Design and Implementation of Small Multiples Matrix-based Visualisation to Monitor and Compare Email Socio-organisational Relationships

One of the fundamental organisational questions is how organisations identify anomalies, monitor and compare email communications between staff-staff or staff-clients or staff-customers relationships on a daily basis. The tenacious and substantial relationships are built by the combination of timely replies, frequent engagement and deep interaction between the individuals. To watchdog this periodically, we need an interactive visualisation tool that can help organisational analysts to reconnect some lost relationships and/or strengthen an existing relationship or in some cases identify inside persons (anomalies). From our point of view, Social Intelligence (SI) in an organisation is a combination of self-, social- and organisational-awareness that will help in managing complex socio-organisational changes and can be interpreted in terms of socio-organisational communication efficacy (that is, one's confidence in one's ability to deal with social and organisational information). We considered a case study, an Enron Organisation Email Scandal, to understand the relationships of staff during various parts of the years and we conducted a workshop study with legal experts to gain insights on how they carry out investigation/analysis with respect to email relationships. The outcomes of the workshop helped us develop a novel small multiples matrix-based visualisation in collaboration with our industrial partner, Red Sift UK, to find anomalies, monitor and compare how email relationships change over time and how it defines the meaning of socio-organisational communication efficacy

Design of Small Multiples Matrix-based Visualisation to Understand E-mail Socio-organisational Relationships

One of the fundamental organisational questions is how organisations identify anomalies, monitor and compare E-mail communications between staff-staff or staff-clients or staff- customers relationships on a daily basis. The tenacious and substantial relationships are built by the combination of timely replies, frequent engagement and deep interaction between the individuals. To watchdog this periodically, we need an interactive visualisation tool that can help organisational analysts to reconnect some lost relationships and/or strengthen an existing relationship or in some cases identify inside persons. From our point of view, Social Intelligence (SI) in an organisation is a combination of self-, social- and organisational-awareness that will help in managing complex socio-organisational changes and can be interpreted in terms of socio-organisational communication efficacy (that is, one’s confidence in one’s ability to deal with social and organisational information). We considered a case study, an Enron E-mail Scandal, to understand the relationships of staff during various parts of the years and we conducted a workshop study with legal experts to gain insights on how they carry out investigation/analysis with respect to E-mail relationships. The outcomes of the workshop helped us develop a novel small multiples matrix-based visualisation in collaboration with the company, Red Sift UK to find anomalies, monitor and compare how email relationships changes over time and how it defines the meaning of socio-organisational communication efficacy

Degradation of Toxic Indigo Carmine Dye by Electrosynthesized Ferrate (VI)

Response surface methodology was applied for optimizing indigo carmine (IC) dye removal by electrochemically produced ferrate (VI). Box-Behnken design was employed in this study, and design parameters were pH, Fe (VI) dose and initial dye concentration (Co). R2 and adjusted R2 values were very high that indicated very good accuracy for the employed model. Optimum operational conditions were: 4.08-7.69 for pH, 24-118.83 mg/L for Fe (VI) dose and 60.68-99.13 mg/L for complete removal of IC. Produced by electrochemical method Ferrate (VI) provides high effectiveness for IC dye-containing synthetic wastewater

A Visual Analytics Approach for User Behaviour Understanding through Action Sequence Analysis

Analysis of action sequence data provides new opportunities to understand and model user behaviour. Such data are often in the form of timestamped and labelled series of atomic user actions. Cyber security is one of the domains that show the value of the analysis of these data. Elaborate and specialised models of user-behaviour are desired for effective decision making during investigation of cyber threats. However, due to their complex nature, activity sequences are not yet well-exploited within cyber security systems. In this paper, we describe the initial phases of a visual analytics approach that aims to enable a rich understanding of user behaviour through the analysis of user activity sequences. First, we discuss a motivating case study and discuss a number of high level requirements as derived from a series of workshops within an ongoing research project. We then present the components of a visual analytics approach that constitutes a novel combination of ``action space'' analysis, pattern mining, and the interactive visual analysis of multiple sequences to take the initial steps towards a comprehensive understanding of user behaviour

Frequency versus time domain parameter estimation: Application to a slot milling operation

The time domain recursive least squares estimation of parameters in the presence of additive output noise of harmonic form leads to biased estimates. However, frequency domain least squares parameter estimation, where the frequency range contaminated by the noise is eliminated, can be used to obtain good parameter estimates. Both the time domain and frequency domain methods are described, and applied to the example of a slot milling operation. A model of the resultant force response to feedrate changes in slot milling, based on previously reported experimental studies, is presented. The force measurement is corrupted by harmonic noise arising from runout on the milling cutter. Simulation studies are performed, using a flat band width multi-frequency test signal to persistently excite the system. The time domain approach leads to poor estimates as expected, while the frequency domain approach gives good parameter estimates. The advantages and disadvantages of both methods are discussed.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/27244/1/0000251.pd

Understanding User Behaviour through Action Sequences: from the Usual to the Unusual

Action sequences, where atomic user actions are represented in a labelled, timestamped form, are becoming a fundamental data asset in the inspection and monitoring of user behaviour in digital systems. Although the analysis of such sequences is highly critical to the investigation of activities in cyber security applications, existing solutions fail to provide a comprehensive understanding due to the complex semantic and temporal characteristics of these data. This paper presents a visual analytics approach that aims to facilitate a user-involved, multi-faceted decision making process during the identification and the investigation of “unusual” action sequences. We first report the results of the task analysis and domain characterisation process. Then we describe the components of our multi-level analysis approach that comprises of constraint-based sequential pattern mining and semantic distance based clustering, and multi-scalar visualisations of users and their sequences. Finally, we demonstrate the applicability of our approach through a case study that involves tasks requiring effective decision-making by a group of domain experts. Although our solution here is tightly informed by a user-centred, domain-focused design process, we present findings and techniques that are transferable to other applications where the analysis of such sequences is of interest

VASABI: Hierarchical User Profiles for Interactive Visual User Behaviour Analytics

User behaviour analytics (UBA) systems offer sophisticated models that capture users’ behaviour over time with an aim to identify fraudulent activities that do not match their profiles. Making decisions based on such systems; however, requires an in-depth understanding of user behaviour both at an individual and at a group level where a group can consist of users with similar roles. We present a visual analytics approach to help analysts gain a comprehensive, multifaceted understanding of user behaviour at multiple levels. We take a user-centred approach to design a visual analytics framework supporting the analysis of collections of users and the numerous sessions of activities they conduct within digital applications. The framework is centred around the concept of hierarchical user profiles, where the profiles are built based on features derived from sessions they perform and visualised with task-informed designs to facilitate interactive exploration and investigation. We also present techniques to extract user tasks that summarise the behaviour and to cluster users according to these tasks for providing hierarchical overviews of groups of users along with individual users and the sessions they conduct. We externalise a series of analysis goals and tasks, and evaluate our methods through a number of use cases that demonstrate how these tasks are addressed. We observe that with the aid of interactive visual hierarchical user profiles, analysts were able to conduct exploratory and investigative analysis effectively, and able to understand the characteristics of user behaviour to make informed decisions whilst evaluating suspicious users and activities

LDA Ensembles for Interactive Exploration and Categorization of Behaviors

We define behavior as a set of actions performed by some agent during a period of time. We consider the problem of analyzing a large collection of behaviors by multiple agents, more specifically, identifying typical behaviors as well as spotting behavior anomalies. We propose an approach leveraging topic modeling techniques -- LDA (Latent Dirichlet Allocation) Ensembles -- for representing categories of typical behaviors by topics obtained through applying topic modeling to a behavior collection. When such methods are applied to text documents, the goodness of the extracted topics is usually judged based on the semantic relatedness of the terms pertinent to the topics. This criterion, however, may not be applicable to topics extracted from non-textual data, such as action sets, since relationships between actions may not be obvious. We have developed a suite of visual and interactive techniques supporting the construction of an appropriate combination of topics based on other criteria, such as distinctiveness and coverage of the behavior set. Our case studies in the operation behaviors in the security management system and visiting behaviors in an amusement park and the expert evaluation of the first case study demonstrate the effectiveness of our approach

Study of thin film poly-crystalline CdTe solar cells presenting high acceptor concentrations achieved by in-situ arsenic doping

Doping of CdTe using Group-V elements (As, P, and Sb) has gained interest in pursuit of increasing the cell voltage of CdTe thin film solar devices. Studies on bulk CdTe crystals have shown that much higher acceptor concentration than the traditional copper treatment is possible with As, P or Sb, enabled by high process temperature and/or rapid thermal quenching under Cd overpressure. We report a comprehensive study on in-situ As doping of poly-crystalline CdTe solar cells by MOCVD, whereby high acceptor densities, approaching 3 × 1016 cm−3 were achieved at low growth temperature of 390 °C. No As segregation could be detected at grain boundaries, even for 1019 As cm−3. A shallow acceptor level (+0.1 eV) due to AsTe substitutional doping and deep-level defects were observed at elevated As concentrations. Devices with variable As doping were analysed. Narrowing of the depletion layer, enhancement of bulk recombination, and reduction in device current and red response, albeit a small near infrared gain due to optical gap reduction, were observed at high concentrations. Device modelling indicated that the properties of the n-type window layer and associated interfacial recombination velocity are highly critical when the absorber doping is relatively high, demonstrating a route for obtaining high cell voltage