OpenRaiser/Intern-Atlas huggingface dataset:
Accelerating Human-in-the-loop Machine Learning: Challenges and Opportunities
Venue: arXiv cs.DB 2018
Year: 2018

Development of machine learning (ML) workflows is a tedious process of iterative experimentation: developers repeatedly make changes to workflows until the desired accuracy is attained. We describe our vision for a "human-in-the-loop" ML system that accelerates this process: by intelligently tracking changes and intermediate results over time, such a system can enable rapid iteration, quick responsive feedback, introspection and debugging, and background execution and automation. We finally describe Helix, our preliminary attempt at such a system that has already led to speedups of up to 10x on typical iterative workflows against competing systems.
Dissipative delay range analysis of coupled differential-difference delay systems with distributed delays
Venue: arXiv cs.SY 2018
Year: 2018

This paper proposes methods to handle the problem of delay range stability analysis for a linear coupled differential-difference system (CDDS) with distributed delays subject to dissipative constraints. The model of linear CDDS contains many models of linear delay systems as special cases. A novel Liapunov-Krasovskii functional with non-constant matrix parameters, which are related to the delay value polynomially, is applied to derive stability conditions. By constructing this new functional, sufficient conditions in terms of robust linear matrix inequalities (LMIs) can be derived, which guarantee range stability of a linear CDDS subject to dissipative constraints. To solve the resulting robust LMIs numerically, we apply the technique of sum of squares programming together with matrix relaxations without introducing any potential conservatism to the original robust LMIs. Furthermore, the proposed methods can be extended to solve delay margin estimation problems for a linear CDDS subject to prescribed dissipative constraints. Finally, numerical examples are presented to demonstrate the effectiveness of the proposed methodologies.
Latent Structure in Collaboration: the Case of Reddit r/place
Venue: arXiv cs.SI 2018
Year: 2018

Many Web platforms rely on user collaboration to generate high-quality content: Wiki, Q&A communities, etc. Understanding and modeling the different collaborative behaviors is therefore critical. However, collaboration patterns are difficult to capture when the relationships between users are not directly observable, since they need to be inferred from the user actions. In this work, we propose a solution to this problem by adopting a systemic view of collaboration. Rather than modeling the users as independent actors in the system, we capture their coordinated actions with embedding methods which can, in turn, identify shared objectives and predict future user actions. To validate our approach, we perform a study on a dataset comprising more than 16M user actions, recorded on the online collaborative sandbox Reddit r/place. Participants had access to a drawing canvas where they could change the color of one pixel at every fixed time interval. Users were not grouped in teams nor were given any specific goals, yet they organized themselves into a cohesive social fabric and collaborated to the creation of a multitude of artworks. Our contribution in this paper is multi-fold: i) we perform an in-depth analysis of the Reddit r/place collaborative sandbox, extracting insights about its evolution over time; ii) we propose a predictive method that captures the latent structure of the emergent collaborative efforts; and iii) we show that our method provides an interpretable representation of the social structure.
A Univariate Bound of Area Under ROC
Venue: arXiv cs.LG 2018
Year: 2018

Area under ROC (AUC) is an important metric for binary classification and bipartite ranking problems. However, it is difficult to directly optimizing AUC as a learning objective, so most existing algorithms are based on optimizing a surrogate loss to AUC. One significant drawback of these surrogate losses is that they require pairwise comparisons among training data, which leads to slow running time and increasing local storage for online learning. In this work, we describe a new surrogate loss based on a reformulation of the AUC risk, which does not require pairwise comparison but rankings of the predictions. We further show that the ranking operation can be avoided, and the learning objective obtained based on this surrogate enjoys linear complexity in time and storage. We perform experiments to demonstrate the effectiveness of the online and batch algorithms for AUC optimization based on the proposed surrogate loss.
A Boosting Framework of Factorization Machine
Venue: arXiv cs.LG 2018
Year: 2018

Recently, Factorization Machines (FM) has become more and more popular for recommendation systems, due to its effectiveness in finding informative interactions between features. Usually, the weights for the interactions is learnt as a low rank weight matrix, which is formulated as an inner product of two low rank matrices. This low rank can help improve the generalization ability of Factorization Machines. However, to choose the rank properly, it usually needs to run the algorithm for many times using different ranks, which clearly is inefficient for some large-scale datasets. To alleviate this issue, we propose an Adaptive Boosting framework of Factorization Machines (AdaFM), which can adaptively search for proper ranks for different datasets without re-training. Instead of using a fixed rank for FM, the proposed algorithm will adaptively gradually increases its rank according to its performance until the performance does not grow, using boosting strategy. To verify the performance of our proposed framework, we conduct an extensive set of experiments on many real-world datasets. Encouraging empirical results shows that the proposed algorithms are generally more effective than state-of-the-art other Factorization Machines.
Reinforced Co-Training
Venue: arXiv cs.CL 2018
Year: 2018

Co-training is a popular semi-supervised learning framework to utilize a large amount of unlabeled data in addition to a small labeled set. Co-training methods exploit predicted labels on the unlabeled data and select samples based on prediction confidence to augment the training. However, the selection of samples in existing co-training methods is based on a predetermined policy, which ignores the sampling bias between the unlabeled and the labeled subsets, and fails to explore the data space. In this paper, we propose a novel method, Reinforced Co-Training, to select high-quality unlabeled samples to better co-train on. More specifically, our approach uses Q-learning to learn a data selection policy with a small labeled dataset, and then exploits this policy to train the co-training classifiers automatically. Experimental results on clickbait detection and generic text classification tasks demonstrate that our proposed method can obtain more accurate text classification results.
A Scalable Permission Management System With Support of Conditional and Customized Attributes
Venue: arXiv cs.CR 2018
Year: 2018

Along with the classical problem of managing multiple identities, actions, devices, APIs etc. in different businesses, there has been an escalating need for having the capability of flexible attribute based access control~(ABAC) mechanisms. In order to fill this gap, several variations of ABAC model have been proposed such as \textit{Amazon's AWS IAM}, which uses JSON as their underlying storage data structure and adds policies/constraints as fields over the regular ABAC. However, these systems still do not provide the capability to have customized permissions and to perform various operations (such as comparison/aggregation) on them. In this paper, we introduce a string based resource naming strategy that supports the customized and conditional permissions for resource access. Further, we propose the basic architecture of our system which, along with our naming scheme, makes the system scalable, secure, efficient, flexible and customizable. Finally, we present the proof of concept for our algorithm as well as the experimental set up and the future trajectory for this work.
Self-Conjugate-Reciprocal Irreducible Monic Factors of $x^n-1$ over Finite Fields and Their Applications
Venue: arXiv cs.IT 2018
Year: 2018

Self-reciprocal and self-conjugate-reciprocal polynomials over finite fields have been of interest due to their rich algebraic structures and wide applications. Self-reciprocal irreducible monic factors of $x^n-1$ over finite fields and their applications have been quite well studied. In this paper, self-conjugate-reciprocal irreducible monic (SCRIM) factors of $x^n-1$ over finite fields of square order have been focused on. The characterization of such factors is given together the enumeration formula. In many cases, recursive formulas for the number of SCRIM factors of $x^n-1$ have been given as well. As applications, Hermitian complementary dual codes over finite fields and Hermitian self-dual cyclic codes over finite chain rings of prime characteristic have been discussed.
On Computing the Total Variation Distance of Hidden Markov Models
Venue: arXiv cs.FL 2018
Year: 2018

We prove results on the decidability and complexity of computing the total variation distance (equivalently, the $L_1$-distance) of hidden Markov models (equivalently, labelled Markov chains). This distance measures the difference between the distributions on words that two hidden Markov models induce. The main results are: (1) it is undecidable whether the distance is greater than a given threshold; (2) approximation is #P-hard and in PSPACE.
Memetic Algorithms Beat Evolutionary Algorithms on the Class of Hurdle Problems
Venue: arXiv cs.NE 2018
Year: 2018

Memetic algorithms are popular hybrid search heuristics that integrate local search into the search process of an evolutionary algorithm in order to combine the advantages of rapid exploitation and global optimisation. However, these algorithms are not well understood and the field is lacking a solid theoretical foundation that explains when and why memetic algorithms are effective. We provide a rigorous runtime analysis of a simple memetic algorithm, the $(1+1)$ MA, on the Hurdle problem class, a landscape class of tuneable difficulty that shows a "big valley structure", a characteristic feature of many hard problems from combinatorial optimisation. The only parameter of this class is the hurdle width w, which describes the length of fitness valleys that have to be overcome. We show that the $(1+1)$ EA requires $Θ(n^w)$ expected function evaluations to find the optimum, whereas the $(1+1)$ MA with best-improvement and first-improvement local search can find the optimum in $Θ(n^2+n^3/w^2)$ and $Θ(n^3/w^2)$ function evaluations, respectively. Surprisingly, while increasing the hurdle width makes the problem harder for evolutionary algorithms, the problem becomes easier for memetic algorithms. We discuss how these findings can explain and illustrate the success of memetic algorithms for problems with big valley structures.
LCMR: Local and Centralized Memories for Collaborative Filtering with Unstructured Text
Venue: arXiv cs.IR 2018
Year: 2018

Collaborative filtering (CF) is the key technique for recommender systems. Pure CF approaches exploit the user-item interaction data (e.g., clicks, likes, and views) only and suffer from the sparsity issue. Items are usually associated with content information such as unstructured text (e.g., abstracts of articles and reviews of products). CF can be extended to leverage text. In this paper, we develop a unified neural framework to exploit interaction data and content information seamlessly. The proposed framework, called LCMR, is based on memory networks and consists of local and centralized memories for exploiting content information and interaction data, respectively. By modeling content information as local memories, LCMR attentively learns what to exploit with the guidance of user-item interaction. On real-world datasets, LCMR shows better performance by comparing with various baselines in terms of the hit ratio and NDCG metrics. We further conduct analyses to understand how local and centralized memories work for the proposed framework.
A D-vine copula-based coupling uncertainty analysis for stiffness predication of variable-stiffness composite
Venue: arXiv cs.CE 2018
Year: 2018

This study suggests a coupling uncertainty analysis method to investigate the stiffness characteristics of variable stiffness (VS) composite. The D-vine copula function is used to address the coupling of random variables. To identify the copula relation between random variables, a novel one-step Bayesian copula model selection (OBCS) method is proposed to obtain a suitable copula function as well as the marginal CDF of random variables. The entire process is Monte Carlo simulation (MCS). However, due to the expensive computational cost of complete finite element analysis (FEA) in MCS, a fast solver, reanalysis method is introduced. To further improve the efficiency of entire procedure, a back propagation neural network (BPNN) model is also introduced based on the reanalysis method. Compared with the reanalysis method, BPNN shows a higher efficiency as well as sufficient accuracy. Finally, the fiber angle deviation of VS composite is investigated by the suggested strategy. Two numerical examples are presented to verify the feasibility of this method.
A Saliency-based Convolutional Neural Network for Table and Chart Detection in Digitized Documents
Venue: arXiv cs.CV 2018
Year: 2018

Deep Convolutional Neural Networks (DCNNs) have recently been applied successfully to a variety of vision and multimedia tasks, thus driving development of novel solutions in several application domains. Document analysis is a particularly promising area for DCNNs: indeed, the number of available digital documents has reached unprecedented levels, and humans are no longer able to discover and retrieve all the information contained in these documents without the help of automation. Under this scenario, DCNNs offers a viable solution to automate the information extraction process from digital documents. Within the realm of information extraction from documents, detection of tables and charts is particularly needed as they contain a visual summary of the most valuable information contained in a document. For a complete automation of visual information extraction process from tables and charts, it is necessary to develop techniques that localize them and identify precisely their boundaries. In this paper we aim at solving the table/chart detection task through an approach that combines deep convolutional neural networks, graphical models and saliency concepts. In particular, we propose a saliency-based fully-convolutional neural network performing multi-scale reasoning on visual cues followed by a fully-connected conditional random field (CRF) for localizing tables and charts in digital/digitized documents. Performance analysis carried out on an extended version of ICDAR 2013 (with annotated charts as well as tables) shows that our approach yields promising results, outperforming existing models.
Simplifying the minimax disparity model for determining OWA weights in large-scale problems
Venue: arXiv cs.AI 2018
Year: 2018

In the context of multicriteria decision making, the ordered weighted averaging (OWA) functions play a crucial role in aggregating multiple criteria evaluations into an overall assessment supporting the decision makers' choice. Determining OWA weights, therefore, is an essential part of this process. Available methods for determining OWA weights, however, often require heavy computational loads in real-life large-scale optimization problems. In this paper, we propose a new approach to simplify the well-known minimax disparity model for determining OWA weights. For this purpose, we use to the binomial decomposition framework in which natural constraints can be imposed on the level of complexity of the weight distribution. The original problem of determining OWA weights is thereby transformed into a smaller scale optimization problem, formulated in terms of the coefficients in the binomial decomposition. Our preliminary results show that a small set of these coefficients can encode for an appropriate full-dimensional set of OWA weights.
Artificial Plants - Vascular Morphogenesis Controller-guided growth of braided structures
Venue: arXiv cs.RO 2018
Year: 2018

Natural plants are exemplars of adaptation through self-organisation and collective decision making. As such, they provide a rich source of inspiration for adaptive mechanisms in artificial systems. Plant growth - a structure development mechanism of continuous material accumulation that expresses encoded morphological features through environmental interactions - has been extensively explored in-silico. However, ex-silico scalable morphological adaptation through material accumulation remains an open challenge. In this paper, we present a novel type of biologically inspired modularity, and an approach to artificial growth that combines the benefits of material continuity through braiding with a distributed and decentralised plant-inspired Vascular Morphogenesis Controller (VMC). The controller runs on nodes that are capable of sensing and communicating with their neighbours. The nodes are embedded within the braided structure, which can be morphologically adapted based on collective decision making between nodes. Human agents realise the material adaptation by physically adding to the braided structure according to the suggestion of the embedded controller. This work offers a novel, tangible and accessible approach to embedding mechanisms of artificial growth and morphological adaptation within physically embodied systems, offering radically new functionalities, innovation potentials and approaches to continuous autonomous or steered design that could find application within fields contributing to the built environment, such as Architecture.
Adaptive control in rollforward recovery for extreme scale multigrid
Venue: arXiv cs.MS 2018
Year: 2018

With the increasing number of compute components, failures in future exa-scale computer systems are expected to become more frequent. This motivates the study of novel resilience techniques. Here, we extend a recently proposed algorithm-based recovery method for multigrid iterations by introducing an adaptive control. After a fault, the healthy part of the system continues the iterative solution process, while the solution in the faulty domain is re-constructed by an asynchronous on-line recovery. The computations in both the faulty and healthy subdomains must be coordinated in a sensitive way, in particular, both under and over-solving must be avoided. Both of these waste computational resources and will therefore increase the overall time-to-solution. To control the local recovery and guarantee an optimal re-coupling, we introduce a stopping criterion based on a mathematical error estimator. It involves hierarchical weighted sums of residuals within the context of uniformly refined meshes and is well-suited in the context of parallel high-performance computing. The re-coupling process is steered by local contributions of the error estimator. We propose and compare two criteria which differ in their weights. Failure scenarios when solving up to $6.9\cdot10^{11}$ unknowns on more than 245\,766 parallel processes will be reported on a state-of-the-art peta-scale supercomputer demonstrating the robustness of the method.
Terrain RL Simulator
Venue: arXiv cs.AI 2018
Year: 2018

We provide $89$ challenging simulation environments that range in difficulty. The difficulty of solving a task is linked not only to the number of dimensions in the action space but also to the size and shape of the distribution of configurations the agent experiences. Therefore, we are releasing a number of simulation environments that include randomly generated terrain. The library also provides simple mechanisms to create new environments with different agent morphologies and the option to modify the distribution of generated terrain. We believe using these and other more complex simulations will help push the field closer to creating human-level intelligence.
Contextualised Browsing in a Digital Library's Living Lab
Venue: arXiv cs.IR 2018
Year: 2018

Contextualisation has proven to be effective in tailoring \linebreak search results towards the users' information need. While this is true for a basic query search, the usage of contextual session information during exploratory search especially on the level of browsing has so far been underexposed in research. In this paper, we present two approaches that contextualise browsing on the level of structured metadata in a Digital Library (DL), (1) one variant bases on document similarity and (2) one variant utilises implicit session information, such as queries and different document metadata encountered during the session of a users. We evaluate our approaches in a living lab environment using a DL in the social sciences and compare our contextualisation approaches against a non-contextualised approach. For a period of more than three months we analysed 47,444 unique retrieval sessions that contain search activities on the level of browsing. Our results show that a contextualisation of browsing significantly outperforms our baseline in terms of the position of the first clicked item in the result set. The mean rank of the first clicked document (measured as mean first relevant - MFR) was 4.52 using a non-contextualised ranking compared to 3.04 when re-ranking the result lists based on similarity to the previously viewed document. Furthermore, we observed that both contextual approaches show a noticeably higher click-through rate. A contextualisation based on document similarity leads to almost twice as many document views compared to the non-contextualised ranking.
Lightweight Hardware Architectures for Efficient Secure Hash Functions ECHO and Fugue
Venue: arXiv cs.CR 2018
Year: 2018

In cryptographic engineering, extensive attention has been devoted to ameliorating the performance and security of the algorithms within. Nonetheless, in the state-of-the-art, the approaches for increasing the reliability of the efficient hash functions ECHO and Fugue have not been presented to date. We propose efficient fault detection schemes by presenting closed formulations for the predicted signatures of different transformations in these algorithms. These signatures are derived to achieve low overhead for the specific transformations and can be tailored to include byte/word-wide predicted signatures. Through simulations, we show that the proposed fault detection schemes are highly-capable of detecting natural hardware failures and are capable of deteriorating the effectiveness of malicious fault attacks. The proposed reliable hardware architectures are implemented on the application-specific integrated circuit (ASIC) platform using a 65-nm standard technology to benchmark their hardware and timing characteristics. The results of our simulations and implementations show very high error coverage with acceptable overhead for the proposed schemes.