An IoT based industry 4.0 architecture for integration of design and manufacturing systems.

This paper proposes an Internet of Things (IoT) based 5-stage Industry 4.0 architecture to integrate the design and manufacturing systems in a Cyber Physical Environment (CPE). It considers the transfer of design and manufacturing systems data through the Cloud/Web-based (CW) services and discusses an effective way to integrate them. In the 1st stage, a Radio-Frequency IDentification (RFID) technology containing Computer Aided Design (CAD) data/models of the product with the ability to design / redesign is scanned and sent to a secure Internet/Cloud Server (CS). Here the CAD models are auto identified and displayed in the Graphical User Interface (GUI) developed for the purpose. From the scanned RFID CAD data/models, the 2nd stage adopts unique machine learning technique(s) and identifies the design & manufacturing features information required for product manufacture. Once identified, the 3rd stage handles the necessary modelling changes as required to manufacture the part by verifying the suitability of process-based product design through user input from the GUI. Then, it performs a Computer Aided Process Planning (CAPP) sequence in a secure design cloud server designed using web-based scripting language. After this, the 4th stage generates Computer Aided Manufacturing (CAM) toolpaths by continuous data retrieval of design and tooling database in the web server by updating the RFID technology with all the information. The various processes involved the 3rd and 4th stages are completed by using ‘Agents’ (a smart program) which uses various search and find algorithms with the ability to handle the changes to the process plan as required. Finally, the 5th stage, approves the product manufacture instructions by completing the production plan with the approved sheets sent to the Computer Numerical Control (CNC) machine. In this article, the proposed architecture is explained through the concept of IoT data transfer to help industries driving towards Industry 4.0 by improving productivity, reducing lead time, protecting security and by maintaining internationals standards / regulations applied in their workplace

A chiral route to spontaneous entanglement generation

We study the generation of spontaneous entanglement between two qubits chirally coupled to a waveguide. The maximum achievable concurrence is demonstrated to increase by a factor of $4/e \sim 1.5$ as compared to the non-chiral coupling situation. The proposed entanglement scheme is shown to be robust against variation of the qubit properties such as detuning and separation, which are critical in the non-chiral case. This result relaxes the restrictive requirements of the non-chiral situation, paving the way towards a realistic implementation. Our results demonstrate the potential of chiral waveguides for quantum entanglement protocols.Comment: 5 pages + 1 page supplemental, 4 figure

Class-Decomposition and Augmentation for Imbalanced Data Sentiment Analysis

Significant progress has been made in the area of text classification and natural language processing. However, like many other datasets from across different domains, text-based datasets may suffer from class-imbalance. This problem leads to model's bias toward the majority class instances. In this paper, we present a new approach to handle class-imbalance in text data by means of unsupervised learning algorithms. We present class-decomposition using two different unsupervised methods, namely k-means and Density-Based Spatial Clustering of Applications with Noise, applied to two different sentiment analysis data sets. The experimental results show that utilizing clustering to find within-class similarities can lead to significant improvement in learning algorithm's performances as well as reducing the dominance of the majority class instances without causing information loss

Student interaction with a virtual learning environment: an empirical study of online engagement behaviours during and since the time of COVID-19.

This paper presents an experience report of online attendance and associated behavioural patterns during a module in the first complete semester undertaken fully online in the autumn of 2020, and the corresponding module deliveries in 2021 and 2022. The COVID-19 pandemic of 2020 resulted in a sudden move of most university teaching online, at a global and large-scale level. This, combined with the need to maintain "business as usual" resulted in new levels of student engagement data for largely unchanged pedagogical processes. Engagement data continued to be gathered throughout the subsequent, phased return to face-to-face and hybrid learning, although at a lesser level of granularity. The wealth of student engagement data gathered during this time allows quantitative insights into how student behaviour continued to adapt during and after the enforced online learning during the COVID-19 pandemic. The anonymous subjects of this case study are computing science students in their final year of undergraduate study. We examine their engagement with the virtual learning environment, including engagement with recorded lecture material, attendance in online sessions and engagement during in-person labs. We relate this to both the students' final grades and the content of the module itself. A number of conclusions are drawn based on this empirical data, relating to observations made by staff and pedagogical theory. There was a moderate, but significant, correlation between engagement in synchronous online lecture sessions and grades during thelockdown phase, but the strength of this correlation has reduced in subsequent years as normality has returned. From monitoring behaviour in online sessions down to minute-by-minute accuracy, it can also be seen that some students strategised their engagement based on sessions they perceived to be most directly contributory to their assessment, placing little value on live guest lecturer sessions. During enforced online learning, the most successful students, on average, engaged with less repeat content than less successful students, instead apparently utilising lecture recordings to "catch up" with missed live lectures

Deep learning for text detection and recognition in complex engineering diagrams.

Engineering drawings such as Piping and Instrumentation Diagrams contain a vast amount of text data which is essential to identify shapes, pipeline activities, tags, amongst others. These diagrams are often stored in undigitised format, such as paper copy, meaning the information contained within the diagrams is not readily accessible to inspect and use for further data analytics. In this paper, we make use of the benefits of recent deep learning advances by selecting models for both text detection and text recognition, and apply them to the digitisation of text from within real world complex engineering diagrams. Results show that 90% of text strings were detected including vertical text strings, however certain non text diagram elements were detected as text. Text strings were obtained by the text recognition method for 86% of detected text instances. The findings show that whilst the chosen Deep Learning methods were able to detect and recognise text which occurred in simple scenarios, more complex representations of text including those text strings located in close proximity to other drawing elements were highlighted as a remaining challenge

Semi-automatic pose estimation of a fleet of robots with embedded stereoscopic cameras.

Given a fleet of robots, automatic estimation of the relative poses between them could be inaccurate in specific environments. We propose a framework composed by the fleet of robots with embedded stereoscopic cameras providing 2D and 3D images of the scene, a human coordinator and a Human-Machine Interface. We suppose auto localising each robot through GPS or landmarks is not possible. 3D-images are used to automatically align them and deduce the relative position between robots. 2Dimages are used to reduce the alignment error in an interactive manner. A human visualises both 2D-images and the current automatic alignment, and imposes a new alignment through the Human-Machine Interface. Since the information is shared through the whole fleet, robots can deduce the position of other ones that do not visualise the same scene. Practical evaluation shows that in situations where there is a large difference between images, the interactive processes are crucial to achieve an acceptable result

Harvesting Excitons Through Plasmonic Strong Coupling

Exciton harvesting is demonstrated in an ensemble of quantum emitters coupled to localized surface plasmons. When the interaction between emitters and the dipole mode of a metallic nanosphere reaches the strong coupling regime, the exciton conductance is greatly increased. The spatial map of the conductance matches the plasmon field intensity profile, which indicates that transport properties can be tuned by adequately tailoring the field of the plasmonic resonance. Under strong coupling, we find that pure dephasing can have detrimental or beneficial effects on the conductance, depending on the effective number of participating emitters. Finally, we show that the exciton transport in the strong coupling regime occurs on an ultrafast timescale given by the inverse Rabi splitting ($\sim10~$fs), orders of magnitude faster than transport through direct hopping between the emitters.Comment: 5 pages, 3 figure

AGREE: a feature attribution aggregation framework to address explainer disagreements with alignment metrics.

As deep learning models become increasingly complex, practitioners are relying more on post hoc explanation methods to understand the decisions of black-box learners. However, there is growing concern about the reliability of feature attribution explanations, which are key to explaining machine learning models. Studies have shown that some explainable artificial intelligence (XAI) methods are highly sensitive to noise and that explanations can vary significantly between techniques. As a result, practitioners often employ multiple methods to reach a consensus on the reliability of their models, which can lead to disagreements among explainers. Although some literature has formalised and reviewed this problem, few solutions have been proposed. In this paper, we propose a novel case-based approach to evaluating disagreement among explainers and advance AGREE-an explainer aggregation approach to resolving the disagreement problem based on explanation weights. Our approach addresses the problem of both local and global explainer disagreement by utilising information from the neighbourhood spaces of feature attribution vectors. We evaluate our approach against simpler feature overlap metrics by weighting the latent space of a k-NN predictor using consensus feature importance and observing the performance degradation. For local explanations in particular, our method captures a more precise estimate of disagreement than the baseline methods and is robust against high dimensionality. This can lead to increased trust in ML models, which is essential for their successful adoption in real-world applications

Digitisation of assets from the oil and gas industry: challenges and opportunities.

Automated processing and analysis of legacies of printed documents across the Oil & Gas industry provide a unique opportunity and at the same time pose a significant challenge. One particular example is the case of Piping and Instrumentation Diagrams (P&IDs). These are complex engineering drawings that are extensively used in the Oil & Gas industry, which contain critical information for risk assessment, and require highly skilled people to provide an accurate interpretation and analysis of their contents. This paper provides an overview of the P&IDs digitisation problem. We outline the opportunities and key challenges, discuss recent relevant work and state of the art and outline possible future direction to solve the problem. During a two-years collaborative project with an industrial partner from the Oil & Gas sector, we have encountered three main challenges other than traditional inherent image and document related challenges. These are, documents quality, skewed distribution of data and topology. In this paper, we discuss these challenges in depth and survey the main state-of-the art methodologies that may solve them