1,722,195 research outputs found
Investigating Brain Functional Networks in a Riemannian Framework
No full textThe brain is a complex system of several interconnected components which can be categorized at different Spatio-temporal levels, evaluate the physical connections and the corresponding functionalities. To study brain connectivity at the macroscale, Magnetic Resonance Imaging (MRI) technique in all the different modalities has been exemplified to be an important tool. In particular, functional MRI (fMRI) enables to record the brain activity either at rest or in different conditions of cognitive task and assist in mapping the functional connectivity of the brain.
The information of brain functional connectivity extracted from fMRI images can be defined using a graph representation, i.e. a mathematical object consisting of nodes, the brain regions, and edges, the link between regions. With this representation, novel insights have emerged about understanding brain connectivity and providing evidence that the brain networks are not randomly linked. Indeed, the brain network represents a small-world structure, with several different properties of segregation and integration that are accountable for specific functions and mental conditions. Moreover, network analysis enables to recognize and analyze patterns of brain functional connectivity characterizing a group of subjects.
In recent decades, many developments have been made to understand the functioning of the human brain and many issues, related to the biological and the methodological perspective, are still need to be addressed. For example, sub-modular brain organization is still under debate, since it is necessary to understand how the brain is functionally organized. At the same time a comprehensive organization of functional connectivity is mostly unknown and also the dynamical reorganization of functional connectivity is appearing as a new frontier for analyzing brain dynamics. Moreover, the recognition of functional connectivity patterns in patients affected by mental disorders is still a challenging task, making plausible the development of new tools to solve them.
Indeed, in this dissertation, we proposed novel methodological approaches to answer some of these biological and neuroscientific questions. We have investigated methods for analyzing and detecting heritability in twin's task-induced functional connectivity profiles. in this approach we are proposing a geodesic metric-based method for the estimation of similarity between functional connectivity, taking into account the manifold related properties of symmetric and positive definite matrices.
Moreover, we also proposed a computational framework for classification and discrimination of brain connectivity graphs between healthy and pathological subjects affected by mental disorder, using geodesic metric-based clustering of brain graphs on manifold space. Within the same framework, we also propose an approach based on the dictionary learning method to encode the high dimensional connectivity data into a vectorial representation which is useful for classification and determining regions of brain graphs responsible for this segregation. We also propose an effective way to analyze the dynamical functional connectivity, building a similarity representation of fMRI dynamic functional connectivity states, exploiting modular properties of graph laplacians, geodesic clustering, and manifold learning
Modelling and Analyzing Attack- Defense Scenarios for Cyber- Ranges
Full text linkRome was not built in a day, but it was burnt to the ground in only six. Wood naturally catches fire, and without adequate engineering, fireproof houses and training for firefighters, destruction caused by fire is inevitable. In the 21st century, our modern world is built not on wood but on a digital infrastructure that was proposed in the 20th century with very little thought to security. This has resulted in a countless number of incidents in which that infrastructure has been compromised, from hospitals serving critically ill patients to gas pipelines providing necessary heating to people living in adverse climate conditions.
The current state of affairs is unacceptable, and serious efforts are needed to design and build a secure digital world and train individuals to use and operate it securely. Engineers and scientists design road infrastructure with great safety measures, but traffic accidents still happen. Indeed, they remain one of the leading causes of death in the world, and most traffic accidents are caused by human error or negligence. Similarly, the digital infrastructure can be designed and deployed securely, but its overall security and safety depend upon the humans who are operating and using it. Therefore, there is a great need to train individuals to operate the digital infrastructure in a secure manner.
Multiple efforts are being made to provide this training. These efforts include cybersecurity education and training based on different pedagogical methods involving classroom teaching, workshops, seminars, conferences and hands-on training. However, the effects of these efforts are not yet visible, as we experience ever-increasing damage caused by cyber-attacks. Traditionally, most cybersecurity awareness and training has been achieved through classrooms and workshops. Little focus has been on hands-on cybersecurity exercises. This is because designing and deploying infrastructure to deliver realistic hands-on exercises is a resource- intensive, complex and difficult task that requires considerable manual technical expertise. This makes the training very expensive and the process error-prone and difficult to standardize.
In order to solve these issues, different researchers have tried to remove inefficiencies in cybersecurity exercises by automating different phases of the exercises with limited success. Some efforts yielded very specific testbed-related artifacts, which were only applicable to that specific testbed, while other efforts lacked the complexity required for realistic cybersecurity exercises. Moreover, there is a lack of consensus among the community on defining the training scenarios that can be used in such exercises. Therefore, standard specifications of scenarios that can be executed in a cybersecurity exercise environment are needed. In this work, I attempt to overcome and address these issues by enhancing efficiency, realism and standardization with a novel method of modeling and executing cybersecurity exercise scenarios in a cybersecurity exercise environment, or a cyber range.
This is achieved through the development of a domain-specific language that is used to model and specify the technical requirements for cybersecurity exercises at an abstract level. The model of the exercise scenario is formalized and verified through logic programming, and then the technical requirements are translated into operational artifacts through an orchestrator. The operational artifacts contain an exercise infrastructure with vulnerabilities, traffic generators and attack/defense agents that can exploit or defend those vulnerabilities at an operational level in a cyber range. The proposed system goes beyond the state of the art by overcoming many inefficiencies in cybersecurity exercise scenario modeling and deployment, making their execution efficient, realistic and computationally repeatable. The proposed artifacts and solutions were tested in Norway’s national cybersecurity competitions, university classrooms and other cybersecurity exercises with positive results
Organ Segmentation with Recursive Data Augmentation for Deep Models
No full textThe precise segmentation of organs from computed tomography is a fundamental and pivotal task for correct diagnosis and proper treatment of diseases. Neural network models are widely explored for their promising performance in the segmentation of medical images. However, the small dimension of available datasets is affecting the biomedical imaging domain significantly and has a huge impact in training of deep learning models. In this paper we try to address this issue by iteratively augmenting the dataset with auxiliary task-based information. This is obtained by introducing a recursive training approach, where a new set of segmented images is generated at each iteration and then concatenated with the original input data as organ attention maps. In the experimental evaluation two different datasets were tested and the results produced from the proposed approach have shown significant improvements in organ segmentation as compared to a standard non-recursive approach
Searching for Functional Neuromarkers of Multiple Sclerosis: A Geodesic Distance-Based Framework on Riemannian Manifolds of Brain Connectivity
No full textMultiple Sclerosis (MS), a dynamic and dispersing disease that not only reflects the structural damages but also causes functional connectivity (FC) imbalance in and between brain networks of patients. Identifying novel neuromarkers for this disease remains a crucial challenge in brain connectivity analysis. To address this, we propose a two-stage mathematical framework capable of discriminating between healthy subjects and MS patients. Our proposed method leverages the properties of positive-definiteness of covariance-based connectivity matrices, which reside on a Riemannian manifold. It allows us to define and utilize a geodesic distance metric within this space. To construct a meaningful vector description for classification and neuromarker identification, we encode the data utilizing a set of reference networks established through geodesic clustering of FC matrices. These cluster centroids (reference networks) act as a dictionary, that enables us to encode each subject's connectivity matrix as a vector of geodesic distance. Finally, we employ a logistic regression classifier to differentiate between healthy and MS groups. A sensitivity analysis of trained classifier weights allowed the identification of the best discriminating feature and therefore led us to the identification of novel neuromarkers that best discriminate between the two groups. We demonstrate that our approach can successfully, discriminate between two groups and also identify neurophysiological markers of disease severity
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Perlindungan Hukum terhadap Kreditur atas Objek Tanah yang telah Dipasang Hak Tanggungan yang Kemudian dibatalkan oleh Pengadilan (Studi Putusan Mahkamah Agung Nomor 2301K/PDT/2007).
No full textThe object of guarantee for mortgage right on The Supreme Court Verdict
Number 2301K/Pdt/2007 is obtained from the transfer of joint property rights.
However, the transfer of rights is legally flawed as it is carried out without the
consent of the other party who owns the joint property object, causing the transfer
of roghts is canceled along with its derivative products. This also causes losses for
creditors whose collateral objects are legally disabled because creditors lose its
collaretal objects for their mortgage right as proof of settlement for creditors. Not
only dose it lose the special guarantee and the privileges attached to it, the Bank
also loses the guarantee of certainty of repayment.
The purpose of this research is to find out the form of legal protection for
creditors which in the Supreme Court Decision does not provide protection and
legal certainty for creditors and what efforts can be made by creditors to fulfill
creditors rights.
The research method used is normative juridical research method. The type
of data used is secondary data consisting of primary, secondary and tertiary legal
materials. The data collection technique used is library research. The data that has
been obtained is then analyzed qualitatively to obtain descriptive research results.
This research shows that Supreme Court Decision No. 2301K/Pdt/2007 does
not provide legal protection for creditors, and Law No. 10/1998 concerning
Banking and Law No. 4/1996 concerning Mortgage which specifically regulates
creditor rights do not explicitly regulate certificate status. Mortgage rights whose
object is erased so that this condition is a settlement of the norms in the Mortgage
Law. Therefore according to the author, creditors can still get legal protection
based on Articles 1131 and 1132 of the Civil Code.120 HalamanTesis Magiste
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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