nils-herrmann/clean_scirepeval huggingface dataset:
Adaptive Hypergraph Learning and its Application in Image Classification

Recent years have witnessed a surge of interest in graph-based transductive image classification. Existing simple graph-based transductive learning methods only model the pairwise relationship of images, however, and they are sensitive to the radius parameter used in similarity calculation. Hypergraph learning has been investigated to solve both difficulties. It models the high-order relationship of samples by using a hyperedge to link multiple samples. Nevertheless, the existing hypergraph learning methods face two problems, i.e., how to generate hyperedges and how to handle a large set of hyperedges. This paper proposes an adaptive hypergraph learning method for transductive image classification. In our method, we generate hyperedges by linking images and their nearest neighbors. By varying the size of the neighborhood, we are able to generate a set of hyperedges for each image and its visual neighbors. Our method simultaneously learns the labels of unlabeled images and the weights of hyperedges. In this way, we can automatically modulate the effects of different hyperedges. Thorough empirical studies show the effectiveness of our approach when compared with representative baselines.
Driver support and cooperative systems control design: Framework and preliminary results

Driver support systems aim to support or take over part of the driving task in order to drive vehicles in a safe, comfortable and efficient way. To this end, in literature, different controller designs have been put forward over the past two decades. This paper proposes an optimal control framework to model driver support systems. In this approach, the acceleration of vehicles in a platoon following a platoon leader is controlled in such a way that a generic objective function is optimized. To solve the optimization problem, a numerical solution approach is put forward applying Pontryagin's minimum principle. Both competitive and cooperative controllers are put forward, in which each vehicle either optimizes its own situation, or where all vehicles cooperate to optimize the overall performance of the platoon. We present several examples showing the difference between the strategies. Results show that the computational complexity of the proposed approach is sufficiently small to allow for real-time computation of car-following strategies, compared to previous approaches. An important property is that this complexity does not increase exponentially with the complexity of the prediction model, nor with the size of the control vector.
An automated feature extraction method for recognition of Petechia Spot in Tongue Diagnosis

Petechia Spot ranks itself among the most significant pathological features utilized in the practice of the Tongue Diagnosis because it can reveal the existence of tiny hemorrhage in some organ inside the body. In this paper, an automated feature extraction method for recognition of Petechia Spot in tongue image is proposed employing marker-based watershed transform and morphological filter. An efficient preprocessing method including gray-level contrast-stretch is designed before extraction to make Petechia Spot more apparent. Moreover, algorithm erasing falsely selected region is presented to avoid misjudgment. Experiment to test availability of the method is carried out, and result is proved to be satisfactory.
Touch Saliency: Characteristics and Prediction

In this work, we propose an alternative ground truth to the eye fixation map in visual attention study, called touch saliency. As it can be directly collected from the recorded data of users' daily browsing behavior on widely used smart phone devices with touch screens, the touch saliency data is easy to obtain. Due to the limited screen size, smart phone users usually move and zoom in the images, and fix the region of interest on the screen when browsing images. Our studies are two-fold. First, we collect and study the characteristics of these touch screen fixation maps (named touch saliency) by comprehensive comparisons with their counterpart, the eye-fixation maps (namely, visual saliency). The comparisons show that the touch saliency is highly correlated with the eye fixations for the same stimuli, which indicates its utility in data collection for visual attention study. Based on the consistency between both touch saliency and visual saliency, our second task is to propose a unified saliency prediction model for both visual and touch saliency detection. This model utilizes middle-level object category features extracted from pre-segmented image superpixels as input to the recently proposed multitask sparsity pursuit (MTSP) framework for saliency prediction. Extensive evaluations show that the proposed middle-level category features can considerably improve the saliency prediction performance when taking both touch saliency and visual saliency as ground truth.
A Formal Specification and Verification Framework for Designing and Verifying Reliable and Dependable Software for Computerized Numerical Control (CNC) Systems

As a distributed computing system, a CNC system needs to be operated reliably, dependably and safely. How to design reliable and dependable software and perform effective verification for CNC systems becomes an important research problem. In this paper, we propose a new modeling method called TTM/ATRTTL (Timed Transition Models/All-Time Real-Time Temporal Logics) for specifying CNCsystems. TTM/ATRTTL provides full supports for specifying hard real-time and feedback that are needed for modeling CNC systems. We also propose a verification framework with verification rules and theorems and implement it with STeP and SF2STeP. The proposed verification framework can check reliability, dependability and safety of systems specified by our TTM/ATRTTL method. We apply our modeling and verification techniques on an open architecture CNC (OAC) system and conduct comprehensive studies on modeling and verifying a logical controller that is the key part of OAC. The results show that our method can effectively model and verify CNC systems and generate CNC software that can satisfy system requirements in reliability, dependability and safety.
A novel implantless MOS thin-film transistor with simple processing, excellent performance and ambipolar operation capability

A novel thin-film transistor (TFT) device that requires no implant step and is capable of ambipolar operation is proposed and successfully demonstrated. The new structure features an undoped Si active channel, a tap metal field-plate (i.e., the sub-gate), and Schottky source/drain. The equivalent circuit of the device is given. During device operation, a high fixed voltage is applied to the sub-gate to form a field-induced source/drain layer under the sub-gate region. Depending on the polarity of the sub-gate bias, the device can be set for n-channel operation with positive sub-gate bias, and p-channel operation with negative sub-gate bias. The new device is similar to conventional Schottky barrier (SB) MOSFET devices, with the exception of a field-induced source/drain region between the channel and Schottky source/drain. The existence of the field-induced source/drain region serves to supply abundant channel carriers during on-state, while reducing the notorious off-state leakage that has plagued all previous SB MOSFETs. The new device is also similar to MOSFETs with field-induced drain (FID), except that the heavily-doped source/drain region is replaced by Schottky source/drain. While retaining all the advantages of FID such as low off-state leakage and low junction leakage, the use of Schottky source/drain not only reduces processing steps (i.e., implant and annealing), but also allows ambipolar operation, thus greatly simplify processing steps especially for CMOS process integration.
An Energy-Balanced Clustering Routing Algorithm for Wireless Sensor Networks

When cluster heads transmit their data to the sink via multi-hop communication, the cluster heads closer to the sink not only gather the data in its cluster but also forward the data for other cluster heads. These key nodes are burdened with heavy relay traffic and tend to die early. An Energy-balanced Clustering Routing algorithm--EBC is proposed, which adjusts the number of member in a cluster to balance the energy dissipation of every cluster head nodes. The total energy dissipation caused by EBC will be less and it will avoid the key nodes premature failure. Simulation results show that the routing protocol effectively balances the energy consumption among cluster heads and achieves an obvious improvement on the network lifetime.
On state estimation with bad data detection

We consider the problem of state estimation through observations corrupted by both bad data and additive observation noises. A mixed ℓ1 and ℓ2 convex programming is used to separate both sparse bad data and additive noises from the observations. Using the almost Euclidean property for a linear subspace, we derive a new performance bound for the state estimation error under sparse bad data and additive observation noises. Our main contribution is to provide sharp bounds on the almost Euclidean property of a linear subspace, using the “escape-through-a-mesh” theorem from geometric functional analysis. We also propose and numerically evaluate an iterative convex programming approach to solve bad data detection problems in electrical power networks.
Fuzzy Comprehensive Evaluation Model of Railway Design Quality

Based on the present situation of railway design quality, an evaluation index system of railway design quality is setup, and multiple fuzzy comprehensive evaluation method is used to evaluate railway design quality. Then, an empirical analysis is carried on a railway. The results showed that the index system and evaluation method has a strong feasibility and operability. It will be widely popularized and applied in railway construction project. This not only helps further improvement of the railway institute’ s design level, but also promotes the quality supervision work of railway departments.
Research on multi cross-section stage-discharge relationship based on BP neural network

To overcome the deficiencies of discharge measurement method in open channel, BP neural network (BPNN) is applied in fitting stage-discharge calculation, based on which design a multi cross-section stage-discharge method. BPNN-based single stage-discharge fitting model and BPNN-based dual stage-discharge fitting model were established as the research objects. The stage-discharge data of open channel acquired from the velocity-area method are used to train and test the model. Experiment and simulation results show that, compared with the single cross-section stage-discharge fitting model, the multi stage-discharge measurement model has higher prediction accuracy, and can exclude effect of backwater, it has been applied in flow prediction of open channels.
Modified semi-orthogonal User Scheduling scheme with optimized user selection parameter

In the original Semi-orthogonal User Scheduling (SUS) scheme, there is a parameter that controls the degree of orthogonality of channel directions between co-scheduled users. However, choosing a good value for the parameter is known to be hard. In this paper, we consider optimizing the parameter in a slightly modified setup, where users are scheduled purely based on their channel directions. With the objective to maximize the system throughput, we demonstrate how to optimize the parameter in the case of perfect feedback (i.e. the base station has the perfect knowledge of users' channel directions) as well as in the case of limited feedback.
Extract summarization using Concept-Obtained and Hybrid Parallel Genetic Algorithm

This paper proposes a special Chinese automatic summarization method based on Concept-Obtained and Hybrid Parallel Genetic Algorithm. The idea of our approach is to obtain concepts of words using HowNet as tool, and use concepts as feature, not words. We construct conceptual vector space model and use Hybrid Parallel Genetic Algorithm to form summary of documents. The automatic evaluation of document's summaries with n-gram score shows the system's effectiveness and feasibility.
iBotGuard: an Internet-based Intelligent Robot security system using Invariant Face Recognition against intruder

One crucial application of intelligent robotic systems is remote surveillance using a security robot. A fundamental need in security is the ability to automatically verify an intruder into a secure or restricted area, to alert remote security personnel, and then to enable them to track the intruder. In this article, we propose an Internet-based security robot system. The face recognition approach possesses "invariant" recognition characteristics, including face recognition where facial expressions, viewing perspectives, three-dimensional poses, individual appearance, and lighting vary and occluding structures are present. The experiment uses a 33.6-kb/s modem Internet connection to successfully remotely control a mobile robot, proving that the streaming technology-based approach greatly improves the "sensibility" of robot teleoperation. This improvement ensures that security personnel can effectively and at low cost use the Internet to remotely control a mobile robot to track and identify a potential intruder.
Robust Face Recognition via Adaptive Sparse Representation

Sparse representation (or coding)-based classification (SRC) has gained great success in face recognition in recent years. However, SRC emphasizes the sparsity too much and overlooks the correlation information which has been demonstrated to be critical in real-world face recognition problems. Besides, some paper considers the correlation but overlooks the discriminative ability of sparsity. Different from these existing techniques, in this paper, we propose a framework called adaptive sparse representation-based classification (ASRC) in which sparsity and correlation are jointly considered. Specifically, when the samples are of low correlation, ASRC selects the most discriminative samples for representation, like SRC; when the training samples are highly correlated, ASRC selects most of the correlated and discriminative samples for representation, rather than choosing some related samples randomly. In general, the representation model is adaptive to the correlation structure that benefits from both ℓ1-norm and ℓ2-norm. Extensive experiments conducted on publicly available data sets verify the effectiveness and robustness of the proposed algorithm by comparing it with the state-of-the-art methods.
Empirical Evaluation of Test Coverage for Functional Programs

The correlation between test coverage and test effectiveness is important to justify the use of coverage in practice. Existing results on imperative programs mostly show that test coverage predicates effectiveness. However, since functional programs are usually structurally different from imperative ones, it is unclear whether the same result may be derived and coverage can be used as a prediction of effectiveness on functional programs. In this paper we report the first empirical study on the correlation between test coverage and test effectiveness on functional programs. We consider four types of coverage: as input coverages, statement/branch coverage and expression coverage, and as oracle coverages, count of assertions and checked coverage. We also consider two types of effectiveness: raw effectiveness and normalized effectiveness. Our results are twofold. (1) In general the findings on imperative programs still hold on functional programs, warranting the use of coverage in practice. (2) On specific coverage criteria, the results may be unexpected or different from the imperative ones, calling for further studies on functional programs.
The doping effect on the electrochemical performance of LiNi0.305Mn0.33Co0.33M0.025O2 (M=Al, Y, Cr) for lithium-ion batteries

LiNi<sub>0.305</sub>Mn<sub>0.33</sub>Co<sub>0.33</sub>M<sub>0.025</sub>O<sub>2</sub> (M=Al, Y, Cr) materials were synthesized via the sol-gel method. The structure and electrochemical properties were characterized by X-ray diffraction (XRD), scanning electron microscope (SEM), cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS) and charge/discharge tests. The materials were maintained the α-NaFeO<sub>2</sub> type layered structure and the discharge capacity was also increased by doping. The Y- and Al- doping slightly decrease the initial discharge capacity of LiNi<sub>0.33</sub>Mn<sub>0.33</sub>Co<sub>0.33</sub>O<sub>2</sub> while the Cr-doping can increase the initial discharge capacity. The cycling performances are improved after doping. When cycled at 1.0C, about 99%, 98.6% and 97.1% of their initial capacities can be retained after 40 cycles for Y-, Al- and Cr-doped materials, respectively. Their rate capabilities are also better than that of the un-doped one. EIS measurement shows that the Y-doped electrode has the lowest resistance impedance value during cycling.
Analysis on the interests of construction parties in project management model based on the game theory

The participants in building projects have worked together in the low efficiency state for many years, which makes it difficult to maximize the interests of all participants. Game theory can explain strategy of all parties involved in the project and benefit from the situation and find the crux of the situation. Firstly, through the establishment of complete information static game model and creation of the benefit function of the project participants with the linear function, a game matrix can be constituted and the Nash equilibrium of the game model may be calculated. The Nash equilibrium of the game can express the conflict between the individual rational and the collective rational in the game of building projects. Secondly, the benefit function of the two sides of the game will be analysed and the benefit graphics of the participants can be drawn according to the benefit function. The graphics can be used to explain that the benefit of participants in the partnering model is greater than the benefit of participants in the traditional model of construction projects. So, the best strategy of two sides in Game is to adopt partnering model, and all research work can provide a theoretical basis for analysis of the superiority of the partnership model.
Fusion visualization for cardiac anatomical and ischemic models with depth weighted optic radiation function

Despite the emerging of various innovations of visualization methods, highlighting and exploring the important function of interest of computational cardiac model still remain a challenging problem. Due to the complex and fine geometries of heart, the silhouette of the whole volume structure may be destroyed when there is a necessity to demonstrate cardiac electrophysiological behaviors. In this paper, we proposed a fusion visualization framework, which combines the electrophysiology pattern with the anatomy pattern through a novel multi-dimensional fusion transfer function. A depth weighted optic radiation model is thus presented in the framework to inspect the occluded cardiac ischemia information. According to the depth to the tissue boundary, the hidden ischemia region of pathological tissue can be revealed from complex overlapping features. Experiment results on the cardiac anatomy and electrophysiological simulation data verifies the effectiveness of the proposed method for intuitively exploring and inspecting electrophysiological activities with the preserved authentic context of anatomical structure, which is found to be useful in analyzing and explaining cardiac functioning phenomena.
A QoS-Aware Web Service Selection Algorithm Based on Clustering

With the prevalence of SOA, an increasing number of Web services are created and composed to construct complex business processes. Selecting an appropriate service from a lot of independently developed services which have the same functionality but different QoS properties is essential for the effect of the composite service according to users' preference. Moreover, the efficiency and effect of the service selection algorithm also play an important role. In this paper, we propose a novel algorithm, named QSSAC, for service selection problem. This algorithm is based on the service clustering which can cluster a lot of atomic services of each task into a few classes according to their QoS properties. With the help of service clustering, our algorithm is able to reduce the execution time and guarantee the near-optimal result as well. Finally, three strategies are provided for re-selecting atomic services in dynamic environment. In experiment, we study the performance of QSSAC algorithm, and its feasibility has been demonstrated by simulation.
Design and Implementation of a High-Performance Stream-Oriented Big Data Processing System

While the real-time processing applications are ubiquitous, which lead to the sprout of many distributed real-time stream processing systems. In this paper, we design a high-performance streamed-oriented big data processing system, including a messaging queue on Kafka and a Spark Streaming-based processing engine, and we evaluate the effectiveness of our system through a real-time traffic statistics application. Then we focus on the relationship between the parameters of Kafka and Spark Streaming and the performance of our system. In the last, we discuss the scalability of the optimized system of Kafka and Spark Streaming.
One statistics-based fault classification technique for cascaded inverter

A fault classification and location method for a 5-level Cascaded H-Bridges Inverter based on BP Neural Network is proposed in this paper. Meanwhile, Principal Component Analysis (PCA) is used in the Neural Network,which we call PCA-NN, to simplify the training data and save the training time. On the other hand, a BP Neural Network without PCA, which is called NN in the following sector, is also proposed. Simulation is done through MATLAB to certify the feasibility of the proposed networks. At the same time, a contrast is made to compare the performances of the two Neural Networks and conclusions are drawn. From the contrast it can be seen that PCA-BP is 5 percent more accurate than NN and the PCA-NN considerably cut down the dimension of training data from 30 to 7 which is favorable for saving training time and improving mapping performance.
Real-Time Event Identification Through Low-Dimensional Subspace Characterization of High-Dimensional Synchrophasor Data

This paper proposes a data-driven real-time event identification method based on the measurements of Phasor Measurement Units (PMUs). The central idea is to characterize an event by the low-dimensional subspace spanned by the dominant singular vectors of a data matrix that contains spatial–temporal blocks of PMU data. The subspace representation is robust to system initial conditions and characterizes the system dynamics. A dictionary of subspaces that correspond to different events are established offline, and an event is identified online with the most similar event in the dictionary through subspace comparison. The compact subspace representation reduces the dictionary size and the computational time of the event identification method. Numerical experiments on both simulated events in an IEEE 68-bus power system and the recorded data in New England validate the proposed method.
An Improved Multi-stage Vector Quantization for Image Coding

The traditional full search vector quantization (FSVQ) can give a desirable quality for image coding, but it consumes too much time and memory space. The multistage vector quantization (MSVQ) was proposed to alleviate the computational complexity of FSVQ. However, MSVQ cannot give a comparable performance as FSVQ for image coding. To better tradeoff the coding performance and computational complexity, we propose a classified multistage vector quantization (CMSVQ) in this paper. The simulation results show that the CMSVQ scheme exhibits higher codewords' use efficiency a better trade-off between the image quality and the computational complexity.