Reciprocal Recommender System for Learners in Massive Open Online Courses (MOOCs)

Massive open online courses (MOOC) describe platforms where users with completely different backgrounds subscribe to various courses on offer. MOOC forums and discussion boards offer learners a medium to communicate with each other and maximize their learning outcomes. However, oftentimes learners are hesitant to approach each other for different reasons (being shy, don't know the right match, etc.). In this paper, we propose a reciprocal recommender system which matches learners who are mutually interested in, and likely to communicate with each other based on their profile attributes like age, location, gender, qualification, interests, etc. We test our algorithm on data sampled using the publicly available MITx-Harvardx dataset and demonstrate that both attribute importance and reciprocity play an important role in forming the final recommendation list of learners. Our approach provides promising results for such a system to be implemented within an actual MOOC.Comment: 10 pages, accepted as full paper @ ICWL 201

Improving Search through A3C Reinforcement Learning based Conversational Agent

We develop a reinforcement learning based search assistant which can assist users through a set of actions and sequence of interactions to enable them realize their intent. Our approach caters to subjective search where the user is seeking digital assets such as images which is fundamentally different from the tasks which have objective and limited search modalities. Labeled conversational data is generally not available in such search tasks and training the agent through human interactions can be time consuming. We propose a stochastic virtual user which impersonates a real user and can be used to sample user behavior efficiently to train the agent which accelerates the bootstrapping of the agent. We develop A3C algorithm based context preserving architecture which enables the agent to provide contextual assistance to the user. We compare the A3C agent with Q-learning and evaluate its performance on average rewards and state values it obtains with the virtual user in validation episodes. Our experiments show that the agent learns to achieve higher rewards and better states.Comment: 17 pages, 7 figure

Linking bacterial population dynamics and nutrient removal in the granular sludge biofilm ecosystem engineered for wastewater treatment

Intensive nutrient removal from wastewater in anaerobic-aerobic systems using granular sludge should rely on optimal balances at biofilm and microbial ecology levels. This study targets the impacts of reactor characteristics and fluctuations in operation conditions on nutrient removal and bacterial community structures by means of microbial and numerical ecology methods. The dynamics of both predominant and accompanying populations were investigated with high resolution on temporal and phylogenetic scales in two reactors operated during 5 months with synthetic wastewater. Multivariate analyses highlighted significant correlations from process to microbial scales in the first reactor, whereas nitrification and phosphorus removal might have been affected by oxygen mass transfer limitations with no impact at population level in the second system. The bacterial community continuum of the first reactor was composed of two major antagonistic Accumulibacter-Nitrosomonas-Nitrospira and Competibacter-Cytophaga-Intrasporangiaceae clusters that prevailed under conditions leading to efficient P- (> 95%) and N-removal (> 65%) and altered P- (< 90%) and N-removal (< 60%), respectively. A third cluster independent of performances was dominated by Xanthomonadaceae affiliates that were on average more abundant at 25 °C (31 ± 5%) than at 20 °C (22 ± 4%). Starting from the physiological traits of the numerous phylotypes identified, a conceptual model is proposed as a base for functional analysis in the granular sludge microbiome and for future investigations with complex real wastewate

Neural Attentive Session-based Recommendation

Given e-commerce scenarios that user profiles are invisible, session-based recommendation is proposed to generate recommendation results from short sessions. Previous work only considers the user's sequential behavior in the current session, whereas the user's main purpose in the current session is not emphasized. In this paper, we propose a novel neural networks framework, i.e., Neural Attentive Recommendation Machine (NARM), to tackle this problem. Specifically, we explore a hybrid encoder with an attention mechanism to model the user's sequential behavior and capture the user's main purpose in the current session, which are combined as a unified session representation later. We then compute the recommendation scores for each candidate item with a bi-linear matching scheme based on this unified session representation. We train NARM by jointly learning the item and session representations as well as their matchings. We carried out extensive experiments on two benchmark datasets. Our experimental results show that NARM outperforms state-of-the-art baselines on both datasets. Furthermore, we also find that NARM achieves a significant improvement on long sessions, which demonstrates its advantages in modeling the user's sequential behavior and main purpose simultaneously.Comment: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. arXiv admin note: text overlap with arXiv:1511.06939, arXiv:1606.08117 by other author

Hidden Markov Models and their Application for Predicting Failure Events

We show how Markov mixed membership models (MMMM) can be used to predict the degradation of assets. We model the degradation path of individual assets, to predict overall failure rates. Instead of a separate distribution for each hidden state, we use hierarchical mixtures of distributions in the exponential family. In our approach the observation distribution of the states is a finite mixture distribution of a small set of (simpler) distributions shared across all states. Using tied-mixture observation distributions offers several advantages. The mixtures act as a regularization for typically very sparse problems, and they reduce the computational effort for the learning algorithm since there are fewer distributions to be found. Using shared mixtures enables sharing of statistical strength between the Markov states and thus transfer learning. We determine for individual assets the trade-off between the risk of failure and extended operating hours by combining a MMMM with a partially observable Markov decision process (POMDP) to dynamically optimize the policy for when and how to maintain the asset.Comment: Will be published in the proceedings of ICCS 2020; @Booklet{EasyChair:3183, author = {Paul Hofmann and Zaid Tashman}, title = {Hidden Markov Models and their Application for Predicting Failure Events}, howpublished = {EasyChair Preprint no. 3183}, year = {EasyChair, 2020}

Verification and Control of Partially Observable Probabilistic Real-Time Systems

We propose automated techniques for the verification and control of probabilistic real-time systems that are only partially observable. To formally model such systems, we define an extension of probabilistic timed automata in which local states are partially visible to an observer or controller. We give a probabilistic temporal logic that can express a range of quantitative properties of these models, relating to the probability of an event's occurrence or the expected value of a reward measure. We then propose techniques to either verify that such a property holds or to synthesise a controller for the model which makes it true. Our approach is based on an integer discretisation of the model's dense-time behaviour and a grid-based abstraction of the uncountable belief space induced by partial observability. The latter is necessarily approximate since the underlying problem is undecidable, however we show how both lower and upper bounds on numerical results can be generated. We illustrate the effectiveness of the approach by implementing it in the PRISM model checker and applying it to several case studies, from the domains of computer security and task scheduling

A personalized and context-aware news offer for mobile devices

For classical domains, such as movies, recommender systems have proven their usefulness. But recommending news is more challenging due to the short life span of news content and the demand for up-to-date recommendations. This paper presents a news recommendation service with a content-based algorithm that uses features of a search engine for content processing and indexing, and a collaborative filtering algorithm for serendipity. The extension towards a context-aware algorithm is made to assess the information value of context in a mobile environment through a user study. Analyzing interaction behavior and feedback of users on three recommendation approaches shows that interaction with the content is crucial input for user modeling. Context-aware recommendations using time and device type as context data outperform traditional recommendations with an accuracy gain dependent on the contextual situation. These findings demonstrate that the user experience of news services can be improved by a personalized context-aware news offer

Probabilistic Bisimulation: Naturally on Distributions

In contrast to the usual understanding of probabilistic systems as stochastic processes, recently these systems have also been regarded as transformers of probabilities. In this paper, we give a natural definition of strong bisimulation for probabilistic systems corresponding to this view that treats probability distributions as first-class citizens. Our definition applies in the same way to discrete systems as well as to systems with uncountable state and action spaces. Several examples demonstrate that our definition refines the understanding of behavioural equivalences of probabilistic systems. In particular, it solves a long-standing open problem concerning the representation of memoryless continuous time by memory-full continuous time. Finally, we give algorithms for computing this bisimulation not only for finite but also for classes of uncountably infinite systems

Vitamin D deficiency in Malawian adults with pulmonary tuberculosis : risk factors and treatment outcomes

The study was supported by a Wellcome Trust (London, UK) Clinical PhD Fellowship awarded to DS (086757/Z/08/A) and the Malawi Liverpool Wellcome Trust (MLW) Core grant from the Wellcome Trust.SETTING: Vitamin D deficiency is common in African adults with tuberculosis (TB), and may be exacerbated by the metabolic effects of anti-tuberculosis drugs and antiretroviral therapy (ART). It is unclear whether vitamin D deficiency influences response to antituberculosis treatment. OBJECTIVES : To describe risk factors for baseline vitamin D deficiency in Malawian adults with pulmonary TB, assess the relationship between serum 25-hydroxy vitamin D (25[OH]D) concentration and treatment response, and evaluate whether the administration of anti-tuberculosis drugs and ART is deleterious to vitamin D status during treatment. DESIGN: A prospective longitudinal cohort study. RESULTS : The median baseline 25(OH)D concentration of the 169 patients (58% human immunodeficiency virus [HIV] infected) recruited was 57 nmol/l; 47 (28%) had vitamin D deficiency (<50 nmol/l). Baseline 25(OH)D concentrations were lower during the cold season (P < 0.001), with food insecurity (P = 0.034) or in patients who consumed alcohol (P = 0.019). No relationship between vitamin D status and anti-tuberculosis treatment response was found. 25(OH)D concentrations increased during anti-tuberculosis treatment, irrespective of HIV status or use of ART. CONCLUSIONS : Vitamin D deficiency is common among TB patients in Malawi, but this does not influence treatment response. Adverse metabolic effects of drug treatment may be compensated by the positive impact of clinical recovery preventing exacerbation of vitamin D deficiency during anti-tuberculosis treatment.Publisher PDFPeer reviewe