Exploring Text Mining and Analytics for Applications in Public Security: An in-depth dive into a systematic literature review

Text mining and related analytics emerge as a technological approach to support human activities in extracting useful knowledge through texts in several formats. From a managerial point of view, it can help organizations in planning and decision-making processes, providing information that was not previously evident through textual materials produced internally or even externally. In this context, within the public/governmental scope, public security agencies are great beneficiaries of the tools associated with text mining, in several aspects, from applications in the criminal area to the collection of people's opinions and sentiments about the actions taken to promote their welfare. This article reports details of a systematic literature review focused on identifying the main areas of text mining application in public security, the most recurrent technological tools, and future research directions. The searches covered four major article bases (Scopus, Web of Science, IEEE Xplore, and ACM Digital Library), selecting 194 materials published between 2014 and the first half of 2021, among journals, conferences, and book chapters. There were several findings concerning the targets of the literature review, as presented in the results of this article

Mind the Gap: Developments in Autonomous Driving Research and the Sustainability Challenge

Scientific knowledge on autonomous-driving technology is expanding at a faster-than-ever pace. As a result, the likelihood of incurring information overload is particularly notable for researchers, who can struggle to overcome the gap between information processing requirements and information processing capacity. We address this issue by adopting a multi-granulation approach to latent knowledge discovery and synthesis in large-scale research domains. The proposed methodology combines citation-based community detection methods and topic modeling techniques to give a concise but comprehensive overview of how the autonomous vehicle (AV) research field is conceptually structured. Thirteen core thematic areas are extracted and presented by mining the large data-rich environments resulting from 50 years of AV research. The analysis demonstrates that this research field is strongly oriented towards examining the technological developments needed to enable the widespread rollout of AVs, whereas it largely overlooks the wide-ranging sustainability implications of this sociotechnical transition. On account of these findings, we call for a broader engagement of AV researchers with the sustainability concept and we invite them to increase their commitment to conducting systematic investigations into the sustainability of AV deployment. Sustainability research is urgently required to produce an evidence-based understanding of what new sociotechnical arrangements are needed to ensure that the systemic technological change introduced by AV-based transport systems can fulfill societal functions while meeting the urgent need for more sustainable transport solutions

Artificial Intelligence-Powered Chronic Wound Management System: Towards Human Digital Twins

Artificial Intelligence (AI) has witnessed increased application and widespread adoption over the past decade. AI applications to medical images have the potential to assist caregivers in deciding on a proper chronic wound treatment plan by helping them to understand wound and tissue classification and border segmentation, as well as visual image synthesis. This dissertation explores chronic wound management using AI methods, such as Generative Adversarial Networks (GAN) and Explainable AI (XAI) techniques. The wound images are collected, grouped, and processed. One primary objective of this research is to develop a series of AI models, not only to present the potential of AI in wound management but also to develop the building blocks of human digital twins. First of all, motivations, contributions, and the dissertation outline are summarized to introduce the aim and scope of the dissertation. The first contribution of this study is to build a chronic wound classification and its explanation utilizing XAI. This model also benefits from a transfer learning methodology to improve performance. Then a novel model is developed that achieves wound border segmentation and tissue classification tasks simultaneously. A Deep Learning (DL) architecture, i.e., the GAN, is proposed to realize these tasks. Another novel model is developed for creating lifelike wounds. The output of the previously proposed model is used as an input for this model, which generates new chronic wound images. Any tissue distribution could be converted to lifelike wounds, preserving the shape of the original wound. The aforementioned research is extended to build a digital twin for chronic wound management. Chronic wounds, enabling technologies for wound care digital twins, are examined, and a general framework for chronic wound management using the digital twin concept is investigated. The last contribution of this dissertation includes a chronic wound healing prediction model using DL techniques. It utilizes the previously developed AI models to build a chronic wound management framework using the digital twin concept. Lastly, the overall conclusions are drawn. Future challenges and further developments in chronic wound management are discussed by utilizing emerging technologies

Edge/Fog Computing Technologies for IoT Infrastructure

The prevalence of smart devices and cloud computing has led to an explosion in the amount of data generated by IoT devices. Moreover, emerging IoT applications, such as augmented and virtual reality (AR/VR), intelligent transportation systems, and smart factories require ultra-low latency for data communication and processing. Fog/edge computing is a new computing paradigm where fully distributed fog/edge nodes located nearby end devices provide computing resources. By analyzing, filtering, and processing at local fog/edge resources instead of transferring tremendous data to the centralized cloud servers, fog/edge computing can reduce the processing delay and network traffic significantly. With these advantages, fog/edge computing is expected to be one of the key enabling technologies for building the IoT infrastructure. Aiming to explore the recent research and development on fog/edge computing technologies for building an IoT infrastructure, this book collected 10 articles. The selected articles cover diverse topics such as resource management, service provisioning, task offloading and scheduling, container orchestration, and security on edge/fog computing infrastructure, which can help to grasp recent trends, as well as state-of-the-art algorithms of fog/edge computing technologies

Advanced analytical methods for fraud detection: a systematic literature review

The developments of the digital era demand new ways of producing goods and rendering services. This fast-paced evolution in the companies implies a new approach from the auditors, who must keep up with the constant transformation. With the dynamic dimensions of data, it is important to seize the opportunity to add value to the companies. The need to apply more robust methods to detect fraud is evident. In this thesis the use of advanced analytical methods for fraud detection will be investigated, through the analysis of the existent literature on this topic. Both a systematic review of the literature and a bibliometric approach will be applied to the most appropriate database to measure the scientific production and current trends. This study intends to contribute to the academic research that have been conducted, in order to centralize the existing information on this topic

A fuzzy approach to text classification with two-stage training for ambiguous instances

Sentiment analysis is a very popular application area of text mining and machine learning. The popular methods include Support Vector Machine, Naive Bayes, Decision Trees and Deep Neural Networks. However, these methods generally belong to discriminative learning, which aims to distinguish one class from others with a clear-cut outcome, under the presence of ground truth. In the context of text classification, instances are naturally fuzzy (can be multi-labeled in some application areas) and thus are not considered clear-cut, especially given the fact that labels assigned to sentiment in text represent an agreed level of subjective opinion for multiple human annotators rather than indisputable ground truth. This has motivated researchers to develop fuzzy methods, which typically train classifiers through generative learning, i.e. a fuzzy classifier is used to measure the degree to which an instance belongs to each class. Traditional fuzzy methods typically involve generation of a single fuzzy classifier and employ a fixed rule of defuzzification outputting the class with the maximum membership degree. The use of a single fuzzy classifier with the above fixed rule of defuzzification is likely to get the classifier encountering the text ambiguity situation on sentiment data, i.e. an instance may obtain equal membership degrees to both the positive and negative classes. In this paper, we focus on cyberhate classification, since the spread of hate speech via social media can have disruptive impacts on social cohesion and lead to regional and community tensions. Automatic detection of cyberhate has thus become a priority research area. In particular, we propose a modified fuzzy approach with two stage training for dealing with text ambiguity and classifying four types of hate speech, namely: religion, race, disability and sexual orientation - and compare its performance with those popular methods as well as some existing fuzzy approaches, while the features are prepared through the Bag-of-Words and Word Embedding feature extraction methods alongside the correlation based feature subset selection method. The experimental results show that the proposed fuzzy method outperforms the other methods in most cases

Application of Archaeometric Techniques in the Study of Wall Painting on the Example of Fragments of Frescoe Paintings from the Church of St.Nicholas (Crkva svetog Nikole) in Baljevac, Serbia

During the archaeological research of the Church of St. Nicholas, in Baljevac, Serbia, fragments of wall paintings were found in Pit no. 1, located in the nave area. The fragments were determined to be from the second phase of the construction of the church (13th century). Several fragments of different and pure tones were selected to examine the composition of the mortar and pigments, as well as the painting technique. Analytical techniques and the results obtained by their use during the examination of the selected fragments are presented in this paper. With a suitable selection of analytical techniques, all the pigments that had been used were identified, the chemical composition of the mortar determined and a parallel made with the materials analysed so far from wall paintings from similar periods. The importance of modern archaeometric tests in modern conservation-restoration practice is highlighted and guidelines for continuing research are presented.U radu su prikazane analitičke tehnike i rezultati koji su dobijeni njihovim korišćenjem prilikom ispitivanja izabranih fragmenata zidnih slika, pronađenih u Crkvi Sv. Nikole u Baljevcu. Prilikom arheoloških istraživanja crkve, u Jami br. 1, koja se nalazila u prostoru naosa, pronađeni su fragmenti zidnog slikarstva datovani u XIII vek. Odabrano je nekoliko fragmenata različitih i čistih tonova za ispitivanje sastava maltera i pigmenata, kao i tehnike oslikavanja. Analitičke tehnike koje se primenjuju u ispi- tivanju zidnog slikarstva mogu biti nedestruktivne, mikrodestruktivne i destruktivne. Nedestruktivne i mikrodestruktivne analitičke tehnike su poželjne u radu sa arheološkim i muzejskim materijalom. Tehnike koje se primenjuju za analizu bojenih slojeva su raznovrsne, a u ovom radu su korišćene: pEDXRF, FTIR, i Ramanska spektrometrija, kao i OM. Za stručnjake koji se bave zaštitom kulturnog nasleđa najbitnije je da znaju šta pojedine anali- tičke tehnike mogu otkriti. Često nije neophodno primeniti veliki broj različitih tehnika, već imati jasan cilj i izabrati one tehnike koje nam mogu obezbediti potrebne podatke. Da bi rezultati bili što tačniji, važno je odabrati adekvatan uzorak. Poznato je da su uzorci čistih, intenzivnih i debljih slojeva boja daju jasnije razultate nego oni sa la- zurnim slojevima. Kod fragmenata iz Crkve Svetog Nikole u Baljevcu, upotrebom analitičkih tehnika pEDXRF, Ramanska i FTIR spektrometrija, zaključeno je da su pri oslikavanju druge faze crkve korišćeni sle- deći pigmenti: žuti oker, crvena zemlja, cinober, olovno crvena, zelena zemlja, krečno bela i crna od uglja. Malter je po sastavu krečni (kalcijum karbonat) i sadrži agregate istog hemijskog sastava. Kalcijum karbonat je korišćen i za posvetljavanje tona nekih pigmenata, tako što je mešan s njima. Na osnovu poprečnog preseka fragmenata i snimanja pod mikroskopom može se zaključiti da je primenjena fresko tehnika slikanja. Kod dva uzorka plave boje dobijeni su ne- očekivani rezultati. Kod jednog od njih je detektovana zelena zemlja. Kod drugog uzorka, čija je boja označena kao plava, na osnovu dobijenih rezultata zaključeno je da je korišćena crna-ugljenik, koja zbog fresko tehnike i kalcijum karbonata stvara privid plave boje. Crvena boja – od oksida gvožđa je kombinovana sa cinoberom i olovno crvenom bojom. Najbolju analogiju za poređenje sa pigmentima iz sličnog perioda predstavljaju pigmenti korišćeni za oslikavanje manastira Žiča. U ovom manastiru su detektovani skoro isti pigmenti kao na ispitivanim fragmentima, osim lapis lazulija, koji je odsutan na fragmentima crkve u Baljevcu

Digital Skills Colloquium 2020: Enhancing Human Capacity for Digital Transformation

The theme for the Digital Skills 2020 Colloquium and Postgraduate Symposium was “Enhancing Human Capacity for Digital Transformation: It is about people”. It is widely accepted that current digital changes that are sweeping through the world are significantly altering the environment in which every organisation, including government, is operating. The scale and scope of the change is what makes all the difference. The way in which organisations respond to these environmental changes will determine their survival. The nature of a digitally transformed organisation cannot be foretold as every organisation will respond according to their local and global environment. There are, however, some uncomfortable realities; manufacturing jobs will not be reinstated, and even if they did, the manufacturing industries are necessarily more capital and not labour intensive (Stiglitz, 2017). Globally, we are experiencing rising unemployment and income inequality as well as increased demand for high skilled labour (Glenn, Florescu & Project, 2019). Accordingly, the Colloquium sought to explore the role played by digital skills in our rapidly transforming realities. The event attracted full academic research papers, case studies, research work that still in progress and practitioner reports and models that portray the NEMISA collaborative ethos involving government, industry and other sectors. Some plenary sessions and guest speakers shared insights on topics such as emerging technologies, blockchain, machine learning, gamification in education, application of 3D printing, upscaling of ICT for development programmes and citizen online safety.School of Computin

Uncertain Multi-Criteria Optimization Problems

Most real-world search and optimization problems naturally involve multiple criteria as objectives. Generally, symmetry, asymmetry, and anti-symmetry are basic characteristics of binary relationships used when modeling optimization problems. Moreover, the notion of symmetry has appeared in many articles about uncertainty theories that are employed in multi-criteria problems. Different solutions may produce trade-offs (conflicting scenarios) among different objectives. A better solution with respect to one objective may compromise other objectives. There are various factors that need to be considered to address the problems in multidisciplinary research, which is critical for the overall sustainability of human development and activity. In this regard, in recent decades, decision-making theory has been the subject of intense research activities due to its wide applications in different areas. The decision-making theory approach has become an important means to provide real-time solutions to uncertainty problems. Theories such as probability theory, fuzzy set theory, type-2 fuzzy set theory, rough set, and uncertainty theory, available in the existing literature, deal with such uncertainties. Nevertheless, the uncertain multi-criteria characteristics in such problems have not yet been explored in depth, and there is much left to be achieved in this direction. Hence, different mathematical models of real-life multi-criteria optimization problems can be developed in various uncertain frameworks with special emphasis on optimization problems