2,620 research outputs found
A survey on Human Mobility and its applications
Human Mobility has attracted attentions from different fields of studies such
as epidemic modeling, traffic engineering, traffic prediction and urban
planning. In this survey we review major characteristics of human mobility
studies including from trajectory-based studies to studies using graph and
network theory. In trajectory-based studies statistical measures such as jump
length distribution and radius of gyration are analyzed in order to investigate
how people move in their daily life, and if it is possible to model this
individual movements and make prediction based on them. Using graph in mobility
studies, helps to investigate the dynamic behavior of the system, such as
diffusion and flow in the network and makes it easier to estimate how much one
part of the network influences another by using metrics like centrality
measures. We aim to study population flow in transportation networks using
mobility data to derive models and patterns, and to develop new applications in
predicting phenomena such as congestion. Human Mobility studies with the new
generation of mobility data provided by cellular phone networks, arise new
challenges such as data storing, data representation, data analysis and
computation complexity. A comparative review of different data types used in
current tools and applications of Human Mobility studies leads us to new
approaches for dealing with mentioned challenges
Obfuscation-resilient Android Malware Analysis Based on Contrastive Learning
Due to its open-source nature, Android operating system has been the main
target of attackers to exploit. Malware creators always perform different code
obfuscations on their apps to hide malicious activities. Features extracted
from these obfuscated samples through program analysis contain many useless and
disguised features, which leads to many false negatives. To address the issue,
in this paper, we demonstrate that obfuscation-resilient malware analysis can
be achieved through contrastive learning. We take the Android malware
classification as an example to demonstrate our analysis. The key insight
behind our analysis is that contrastive learning can be used to reduce the
difference introduced by obfuscation while amplifying the difference between
malware and benign apps (or other types of malware).
Based on the proposed analysis, we design a system that can achieve robust
and interpretable classification of Android malware. To achieve robust
classification, we perform contrastive learning on malware samples to learn an
encoder that can automatically extract robust features from malware samples. To
achieve interpretable classification, we transform the function call graph of a
sample into an image by centrality analysis. Then the corresponding heatmaps
are obtained by visualization techniques. These heatmaps can help users
understand why the malware is classified as this family. We implement IFDroid
and perform extensive evaluations on two widely used datasets. Experimental
results show that IFDroid is superior to state-of-the-art Android malware
familial classification systems. Moreover, IFDroid is capable of maintaining
98.2% true positive rate on classifying 8,112 obfuscated malware samples
Transport infrastructures and regional development: The discovery of centrality of the container terminal of Gioia Tauro in the Mediterranian
European container terminals of Mediterranean are enjoying of a discovery on the more dynamics routes of the whole world that link Europe to Far East-Pacific. This centrality consists in the acquisition of a relative advantage in terms of 6 -7 days less of navigation respect to big ports of call of North Europe. In this framework, the new installation of Gioia Tauro?s container terminal is demonstrating to be one of the most important junctions of transhipment of South Europe. This could manifest completely his potentialities in the same measures in which will be guided at the same time adequate territorial policies and lay the bases for logistic systems of high level. This means to direct in the national and continental hinterland the construction of a system of infrastructures of road and railway junctions necessarily adequate and advanced in terms of technician level and capacity of response. Only in this sense it will be possible to initiate a competition forms among economic systems in a territorial base rather than among single productive units. In fact, after a ten-year period of interventions for sectors, transeuropean network of transportation could start to considered as a first systematic tentative of integrated actions among transportation politics and regional politics of European Union. By starting, therefore, by a systemic approach, the paper turns to a simulation model and to specific inquiry on the field to gather the impacts of the container terminal to territorial level. The aim is to verify if and in which way the politics of national and community transport linked to the birth of container port of Gioia Tauro, will assume the features of a complex regional development politics able to mitigate and/or prevent the imbalances become worse in the ambit of European Union.
Electrophysiological network alterations in adults with copy number variants associated with high neurodevelopmental risk
Rare copy number variants associated with increased risk for neurodevelopmental and psychiatric disorders (referred to as ND-CNVs) are characterized by heterogeneous phenotypes thought to share a considerable degree of overlap. Altered neural integration has often been linked to psychopathology and is a candidate marker for potential convergent mechanisms through which ND-CNVs modify risk; however, the rarity of ND-CNVs means that few studies have assessed their neural correlates. Here, we used magnetoencephalography (MEG) to investigate resting-state oscillatory connectivity in a cohort of 42 adults with ND-CNVs, including deletions or duplications at 22q11.2, 15q11.2, 15q13.3, 16p11.2, 17q12, 1q21.1, 3q29, and 2p16.3, and 42 controls. We observed decreased connectivity between occipital, temporal and parietal areas in participants with ND-CNVs. This pattern was common across genotypes and not exclusively characteristic of 22q11.2 deletions, which were present in a third of our cohort. Furthermore, a data-driven graph theory framework enabled us to successfully distinguish participants with ND-CNVs from unaffected controls using differences in node centrality and network segregation. Together, our results point to alterations in electrophysiological connectivity as a putative common mechanism through which genetic factors confer increased risk for neurodevelopmental and psychiatric disorders
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Single-participant structural similarity matrices lead to greater accuracy in classification of participants than function in autism in MRI.
BackgroundAutism has previously been characterized by both structural and functional differences in brain connectivity. However, while the literature on single-subject derivations of functional connectivity is extensively developed, similar methods of structural connectivity or similarity derivation from T1 MRI are less studied.MethodsWe introduce a technique of deriving symmetric similarity matrices from regional histograms of grey matter volumes estimated from T1-weighted MRIs. We then validated the technique by inputting the similarity matrices into a convolutional neural network (CNN) to classify between participants with autism and age-, motion-, and intracranial-volume-matched controls from six different databases (29,288 total connectomes, mean age = 30.72, range 0.42-78.00, including 1555 subjects with autism). We compared this method to similar classifications of the same participants using fMRI connectivity matrices as well as univariate estimates of grey matter volumes. We further applied graph-theoretical metrics on output class activation maps to identify areas of the matrices that the CNN preferentially used to make the classification, focusing particularly on hubs.LimitationsWhile this study used a large sample size, the majority of data was from a young age group; furthermore, to make a viable machine learning study, we treated autism, a highly heterogeneous condition, as a binary label. Thus, these results are not necessarily generalizable to all subtypes and age groups in autism.ResultsOur models gave AUROCs of 0.7298 (69.71% accuracy) when classifying by only structural similarity, 0.6964 (67.72% accuracy) when classifying by only functional connectivity, and 0.7037 (66.43% accuracy) when classifying by univariate grey matter volumes. Combining structural similarity and functional connectivity gave an AUROC of 0.7354 (69.40% accuracy). Analysis of classification performance across age revealed the greatest accuracy in adolescents, in which most data were present. Graph analysis of class activation maps revealed no distinguishable network patterns for functional inputs, but did reveal localized differences between groups in bilateral Heschl's gyrus and upper vermis for structural similarity.ConclusionThis study provides a simple means of feature extraction for inputting large numbers of structural MRIs into machine learning models. Our methods revealed a unique emphasis of the deep learning model on the structure of the bilateral Heschl's gyrus when characterizing autism
Graph analysis of functional brain networks: practical issues in translational neuroscience
The brain can be regarded as a network: a connected system where nodes, or
units, represent different specialized regions and links, or connections,
represent communication pathways. From a functional perspective communication
is coded by temporal dependence between the activities of different brain
areas. In the last decade, the abstract representation of the brain as a graph
has allowed to visualize functional brain networks and describe their
non-trivial topological properties in a compact and objective way. Nowadays,
the use of graph analysis in translational neuroscience has become essential to
quantify brain dysfunctions in terms of aberrant reconfiguration of functional
brain networks. Despite its evident impact, graph analysis of functional brain
networks is not a simple toolbox that can be blindly applied to brain signals.
On the one hand, it requires a know-how of all the methodological steps of the
processing pipeline that manipulates the input brain signals and extract the
functional network properties. On the other hand, a knowledge of the neural
phenomenon under study is required to perform physiological-relevant analysis.
The aim of this review is to provide practical indications to make sense of
brain network analysis and contrast counterproductive attitudes
Architecture Information Communication in Two OSS Projects: the Why, Who, When, and What
Architecture information is vital for Open Source Software (OSS) development,
and mailing list is one of the widely used channels for developers to share and
communicate architecture information. This work investigates the nature of
architecture information communication (i.e., why, who, when, and what) by OSS
developers via developer mailing lists. We employed a multiple case study
approach to extract and analyze the architecture information communication from
the developer mailing lists of two OSS projects, ArgoUML and Hibernate, during
their development life-cycle of over 18 years. Our main findings are: (a)
architecture negotiation and interpretation are the two main reasons (i.e.,
why) of architecture communication; (b) the amount of architecture information
communicated in developer mailing lists decreases after the first stable
release (i.e., when); (c) architecture communications centered around a few
core developers (i.e., who); (d) and the most frequently communicated
architecture elements (i.e., what) are Architecture Rationale and Architecture
Model. There are a few similarities of architecture communication between the
two OSS projects. Such similarities point to how OSS developers naturally
gravitate towards the four aspects of architecture communication in OSS
development.Comment: Preprint accepted for publication in Journal of Systems and Software,
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