varunr14/newsummary huggingface dataset:
The integration of multiple features is important for action categorization and object recognition in videos because single feature based representation hardly captures imaging variations and individual attributes . In this paper a novel formulation named Multivariate video Information Bottleneck is defined . It is an extensional type of multivariate information bottleneck and can discover categories from a collection of unlabeled videos automatically . Differing from the original multivariate information bottleneck the novel approach extracts the video categories from multiple features simultaneously such as local static and dynamic feature each type of feature is treated as a relevant variable . Specifically by preserving the relevant information with respect to these feature variables maximally the MvIB method is able to integrate various aspects of semantic information into the final video partitioning results and thus captures the complementary information resided in multiple feature variables . Extensive experimental results on five challenging video data sets show that the proposed approach can consistently and significantly outperform other state of the art unsupervised learning methods . 


Summary: Novel multivariate IB model is proposed for unsupervised video categorization. Effective solution is designed to integrate multiple features simultaneously. Information theoretic optimization is constructed to alleviate the semantic gap.
A parallel computing region growing algorithm for surface reconstruction from unorganized point clouds is proposed in this research . The traditional region growing algorithm belongs to sequential process and needs to update the topology information continuously to maintain the boundaries of the growing region . This constraint becomes a bottleneck for efficiency improvement . The proposed GPU based region growing algorithm is to decompose the traditional sequence and re plan specific framework for the purpose of utilizing parallel computation . Then a graphics card with multi processing units will be used to build triangles in the parallel computing mode . In our GPU based reconstruction process each sampling point is regarded as an independent seed and expands simultaneously until all surrounding patches overlap each other . Following this the overlapping patches are removed and holes are filled by the GPU based calculation . Finally a complete model is created . In order to validate the algorithm proposed the unorganized point cloud was obtained by a 3D scanner and then reconstructed using the parallel computing region growing algorithm . According to the results obtained the algorithm proposed here shows 10 times better performance when compared to the traditional region growing method . 


Summary: We develop a region growing algorithm based on parallel computation architecture. The old algorithm needs to be redesigned to fit the requirement of GPU architecture. Our algorithm can speed up the mesh reconstruction over 10 times. The proposed algorithm also have the same quality compared to original algorithm.
In multi agent systems stereotypical trust models are widely used to bootstrap a priori trust in case historical trust evidences are unavailable . These models can work well if and only if malicious agents share some common features in their profiles and these features can be detected . However this condition may not hold for all the adversarial scenarios . Smart attackers can show different trustworthiness to different agents and services . In this paper we propose CAST a novel Context Aware Stereotypical Trust deep learning framework . CAST coins a comprehensive set of seven context aware stereotypes each of which can capture a unique type of context correlated attacks as well as a deep learning architecture to keep the trust stereotyping robust . The basic idea is to construct a multi layer perceptive structure to learn the latent correlations between context aware stereotypes and the trustworthiness and thus can estimate the new trust by taking into account the context information . We have evaluated CAST using a rich set of experiments over a simulated multi agent system . The experimental results have successfully confirmed that our CAST can achieve approximately tens of times higher trust inference accuracy in average than the competing algorithms in the presence of context correlated attacks and more importantly can maintain a much better trust inference robustness against stereotyping errors . 


Summary: We have proposed CAST a new context aware stereotypical trust model. We have considered a comprehensive set of seven context aware stereotypes. We have applied a deep learning architecture to keep trust stereotyping robust. We have confirmed the effectiveness of CAST using a rich set of experiments.
The paper is focussed on the robustness of parallel computation in the case of buckling and post buckling analyses . In the nonlinear context domain decomposition methods are mainly used as a solver for the tangent problem solved at each iteration of a Newton Raphson algorithm . In case of strongly nonlinear and heterogeneous problems as those encountered in buckling and post buckling this procedure may lead to severe difficulties regarding convergence and efficiency . The problem of convergence is regarded as the most critical issue at the industrial level . Indeed if a method which can show efficiency for some problems is not robust with respect to convergence the method will not be implemented by industrial end users . Therefore two paths are explored to gain robustness when making use of domain decomposition methods a nonlinear localization strategy which may also improve the robustness by treating the nonlinearity at the subdomain level and a mixed framework allowing to circumvent the problem of local divergence . It is to be noted that those two ingredients may also be used to improve the numerical efficiency of the method but this is not the main focus of the paper . Simple structures are first considered to illustrate the method performances . Results obtained in the case of a boxed structure and of a stiffened panel are then discussed . 


Summary: Nonlinear localization can deal with larger increments than the classical NKS methods. Mixed DDM can use larger increments and pass limit points with respect to NKS methods. Important reduction on the number of global iterations. The amount of exchanged data between processors is reduced. Sensitivity study for the Robin operator.
Accurate interval forecasting of agricultural commodity futures prices over future horizons is challenging and of great interests to governments and investors by providing a range of values rather than a point estimate . Following the well established linear and nonlinear modeling framework this study extends it to forecast interval valued agricultural commodity futures prices with vector error correction model and multi output support vector regression which is capable of capturing the linear and nonlinear patterns exhibited in agricultural commodity futures prices . Two agricultural commodity futures prices from Chinese futures market are used to justify the performance of the proposed VECM MSVR method against selected competitors . The quantitative and comprehensive assessments are performed and the results indicate that the proposed VECM MSVR method is a promising alternative for forecasting interval valued agricultural commodity futures prices . 


Summary: Proposing an interval forecasting method for agricultural commodity futures prices. Extending the linear and nonlinear modeling framework for ITS forecasting. VECM and MSVR are integrated abbreviated as VECM MSVR . The experimental analysis is based on one step ahead and multi step ahead forecasts. VECM MSVR is a promising method for interval forecasting in future markets.
We study the problem of clustering uncertain objects whose locations are uncertain and described by probability density functions . We analyze existing pruning algorithms and experimentally show that there exists a new bottleneck in the performance due to the overhead of pruning candidate clusters for assignment of each uncertain object in each iteration . In this article we will show that by considering squared Euclidean distance UK means is reduced to K means and performs much faster than pruning algorithms however with some discrepancies in the clustering results due to using different distance functions . Thus we propose Approximate UK means to heuristically identify objects of boundary cases and re assign them to better clusters . Three models for the representation of cluster representative are proposed to calculate expected squared Euclidean distance between objects and cluster representatives in this paper . Our experimental results show that on average the execution time of Approximate UK means is only 25 more than K means and our approach reduces the discrepancies of K means clustering results by up to 70 . 


Summary: We reduce UK means to K means. We experimentally show that K means performs much faster than existing pruning algorithms. We propose Approximate UK means to heuristically identify boundary objects and re assign them to better clusters. We propose three models for the representation of cluster representative. To our knowledge this is the first time to introduce uncertain model of cluster representative.
Covering rough sets are a generalization of Pawlak rough sets in which a partition of the universal set induced by an equivalence relation is replaced by a covering . In this paper covering rough sets are transformed into generalized rough sets induced by binary relations . The paper discusses three theoretical topics . First we consider a special type of covering in which the neighborhoods form a reduction of the covering and we obtain necessary and sufficient conditions for neighborhoods in a covering form a reduction of the covering . Second we study another special type of covering and give conditions for the covering lower and upper approximations to be dual to each other . Finally we give an axiomatic system that characterizes the lower and upper approximations of rough sets based on a partial order . 


Summary: Give conditions for neighborhoods in a covering form a reduction of the covering. Give conditions for covering lower and upper approximations are dual to each other. Axiomatize lower and upper rough approximations based on partial orders.
This study proposes a new robust multi objective maintenance planning approach of the deteriorating bridges against uncertainty in performance degradation model . The main focus is to guarantee the performance requirements of the bridge by the scheduled maintenance interventions even in the presence of uncertainty in time dependent performance degradation model . The uncertainties are modeled as the perturbation of the system parameters . These are simulated by a sampling method and incorporated into the GA based multi objective optimization framework which produces a set of optimal preventive maintenance scenarios . In order to focus the searching on the most preferable region the performance models of the bridge components are all integrated into single overall performance measure by using the preference based objective space reduction method . Numerical example of a typical prestressed concrete girder bridge is provided to demonstrate the new robust maintenance scheduling approach . For comparison purpose non robust multi objective maintenance planning without considering uncertainty of the bridge performance is also provided . It is verified that the proposed approach can produce successfully performing maintenance scenarios under the perturbation of bridge condition grades while maintaining well balanced maintenance strategy both in terms of bridge performance and maintenance cost . 


Summary: We propose a new robust multi objective maintenance planning method of deteriorating bridges against model uncertainties. Simulated system uncertainties are incorporated into the GA based multi objective optimization framework. The preference based objective space reduction method is utilized to enhance searching efficiency. Numerical example of a typical prestressed concrete girder bridge is provided. Proposed method produces well balanced maintenance strategy both in terms of bridge performance and maintenance cost.
This work describes a technique for generating two dimensional triangular meshes using distributed memory parallel computers based on a master slaves model . This technique uses a coarse quadtree to decompose the domain and a serial advancing front technique to generate the mesh in each subdomain concurrently . In order to advance the front to a neighboring subdomain each subdomain suffers a shift to a Cartesian direction and the same advancing front approach is performed on the shifted subdomain . This shift and remesh procedure is repeatedly applied until no more mesh can be generated shifting the subdomains to different directions each turn . A finer quadtree is also employed in this work to help estimate the processing load associated with each subdomain . This load estimation technique produces results that accurately represent the number of elements to be generated in each subdomain leading to proper runtime prediction and to a well balanced algorithm . The meshes generated with the parallel technique have the same quality as those generated serially within acceptable limits . Although the presented approach is two dimensional the idea can be easily extended to three dimensions . 


Summary: We present a parallel technique for generating two dimensional triangular meshes. Generated meshes show the same quality as those generated with serial approaches. Our parallel technique presents a fairly good speed up. Our load estimation is very effective and can be used for efficient load balancing.
This paper aims to develop and compare several elicitation criterions for decision making of incomplete soft sets which are generated by restricted intersection . One time elicitation process is divided into two steps . Using the greedy idea four criterions for elicitation of objects are built based on maximax maximin minimax regret and combination of expected choice values and elicitation times . Then these initial unknown values which produce incomplete values together with known information are in priority . Fast methods for computing possibly and necessarily optimal solutions before or in the elicitation process are invented . As far as the sizes of soft sets used in the simulation experiments it is found statistically that we should choose the criterion based on the combination of expected choice value and expected elicitation times in the first step of one time elicitation . The developed methods can be used for decision making of incomplete 0 1 information systems which are generated by the conjunction of two experts incomplete 0 1 evaluation results . Whenever the available information is not enough for choosing a necessarily optimal solution the elicitation algorithms can help elicitate as few unknown values as possible until an optimal result is found . An elicitation system is made to show that our elicitation methods can potentially be embedded in recommender or decision support systems . The elicitation problems are proposed for decision making of operation generated soft sets by extracting from some practical problems . The concept of expected elicitation times of objects is defined and used for developing one type of elicitation strategy . 


Summary: A fast method for computing necessarily optimal solutions is developed. Four kinds of elicitation criterions for one time elicitation are explored. Idea of expected elicitation times is used in one elicitation criterion. A system is built to implement the proposed elicitation criterions.
Ultra large scale systems are a new generation of distributed software system that are composed of various changing inconsistent or even conflicting components that are distributed in a wide domain . Some important characteristics of these systems include their very large size global geographical distribution operational and managerial independence of their member systems . The main function of these systems arises from the interoperability between their components . Nowadays one of the most important challenges facing ultra large scale systems is the interoperability of their component systems . Interoperability is the ability by which system elements can exchange and understand the information required with each other . This paper aims to solve the mentioned challenge which is divided into two main parts . In the first part this paper presents a maturity model for the interoperability of ultra large scale systems by using the interoperability level of the component system of one ultra large scale system its maturity level can be determined . In the second part by proposing a framework we try to increase the interoperability of the component systems in ultra large scale systems based on the interoperability maturity levels determined in the first part . Consequently their interoperability is improved . 


Summary: We provide a maturity model for interoperability of ultra large scale systems. We provide a framework for interoperability of ultra large scale systems. Increasing interoperability of component systems in ultra large scale systems.
We mainly study the low rank image recovery problem by proposing a bilinear low rank coding framework called Tensor Low Rank Representation . For enhanced low rank recovery and error correction our method constructs a low rank tensor subspace to reconstruct given images along row and column directions simultaneously by computing two low rank matrices alternately from a nuclear norm minimization problem so both column and row information of data can be effectively preserved . Our bilinear approach seamlessly integrates the low rank coding and dictionary learning into a unified framework . Thus our formulation can be treated as enhanced Inductive Robust Principal Component Analysis with noise removed by low rank representation and can also be considered as the enhanced low rank representation with a clean informative dictionary via low rank embedding . To enable our method to include outside images the out of sample extension is also presented by regularizing the model to correlate image features with the low rank recovery of the images . Comparison with other criteria shows that our model exhibits stronger robustness and enhanced performance . We also use the outputted bilinear low rank codes for feature learning . Two unsupervised local and global low rank subspace learning methods are proposed for extracting image features for classification . Simulations verified the validity of our techniques for image recovery representation and classification . 


Summary: We mainly explore the low rank image recovery problem. A bilinear low rank image coding framework is proposed. For recovery TLRR preserves both column and row information of given data. Out of sample extension of TLRR is presented for handling outside data. We propose two local and global low rank subspace learning methods for feature learning.
In this paper an effective bi population estimation of distribution algorithm is presented to solve the no idle permutation flow shop scheduling problem with the total tardiness criterion . To enhance the search efficiency and maintain the diversity of the whole population two sub populations are used in the BEDA . The two sub populations are generated by sampling the probability models that are updated differently for the global exploration and the local exploitation respectively . Meanwhile the two sub populations collaborate with each other to share search information for adjusting the models . To well adjust the models for generating promising solutions the global probability model is updated during the evolution with the superior population and the local probability model is updated with the best solution that has been explored . To further enhance exploitation in the promising region the insertion operator is used iteratively as the local search procedure . To investigate the influence of parameter setting numerical study based on the Taguchi method of design of experiment is carried out . The effectiveness of the bi population strategy and local search procedure is shown by numerical comparisons and the comparisons with the recently published algorithms by using the benchmarking instances also demonstrate the effectiveness of the proposed BEDA . 


Summary: A novel bi population EDA for NIFPSP. Suitable adjusting mechanisms for adjusting probability models. Insertion based local search procedure to enhance exploitation. Better results obtained than those by the existing meta heuristics.
Noise components are a major cause of poor performance in document analysis . To reduce undesired components most recent research works have applied an image processing technique . However the effectiveness of these techniques is suitable only for a Latin script document but not a non Latin script document . The characteristics of the non Latin script document such as Thai are considerably more complicated than the Latin script document and include many levels of character alignment no word or sentence separator and variability in a character s size . When applying an image processing technique to a Thai document we usually remove the characters that are relatively close to noise . Hence in this paper we propose a novel noise reduction method by applying a machine learning technique to classify and reduce noise in document images . The proposed method uses a semi supervised cluster and label approach with an improved labeling method namely feature selected sub cluster labeling . Feature selected sub cluster labeling focuses on the clusters that are incorrectly labeled by conventional labeling methods . These clusters are re clustered into small groups with a new feature set that is selected according to class labels . The experimental results show that this method can significantly improve the accuracy of labeling examples and the performance of classification . We compared the performance of noise reduction and character preservation between the proposed method and two related noise reduction approaches i.e . a two phased stroke like pattern noise removal and a commercial noise reduction software called ScanFix Xpress 6.0 . The results show that semi supervised noise reduction is significantly better than the compared methods of which an F measure of character and noise is 86.01 and 97.82 respectively . 


Summary: We proposed a novel noise reduction method for document images. Semi supervised learning is applied to classify noise from character components. The proposed method is suitable for Non Latin based scripts i.e. Thai document image. We proposed an enhance labeling method of semi supervised cluster and label approach. The performance of proposed methods are significantly better than comparison methods.