The importance of a research article is routinely measured by counting how many times it has been cited. However, treating all citations with equal weight ignores the wide variety of functions that citations perform. We want to automatically identify the subset of references in a bibliography that have a central academic influence on the citing paper. For this purpose, we examine the effectiveness of a variety of features for determining the academic influence of a citation.By asking authors to identify the key references in their own work, we created a dataset in which citations were labeled according to their academic influence. Using automatic feature selection with supervised machine learning, we found a model for predicting academic influence that achieves good performance on this dataset using only four features.The best features, among those we evaluated, were features based on the number of times a reference is mentioned in the body of a citing paper. The performance of these features inspired us to design an influence-primed h-index (the hip-index). Unlike the conventional h-index, it weights citations by how many times a reference is mentioned. According to our experiments, the hip-index is a better indicator of researcher performance than the conventional h-index.

Mathematical models for predicting the fate of pollutants in the environment require reactivity parameter values —that is, the physical and chemical constants that govern reactivity. Although empirical structure‐activity relationships have been developed that allow estimation of some constants, such relationships generally hold only within limited families of chemicals. Computer programs are under development that predict chemical reactivity strictly from molecular structure for a broad range of molecular structures. A prototype computer system called SPARC (SPARC Performs Automated Reasoning in Chemistry) uses computational algorithms based on fundamental chemical structure theory to estimate a variety of reactivity parameters (e.g., equilibrium/rate constants, UV‐visible absorption spectra, etc.). This capability crosses chemical family boundaries to cover a broad range of organic compounds. SPARC does not do “first principles” computation, but seeks to analyze chemical structure relative to a specific reactivity query in much the same manner in which an expert chemist would do so. Molecular structures are broken into functional units with known intrinsic reactivity. This intrinsic behavior is modified for a specific molecule in question with mechanistic perturbation models. To date, computational procedures have been developed for UV‐visible light absorption spectra, ionization pKa, hydrolysis rate constants, and numerous physical properties. This paper describes the logic of the approach to chemistry prediction and provides an overview of the computational procedures. Additional papers are in preparation describing in detail the chemical models and specific applications.

Abstract. Autonomous robots such as self-driving cars are already able to make decisions that have ethical consequences. As such machines make increasingly complex and important decisions, we will need to know that their decisions are trustworthy and ethically justified. Hence we will need them to be able to explain the reasons for these decisions: ethical decision-making requires that decisions be explainable with reasons. We argue that for people to trust autonomous robots we need to know which ethical principles they are applying and that their application is deterministic and predictable. If a robot is a self-improving, self-learning type of robot whose choices and decisions are based on past experience, which decision it makes in any given situation may not be entirely predictable ahead of time or explainable after the fact. This combination of non-predictability and autonomy may confer a greater degree of responsibility to the machine but it also makes them harder to trust.

Mathematical models for predicting the fate of pollutants in the environment require reactivity parameter values-that is, the physical and chemical constants that govern reactivity. Although empirical structure-activity relationships have been developed that allow estimation of some constants, such relationships generally hold only within limited families of chemicals. Computer programs are under development that predict chemical reactivity strictly from molecular structure for a broad range of molecular structures. A prototype computer system called SPARC (SPARC Performs Automated Reasoning in Chemistry) uses computational algorithms based on fundamental chemical structure theory to estimate a variety of reactivity parameters (e.g., equilibrium/rate constants , UV-visible absorption spectra, etc.). This capability crosses chemical family boundaries to cover a broad range of organic compounds. SPARC does not do "first principles" computation, but seeks to analyze chemical structure relative to a specific reactivity query in much the same manner in which an expert chemist would do so. Molecular structures are broken into functional units with known intrinsic reactivity. This intrinsic behavior is modified for a specific molecule in question with mechanistic perturbation models. To date, computational procedures have been developed for UV-visible light absorption spectra, ionization pK,, hydrolysis rate constants, and numerous physical properties. This paper describes the logic of the approach to chemistry prediction and provides an overview of the computational procedures. Additional papers are in preparation describing in detail the chemical models and specific applications.