Assisted labeling for spam account detection on twitter

Online Social Networks (OSNs) have become increasingly popular both because of their ease of use and their availability through almost any smart device. Unfortunately, these characteristics make OSNs also target of users interested in performing malicious activities, such as spreading malware and performing phishing attacks. In this paper we address the problem of spam detection on Twitter providing a novel method to support the creation of large-scale annotated datasets. More specifically, URL inspection and tweet clustering are performed in order to detect some common behaviors of spammers and legitimate users. Finally, the manual annotation effort is further reduced by grouping similar users according to some characteristics. Experimental results show the effectiveness of the proposed approach

A Smart Assistant for Visual Recognition of Painted Scenes

Nowadays, smart devices allow people to easily interact with the surrounding environment thanks to existing communication infrastructures, i.e., 3G/4G/5G or WiFi. In the context of a smart museum, data shared by visitors can be used to provide innovative services aimed to improve their cultural experience. In this paper, we consider as case study the painted wooden ceiling of the Sala Magna of Palazzo Chiaramonte in Palermo, Italy and we present an intelligent system that visitors can use to automatically get a description of the scenes they are interested in by simply pointing their smartphones to them. As compared to traditional applications, this system completely eliminates the need for indoor positioning technologies, which are unfeasible in many scenarios as they can only be employed when museum items are physically distinguishable. Experimental analysis aimed to evaluate the performance of the system in terms of accuracy of the recognition process, and the obtained results show its effectiveness in a real-world application scenario

Laparoscopic right hemicolectomy: the SICE (Societ\ue0 Italiana di Chirurgia Endoscopica e Nuove Tecnologie) network prospective trial on 1225 cases comparing intra corporeal versus extra corporeal ileo-colic side-to-side anastomosis

Background: While laparoscopic approach for right hemicolectomy (LRH) is considered appropriate for the surgical treatment of both malignant and benign diseases of right colon, there is still debate about how to perform the ileo-colic anastomosis. The ColonDxItalianGroup (CoDIG) was designed as a cohort, observational, prospective, multi-center national study with the aims of evaluating the surgeons\u2019 attitude regarding the intracorporeal (ICA) or extra-corporeal (ECA) anastomotic technique and the related surgical outcomes. Methods: One hundred and twenty-five Surgical Units experienced in colorectal and advanced laparoscopic surgery were invited and 85 of them joined the study. Each center was asked not to change its surgical habits. Data about demographic characteristics, surgical technique and postoperative outcomes were collected through the official SICE website database. One thousand two hundred and twenty-five patients were enrolled between March 2018 and September 2018. Results: ICA was performed in 70.4% of cases, ECA in 29.6%. Isoperistaltic anastomosis was completed in 85.6%, stapled in 87.9%. Hand-sewn enterotomy closure was adopted in 86%. Postoperative complications were reported in 35.4% for ICA and 50.7% for ECA; no significant difference was found according to patients\u2019 characteristics and technologies used. Median hospital stay was significantly shorter for ICA (7.3 vs. 9 POD). Postoperative pain in patients not prescribed opioids was significantly lower in ICA group. Conclusions: In our survey, a side-to-side isoperistaltic stapled ICA with hand-sewn enterotomy closure is the most frequently adopted technique to perform ileo-colic anastomosis after any indications for elective LRH. According to literature, our study confirmed better short-term outcomes for ICA, with reduction of hospital stay and postoperative pain. Trial registration: Clinical trial (Identifier: NCT03934151)

NEOTROPICAL XENARTHRANS: a data set of occurrence of xenarthran species in the Neotropics

Xenarthrans – anteaters, sloths, and armadillos – have essential functions for ecosystem maintenance, such as insect control and nutrient cycling, playing key roles as ecosystem engineers. Because of habitat loss and fragmentation, hunting pressure, and conflicts with 24 domestic dogs, these species have been threatened locally, regionally, or even across their full distribution ranges. The Neotropics harbor 21 species of armadillos, ten anteaters, and six sloths. Our dataset includes the families Chlamyphoridae (13), Dasypodidae (7), Myrmecophagidae (3), Bradypodidae (4), and Megalonychidae (2). We have no occurrence data on Dasypus pilosus (Dasypodidae). Regarding Cyclopedidae, until recently, only one species was recognized, but new genetic studies have revealed that the group is represented by seven species. In this data-paper, we compiled a total of 42,528 records of 31 species, represented by occurrence and quantitative data, totaling 24,847 unique georeferenced records. The geographic range is from the south of the USA, Mexico, and Caribbean countries at the northern portion of the Neotropics, to its austral distribution in Argentina, Paraguay, Chile, and Uruguay. Regarding anteaters, Myrmecophaga tridactyla has the most records (n=5,941), and Cyclopes sp. has the fewest (n=240). The armadillo species with the most data is Dasypus novemcinctus (n=11,588), and the least recorded for Calyptophractus retusus (n=33). With regards to sloth species, Bradypus variegatus has the most records (n=962), and Bradypus pygmaeus has the fewest (n=12). Our main objective with Neotropical Xenarthrans is to make occurrence and quantitative data available to facilitate more ecological research, particularly if we integrate the xenarthran data with other datasets of Neotropical Series which will become available very soon (i.e. Neotropical Carnivores, Neotropical Invasive Mammals, and Neotropical Hunters and Dogs). Therefore, studies on trophic cascades, hunting pressure, habitat loss, fragmentation effects, species invasion, and climate change effects will be possible with the Neotropical Xenarthrans dataset

A Resilient Smart Architecture for Road Surface Condition Monitoring

Nowadays, road surface condition monitoring is a challenging problem that cannot be addressed with traditional techniques. In this paper we propose an architecture for monitoring the condition of road surfaces based on the paradigm of Mobile Crowdsensing. First, a surface detection module extracts high level features from raw data, indicating the presence of hazards. Then, in order to make the system resilient to attacks, the system exploits a reputation module to identify malicious users and filter out unreliable data. Finally, a truth discovery module aggregates the resulting information to obtain the desired truth values. Experiments carried out on a real world dataset prove the resilience of the proposed system to different attacks and the accuracy achieved

WiP: Smart services for an augmented campus

Technological progress in recent years has allowed the design of new intelligent learning systems in smart environments aiming to facilitate users' lives. As a consequence, besides making use of traditional sensors for monitoring the quantities of interest, such systems can also benefit from information obtained from the users' smart devices, which can now be considered as additional sensing tools. In this article, we present the design of a novel system based on the fog computing paradigm that can improve the services offered to users on a smart campus by using different smart devices, i.e., smartphones, smartwatches, tablets, smartcameras and so on. In particular, we will describe a system in which several smart devices will collect sensory and context information, whilst the cloud will aggregate and analyze this data to extract information of particular interest. The main challenge of this project is to create an intelligent platform that allows new software modules to be added without having to re-design the entire architecture, and that can provide new services to campus users or improve existing ones

SmartWave: A Smart Platform for Marine Environmental Monitoring

In recent years, the interest in the study of seas and oceans has dramatically increased as they are considered of primary importance for forecasting catastrophic events or for supporting blue economy, as well as the marine tourism, improving the tourist reception or enhancing any marine-related activity. This led to the development of IT platforms that allow to monitor the marine environment and provide a number of services to different kinds of final users, whether they are private individuals interested in the status of the seas, or companies whose business depends on the marine environmental monitoring. The main limitations of current platforms are due to such a difference between free trials, which often focus only on specific aspects of deep waters, and subscriptions, which provide analyzes whose reliability is generally not proportional to the costs. This paper presents SmartWave, a project funded by Regione Sicilia (European Regional Development Fund), that aims to develop a novel IT platform to observe and predict phenomena that characterize the marine environment, while also providing the consumer with a unified portal to collect, access and analyze marine-related information. To achieve this goal, one of the main challenges of this project is to aggregate and standardize heterogeneous data from multiple sources in order to offer very accurate information to private or business consumers

Towards a smart campus through participatory sensing

In recent years, the percentage of the population owning a smartphone has increased significantly. These devices provide users with more and more functions that make them real sensing platforms. Exploiting the capabilities offered by smartphones, users can collect data from the surrounding environment and share them with other entities in the network thanks to existing communication infrastructures, i.e., 3G/4G/5G or WiFi. In this work, we present a system based on participatory sensing paradigm using smartphones to collect and share local data in order to monitor make a campus 'smart'. In particular, our system infers the activities performed by users (e.g., students) in a campus in order to identify trends and behavioral patterns. This information allows the system to decide in real-Time which actions are needed to provide the best possible services to users, according to their needs and preferences