Past attempts to measure the quality of peer review have relied on either subjective ratings or tangentially related factors such as the sheer number or length of reviews. Previously, we introduced the Fair Attribution to Indexed Reports (FAIR) Metrics to quantify adherence to good citation practices via systematic semantic comparison of statements in the target document to those found in cited and uncited prior reports. In the present work, we define new FAIR Metrics for assessing the quality of peer review, extend the FAIR Metrics module of the PDP-DREAM Ontology with additional classes and properties needed to record FAIR Metrics analysis of a peer review, and demonstrate use with a simple example.

The FAIR Metrics, with acronym FAIR for Fair Acknowledgment of Information Records and Fair Attribution to Indexed Reports, measure how appropriately a document cites prior literature. We demonstrate use of a novel workflow for manual evaluation of the FAIR Metrics on five example publications, three of which were retracted for plagiarism. We recorded results of the analyses in Nexus-PORTAL-DOORS-Scribe (NPDS) records as an open access data set for continuing development of automated plagiarism detection tools.

Smart cities are a growing paradigm in the design of systems that interact with one another for informed and efficient decision making, empowered by data and technology, of resources in a city. The diffusion of information to citizens in a smart city will rely on social trends and smart advertisement. Online social networks (OSNs) are prominent and increasingly important platforms to spread information, observe social trends, and advertise new products. To maximize the benefits of such platforms in sharing information, many groups invest in finding ways to maximize the expected number of clicks as a proxy of these platform's performance. As such, the study of click-through rate (CTR) prediction of advertisements, in environments like online social media, is of much interest. Prior works build machine learning (ML) using user-specific data to classify whether a user will click on an advertisement or not. For our work, we consider a large set of Facebook advertisement data (with no user data) and categorize targeted interests into thematic groups we call conceptual nodes. ML models are trained using the advertisement data to perform CTR prediction with conceptual node combinations. We then cast the problem of finding the optimal combination of conceptual nodes as an optimization problem. Given a certain budget k, we are interested in finding the optimal combination of conceptual nodes that maximize the CTR. We discuss the hardness and possible NP-hardness of the optimization problem. Then, we propose a greedy algorithm and a genetic algorithm to find near-optimal combinations of conceptual nodes in polynomial time, with the genetic algorithm nearly matching the optimal solution. We observe that simple ML models can exhibit the high Pearson correlation coefficients w.r.t. click predictions and real click values. Additionally, we find that the conceptual nodes of “politics”, “celebrity”, and “organization” are notably more influential than other considered conceptual nodes.

The PORTAL-DOORS system (PDS) has been designed as a resource metadata management system intended to support applications such as automated searches of online resources and meta-analyses of published literature. PDS comprises a network of Problem Oriented Registry of Tags and Labels (PORTAL) lexical registries and Domain Ontology Oriented Resource System (DOORS) semantic directories. Here we introduce a PDS-compliant concept-validating registry and hypothesis-exploring ontology that organizes focal-onset dementias including Sensory-Onset, Language-Onset and Motor-ONset (SOLOMON) dementias with novel classifying and relating concepts. This approach facilitates semantic search of resources and exploration of hypotheses related to neurodegeneration. SOLOMON interoperates with other PDS registries and ontologies including BrainWatch, ManRay and GeneScene.