nils-herrmann/clean_scirepeval huggingface dataset:
Inertia-based video cut detection and its integration with video coder

An encoder-dependent video cut detection algorithm is proposed. Based on the inertia property of natural videos, the proposed algorithm detects video cuts in the video-coding loop by making use of the intermediate results of video compression. Experiments show that the detection performance of the proposed algorithm is quite good as compared to previous works, and when it is integrated with the coder, not only is the content accessibility of the output code stream improved but also the compression ratio is improved. The authors regard this inertia-based algorithm as a step towards the integration of video compression and content-based video retrieval.
Comprehensive evaluation and suggestion about the real estate macroeconomic regulation and control policy

This article takes the real estate as the object of study, uses the theoretical analysis and the empirical analysis unifying the research technique, including qualitative and the quantitative analysis in the union real estate industry characteristic and real estate macroeconomic regulation and control process physical demand, carries on a more complete analysis to our country real estate macroeconomic regulation and control policy movement process. This paper designed the evaluation model of real estate macroeconomic regulation and control policy effects, established the policy which can influence real estate market development as a evaluating indicator system, finally analyzed the effects of policy and put forward the proposals.
Integration and Optimization Analysis of Ecological Industry Chain of Mining Industry

On one hand, the integration of ecological industry chain in mining area is affected by technology development level, market demand, industrial policy and the use of products, by-products and wastes. On the other hand, it is also limited by the optimal production scale, the inflows and outflows of the resources of industry units. Therefore, through the integration of optimal production scale of industry units, optimization analysis of the inflows and outflows of resources, and the establishment of environmental control indexes and comprehensive benefit control index and resource flowing control indexes, we can judge the feasibility and necessity of the integration of the ecological industry chain scientifically.
An Improved Ant Colony Optimization for Flexible Job Shop Scheduling Problems

An Improved Ant Colony Optimization (IACO) algorithm is proposed to the Flexible Job Shop Scheduling Problem (FJSSP) in this paper. IACO algorithm provides an effective integration between Ant Colony Optimization (ACO) model and knowledge model. In the IACO algorithm, knowledge model learns some available knowledge from the optimization of ACO, and then employs the existing knowledge to guide the current heuristic searching. The performance of IACO was evaluated by many benchmark instances taken from literature. Final experimental results indicate that the proposed IACO algorithm outperforms some current approaches in the quality of schedules.
EmailTime: Visual analytics of emails

Although the discovery and analysis of communication patterns in large and complex email datasets are difficult tasks, they can be a valuable source of information. This paper presents EmailTime's capabilities through several examples. EmailTime is a visual analysis of email correspondence patterns over the course of time that interactively portrays personal and interpersonal networks using the correspondence in the email dataset. We suggest that integrating both statistics and visualizations in order to display information about the email datasets may simplify its evaluation.
Adaptive Load Balancing for Lookups in Heterogeneous DHT

In DHT based P2P networks, the heterogeneity of node capacity and the skewness in query pattern could result in lookup load imbalance. Indegree adjustment is an effective, light-weighted and heterogeneity-aware approach to balance lookup traffic in local area, but not globally. In this paper we present a hybrid algorithm combining in degree adjustment and virtual server (VS) approaches to address both local and global lookup imbalance in DHT networks. We divide local nodes into zones, and select strong nodes to form a virtual server group (VSG). Hot zones can apply for virtual servers from VSG in order to distribute load in system wide. Nodes and VSs in the zones use indegree adjustment to maintain zone balance. We can therefore significantly improve the load balancing of traffic in these systems, and consequently their performance.Results from experimental evaluation demonstrate the effectiveness of our approach.
A new fast prediction of the coverage area in WCDMA networks

The paper provides a forward channel pilot strength coverage prediction method to aid in the RF optimization of WCDMA wireless networks. A novel prediction algorithm for wave propagation environments without using a 3D digital map is proposed; it is computed using an inversion technique from measured data. This algorithm is accurate and fast enough to predict efficiently the coverage area which is one of the key tasks in the process of optimizing WCDMA networks.
An Act Indexing Information Model for Clinical Data Integration

Making accurate clinical diagnoses, treatment plans and preventive measures relies on the comparison and inter-confirmation among various kinds of clinical data. Due to the complexity of the hospital environment, the clinical data always originate from different medical information systems with incompatible structures. These heterogeneous clinical data are stored dispersedly and isolated from one another. It is very difficult to accomplish integrated query and visualization. This paper brings forward a Healthcare Act Indexing Information Model (HAIIM) to build a centralized index of heterogeneous clinical data. It organizes these data effectively according to the act-centered view of healthcare from Health Level Seven (HL7) Reference Information Model (RIM). We incorporated the resulting model into building of a "virtual" clinical data center, which enables the integrated query of heterogeneous clinical data. Based on HAIIM, we designed and implemented an integrated viewer to visualize clinical data according to the two dimensional act-time relationships.
Chinese Named Entity Recognition using Support Vector Machines

Named entity recognition (NER) is low-level semantics technology. Since it is simple and efficient, it has been widely applied in many systems such as machine translation, information retrieval, information extraction, question answering and summarization. The goal of named entity recognition is to classify names into some particular categories from text, such as the names of people, places, and organizations. Previous studies focus on combining abundant rules or trigger words to enhance the system performance. These methods require domain experts to build up the rules and word set. In this paper, we present a robust named entity recognition system based on support vector machines (SVM). In the experiment, we perform the one-against-one SVM algorithm and a feature extraction method to achieve high accuracy
Keyword spotting in degraded document using mixed OCR and word shape coding

This paper presents a new way for keyword spotting in degraded imaged document. Two prevalent word indexing, OCR and word shape coding, are combined compactly based on the recognition confidence evaluation. The basic procedures are as follows. First, OCR candidates are used for OCR indexing. Second, a new stoke feature and convex-concave feature of word are adopted for word shape coding. Furthermore, an intelligent indexing based on recognition confidence is introduced, which is adaptive to image quality. Finally, an inexact matching is used for word spotting. A collection from NLM, including 1553 scanned imaged documents, is used to evaluate our method. The results confirm the validity of our method.
Inventory Rationing in a Capacitated System with Backorders and Lost Sales

In this paper, we study a periodic review, capacitated, make-to-stock system with two demand classes. The first class demand comes from customers who have long term and profitable relationships with the manufacturer. The second class demand comes from the retail market. The first class demand not satisfied immediately is backordered, whereas the unsatisfied second class demand is lost. There are two fundamental decisions for managing such a system, i.e., production decision and rationing decision. We prove that a base stock rationing policy is optimal. Furthermore, through an extensive numerical study, we study the cost savings of the optimal policy compared with two common used heuristics and show that the benefit of the optimal control policy can be quite significant.
Changes of Hydraulic Conductivity in Coastal Aquifers with the Existence of Illites

Hydraulic conductivity of aquifers is one of important hydrogeological parameters in the seawater-freshwater field, which has a great influence on the scale and extent of seawater intrusion. Columns were subject to a pulse of seawater followed by fresh water or brackish water. Outflow rates and changes in hydraulic head as well as electric conductivity and particle concentrations were monitored over time. Permeability decrease occurred and no particles were found in the outflow when seawater was flushed by fresh water. However, when the concentration of the inflow solution for this replacement decreased gradually, particle releases occurred and permeability also decreased. Results indicate that there exists a distinct variation of permeability of aquifer media in the seawater-freshwater interface, namely a critical value existing in those experiments. The analysis of the experiments suggests deflocculation and release process of illite are the main factors of permeability reduction of sand columns.
Estimation of Breast Skin-Line Using Least Square Estimation and Watershed Segmentation

Breast cancer has become one of the most dangerous tumors for middle-aged and older women in China recently. Mammography is its most reliable detection method in the clinic, and computer-aided diagnosis (CAD) could assist the radiologists in reading the mammograms. In this paper, a new algorithm was proposed to estimate the skin-line of the breast automatically, which could be divided into four steps. It firstly used iterative thresholding to remove the backgrounds. And then, it applied the least square estimation (LSE) method to approach the horizontal frames, and proposed an elastic thread algorithm to remove them. Finally, it used the watershed algorithm to fine the breast boundary. With the experimental results, the advantages of this method over the conventional ones could be proved.
MIMO with network coding in relay networks: A combination of multiplexing and diversity

In this paper, a new two-step communication protocol combined with MIMO technologies is proposed in this paper which allows improving the system throughput in a cooperative networks. The protocol is termed MINEC (MIMO Network Coding). A three nodes network with multi-antennas on relay node is taken as an illustrative example of MINEC. In step one of MINEC, two source nodes transmit message simultaneously; in step two, the relay node broadcasts coded data with network and space-time coding. A binary symmetric relay channel model is used to carry out the theoretical performance analysis of MINEC. The theoretical findings are validated by Monte-Carlo-simulation. It is found that MINEC facilitates a performance equivalent to a 2×2 V-BLAST MIMO and a two 2×1 Alamouti MIMO in transmitting phase and forwarding phase, respectively.
A Low-order hybrid dynamic model for evaporators

A low-order hybrid model, which can present the transient behaviors of evaporators, is proposed in this paper. The model is derived from the fundamental conversation of energy and mass of the working fluid flowing though the evaporator, while its geometrical dimensions, thermodynamic coefficients that can be considered as constants and some variables that keep small variety during the operation (such as cross sectional areas of tube, mean void fraction, the density of the working fluid in the superheated section, etc.), are lumped into unknown parameters. The linear least squares method is employed to estimate these unknown parameters using measured experimental data collected from the experimental testing facility. The model is just two orders, but can accurately capture the transient behaviors of evaporators. Validation results confirm a good agreement between simulation results and experimental data. The developed models will be suitable for the dynamic behavior analysis and the advanced controller design.
An Automatic Knowledge Acquisition Mechanism for Independent Inference Engine Module of CDSS

Clinical decision support system (CDSS) has been increasingly applied to help clinicians analyze patient data and make clinical decisions at present. The kernel part of CDSS is inference engine (IE) module whose independence is very important, which separates the knowledge reasoning from knowledge representation and causes them to turn two independent processes while most of IE modules currently used haven't achieved this goal yet. IE module consists of two parts: an independent IE and the automatic knowledge acquisition mechanism for IE. In this paper, CLIPS is chosen as independent IE and we devoted to establishing the automatic knowledge acquisition mechanism using two methods proposed by us which were named Multi-Rule Sets Abstracting and Single-Rule Series Abstracting. Via comparison, the latter was demonstrated to be more effective for purpose. In practice, an experiment on IE module indicated that this automatic knowledge acquisition mechanism realized its independence.
DL-PRO: A novel deep learning method for protein model quality assessment

Computational protein structure prediction is very important for many applications in bioinformatics. In the process of predicting protein structures, it is essential to accurately assess the quality of generated models. Although many single-model quality assessment (QA) methods have been developed, their accuracy is not high enough for most real applications. In this paper, a new approach based on C-a atoms distance matrix and machine learning methods is proposed for single-model QA and the identification of native-like models. Different from existing energy/scoring functions and consensus approaches, this new approach is purely geometry based. Furthermore, a novel algorithm based on deep learning techniques, called DL-Pro, is proposed. For a protein model, DL-Pro uses its distance matrix that contains pairwise distances between two residues' C-a atoms in the model, which sometimes is also called contact map, as an orientation-independent representation. From training examples of distance matrices corresponding to good and bad models, DL-Pro learns a stacked autoencoder network as a classifier. In experiments on selected targets from the Critical Assessment of Structure Prediction (CASP) competition, DL-Pro obtained promising results, outperforming state-of-the-art energy/scoring functions, including OPUS-CA, DOPE, DFIRE, and RW.
Independent Component Analysis Applied to Multiple Antenna Space-Time Systems

Theoretical investigation have shown that multiple antenna schemes can improve link reliability and provide high spectral efficiency. Space-time block coding (STBC) and vertical Bell Labs layered space time (V-BLAST) systems are capable of meeting these requirements by using multiple antenna strategies, respectively. However, in order to achieve largely improved performance they need precise channel information at the receiver, which is difficult to be guaranteed in some specific scenarios. In this paper, independent component analysis (ICA) technique is exploited to detect the transmitted signals blindly, which needs not the channel estimation. By analyzing the essential structures of STBC and V-BLAST systems, the specific models suitable to ICA are established. The robustness against channel estimation error and flexibility of system design can be acquired by using the ICA based scheme. Simulation results demonstrate the validity of proposed approach and the feasibility of ICA based scheme is also discussed
Differential evolution with multi-stage strategies for global optimization

Differential evolution is a fast, robust, and simple population-based stochastic search algorithm for global optimization, which has been widely applied in various fields. However, there are many mutation strategies in DE, which have their own characteristics. Therefore, choosing a best mutation strategy is not easy for a specific problem. Different mutation strategies may be appropriate during different stages of the evolution. In this paper, we propose a DE with multi-stage strategies (DEMS). In DEMS, the evolution process of DE is divided into multiple stages according to the average distance between each individual in the initial population. Each stage has its own strategy candidate pool which includes multiple effective strategies. At the beginning of each generation, the average distance between each individual is first calculated to determine the evolution stage. Then for each target vector in the current population, a mutation strategy is randomly selected from the strategy candidate pool with respect to the stage to produce a offspring vector. Numerical experiments on 15 well-known benchmark functions and the CEC 2015 benchmark sets show that the proposed DEMS is significantly better than, or at least comparable to several state-of-the-art DE variants, in terms of the quality of the final solutions and the convergence rate.
Model of airport runway frictional coefficient measure vehicle based on impedance diagrams

A friction coefficient is an important parameter for assess the airport runway and assure the airplane sliding safely, which can be measured by the airport runway frictional coefficient measure vehicle. There are many factors affect the measure precision of the measure vehicle, one of which is the vehicle vibration caused by fluctuation of runway. An impedance diagrams method is used to model for the hanging system and measuring system of measure vehicle which can reduce the effect of runway fluctuation on the measurement result.
Inferential modelling and decision making with data

In this paper, we introduce the main concepts of a new maximum livelihood evidential reasoning (MAKER) framework for data-driven inferential modelling and decision making under different types of uncertainty. It consists of two types of model: state space model (SSM) and evidence space model (ESM), driven by the data that reflects the relationships between system inputs and output. SSM is constructed to describe different system states and changes. ESM is established by mapping data to a set of evidence that is partitioned into evidential elements each pointing to a system state set and together represents system behaviours in a probabilistic and distributed manner. The reliability of evidence and interdependence between a pair of evidence are explicitly measured. It is in the joint evidence-state space that multiple pieces of evidence with different degrees of interdependence and reliability are acquired from system inputs and combined to inference system output. A general optimal learning model is constructed, where evidence reliability can be learnt from historical data by maximising the likelihood of true state. In the MAKER framework, different types of uncertainty can be taken into account for inferential modelling, probabilistic prediction and decision making.
Modelling User Behaviour for Web Recommendation Using LDA Model

Web users exhibit a variety of navigational interests through clicking a sequence of Web pages. Analysis of Web usage data will lead to discover Web user access pattern and facilitate users locate more preferable Web pages via collaborative recommending technique. Meanwhile, latent semantic analysis techniques provide a powerful means to capture user access pattern and associated task space. In this paper, we propose a collaborative Web recommendation framework, which employs Latent Dirichlet Allocation (LDA) to model underlying topic-simplex space and discover the associations between user sessions and multiple topics via probability inference. Experiments conducted on real Website usage dataset show that this approach can achieve better recommendation accuracy in comparison to existing techniques. The discovered topic-simplex expression can also provide a better interpretation of user navigational preference.
Decentralized Cooperative H∞ Robust Control for Linear Large-Scale System

A decentralized cooperative robust H∞ controller was proposed for a linear time invariant (LTI) system via a multilevel control approach. This system was modeled as interconnection of subsystems with parameter uncertainty. The multilevel control approach was a two-stage controller design in which the first stage involved constructing a decentralized robust controller using only its states for every uncertain subsystem via linear matrix inequality (LMI) approach to ensure the subsystem stability and achieve a given H∞ performance level and the second stage carried out a cooperative controller design to reduce the effect of other subsystems and improve the overall system performance. The controller, obtained applying this idea, not only makes the overall system inner stabilized and achieves the H∞ performance requirements, but also is easy to realize.
Image Classification by Cross-Media Active Learning With Privileged Information

In this paper, we propose a novel cross-media active learning algorithm to reduce the effort on labeling images for training. The Internet images are often associated with rich textual descriptions. Even though such textual information is not available in test images, it is still useful for learning robust classifiers. In light of this, we apply the recently proposed supervised learning paradigm, learning using privileged information, to the active learning task. Specifically, we train classifiers on both visual features and privileged information, and measure the uncertainty of unlabeled data by exploiting the learned classifiers and slacking function. Then, we propose to select unlabeled samples by jointly measuring the cross-media uncertainty and the visual diversity. Our method automatically learns the optimal tradeoff parameter between the two measurements, which in turn makes our algorithms particularly suitable for real-world applications. Extensive experiments demonstrate the effectiveness of our approach.
Compression algorithm of road traffic data in time series based on temporal correlation

The numerous applications of urban traffic detection technology in road traffic data acquisition bring new challenges for transportation and storage of road traffic big data. The travel demand and travel time of travel participants present certain specific regularity; thus, a compression algorithm for road traffic data in time series based on temporal correlation was proposed in this study. First, the temporal correlation of the road traffic data in time series was analysed. Second, the reference sequences of road traffic characteristics were constructed to acquire the base data under different modes. Third, the training data under the same mode were extracted to acquire the difference data between training and base data. Then the optimal threshold of the difference data was trained. Fourth, the optimal threshold was introduced into the difference data between real-time and base data in time series, combining with Lempel-Ziv-Welch (LZW) encoding to achieve the compression of difference data. Finally, the reconstruction of real-time road traffic data in time series was accomplished based on LZW decoding technology. Six typical road segments in Beijing were adopted for case studies. The final results prove the feasibility of the algorithm, and that the reconstructed data can achieve high accuracy.