nils-herrmann/clean_scirepeval huggingface dataset:
Spatial-HMM:A new approach for Semantic Annotation of Histological

This paper presents a new spatial-HMM for automatically classifying and annotating histological images. Our model is a 2D generalization of HMM. Given a matrix of feature vectors for all blocks in an image, the most appropriate semantic labels determined by our models are used for annotation. Our experimental results showed that our model is superior to HMM in both recognition and annotation accuracy
Personalized Search of Educational Content Based on Multiple Ontologies

In this paper, we introduce the design of a personalized education (PE) search approach that employs multiple ontologies to automatically generate queries for educational resources retrieval based on a high level specification of the teaching/learning needs of a user. Central to this approach is the design of PEOnto which is an educational ontology that consists of FIVE interrelated ontologies that supports the delivery of various services in a personalized education system. We illustrate the feasibility of PEOnto design through a scenario walkthrough in the context of grade 4 English language learning
An intelligent system for integrating semantic and iconic features for image retrieval

The I-Browse project aimed to develop a prototype system to provide facilities for supporting intelligent retrieval of medical images through a combination of iconic and semantic content. The resulting prototype system, I-Browse, is able to extract and represent relevant iconic and semantic information from input images and to automatically generate textual annotations for images. Techniques were also developed for retrieving relevant images from the database given either an example image query or a textual query. The facilities provided by I-Browse were evaluated by medical colleagues and judged to have the potential of alleviating some of the time-consuming tasks that doctors now have to perform daily and providing a means of identifying previously unknown relationships between visual appearance and histological events.
Automatic segmentation and tracking of vasculature from confocal scanning laser fluorescence microscope using multi-orientation dissections

This paper describes automated techniques for segmenting, tracking and identifying the individual vessels within the vascular network of the tail of zebrafish embryo from serial images obtained from fluorescence Confocal Scanning Laser Microscope (CSLM). Our approach digitally dissects the voxel data and obtained multi-orientation serial sections along different cutting planes of the zebrafish to simplify the vessel segmentation and tracking processes. This approach is particularly effective for the zebrafish embryo tail as its vasculature exhibits distinctly horizontally and longitudinally oriented vascular components. The resulting algorithm successfully identified and labeled not only major vessels such as Caudal Vein and Caudal Artery but also minor ones such as Intersegmental Vessel. The labeled voxels are represented by an Attributed Vessel Representation Graph, which contains low level geometric information of the vessel as well as semantic information for high level analysis and inter-vasculature comparison.
Concealment of damaged blocks by neighborhood regions partitioned matching

This paper presents a new error concealment algorithm for the masking of damaged blocks due to data loss during the transmission of DCT based images over a packet network. The algorithm is called neighborhood regions partitioned matching (NRPM). Instead of repairing damaged block as a whole, each damaged block is divided into a number of sub-blocks. Each sub-block is recovered utilizing not only the surrounding pixels, but also remote pixels which share similar local image characteristics. The approach yields good a quality repair and since smaller block size is used, it can significantly reduce the computational time for the reconstruction process.
3D object retrieval based on visual keywords using Relative Angle Context Distribution

In this paper, we proposed a new 3D object retrieval method based on the visual keywords. In our method, the visual keywords are generated from the clusters of Relative Angle Context Distribution, which provides a statistical shape context that captures local shape characters and is also rotational and scale invariant. We also adopt the term frequency model commonly used for text retrieval to compare two 3D objects according to their keyword sets based on a cosine similarity measure. Our experiments have demonstrated that this method is robust for its invariance with respect to object orientation and scaling, and yields a higher precision when compared to existing retrieval methods such as the Distance Map.
Data recovery for medical organs based on a joint Statistical Deformable Model that incorporates prior knowledge of the missing data

In this paper, we proposed a novel joint statistical deformable model (SDM) that incorporates prior knowledge of the missing data into the data recovery process for medical organs. Our approach formalized the prior knowledge of the missing data as the probability of the sample belonging to the SDM from the training data set, and together with the known points to provide accurate data recovery. Our experiments have shown that, the proposed approach achieves a higher accuracy than a state of the art technique for highly variable (soft tissue) organs particularly when the number of known points is small compared with that of the missing data. This results in a unique and robust framework of data recovery that is suitable for both rigid and highly flexible medical organs.
Spectral learning of latent semantics for action recognition

This paper proposes novel spectral methods for learning latent semantics (i.e. high-level features) from a large vocabulary of abundant mid-level features (i.e. visual keywords), which can help to bridge the semantic gap in the challenging task of action recognition. To discover the manifold structure hidden among mid-level features, we develop spectral embedding approaches based on graphs and hypergraphs, without the need to tune any parameter for graph construction which is a key step of manifold learning. In particular, the traditional graphs are constructed by linear reconstruction with sparse coding. In the new embedding space, we learn high-level latent semantics automatically from abundant mid-level features through spectral clustering. The learnt latent semantics can be readily used for action recognition with SVM by defining a histogram intersection kernel. Different from the traditional latent semantic analysis based on topic models, our two spectral methods for semantic learning can discover the manifold structure hidden among mid-level features, which results in compact but discriminative high-level features. The experimental results on two standard action datasets have shown the superior performance of our spectral methods.
Generalized Competitive Learning of Gaussian Mixture Models

When fitting Gaussian mixtures to multivariate data, it is crucial to select the appropriate number of Gaussians, which is generally referred to as the model selection problem. Under regularization theory, we aim to solve this model selection problem through developing an entropy regularized likelihood (ERL) learning on Gaussian mixtures. We further present a gradient algorithm for this ERL learning. Through some theoretic analysis, we have shown a mechanism of generalized competitive learning that is inherent in the ERL learning, which can lead to automatic model selection on Gaussian mixtures and also make our ERL learning algorithm less sensitive to the initialization as compared to the standard expectation-maximization algorithm. The experiments on simulated data using our algorithm verified our theoretic analysis. Moreover, our ERL learning algorithm has been shown to outperform other competitive learning algorithms in the application of unsupervised image segmentation.
Compressed domain feature transformation using evolutionary strategies for image classification

Recently, a number of approaches have been proposed which use compressed domain features for image retrieval and classification. While the main motivation of these approaches is to improve processing efficiency and reduce computational requirements, we propose a method which also aims at enhancing the content characterization capabilities of the compressed domain features in addition to efficiency improvement. We model the compressed domain feature values as random variables and approximate their associated probability mass functions as histograms. We then transform these histograms in such a way that the resulting classification rate based on these transformed histograms is improved. With a large number of possible transformations, we adopt an evolutionary strategy (ES) to search for the. optimal one. Experiments show that our proposed approach is able to obtain a better classification rate while the efficiency advantage of using compressed domain features is retained.
An abstract layered model for hypermedia document system

We propose a layered model for hypermedia document systems and use this model in the design and implementation of a prototype hypermedia document system. The model consists of three layers: a runtime layer (RTL) a document description layer (DOCDL) and a storage layer (SRL). The DOCDL contributes to defining the logical structure. It not only constrains the structure of the SRL, but also forms a bridge between the RTL and the SRL. In the DOCDL, an extended open document architecture (ODA) is employed for describing the logical structure of the document. In addition, we present a technique for automatically generating and updating database schema given a document logical structure. For the RTL, the document logical structure is visualized to help users to retrieve information, and as a means for multimedia data capture. The advantages and benefits of this approach are to allow the design and implementation of hypermedia systems to be automated and much simplified. The approach also caters for the continuous updating of the document logical structure such as adding an extra data object. The paper applies the proposed model to the design and implementation of a multimedia document system for medical documents.
Image categorization with spatial mismatch kernels

This paper presents a new class of 2D string kernels, called spatial mismatch kernels, for use with support vector machine (SVM) in a discriminative approach to the image categorization problem. We first represent images as 2D sequences of those visual keywords obtained by clustering all the blocks that we divide images into on a regular grid. Through decomposing each 2D sequence into two parallel 1D sequences (i.e. the row-wise and column-wise ones), our spatial mismatch kernels can then measure 2D sequence similarity based on shared occurrences of k-length 1D subsequences, counted with up to m mismatches. While those bag-of-words methods ignore the spatial structure of an image, our spatial mismatch kernels can capture the spatial dependencies across visual keywords within the image. Experiments on the natural and histological image databases then demonstrate that our spatial mismatch kernel methods can achieve superior results.
Brush Writing Style Classification from Individual Chinese Characters

Chinese calligraphic artwork usually contains a few or even a single character. In order to perform automatic brush writing style classification on these kinds of Chinese calligraphic images, existing approaches need to be modified because they assume that many characters of the same style exist in the input image. A novel approach is proposed to address the brush writing style classification problem for single-character Chinese calligraphic images by combining the texture analysis and the structural information through a set of parameterised ellipses
Finding the maximum modulus roots of polynomials based on constrained neural networks

This paper focuses on how to find the maximum modulus root (MMR) (real or complex) of an arbitrary polynomial. Efficient solution to this problem is important for many fields including neural computation and digital signal processing etc. We present neural networks technique for solving this problem. Our neural root finder (NRF) is designed based on partitioning feedforward neural networks (FNN) trained with a constrained learning algorithm (CLA) by imposing the a priori information about the root moment from polynomial into the error cost function. Experimental results show that this neural root-finding method is able to find the maximum modulus roots of polynomials rapidly and efficiently.
Robust watermarking of 3D polygonal models based on vertex scrambling

We propose a robust 3D model watermarking scheme based on distributing the information corresponds to a bit of the watermark over the entire model via vertex scrambling. The scheme also adaptively varies the strength of the watermark signal with reference to the local geometry and embeds watermark information into the model by modifying the length of the vectors that link vertices to the centre of the model. Experiments show that this watermarking scheme is robust against attacks such as mesh simplification, addition of noise, and model cropping.
Combining Context, Consistency, and Diversity Cues for Interactive Image Categorization

This paper presents a novel graph-based framework which can combine context, consistency, and diversity cues for interactive image categorization. The image representation is first formed with visual keywords by dividing images into blocks and then performing clustering on these blocks. The context across visual keywords within an image is further captured by proposing a 2-D spatial Markov chain model. To develop a graph-based approach to image categorization, we incorporate intra-image context into a new class of kernel called spatial Markov kernel which can be used to define the affinity matrix for a graph. After graph construction with this kernel, the large unlabeled data can be exploited by graph-based semi-supervised learning through label propagation with inter-image consistency. For interactive image categorization, we further combine this semi-supervised learning with active learning by defining a new diversity-based data selection criterion using spectral embedding. Experiments then demonstrate that the proposed framework can achieve promising results.
Cyber Composer: Hand Gesture-Driven Intelligent Music Composition and Generation

Cyber Composer is a novel and interactive cyber instrument that enables both musicians and music laypersons to dynamically control the tonality and the melody of the music that they generate/compose through hand motion and gestures. Cyber Composer generates music according to hand motions and gestures of the users in the absence of real musical instruments. Music theories are embedded in the design so that melody flow and musical expressions like the pitch, rhythm and volume of the melody can be controlled and generated in real-time by wearing a pair of motion-sensing gloves. Also central to the design is the mapping of the hand motions and gestures to musical expressions that is intuitive and requires minimal training. Cyber Composer is expected to find applications in the fields of performance, composing, entertainment, education as well as psychotherapy.
Java-based approaches for accessing databases on the Internet and a JDBC-ODBC implementation

An important area of Java development is to create Java based software or tools with the capability of accessing databases over the Internet. JDBC and its various properties are explored. Several Java based technical approaches for accessing databases on the Internet are proposed and investigated in detail. A design and implementation of the Java software for accessing databases on the Internet is proposed and described, where the design scheme of an interactive applet by using Java JDBC programming is proposed and investigated, the main JDBC classes used in the Java program and the relationship between them are discussed, the implementation of the software is provided, and some instances for running the Java program are given. This Java software provides an interactive and user friendly interface on the browser which supports Java JDK 1.1 or later and the software has the capability for the connection, SQL access, retrieval and display of a database on the Internet.
Evidential reasoning in foreign exchange rates forecasting

This paper presents a novel approach to foreign exchange rates forecasting based on the accumulation of and reasoning with supporting and refuting evidence which is brought to bear upon a set of hypotheses about exchange rates movements. The mathematical basis of this approach is provided by the Dempster-Shafer theory of evidence. This paper describes the basics of Dempster-Shafer theory of evidence and how the problem domain of foreign exchange forecasting can be mapped into the framework of the theory. It presents an application of the theory to foreign exchange forecasting and the forecasting system architecture. A summary and discussion of the approach is provided.<<ETX>>
Calligraphic character synthesis using a brush model

The paper proposes a novel methodology which allows calligraphic writing to be synthesized realistically. The approach models the physical process of brush stroke creation and consists of three separate aspects, namely, the physical geometry of the writing brush, the dynamic movement, e.g., the position and orientation, of the brush along the stroke trajectory and the amount of ink absorbed in the brush bundle as well as the ink depositing process. By controlling these physical parameters associated with the writing process, very realistic calligraphic writing can be generated. In particular, the aesthetic features commonly associated with calligraphy, such as the varying widths of a stroke, the impression of physical rubbing between the brush and the underlying paper, the varying shades of grey caused by different degrees of ink content in the brush, and the black and white trails created by fast movement of a drying brush can be simulated. This is the first time physically-based model of a brush has been used to synthesize calligraphic writing and the model has been implemented on a PC-based platform.
Contextual kernel and spectral methods for learning the semantics of images.

This paper presents contextual kernel and spectral methods for learning the semantics of images that allow us to automatically annotate an image with keywords. First, to exploit the context of visual words within images for automatic image annotation, we define a novel spatial string kernel to quantify the similarity between images. Specifically, we represent each image as a 2-D sequence of visual words and measure the similarity between two 2-D sequences using the shared occurrences of s -length 1-D subsequences by decomposing each 2-D sequence into two orthogonal 1-D sequences. Based on our proposed spatial string kernel, we further formulate automatic image annotation as a contextual keyword propagation problem, which can be solved very efficiently by linear programming. Unlike the traditional relevance models that treat each keyword independently, the proposed contextual kernel method for keyword propagation takes into account the semantic context of annotation keywords and propagates multiple keywords simultaneously. Significantly, this type of semantic context can also be incorporated into spectral embedding for refining the annotations of images predicted by keyword propagation. Experiments on three standard image datasets demonstrate that our contextual kernel and spectral methods can achieve significantly better results than the state of the art.
Human 3D Motion Recognition Based on Spatial-Temporal Context of Joints

The paper presents a novel human motion recognition method based on a new form of the Hidden Markov Models, called spatial-temporal hidden markov models (ST-HMM), which can be learnt from a sequence of joints positions. To cope with the high dimensionality of the pose space, in this paper, we exploit the spatial dependency between each pair of spatially connected joints in the articulated skeletal structure, as well as the temporal dependency due to the continuous movement of each of the joints. The spatial-temporal contexts of these joints are learnt from the sequences of joints movements and captured by our ST-HMM. Results of recognizing 11 different action classes on a large number of motion capture sequences as well as synthetic tracking data show that our approach outperforms traditional HMM approach in terms of robustness and recognition rates.
Combining multiple spatial hidden Markov models in image semantic classification and annotation

The spatial-hidden Markov model (SHMM) is a two dimensional generalization of the traditional hidden Markov model (HMM), with the capability of blockbased semantic annotation as well as classification of images. In this paper, we conduct a sensitivity analysis of SHMM in semantic classification with respect to different block sizes and from this analysis, we propose a novel multi-scales SHMM that combines multiple SHMMs, each classifying the image on a different scale. By regarding each SHMM as distinct classifiers, classifier combination algorithm can be applied to integrate the outputs of the respective SHMMs to improve image classification accuracy. Experiment results demonstrate that the multi-scale SHMM consistently outperforms single SHMMin image semantic classifications. The proposed approach can be extended to other block-based image classification algorithms.
A Statistical Assembled Model for Segmentation of Entire 3D Vasculature

We introduce a novel statistical deformable model called SAMTUS for the segmentation of soft tissue tubular structures. The model is composed of an assembly of statistically deformable tubular segments whereby the junctions of the tubular branches are used as landmarks for constructing the underlying point distribution model. The flexibility of SAMTUS is governed by two independent statistical models that describe the axis variation (statistical axis model, or SAM) and the cross-sectional radius variation (statistical surface model, or SSM) respectively. We also propose a SAMTUS based segmentation algorithm for an entire tubular structure. The approach has been applied to the segmentation of the three-dimensional vasculature of zebrafish embryo. The efficiency and robustness of this method is evaluated through quantification results on both sectional level and volumetric level
Complexity Control in the HEVC Intracoding for Industrial Video Applications

A large number of industrial video applications are expected to work in real-time and power-constrained scenarios. For such applications, the coding complexity has a great impact on their performance. In this paper, we propose a complexity control method in the high-efficiency video coding intracoding to facilitate these video applications. The proposed method is performed on the coding tree unit (CTU) level, which consists of three steps, namely complexity estimation, complexity allocation, and prediction unit (PU) adaption. In the first step, a complexity estimation model is proposed to estimate the coding complexity of each CTU based on the sum of absolute transformed difference. Then, the complexity budget is allocated to each CTU proportionally to its estimated coding complexity. In PU adaption, only a subset of PU sizes are selected for the CTU according to its allocated complexity and prediction performance. A feedback-based error elimination scheme removes the complexity error during the encoding process. Experimental results show that the proposed method is able to adjust the complexity ratio from 100% to 20%. Meanwhile, the rate-distortion performance and complexity control accuracy of the proposed method are superior to those of the state-of-the-art methods.