nils-herrmann/clean_scirepeval huggingface dataset:
Counter based adaptation for CAVLC in HEVC

Compared to the CAVLC in H.264/AVC, CAVLC in HEVC has been improved much. One mechanism that contributes to the improvement is adaptive code word swapping based on previous coded symbol in the early design of HEVC. However, such a instantly swapping strategy often makes a wrong estimation of the real statistics and may lead to inefficient coding that may even worse than without adaptation. In this paper, we analyze potential loss caused by instantly swapping and propose a counter-based adaptation scheme to further improve the entropy coding efficiency. We discuss the bit-depth of those counters that achieves a trade-off between the adaptation capability and a good estimation of the statistics. The proposed scheme introduces a sum counter for each code table and a counter for each code. The sum counter determines when to shift all the counters to prevent overflow and make sure symbols far away influence less. The experimental results show that the counter based algorithm brings about 0.7% bits saving compared with the instantly swapping adaptation algorithm. Compared with the CAVLC without adaptation, the performance gain of the counter adaptation algorithm is about 4.0% bits saving. A variant of the proposed counter-based adaptation has been adopted by HEVC design.
Location aided transmit strategy in two-way relay networks

In this paper, a simple transmit beamforming scheme over MISO channel in two-way relay networks is proposed. Beam in the proposed scheme is built to maximize sum of the receive power of the two receivers according to their geographic location. The key consideration is to estimate signal direction of arrival (DOA) with the location information supported by localization and tracking instead of using complex DOA estimation algorithm. System performance impacted by DOA of signal of each receiver is analyzed. Two special DOA cases-coherent position and orthogonal position are shown, which bound the performance of this scheme. In coherent position, both receivers will benefit 3 dB array gain, while in orthogonal position system performs as two separate SISO channel. Theoretical and simulative analyses of the transmit strategy is carried out. It is found that the proposed scheme can support simple and higher rate transmission over MISO channel, and suitable to relay networks.
A HLA-based hierarchical architecture for the CTCS hardware-in-the-loop simulation system

In this paper, a HLA-based hierarchical architecture for the CTCS HILS system is proposed to achieve better structural properties including expansibility, reconfigurability, loose coupling and finer modularization. The overall structure and some key points of the architecture (i.e. HLA infrastructure, event scheduling mechanism, message dispatch mechanism, and equipments access mechanism) are explained in sequence. The effective solutions to some special problems (i.e. the federate division and the train transfer) are prproposed, too. The successful application of this architecture shows that it can well satisfy the requirements for constructing a large-scale complex distributed HILS system, such as the CTCS HILS system.
Description of Boundary Irregularity On Multi-Scale Local FD for Melanomas

Boundary irregularity of melanomas is one of most important clinical indicators to discriminate from benign moles. Conventional fractal dimension (FD) is utilized to describe boundary complexity with weaker criminative power. The objective of the paper is to explore new boundary irregularity descriptors at different scales. In the paper, there are two novel measures of boundary's structural irregularities and multiscale features related to boundary roughness are proposed using an integrated quantitative model combining Gaussian filtering and local FDs. There are two key elements in the scheme: (1) Local fractal distributions are taken as a tool to describe local irregularity along boundaries at different scales with meaningful diagnosis features; (2) multi-scaled boundaries generated by Gaussian filters could produce boundary's outlines important to measure of structural irregularity. The advantage of the approach is that feature clusters of boundary irregularity are formed at different scales, so that extracted measurements from multi-scales could provide effective descriptors compared to the conventional FD and other boundary based methods. Experiments show that the proposed irregular descriptors not only enhance finer expressions of boundary complexity but also could effectively discriminate melanomas among moles using statistical features of boundary roughness at different scales.
Academic Recommendation on Graph with Dynamic Transfer Chain

Academic contents update and learner's capability change over time. But nowadays, academic recommendation system does not take time factors into account. There are two challenges to capture learner's preferences and learning context accurately and dynamically. First modeling academic trend and user's cognitive level transferred by time is a hard problem. And designing dynamic algorithm to improve recommendation accuracy with implicit behavior data is difficult. In this paper, we propose Dynamic Transfer Chain (DTC) to model user's preferences and academic context over time on transaction data. Based on DTC model, we present a novel algorithm Dynamic Academic Recommendation on Graph (DARG). We evaluate the effectiveness of our method using an open dataset named CiteULike, including 9170 users, 11343 papers, 194596 user-paper pairs. The evaluation metric we used is Hit Ratio. The results show that our proposed approach gives 12.873% to 33.852% improvement over the previous counterpart, including User-KNN, Item-KNN, TUser-KNN, TItem-KNN.
Formalizing the Structure and Behaviour of Context-Aware Systems in Bigraphs

Context-aware Computing has been one of the important aspects of Ubiquitous Computing, Cloud Computing, Cyber-physical Systems, etc., recently. To date, very few works can be found on formal approaches for this area. Bigraph and its related theories are introduced to formalize the modeling of context-aware systems in this paper. We use bigraphs to model the structure of context-aware system, whereas the corresponding behavior of context-aware system is modeled using bigraphical reactive system. Here we make a mapping from the elements of context-awareness to the components of bigraph. Consequently, we present an approach to translating scenarios in real life into bigraphical models. Furthermore, the formalisms are explicitly illustrated through some simple but non-trivial examples.
A Model-based Algorithm for Mass Segmentation in Mammograms

A novel mass segmentation algorithm is proposed in this paper. It establishes two mass models to represent the various masses, uses iterative thresholding to extract the suspicious area, and applies a DWT-based approach to locate the masses. And then, a region growing process restricted by Canny edge detection is carried out to extract the rough mass regions, and finally active contour model is used to segment the masses accurately. The clinical experiment has demonstrated that our algorithm has higher performance than conventional methods
Miniaturized Tapered Slot Antenna With Signal Rejection in 5–6-GHz Band Using a Balun

A miniaturized elliptically tapered slot antenna fed by a novel balun is proposed for directional ultrawideband (UWB) applications. The feeding configuration consists of a stepped microstrip line and a folded slotline connected with a circular slot. An arc-shaped parasitic strip is printed inside the circular slot. This design achieves the minimization of the physical size and the capability of signal rejection at 5-6 GHz with significant gain suppression of 15 dB. Both simulated and measured results confirm the advantage of the design in reducing the size of the antenna while maintaining the UWB performance with band-notched function.
A Bayesian view on the polynomial distribution model in estimation of distribution algorithms

Estimation of distribution algorithms(EDA) are a class of recently-developed evolutionary algorithms in which the probabilistic model are used to explicitly characterize the distribution of the population and to generate new individuals. The polynomial distribution is applied by discrete EDAs and continuous EDAs based on discretization of the domain such as histogram-based EDA. We can unify those kinds of EDA from their distribution and call them PolyEDA. In this paper, we theoretically analyze PolyEDA from a Bayesian analysis view. Our analysis is based on the assumption that the prior distribution of the parameters satisfies a Dirichlet distribution, because under this assumption the formulation can be analytically solved. Furthermore, we notice that the prior distribution is always overlooked by previous algorithms, so we follow this way and propose some strategies to improve the PolyEDA. The experimental results show that these new strategies can help the polynomial model based estimation of distribution algorithms achieve better convergence and diversity.
A new clustering-based method for protein structure selection

In protein tertiary structure prediction, it is a crucial step to select near-native structures from a large number of candidate structural models. Despite much effort to tackle the problem of protein structure selection, the discerning power of current scoring functions is still unsatisfactory. In this paper, we developed a new clustering-based method for selecting near-native protein structures. Our method consists of three phases: filtering, clustering and cluster reduction, and centroid construction. Given a set of Calpha protein structures, we apply one or multiple existing scoring functions to filter out bad structures. Then, we group the remaining structures into clusters based on pair-wise similarity measured by RMSD. Each cluster is reduced iteratively to remove outliers and bad structures. Finally, we construct a centroid for each cluster by applying multi-dimensional scale techniques. The centroids are the final models. In experiments, we applied our method to a test set of representative proteins and obtained significant improvement over existing methods.
A study of Chinese text summarization using adaptive clustering of paragraphs

Automatic summarization is an important research issue in natural language processing. This paper presents a special summarization method to generate single-document summary with maximum topic completeness and minimum redundancy. It initially implements the semantic-class-based vector representations of various kinds of linguistic units in a document by means of HowNet (an existing ontology), which can improve the representation quality of traditional term-based vector space model in a certain degree. Then, by adopting K-means clustering algorithm as well as a clustering analysis algorithm, we can capture the number of different latent topic regions in a document adoptively. Finally, topic representative sentences are selected from each topic region to form the final summary. In order to evaluate the effectiveness of the proposed summarization method, a novel metric which is known as representation entropy is used for summarization redundancy evaluation. Preliminary experimental results show that the proposed method outperforms the conventional basic summarization method under the evaluation scheme when dealing with diverse genres of Chinese documents with free writing style and flexible topic distribution.
Feasibility analysis of No.10 coal seam in the first mining area of Yuandian coal mine

The no.10 coal seam in the first mining area of Yuandian coal mine lies in “Taiyuan Group limestone” aquifer with rich water, the underlying limestone water is an important danger for mine water-filling. To ensure the safety of mining, it is necessary to study and analyze the mining conditions. On this basis, with the design of targeted prevention and treatment of water, we achieve the purpose of safe mining.
A Visual Based Extended Monte Carlo Localization for Autonomous Mobile Robots

As a probabilistic localization algorithm, Monte Carlo localization (MCL) method has been widely used for mobile robot localization over the past decade. In this paper, an extended MCL method (EMCL) is developed by incorporating two different resampling processes, namely importance resampling and sensor-based resampling, to conventional MCL for improvement of localization performance. Different resampling processes are utilized based on a matching of sample distribution and observations. Two additional processes for validating over-convergence and uniformity are introduced for examination of such matching. A visual based EMCL is further implemented using a triangulation-based resampling from visual features recognized by Bayesian networks. Experiments are conducted to demonstrate the validity of the proposed approach
Axial-mode elliptical helical antenna with variable pitch angle

To improve an elliptical cross-section helical antenna's radition performance, an elliptical helical antenna with a variable pitch angle is proposed. Its radiation patterns are simulated by the moment method and FEKO software. The results show that its circular polarisation as well as its directivity can be improved compared with an elliptical helical antenna of the same coil number, axial size and arm length.
Design ETL Metamodel Based on UML Profile

Data Warehouse plays an important part in the process of decision-making for Enterprise, as a key component of the data warehouse architecture, the tool that support data extraction, transformation, loading(ETL, Extract \ Transformation \ Load) is a critical success factor for any data warehouse projects. Traditional methods of ETL development are difficulty to meet the Enterprise decision-making needs. To deal with these problems, we put forward a method of designing the ETL meta-model based on an integrated modeling framework of DW, which takes place of ETL modeling in the special data warehouse, and the design of ETL can be specified in the early stages of a DW project. It achieved the seamless Integration between ETL and data warehouse designed on conceptual level, reduced the development time and cost. Finally, we prove our approach usability and efficiency by practical application of this model.
Applications of Genetic Neural Network to Deformation Prediction of Face Rockfill Dam

Genetic neural network can improve optimization ability of the neural network model and effectively overcome the slow learning speed of BP neural network, there are the inherent local minimum point defects, and previous studies show that the method can effectively improve the prediction accuracy and speed. In this paper, using better representation at home and abroad and more comprehensive information on the actual engineering data, to explore the application of Genetic Neural Network to Deformation Prediction of Face Rockfill Dam, and to provide reference for Deformation Prediction of Face Rockfill Dam.
Common Substructure Extraction of Proteins by Geometric Invariants

Structure alignment could help to find shape similarities between proteins and guide structure classification and fold recognition. Common substructure detection and extraction are especially important, for which could guide the biologist to discover binding site or active site. We represent each segment of alpha-carbon backbone by using dihedral angles and curve moment invariants. Then, local and global structure alignment could be performed by iterative closest point algorithm. Maximum common substructures between a pair of proteins or within a protein could be found. Active sites also could be detected by the proposed algorithm.
Foveation based error resilience for wireless video communication

Based on the observation that foveation analysis can be used to find most sensitive content of human eyes in video, one effective error resilience method is proposed for robust transmission of H.264 inter-coded frames in wireless channel. It firstly exploits the results of automatic foveation selection to find foveated area in inter-coded frames, and then considers the results of pre-error concealment effect analysis to search for error resilient alignment order of MB used in video encoder. Extensive experimental results on different portrait video sequences over random bit error wireless channel demonstrate that this proposed method can achieve better subjective and objective effect than original JM 8.2 H.264 video codec and H.264 its own error resilient tool.
Modeling and control of parallel dual-active-bridge converter in PET

This paper proposed a control and modeling method for parallel dual-active-bridge (DAB). A double loop control with DC bus voltage outer loop and current inner loop control method had adopted. A small signal modeling method for multiple DAB parallel systems is presented, and a small signal model of multiple DAB converters is established. Influence of voltage regulator and current regulator parameters on the system characteristics is analyzed, which provides a theoretical basis for parameter selection. Finally, the effectiveness of the proposed control method and the accuracy of the modeling method are verified by simulation analysis.
Learning Prototype Hyperplanes for Face Verification in the Wild

In this paper, we propose a new scheme called Prototype Hyperplane Learning (PHL) for face verification in the wild using only weakly labeled training samples (i.e., we only know whether each pair of samples are from the same class or different classes without knowing the class label of each sample) by leveraging a large number of unlabeled samples in a generic data set. Our scheme represents each sample in the weakly labeled data set as a mid-level feature with each entry as the corresponding decision value from the classification hyperplane (referred to as the prototype hyperplane) of one Support Vector Machine (SVM) model, in which a sparse set of support vectors is selected from the unlabeled generic data set based on the learnt combination coefficients. To learn the optimal prototype hyperplanes for the extraction of mid-level features, we propose a Fisher's Linear Discriminant-like (FLD-like) objective function by maximizing the discriminability on the weakly labeled data set with a constraint enforcing sparsity on the combination coefficients of each SVM model, which is solved by using an alternating optimization method. Then, we use the recent work called Side-Information based Linear Discriminant (SILD) analysis for dimensionality reduction and a cosine similarity measure for final face verification. Comprehensive experiments on two data sets, Labeled Faces in the Wild (LFW) and YouTube Faces, demonstrate the effectiveness of our scheme.
A localization and tracking application for UWB

An increasing demand for applications in the area of Ultra-Wideband Radio Technology led to the EUWB (Coexisting Short Range Radio by Advanced Ultra-Wideband Radio Technology, FP7-ICT-215669, www.euwb.eu) project, which has been started in April 2008. The goal of this industry-led initiative of major industrial and excellent academic organizations is the improvement, integration, and the applications of UltraWideband Radio Technology (UWB-RT). The EUWB project has significantly enhanced the scientific knowledge base in the 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 networks. Without a doubt, the efficient exploitation of cross-layer functionalities will lead to successful applications of UWB technologies. Beside the cognitive radio related technology, namely the Detect-And-Avoid (DAA) technology, the localization and tracking (LT) technology is a major aspect of EUWB, in particular the underlying LT protocol. Within the scope of this manuscript, the authors will give a brief overview of EUWB by illustrating its technical approach. Furthermore, the authors will describe a novel LT protocol as well as its application to a typical scenario. This application is tailored to operate also with UWB devices that do not fully comply with the previously proposed LT schemes which need specific physical and medium access control layer functionalities.
A series-input forward converter with shared RCD cell for high-reliability and wide input voltage range applications

An interleaved forward converter, adopting the series-input series-rectifier structure with a shared RCD reset branch, is proposed in this paper. Only one RCD branch is shared by two forward cells as a common transformer reset circuit. The duty ratio of each forward cell can exceed 50%, as a result, the effective duty ratio of the two cells can be more than 100%, which makes the proposed converter suitable for wide-voltage-range application. Compared with the conventional RCD reset single switch forward converter, it maintains the merits of low voltage stress on the main switches, high efficiency because only part of the magnetizing power is dissipated by the reset resistor, and the voltage across the reset capacitor is lower as well. All these features make the proposed converter promising for wide-input-voltage applications with high reliability, high efficiency and a simple structure. Operation principles and characteristics are given in detail and verified with a 200W prototype.
Integration of IHE-Based Systems with Petri Net Workflow Management System

Workflow management is crucial to elevate the efficiency and quality of healthcare services, and current healthcare IT solutions typically lack support to process oriented requirement. In order to introduce workflow management to healthcare information systems, the heterogeneous systems that coexist in healthcare environment have to be integrated for integral workflow. At build-time, the workflow roles played by the systems have to be identified to establish the workflow model, and with this model the systems are integrated at run-time. Integrating the Healthcare Enterprise (IHE) proposes some profiles for healthcare system workflow integration, and IHE- based systems typically state conformance to certain actors in profiles. Therefore, it is possible to start from these integration statements to model workflow and integrate the systems. In this paper, we present the method of translating integration profiles of IHE into Petri Net based workflow models and integrating these systems with Petri net Workflow Management System (WFMS). This method provides a starting point to bridge the gap between traditional applications and process-oriented healthcare solutions.
Mecanum wheeled motion system with three wheels

Omni-directional wheeled motion system with its flexible steering, stable structure and reliable control precision, become the basis of the mobile robot system which used indoor. In this paper two kinds of Omni-directions platform which wheeled by three Mecanum wheels are built up, by setting up the dynamical simulations about the two model based in simulation software, and according to the dynamic simulation analysis, we try to find the affections of the platform motion caused by the drive force on Mecanum wheels, and choose the better model. All of the work will also make the basis for the control theory which is needed by the next step.
Generic Expert System and Its Application in Knowledge Modelling and Inference

In this paper, we present a generic expert system software tool and its application in expert knowledge modelling and inference. The system is able to model expert rule-based knowledge and provide predictions for unseen data by the application of inductive inference. The theoretical framework for modelling expert knowledge is described independently of its implementation as a generic expert system. The framework is based on a review of the literature on belief rule-based inference methodology using the evidential reasoning approach (RIMER) and its applications. The application of the generic expert system is demonstrated by a case study which uses a guideline model for clinical risk assessment of acute upper gastrointestinal bleeding.