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

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

Adaptive Ensemble Learning for Intrusion Detection Systems

For years, the European Commission has highlighted the need to invest in cybersecurity as a means of protecting institutions and citizens from the many threats in cyberspace. Attacks perpetrated through the network are extremely dangerous, also because their mitigation is complex, making it difficult to ensure an adequate level of security. One of the crucial elements in building an overall system of protection against network-based cyber attacks are Intrusion Detection Systems (IDSs), whose goal is to detect and identify such attacks and misuse of computer networks in a timely manner. Nowadays, the most effective IDSs are based on Machine Learning (ML) and are able to combine and analyze information from heterogeneous sources, such as network traffic, user activity patterns, and data extracted from system logs. However, these tools commonly exploit specific classifiers, whose performance is highly dependent on the attacks being considered, and are unable to generalize adequately enough to be applied in different contexts. The research laboratories of Networking and Distributed Systems and Artificial Intelligence at the University of Palermo are carrying out research activities in order to address these issues, with the main goal of designing a new generation of IDSs that, by dynamically and adaptively combining multiple classifiers, are able to overcome the limitations of state-of-the-art solutions

Doenças e práticas terapêuticas entre os Teréna de Mato Grosso do Sul

Resumo Este artigo propõe-se a descrever as visões sobre saúde e doença, enfatizando a identificação, as interpretações diagnósticas e as práticas tera pêuticas relacionadas à doença crônica entre os Teréna da Terra Indígena Buriti de Mato Grosso do Sul. Realizou-se um estudo qualitativo por meio de entrevista semiestruturada com 24 indígenas e de observação participante, com registro em diário de campo, entre março e agosto de 2010. Para os Teréna, o entendimento de saúde e doença compõe questões de sua vida cotidiana: redução na disponi bilidade de terra, mudanças climáticas, influências do meio urbano e quebra de regras. Quanto aos processos terapêuticos, observou-se a busca pelo aconselhamento e pelo cuidado familiar e religioso, e o atendimento biomédico obtido no posto de saúde, sendo estes vistos como complementares. Os es quemas interpretativos mencionados pelos Teréna para a causalidade da hipertensão arterial indicam relação com as condições de vida, contaminações do meio ambiente, mudanças na dieta alimentar, feitiços e desobediência aos mais velhos. As expe riências vivenciadas pelos Teréna no entendimento de suas doenças e na busca pela cura são processos resultantes dos saberes locais, da participação de diversos atores envolvidos e dos recursos e tecno logias disponíveis, todos inscritos em um contexto cultural e social dinâmico

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)

Deep learning neural network prediction of postoperative complications in patients undergoing laparoscopic right hemicolectomy with or without CME and CVL for colon cancer: insights from SICE (Società Italiana di Chirurgia Endoscopica) CoDIG data

BackgroundPostoperative complications in colorectal surgery can significantly impact patient outcomes and healthcare costs. Accurate prediction of these complications enables targeted perioperative management, improving patient safety and optimizing resource allocation. This study evaluates the application of machine learning (ML) models, particularly deep learning neural networks (DLNN), in predicting postoperative complications following laparoscopic right hemicolectomy for colon cancer.MethodsData were drawn from the CoDIG (ColonDx Italian Group) multicenter database, which includes information on patients undergoing laparoscopic right hemicolectomy with complete mesocolic excision (CME) and central vascular ligation (CVL). The dataset included demographic, clinical, and surgical factors as predictors. Models such as decision trees (DT), random forest (RF), and DLNN were trained, with DLNN evaluated using cross-validation metrics. To address class imbalance, the synthetic minority over-sampling technique (SMOTE) was employed. The primary outcome was the prediction of postoperative complications within 1 month of surgery.ResultsThe DLNN model outperformed other models, achieving an accuracy of 0.86, precision of 0.84, recall of 0.90, and an F1 score of 0.87. Relevant predictors identified included intraoperative minimal bleeding, blood transfusion, and adherence to the fast-track recovery protocol. The absence of intraoperative bleeding, intracorporeal anastomosis, and fast-track protocol adherence were associated with a reduced risk of complications.ConclusionThe DLNN model demonstrated superior predictive performance for postoperative complications compared to other ML models. The findings highlight the potential of integrating ML models into clinical practice to identify high-risk patients and enable tailored perioperative care. Future research should focus on external validation and implementation of these models in diverse clinical settings to further optimize surgical outcomes

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