A framework for the forensic investigation of unstructured email relationship data

Our continued reliance on email communications ensures that it remains a major source of evidence during a digital investigation. Emails comprise both structured and unstructured data. Structured data provides qualitative information to the forensics examiner and is typically viewed through existing tools. Unstructured data is more complex as it comprises information associated with social networks, such as relationships within the network, identification of key actors and power relations, and there are currently no standardised tools for its forensic analysis. Moreover, email investigations may involve many hundreds of actors and thousands of messages. This paper posits a framework for the forensic investigation of email data. In particular, it focuses on the triage and analysis of unstructured data to identify key actors and relationships within an email network. This paper demonstrates the applicability of the approach by applying relevant stages of the framework to the Enron email corpus. The paper illustrates the advantage of triaging this data to identify (and discount) actors and potential sources of further evidence. It then applies social network analysis techniques to key actors within the data set. This paper posits that visualisation of unstructured data can greatly aid the examiner in their analysis of evidence discovered during an investigation

Emerging risks identification on food and feed - EFSA

The European Food Safety Authority's has established procedures for the identification of emerging risk in food and feed. The main objectives are to: (i) to carry out activities aiming at identifying, assessing and disseminating information on emerging issues and ensure coordination with relevant networks and international organisations; (ii) promote the identification of data sources and data collection and /or data generation in prioritised emerging issues; and the (iii) evaluate of the collected information and identify of emerging risks. The objective(s) of the Standing Working Group on Emerging Risks (SWG‐ER) is to collaborate with EFSA on the emerging risks identification (ERI) procedure and provide strategic direction for EFSA work building on past and ongoing projects related to EFSA ERI procedure. The SWG‐ER considered the ERI methodologies in place and results obtained by EFSA. It was concluded that a systematic approach to the identification of emerging issues based on experts’ networks is the major strength of the procedure but at present, it is mainly focused on single issues, over short to medium time horizons, no consistent weighting or ranking is applied and clear governance of emerging risks with follow‐up actions is missing. The analysis highlighted weaknesses with respect to data collection, analysis and integration. No methodology is in place to estimate the value of the procedure outputs in terms of avoided risk and there is urgent need for a communication strategy that addresses the lack of data and knowledge uncertainty and addresses risk perception issues. Recommendations were given in three areas: (i) Further develop a food system‐based approach including the integration of social sciences to improve understanding of interactions and dynamics between actors and drivers and the development of horizon scanning protocols; (ii) Improve data processing pipelines to prepare big data analytics, implement a data validation system and develop data sharing agreements to explore mutual benefits; and (iii) Revise the EFSA procedure for emerging risk identification to increase transparency and improve communication

Data Mining Paradigm in the Study of Air Quality

Air pollution is a serious global problem that threatens human life and health, as well as the environment. The most important aspect of a successful air quality management strategy is the measurement analysis, air quality forecasting, and reporting system. A complete insight, an accurate prediction, and a rapid response may provide valuable information for society’s decision-making. The data mining paradigm can assist in the study of air quality by providing a structured work methodology that simplifies data analysis. This study presents a systematic review of the literature from 2014 to 2018 on the use of data mining in the analysis of air pollutant measurements. For this review, a data mining approach to air quality analysis was proposed that was consistent with the 748 articles consulted. The most frequent sources of data have been the measurements of monitoring networks, and other technologies such as remote sensing, low-cost sensors, and social networks which are gaining importance in recent years. Among the topics studied in the literature were the redundancy of the information collected in the monitoring networks, the forecasting of pollutant levels or days of excessive regulation, and the identification of meteorological or land use parameters that have the most substantial impact on air quality. As methods to visualise and present the results, we recovered graphic design, air quality index development, heat mapping, and geographic information systems. We hope that this study will provide anchoring of theoretical-practical development in the field and that it will provide inputs for air quality planning and management.Facultad de Ciencias Exacta

Machine Learning and MADIT methodology for the fake news identification: the persuasion index

[EN] The phenomenon of fake news has grown concurrently with the rise of social networks that allow people to directly access news without the mediation of reliable sources. Recognizing news as fake is a difficult task for humans, and even tougher for a machine. This proposal aims to redesign the problem: from a check of truthfulness of news content, to the analysis of texts’ persuasion level. That is how information is introduced to the reader, assuming that fake news is aimed at persuading towards the reality of sense they intend to convey. M.A.D.I.T. methodology has been chosen. It is useful to describe how texts are built, overcoming the content/structure analysis level and stressing the study of Discursive Repertories: discursive modalities of reality of sense building, classified into real and fake news categories thanks to the Machine learning application. For the dataset building 7,387 news have been analysed. The results highlight different profiles of text building between the two groups: the different and typical discursive repertories allow to validate the methodological approach as a good predictor of the persuasion level of texts, not only of news, but also of information in domains such as the economic financial one (e.g. GameStop event).Orrù, L.; Moro, C.; Cuccarini, M.; Paita, M.; Dalla Riva, MS.; Bassi, D.; Da San Martino, G.... (2022). Machine Learning and MADIT methodology for the fake news identification: the persuasion index. En 4th International Conference on Advanced Research Methods and Analytics (CARMA 2022). Editorial Universitat Politècnica de València. 165-172. https://doi.org/10.4995/CARMA2022.2022.1508116517

A comparison of theory and practice in market intelligence gathering for Australian micro-businesses and SMEs

Recent government sponsored research has demonstrated that there is a gap between the theory and practice of market intelligence gathering within the Australian micro, small and medium businesses (SMEs). Typically, there is a significant amount of information in literature about 'what needs to be done', however, there is little insight in terms of how market intelligence gathering should occur. This paper provides a novel insight and a comparison between the theory and practices of market intelligence gathering of micro-business and SMEs in Australia and demonstrates an anomoly in so far as typically the literature does not match what actually occurs in practice. A model for market intelligence gathering for micro-businesses and SMEs is also discussed

A comparison of theory and practice in market intelligence gathering for Australian micro-businesses and SMEs

Recent government sponsored research has demonstrated that there is a gap between the theory and practice of market intelligence gathering within the Australian micro, small and medium businesses (SMEs). Typically, there is a significant amount of information in literature about 'what needs to be done', however, there is little insight in terms of how market intelligence gathering should occur. This paper provides a novel insight and a comparison between the theory and practices of market intelligence gathering of micro-business and SMEs in Australia and demonstrates an anomoly in so far as typically the literature does not match what actually occurs in practice. A model for market intelligence gathering for micro-businesses and SMEs is also discussed