The identification of cyberattacks which target information and communication systems has been a focus of the research community for years. Network intrusion detection is a complex problem which presents a diverse number of challenges. Many attacks currently remain undetected, while newer ones emerge due to the proliferation of connected devices and the evolution of communication technology. In this survey, we review the methods that have been applied to network data with the purpose of developing an intrusion detector, but contrary to previous reviews in the area, we analyze them from the perspective of the Knowledge Discovery in Databases (KDD) process. As such, we discuss the techniques used for the capture, preparation and transformation of the data, as well as, the data mining and evaluation methods. In addition, we also present the characteristics and motivations behind the use of each of these techniques and propose more adequate and up-to-date taxonomies and definitions for intrusion detectors based on the terminology used in the area of data mining and KDD. Special importance is given to the evaluation procedures followed to assess the different detectors, discussing their applicability in current real networks. Finally, as a result of this literature review, we investigate some open issues which will need to be considered for further research in the area of network security.

A bstract Continuous high frequency water quality monitoring is becoming a critical task to support water management. Despite the advancement s in sensor technologies, certain variables cannot be easily and/or economically monitored in - situ and in real time. In these cases, surrogate measures can be used to make estimations by means of data - driven models. In th is work, variables that are commonly measured in - situ are used as surrogates to estimate the concentration s of nutrients in a rural catchment and in an urban one, making use of machine learning models, specifically Random Forests . The results are compared with those of linear modelling using the same number of surrogates, obtaining a reduction in the Root Mean Squared Error (RMSE) of up to 60.1% . Th e profit from including up to seven surrogate sensors was computed, concluding that adding more than 4 and 5 sensors in each of the catchments respectively was not worthy in terms of error improvement. Keywords water monitoring, water quality, surrogate parameters, random forests, soft - sensors, machine learning 2 1. Introduction Waterb odies must maintain a good ecological and chemical status in order to protect human health, preserve water supply and safeguard natural ecosystems and biodiversity. The assessment of the ecological status of these waterbodies in a coherent and comprehensiv e way would benefit from improving water quality monitoring progra mmes (Voulvoulis et al., 2017) . To date, many substa nces like major nutrients (nitrogen (N) and phosphorus (P)) are mostly monitored by means of analytical discrete campaigns with low sampling frequenc y . Nutrient monitoring is of great importance to reduce the risk of eutrophication, a water quality problem that leads to numerous negative impacts like public health issues, fish mortality and unhealthy ecosystems, among others .

Peer Assessment is a task of analysis and commenting on student's writing by peers, is core of all educational components both in campus and in MOOC's. However, with the sheer scale of MOOC's & its inherent personalised open ended learning, automatic grading and tools assisting grading at scale is highly important. Previously we presented survey on tasks of post classification, knowledge tracing and ended with brief review on Peer Assessment (PA), with some initial problems. In this review we shall continue review on PA from perspective of improving the review process itself. As such rest of this review focus on three facets of PA namely Auto grading and Peer Assessment Tools (we shall look only on how peer reviews/auto-grading is carried), strategies to handle Rogue Reviews, Peer Review Improvement using Natural Language Processing. The consolidated set of papers and resources so used are released in https://github.com/manikandan-ravikiran/cs6460-Survey-2.

Learning Management Systems (LMS) and Educational Data Mining (EDM) are two important parts of online educational environment with the former being a centralised web-based information systems where the learning content is managed and learning activities are organised (Stone and Zheng,2014) and latter focusing on using data mining techniques for the analysis of data so generated. As part of this work, we present a literature review of three major tasks of EDM (See section 2), by identifying shortcomings and existing open problems, and a Blumenfield chart (See section 3). The consolidated set of papers and resources so used are released in https://github.com/manikandan-ravikiran/cs6460-Survey. The coverage statistics and review matrix of the survey are as shown in Figure 1 & Table 1 respectively. Acronym expansions are added in the Appendix Section 4.1.

The need for transparency of predictive systems based on Machine Learning algorithms arises as a consequence of their ever-increasing proliferation in the industry. Whenever black-box algorithmic predictions influence human affairs, the inner workings of these algorithms should be scrutinised and their decisions explained to the relevant stakeholders, including the system engineers, the system's operators and the individuals whose case is being decided. While a variety of interpretability and explainability methods is available, none of them is a panacea that can satisfy all diverse expectations and competing objectives that might be required by the parties involved. We address this challenge in this paper by discussing the promises of Interactive Machine Learning for improved transparency of black-box systems using the example of contrastive explanations -- a state-of-the-art approach to Interpretable Machine Learning. Specifically, we show how to personalise counterfactual explanations by interactively adjusting their conditional statements and extract additional explanations by asking follow-up "What if?" questions. Our experience in building, deploying and presenting this type of system allowed us to list desired properties as well as potential limitations, which can be used to guide the development of interactive explainers. While customising the medium of interaction, i.e., the user interface comprising of various communication channels, may give an impression of personalisation, we argue that adjusting the explanation itself and its content is more important. To this end, properties such as breadth, scope, context, purpose and target of the explanation have to be considered, in addition to explicitly informing the explainee about its limitations and caveats...

Deep Reinforcement Learning (DRL) has numerous applications in the real world thanks to its outstanding ability in quickly adapting to the surrounding environments. Despite its great advantages, DRL is susceptible to adversarial attacks, which precludes its use in real-life critical systems and applications (e.g., smart grids, traffic controls, and autonomous vehicles) unless its vulnerabilities are addressed and mitigated. Thus, this paper provides a comprehensive survey that discusses emerging attacks in DRL-based systems and the potential countermeasures to defend against these attacks. We first cover some fundamental backgrounds about DRL and present emerging adversarial attacks on machine learning techniques. We then investigate more details of the vulnerabilities that the adversary can exploit to attack DRL along with the state-of-the-art countermeasures to prevent such attacks. Finally, we highlight open issues and research challenges for developing solutions to deal with attacks for DRL-based intelligent systems.

On October 2019, sitting amidst the audience at the World AI Summit conference held in Amsterdam, I watched with excitement as the Secretary of State (Ministry of Economic Affairs), Mrs Mona Keijzer, took to the stage to announce the recent launch of the AI Coalition in the Netherlands, and consequently the Strategic AI Action Plan that resulted from the work of its 65 parties. Mrs Keijzer seemed quite optimistic in their ability to compete at the global level. This optimism is indeed justified by key ingredients that make it ready for embracing such a leap forward. For instance, the Netherlands is one of the most data-connected countries in the world, according to DHL research, and on top of this, the country has achieved a strong ecosystem of public-private partnerships (PPP), in addition to a leading European position in high quality research. A McKinsey Report on AI in Europe ranked the Netherlands above average when it comes to AI readiness, with top 25% scores for Automation, Digital Readiness and Innovation.

Analyst firm Cognilytica recently concluded a survey of global Artificial Intelligence adoption patterns with some surprising and striking results. On the whole, adoption of AI continues to grow strongly in all regions of the world, with specific interest in particular patterns of AI. In a survey sent to more than 1500 decision makers in multiple industries and regions, the general consensus seems to be that over 40% of respondents indicate that they are currently implementing at least one AI project or plan to do so within the short term. However, in the same survey, over 90% of respondents indicate that they plan to implement one of the patterns of AI in the short term, if not already. The chart below shows the findings in more detail.

How AI and ML technologies fight against the growing fraud threat?

It is well understood that implicit bias is a factor in adverse effects against subpopulations in many societal contexts [1,6,42] as also highlighted by recent events in the popular press [22,38,61]. For instance, in employment decisions, men are perceived as more competent and given a higher starting salary even when qualifications are the same [52], and in managerial jobs, it was observed that women had to show roughly twice as much evidence of competence as men to be seen as equally competent [37,59]. In education, implicit biases have been shown to exist in ways that exacerbate the achievement gap for racial and ethnic minorities [53] and female students [41], and add to the large racial disparities in school discipline which particularly affect black students' school performance and future prospects [45]. Beyond negatively impacting social opportunities, implicit biases have been shown to put lives at stake as they are a factor in police decisions to shoot, negatively impacting people who are black [20] and of other racial or ethnic minorities [48]. Furthermore, decision making that relies on biased measures of quantities such as utility can not only adversely impact those perceived more negatively, but can also lead to sub-optimal outcomes for those harboring these unconscious biases. To combat this, a significant effort has been placed in developing anti-bias training with the goal of eliminating or reducing implicit biases [24, 39, 64].