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Online Social Networks have revolutionized how we consume and share information, but they have also led to a proliferation of content not always reliable and accurate. One particular type of social accounts is known to promote unreputable content, hyperpartisan, and propagandistic information. They are automated accounts, commonly called bots. Focusing on Twitter accounts, we propose a novel approach to bot detection: we first propose a new algorithm that transforms the sequence of actions that an account performs into an image; then, we leverage the strength of Convolutional Neural Networks to proceed with image classification. We compare our performances with state-of-the-art results for bot detection on genuine accounts / bot accounts datasets well known in the literature. The results confirm the effectiveness of the proposal, because the detection capability is on par with the state of the art, if not better in some cases., Comment: Accepted @ICCS2023, 23th International Conference on Computational Science, 3-5 July, 2023. The present version is a preprint

PurposeInvestigate the behaviour and the habits of the consumers from central-southern Italy in relation to extra olive oil consumption, focussing on the impact of protected designation of origin (PDO) and EU–organic certification on purchase intention and quality perception.Design/methodology/approachA specific questionnaire was submitted to 160 consumers; a subsample of ten experts, ten semi-experts and ten habitual consumers of olive oil tested, through a blind test first and a normal one then, three Italian samples: an extra-virgin olive oil (EVOO) without certification, an organic EVOO and a PDO EVOO, which were characterised also from a chemical-physical point of view. The answers provided during the tastings were statistically analysed and compared.FindingsPeople interviewed prefer local olive oils; they are positively influenced by PDO/organic certification, while price is not a decisive factor on the purchasing choices. According to tasting panel results: experts gave consistent answers preferring organic olive oil, semi-experts are positively influenced by the PDO brand contrary to what they claimed; non-experts would buy EVOO, although they are positively influenced by the PDO brand and negatively by the organic certification.Practical implicationsOnly knowledge and experience can aid consumers make consistent and aware choices. Information campaigns could help them to distinguish products, correctly identify food attributes and overcome their scepticism towards quality of organic products.Originality/valueFew works investigated the impact of quality and sustainability labelling on perception of olive oils, valuing the consistency between answers provided before and after sensory assessments.

Portugal was a pioneer in state-led cooperative development. In 1867, the parliament passed legislation encouraging workers to organize their own collective businesses. In the view of the ruling elite, this would prevent the emergence of a class cleavage between labor and capital, contributing to the stability of the liberal economic and political order. Combining the historical method with John Kingdon’s multiple-streams approach to policy formulation, this article examines the complex array of domestic and external factors that shaped this policy intervention. Additionally, the study explores the impact of the policy on the involved stakeholders. Far from fulfilling the expectations of its promoters, the law on cooperatives seems to have only marginally stimulated the growth of the sector. Moreover, the government’s support to cooperatives seems to have undermined the legitimacy of the model in the eyes of a labor movement that was starting to see its interests as opposed to those of the ruling class.

This study sought to evaluate perisplenic artery nerve distribution and the feasibility of splenic artery denervation (SDN). The NEXION radiofrequency catheter was used to perform SDN in healthy and inflammatory arthritis pigs. Splenic artery anatomy, nerve distribution, and splenic norepinephrine (NEPI) levels were evaluated before and after SDN. Perisplenic artery nerves were primarily distributed within 2.5 mm of the arterial lumen and were largely sympathetic on the basis of tyrosine hydroxylase expression. The pancreas, tended to be circumferentially positioned around the proximal splenic artery, typically >2.5 mm from the lumen, ensuring that most of the nerves could be targeted without affecting this sensitive organ. The mid segment of the splenic artery was relatively free of contact with the adjacent pancreas. Splenic NEPI levels and nerve abundance followed a decreasing gradient from the proximal to distal splenic artery. SDN resulted in significant reductions in splenic NEPI levels at day 14 (60.7%, P = 0.024) in naive pigs and day 45 (100%, P = 0.001) in inflammatory arthritis pigs. There was no significant effect of SDN on joint soft tissue injury or circulating inflammatory markers in the inflammatory arthritis model. The majority of perisplenic arterial nerves are within close proximity of the lumen and are primarily sympathetic efferent fibers. Nerves in the mid-segment may be the preferred SDN target given their proximity to the artery and paucity of periarterial off-target organs. SDN appears safe and effective at reducing splenic NEPI levels.

Aims: With increasing attention to renovascular causes and targets for hypertension there arises a critical need for more detailed knowledge of renal arterial anatomy. However, a standardised nomenclature is lacking. The present study sought to develop a standardised nomenclature for renal anatomy considering the complexity and variation of the renal arterial tree and to assess the applicability of the nomenclature. Methods and results: One thousand hypertensive patients underwent invasive selective renal artery angiography in nine centres. Further, renovasography was performed in 249 healthy swine as a surrogate for normotensive anatomy. Anatomical parameters were assessed by quantitative vascular analysis. Patients' mean blood pressure was 168/90±26/17 mmHg. The right main renal artery was longer than the left (41±15 mm vs. 35±13 mm, p, National Institutes of Health (U.S.) (Grant GM 49039)

We evaluated the preclinical safety of a novel paclitaxel-coated balloon catheter (PCB) with a nitinol constraining structure positioned over it, designed to improve acute treatment of stenotic and occluded arteries. PCBs (TriReme Medical, Singapore) with 5.53mg PTX and (3μg/mm2, 6×80 mm) and a

Radiofrequency renal denervation (RDN) is under investigation for treatment of hypertension with variable success. We developed preclinical models to examine the dependence of ablation biomarkers on RDN treatment parameters and anatomical variables. 129 porcine renal arteries were denervated with an irrigated RF catheter with multiple helically arrayed electrodes. Nerve effects and ablation geometries at 7d were characterized histomorphometrically and correlated with associated renal norepinephrine (NEPI) levels. NEPI exhibited a threshold dependence on the percentage of affected nerves (%AN) across the range of treatment durations (30–60sec) and power set-points (6–20W). For 15W/30sec treatments, NEPI reduction and %AN tracked with number of electrode treatments, confirming additive effects of helically staggered ablations. Threshold effects were only attained when ≥four electrodes were powered. Histomorphometry and computational modeling both illustrated that RF treatments directed at large neighboring veins resulted in sub-average ablation areas, and therefore contributed suboptimally to efficacy. Account for measured nerve distribution patterns and the annular geometry of the artery revealed that, regardless of treatment variables, total ablation area and circumferential coverage were the prime determinants of RDN efficacy, with increased efficacy at smaller diameters. SUMMARY: Large animal experiments supported by computational and statistical models that account for arterial microanatomy and nerve distribution explain the dependence of RDN efficacy on procedural parameters, e.g. number of electrode treatments, and anatomic parameters, e.g. arterial diameter and the proximity of veins to ablation sites.

The arm race between spambots and spambot-detectors is made of several cycles (or generations): a new wave of spambots is created (and new spam is spread), new spambot filters are derived and old spambots mutate (or evolve) to new species. Recently, with the diffusion of the adversarial learning approach, a new practice is emerging: to manipulate on purpose target samples in order to make stronger detection models. Here, we manipulate generations of Twitter social bots, to obtain - and study - their possible future evolutions, with the aim of eventually deriving more effective detection techniques. In detail, we propose and experiment with a novel genetic algorithm for the synthesis of online accounts. The algorithm allows to create synthetic evolved versions of current state-of-the-art social bots. Results demonstrate that synthetic bots really escape current detection techniques. However, they give all the needed elements to improve such techniques, making possible a proactive approach for the design of social bot detection systems., Comment: This is the pre-final version of a paper accepted @ 11th ACM Conference on Web Science, June 30-July 3, 2019, Boston, US

Social capital is widely regarded as a collective resource with positive effects on the economic performance of cooperatives. This conclusion is based on the implicit assumption that social interactions between cooperative members would inexorably lead to the development of networks, norms and trust. This paper challenges the validity of this assumption. Conceptualizing social capital as a resource of the individual, it is argued that the interactions between cooperative members may lead to the establishment of a variety of complex social ties, some of which can negatively affect the economic performance of the organization. To illustrate this argument, the paper presents an exploratory case study of a small, manufacturing worker cooperative. Drawing on ethnographic techniques, the study identifies four organizational dynamics which are presumably affected by social capital: (1) the rule of surplus distribution; (2) the style of leadership; (3) the mechanisms of control; and (4) the criteria for recruiting and evaluating new members.

Fraglets represent an execution model for communication protocols that resembles the chemical reactions in living organisms. The strong connection between their way of transforming and reacting and formal rewriting systems makes a fraglet program amenable to automatic verification. Grounded on past work, this paper investigates feasibility of adopting fraglets as model for specifying security protocols and analysing their properties. In particular, we give concrete sample analyses over a secure RFID protocol, showing evolution of the protocol run as chemical dynamics and simulating an adversary trying to circumvent the intended steps. The results of our analysis confirm the effectiveness of the cryptofraglets framework for the model and analysis of security properties and eventually show its potential to identify and uncover protocol flaws.

In this paper, we propose and test an approach based on regression models, to predict the review score of an item, across different reviewer categories. The analysis is based on a public dataset with more than 2.5 million hotel reviews, belonging to five specific reviewers’ categories. We first compute the relation between the average scores associated with the different categories and generate the corresponding regression model. Then, the extracted model is used for prediction: given the average score of a hotel according to a reviewer category, it predicts the average score associated with another category.

Major search engines deploy personalized Web results to enhance users' experience, by showing them data supposed to be relevant to their interests. Even if this process may bring benefits to users while browsing, it also raises concerns on the selection of the search results. In particular, users may be unknowingly trapped by search engines in protective information bubbles, called "filter bubbles", which can have the undesired effect of separating users from information that does not fit their preferences. This paper moves from early results on quantification of personalization over Google search query results. Inspired by previous works, we have carried out some experiments consisting of search queries performed by a battery of Google accounts with differently prepared profiles. Matching query results, we quantify the level of personalization, according to topics of the queries and the profile of the accounts. This work reports initial results and it is a first step a for more extensive investigation to measure Web search personalization., In Proceedings WWV 2015, arXiv:1508.03389

Unattended Wireless Sensor Networks (UWSNs) operate in autonomous or disconnected mode: sensed data is collected periodically by an itinerant sink. Between successive sink visits, sensor-collected data is subject to some unique vulnerabilities. In particular, while the network is unattended, a mobile adversary (capable of subverting up to a fraction of sensors at a time) can migrate between compromised sets of sensors and inject fraudulent data. In this paper, we provide two collaborative authentication techniques that allow an UWSN to maintain integrity and authenticity of sensor data-in the presence of a mobile adversary-until the next sink visit. Proposed schemes use simple, standard, and inexpensive symmetric cryptographic primitives, coupled with key evolution and few message exchanges. We study their security and effectiveness, both analytically and via simulations. We also assess their robustness and show how to achieve the desired trade-off between performance and security.

The Internet of Things (IoT) revolution promises to make our lives easier by providing cheap and always connected smart embedded devices, which can interact on the Internet and create added values for human needs. But all that glitters is not gold. Indeed, the other side of the coin is that, from a security perspective, this IoT revolution represents a potential disaster. This plethora of IoT devices that flooded the market were very badly protected, thus an easy prey for several families of malwares that can enslave and incorporate them in very large botnets. This, eventually, brought back to the top Distributed Denial of Service (DDoS) attacks, making them more powerful and easier to achieve than ever. This paper aims at provide an up-to-date picture of DDoS attacks in the specific subject of the IoT, studying how these attacks work and considering the most common families in the IoT context, in terms of their nature and evolution through the years. It also explores the additional offensive capabilities that this arsenal of IoT malwares has available, to mine the security of Internet users and systems. We think that this up-to-date picture will be a valuable reference to the scientific community in order to take a first crucial step to tackle this urgent security issue.

The Internet of Things revolution promises to make our lives much easier by providing us cheap and convenient smart devices, but all that glitters is not gold. This plethora of devices that flooded the market, generally poorly designed with respect to security aspects, brought back to the top Distributed Denial of Service (DDoS) attacks which are now even more powerful and easier to achieve than the past. Understanding how these attacks work, in all their different forms, represents a first crucial step to tackle this urgent issue. To this end, in this paper we propose a new up-to-date taxonomy and a comprehensive classification of current DDoS attacks.

The burst in smartphone use, handy design in laptops and tablets as well as other smart products, like cars with the ability to drive you around, manifests the exponential growth of network usage and the demand of accessing remote data on a large variety of services. However, users notoriously struggle to maintain distinct accounts for every single service that they use. The solution to this problem is the use of a Single Sign On (SSO) framework, with a unified single account to authenticate user’s identity throughout the different services. In April 2007, AOL introduced OpenAuth framework. After several revisions and despite its wide adoption, OpenAuth 2.0 has still several flaws that need to be fixed in several implementations. In this paper, we present a thorough review about both benefits of this single token authentication mechanism and its open flaws.

Recent studies in social media spam and automation provide anecdotal argumentation of the rise of a new generation of spambots, so-called social spambots. Here, for the first time, we extensively study this novel phenomenon on Twitter and we provide quantitative evidence that a paradigm-shift exists in spambot design. First, we measure current Twitter's capabilities of detecting the new social spambots. Later, we assess the human performance in discriminating between genuine accounts, social spambots, and traditional spambots. Then, we benchmark several state-of-the-art techniques proposed by the academic literature. Results show that neither Twitter, nor humans, nor cutting-edge applications are currently capable of accurately detecting the new social spambots. Our results call for new approaches capable of turning the tide in the fight against this raising phenomenon. We conclude by reviewing the latest literature on spambots detection and we highlight an emerging common research trend based on the analysis of collective behaviors. Insights derived from both our extensive experimental campaign and survey shed light on the most promising directions of research and lay the foundations for the arms race against the novel social spambots. Finally, to foster research on this novel phenomenon, we make publicly available to the scientific community all the datasets used in this study., Comment: Post-print of the article published in the Proceedings of 26th WWW, 2017, Companion Volume (Web Science Track, Perth, Australia, 3-7 April, 2017)

The failure of the prospective, randomized, sham-controlled SYMPLICITY HTN-3 trial to show significant difference in reduction of systolic blood pressure (SBP) in patients with resistant hypertension 6 months after renal denervation (RDN) compared with control subjects was attributed in part to

Android platform has become a primary target for malware. In this paper we present SafeDroid, an open source distributed service to detect malicious apps on Android by combining static analysis and machine learning techniques. It is composed by three micro-services, working together, combining static analysis and machine learning techniques. SafeDroid has been designed as a user friendly service, providing detailed feedback in case of malware detection. The detection service is optimized to be lightweight and easily updated. The feature set on which the micro-service of detection relies on on has been selected and optimized in order to focus only on the most distinguishing characteristics of the Android apps. We present a prototype to show the effectiveness of the detection mechanism service and the feasibility of the approach.

Spanish Abstract: En este trabajo se describe la evolucion de las cooperativas de credito en Argentina desde finales de 1880 hasta la actualidad. El analisis presta particular atencion a la influencia de factores politicos e institucionales. En terminos mas especificos, muestra como reiterados cambios en el marco legal y regulatorio, inducidos por la profunda inestabilidad del contexto politico argentino, han afectado sustancialmente la dimension y la estructura del sistema de cooperativas de credito.English Abstract: This paper traces the evolution of credit cooperatives in Argentina from the late 1880s to present. The analysis pays particular attention to the influence of institutional and political factors. More specifically, it shows how repeated changes in the legal and regulatory framework, induced by the profound instability of the Argentine political environment, have substantially affected the size and structure of the credit cooperative system.

Automatic quality evaluation of Web information is a task with many fields of applications and of great relevance, especially in critical domains like the medical one. We move from the intuition that the quality of content of medical Web documents is affected by features related with the specific domain. First, the usage of a specific vocabulary (Domain Informativeness); then, the adoption of specific codes (like those used in the infoboxes of Wikipedia articles) and the type of document (e.g., historical and technical ones). In this paper, we propose to leverage specific domain features to improve the results of the evaluation of Wikipedia medical articles. In particular, we evaluate the articles adopting an "actionable" model, whose features are related to the content of the articles, so that the model can also directly suggest strategies for improving a given article quality. We rely on Natural Language Processing (NLP) and dictionaries-based techniques in order to extract the bio-medical concepts in a text. We prove the effectiveness of our approach by classifying the medical articles of the Wikipedia Medicine Portal, which have been previously manually labeled by the Wiki Project team. The results of our experiments confirm that, by considering domain-oriented features, it is possible to obtain sensible improvements with respect to existing solutions, mainly for those articles that other approaches have less correctly classified. Other than being interesting by their own, the results call for further research in the area of domain specific features suitable for Web data quality assessment.

Periarterial lymph nodes and blood vessels can shield nerves from radiofrequency heating and limit the efficacy of catheter based renal denervation (RDN). We quantified the distributions of accessible and shielded nerves surrounding porcine renal arteries. Left and right renal arteries from 9 swine

The spleen plays an important role in the immune response to inflammatory conditions including cancer, myocardial infarction and atherosclerosis. Device-based neuromodulation of the spleen’s neural pathways is an emerging therapeutic approach to regulating the immune response in various

In RFID systems addressing security issues, many authentication techniques require the tag to keep some sort of synchronization with the reader. In particular, this is true in those proposals that leverage hash chains. When the reader and the tag get de-synchronized, possibly by an attacker, this paves the way to several denial of service (DoS) attacks, as well as threatening privacy (e.g., via the timing attack). Even if de-synchronization happens for non-malicious causes, this event has a negative effect on performances (for instance, slowing down the authentication process). In this paper, we provide a solution to cope with the de-synchronization between the tag and the reader when hash chains are employed. In particular, our solution relies on mutual reader-tag authentication, achieved via hash traversal and Merkle tree techniques. We show that this techniques applied to an existing security protocol for RFID systems, such as RIPP-FS, make timing attacks hard to succeed. Moreover, the proposed solutions can be transparently and independently adopted by similar security protocols as well to thwart timing attack and/or to provide reader-tag mutual authentication. Finally, extensive simulations show that our proposal introduces a negligible overhead to recover de-synchronization. Copyright © 2009 John Wiley & Sons, Ltd.

Fraglets represent an execution model for communication protocols that resembles the chemical reactions in living organisms. The strong connection between their way of transforming and reacting and formal rewriting systems makes a fraglet program amenable to automatic verification. Starting from past work where the model has been enriched and executed to specify security protocols and properties (leading to the definition of cryptofraglets), this paper updates cryptofraglets and gives examples of concrete sample analyses over a secure RFID protocol.

$\textit{Fake followers}$ are those Twitter accounts specifically created to inflate the number of followers of a target account. Fake followers are dangerous for the social platform and beyond, since they may alter concepts like popularity and influence in the Twittersphere - hence impacting on economy, politics, and society. In this paper, we contribute along different dimensions. First, we review some of the most relevant existing features and rules (proposed by Academia and Media) for anomalous Twitter accounts detection. Second, we create a baseline dataset of verified human and fake follower accounts. Such baseline dataset is publicly available to the scientific community. Then, we exploit the baseline dataset to train a set of machine-learning classifiers built over the reviewed rules and features. Our results show that most of the rules proposed by Media provide unsatisfactory performance in revealing fake followers, while features proposed in the past by Academia for spam detection provide good results. Building on the most promising features, we revise the classifiers both in terms of reduction of overfitting and cost for gathering the data needed to compute the features. The final result is a novel $\textit{Class A}$ classifier, general enough to thwart overfitting, lightweight thanks to the usage of the less costly features, and still able to correctly classify more than 95% of the accounts of the original training set. We ultimately perform an information fusion-based sensitivity analysis, to assess the global sensitivity of each of the features employed by the classifier. The findings reported in this paper, other than being supported by a thorough experimental methodology and interesting on their own, also pave the way for further investigation on the novel issue of fake Twitter followers.

Background— The long-term efficacy of radiofrequency ablation of renal autonomic nerves has been proven in nonrandomized studies. However, long-term safety of the renal artery (RA) is of concern. The aim of our study was to determine if cooling during radiofrequency ablation preserved the RA while allowing equivalent nerve damage. Methods and Results— A total of 9 swine (18 RAs) were included, and allocated to irrigated radiofrequency (n=6 RAs, temperature setting: 50°C), conventional radiofrequency (n=6 RAs, nonirrigated, temperature setting: 65°C), and high-temperature radiofrequency (n=6 RAs, nonirrigated, temperature setting: 90°C) groups. RAs were harvested at 10 days, serially sectioned from proximal to distal including perirenal tissues and examined after paraffin embedding, and staining with hematoxylin-eosin and Movat pentachrome. RAs and periarterial tissue including nerves were semiquantitatively assessed and scored. A total of 660 histological sections from 18 RAs were histologically examined by light microscopy. Arterial medial injury was significantly less in the irrigated radiofrequency group (depth of medial injury, circumferential involvement, and thinning) than that in the conventional radiofrequency group ( P P =0.003 for thinning). Severe collagen damage such as denatured collagen was also significantly less in the irrigated compared with the conventional radiofrequency group ( P P =0.36), there was a trend toward less nerve damage in the irrigated compared with conventional. Compared to conventional radiofrequency, circumferential medial damage in highest-temperature nonirrigated radiofrequency group was significantly greater ( P Conclusions— Saline irrigation significantly reduces arterial and periarterial tissue damage during radiofrequency ablation, and there is a trend toward less nerve damage.

Wireless Sensor Networks (WSNs) pose a few unique security challenges due to the fact that they (often) run unattended, do not rely on tamper-resistant hardware, and are severely resource constrained--to name a few. In this context, a particularly dreadful attack is the replica attack. That is, sensors are captured and their state seized, and replicated in reprogrammed sensors eventually performing some rogue activities. While some solutions to this problem do exist in static WSNs, mobile WSNs lack of solutions that are both effective and efficient, due to the complexity added by sensor mobility. In this paper, we propose a novel solution against the replica attack in mobile WSNs. In particular, we provide the following contributions: we first introduce a novel realistic attacker model that can be used to assess the quality of the solutions provided. Later, we detail a distributed, efficient, and cooperative protocol to detect replica. Leveraging just local (one-hop) communications and node mobility our solution enforces the emergent property of replica detection. Finally, our solution is tested against the introduced attacker model. Simulation results show that our solution is effective and efficient--providing high detection rate while incurring limited overhead.

The analysis of large amount of traffic data is the daily routine of Autonomous Systems and ISP operators. The detection of anomalies like denial-of-service (DoS) or distributed denial-of-service (DDoS) is also one of the main issues for critical services and infrastructures. The suitability of metrics coming from the information theory for detecting DoS and DDoS episodes has been widely analyzed in the past. Unfortunately, their effectiveness are often evaluated on synthetic data set, or, in other cases, on old and unrepresentative data set, e.g. the DARPA network dump. This paper presents the evaluation by means of main metrics proposed in the literature of a real and large network flow dataset, collected from an Italian transit tier II Autonomous System (AS) located in Rome. We show how we effectively detected and analyzed several attacks against Italian critical IT services, some of them also publicly announced. We further report the study of others legitimate and malicious activities we found by ex-post analysis.

Review-based websites such as, e.g., Amazon, eBay, Trip-Advisor, and Booking have gained an extraordinary popularity, with millions of users daily consulting online reviews to choose the best services and products fitting their needs. Some of the most popular review-based websites rank products by sorting them aggregating the single ratings through their arithmetic mean. In contrast, recent studies have proved that the median is a more robust aggregator, in terms of ad hoc injections of outlier ratings. In this paper, we focus on four different types of ratings aggregators. We propose to the slotted mean and the slotted median, and we compare their mathematical properties with the mean and the median. The results of our experiments highlight advantages and drawbacks of relying on each of these quality indexes. Our experiments have been carried out on a large data set of hotel reviews collected from Booking. com, while our proposed solutions are rooted on sound statistical theory. The results shown in this paper, other than being interesting on their own, also call for further investigations.

The Internet is naturally a simple and immediate mean to retrieve information. However, not everything one can find is equally ac- curate and reliable. In this paper, we continue our line of research towards effective techniques for assessing the quality of online content. Focusing on the Wikipedia Medicinal Portal, in a previous work we implemented an automatic technique to assess the quality of each article and we com- pared our results to the classification of the articles given by the portal itself, obtaining quite different outcomes. Here, we present a lightweight instantiation of our methodology that reduces both redundant features and those not mentioned by the WikiProject guidelines. What we obtain is a fine-grained assessment and a better discrimination of the articles' quality, w.r.t. previous work. Our proposal could help to automatically evaluate the maturity of Wikipedia medical articles in an efficient way.

In the next future radio frequency identiflcation (RFID) systems will be so pervasive that everyone will wear (maybe unawares) a kit of personal tags. However, a major concern that prevents a wide diffusion of this emerging technology is privacy. Several proposed protocols try to solve the privacy issue, but are exposed to many others security flaws, such as denial of service or lack of forward secrecy. One recently proposed solution addressing all these problems is RIPP-FS: an RFID identification protocol that guarantees privacy, authentication and forward secrecy, allowing the reading of many tags at once. While efficient, RIPP-FS requires some extra computations on the tag side when a tag misses several consecutive queries from the reader. In this paper we propose FastRIPP, an efficient protocol that significantly reduces the exposed overhead. This improvement is inspired by an efficient hash traversal amortization technique that uses fractal hash sequence representation. We analyzed the performances of the proposed protocol compared with the original version of RIPP-FS. The results of extensive simulations confirm the gain of performance of the proposed protocol. Finally, note that the proposed solution could be adopted by other RFID identification protocols as well.

Drug release from spray coatings applied to cylindrical stents is difficult to customize due to intermixing of successively coated layers. Flat sheets of electropolished L-605 Cobalt Chromium alloy were spray coated with a solution of Ridaforolimus and two polymers, folded and welded into cylinders

Unattended Wireless Sensors Networks (UWSNs) are emerging Wireless Sensor Networks (WSNs) characterized by periodic absence of trusted entities, like the Sink Collectors. The time periods with no sink attendance, force the network sensors to locally store collected data until the next sink visit. This means that data collection is not performed in real time, but it falls at regular intervals. Such conditions define a different paradigm regarding traditional WSNs and introduce several new security issues, data survival above all. At the best of our knowledge, only two strategies were proposed to mitigate mobile adversary data deletion attack : encryption protocols (implementing backward and/or forward secrecy) and replication. While encryption protocols can impose an extensive computational effort and experience several security issues (like key exposure), replication schemes seem to capture a renewed interest from researchers, since their power requirement seem compatible with the constraints of wireless sensors. In this paper we focus on a new replication scheme and compare its performances with some other proposals, previously presented in literature. The schemes share some model assumptions, in terms of network and adversary attack strategy and objectives.

Unattended Wireless Sensor Networks (UWSNs) are networks that operate without on-line data collection entities (Sink Collectors). This configuration enriches their flexibility but, at the same time, introduces new security issues. A challenging issue is data survival: in absence of the sink, in fact, data sensed by network sensors cannot be kept clear from the adversary. Cryptographic schemes, moreover, cannot be easily implemented: cryptographic material as well as sensed data are continuously exposed to the activity of a smart adversary. Several strategies were proposed in literature which collectively attempt to mitigate this challenge. In this paper we investigate the use of replication as a mean to enhance the survival probability of collected data. We propose a scheme that deterministically grants data survival against a focused mobile adversary. We analyze the behavior of our proposal and study its security and efficiency properties to prove its suitability for UWSNs.

Peripheral vascular interventions frequently employ guiding sheaths with hydrophilic coatings raising the concern for the embolization of this material clinically as increasingly documented. A peripheral stent and delivery system (SDS) were deployed in the iliac and/or carotid arteries of 23

This paper proposes a new scheme for p2p networks. The main contributions are an extensive use of randomization techniques and a novel usage of tree-data structure. The combination of these two ingredients allows a great flexibility of network parameters, such as: load balance among the peers, fast lookups and reduced memory usage. For instance, with routing tables of size (d-1) log/sub d/n, the average number of hops for a lookup is of the order of 1/d ((d-1) log/sub d/n+1), where n is the number of peers in the network and d is the variety of the tree data structure. Further, we propose a few new optimization mechanisms that can be adopted in DHT Extensive simulations support these results.

A menudo, los estudiosos latinoamericanos de la economía solidaria tras anua línea divisoria entre las pequeñas y las grandes cooperativas. Las primeras son presentadas como organizaciones solidarias, orientadas a lograr una amplia gama de objetivos política y socialmente deseables; las segundas como empresas motivadas por el lucro, únicamente guindadas por los imperativoscomerciales del mercado. El presente artículo cuestiona esta visión. Tomando como marco de referencia la experiencia del cooperativismo de crédito en Argentina, se argumenta que el aumento de la dimensión y de la complejidad de una cooperativa no necesariamente afecta sus fundamentos solidarios o su compromiso político. El artículo también destaca las diferentes lógicas dela cooperación, alertando sobre la necesidad de evitar el uso del término “capitalista” para referirse a aquellas cooperativas que están fundadas sobre el principio dela racionalidad instrumental.

Security and privacy issues are rarely taken into account in automated systems for monitoring elderly people in their home, exposing inhabitants to a number of threats they are usually not aware of. As a case study to expose the major vulnerabilities these systems are exposed to, this paper reviews a generic example of automated healthcare monitoring system. The security and privacy issues identified in this case study can be easily generalised and regarded as alarm bells for all the pervasive healthcare professionals.