SearcharxivSearch

arXiv subjects

Benjamin Johnston

Publications and source records attributed to Benjamin Johnston.

10 recordsLinked to original sources

Optical Fiber-Waveguide Hybrid Architecture for Mid-Infrared Ring Lasers

We report a mid-infrared ring laser via fiber-to-waveguide hybrid integration enabled by directional couplers fabricated in fluoride glasses. Directional couplers were inscribed into fluoride glass using femtosecond laser direct writing, forming low-loss waveguides with a high positive refractive index change. The waveguides were optimized for efficient guidance at a wavelength of 2.7 {\textmu}m. Directional couplers with systematically varied coupling gap and interaction lengths were fabricated and experimentally characterized, demonstrating controllable power splitting ratios spanning from complete transmission (100:0) to balanced coupling (50:50). A directional coupler with a 7 {\textmu}m coupling gap and a 1.5 mm interaction length, providing an 80:20 power splitting ratio, was integrated with a cladding-pumped Er:ZBLAN fiber to realize a ring laser cavity. The resulting device produced stable lasing at 2716.7 nm with an output power of 14.2 mW. This work represents the first realization of a fully fiber-chip integrated mid-infrared laser source and opens new avenues for 3D photonic integration in fluoride glasses, enabling design flexibility previously unattainable in traditional fiber-optics architectures.

physics.optics

Predicting Skill Shortages in Labor Markets: A Machine Learning Approach

Skill shortages are a drain on society. They hamper economic opportunities for individuals, slow growth for firms, and impede labor productivity in aggregate. Therefore, the ability to understand and predict skill shortages in advance is critical for policy-makers and educators to help alleviate their adverse effects. This research implements a high-performing Machine Learning approach to predict occupational skill shortages. In addition, we demonstrate methods to analyze the underlying skill demands of occupations in shortage and the most important features for predicting skill shortages. For this work, we compile a unique dataset of both Labor Demand and Labor Supply occupational data in Australia from 2012 to 2018. This includes data from 7.7 million job advertisements (ads) and 20 official labor force measures. We use these data as explanatory variables and leverage the XGBoost classifier to predict yearly skills shortage classifications for 132 standardized occupations. The models we construct achieve macro-F1 average performance scores of up to 83 per cent. Our results show that job ads data and employment statistics were the highest performing feature sets for predicting year-to-year skills shortage changes for occupations. We also find that features such as 'Hours Worked', years of 'Education', years of 'Experience', and median 'Salary' are highly important features for predicting occupational skill shortages. This research provides a robust data-driven approach for predicting and analyzing skill shortages, which can assist policy-makers, educators, and businesses to prepare for the future of work.

econ.GN

Adaptively selecting occupations to detect skill shortages from online job ads

Labour demand and skill shortages have historically been difficult to assess given the high costs of conducting representative surveys and the inherent delays of these indicators. This is particularly consequential for fast developing skills and occupations, such as those relating to Data Science and Analytics (DSA). This paper develops a data-driven solution to detecting skill shortages from online job advertisements (ads) data. We first propose a method to generate sets of highly similar skills based on a set of seed skills from job ads. This provides researchers with a novel method to adaptively select occupations based on granular skills data. Next, we apply this adaptive skills similarity technique to a dataset of over 6.7 million Australian job ads in order to identify occupations with the highest proportions of DSA skills. This uncovers 306,577 DSA job ads across 23 occupational classes from 2012-2019. Finally, we propose five variables for detecting skill shortages from online job ads: (1) posting frequency; (2) salary levels; (3) education requirements; (4) experience demands; and (5) job ad posting predictability. This contributes further evidence to the goal of detecting skills shortages in real-time. In conducting this analysis, we also find strong evidence of skills shortages in Australia for highly technical DSA skills and occupations. These results provide insights to Data Science researchers, educators, and policy-makers from other advanced economies about the types of skills that should be cultivated to meet growing DSA labour demands in the future.

cs.CY

Fog Robotics: A Summary, Challenges and Future Scope

Human-robot interaction plays a crucial role to make robots closer to humans. Usually, robots are limited by their own capabilities. Therefore, they utilise Cloud Robotics to enhance their dexterity. Its ability includes the sharing of information such as maps, images and the processing power. This whole process involves distributing data which intend to rise enormously. New issues can arise such as bandwidth, network congestion at backhaul and fronthaul systems resulting in high latency. Thus, it can make an impact on seamless connectivity between the robots, users and the cloud. Also, a robot may not accomplish its goal successfully within a stipulated time. As a consequence, Cloud Robotics cannot be in a position to handle the traffic imposed by robots. On the contrary, impending Fog Robotics can act as a solution by solving major problems of Cloud Robotics. Therefore to check its feasibility, we discuss the need and architectures of Fog Robotics in this paper. To evaluate the architectures, we used a realistic scenario of Fog Robotics by comparing them with Cloud Robotics. Next, latency is chosen as the primary factor for validating the effectiveness of the system. Besides, we utilised real-time latency using Pepper robot, Fog robot server and the Cloud server. Experimental results show that Fog Robotics reduces latency significantly compared to Cloud Robotics. Moreover, advantages, challenges and future scope of the Fog Robotics system is further discussed.

cs.RO

Fog Robotics for Efficient, Fluent and Robust Human-Robot Interaction

Active communication between robots and humans is essential for effective human-robot interaction. To accomplish this objective, Cloud Robotics (CR) was introduced to make robots enhance their capabilities. It enables robots to perform extensive computations in the cloud by sharing their outcomes. Outcomes include maps, images, processing power, data, activities, and other robot resources. But due to the colossal growth of data and traffic, CR suffers from serious latency issues. Therefore, it is unlikely to scale a large number of robots particularly in human-robot interaction scenarios, where responsiveness is paramount. Furthermore, other issues related to security such as privacy breaches and ransomware attacks can increase. To address these problems, in this paper, we have envisioned the next generation of social robotic architectures based on Fog Robotics (FR) that inherits the strengths of Fog Computing to augment the future social robotic systems. These new architectures can escalate the dexterity of robots by shoving the data closer to the robot. Additionally, they can ensure that human-robot interaction is more responsive by resolving the problems of CR. Moreover, experimental results are further discussed by considering a scenario of FR and latency as a primary factor comparing to CR models.

cs.RO

A Fully Automated System for Sizing Nasal PAP Masks Using Facial Photographs

We present a fully automated system for sizing nasal Positive Airway Pressure (PAP) masks. The system is comprised of a mix of HOG object detectors as well as multiple convolutional neural network stages for facial landmark detection. The models were trained using samples from the publicly available PUT and MUCT datasets while transfer learning was also employed to improve the performance of the models on facial photographs of actual PAP mask users. The fully automated system demonstrated an overall accuracy of 64.71% in correctly selecting the appropriate mask size and 86.1% accuracy sizing within 1 mask size.

cs.CV

Semi-Automated Nasal PAP Mask Sizing using Facial Photographs

We present a semi-automated system for sizing nasal Positive Airway Pressure (PAP) masks based upon a neural network model that was trained with facial photographs of both PAP mask users and non-users. It demonstrated an accuracy of 72% in correctly sizing a mask and 96% accuracy sizing to within 1 mask size group. The semi-automated system performed comparably to sizing from manual measurements taken from the same images which produced 89% and 100% accuracy respectively.

cs.CV

The face-space duality hypothesis: a computational model

Valentine's face-space suggests that faces are represented in a psychological multidimensional space according to their perceived properties. However, the proposed framework was initially designed as an account of invariant facial features only, and explanations for dynamic features representation were neglected. In this paper we propose, develop and evaluate a computational model for a twofold structure of the face-space, able to unify both identity and expression representations in a single implemented model. To capture both invariant and dynamic facial features we introduce the face-space duality hypothesis and subsequently validate it through a mathematical presentation using a general approach to dimensionality reduction. Two experiments with real facial images show that the proposed face-space: (1) supports both identity and expression recognition, and (2) has a twofold structure anticipated by our formal argument.

cs.CV

Socially Impaired Robots: Human Social Disorders and Robots' Socio-Emotional Intelligence

Social robots need intelligence in order to safely coexist and interact with humans. Robots without functional abilities in understanding others and unable to empathise might be a societal risk and they may lead to a society of socially impaired robots. In this work we provide a survey of three relevant human social disorders, namely autism, psychopathy and schizophrenia, as a means to gain a better understanding of social robots' future capability requirements. We provide evidence supporting the idea that social robots will require a combination of emotional intelligence and social intelligence, namely socio-emotional intelligence. We argue that a robot with a simple socio-emotional process requires a simulation-driven model of intelligence. Finally, we provide some critical guidelines for designing future socio-emotional robots.

cs.RO

Affective Facial Expression Processing via Simulation: A Probabilistic Model

Understanding the mental state of other people is an important skill for intelligent agents and robots to operate within social environments. However, the mental processes involved in `mind-reading' are complex. One explanation of such processes is Simulation Theory - it is supported by a large body of neuropsychological research. Yet, determining the best computational model or theory to use in simulation-style emotion detection, is far from being understood. In this work, we use Simulation Theory and neuroscience findings on Mirror-Neuron Systems as the basis for a novel computational model, as a way to handle affective facial expressions. The model is based on a probabilistic mapping of observations from multiple identities onto a single fixed identity (`internal transcoding of external stimuli'), and then onto a latent space (`phenomenological response'). Together with the proposed architecture we present some promising preliminary results

cs.CV