varunr14/newsummary huggingface dataset:
Archives are an extremely valuable part of our cultural heritage since they represent the trace of the activities of a physical or juridical person in the course of their business . Despite their importance the models and technologies that have been developed over the past two decades in the Digital Library field have not been specifically tailored to archives . This is especially true when it comes to formal and foundational frameworks as the Streams Structures Spaces Scenarios Societies model is . Therefore we propose an innovative formal model called NEsted SeTs for Object hieRarchies for archives explicitly built around the concepts of context and hierarchy which play a central role in the archival realm . NESTOR is composed of two set based data models the Nested Sets Model and the Inverse Nested Sets Model that express the hierarchical relationships between objects through the inclusion property between sets . We formally study the properties of these models and prove their equivalence with the notion of hierarchy entailed by archives . We then use NESTOR to extend the 5S model in order to take into account the specific features of archives and to tailor the notion of digital library accordingly . This offers the possibility of opening up the full wealth of DL methods and technologies to archives . We demonstrate the impact of NESTOR on this problem through three example use cases . 


Summary: A set theoretical formal model for digital archives is presented. The 5S model is extended for digital archives and concrete applications are given. Address EAD issues in the Web and with compound digital objects. Use cases based on OAI PMH OAI ORE and annotation frameworks for digital archives. Properties and mappings between the tree and NESTOR are formally proved.
This paper presents an unsupervised deep learning framework that derives spatio temporal features for human robot interaction . The respective models extract high level features from low level ones through a hierarchical network viz . the Hierarchical Temporal Memory providing at the same time a solution to the curse of dimensionality in shallow techniques . The presented work incorporates the tensor based framework within the operation of the nodes and thus enhances the feature derivation procedure . This is due to the fact that tensors allow the preservation of the initial data format and their respective correlation and moreover attain more compact representations . The computational nodes form spatial and temporal groups by exploiting the multilinear algebra and subsequently express the samples according to those groups in terms of proximity . This generic framework may be applied in a diverse of visual data while it has been examined on sequences of color and depth images exhibiting remarkable performance . 


Summary: We propose a novel deep learning model for action recognition. The deep learning model exploits multilinear algebra. The method derives spatio temporal features.
In this article we propose a novel formalism to model and analyse gene regulatory networks using a well established formal verification technique . We model the possible behaviours of networks by logical formulae in linear temporal logic . By checking the satisfiability of LTL it is possible to check whether some or all behaviours satisfy a given biological property which is difficult in quantitative analyses such as the ordinary differential equation approach . Owing to the complexity of LTL satisfiability checking analysis of large networks is generally intractable in this method . To mitigate this computational difficulty we developed two methods . One is a modular checking method where we divide a network into subnetworks check them individually and then integrate them . The other is an approximate analysis method in which we specify behaviours in simpler formulae which compress or expand the possible behaviours of networks . In the approximate method we focused on network motifs and presented approximate specifications for them . We confirmed by experiments that both methods improved the analysis of large networks . 


Summary: We propose a novel qualitative method for analysing gene networks based on formal verification technique. Behaviours and properties of networks are described in temporal logic formulae. By checking satisfiability of the formula we can analyse properties of the network. To improve the efficiency of analysis we developed the modular and approximate method.
The ability of most existing approaches to classify abandoned and removed objects in images is affected by external environmental conditions such as illumination and traffic volume because the approaches use several pre defined threshold values and generate many falsely classified static regions . To reduce these effects we propose an accurate ARO classification method using a hierarchical finite state machine that consists of pixel layer region layer and event layer FSMs where the result of the lower layer FSM is used as the input of the higher layer FSM . Each FSM is defined by a Mealy state machine with three states and several state transitions where a support vector machine determines the state transition based on the current state and input features such as area intensity motion shape time duration color and edge . Because it uses the hierarchical FSM structure with features that are optimally trained by SVM classifiers the proposed ARO classification method does not require threshold values and guarantees better classification accuracy under severe environmental changes . In experiments the proposed ARO classification method provided much higher classification accuracy and lower false alarm rate than the state of the art methods in both public databases and a commercial database . The proposed ARO classification method can be applied to many practical applications such as detection of littering illegal parking theft and camouflaged soldiers . 


Summary: We propose a novel and accurate ARO classification method. We propose a hierarchical FSM consisting of pixel region and event layers. State transition is done by the pre trained SVM using 7 different input features. The proposed ARO method shows higher classification and low false alarm. The proposed ARO method can be applied to many practical applications.
When a videometric system operates over a long period temperature variations in the camera and its environment will affect the measurement results which can not be ignored . How to eliminate or compensate for the effects of such variations in temperature is an emergent problem . Starting with the image drift phenomenon this paper presents an image drift model that analyzes the relationship between variations in the camera parameters and drift in the coordinates of the image . A simplified model is then introduced by analyzing the coupling relationships among the variations in the camera parameters . Furthermore a model of the relationship between the camera parameters and temperature variations is established with the system identification method . Finally several compensation experiments on image drift are carried out using the parameter temperature relationship model calibrated with one arbitrary data set to compensate the others . The analyses and experiments demonstrate the feasibility and efficiency of the proposed method . 


Summary: This paper proposed a compensation method for eliminating the effects of temperature variation in the long duration application of image vision. A model of the relationship between the camera parameters and temperature variations is established with the system identification method. Experiments are carried on. The analyses and experiments demonstrate the feasibility and efficiency of the proposed method.
In recent years monitoring the compliance of business processes with relevant regulations constraints and rules during runtime has evolved as major concern in literature and practice . Monitoring not only refers to continuously observing possible compliance violations but also includes the ability to provide fine grained feedback and to predict possible compliance violations in the future . The body of literature on business process compliance is large and approaches specifically addressing process monitoring are hard to identify . Moreover proper means for the systematic comparison of these approaches are missing . Hence it is unclear which approaches are suitable for particular scenarios . The goal of this paper is to define a framework for Compliance Monitoring Functionalities that enables the systematic comparison of existing and new approaches for monitoring compliance rules over business processes during runtime . To define the scope of the framework at first related areas are identified and discussed . The CMFs are harvested based on a systematic literature review and five selected case studies . The appropriateness of the selection of CMFs is demonstrated in two ways a systematic comparison with pattern based compliance approaches and a classification of existing compliance monitoring approaches using the CMFs . Moreover the application of the CMFs is showcased using three existing tools that are applied to two realistic data sets . Overall the CMF framework provides powerful means to position existing and future compliance monitoring approaches . 


Summary: Framework of ten compliance monitoring functionalities CMF in business processes is defined. Related techniques and enabling technologies are discussed. Systematic literature review and presentation of real world business constraints for compliance are extracted from five case studies. Systematic comparison with compliance patterns and classification of existing approaches using the framework is presented. CMF framework based on two realistic data sets using three different compliance monitoring tools is applied.
In accordance with Basel Capital Accords the Capital Requirements for market risk exposure of banks is a nonlinear function of Value at Risk . Importantly the CR is calculated based on a bank s actual portfolio i.e . the portfolio represented by its current holdings . To tackle mean VaR portfolio optimization within the actual portfolio framework we propose a novel mean VaR optimization method where VaR is estimated using a univariate Generalized AutoRegressive Conditional Heteroscedasticity volatility model . The optimization was performed by employing a Nondominated Sorting Genetic Algorithm . On a sample of 40 large US stocks our procedure provided superior mean VaR trade offs compared to those obtained from applying more customary mean multivariate GARCH and historical VaR models . The results hold true in both low and high volatility samples . 


Summary: We introduce mean univariate GARCH VaR actual portfolio optimization in accordance with Basel Capital Accords. We develop optimization software that combines NSGA II algorithm and R statistical software. Computational results show that our approach provides better mean univariate GARCH VaR trade offs of actual portfolios in comparison to benchmarks estimated by analytical methods. Empirical tests cover both low and high volatility samples.
Knowledge acquisition and bilingual terminology extraction from multilingual corpora are challenging tasks for cross language information retrieval . In this study we propose a novel method for mining high quality translation knowledge from our constructed Persian English comparable corpus University of Tehran Persian English Comparable Corpus . We extract translation knowledge based on Term Association Network constructed from term co occurrences in same language as well as term associations in different languages . We further propose a post processing step to do term translation validity check by detecting the mistranslated terms as outliers . Evaluation results on two different data sets show that translating queries using UTPECC and using the proposed methods significantly outperform simple dictionary based methods . Moreover the experimental results show that our methods are especially effective in translating Out Of Vocabulary terms and also expanding query words based on their associated terms . 


Summary: We propose novel method for mining high quality translation from comparable corpus. We introduce Term Association Network TAN for mining Translation knowledge. We propose a new method for term translation validity using cross outlier detection. Results show that proposed methods significantly outperforms dictionary based method. Our methods are specially effective in translating OOV terms by expanding query words.
Expertise seeking is the activity of selecting people as sources for consultation about an information need . This review of 72 expertise seeking papers shows that across a range of tasks and contexts people in particular work group colleagues and other strong ties are among the most frequently used sources . Studies repeatedly show the influence of the social network of friendships and personal dislikes on the expertise seeking network of organisations . In addition people are no less prominent than documentary sources in work contexts as well as daily life contexts . The relative influence of source quality and source accessibility on source selection varies across studies . Overall expertise seekers appear to aim for sufficient quality composed of reliability and relevance while also attending to accessibility composed of access to the source and access to the source information . Earlier claims that seekers disregard quality to minimise effort receive little support . Source selection is also affected by task related seeker related and contextual factors . For example task complexity has been found to increase the use of information sources whereas task importance has been found to amplify the influence of quality on source selection . Finally the reviewed studies identify a number of barriers to expertise seeking . 


Summary: Expertise seeking involves selecting people for consultation about info needs. People e.g. work group colleagues are among the most frequent sources. Seekers balance quality and accessibility in their selection of sources. Source selection is affected by task related seeker related and contextual factors. Multiple barriers complicate degrade or prevent expertise seeking.
In this paper we propose a robust dense stereo reconstruction algorithm using a random walk with restart . The pixel wise matching costs are aggregated into superpixels and the modified random walk with restart algorithm updates the matching cost for all possible disparities between the superpixels . In comparison to the majority of existing stereo methods using the graph cut belief propagation or semi global matching our proposed method computes the final reconstruction through the determination of the best disparity at each pixel in the matching cost update . In addition our method also considers occlusion and depth discontinuities through the visibility and fidelity terms . These terms assist in the cost update procedure in the calculation of the standard smoothness constraint . The method results in minimal computational costs while achieving high accuracy in the reconstruction . We test our method on standard benchmark datasets and challenging real world sequences . We also show that the processing time increases linearly in relation to an increase in the disparity search range . 


Summary: We develop a robust stereo matching algorithm using a random walk with restart. The proposed method considers occlusion and depth discontinuities. The method achieves high accuracy with low computational costs in the reconstruction. Our method works well on the varying illumination and exposure test.
On the onset of the second decade of research in eye movement biometrics the already demonstrated results strongly support the promising perspectives of the field . This paper presents a description of the research conducted in eye movement biometrics based on an extended analysis of the characteristics and results of the BioEye 2015 Competition on Biometrics via Eye Movements . This extended presentation can contribute to the understanding of the current level of research in eye movement biometrics covering areas such as the previous work in the field the procedures for the creation of a database of eye movement recordings and the different approaches that can be used for the analysis of eye movements . Also the presented results from BioEye 2015 competition can demonstrate the potential identification accuracy that can be achieved under easier and more difficult scenarios . Based on the provided presentation we discuss topics related to the current status in eye movement biometrics and suggest possible directions for the future research in the field . 


Summary: We present a review of the state of the art in eye movement biometrics. We explain the general steps for the creation of a database of eye movement recordings. We describe basic eye movement features and methodologies with application in biometrics. We present extended analysis and results for the BioEye 2015 competition.
Human action recognition from still image has recently drawn increasing attention in human behavior analysis and also poses great challenges due to the huge inter ambiguity and intra variability . Vector of locally aggregated descriptors has achieved state of the art performance in many image classification tasks based on local features . The great success of VLAD is largely due to its high descriptive ability and computational efficiency . In this paper towards optimal VLAD representations for human action recognition from still images we improve VLAD by tackling three important issues including empty cavity ambiguity and pooling strategies . The empty cavity limits the performance of VLAD and has long been overlooked . We investigate the empty cavity and provide an effective solution to deal with it which improves the performance of VLAD we enhance the codewords with middle level of assignments which are more reliable and can provide more useful information for realistic activity we propose incorporating the generalized max pooling to replace sum pooling in VLAD which is more reliable for the final representation . We have conducted extensive experiments on four widely used benchmarks to validate the proposed method for human action recognition from still images . Our method produces competitive performance with state of the art algorithms . 


Summary: Tackle the empty cavity issue by properly selecting reference points. Reveal codeword ambiguity by imbalance assignment and enhance the reliable codeword. Incorporate GMP into VLAD.
This paper proposes a unified multi lateral filter to efficiently increase the spatial resolution of low resolution and noisy depth maps in real time . Time of Flight cameras have become a very promising alternative to stereo based range sensing systems as they provide depth measurements at a high frame rate . However there are actually two main drawbacks that restrict their use in a wide range of applications namely their fairly low spatial resolution as well as the amount of noise within the depth estimation . In order to address these drawbacks we propose a new approach based on sensor fusion . That is we couple a ToF camera of low resolution with a 2 D camera of higher resolution to which the low resolution depth map will be efficiently upsampled . In this paper we first review the existing depth map enhancement approaches based on sensor fusion and discuss their limitations . We then propose a unified multi lateral filter that accounts for the inaccuracy of depth edges position due to the low resolution ToF depth maps . By doing so unwanted artefacts such as texture copying and edge blurring are almost entirely eliminated . Moreover the proposed filter is configurable to behave as most of the alternative depth enhancement approaches . Using a convolution based formulation and data quantization and downsampling the described filter has been effectively and efficiently implemented for dynamic scenes in real time applications . The experimental results show a sensitive qualitative as well as quantitative improvement on raw depth maps outperforming state of the art multi lateral filters . 


Summary: New multi lateral filter to efficiently increase the spatial resolution of low resolution and noisy depth maps in real time. ToF camera coupled with a 2 D camera of higher resolution to which the low resolution depth map will upsampled. We account for the inaccuracy of depth edges position due to the low resolution ToF depth maps. Unwanted artefacts such as texture copying and edge blurring are almost entirely eliminated. The proposed filter is convolution based and achives a real time performance by data quantization and downsampling. The proposed filter has been effectively and efficiently implemented for dynamic scenes in real time applications. The proposed filter can be easily adapted for alternative depth sensing systems than ToF cameras.
We propose a holistic approach to the problem of re identification in an environment of distributed smart cameras. We model the re identification process in a distributed camera network as a distributed multi class classifier composed of spatially distributed binary classifiers. We treat the problem of re identification as an open world problem and address novelty detection and forgetting. As there are many tradeoffs in design and operation of such a system we propose a set of evaluation measures to be used in addition to the recognition performance. The proposed concept is illustrated and evaluated on a new many camera surveillance dataset and SAIVT SoftBio dataset. 


Summary: Formalization of object re identification problem in a distributed environment Re identification treated as an open world problem Novelty detection and forgetting included in the scheme A set of performance measures geared towards open world distributed surveillance Experiments on a many camera 36 surveillance dataset and publicly available source code
This paper presents a novel stereo disparity estimation method which combines three different cost metrics defined using RGB information the CENSUS transform as well as Scale Invariant Feature Transform coefficients . The selected cost metrics are aggregated based on an adaptive weight approach in order to calculate their corresponding cost volumes . The resulting cost volumes are then merged into a combined one following a novel two phase strategy which is further refined by exploiting scanline optimization . A mean shift segmentation driven approach is exploited to deal with outliers in the disparity maps . Additionally low textured areas are handled using disparity histogram analysis which allows for reliable disparity plane fitting on these areas . Finally an efficient two step approach is introduced to refine disparity discontinuities . Experiments performed on the four images of the Middlebury benchmark demonstrate the accuracy of this methodology which currently ranks first among published methods . Moreover this algorithm is tested on 27 additional Middlebury stereo pairs for evaluating thoroughly its performance . The extended comparison verifies the efficiency of this work . 


Summary: A two phase strategy for combining separate cost volumes is described. A mean shift segmentation driven approach to handle disparity outliers is utilized. Low textured area plane fitting is fostered by using disparity histogram analysis. Our method ranks first among published methods in the Middlebury evaluation system.