There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

The Stuff of Thought: Language as a Window into Human Nature

LACO: Lightweight Three-Factor Authentication, Access Control and Ownership Transfer Scheme for E-Health Systems in IoT

Smart Machines begins by hyping IBM’s latest “grand challenge” intended to “demonstrate IBM’s scientific and technological prowess” (27), namely Watson’s winning performance on the TV show Jeopardy...

Smart Machines: IBM’s Watson and the Era of Cognitive Computing

Research, the universal pursuit of new knowledge, is embarking on a fresh journey into artificial intelligence (AI) Nature reports that AI arose nine places to the fourth-most popular search term and that search terms machine learning and deep learning appeared in the top 20 for the first time since 2018 It is pertinent for industrial informatics to embrace this renewed surge of interest in AI with a clear direction and purpose that engages scholars, practitioners, and professionals alike

/pdf/toward-intelligent-industrial-informatics-a-review-of-4morodv641.pdf

Toward Intelligent Industrial Informatics: A Review of Current Developments and Future Directions of Artificial Intelligence in Industrial Applications

A data integration platform for patient-centered e-healthcare and clinical decision support

One's personality is widely accepted as an indicator of job performance, job satisfaction and tenure intention The ability to measure an applicant's personality in the selection process helps recruiters, hiring managers and the applicant make better hiring decisions Our work shows that textual content of answers to standard interview questions related to past behaviour and situational judgement can be used to reliably infer personality traits We used data from over 46,000 job applicants who completed an online chat interview that also included a personality questionnaire based on the six-factor HEXACO personality model to self-rate their personality Using natural language processing (NLP) and machine learning methods we built a regression model to infer HEXACO trait values from textual content We compared the performance of five different text representation methods and found that term frequency-inverse document frequency (TF-IDF) with Latent Dirichlet Allocation (LDA) topics performed the best with an average correlation of r = 039 As a comparison, a large study of Facebook messages based inference of Big 5 personality found an average correlation of r = 035 and IBM's Personality Insights service built using twitter text data reports an average correlation of r = 031 We further validated our model with a group of 117 volunteers who used an agreement scale of yes/no/maybe to rate the individual trait descriptors generated based on the model outcomes On average, 8783% of the participants agreed with the personality description given for each of the six traits The ability of algorithms to objectively infer a candidate's personality using only the textual content of interview answers presents significant opportunities to remove the subjective biases involved in human interviewer judgement of candidate personality

/pdf/predicting-personality-using-answers-to-open-ended-interview-27ccxmebs7.pdf

Predicting Personality Using Answers to Open-Ended Interview Questions

The fusion of sensor data is an essential requirement for active perception in autonomous robots in order to accurately perceive an environment, make appropriate adjustments that improve the understanding of the environment and generate actionable insights. Despite a large body of literature in this domain, only a handful of research focuses on unsupervised machine learning, which is increasingly important in unlabeled, unknown environments. This paper proposes a scalable self-organizing neural architecture for environmental perception based on multimodal fusion using unsupervised machine learning. Inspired by the biological counterpart, the neural architecture consists of topographic maps across each modality and incorporates a novel scalable self-organization process that handles high volume, high-velocity sensor data. The co-occurrence relationships across modalities are captured in cross-modal connections and the reverse Hebbian projection is used for multimodal representation. This proposed unsupervised machine learning-based scalable fusion technique was implemented on Apache Hadoop and Apache Spark. It was validated using an extensive multimodal human activity sensor dataset to demonstrate efficient representation and multimodal fusion for active perception. Note to Practitioners —Heterogeneous sensors are increasingly used to capture different aspects of an event or an environment. This paper presents a multimodal fusion mechanism to fuse such heterogeneous sensor data to support the accurate perception of the surrounding. The proposed mechanism uses natural regularities in patterns observed across multiple modalities and enriches each modality with information from other co-occurring modalities to achieve a multimodal clustering. The cluster can be used for online prediction and offline action planning of the robot by matching the current sensory inputs. The algorithm uses unsupervised machine learning, and hence, can be used with any multimodal dataset without being restricted by the labeling of the dataset. In addition, we adapt the algorithm for multiple big data platforms so that it is effective and pragmatic for large-scale sensor data fusion tasks.

Unsupervised Machine Learning Based Scalable Fusion for Active Perception

The proliferation of Internets of Things (IoT) technologies in both industrial and non-industrial settings has led to the accumulation of Big Data sets. Analysis of these high-volume, high-velocity datasets require advanced processing techniques that incorporate parallel and distributed computations. In this paper, we present a novel distributed self-adaptive neural-network algorithm, the Distributed Growing Self-Organizing Map (DGSOM) algorithm to address the growing need for unsupervised machine learning of Big Data sets on distributed computing environments. The algorithm was tested on a Big Data set of sensor recordings of human activity collected from wearable devices, 2.8 million records. Results indicate that the distributed algorithm significantly reduces execution time compared to its serial counterpart. Moreover, the self-adaptive nature and controlled growth of the algorithm demonstrates data-driven structure adaptation and multi-granular pattern analysis. Overall, the proposed algorithm addresses the need for pattern discovery and visualization from Big Data sets generated by IoT devices which are increasingly commonplace in industrial scenarios.

Apache spark based distributed self-organizing map algorithm for sensor data analysis

Multimodal sensory fusion is a fundamental requirement for autonomous robots to form an unambiguous and meaningful representation of their surroundings. In this paper, we propose a multisensory self-organizing neural architecture for multimodal fusion using unsupervised machine learning. Inspired by biological evidence from the organization of the human sensory system, the proposed architecture consists of self-organizing neural layers for learning individual modalities. We have incorporated scalable computing for self-organization, so the processing can be scaled to support large datasets and short computation times. The lateral associative connections capture the co-occurrence relationships across individual modalities for cross-modal fusion in obtaining a multimodal representation. Experiments are conducted on an audio-visual dataset consisting of utterances to evaluate the quality of multimodal fusion over individual unimodal representations. Multimodal representation achieves significant improvements over the unimodal representations. These results indicate the proposed architecture is capable of forming effective multimodal representations in short computation times from congruent multisensory stimuli.

Madhura Jayaratne

Papers

A data integration platform for patient-centered e-healthcare and clinical decision support

Predicting Personality Using Answers to Open-Ended Interview Questions

Unsupervised Machine Learning Based Scalable Fusion for Active Perception

Apache spark based distributed self-organizing map algorithm for sensor data analysis

Bio-Inspired Multisensory Fusion for Autonomous Robots