Transfer Learning across Networks for Collective Classification

This paper addresses the problem of transferring useful knowledge from a source network to predict node labels in a newly formed target network. While existing transfer learning research has primarily focused on vector-based data, in which the instances are assumed to be independent and identically distributed, how to effectively transfer knowledge across different information networks has not been well studied, mainly because networks may have their distinct node features and link relationships between nodes. In this paper, we propose a new transfer learning algorithm that attempts to transfer common latent structure features across the source and target networks. The proposed algorithm discovers these latent features by constructing label propagation matrices in the source and target networks, and mapping them into a shared latent feature space. The latent features capture common structure patterns shared by two networks, and serve as domain-independent features to be transferred between networks. Together with domain-dependent node features, we thereafter propose an iterative classification algorithm that leverages label correlations to predict node labels in the target network. Experiments on real-world networks demonstrate that our proposed algorithm can successfully achieve knowledge transfer between networks to help improve the accuracy of classifying nodes in the target network.Comment: Published in the proceedings of IEEE ICDM 201

Mining Brain Networks using Multiple Side Views for Neurological Disorder Identification

Mining discriminative subgraph patterns from graph data has attracted great interest in recent years. It has a wide variety of applications in disease diagnosis, neuroimaging, etc. Most research on subgraph mining focuses on the graph representation alone. However, in many real-world applications, the side information is available along with the graph data. For example, for neurological disorder identification, in addition to the brain networks derived from neuroimaging data, hundreds of clinical, immunologic, serologic and cognitive measures may also be documented for each subject. These measures compose multiple side views encoding a tremendous amount of supplemental information for diagnostic purposes, yet are often ignored. In this paper, we study the problem of discriminative subgraph selection using multiple side views and propose a novel solution to find an optimal set of subgraph features for graph classification by exploring a plurality of side views. We derive a feature evaluation criterion, named gSide, to estimate the usefulness of subgraph patterns based upon side views. Then we develop a branch-and-bound algorithm, called gMSV, to efficiently search for optimal subgraph features by integrating the subgraph mining process and the procedure of discriminative feature selection. Empirical studies on graph classification tasks for neurological disorders using brain networks demonstrate that subgraph patterns selected by the multi-side-view guided subgraph selection approach can effectively boost graph classification performances and are relevant to disease diagnosis.Comment: in Proceedings of IEEE International Conference on Data Mining (ICDM) 201

Mindful reflexivity: Unpacking the process of transformative learning in mindfulness and discernment

Can spiritual practice encourage transformative learning? In this article, we unpack how spiritual practices from the Buddhist tradition—mindfulness—and the Quaker tradition—discernment—encourage the attainment of moral reflexivity and the capacity to transform self in individual and relational organizational contexts, respectively. We also show how moral reflexivity and self-transformation are mutually reinforcing and promote a transformational cycle of management learning. We propose that “mindful reflexivity”, a foundational model of spiritually informed moral reflexivity, can contribute to new ways of management learning through its context sensitivity and ethical orientation to foster the kinds of reflexivity needed for responsible management. Our article concludes with implications for management learning theory and practice, and we offer pathways for future research

Visual Information Literacy Via Visual Means: Three Heuristics

To offer definitions and application scenarios for three interdisciplinary heuristics designed to encourage a more holistic view of texts with the objective of raising awareness and enhancing the information literacy of student researchers

Conceptualizing and supporting awareness of collaborative argumentation

In this thesis, we introduce “Argue(a)ware”. This is a concept for an instructional group awareness tool which aims at supporting social interactions in co-located computer-supported collaborative argumentation settings. Argue(a)ware is designed to support the social interactions in the content (i.e., task-related) and in the relational (i.e., social and interpersonal) space of co-located collaborative argumentation (Barron, 2003). The support for social interactions in the content space of collaboration is facilitated with the use of collaborative scripts for argumentation (i.e., instructions and scaffolds of argument construction) as well with the use of an argument mapping tool (i.e., visualization of argumentation outcomes in a form of diagrams) (Stegmann, Weinberger, & Fischer, 2007; van Gelder, 2013). The support for social interactions in the relational space of collaboration is facilitated with the use of different awareness mechanisms from the CSCL and the CSCW research fields (i.e., monitoring, mirroring and awareness notification tools). In this thesis, we examined how different awareness mechanisms facilitate the regulation of collaborative processes in the relational space of collaborative argumentation. Moreover, we studied how they affect the perceived team effectiveness (i.e., process outcome) and group performance (i.e., learning outcome) in the content space of collaboration. Thereby, we studied also the effects of the design of the awareness mechanisms on the application of the mechanisms and the user experience with them. In line with the design-based research paradigm, we attempted to simultaneously improve and study the effect of Argue(a)ware on collaborative argumentation (Herrington, McKenney, Reeves & Oliver, 2007). Through a series of design-based research studies we tested and refined the prototypes of the instructional group awareness tool. Moreover, we studied the ecological validity of dominant awareness and instructional theories in the context of co-located computer-supported collaborative argumentation. The underlying premise of the Argue(a)ware tool is that a combination of awareness and instructional support will result in increased awareness of collaboration, which will, in turn, mediate the regulation of collaborative processes. Moreover, we assume that successful regulation of collaboration will result in high perceived team effectiveness and the group performance in turn. In the first phase of development of the Argue(a)ware tool, we built support of the content space of collaborative argumentation with argument scaffold elements in a pedagogical face-to-face macro-script and an argument mapping tool. Furthermore, we extended the use of the script for supporting the relational space of collaboration by embedding awareness prompts for reflecting on collaboration during regular breaks in the script. Following, we designed two variations of the same pedagogical face-to-face macro-script which differ with respect to the type of group awareness prompts they used for supporting the relational space of collaboration i.e. behavioral and social. Upon designing the two script variations, we conducted a longitudinal, multiple-case study with ten groups of Media Informatics master students (n = 28, in groups of three or two, group=case, 4 sessions x70 min, Behavioural Awareness Script group= 5, Social Awareness Script group =5.) where each group was conceptualized as a case. Students collaborated every time for arguing to solve one different ill-structured problem and for transferring their arguments in the argument mapping tool Rationale. Thereby, we intended to investigate the effects of different awareness prompts on (a) collaborative metacognitive processes i.e., regulation, reflection, and evaluation (b) the relation between collaborative metacognitive processes and the quality of collaborative argumentation as well as (c) the impact of the two script variations on perceived team effectiveness and (d) what was experience with the different parts of the script variations in the two groups and how this fits into the design framework by Buder (2011). The quantitative analysis of argument outcomes from the groups yield no significant difference between the groups that worked with the BAS and the SAS variations. No significant difference between the script variations with respect to the results from the team effectiveness questionnaires was found either. Prompts for regulating collaboration processes were found to be the most successfully and consistently applied ones, especially in the most successful cases from both script variations and have influenced the argumentation outcomes. The awareness prompts afforded an explicit feedback display format (e.g. assessment of participation levels of self- and others) through discussion (Buder, 2011). The prompted explicit feedback display format (i.e., ratings of one’s self and of others) was criticized for running only on subjective awareness information on participation, contribution efforts and performance in the role. This resulted in evaluation apprehension phenomena (Cottrell, 1972) and evaluation bias (i.e., users may have not assessed themselves or others frankly) (Ghadirian et al., 2016). The awareness prompts for reflection and evaluation did reveal frictions in the plan making process (i.e., dropping out of the plan for collaboration) in the least successful groups. Problems with group dynamics (i.e., free-loading and presence of dominance) but were not powerful enough to trigger the desired changes in the behaviors of the students. The prompts for evaluating the collaboration in both script variations had no apparent connection to argumentation outcomes. The results indicated that dominant presence phenomena inhibited substantive argumentation in the least successful groups. They also indicated that the role-assignment influenced the group dynamics by helping student’s making clear the labor division in the group. In the second phase of development of the Argue(a)ware tool, the focus is on structuring and regulating social interactions in the relational space of collaborative argumentation by means of scripted roles and role-based awareness scaffolds. We designed support for mirroring participation in the role (i.e., a role-based awareness visualization) and support for monitoring participation, coordination and collaboration efforts in the role (i.e., self-assessment questionnaire). Moreover, we designed additional support for guiding participation in the role i.e., role-based reminders as notifications on smartwatches. In a between-subjects study, ten groups of three university students each (n = 30, Mage =22y, mixed educational backgrounds, 1x90min) worked with two variants of the Argue(a)ware for arguing to solve one ill-structured problem and transferring their arguments in the argument mapping tool Rationale. Next, to that, students should monitor their progress in their role with the role-based awareness visualization and the self-assessment questionnaire with the basic awareness support (role-based awareness visualization with the intermediate self-assessment) and the enhanced awareness support (additional role-based awareness reminders). Half of the groups worked only with the role-based awareness visualization and the self-assessment questionnaire (Basic Awareness Condition-BAC) while the other half groups received additional text-based awareness notifications via smartwatches that were sent to students privately (Enhanced Awareness Condition- EAC). Thereby, we tested the use of different degrees of awareness support in the two conditions with respect to their impact on a) self-perceived awareness of performance in the role and of collaboration and coordination efforts (measured with the same questionnaire at two time points), b) on perceive team effectiveness, c) group performance. We hypothesized that students in EAC will perform better thanks to the additional awareness reminders that increased the directivity and influenced their awareness in the role. The mixed methods analysis revealed that the awareness reminders, when perceived on time, succeeded in guiding collaboration (i.e., resulted in more role-specific behaviors). Students in the EAC condition improved their awareness over time (between the two measurements). These results indicated that enhanced awareness support in the form of additional guidance through awareness reminders can boost the awareness of students’ performance in the role as well as the awareness of their coordination and collaboration efforts over time by directing them back to the mirroring and monitoring tools. Moreover, students in EAC exhibited higher perceived team effectiveness than the students in BAC. However, no significant differences in building of shared mental models or performing in mutual performance monitoring were found between the groups. However, students in BAC and EAC did not differ significantly with respect to the formal correctness or evidence sufficiency of their group argumentation outcomes. Moreover, technical difficulties with the smartphones used as delivery devices for the awareness reminders (i.e., low vibration modus) hindered the timely perception of the reminders and thus their effect on participation. Finally, the questionnaire on the experience with the different parts of Argue(a)ware system indicated the need for exploring further media for supporting the awareness reminders to avoid the overwhelming effects of the multiple displays of the system and enhancing higher perceptiveness of the reminders with low interruption costs for other group members. The rather high satisfaction with the use of the role-based awareness visualization and the positive comments on the motivating aspects of monitoring how the personal success contributes to the group performance indicate that the group mirror succeeded in making group norms visible to group members in a non-obtrusive way. The high interpersonal comparability of performances without moderating the group ‘s interaction directly in the basic awareness condition was proven to be the favored design approach compared to the combination of group mirror and awareness reminders in the enhance awareness condition. In the third phase of development of Argue(a)ware, we focused on designing and testing different notification modes on different ubiquitous mobile devices for facilitating the next prototype of a notification system for role-based awareness reminders. Thereby, the aim of the system was again to guide students’ active participation in collaborative argumentation. More specifically, we focused on raising students’ attention to the reminders and triggering a prompter reaction to the contents of the reminders whilst avoiding a high interruption cost for the primary task (i.e., arguing for solving the problem at hand) in the group. These goals were translated into design challenges for the design of the role-based awareness notification system. The system should afford low interruptions, high reaction and high comprehension of notifications. Notification systems with this particular configuration of IRC values are known as "secondary display" systems (McCrickard et al., 2003). Next, we designed three low-fidelity prototypes for a role-based notification system for delivering awareness reminders: The first ran on a smartwatch and afforded text-based information with vibration and light notification modalities. The second ran on smartphone and afforded text-based information with vibrotactile and light-based notification modalities. Finally, the third prototype run on a smart-ring which afforded graphical- based (i.e. abstract light) information with and light and vibration notification modalities. To test the suitability of these prototypes for acting as “secondary display” systems, we conducted a within-subjects user study where three university students (n= 3, Mage=28, mixed educational background) argued for solving three different problem cases and producing an argument map in each of the three consecutive meetings (max 90min) in the Argue(a)ware instructional system. Students were assigned the roles of writer, corrector and devil`s advocate and were instructed to maintain the same role across the three meetings. In each meeting, students worked with a different role-based awareness notification prototype, where they received a notification indicating their balloon is not growing bigger after five minutes of not exhibiting any role-specific behaviors. The role-based awareness notification prototypes aimed at introducing timely interventions which would prompt students to check on their own progress in the role and the group progress as visualized by the role-based awareness visualization on the large display. Ultimately, this should prompt them to reflect on the awareness information from the visualization and adapt their behaviors to the desired behavior standards over time. Results showed that students perceived the notifications from all media mostly based on vibration cues. Thereby, the vibration cues on the wrist (smartwatch) were considered the least disruptive to the main task compared to the vibration cues on finger (smartwatch) and the vibration cues on the desk (smartphone). Students also declared that vibration cues on wrist prompted the fastest reaction i.e., attending to notification by interacting with the smartwatch. These results indicate that vibration cues on the wrist can be a suitable notification mechanism for increasing the perceived urgency of the message and prompting the reaction on it without causing great distraction to the main task, as studies previous studies showed before (Pielot, Church, & deOliveira, 2013; Hernández-Leo, Balestrini, Nieves & Blat, 2012). Based on very limited qualitative data on light as notification modality and awareness representation type no inferences could be made about its influence on the cost of interruption, reaction and comprehension parameters comprehensiveness. The qualitative and quantitative data on the experience with different media as awareness notification systems indicate that smartwatches may be the most suitable medium for acting as awareness notification medium with a “secondary display” IRC configuration (low-high-high). However, this inference needs to be tested in terms of a follow up study. In the next study, the great limitations of study (limited data due to low power and mal-structured measurement instruments) need to be repaired. Finally, the focus should be on comparing notification modalities of one medium (e.g., smartphone) based on a larger set of participants and with the use of objective measurements for the IRC parameter values (Chewar, McCrickard & Sutcliffe, 2004). Finally, we draw conclusions based on the findings from the three studies with respect to the role of awareness mechanisms for facilitating collaborative processes and outcomes and provide replicable and generalizable design principles. These principles are formed as heuristic statements and are subject to refinement by further research (Bell, Hoadley, & Linn, 2004; Van den Akker, 1999). We conclude with the limitations of the study and ideas for future work with Argue(a)ware

ALOJA: A benchmarking and predictive platform for big data performance analysis

The main goals of the ALOJA research project from BSC-MSR, are to explore and automate the characterization of cost-effectivenessof Big Data deployments. The development of the project over its first year, has resulted in a open source benchmarking platform, an online public repository of results with over 42,000 Hadoop job runs, and web-based analytic tools to gather insights about system's cost-performance1. This article describes the evolution of the project's focus and research lines from over a year of continuously benchmarking Hadoop under dif- ferent configuration and deployments options, presents results, and dis cusses the motivation both technical and market-based of such changes. During this time, ALOJA's target has evolved from a previous low-level profiling of Hadoop runtime, passing through extensive benchmarking and evaluation of a large body of results via aggregation, to currently leveraging Predictive Analytics (PA) techniques. Modeling benchmark executions allow us to estimate the results of new or untested configu- rations or hardware set-ups automatically, by learning techniques from past observations saving in benchmarking time and costs.This work is partially supported the BSC-Microsoft Research Centre, the Span- ish Ministry of Education (TIN2012-34557), the MINECO Severo Ochoa Research program (SEV-2011-0067) and the Generalitat de Catalunya (2014-SGR-1051).Peer ReviewedPostprint (author's final draft

Efficient instance and hypothesis space revision in Meta-Interpretive Learning

Inductive Logic Programming (ILP) is a form of Machine Learning. The goal of ILP is to induce hypotheses, as logic programs, that generalise training examples. ILP is characterised by a high expressivity, generalisation ability and interpretability. Meta-Interpretive Learning (MIL) is a state-of-the-art sub-field of ILP. However, current MIL approaches have limited efficiency: the sample and learning complexity respectively are polynomial and exponential in the number of clauses. My thesis is that improvements over the sample and learning complexity can be achieved in MIL through instance and hypothesis space revision. Specifically, we investigate 1) methods that revise the instance space, 2) methods that revise the hypothesis space and 3) methods that revise both the instance and the hypothesis spaces for achieving more efficient MIL. First, we introduce a method for building training sets with active learning in Bayesian MIL. Instances are selected maximising the entropy. We demonstrate this method can reduce the sample complexity and supports efficient learning of agent strategies. Second, we introduce a new method for revising the MIL hypothesis space with predicate invention. Our method generates predicates bottom-up from the background knowledge related to the training examples. We demonstrate this method is complete and can reduce the learning and sample complexity. Finally, we introduce a new MIL system called MIGO for learning optimal two-player game strategies. MIGO learns from playing: its training sets are built from the sequence of actions it chooses. Moreover, MIGO revises its hypothesis space with Dependent Learning: it first solves simpler tasks and can reuse any learned solution for solving more complex tasks. We demonstrate MIGO significantly outperforms both classical and deep reinforcement learning. The methods presented in this thesis open exciting perspectives for efficiently learning theories with MIL in a wide range of applications including robotics, modelling of agent strategies and game playing.Open Acces

Predicting trucking accidents with truck drivers 'safety climate perception across companies: A transfer learning approach

There is a rising interest in using artificial intelligence (AI)-powered safety analytics to predict accidents in the trucking industry. Companies may face the practical challenge, however, of not having enough data to develop good safety analytics models. Although pretrained models may offer a solution for such companies, existing safety research using transfer learning has mostly focused on computer vision and natural language processing, rather than accident analytics. To fill the above gap, we propose a pretrain-then-fine-tune transfer learning approach to help any company leverage other companies' data to develop AI models for a more accurate prediction of accident risk. We also develop SafeNet, a deep neural network algorithm for classification tasks suitable for accident prediction. Using the safety climate survey data from seven trucking companies with different data sizes, we show that our proposed approach results in better model performance compared to training the model from scratch using only the target company's data. We also show that for the transfer learning model to be effective, the pretrained model should be developed with larger datasets from diverse sources. The trucking industry may, thus, consider pooling safety analytics data from a wide range of companies to develop pretrained models and share them within the industry for better knowledge and resource transfer. The above contributions point to the promise of advanced safety analytics to make the industry safer and more sustainable.Comment: submitted to journal: accident analysis and preventio