Assessing the forecasting model ability in measuring the prevention transmission of COVID-19 pandemic: An application of visibility analysis using Inductive logic

Forecasting is an integral approach due to its ability to make informed act decisions and develop data-driven strategies. It's also used to make decisions related to current circumstances and predictions on future conditions. An integral part has been developed using visibility analysis for COVID-19 Outbreak, a lesson from Indonesia. The author identified that its topic has limited attention, especially in assessing the forecasting models. The issue comes from predicted results that are questionable or cannot be trusted without applying the visibility analysis in the forecasting model. The visibility analysis is required to assess the model's ability to forecast future events. In conjunction with the issue, this paper introduces the analysis of visibility error with the different concepts during model development for the transmission prevention measures in making the decision. This study applied a statistical approach to assess the visibility error of forecasting performance in determining how long periods of forecasting and deciding for transmission prevention measures COVID-19 pandemics. Also, we developed the visibility error of time-variant using inductive logic. The result indicated that the number of data required to perform forecasting work on the basis of forecasting model specifications. In conclusion, this study has been completed to develop the statistical formula for identifying the largest time horizon in forecasting model N = V + 2. Also, this developed model can assist the stakeholder in forecasting the number of transmission prevention and making the decision in case of COVID-19 pandemic.

Pertanggungjawaban Pidana Korporasi Terhadap Pencemaran Lingkungan Hidup Ditinjau dari Undang-undang Cipta Kerja

Human activities that are less controlled, make a clean and healthy living environment less and less, this is because the earth is currently getting older and the activities of humans themselves are not properly preserving the environment. Humans have a role and responsibility to empower the environment to maintain the ecosystem. However, the current reality is that most environmental crimes often involve corporations. This study aims to examine the regulation of criminal acts by corporations in the perspective of the Copyright Act and to reveal criminal sanctions against corporations that commit acts of environmental pollution in terms of the Copyright Act. This research uses a normative research type, with a Legislative approach. As for what is used as primary data, namely Law Number 32 of 2009, Law Number 11 of 2020 concerning Job Creation is the legal basis for knowing criminal arrangements and criminal sanctions against corporate criminal liability for environmental pollution in terms of the work copyright law. Data were collected using library research techniques. After the research data has been collected, it is processed by elaboration, namely combining the sources of the primary, secondary and tertiary legal materials with deductive and inductive logic. The results of this study indicate that corporate crime is essentially a functional act and is in the form of an inclusion offense. The criminal sanction of imprisonment is 1/3 to the management of the corporation.

Symbolic AI for XAI: Evaluating LFIT Inductive Programming for Explaining Biases in Machine Learning

Machine learning methods are growing in relevance for biometrics and personal information processing in domains such as forensics, e-health, recruitment, and e-learning. In these domains, white-box (human-readable) explanations of systems built on machine learning methods become crucial. Inductive logic programming (ILP) is a subfield of symbolic AI aimed to automatically learn declarative theories about the processing of data. Learning from interpretation transition (LFIT) is an ILP technique that can learn a propositional logic theory equivalent to a given black-box system (under certain conditions). The present work takes a first step to a general methodology to incorporate accurate declarative explanations to classic machine learning by checking the viability of LFIT in a specific AI application scenario: fair recruitment based on an automatic tool generated with machine learning methods for ranking Curricula Vitae that incorporates soft biometric information (gender and ethnicity). We show the expressiveness of LFIT for this specific problem and propose a scheme that can be applicable to other domains. In order to check the ability to cope with other domains no matter the machine learning paradigm used, we have done a preliminary test of the expressiveness of LFIT, feeding it with a real dataset about adult incomes taken from the US census, in which we consider the income level as a function of the rest of attributes to verify if LFIT can provide logical theory to support and explain to what extent higher incomes are biased by gender and ethnicity.

Inductive logic programming at 30

Inductive logic programming (ILP) is a form of logic-based machine learning. The goal is to induce a hypothesis (a logic program) that generalises given training examples and background knowledge. As ILP turns 30, we review the last decade of research. We focus on (i) new meta-level search methods, (ii) techniques for learning recursive programs, (iii) new approaches for predicate invention, and (iv) the use of different technologies. We conclude by discussing current limitations of ILP and directions for future research.

Applying organisational behaviour theory to aid emergency staff retention

The purpose of this theoretical concept article is to spark a dialogue on the use of organisational behaviour theory to address emergency responder retention. In the United States, emergency medical services (EMS) appear to be burdened with continuing problems of retaining staff. Poor responder retention affects the ability of EMS to deliver high-quality services; without trained, educated and experienced first responders, the EMS system struggles, and what suffers is the ability to provide medical care. The authors set out to construct a pathway for addressing the underlying issues leading to the exodus of professionals using organisational behaviour theory. To develop the idea, an inductive logic approach was used to address underlying negative factors influencing poor retention and discuss the promise of organisational behaviour theory in improving the retention of responders.

Generating contrastive explanations for inductive logic programming based on a near miss approach

In recent research, human-understandable explanations of machine learning models have received a lot of attention. Often explanations are given in form of model simplifications or visualizations. However, as shown in cognitive science as well as in early AI research, concept understanding can also be improved by the alignment of a given instance for a concept with a similar counterexample. Contrasting a given instance with a structurally similar example which does not belong to the concept highlights what characteristics are necessary for concept membership. Such near misses have been proposed by Winston (Learning structural descriptions from examples, 1970) as efficient guidance for learning in relational domains. We introduce an explanation generation algorithm for relational concepts learned with Inductive Logic Programming (GeNME). The algorithm identifies near miss examples from a given set of instances and ranks these examples by their degree of closeness to a specific positive instance. A modified rule which covers the near miss but not the original instance is given as an explanation. We illustrate GeNME with the well-known family domain consisting of kinship relations, the visual relational Winston arches domain, and a real-world domain dealing with file management. We also present a psychological experiment comparing human preferences of rule-based, example-based, and near miss explanations in the family and the arches domains.