Henrique O. Santos

Rensselaer Polytechnic Institute | RPI · Tetherless World Constellation

The research discussed in this paper tackles the multifaceted problems of distributed ontologies, simulation, and computation in building systems and environments, aiming to address both the building modeling and performance aspects and their dual in the distributed computational, sensor and AI space. The work is developing both the interoperabilit...

With the advent of large language models, evaluating and benchmarking these systems on important AI problems has taken on newfound importance. Such benchmarking typically involves comparing the predictions of a system against human labels (or a single ‘ground-truth’). However, much recent work in psychology has suggested that most tasks involving s...

Disparities in healthcare access and utilization associated with demographic and socioeconomic status hinder advancement of health equity. Thus, we designed a novel equity-focused approach to quantify variations of healthcare access/utilization from the expectation in national target populations. We additionally applied survey-weighted logistic reg...

Achieving machine common sense has been a longstanding problem within Artificial Intelligence. Thus far, benchmark data sets that are grounded in a theory of common sense and can be used to conduct rigorous, semantic evaluations of common sense reasoning (CSR) systems have been lacking. One expectation of the AI community is that neuro-symbolic rea...

Machine Common Sense Reasoning is the subfield of Artificial Intelligence that aims to enable machines to behave or make decisions similarly to humans in everyday and ordinary situations. To measure progress, benchmarks in the form of question-answering datasets have been developed and published in the community to evaluate machine commonsense mode...

With the advent of large language models, evaluating and benchmarking these systems on important AI problems has taken on newfound importance. Such benchmarking typically involves comparing the predictions of a system against human labels (or a single 'ground-truth'). However, much recent work in psychology has suggested that most tasks involving s...

Buildings consume one-third of the world's energy and are some of the major energy consumers on the planet. Occupants use this energy for enhancing indoor environmental quality (IEQ) which is affected by many factors including temperature, humidity, airflow, air quality, etc.; however, it is difficult to find a suitable general solution to improve...

Building machines capable of common sense reasoning is an important milestone in achieving Artificial General Intelligence (AGI). While recent advances, such as large language models, are promising, systematic and sufficiently robust evaluations of these models on common sense have been inadequate, and designed for an earlier generation of models....

Disparities in healthcare access and utilization associated with demographic and socioeconomic status hinder advancement of health equity. Thus, we designed a novel equity-focused approach to quantify variations of healthcare access/utilization from the expectation in national target populations. We additionally applied survey-weighted logistic reg...

Common-sense reasoning has recently emerged as an important test for artificial general intelligence, especially given the much-publicized successes of language representation models such as T5, BERT and GPT-3. Currently, typical benchmarks involve question answering tasks, but to test the full complexity of common-sense reasoning, more comprehensi...

Programming machines with commonsense reasoning (CSR) abilities is a longstanding challenge in the Artificial Intelligence community. Current CSR benchmarks use multiple-choice (and in relatively fewer cases, generative) question-answering instances to evaluate machine commonsense. Recent progress in transformer-based language representation models...

Policies are often crucial for decision-making in a wide range of domains. Typically they are written in natural language, which leaves room for different individual interpretations. In contrast, computable policies offer standardization for the structures that encode information, which can help decrease ambiguity and variability of interpretations...

Policies are authoritative assets that are present in multiple domains to support decision-making. They describe what actions are allowed or recommended when domain entities and their attributes satisfy certain criteria. It is common to find policies that contain geographical rules, including distance and containment relationships among named locat...

Developing agents capable of commonsense reasoning is an important goal in Artificial Intelligence (AI) research. Because commonsense is broadly defined, a computational theory that can formally categorize the various kinds of commonsense knowledge is critical for enabling fundamental research in this area. In a recent book, Gordon and Hobbs descri...

Commonsense question-answering (QA) tasks, in the form of benchmarks, are constantly being introduced for challenging and comparing commonsense QA systems. The benchmarks provide question sets that systems' developers can use to train and test new models before submitting their implementations to official leaderboards. Although these tasks are crea...

Because radio spectrum is a finite resource, its usage and sharing is regulated by government agencies. These agencies define policies to manage spectrum allocation and assignment across multiple organizations, systems, and devices. With more portions of the radio spectrum being licensed for commercial use, the importance of providing an increased...

Because radio spectrum is a finite resource, its usage and sharing is regulated by government agencies. These agencies define policies to manage spectrum allocation and assignment across multiple organizations, systems, and devices. With more portions of the radio spectrum being licensed for commercial use, the importance of providing an increased...

It is common practice for data providers to include text descriptions for each column when publishing data sets in the form of data dictionaries. While these documents are useful in helping an end-user properly interpret the meaning of a column in a data set, existing data dictionaries typically are not machine-readable and do not follow a common s...

To investigate the cause and progression of a phenomenon, such as chronic disease, it is essential to collect a wide variety of data that together explains the complex interplay of different factors, e.g., genetic, lifestyle, environmental and social. Sharing information between studies is therefore of paramount importance. However, data that needs...

Ontologies are being widely used across many scientific fields, most notably in roles related to acquiring, preparing, integrating and managing data resources. Data acquisition and preparation activities are often difficult to reuse since they tend to be domain dependent, as well as dependent on how data is acquired: through measurement, subject-el...

As part of Smart Cities initiatives, national, regional and local governments all over the globe are under the mandate of being more open regarding how they share their data. Under this mandate, many of these governments are publishing data under the umbrella of open government data, which includes measurement data from city-wide sensor networks. F...

Significant efforts have been made to understand and document knowledge related to scientific measurements. Many of those efforts resulted in one or more high-quality ontologies that describe some aspects of scientific measurements, but not in a comprehensive and coherently integrated manner. For instance, we note that many of these high-quality on...

In the context of Smart Cities, indicator definitions have been used to calculate values that enable the comparison among different cities. The calculation of an indicator values has challenges as the calculation may need to combine some aspects of quality while addressing different levels of abstraction. Knowledge graphs (KGs) have been used succe...

In this paper we describe an automatic generator to support the data scientist to construct, in a user-friendly way, dashboards from data represented as networks. The generator called SBINet (Semantic for Business Intelligence from Networks) has a semantic layer that, through ontologies, describes the data that represents a network as well as the p...

In the Law Enforcement context, more and more data about crime occurrences are becoming available to the general public. For an effective use of open data, it is desirable that the different sources of information follow a pattern, which allows reliable comparisons. In addition, it is expected that the task of creating a correspondence between the...

Although there are increasingly more initiatives for the generation of semantic knowledge based on user participation, there is still a shortage of platforms for regular users to create applications on which semantic data can be exploited and generated automatically. We propose an architecture, called Semantic Maps (SeMaps), for assisting the autho...

The authors describe WikiCrimes, a project founded on a website of the same name that aims to offer a common interaction space for the general public where they can note criminal activity and track the locations where such crimes occur. The goal is to encourage collaborative participation that generates useful information for everyone. The authors...

The use of widgets is a very popular manner to make a website customization. From widget's creation the content creator configures the website with functions that he/she consider adequate to the users. Typically, widgets relies on syndication (RSS) in which a website content is made available to other websites. Even though there is a huge popularit...