Simplified Video Surveillance Framework for Dynamic Object Detection under Challenging Environment

An effective video surveillance system is highly essential in order to ensure constructing better form of video analytics. Existing review of literatures pertaining to video analytics are found to directly implement algorithms on the top of the video file without much emphasis on following problems i.e. i) dynamic orientation of subject, ii)poor illumination condition, iii) identification and classification of subjects, and iv) faster response time. Therefore, the proposed system implements an analytical concept that uses depth-image of the video feed along with the original colored video feed to apply an algorithm for extracting significant information about the motion blob of the dynamic subjects. Implemented in MATLAB, the study outcome shows that it is capable of addressing all the above mentioned problems associated with existing research trends on video analytics by using a very simple and non-iterative process of implementation. The applicability of the proposed system in practical world is thereby proven

Big Data Security (Volume 3)

After a short description of the key concepts of big data the book explores on the secrecy and security threats posed especially by cloud based data storage. It delivers conceptual frameworks and models along with case studies of recent technology

Edge Video Analytics: A Survey on Applications, Systems and Enabling Techniques

Video, as a key driver in the global explosion of digital information, can create tremendous benefits for human society. Governments and enterprises are deploying innumerable cameras for a variety of applications, e.g., law enforcement, emergency management, traffic control, and security surveillance, all facilitated by video analytics (VA). This trend is spurred by the rapid advancement of deep learning (DL), which enables more precise models for object classification, detection, and tracking. Meanwhile, with the proliferation of Internet-connected devices, massive amounts of data are generated daily, overwhelming the cloud. Edge computing, an emerging paradigm that moves workloads and services from the network core to the network edge, has been widely recognized as a promising solution. The resulting new intersection, edge video analytics (EVA), begins to attract widespread attention. Nevertheless, only a few loosely-related surveys exist on this topic. The basic concepts of EVA (e.g., definition, architectures) were not fully elucidated due to the rapid development of this domain. To fill these gaps, we provide a comprehensive survey of the recent efforts on EVA. In this paper, we first review the fundamentals of edge computing, followed by an overview of VA. The EVA system and its enabling techniques are discussed next. In addition, we introduce prevalent frameworks and datasets to aid future researchers in the development of EVA systems. Finally, we discuss existing challenges and foresee future research directions. We believe this survey will help readers comprehend the relationship between VA and edge computing, and spark new ideas on EVA.Comment: 31 pages, 13 figure

Big Data

Η εργασία στοχεύει στην ανάλυση της αγοράς των μεγάλων δεδομένων, Περιλαμβάνονται οι πάροχοι μαζί με κάποιες ενδιαφέρουσες περιπτώσεις χρήσης.Nowadays, term big data, draws a lot of attention, both for Business and person perspective. For decades, companies have been making business decisions through its Business Intelligence department, based on transactional data which were basically stored in relational databases. However, regulatory compliance, increased competition, and other pressures have created an insatiable need for companies to accumulate and analyze large, fast-growing quantities of data that was beyond the critical data

AI and blockchain adoption in corporate governance

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonPurpose The purpose of this doctoral thesis sets out to explore and elaborate on the impact of artificial intelligence (AI) and blockchain adoption in corporate governance from ethical perspectives. Positioned within the corporate governance domain, this study adopts an explicit business perspective to study corporate governance change with emerging AI and blockchain technological tools in general and focuses on the ethical use of technologies specifically. As such, this empirical investigation aims to help organizations understand the ethical benefits and ethical dilemmas of using AI and blockchain in businesses and draw plans on how to govern these technologies ethically for the benefit of the business and society. Design/Methodology/Approach: This study adopts specific techniques and a pragmatic, step-by-step netnography approach to investigate online traces from social media sites and extends these online explorations with online semi-structured interviews. The research design of this investigation follows step-by-step procedures that are methodologically sound to ensure rigor in this investigation to enhance the trustworthiness of this study. In total, this research collects an abundance of data: 34 LinkedIn Posts with Comments; 12 Webinars; 22 YouTube Videos; 19 Videos; 10 Podcasts, and 17 semi-structured interview videos. The video, audio, and interview data have been transcribed into textual data total of 453065 words for thematic analysis using NVivo software. Enough time has been allocated to the iterative process of data collection and data analysis. The analysis moves back and forth to the point when theoretical saturation is achieved. The data structure extracts from data in this study illustrate the analytic claims that match the analysis and data together, to ensure a good fit between described method and reported analysis are consistent. Findings: This study develops a thematic framework that constitutes the corporate governance transformation with the ethical use of AI and blockchain technology. This framework provides a holistic understanding of why corporate governance needs to change, especially with the emergence of blockchain and AI technologies, what changes will corporate governance encounter, and how corporate governance can imperatively respond to the ethical use of these technologies. Specifically, it explicitly provides comprehensive understanding of the ethical benefits and ethical concerns of using AI and blockchain technologies in corporate governance, and reveals how companies can govern the use of these technologies ethically. In general terms, the findings of this study support the notion of corporate governance change to transform business models and processes to leverage the new capabilities of AI and blockchain technologies, to priories creativity, speed, and accountability, to replace the old business model, to foster agile or collaborative governance to deal with uncertainty, agility, adaptiveness, and cooperation in the digital world, to foster a network and platform strategies to drive success. This study goes beyond the extant corporate governance scholarship to assess the technological impact to capture values for companies in ethical ways to sustain future growth. Additionally, the notion of corporate governance is further specified and significantly expanded by this study to assess the adoption of AI and blockchain as new corporate governance tools or mechanisms, to enhance ethical benefits when used properly, and mitigate ethical dilemmas with proper checks and balances, safeguards in place, to help organizations stay relevant in this digital transformation and be ethical and sustainable. This study empirically corroborates that in theory, the use of blockchain and AI can enhance ethical practice by detecting fraud and anomaly activities, due to the unique capabilities of blockchain and AI technologies. Further, this research adds depth and specificity by identifying the ethical concerns of using blockchain and AI in corporate governance. The study empirically reveals the ethical concerns of privacy issues, unethical use of data, job transformation and replacement, and algorithm bias that companies will encounter when they use these technologies. In addition, the findings of this study suggest how companies can ethically govern the use of these technologies in socially responsible ways as they transform digitally. Originality/Value: The emergent thematic framework is constructed from the empirical and analytical procedures specifically and purposely designed for this study. This study makes theoretical contributions to knowledge and enriches the extant works of literature, and also provides practical contributions to the ethical use of disruptive technologies, future workforce, and regulations. However, the study was conducted within certain theoretical, methodological, empirical, and pragmatic conditions, which might constitute particular limitations and constraints. Therefore, the last section of this thesis elucidates and suggests the directions for future research

BIG DATA и анализ высокого уровня : материалы конференции

В сборнике опубликованы результаты научных исследований и разработок в области BIG DATA and Advanced Analytics для оптимизации IT-решений и бизнес-решений, а также тематических исследований в области медицины, образования и экологии

Machine learning for the sustainable energy transition: a data-driven perspective along the value chain from manufacturing to energy conversion

According to the special report Global Warming of 1.5 °C of the IPCC, climate action is not only necessary but more than ever urgent. The world is witnessing rising sea levels, heat waves, events of flooding, droughts, and desertification resulting in the loss of lives and damage to livelihoods, especially in countries of the Global South. To mitigate climate change and commit to the Paris agreement, it is of the uttermost importance to reduce greenhouse gas emissions coming from the most emitting sector, namely the energy sector. To this end, large-scale penetration of renewable energy systems into the energy market is crucial for the energy transition toward a sustainable future by replacing fossil fuels and improving access to energy with socio-economic benefits. With the advent of Industry 4.0, Internet of Things technologies have been increasingly applied to the energy sector introducing the concept of smart grid or, more in general, Internet of Energy. These paradigms are steering the energy sector towards more efficient, reliable, flexible, resilient, safe, and sustainable solutions with huge environmental and social potential benefits. To realize these concepts, new information technologies are required, and among the most promising possibilities are Artificial Intelligence and Machine Learning which in many countries have already revolutionized the energy industry. This thesis presents different Machine Learning algorithms and methods for the implementation of new strategies to make renewable energy systems more efficient and reliable. It presents various learning algorithms, highlighting their advantages and limits, and evaluating their application for different tasks in the energy context. In addition, different techniques are presented for the preprocessing and cleaning of time series, nowadays collected by sensor networks mounted on every renewable energy system. With the possibility to install large numbers of sensors that collect vast amounts of time series, it is vital to detect and remove irrelevant, redundant, or noisy features, and alleviate the curse of dimensionality, thus improving the interpretability of predictive models, speeding up their learning process, and enhancing their generalization properties. Therefore, this thesis discussed the importance of dimensionality reduction in sensor networks mounted on renewable energy systems and, to this end, presents two novel unsupervised algorithms. The first approach maps time series in the network domain through visibility graphs and uses a community detection algorithm to identify clusters of similar time series and select representative parameters. This method can group both homogeneous and heterogeneous physical parameters, even when related to different functional areas of a system. The second approach proposes the Combined Predictive Power Score, a method for feature selection with a multivariate formulation that explores multiple sub-sets of expanding variables and identifies the combination of features with the highest predictive power over specified target variables. This method proposes a selection algorithm for the optimal combination of variables that converges to the smallest set of predictors with the highest predictive power. Once the combination of variables is identified, the most relevant parameters in a sensor network can be selected to perform dimensionality reduction. Data-driven methods open the possibility to support strategic decision-making, resulting in a reduction of Operation & Maintenance costs, machine faults, repair stops, and spare parts inventory size. Therefore, this thesis presents two approaches in the context of predictive maintenance to improve the lifetime and efficiency of the equipment, based on anomaly detection algorithms. The first approach proposes an anomaly detection model based on Principal Component Analysis that is robust to false alarms, can isolate anomalous conditions, and can anticipate equipment failures. The second approach has at its core a neural architecture, namely a Graph Convolutional Autoencoder, which models the sensor network as a dynamical functional graph by simultaneously considering the information content of individual sensor measurements (graph node features) and the nonlinear correlations existing between all pairs of sensors (graph edges). The proposed neural architecture can capture hidden anomalies even when the turbine continues to deliver the power requested by the grid and can anticipate equipment failures. Since the model is unsupervised and completely data-driven, this approach can be applied to any wind turbine equipped with a SCADA system. When it comes to renewable energies, the unschedulable uncertainty due to their intermittent nature represents an obstacle to the reliability and stability of energy grids, especially when dealing with large-scale integration. Nevertheless, these challenges can be alleviated if the natural sources or the power output of renewable energy systems can be forecasted accurately, allowing power system operators to plan optimal power management strategies to balance the dispatch between intermittent power generations and the load demand. To this end, this thesis proposes a multi-modal spatio-temporal neural network for multi-horizon wind power forecasting. In particular, the model combines high-resolution Numerical Weather Prediction forecast maps with turbine-level SCADA data and explores how meteorological variables on different spatial scales together with the turbines' internal operating conditions impact wind power forecasts. The world is undergoing a third energy transition with the main goal to tackle global climate change through decarbonization of the energy supply and consumption patterns. This is not only possible thanks to global cooperation and agreements between parties, power generation systems advancements, and Internet of Things and Artificial Intelligence technologies but also necessary to prevent the severe and irreversible consequences of climate change that are threatening life on the planet as we know it. This thesis is intended as a reference for researchers that want to contribute to the sustainable energy transition and are approaching the field of Artificial Intelligence in the context of renewable energy systems

Is operational research in UK universities fit-for-purpose for the growing field of analytics?

Over the last decade considerable interest has been generated into the use of analytical methods in organisations. Along with this, many have reported a significant gap between organisational demand for analytical-trained staff, and the number of potential recruits qualified for such roles. This interest is of high relevance to the operational research discipline, both in terms of raising the profile of the field, as well as in the teaching and training of graduates to fill these roles. However, what is less clear, is the extent to which operational research teaching in universities, or indeed teaching on the various courses labelled as analytics , are offering a curriculum that can prepare graduates for these roles. It is within this space that this research is positioned, specifically seeking to analyse the suitability of current provisions, limited to master s education in UK universities, and to make recommendations on how curricula may be developed. To do so, a mixed methods research design, in the pragmatic tradition, is presented. This includes a variety of research instruments. Firstly, a computational literature review is presented on analytics, assessing (amongst other things) the amount of research into analytics from a range of disciplines. Secondly, a historical analysis is performed of the literature regarding elements that can be seen as the pre-cursor of analytics, such as management information systems, decision support systems and business intelligence. Thirdly, an analysis of job adverts is included, utilising an online topic model and correlations analyses. Fourthly, online materials from UK universities concerning relevant degrees are analysed using a bagged support vector classifier and a bespoke module analysis algorithm. Finally, interviews with both potential employers of graduates, and also academics involved in analytics courses, are presented. The results of these separate analyses are synthesised and contrasted. The outcome of this is an assessment of the current state of the market, some reflections on the role operational research make have, and a framework for the development of analytics curricula. The principal contribution of this work is practical; providing tangible recommendations on curricula design and development, as well as to the operational research community in general in respect to how it may react to the growth of analytics. Additional contributions are made in respect to methodology, with a novel, mixed-method approach employed, and to theory, with insights as to the nature of how trends develop in both the jobs market and in academia. It is hoped that the insights here, may be of value to course designers seeking to react to similar trends in a wide range of disciplines and fields