With the rise of the Internet of Things (IoT), applications have become smarter and connected devices give rise to their exploitation in all aspects of a modern city. As the volume of the collected data increases, Machine Learning (ML) techniques are applied to further enhance the intelligence and the capabilities of an application. The field of smart transportation has attracted many researchers and it has been approached with both ML and IoT techniques. In this review, smart transportation is considered to be an umbrella term that covers route optimization, parking, street lights, accident prevention/detection, road anomalies, and infrastructure applications. The purpose of this paper is to make a self-contained review of ML techniques and IoT applications in Intelligent Transportation Systems (ITS) and obtain a clear view of the trends in the aforementioned fields and spot possible coverage needs. From the reviewed articles it becomes profound that there is a possible lack of ML coverage for the Smart Lighting Systems and Smart Parking applications. Additionally, route optimization, parking, and accident/detection tend to be the most popular ITS applications among researchers.

https://www.mdpi.com/1999-5903/11/4/94/pdf

A Review of Machine Learning and IoT in Smart Transportation

Research consistently links adolescents' and young adults' drinking with their peers' alcohol intake. In interpreting this correlation, 2 essential questions are often overlooked. First, which peers are more important, best friends or broader social networks? Second, do peers cause increased drinking, or do young people select friends whose drinking habits match their own? The present study combines social network analyses with family (twin and sibling) designs to answer these questions via data from the National Longitudinal Study of Adolescent Health. Analysis of peer nomination data from 134 schools (n = 82,629) and 1,846 twin and sibling pairs shows that peer network substance use predicts changes in drinking from adolescence into young adult life even after controlling for genetic and shared environmental selection, as well as best friend substance use. This effect was particularly strong for high-intensity friendships. Although the peer-adolescent drinking correlation is partially explained by selection, the present finding offers powerful evidence that peers also cause increased drinking.

/pdf/peer-network-drinking-predicts-increased-alcohol-use-from-48gmkvkj0x.pdf

Peer network drinking predicts increased alcohol use from adolescence to early adulthood after controlling for genetic and shared environmental selection.

Topological conﬂicts frequently arise among source trees for methodological or biological reasons, such as long branch attraction, lateral gene transfers, gene duplication/loss or deep gene coalescence. When topological conﬂicts occur among source trees, liberal methods infer supertrees containing the most frequent alternative, while veto methods infer supertrees not contradicting any source tree, i.e. discard all conﬂicting resolutions. When the source trees host a signiﬁcant number of topological conﬂicts or have a small taxon overlap, supertree methods of both kinds can propose poorly resolved, hence uninformative, supertrees. Results: To overcome this problem, we propose to infer non-plenary supertrees, i.e. supertrees that do not necessarily contain all the taxa present in the source trees, discarding those whose position greatly diﬀers among source trees or for which insuﬃcient information is provided. We detail a variant of the PhySIC veto method called PhySIC IST that can infer non-plenary supertrees. PhySIC IST aims at inferring supertrees that satisfy the same appealing theoretical properties as with PhySIC, while being as informative as possible under this constraint. The informativeness of a supertree is estimated using a variation of the CIC (Cladistic Information Content) criterion, that takes into account both the presence of multifurcations and the absence of some taxa. Additionally, we propose a statistical preprocessing step called STC (Source Trees Correction) to correct the source trees prior to the supertree inference. STC is a liberal step that removes the parts of each source tree that signiﬁcantly conﬂict with other source trees. Combining STC with a veto method allows an explicit trade-oﬀ between veto and liberal approaches, tuned by a single parameter. Performing large-scale simulations, we observe that STC+PhySIC IST infers much more informative supertrees than PhySIC, while preserving low type I error compared to the well-known MRP method. Two biological case studies on animals conﬁrm that the STC preprocess successfully detects anomalies in the source trees while STC+PhySIC IST provides well-resolved supertrees agreeing with current knowledge in systematics. Conclusions: The paper introduces and tests two new methodologies, PhySIC IST and STC, that demonstrate the interest in inferring non-plenary supertrees as well as preprocessing the source trees. An implementation of the methods is available at: http://www.atgc-montpellier.fr/physic ist/.

/pdf/physic-ist-cleaning-source-trees-to-infer-more-informative-2we23vaimd.pdf

PhySIC_IST: cleaning source trees to infer more informative supertrees

Incremental updating of rough approximations in interval-valued information systems under attribute generalization

Collaborative tagging systems, also known as folksonomies, have grown in popularity over the Web on account of their simplicity to organize several types of content e.g., Web pages, pictures, and video using open-ended tags. The rapid adoption of these systems has led to an increasing amount of users providing information about themselves and, at the same time, a growing and rich corpus of social knowledge that can be exploited by recommendation technologies. In this context, tripartite relationships between users, resources, and tags contained in folksonomies set new challenges for knowledge discovery approaches to be applied for the purposes of assisting users through recommendation systems. This review aims at providing a comprehensive overview of the literature in the field of folksonomy-based recommender systems. Current recommendation approaches stemming from fields such as user modeling, collaborative filtering, content, and link-analysis are reviewed and discussed to provide a starting point for researchers in the field as well as explore future research lines.

Ale iberna

Papers

Data Science and Classification (Studies in Classification, Data Analysis, and Knowledge Organization)