few annotated samples for each individual, and extract image features using the convolutional neural nets. Then, a number of candidates are selected from the unannotated samples for classifier updating, in which we apply the current classifiers ranking the samples by the prediction confidence. In particular, our approach utilizes the high-confidence and low-confidence samples in the self-paced and the active user-query way, respectively. The neural nets are later fine-tuned based on the updated classifiers. Such heuristic implementation is formulated as solving a concise active SPL optimization problem, which also advances the SPL development by supplementing a rational dynamic curriculum constraint. The new model finely accords with the “instructor-student-collaborative” learning mode in human education. The advantages of this proposed framework are two-folds: i) The required number of annotated samples is significantly decreased while the comparable performance is guaranteed. A dramatic reduction of user effort is also achieved over other state-of-the-art active learning techniques. ii) The mixture of SPL and AL effectively improves not only the classifier accuracy compared to existing AL/SPL methods but also the robustness against noisy data. We evaluate our framework on two challenging datasets, which include hundreds of persons under diverse conditions, and demonstrate very promising results. Please find the code of this project at:  http://hcp.sysu.edu.cn/projects/aspl/.
An End-to-End Energy Saving Algorithm Based on Cooperative Game Theory under Time Delay Constraint

The next generation of the Internet is a multi-service network with data, voice, video and multimedia, which puts forward the higher requirements of real-time performance and effectiveness for the end-to-end communication network. However, higher requirements of time delay will lead to higher energy consumption. Since different communication services have different requirements of time delay, an end-to-end energy saving algorithm based on cooperative game under time delay constraint was proposed in this paper. Firstly we established a cooperative game model based on the utility function of each node's energy consumption, secondly proved the existence and uniqueness of the Nash bargaining Solution (NBS) for this model, and then the NBS was solved by the Karush - Kuhn - Tucker (KKT) conditions, thus an end-to-end energy saving algorithm based on cooperative game under time delay constraint was proposed for delay allocation and rate control. The optimal utility was achieved after the 11 iterations in simulation experiment, the delay and the rate can be reasonably distributed respectively in the case of different data packets and different delay constraints, compared with the non-cooperative game and random delay distribution, the proposed algorithm achieves the reduction of the energy consumption in the multi-node network communication system, and guarantees the fairness of the nodes.
A History-Based Dynamic Random Software Testing

Following the idea of software cybernetics, Dynamic Random Testing (DRT) was proposed to improve the traditional random testing and random-partition testing strategies. The essential idea of Dynamic Random Testing is to update the testing profile dynamically during the software testing according to test data collected online. As a result, the sub domains which have higher defect detection rate are selected more easily. There are some shortcomings in the original DRT, such as it is not rigorous to update the testing profile each time merely according to whether a test case reveals defects or not. In order to overcome the shortcomings in the original DRT, a History-based Dynamic Random Testing is proposed on the basis of DRT and it is denoted as DRT-h. In DRT-h strategy, the estimate of the defect detection rate of each sub domain is calculated in real-time during the software testing by using history testing information. In this paper some real-life software were used to verify the effect of DRT and DRT-h, and the experiments show that DRT-h outperforms random testing, random-partition testing and Dynamic Random Testing greatly.
Combined memory polynomial model for Doherty power amplifiers with memory effects

This paper presents a combined memory polynomial model for Doherty power amplifiers with memory effects. The model combines the memory polynomial model with an envelope memory term. Levenberg-Marquardt Method is applied to estimate the model parameters. The model has been successfully validated with 20MHz LTE signals in the test bench, in which the input and output signals of a Doherty power amplifier are sampled by a vector signal analyzer (VSA). Good modeling accuracy can be achieved with normalized mean square errors (NMSE) of about -40 dB by comparison between measurements and modeling results in frequency domain.
A role-based distributed publish/subscribe system in IoT

With the emerging of IoT (Internet of Things), the scale of Internet is extended extremely and large amounts of information are produced. Most previous work only focuses on the interconnection and interoperability problems among different entities and enterprise application systems in a dynamic interaction environment. However, the problems of how to efficiently disseminate the sensing information among the information providers and consumers on-demand still have not been comprehensively explored. In order to delivery various types of data in IoT on-demand, this paper proposed a role-based distributed publish/subscribe system (RBDS). RBDS is based on a hierarchy routing strategy of Grid. Firstly, the subscribers are mapped to appropriate role broker node by the organization of IoT data. Secondly, divide broker nodes into different groups based on role. In each group, broker nodes are separated into super nodes and common nodes according to their ability. Those super nodes are organized in upper Grid structure to distribute events in different groups. And the common nodes are organized in lower Grid structure to distribute events in the same group. It improves the scalability and decreases latency. At last, calculate the load of each broker node dynamically based on its capacity and its subscribers. Thereby the total number of subscribers on each broker node is efficiently balanced. Experimental results show that the RBDS can improve scalability, decrease latency and balance load effectively.
An Efficient Globally Optimal Algorithm for Asymmetric Point Matching.

Although the robust point matching algorithm has been demonstrated to be effective for non-rigid registration, there are several issues with the adopted deterministic annealing optimization technique. First, it is not globally optimal and regularization on the spatial transformation is needed for good matching results. Second, it tends to align the mass centers of two point sets. To address these issues, we propose a globally optimal algorithm for the robust point matching problem in the case that each model point has a counterpart in scene set. By eliminating the transformation variables, we show that the original matching problem is reduced to a concave quadratic assignment problem where the objective function has a low rank Hessian matrix. This facilitates the use of large scale global optimization techniques. We propose a modified normal rectangular branch-and-bound algorithm to solve the resulting problem where multiple rectangles are simultaneously subdivided to increase the chance of shrinking the rectangle containing the global optimal solution. In addition, we present an efficient lower bounding scheme which has a linear assignment formulation and can be efficiently solved. Extensive experiments on synthetic and real datasets demonstrate the proposed algorithm performs favorably against the state-of-the-art methods in terms of robustness to outliers, matching accuracy, and run-time.
Characteristics of the Bio-enhanced powder activated carbon dynamic membrane reactor for municipal wastewater treatment

A Bio-enhanced powder activated carbon dynamic membrane reactor (BPDMR) was designed, which combined the advantages of both microbial reactor and dynamic membrane, and is a new municipal wastewater treatment technology. Based on a laboratory-scale anoxic-oxic (A/O) process, the dynamic membrane is used as the key factor of intercepting suspended solids (SS) and colloidal particles. Simultaneously, experimental results indicated that the system had the advantages of high filtration flux, low filtration resistance, easy back washing, and higher removal efficiency of the pollutants.
Direct yaw-moment control of a FWIA EV based on sliding model control and torque allocation optimization

In this paper, a direct yaw-moment control (DYC) scheme is proposed for a four-wheel-independently-actuated electric vehicle (FWIA EV). An upper controller is based on a reference model with two degrees of freedom (2-DOF) that generates the desired yaw rate for a sliding mode controller to track so as to improve the vehicle dynamic stability. A torque optimization distribution strategy is designed in the lower controller to allocate the required torques to each in-wheel motor for vehicle stability enhancement. The proposed DYC scheme is implemented in a Carmaker vehicle model and a MATLAB/Simulink control model and evaluated in simulations of a snake-lane-change and a double-lane-change maneuver. The results show that the side slip angle and tire load rate have been reduced, on average, by 2/3 using the DYC system compared with those without control. The cases with DYC also provide better tracking of the desired trajectory and yaw rate with smaller steering angle than those without control.
A novel approach of components retrieval in large-scale component repositories

For improving the retrieval performance of large-scale component repositories, a novel approach of components retrieval, Automatic Tags Extraction(ATE) retrieval, is proposed in this paper. In this method, component tags are extracted automatically from application domain terms, high-frequency terms, high-weight terms and facet terms in description document of component at first, and then the improved VSM (Vector Space Mode) similarity algorithm is used to retrieve on the tags. Our experiments show that, compared with some common retrieval methods, the ATE retrieval is more feasible and efficient.
Advanced packaging stepper for 300mm wafer process

With development of advanced package technology, requirements for lithographic process become tighter than before. When dealing with 200mm or 300mm wafer, stepper shows better uniformity of CD and overlay. SSB500 stepper can be used in RDL and Bump process. This paper introduces features of SSB500 stepper, such as wide band exposure light source, large field size, flexible alignment strategy, direct focus measurement, and proposes absolute overlay error concept and measurement method based on golden wafer. It also provides test data for CDU, UDoF, overlay for single machine and matching, thick photo resist performance, which shows specification of the stepper meeting well with advanced package process.
Design of Data Detection System Based on ARM Embedded System

Data detection system is an integrated measurement and control system. In order to enhance the performance of real-time response and ability to multi-task procession, this paper introduces a design of data detection system based on ARM9 hardware and μC/OS-??? embedded operating system, which has the advantages such as detection data is demendable and construction is simple in worksite. It can be applied in microbiology fermentation process detection system, and has broad market prospects.
Discriminant analysis with tensor representation

In this paper, we present a novel approach to solving the supervised dimensionality reduction problem by encoding an image object as a general tensor of 2nd or higher order. First, we propose a discriminant tensor criterion (DTC), whereby multiple interrelated lower-dimensional discriminative subspaces are derived for feature selection. Then, a novel approach called k-mode cluster-based discriminant analysis is presented to iteratively learn these subspaces by unfolding the tensor along different tensor dimensions. We call this algorithm discriminant analysis with tensor representation (DATER), which has the following characteristics: 1) multiple interrelated subspaces can collaborate to discriminate different classes; 2) for classification problems involving higher-order tensors, the DATER algorithm can avoid the curse of dimensionality dilemma and overcome the small sample size problem; and 3) the computational cost in the learning stage is reduced to a large extent owing to the reduced data dimensions in generalized eigenvalue decomposition. We provide extensive experiments by encoding face images as 2nd or 3rd order tensors to demonstrate that the proposed DATER algorithm based on higher order tensors has the potential to outperform the traditional subspace learning algorithms, especially in the small sample size cases.
A visual modeling method of entity-based network architecture

With the development of Internet technology, the traditional network is more difficult to meet the needs of new applications. Researchers make a thorough analysis and research of the next generation Internet at home and abroad. This paper proposes a visual modeling method of entity-based network architecture, by making a comprehensive analysis and design of network systems from the perspective of the architecture. This method is the further study of the formal modeling method of network architecture. This paper offers the researchers practical solutions to design, manage and maintain new network architecture graphically. After that, based on object-oriented idea, the network architecture visual modeling tool is designed and implemented. The tool is called Designer for Network Architecture. At last, a router is modeled using the visual modeling tool as an example.
A method of color optical flow computation with local and global model combined

It is a problem that the results are unstable when local method is used to estimate optical flow at smooth color points. A method was presented to estimate stability through the condition number of local coefficients matrix. And the color results of global smooth were used to replace the unstable results of local color method. The combined model was provided with the advantages of high precision of local model and density of global model. Finally, the fusion optical flow result was filtered by median filter. Experiments show that the method produces density optical flow field and precision of the result can be improved.