varunr14/newsummary huggingface dataset:
Comparison of human brain MR images is often challenged by large inter subject structural variability . To determine correspondences between MR brain images most existing methods typically perform a local neighborhood search based on certain morphological features . They are limited in two aspects pre defined morphological features often have limited power in characterizing brain structures thus leading to inaccurate correspondence detection and correspondence matching is often restricted within local small neighborhoods and fails to cater to images with large anatomical difference . To address these limitations we propose a novel method to detect distinctive landmarks for effective correspondence matching . Specifically we first annotate a group of landmarks in a large set of training MR brain images . Then we use regression forest to simultaneously learn the optimal sets of features to best characterize each landmark and the non linear mappings from the local patch appearances of image points to their 3D displacements towards each landmark . The learned regression forests are used as landmark detectors to predict the locations of these landmarks in new images . Because each detector is learned based on features that best distinguish the landmark from other points and also landmark detection is performed in the entire image domain our method can address the limitations in conventional methods . The deformation field estimated based on the alignment of these detected landmarks can then be used as initialization for image registration . Experimental results show that our method is capable of providing good initialization even for the images with large deformation difference thus improving registration accuracy . 


Summary: We propose a novel method to detect distinctive landmarks for effective correspondence matching. The method can accurately detect corresponding landmarks in the case of large anatomical variations. The method can automatically identify informative features and search correspondence in the entire image domain. The method can provide good initialization to registration especially for images with large deformation difference.
An interactive loop between motion recognition and motion generation is a fundamental mechanism for humans and humanoid robots . We have been developing an intelligent framework for motion recognition and generation based on symbolizing motion primitives . The motion primitives are encoded into Hidden Markov Models which we call motion symbols . However to determine the motion primitives to use as training data for the HMMs this framework requires a manual segmentation of human motions . Essentially a humanoid robot is expected to participate in daily life and must learn many motion symbols to adapt to various situations . For this use manual segmentation is cumbersome and impractical for humanoid robots . In this study we propose a novel approach to segmentation the Real time Unsupervised Segmentation method which comprises three phases . In the first phase short human movements are encoded into feature HMMs . Seamless human motion can be converted to a sequence of these feature HMMs . In the second phase the causality between the feature HMMs is extracted . The causality data make it possible to predict movement from observation . In the third phase movements having a large prediction uncertainty are designated as the boundaries of motion primitives . In this way human whole body motion can be segmented into a sequence of motion primitives . This paper also describes an application of RUS to AUtonomous Symbolization of motion primitives . Each derived motion primitive is classified into an HMM for a motion symbol and parameters of the HMMs are optimized by using the motion primitives as training data in competitive learning . The HMMs are gradually optimized in such a way that the HMMs can abstract similar motion primitives . We tested the RUS and AUS frameworks on captured human whole body motions and demonstrated the validity of the proposed framework . 


Summary: This paper proposes a framework for real time unsupervised segmentation of human motions and automatic symbolization of the motions. The segmentation is based on prediction uncertainty and symbolization is based on competitive learning of human motion. Their integration was verified on the human motion datasets.
In this study we compare three commonly used methods for hyperspectral image classification namely Support Vector Machines Gaussian Processes and the Spectral Angle Mapper . We assess their performance in combination with different kernels . The assessment is done in two experiments under ideal conditions in the laboratory and separately in the field using natural light . For both experiments independent training and test sets are used . Results show that GPs generally outperform the SVMs irrespective of the kernel used . Furthermore angle based methods including the Spectral Angle Mapper outperform GPs and SVMs when using distance based kernels in the field experiment . A new GP method using an angle based kernel the Observation Angle Dependent covariance function outperforms SAM and SVMs in both experiments using only a small number of training spectra . These findings show that distance based kernels are more affected by changes in illumination between the training and test set than are angular based methods kernels . Taken together this study shows that independent training data can be used for classification of hyperspectral data in the field such as in open pit mines by using Bayesian machine learning methods and non stationary kernels such as GPs and the OAD kernel . This provides a necessary component for automated classifications such as autonomous mining where many images have to be classified without user interaction . 


Summary: Gaussian Processes incorporating SAM at core for hyperspectral data classification. Systematic comparison of machine learning methods under various conditions. Data acquired from different materials sensors and different illumination. Classification using such independent training and test data sets. The presented method provides a Bayesian framework as basis for data fusion.
The development of distributed testing frameworks is more complex where the implementation process must consider the mechanisms and functions required to support interaction as long as the communication and the coordination between distributed testing components . The typical reactions of such systems are the generation of errors set time outs locks observability controllability and synchronization problems . In other side the distributed testing process must not only check if the output events have been observed but also the dates when these events have been occurred . In this paper we show how to cope with these problems by using a distributed testing method including timing constraints . Afterwards a multi agent architecture is proposed in the design process to describe the behavior of testing a distributed chat group application on high level of abstraction . 


Summary: We present some technical issues for testing distributed frameworks with timing constraints. We model an architecture taking into account the delay of messages exchanged between the components of the testing distributed applications. We propose a Multi Agent based system to capture the complex monitoring tasks of distributed tester behaviors
The rise of Internet of Things has been improving the so called mobile Online Social Networks in sense of more ubiquitous inter communication and information sharing . Meanwhile location sharing service is known as the key cornerstone of mOSNs . Unfortunately location sharing has also caused similarly serious concerns on the potential privacy leakage . We propose BMobishare a security enhanced privacy preserving location sharing mechanism . It employs the Bloom Filter to mask sensitive data exchanges such that exchange of both sides can not obtain unauthorized privacy information . Analyses and evaluations show that BMobishare s enhanced location sharing procedure achieves significantly better performance when compared to existing approaches . 


Summary: We make a further research on privacy preserving location sharing on mobile online social networks. BMobishare is proposed a security improved mechanism which employs dummy query and the Bloom Filter to protect privacy. A performance evaluation that attests to the practicality of our solution.
The driving force behind software development of the Electronic Medical Record has been gradually changing . Heterogeneous software requirements have emerged so how to correctly carry out development project has become a complex task . This paper adopts the knowledge engineering and management mechanism i.e . CommonKADS and software quality engineering to improve existing strategic information management plan as a design methodology to help software implementation for medical institutes . We evaluate the adopting performance by a real case that examines the maturity level of the architecture alignment between the target solution in the proposed SIM plan and the built medical system . 


Summary: This proposed methodology can be used to improve the SIM plan. With the proposed SIM plan software development process is improved. The related software architecture is examined in different models. A real case with the working scenario to verify this proposed SIM plan.
Due to the advancements of robotic systems they are able to be employed in more unstructured outdoor environments . In such environments the robot terrain interaction becomes a highly non linear function . Several methods were proposed to estimate the robot terrain interaction machine learning methods iterative geometric methods quasi static and fully dynamic physics simulations . However to the best of our knowledge there has been no systematic evaluation comparing those methods . In this paper we present a newly developed iterative contact point estimation method for static stability estimation of actively reconfigurable robots . This new method is systematically compared to a physics simulation in a comprehensive evaluation . Both interaction models determine the contact points between robot and terrain and facilitate a subsequent static stability prediction . Hence they can be used in our state space global planner for rough terrain to evaluate the robot s pose and stability . The analysis also compares deterministic versions of both methods to stochastic versions which account for uncertainty in the robot configuration and the terrain model . The results of this analysis show that the new iterative method is a valid and fast approximate method . It is significantly faster compared to a physics simulation while providing good results in realistic robotic scenarios . 


Summary: We present a new robot pose prediction method for static stability estimation. The method approximates the terrain by least squares planes to reduce the runtime. A stochastic version accounts for noise in the robot state and the terrain model. We systematically compared it with a physics simulation in many distinct scenarios. The new method is significantly faster and competitive in realistic situations.
Controller Area Network is very popular in networked embedded systems . On the other hand intranets are now ubiquitous in office home and factory environments . Namely the Internet Protocol is the glue that permits any kind of information to be exchanged between devices in heterogeneous systems . In this paper a network architecture to seamlessly integrate CAN busses in intranets is described . Flexibility and scalability were the key design requirements to conceive a comprehensive solution that suits both inexpensive and very complex applications . A prototype implementation has been tested to confirm architecture feasibility and assess the performance it can achieve . 


Summary: An architecture to integrate Controller Area Network CAN in intranets is proposed. Key design requirements were flexibility and scalability. The proposed architecture suits both inexpensive and very complex applications. A prototype implementation has been tested to assess feasibility and performance.
A significant number of recommender systems utilize the k nearest neighbor algorithm as the collaborative filtering core . This algorithm is simple it utilizes updated data and facilitates the explanations of recommendations . Its greatest inconveniences are the amount of execution time that is required and the non scalable nature of the algorithm . The algorithm is based on the repetitive execution of the selected similarity metric . In this paper an innovative similarity metric is presented HwSimilarity . This metric attains high quality recommendations that are similar to those provided by the best existing metrics and can be processed by employing low cost hardware circuits . This paper examines the key design concepts and recommendation quality results of the metric . The hardware design cost of implementation and improvements achieved during execution are also explored . 


Summary: kNN algorithm performance. Collaborative filtering hardware similarity measure. Low cost recommender systems hardware circuits.
The Future Internet is expected to be composed of a mesh of interoperable web services accessed from all over the Web . This approach has been supported by many software providers who have provided a wide range of mash up tools for creating composite applications based on components prepared by the respective provider . These tools aim to achieve the end user development of rich internet applications however most having failed to meet the needs of end users without programming knowledge have been unsuccessful . Thus many studies have investigated success factors in order to propose scales of success factor objectives and assess the adequacy of mashup tools for their purpose . After reviewing much of the available literature this paper proposes a new success factor scale based on human factors human computer interaction factors and the specialization functionality relationship . It brings together all these factors offering a general conception of EUD success factors . The proposed scale was applied in an empirical study on current EUD tools which found that today s EUD tools have many shortcomings . In order to achieve an acceptable success rate among end users we then designed a mashup tool architecture called FAST Wirecloud which was built taking into account the proposed EUD success factor scale . The results of a new empirical study carried out using this tool have demonstrated that users are better able to successfully develop their composite applications and that FAST Wirecloud has scored higher than all the other tools under study on all scales of measurement and particularly on the scale proposed in this paper . 


Summary: Shows a mashup platform that lets end users make their own web applications Proposes a new scale based on a set of objective factors Proposes a reference architecture built taking into account the factors of success This architecture achieves more success among end users that current platforms It is useful to know which decisions are relevant for achieving end user satisfaction.
Objective Machine learning techniques can be used to extract predictive models for diseases from electronic medical records . However the nature of EMRs makes it difficult to apply off the shelf machine learning techniques while still exploiting the rich content of the EMRs . In this paper we explore the usage of a range of natural language processing techniques to extract valuable predictors from uncoded consultation notes and study whether they can help to improve predictive performance . Methods We study a number of existing techniques for the extraction of predictors from the consultation notes namely a bag of words based approach and topic modeling . In addition we develop a dedicated technique to match the uncoded consultation notes with a medical ontology . We apply these techniques as an extension to an existing pipeline to extract predictors from EMRs . We evaluate them in the context of predictive modeling for colorectal cancer a disease known to be difficult to diagnose before performing an endoscopy . Results Our results show that we are able to extract useful information from the consultation notes . The predictive performance of the ontology based extraction method moves significantly beyond the benchmark of age and gender alone of 0.870 versus 0.831 . We also observe more accurate predictive models by adding features derived from processing the consultation notes compared to solely using coded data although the difference is not significant . The extracted features from the notes are shown be equally predictive compared to the coded data of the consultations . Conclusion It is possible to extract useful predictors from uncoded consultation notes that improve predictive performance . Techniques linking text to concepts in medical ontologies to derive these predictors are shown to perform best for predicting CRC in our EMR dataset . 


Summary: The paper studies several natural language processing NLP techniques to extract predictors from uncoded data in electronic medical records EMRs . Some techniques are well known while other have been developed specifically for this research. The approaches have been applied to a large dataset we have access to covering 90 000 patients in general practices. We focus on predictive modelling of colorectal cancer which is a challenging disease to study as it is a common type of cancer while the symptoms are very a specific for the disease. The results show that some of the NLP techniques studied can complement the coded EMR data and hence result in improved predictive models.
Hidden and exposed terminal problems are known to negatively impact wireless communications degrading potential computing services on top . These effects are more significant in Wireless Mesh Sensor Networks and particularly in those based on the IEEE 802.15.5 Low Rate Wireless Personal Area Network standard a promising solution for enabling low power WMSNs . The first contribution of this paper is a quantitative evaluation of these problems under the IEEE 802.15.5 Asynchronous Energy Saving mode which is intended for asynchronous data collection applications . The results obtained show a sharp deterioration of the network performance . Therefore this paper also reviews the most relevant approaches that cope with these problems and are compatible with ASES . Finally a set of these proposals is assessed to find out those more suitable for their potential integration with ASES which constitutes the second major contribution of the paper . 


Summary: The hidden and exposed effects are assessed on Wireless Mesh Sensor Networks WMSNs . Performance evaluation reveals a clear negative impact on WMSNs. Different scientific proposals are discussed in order to mitigate these problems. Some of these proposals are stressed and compared according to WMSN premises. Best solutions of our comparative study are shown to the audience.
Enterprise Resource Planning packages are information systems that automate the business processes of organizations thereby improving their operational efficiency substantially . ERP projects that involve customization are often affected by inaccurate estimation of efforts . Size of the software forms the basis for effort estimation . Methods used for effort estimation either employ function points or lines of code to measure the size of customized ERP packages . Literature review reveals that the existing software size methods which are meant for custom built software products may not be suitable for COTS products such as customized ERP packages . Hence the effort estimation using conventional methods for customized ERP packages may not be accurate . This paper proposes a new approach to estimate the size of customized ERP packages using Package Points . The proposed approach was validated with data collected from 14 ERP projects delivered by the same company . A positive correlation was observed between Package Points and the efforts of these projects . This result indicates the feasibility of our proposed approach as well as the positive climate for its utility by the project managers of future ERP projects . Lastly we examine the implication of these results for practice and future research scope . 


Summary: A unique approach to measure size of the customized ERP package has been proposed. Case study research method has been used to validate the proposed approach. A positive correlation was observed between Package Points and the efforts of these projects. The study presents theoretical and practical implications for better understanding of ERP package size measurement process.
In this study we propose a novel solution for collecting smart meter data by merging Vehicular Ad Hoc Networks and smart grid communication technologies . In our proposed mechanism Vehicular Ad Hoc Networks are utilized for collecting data from smart meters eliminating the need for manpower . To the best of our knowledge this is the first study proposing the utilization of public transportation vehicles for collecting data from smart meters . With this work the use of the IEEE 802.11p protocol has been proposed for the first time for use in smart grid applications . In our scheme data flows first from smart meters to a bus through infrastructure to vehicle communication and then from the bus to a bus stop through vehicle to infrastructure communication . The performance of our proposed mechanism has been investigated in detail in terms of end to end delay and delivery ratio by using Network Simulator 2 and with different routing protocols . 


Summary: The first study which uses public transportation for collecting data from SM Our study only needs the IEEE 802.11p communication protocol capability on WAMR. Unlike other VANET studies our study uses both I2V V2I communications.
Objective Recurrence of cancer after treatment increases the risk of death . The ability to predict the treatment outcome can help to design the treatment planning and can thus be beneficial to the patient . We aim to select predictive features from clinical and PET based features in order to provide doctors with informative factors so as to anticipate the outcome of the patient treatment . Methods In order to overcome the small sample size problem of datasets usually met in the medical domain we propose a novel wrapper feature selection algorithm named HFS which searches forward in a hierarchical feature subset space . Feature subsets are iteratively evaluated with the prediction performance using SVM . All feature subsets performing better than those at the preceding iteration are retained . Moreover as SUV based features have been recognized as significant predictive factors for a patient outcome we propose to incorporate this prior knowledge into the selection procedure to improve its robustness and reduce its computational cost . Results Two real world datasets from cancer patients are included in the evaluation . We extract dozens of clinical and PET based features to characterize the patient s state including SUV parameters and texture features . We use leave one out cross validation to evaluate the prediction performance in terms of prediction accuracy and robustness . Using SVM as the classifier our HFS method produces accuracy values of 100 and 94 on the two datasets respectively and robustness values of 89 and 96 . Without accuracy loss the prior based version improves the robustness up to 100 and 98 on the two datasets respectively . Conclusions Compared with other feature selection methods the proposed HFS and pHFS provide the most promising results . For our HFS method we have empirically shown that the addition of prior knowledge improves the robustness and accelerates the convergence . 


Summary: A novel wrapper method searches forward in a hierarchical feature subset space. Prior domain knowledge is incorporated into the selection procedure. Promising results are obtained on two cancer patient datasets.