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With the emergence of COVID-19, mobile health applications have increasingly become crucial in contact tracing, information dissemination, and pandemic control in general. Apps warn users if they have been close to an infected person for sufficient time, and therefore potentially at risk. The distance measurement accuracy heavily affects the probability estimation of being infected. Most of these applications make use of the electromagnetic field produced by Bluetooth Low Energy technology to estimate the distance. Nevertheless, radio interference derived from numerous factors, such as crowding, obstacles, and user activity can lead to wrong distance estimation, and, in turn, to wrong decisions. Besides, most of the social distance-keeping criteria recognized worldwide plan to keep a different distance based on the activity of the person and on the surrounding environment. In this study, in order to enhance the performance of the COVID-19 tracking apps, a human activity classifier based on Convolutional Deep Neural Network is provided. In particular, the raw data coming from the accelerometer sensor of a smartphone are arranged to form an image including several channels (HAR-Image), which is used as fingerprints of the in-progress activity that can be used as an additional input by tracking applications. Experimental results, obtained by analyzing real data, have shown that the HAR-Images are effective features for human activity recognition. Indeed, the results on the k-fold cross-validation and obtained by using a real dataset achieved an accuracy very close to 100%. [ABSTRACT FROM AUTHOR]

The article discusses tension pneumomediastinum, a rare but serious condition that can lead to cardiogenic shock. It explains that pneumomediastinum is the presence of air in the mediastinum, and can be caused by airway diseases or trauma. While most cases of pneumomediastinum can be managed conservatively, tension pneumomediastinum requires immediate treatment. The article describes a bedside technique for mediastinal decompression using a supra jugular approach, which was successfully used in a case study. It concludes that the choice of technique for decompression depends on the available expertise, and that supra jugular mediastinal decompression is a safe and effective option. [Extracted from the article]

The vast majority of compressed digital data that flows nowadays on modern high-speed networks is directly related to human activity. It describes what we do, what we see and photograph, where we go, whom we meet, and specifically every moment of our lives. This brings up issues and concerns regarding the necessity to safeguard user privacy as well as to protect the digital multimedia contents that are delivered to offer new experiences. In this paper, we explore a unified approach to compression and privacy by considering different types of digital data (text, images, sound, and hyperspectral images). [ABSTRACT FROM AUTHOR]

In the last decades, mobile-based apps have been increasingly used in several application fields for many purposes involving a high number of human activities. Unfortunately, in addition to this, the number of cyber-attacks related to mobile platforms is increasing day-by-day. However, although advances in Artificial Intelligence science have allowed addressing many aspects of the problem, malware classification tasks are still challenging. For this reason, the following paper aims to propose new special features, called permission maps (Perm-Maps), which combine information related to the Android permissions and their corresponding severity levels. Such features have proven to be very effective in classifying different malware families through the usage of a convolutional neural network. Also, the advantages introduced by the Perm-Maps have been enhanced by a training process based on a federated logic. Experimental results show that the proposed approach achieves up to a 3% improvement in average accuracy with respect to J48 trees and Naive Bayes classifier, and up to 16% compared to multi-layer perceptron classifier. Furthermore, the combined use of Perm-Maps and federated logic allows dealing with unbalanced training datasets with low computational efforts. [ABSTRACT FROM AUTHOR]

Resource Planning Optimization (RPO) is a common task that many companies need to face to get several benefits, like budget improvements and run-time analyses. However, even if it is often solved by using several software products and tools, the great success and validity of the Artificial Intelligence-based approaches, in many research fields, represent a huge opportunity to explore alternative solutions for solving optimization problems. To this purpose, the following paper aims to investigate the use of multiple Artificial Neural Networks (ANNs) for solving a RPO problem related to the scheduling of different Combined Heat & Power (CHP) generators. The experimental results, carried out by using data extracted by considering a real Microgrid system, have confirmed the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Due to their open nature and popularity, Android-based devices have attracted several end-users around the World and are one of the main targets for attackers. Because of the reasons given above, it is necessary to build tools that can reliably detect zero-day malware on these devices. At the moment, many of the frameworks that have been proposed to detect malware applications leverage Machine Learning (ML) techniques. However, an essential requirement to build these frameworks consists of using very large and sophisticated datasets for model construction and training purposes. Their success, indeed, strongly depends on the choice of the right features used for building a classification model providing adequate generalisation capability. Furthermore, the creation of a training dataset that well represents the malware properties and behaviour is one of the most critical challenges in malware analysis. Therefore, the main aim of this paper is proposing a new dataset called Unisa Malware Dataset (UMD) available on , which is based on the extraction of static and dynamic features characterising the malware activities. Additionally, we will show some experiments concerning common ML tools to demonstrate how it is possible to build efficient ML-based malware classification frameworks using the proposed dataset. [ABSTRACT FROM AUTHOR]

• We discuss and analyze the main security issues affecting the CAN bus in the automotive. • We study the feasibility of using encryption algorithms for securing CAN-bus messages. • We propose a security solution based on lightweight block ciphers. • The proposed solution is efficient and reasonably secure against targeted attacks. • We use Automotive Grade Linux to evaluate the performance of our solution. In recent years, the automotive industry has made significant investments in the Internet of Things (IoT) paradigm, aiming to develop technologies and services for connected vehicles that focus on user-oriented solutions, providing unique and tailored driving experiences. Nowadays, the vehicles are connected to the car manufacturer network, the road operator networks, and the Internet. Such interconnections are used for infotainment applications and driving assistance services, such as real-time HD-maps, and road safety applications. For these reasons, the concept of the Internet of Vehicles (IoVs) is increasingly emerging and, vehicles become Internet terminals exposed to cyber-attacks. In this context, the main weakness is given by the Controller Area Network (CAN) protocol. This protocol, which governs the in-vehicle network, was designed to reduce transmission error problems and minimize latency times. However, it does not employ any security facility to protect the communication. In particular, one of the main issues of this protocol is that it uses unencrypted communication. Improving the security of this protocol is necessary while preserving its performance in terms of communication efficiency. This paper investigates the possibility of using lightweight block ciphers to secure in-vehicle devices such as microcontrollers, which have constrained hardware and software capabilities. The results obtained have shown that this proposal while guaranteeing a high degree of safety, has a negligible impact on the vehicle's performance. [ABSTRACT FROM AUTHOR]

The complexity of current cyber infrastructures requires specialized security-oriented skills to be acquired by a variety of different actors, in order to defend critical systems and sensitive data against emerging security threats and attacks. In order to facilitate this process, advanced cybersecurity education and training programs should be considered, as well as platforms and tools for simulating realistic training scenarios should be exploited. This paper presents an open-source solution for simulating cyber infrastructures, and reproducing realistic Internet of Things (IoT) scenarios, with specific focus on Edge applications. It can be used for the implementation of new cooperative and competitive cybersecurity training exercises and skills for different application domains, as well as validate solutions that can be used to prevent, detect, mitigate, recovery, and evaluate the attack impact. [ABSTRACT FROM AUTHOR]

Herpes simplex virus (HSV‐1) employs heparan sulfate (HS) as receptor for cell attachment and entry. During late‐stage infection, the virus induces the upregulation of human heparanase (Hpse) to remove cell surface HS allowing viral spread. We hypothesized that inhibition of Hpse will prevent viral release thereby representing a new therapeutic strategy for HSV‐1. A range of HS‐oligosaccharides was prepared to examine the importance of chain length and 2‐O‐sulfation of iduronic moieties for Hpse inhibition. It was found that hexa‐ and octasaccharides potently inhibited the enzyme and that 2‐O‐sulfation of iduronic acid is tolerated. Computational studies provided a rationale for the observed structure–activity relationship. Treatment of human corneal epithelial cells (HCEs) infected with HSV‐1 with the hexa‐ and octasaccharide blocked viral induced shedding of HS which significantly reduced spread of virions. The compounds also inhibited migration and proliferation of immortalized HCEs thereby providing additional therapeutic properties. [ABSTRACT FROM AUTHOR]

Herpes simplex virus (HSV‐1) employs heparan sulfate (HS) as receptor for cell attachment and entry. During late‐stage infection, the virus induces the upregulation of human heparanase (Hpse) to remove cell surface HS allowing viral spread. We hypothesized that inhibition of Hpse will prevent viral release thereby representing a new therapeutic strategy for HSV‐1. A range of HS‐oligosaccharides was prepared to examine the importance of chain length and 2‐O‐sulfation of iduronic moieties for Hpse inhibition. It was found that hexa‐ and octasaccharides potently inhibited the enzyme and that 2‐O‐sulfation of iduronic acid is tolerated. Computational studies provided a rationale for the observed structure–activity relationship. Treatment of human corneal epithelial cells (HCEs) infected with HSV‐1 with the hexa‐ and octasaccharide blocked viral induced shedding of HS which significantly reduced spread of virions. The compounds also inhibited migration and proliferation of immortalized HCEs thereby providing additional therapeutic properties. [ABSTRACT FROM AUTHOR]

The article focuses on the pro-differentiating effects of red photobiomodulation on skeletal myoblasts, with evidence suggesting its potential to enhance myoblast differentiation while maintaining myotube viability and size.

Homeland security represents one of the most relevant application contexts for smart cities, attracting the interest of both authorities and the research community. In case of a crisis event occurring in the urban area, authorities are responsible for effectively managing response operations. A critical challenge in emergency management is the lack of real-time coordinated reaction capabilities driven by integrated decision making facilities based on the information obtained by first responders acting on the crisis site. This work aims at supporting coordinated emergency management in smart cities based on the localization of first responders during crisis events. We present a hybrid cloud architecture for managing computing and storage resources needed by command & control activities in emergency scenarios, complemented by a first responder localization service relying on a novel positioning approach which combines the strength of signals received from landmarks placed by first responders on the crisis site with information obtained from motion sensors. [ABSTRACT FROM AUTHOR]

Despite the wide deployment of advanced wireless coverage infrastructures, finding the best way for achieving secure mobile communication in every-day's life activities is still an open question. Indeed, a large number of mobile terminals, supporting multiple networking technologies, may be used to manage data from everywhere and at anytime. However, the effort required for achieving security, given the complexity of cryptographic algorithms, heavily affects the power consumption of terminals. Such energy demand, together with the one required to manage communication activities, makes energy-efficient secure communication among hardware-constrained handheld devices a challenging topic. In this work, we introduce an analytic energy model for secure communication among multi-mode terminals. This model describes the energy consumption of mobile terminals operating within a dynamic network scenario, considering both their interconnection and secure data exchange issues, in order to develop adaptive strategies for energy-efficient secure communications. Finally, the model has been validated through simulation. [ABSTRACT FROM AUTHOR]

This paper presents a multi-camera target tracking system based on factor graphs, designed to estimate the state of moving targets using a message propagation framework. Inexpensive cameras deployed in the area of interest are the main sensory modality. We discuss the transformation geometry between world points and camera pixels and how the uncertainties in the homographies affect the estimation. The framework has been tested on real data coming from a harbor to track moving vessels. [ABSTRACT FROM PUBLISHER]

This work analyzes a new and very subtle kind of security threat that can affect large-scale cloud-based IT service infrastructures, by exploiting the computational resources of their component data center to waste as much energy as possible. The consequence of these threats ranges from increased costs in the energy bill, to penalization for exceeding the agreed quantity of greenhouse gases (GHG) emissions, up to complete denial of service caused by electrical outages due to power budget exhaustion. We analyzed different types of such attacks with their potential impacts on the energy consumption, modeled their behavior and quantified how current energy-proportional technologies may provide attackers with great opportunities for raising the target facility emissions and costs. These efforts resulted in a simple model with some parametric reference values that can be used to estimate the impact of such attacks also in presence of very large infrastructures containing thousands or millions of servers. [ABSTRACT FROM AUTHOR]

Big Data processing architectures are now widely recognized as one of the most significant innovations in Computing in the last decade. Their enormous potential in collecting and processing huge volumes of data scattered throughout the Internet is opening the door to a new generation of fully distributed applications that, by leveraging the large amount of resources available on the network will be able to cope with very complex problems achieving performances never seen before. However, the Internet is known to have severe scalability limitations in moving very large quantities of data, and such limitations introduce the challenge of making efficient use of the computing and storage resources available on the network, in order to enable data-intensive applications to be executed effectively in such a complex distributed environment. This implies resource scheduling decisions which drive the execution of task towards the data by taking network load and capacity into consideration to maximize data access performance and reduce queueing and processing delays as possible. Accordingly, this work presents a data-centric meta-scheduling scheme for fully distributed Big Data processing architectures based on clustering techniques whose goal is aggregating tasks around storage repositories and driven by a new concept of 'gravitational' attraction between the tasks and their data of interest. This scheme will benefit from heuristic criteria based on network awareness and advance resource reservation in order to suppress long delays in data transfer operations and result into an optimized use of data storage and runtime resources at the expense of a limited (polynomial) computational complexity. [ABSTRACT FROM AUTHOR]

In the last years, an increasing interest in location services characterized the market of mobile ubiquitous devices (smartphones, handhelds, etc.). Several technologies and solutions have been developed to determine the position of mobile devices in their operating space, each with its specific degree of precision and accuracy. In this scenario, the ideal location service should be able of tracking the mobile terminal in any place it moves to, both indoors and outdoors. However, while outdoor location services have already achieved a satisfactory degree of technological maturity and effectiveness, a really ubiquitous location service that works satisfactorily in both indoor and outdoor scenarios is not yet available. In order to cope with the above challenge, this work proposes a hybrid location approach designed to choose and switch among multiple positioning technologies supported by the mobile device and available in the surrounding environment, in a dynamic and transparent way during the user movement. It combines signal strength-based fingerprinting techniques for indoor positioning together with traditional GPS-based positioning for the outdoor localization and performs opportunistic technology switching according to a count-and-threshold mechanism. The resulting solution is able to leverage the different features of the wireless networks and of the global positioning technologies, in order to provide ubiquitous location services across indoor and outdoor scenarios, as well as to minimize power consumption of the mobile device. [ABSTRACT FROM AUTHOR]

Abstract: With the rapid growth and the increasing complexity of network infrastructures and the evolution of attacks, identifying and preventing network abuses is getting more and more strategic to ensure an adequate degree of protection from both external and internal menaces. In this scenario many techniques are emerging for inspecting network traffic and discriminating between anomalous and normal behaviors to detect undesired or suspicious activities. Unfortunately, the concept of normal or abnormal network behavior depends on several factors and its recognition requires the availability of a model aiming at characterizing current behavior, based on a statistical idealization of past events. There are two main challenges when generating the training data needed for effective modeling. First, network traffic is very complex and unpredictable, and second, the model is subject to changes over time, since anomalies are continuously evolving. As attack techniques and patterns change, previously gained information about how to tell them apart from normal traffic may be no longer valid. Thus, a desirable characteristic of an effective model for network anomaly detection is its ability to adapt to change and to generalize its behavior to multiple different network environments. In other words, a self-learning system is needed. This suggests the adoption of machine learning techniques to implement semi-supervised anomaly detection systems where the classifier is trained with “normal” traffic data only, so that knowledge about anomalous behaviors can be constructed and evolve in a dynamic way. For this purpose we explored the effectiveness of a detection approach based on machine learning, using the Discriminative Restricted Boltzmann Machine to combine the expressive power of generative models with good classification accuracy capabilities to infer part of its knowledge from incomplete training data. [Copyright &y& Elsevier]

Abstract: Energy consumption is now one of the most important issues for network carriers, since the majority of the energy needed for their operation is consumed in the wireless access and optical transport networks. The continuous growth in the wireless customers and traffic volumes and the consequent energy demand on modern carriers’ broadband infrastructures require reconsidering their energy efficiency, by starting from the formulation of new, more complete and representative network models that should become the foundations for modern energy-aware control plane architectures. Accordingly, this work presents a novel comprehensive energy model for next-generation wireless access-over-optical-transport networks characterized by hybrid power systems (i.e., multiple dynamically available power sources). The objective is to identify the energy-related information that need to be handled at the control plane layer to support energy-aware networking practices. Such information can be made available to suitable energy-aware routing and wavelength assignment algorithms that may exploit them to optimize the overall network energy-consumption and reducing the associated carbon footprint. The proposed model may be taken as a reference for the implementation of new energy-aware control plane protocols (routing and signaling) that make use of power-related considerations to achieve energy-efficiency and energy-awareness in wavelength-routed network infrastructures. [Copyright &y& Elsevier]

Abstract: Lack of knowledge of the prior distribution in classification problems that operate on small data sets may make the application of Bayes’ rule questionable. Uniform or arbitrary priors may provide classification answers that, even in simple examples, may end up contradicting our common sense about the problem. Entropic priors (EPs), via application of the maximum entropy (ME) principle, seem to provide good objective answers in practical cases leading to more conservative Bayesian inferences. EP are derived and applied to classification tasks when only the likelihood functions are available. In this paper, when inference is based only on one sample, we review the use of the EP also in comparison to priors that are obtained from maximization of the mutual information between observations and classes. This last criterion coincides with the maximization of the KL divergence between posteriors and priors that for large sample sets leads to the well-known reference (or Bernardo’s) priors. Our comparison on single samples considers both approaches in prospective and clarifies differences and potentials. A combinatorial justification for EP, inspired by Wallis’ combinatorial argument for entropy definition, is also included. The application of the EP to sequences (multiple samples) that may be affected by excessive domination of the class with the maximum entropy is also considered with a solution that guarantees posterior consistency. An explicit iterative algorithm is proposed for EP determination solely from knowledge of the likelihood functions. Simulations that compare EP with uniform priors on short sequences are also included. [Copyright &y& Elsevier]

In this paper, a new traffic engineering-capable routing and wavelength assignment scheme is proposed to efficiently handle LSP and lightpath setup requests with different QoS requirements on modern multi-layer (fully optical core and time-division multiplexed edge) transport networks. The objectives of the proposed algorithm are to minimize the rejection probability by maximizing the network load balancing and efficiently handling the grooming of several LSPs on the same lightpath while respecting the constraints of the optical node architecture and considering both traffic engineering and QoS requirements. The proposed solution consists of a two-stage RWA algorithm: each time a new request arrives, an on-line dynamic grooming scheme finds a set of feasible lightpaths which fulfill the QoS and traffic engineering requirements; then, the best feasible lightpath is selected, aiming to keep the network unbalancing and blocking probability as low as possible in the medium and long term, according to a novel global path affinity minimization concept. Extensive simulation experiments have been performed in which our on-line dynamic RWA algorithm demonstrated significant performances. Thanks to its optimal network resource usage and to its reasonable computational space and time complexity, the algorithm can be very attractive for the next-generation optical wavelength-switched networks. [ABSTRACT FROM AUTHOR]

Abstract: Purpose: To evaluate the time required, the accuracy and the precision of a model-based image analysis software tool for the diagnosis of osteoporotic fractures using a 6-point morphometry protocol. Materials and methods: Lateral dorsal and lumbar radiographs were performed on 92 elderly women (mean age 69.2±5.7 years). Institutional review board approval and patient informed consent were obtained for all subjects. The semi-automated and the manual correct annotations of 6-point placement were compared to calculate the time consumed and the accuracy of the software. Twenty test images were randomly selected and the data obtained by multiple perturbed initialisation points on the same image were compared to assess the precision of the system. Results: The time requirement data of the semi-automated system (420±67s) were statistically different (p <0.05) from that of manual placement (900±77s). In the accuracy test, the mean reproducibility error for semi-automatic 6-point placement was 2.50±0.72% [95% CI] for the anterior–posterior reference and 2.16±0.5% [95% CI] for the superior–inferior reference. In the precision test the mean error resulted averaged over all vertebrae was 2.6±1.3% in terms of vertebral width. Conclusions: The technique is time effective, accurate and precise and can, therefore, be recommended in large epidemiological studies and pharmaceutical trials for reporting of osteoporotic vertebral fractures. [Copyright &y& Elsevier]

Advances in optical technologies have enabled the deployment of wavelength division-multiplexed (WDM) transmission systems capable of providing huge amounts of bandwidth across long distances. In this scenario, dynamic routing for direct provisioning of optical paths at the WDM layer becomes a challenging problem. Any distributed algorithm for routing dynamic traffic demands on optical transport infrastructures should be simple, flexible, efficient and scalable. The contribution of this paper is a novel integrated routing and grooming scheme for setting-up bandwidth guaranteed paths on hybrid wavelength and label switched networks. Our proposal exploits and refines the minimum interference routing idea according to an improved and re-optimized resource and traffic-aware approach, where critical links are detected and weighted according to a low complexity all-pairs minimum cut strategy that substantially reduce the overall number of calculations and hence the computational cost. The valuable results achieved in the comparison against other well-known reference techniques clearly demonstrate that our algorithm is very time-efficient while performing better in terms of blocking probability. [ABSTRACT FROM AUTHOR]

The purpose of our study is to demonstrate the uncovertebral mal-alignment as a reliable indirect sign of cervical facet joint dislocation. We examined the uncovertebral axial plane alignment of 12 patients with unilateral and bilateral cervical facet joint dislocation (UCFJD and BCFJD, respectively), comparing its frequency to the reverse hamburger bun sign on CT and MR axial images. Of the seven cases with BCFJD, five clearly demonstrated the diagnostic reverse facet joint hamburger bun sign on CT and MR images, but in two cases this sign was not detectable. In the five cases with UCFJD, four demonstrated the reverse hamburger bun sign on both CT and MRI. In one case the reverse hamburger bun sign was not seen adequately with either image modality, but the facet dislocation was identified on sagittal imaging. The uncovertebral mal-alignment was detected in all 12 cases. Normally, the two components of the uncovertebral joint enjoy a concentric relationship that in the axial plane is reminiscent of the relationship of headphones with the wearer's head. We name this appearance the 'headphones' sign. Radiologists should be aware of the headphones sign as a reliable indicator of facet joint dislocation on axial imaging used in the assessment of cervical spine injuries. [ABSTRACT FROM AUTHOR]

This work presents a new numerical method for solving Volterra-type integro functional equations with variable bounds and mixed delay. This paper applies discrete orthogonal Hahn polynomials and their properties numerically. A discrete scalar product is associated with discrete orthogonal polynomials. Several numerical experiments (including linear and nonlinear) for multiple test problems are provided to validate the accuracy of this method. [ABSTRACT FROM AUTHOR]

Presents information on a study which improved the techniques for obtaining a solution for the sparsity problem of a support vector (SV) expansion. Overview on engineering problem solving; Examples of sparsity; Comparison between SV approach and cross-correlation based method; Results and conclusion.

Presents a study on the behavior of anti-Hebbian synapses at a neural node that contains the standard sigmoidal non-linearity. Discussion on nonlinear neural node; Equivalence with the linear neuron; Simulation results; Conclusion.

Despite the large diffusion and use of embedding generated through Word2Vec, there are still many open questions about the reasons for its results and about its real capabilities. In particular, to our knowledge, no author seems to have analysed in detail how learning may be affected by the various choices of hyperparameters. In this work, we try to shed some light on various issues focusing on a typical dataset. It is shown that the learning rate prevents the exact mapping of the co-occurrence matrix, that Word2Vec is unable to learn syntactic relationships, and that it does not suffer from the problem of overfitting. Furthermore, through the creation of an ad-hoc network, it is also shown how it is possible to improve Word2Vec directly on the analogies, obtaining very high accuracy without damaging the pre-existing embedding. This analogy-enhanced Word2Vec may be convenient in various NLP scenarios, but it is used here as an optimal starting point to evaluate the limits of Word2Vec. [ABSTRACT FROM AUTHOR]

Bayesian networks in their Factor Graph Reduced Normal Form are a powerful paradigm for implementing inference graphs. Unfortunately, the computational and memory costs of these networks may be considerable even for relatively small networks, and this is one of the main reasons why these structures have often been underused in practice. In this work, through a detailed algorithmic and structural analysis, various solutions for cost reduction are proposed. Moreover, an online version of the classic batch learning algorithm is also analysed, showing very similar results in an unsupervised context but with much better performance; which may be essential if multi-level structures are to be built. The solutions proposed, together with the possible online learning algorithm, are included in a C++ library that is quite efficient, especially if compared to the direct use of the well-known sum-product and Maximum Likelihood algorithms. The results obtained are discussed with particular reference to a Latent Variable Model structure. [ABSTRACT FROM AUTHOR]

Hypoxia-inducible factor (HIF)-1α represents an oxygen-sensitive subunit of HIF transcriptional factor, which is usually degraded in normoxia and stabilized in hypoxia to regulate several target gene expressions. Nevertheless, in the skeletal muscle satellite stem cells (SCs), an oxygen level-independent regulation of HIF-1α has been observed. Although HIF-1α has been highlighted as a SC function regulator, its spatio-temporal expression and role during myogenic progression remain controversial. Herein, using biomolecular, biochemical, morphological and electrophysiological analyses, we analyzed HIF-1α expression, localization and role in differentiating murine C2C12 myoblasts and SCs under normoxia. In addition, we evaluated the role of matrix metalloproteinase (MMP)-9 as an HIF-1α effector, considering that MMP-9 is involved in myogenesis and is an HIF-1α target in different cell types. HIF-1α expression increased after 24/48 h of differentiating culture and tended to decline after 72 h/5 days. Committed and proliferating mononuclear myoblasts exhibited nuclear HIF-1α expression. Differently, the more differentiated elongated and parallel-aligned cells, which are likely ready to fuse with each other, show a mainly cytoplasmic localization of the factor. Multinucleated myotubes displayed both nuclear and cytoplasmic HIF-1α expression. The MMP-9 and MyoD (myogenic activation marker) expression synchronized with that of HIF-1α, increasing after 24 h of differentiation. By means of silencing HIF-1α and MMP-9 by short-interfering RNA and MMP-9 pharmacological inhibition, this study unraveled MMP-9's role as an HIF-1α downstream effector and the fact that the HIF-1α/MMP-9 axis is essential in morpho-functional cell myogenic commitment. [ABSTRACT FROM AUTHOR]

Malignant pleural effusion is diagnostically challenging in presence of negative cytology. The assessment of tumor markers in serum has become a standard tool in cancer diagnosis, while pleural fluid sampling has not met universal consensus. The evaluation of a panel of markers both in serum and pleural fluid may be crucial to improve the diagnostic accuracy. Using a machine learning-based approach, we provide a mathematical formula capable to express the complex relation existing among the expressed markers in serum and pleural effusion and the presence of lung cancer. The formula indicates CEA and CYFRA21-1 in pleural fluid as the best diagnostic markers, with 97% accuracy, 98% sensitivity, 95% specificity, 96% area under curve, 98% positive predictive value, and 92% MCC (Matthews correlation coefficient). [ABSTRACT FROM AUTHOR]

Due to their open nature and popularity, Android-based devices represent one of the main targets for malware attacks that may adversely affect the privacy of their users. Considering the huge Android market share, it is necessary to build effective tools able to reliably detect zero-day malware on these platforms. Therefore, several static and dynamic analysis methods based on Neural Networks and Deep Learning have been proposed in the literature. Despite machine learning can be considered the most promising approach for classifying applications into malware or legitimate ones, its success strongly depends on the choice of the right features used for building the detection model. This is definitely not an easy task that requires a systematic solution. Accordingly, this work represents the sequences of API calls invoked by apps during their execution as sparse matrices looking like images (API-images), which can be used as fingerprints of the apps' behavior over time. We also used autoencoders to autonomously extract the most representative and discriminating features from these matrices, that, once provided to an artificial neural network-based classifier have shown to be effective in detecting malware, also when the network is trained on a reduced number of samples. Experimental results show that the resulting framework is able to outperform more complex and sophisticated machine learning approaches in malware classification. • Using Neural Networks and Deep Learning to timely detect zero-day malware on Android platforms. • Building applications' classification model needed for flagging privacy/security breaches. • Modeling application behavior through sparse matrices representing API calls' sequences (API-Images). • Using autoencoders to autonomously extract the most representative and discriminating features. • Performing classification and detection based on softmax multi-class regressors. [ABSTRACT FROM AUTHOR]

Adipokines are peptide hormones produced by the adipose tissue involved in several biological functions. Among adipokines, adiponectin (ADPN) has antidiabetic and anti-inflammatory properties. It can also modulate food intake at central and peripheral levels, acting on hypothalamus and facilitating gastric relaxation. ADPN exerts its action interacting with two distinct membrane receptors and triggering some well-defined signaling cascades. The ceramidase activity of ADPN receptor has been reported in many tissues: it converts ceramide into sphingosine. In turn, sphingosine kinase (SK) phosphorylates it into sphingosine-1 phosphate (S1P), a crucial mediator of many cellular processes including contractility. Using a multidisciplinary approach that combined biochemical, electrophysiological and morphological investigations, we explored for the first time the possible role of S1P metabolism in mediating ADPN effects on the murine gastric fundus muscle layer. By using a specific pharmacological inhibitor of SK2, we showed that ADPN affects smooth muscle cell membrane properties and contractile machinery via SK2 activation in gastric fundus, adding a piece of knowledge to the action mechanisms of this hormone. These findings help to identify ADPN and its receptors as new therapeutic targets or as possible prognostic markers for diseases with altered energy balance and for pathologies with fat mass content alterations. [ABSTRACT FROM AUTHOR]

The article presents a study which outlines the role of magnetic resonance imaging (MRI) in evaluating the heart muscles of athletes. It is inferred that MRI produces highly accurate and reproducible structural and functional cardiac information. The significance of MRI in evaluating myocardial hypertrophy and function is highlighted.

Internet of Things (IoT) is a promising technology for improving our lives and society by integrating smart devices in our environment and paving the way for novel ICT application, spanning from smart cities to energy efficiency and home automation. However, such a vision encompasses the availability of thousands of smart devices, or even more, that continuously exchange a huge volume of data among each other and with cloud-based services, raising a big data problem. Such a problem can be approached by properly applying data fusion practices within an IoT infrastructure. Due to the characteristics and peculiarities of the communications among smart devices within the IoT, an event-based data fusion is needed, where devices exchange notifications of events among each others. Such data fusion should be focused on special devices where notification heterogeneity, and data source trust issues have to be faced with. Accordingly, the contribution of this work is proposing (i) a novel broker-less event-based communication protocol specifically tailored to sensors with constrained resources, (ii) a solution for flexible event-based communications among heterogeneous data sources, and (iii) an approach based on the theory of evidence for data fusion processes that depend on the matching and trust degree of the data to be fused. [ABSTRACT FROM AUTHOR]

Modern fog network architectures, empowered by IoT applications and 5G communications technologies, are characterized by the presence of a huge number of mobile nodes, which undergo frequent handovers, introducing a significant load on the involved network entities. Considering the distributed and flat nature of these architectures, DMM can be the only viable option for efficiently managing handovers in these scenarios. The existing DMM solutions are capable of providing smooth handovers, but lack robustness from the security point of view. Indeed, DMM depends on external mechanisms for handover security and uses a centralized device, which has obvious security and performance implications in flat architectures where hierarchical dependencies can introduce problems. We propose a new DMM schema based on the blockchain, capable of resolving hierarchical security issues without affecting the network layout, and also satisfying fully distributed security requirements with less consumption of energy. [ABSTRACT FROM PUBLISHER]

Human ability to foresee the near future plays a key role in everyone's life to prevent potentially dangerous situations. To be able to make predictions is crucial when people have to interact with the surrounding environment. Modeling such capability can lead to the design of automated warning systems and provide moving robots with an intelligent way of interaction with changing situation. In this work we focus on a typical urban human-scene where we aim at predicting an agent's behavior using a stochastic model. In this approach, we fuse the various factors that would contribute to a human motion in different contexts. Our method uses previously observed trajectories to build point-wise circular distributions that after combination, provide a statistical smooth prediction towards the most likely areas. More specifically, a ray-launching procedure, based on a semantic segmentation, gives a coarse scene representation for collision avoidance; a nearly-constant velocity dynamic model smooths the acceleration progression and knowledge of the agent's destination may further steer the path prediction. Experimental results in structured scenes, validate the effectiveness of the method in predicting paths in comparison to actual trajectories. [ABSTRACT FROM AUTHOR]

Pervasive computing is one of the latest and more advanced paradigms currently available in the computers arena. Its ability to provide the distribution of computational services within environments where people live, work or socialize leads to make issues such as privacy, trust and identity more challenging compared to traditional computing environments. In this work, we review these general issues and propose a pervasive computing architecture based on a simple but effective trust model that is better able to cope with them. The proposed architecture combines some artificial intelligence techniques to achieve close resemblance with human-like decision making. Accordingly, Apriori algorithm is first used in order to extract the behavioral patterns adopted from the users during their network interactions. Naïve Bayes classifier is then used for final decision making expressed in term of probability of user trustworthiness. To validate our approach, we applied it to some typical ubiquitous computing scenarios. The obtained results demonstrated the usefulness of such approach and the competitiveness against other existing ones. [ABSTRACT FROM AUTHOR]

Security is a very complex and challenging problem in Cloud organizations. Ensuring the security of operations within the cloud by also enforcing the users’ own security requirements, usually results in a complex tradeoff with the efficiency of the overall system. In this paper, we developed a novel architectural model enforcing cloud security, based on a multi-agent scheme and a security aware non-deterministic Meta Scheduler driven by genetic heuristics. Such model is explicitly designed to prevent Denial of Service and Timing Attacks over the cloud and has been demonstrated to be integrable within the well-known OpenStack platform. Additionally, we proposed two different models for assuring users security demands. The first is a scoring model that allows scheduling tasks only on the Virtual Machines offering proper security level. The second model takes into account the time spent on the necessary cryptographic operations dedicated to particular task. The above scheduling system has been simulated in order to assess the effectiveness of the proposed security architecture, resulting in an increased system safety and resiliency against attacks, without sensibly impacting the performance of the whole cloud environment. [ABSTRACT FROM AUTHOR]

Presents the authors' response to Yong Gao, following his comments on their paper `Theoretical Analysis of Evolutionary Algorithms with an Infinite Population Size in Continuous Space, Part I: Basic Properties of Selection and Mutation. Author's agreement that the full potential of the genetic algorithm approach can be appreciated if it is supported by a sound mathematical formulation.