Automated image classification for heritage photographs using Transfer Learning of Computer Vision in Artificial Intelligence

There is substantial archival data available in different forms, including manuscripts, printed papers, photographs, videos, audios, artefacts, sculptures, building, and others. Media content like photographs, audios, and videos are crucial content because such content conveys information well. The digital version of such media data is essential as it can be shared easily, available in the online or offline platform, easy to copy, easy to transport, easy to back up and easy to keep multiple copies at different places. The limitation of the digital version of media data is the lack of searchability as it hardly has any text that can be processed for OCR. These important data cannot be analysed and, therefore, cannot be used in a meaningful way. To make this data meaningful, one has to manually identify people in the images and tag them to create metadata. Most of the photographs were possible to search based on very basic metadata. This data, when hosted on the web platform, searching media data is becoming a challenge due to its data formats. Improvement in existing search functionality is required to improve the searchability of the photographs in terms of ease of usage, quick retrieval and efficiency. The recent revolution in machine learning, deep learning and artificial intelligence offers a variety of facilities to process media data and identify meaningful information out of it. This research paper explains the methods to process digital photographs to classify people in the given photographs, tag them and saves that information in the metadata. We will tune various hyperparameter to improve their accuracy. Machine learning, deep learning and artificial intelligence offers several benefits, including auto-identification of people, auto-tagging them, provide insights and finally, the most important part is it improves the searchability of photographs drastically. It was envisaged that about 85% of the manual tagging activity might be reduced and improves the searchability of photographs by 90%.

Auto-identification of unphysical source reconstructions in strong gravitational lens modelling

With the advent of next-generation surveys and the expectation of discovering huge numbers of strong gravitational lens systems, much effort is being invested into developing automated procedures for handling the data. The several orders of magnitude increase in the number of strong galaxy-galaxy lens systems is an insurmountable challenge for traditional modelling techniques. Whilst machine learning techniques have dramatically improved the efficiency of lens modelling, parametric modelling of the lens mass profile remains an important tool for dealing with complex lensing systems. In particular, source reconstruction methods are necessary to cope with the irregular structure of high-redshift sources. In this paper, we consider a Convolutional Neural Network (CNN) that analyses the outputs of semi-analytic methods which parametrically model the lens mass and linearly reconstruct the source surface brightness distribution. We show the unphysical source reconstructions that arise as a result of incorrectly initialised lens models can be effectively caught by our CNN. Furthermore, the CNN predictions can be used to automatically re-initialise the parametric lens model, avoiding unphysical source reconstructions. The CNN, trained on reconstructions of lensed Sérsic sources, accurately classifies source reconstructions of the same type with a precision P > 0.99 and recall R > 0.99. The same CNN, without re-training, achieves P = 0.89 and R = 0.89 when classifying source reconstructions of more complex lensed HUDF sources. Using the CNN predictions to re-initialise the lens modelling procedure, we achieve a 69 per cent decrease in the occurrence of unphysical source reconstructions. This combined CNN and parametric modelling approach can greatly improve the automation of lens modelling.

An Extended Re-selling Protocol For Existing Anti-Counterfeiting Schemes

Product counterfeiting is an on-going problem in supply chains and retail environments, Recently an anti-counterfeiting protocol to address this issue via cost-effective use of auto-identification technologies such as radio-frequency identification (RFID) was proposed by researchers.Yet the use case of re-selling the same product was not been fully addressed which might cause serious problem for the exciting and proposed schemes and transactions. This paper proposes an extended RFID-based anti-counterfeiting to address the use case of the original buyer reselling the same item to a second buyer. The extended scheme will be followed by a formal security analysis to show that the proposed protocol satisfies the requirements of security correctness and is resistant to compromise through security attacks.

An Extended Re-selling Protocol For Existing Anti-Counterfeiting Schemes

Product counterfeiting is an on-going problem in supply chains and retail environments, Recently an anti-counterfeiting protocol to address this issue via cost-effective use of auto-identification technologies such as radio-frequency identification (RFID) was proposed by researchers.Yet the use case of re-selling the same product was not been fully addressed which might cause serious problem for the exciting and proposed schemes and transactions. This paper proposes an extended RFID-based anti-counterfeiting to address the use case of the original buyer reselling the same item to a second buyer. The extended scheme will be followed by a formal security analysis to show that the proposed protocol satisfies the requirements of security correctness and is resistant to compromise through security attacks.

Way to Industry 4.0: Integrated IT monitoring with auto-identification of root cause for heterogeneous environment

For large enterprises driving the nation through their services or for systems addressing mission critical needs of the country even a bare minimum downtime of 0.1 % may prove fatal not just for the enterprise but also for the entire nation and hence must be strengthened with systems capable of detecting failure of components / sub-systems at an early stage. Industry 4.0 entails automation wherein systems visualize the entire operations and make decision autonomously. However, quick detection of faults followed by even faster isolation of the true cause is a longstanding challenge especially when one considers the count of endpoints employed (ranging from few hundred to several thousands) and the heterogeneity involved (be they physical servers or virtual, software or hardware, COTS / enterprise or embedded, applications or databases or middleware). No single product is available off the shelf which caters to such a wide scope in terms of monitoring. Moreover, implementation of different suites of products for monitoring different resources results in scattered, unrelated data which is meaningless unless huge manual effort is invested in analysis of the data to derive meaning out of it.

A Study on Green Characteristics of RFID using Innovation Diffusion Theory

Radio frequency identification (RFID) is one type of auto-identification technology that uses radio frequency (RF) waves to identify, track, and locate individual physical items. This technology has been used in many applications including manufacturing and distribution of product. While RFID is useful in improving several functions within a firm, the authors focus on the logistics function in this chapter. Applying RFID can help improve logistics in several ways. RFID can closely monitor and track positions of vehicles and assist companies to successfully manage their warehouses and supply chains. Additionally, cost savings, supply chain visibility, and new process creation have been identified as three key benefits of RFID adoption. In spite of significant number of research studies on RFID, there is a limited amount of published knowledge on the discussion of the drivers or influencing factors that lead logistics industry to consider RFID. Given the increasing importance of green issues, there is a need to understand how the perceived positive green characteristics are affecting the level of adoption of the RFID technology. The aim of this chapter is therefore to explore the factors affecting logistics service providers' intention to use RFID, with special emphasis on its environmental friendly green characteristics. The theory of diffusion of innovations is used to develop a conceptual model of factors influencing RFID adoption.