Prediction And Recommendations On The It Leaners\u27 Learning Path As A Collective Intelligence Using A Data Mining Technique

With the recent advances in computer technology along with pervasive internet accesses, data analytics is getting more attention than ever before. In addition, research areas on data analysis are diverging and integrating lots of different fields such as a business and social sector. Especially, recent researches focus on the data analysis for a better intelligent decision making and prediction system. This paper analyzes data collected from current IT learners who have already studied various IT subjects to find the IT learners’ learning patterns. The most popular learning patterns are identified through an association rule data mining using an arules package running under R studio. Experimental results are used to recommend the IT learning path to rudimentary IT learners. It is expected that our research promotes IT learning field and results in a platform of IT learning helpful to IT learners

Physiognomic Artificial Intelligence

The reanimation of the pseudosciences of physiognomy and phrenology at scale through computer vision and machine learning is a matter of urgent concern. This Article—which contributes to critical data studies, consumer protection law, biometric privacy law, and antidiscrimination law—endeavors to conceptualize and problematize physiognomic artificial intelligence (“AI”) and offer policy recommendations for state and federal lawmakers to forestall its proliferation. Physiognomic AI, as this Article contends, is the practice of using computer software and related systems to infer or create hierarchies of an individual’s body composition, protected class status, perceived character, capabilities, and future social outcomes based on their physical or behavioral characteristics. Physiognomic and phrenological logics are intrinsic to the technical mechanism of computer vision applied to humans. This Article observes how computer vision is a central vector for physiognomic AI technologies and unpacks how computer vision reanimates physiognomy in conception, form, and practice and the dangers this trend presents for civil liberties. This Article thus argues for legislative action to forestall and roll back the proliferation of physiognomic AI. To that end, it considers a potential menu of safeguards and limitations to significantly limit the deployment of physiognomic AI systems, which hopefully can be used to strengthen local, state, and federal legislation. This Article foregrounds its policy discussion by proposing the abolition of physiognomic AI. From there, it posits regimes of U.S. consumer protection law, biometric privacy law, and civil rights law as vehicles for rejecting physiognomy’s digital renaissance in AI. Specifically, it contends that physiognomic AI should be categorically rejected as oppressive and unjust. Second, it argues that lawmakers should declare physiognomic AI unfair and deceptive per se. Third, it proposes that lawmakers should enact or expand biometric privacy laws to prohibit physiognomic AI. Fourth, it recommends that lawmakers should prohibit physiognomic AI in places of public accommodation. It also observes the paucity of procedural and managerial regimes of fairness, accountability, and transparency in ad- dressing physiognomic AI and attend to potential counterarguments in support of physiognomic AI

Complexity Measures and Features for Times Series classification

Classification of time series is a growing problem in different disciplines due to the progressive digitalization of the world. Currently, the state-of-the-art in time series classification is dominated by The Hierarchical Vote Collective of Transformation-based Ensembles. This algorithm is composed of several classifiers of different domains distributed in five large modules. The combination of the results obtained by each module weighed based on an internal evaluation process allows this algorithm to obtain the best results in state-of-the-art. One Nearest Neighbour with Dynamic Time Warping remains the base classifier in any time series classification problem for its simplicity and good results. Despite their performance, they share a weakness, which is that they are not interpretable. In the field of time series classification, there is a tradeoff between accuracy and interpretability. In this work, we propose a set of characteristics capable of extracting information on the structure of the time series to face time series classification problems. The use of these characteristics allows the use of traditional classification algorithms in time series problems. The experimental results of our proposal show no statistically significant differences from the second and third best models of the state-of-the-art. Apart from competitive results in accuracy, our proposal is able to offer interpretable results based on the set of characteristics proposed.Spanish Government TIN2016-81113-R PID2020-118224RB-I00 BES-2017-080137Andalusian Regional Government, Spain P12-TIC-2958 P18-TP-5168 A-TIC-388-UGR-1

Natural Computational Architectures for Cognitive Info-Communication

Recent comprehensive overview of 40 years of research in cognitive architectures, (Kotseruba and Tsotsos 2020), evaluates modelling of the core cognitive abilities in humans, but only marginally addresses biologically plausible approaches based on natural computation. This mini review presentsa set of perspectives and approaches which have shaped the development of biologically inspired computational models in the recent past that can lead to the development of biologically more realistic cognitive architectures. For describing continuum of natural cognitive architectures, from basal cellular to human-level cognition, we use evolutionary info-computational framework, where natural/ physical/ morphological computation leads to evolution of increasingly complex cognitive systems. Forty years ago, when the first cognitive architectures have been proposed, understanding of cognition, embodiment and evolution was different. So was the state of the art of information physics, bioinformatics, information chemistry, computational neuroscience, complexity theory, selforganization, theory of evolution, information and computation. Novel developments support a constructive interdisciplinary framework for cognitive architectures in the context of computing nature, where interactions between constituents at different levels of organization lead to complexification of agency and increased cognitive capacities. We identify several important research questions for further investigation that can increase understanding of cognition in nature and inspire new developments of cognitive technologies. Recently, basal cell cognition attracted a lot of interest for its possible applications in medicine, new computing technologies, as well as micro- and nanorobotics. Bio-cognition of cells connected into tissues/organs, and organisms with the group (social) levels of information processing provides insights into cognition mechanisms that can support the development of new AI platforms and cognitive robotics