Reviewerly/exHarmony huggingface dataset:
Distributional learning of conjunctive grammars and contextual binary feature grammars

Approaches based on the idea generically called distributional learning have been making great success in the algorithmic learning of several rich subclasses of context-free languages and their extensions. Those language classes are defined by properties concerning string-context relation. In this paper, we present a distributional learning algorithm for conjunctive grammars with the k-finite context property (k-fcp) for each natural number k. We also compare our result with the closely related work by Clark et al. (JMLR 2010) [5] on learning some context-free grammars using contextual binary feature grammars (cbfgs). We prove that the context-free grammars targeted by their algorithm have the k-fcp. Moreover, we show that every exact cbfg has the k-fcp, too, while not all of them are learnable by their algorithm. Clark et al. conjectured a learning algorithm for exact cbfgs should exist. This paper answers their conjecture in a positive way.
Real-Time Recurrent Tactile Recognition: Momentum Batch-Sequential Echo State Networks

Tactile recognition aims at identifying target objects according to tactile sensory readings. Tactile data have two salient properties: 1) sequentially real-time and 2) temporally correlated, which essentially calls for a real-time (i.e., online fixed-budget) and recurrent recognition procedure. Based on an efficient and robust spatio-temporal feature representation for tactile sequences, we handle the problem of real-time recurrent tactile recognition by proposing a bounded online-sequential learning framework, and incorporates the strength of batch-regularization bootstrapping, bounded recursive reservoir, and momentum-based estimation. Experimental evaluations show that it outperforms the state-of-the-art methods by a large margin on test accuracy; and its training performance is superior to most compared models from aspects of average online training error, computational complexity, and storage efficiency.
Fair Online Power Capping for Emergency Handling in Multi-Tenant Cloud Data Centers

In view of the high capital expense for scaling up power capacity to meet the escalating demand, maximizing the utilization of built capacity has become a top priority for multi-tenant data center operators, where many cloud providers house their physical servers. The traditional power provisioning guarantees a high availability, but is very costly and results in a significant capacity under-utilization. On the other hand, power oversubscription (i.e., deploying more servers than what the capacity allows) improves utilization but offers no availability guarantees due to the necessity of power reduction to handle the resulting power emergencies. Given these limitations, we propose a novel hybrid power provisioning approach, called HyPP, which provides a combination of two different power availabilities to tenants: capacity with a very high availability (100 percent or nearly 100 percent), plus additional capacity with a medium availability that may be unavailable for up to a certain amount during each billing period. For HyPP, we design an online algorithm for the operator to coordinate tenants' power reduction at runtime when the tenants' aggregate power demand exceeds the power capacities. Our algorithm aims at achieving long-term fairness in tenants' power reduction (defined as the ratio of total actual power reduction by a tenant to its contracted reduction budget over a billing period). We analyze the theoretical performance of our online algorithm and derive a good competitive ratio in terms of fairness compared to the offline optimum. We also validate our algorithm through simulations under realistic settings.
Mathematical Techniques for Biomedical Image Segmentation

This article presents some mathematical methods for biomedical image segmentation that these authors have used, adapted, and developed. In particular, some segmentation strategies for blood vessel, atherosclerosis, and intracerebral hemorrhage images constitute one of the principal causes of death in all those countries where the classical epidemics do not have an important weight. In the field of biomedical, images have developed sophisticated algorithms for image segmentation, which go from the deformable models, bioinspired algorithms, and neural networks, among others. Many of these strategies for arriving at satisfactory results need a lot of computational time. For this reason, the proposal of simple, fast, and reliable algorithms for biomedical image segmentation will be always welcome. The remainder of the article is presented in section “Materials and Methods.” Section “Mathematical Techniques Theory” outlines some mathematical techniques and theoretical aspects. In section “Algorithms,” we describe some of our algorithms. Section “Segmentation of Blood Vessel Images” demonstrates the experimental results, comparisons, and discussion. Finally, in section “Conclusions,” the most important conclusions are given.
Syntactic parsing and supervised analysis of Sindhi text

This research study addresses the morphological and syntactic problems of Sindhi language text by proposing an Algorithm for tokenization and syntactic parsing. A Sindhi parser is developed on basis of proposed algorithm to perform syntactic parsing on Sindhi text using Sindhi WordNet (SWN) and corpus. Results of Sindhi syntactic parsing are accumulated to develop multi-class and multi-feature based Sindhi dataset in CSV format. Three attributes of Sindhi dataset are labelled as class. All three classes are comprised with different number of categories. SVM, Random forest and K-NN supervised machine learning methods are used and trained to analyze and evaluate the Sindhi dataset. 80% of dataset is used as training set and 20% of dataset is used as test set. In this research study, 10-fold cross validation technique is applied to evaluate and validate the supervised machine learning process. The SVM classifier gives better results on class phrase and UPOS whereas Random forest gives better result on class TagStatus. Precision, recall, f-measure and confusion matrix approve the performance of all supervised classifiers. The better performance of supervised machine learning methods, support the Sindhi dataset and Sindhi online parser for future research. This study opens new doors for research on right hand written languages especially Sindhi language to solve its computational linguistics problems.
Importance sampling from posterior distributions using copula-like approximations

We provide generic approximations to k-dimensional posterior distributions through an importance sampling strategy. The importance function is a product of k univariate of Student-t densities and a k-dimensional beta-Liouville density truncated on the hypercube. The parameters of the densities and the number of components in the mixtures are adaptively optimised along the Monte Carlo sampling. For challenging high dimensional latent Gaussian models we propose a nested importance function approximation. We apply the techniques to a range of econometric models that have appeared in the literature, and we document their satisfactory performance relative to the alternatives.
Recognizable series on graphs and hypergraphs

We introduce the notion of Hypergraph Weighted Model (HWM), a computational model that generically associates a tensor network to a graph or a hypergraph and then computes a value by generalized tensor contractions directed by its hyperedges. A series r defined on a hypergraph family is said to be recognizable if there exists a HWM that computes it. This model generalizes the notion of recognizable series on strings and trees. We present some examples on non classical graphs families such as circular strings and pictures and we study properties of the model such as closure properties and recognizability of finite support series. We conclude by a section exploring the learnability of HWMs defined over the family of circular strings.
Empirical modeling and simulation of an heterogeneous Cloud computing environment

Cloud computing offers users a convenient scalable and flexible scenario for developing, running, and testing different applications in a pay-as-you-go model. However, the increasing demand of computational resources have resulted in more power consumption. In order to address this issue, the use of energy efficient resources such as FPGAs within a Cloud computing environment is nowadays a hot research topic, driven both from industry and academy. The efficient use of this type of resources can lead to increase the profit obtained by Cloud providers by serving more client requests while fulfilling their requirements with the minimum set of resources. In this paper, we survey an architectural framework and algorithms for managing heterogeneous resources, focusing on FPGAs. Based on this architecture, a simulation tool for studying the impact on performance and energy consumption of scaling the number of FPGAs in the system is presented. This tool is based on statistical models of processing time and energy consumption. The scalability study demonstrates that increasing the number of FPGAs can improve energy consumption up to 40%, while also admitting up to 35% more requests into the system.
Deep Geo-Constrained Auto-Encoder for Non-Landmark GPS Estimation

This paper addresses the problem of geotagging images, i.e., assigning GPS coordinates (i.e., latitude, longitude) to images using image contents. Due to the huge appearance variability of visual features across the world, the images' contents and their GPS coordinates may be inconsistent. This means images captured from geographically close areas may appear visually distinct; and images with visually similar contents may be taken from geographically distant areas. In this paper, we propose a deep Geo-constrained Auto-encoder (DGAE) to solve these inconsistency problems. Using clustered GPS data and visual data, our approach identifies inconsistent data pairs (i.e., image, GPS). We then propose a novel deep learning framework that can learn similar feature representations for geographically close images and distinct feature representations for geographically distant images. We introduce two new constraints: the same-area constraint and the easy-confusing constraint to our feature learning networks. The former one penalizes images from the same area but with very distinct visual features, and the latter one penalizes images from distant areas but with very similar visual features. A deep architecture is developed to further improve learning discriminative features, which can disambiguate different geometric locations. Our approach is extensively evaluated on a newly-compiled large image geotagging dataset from large-scale community-contributed images with 664,720 images and outperforms comparison approaches.
Applying Ricci flow to high dimensional manifold learning

In machine learning, a high dimensional data set such as the digital image of a human face is often viewed as a point set distributed on a differentiable manifold. In many cases, the intrinsic dimension of this manifold is low but the representation dimension of the data points is high. To ease data processing requirements, manifold learning (ML) techniques can be used to reduce a high dimensional manifold (HDM) to a low dimensional one while keeping the essential geometric properties, such as relative distances between points, unchanged. Traditional ML algorithms often assume that the local neighborhood of any point on an HDM is roughly equal to the tangent space at that point. This assumption leads to the disadvantage that the neighborhoods of points on the manifold, though they have a very different curvature, will be treated equally and will be projected to a lower dimensional space. The curvature is a different way of manifold processing, where traditional dimension reduction is ineffective at preserving the neighborhood. To overcome this obstacle, we perform an "operation" on the HDM using Ricci flow before a manifold's dimension reduction. More precisely, with the Ricci flow, we transform each local neighborhood of the HDM to a constant curvature patch. The HDM, as a whole, is then transformed into a subset of a sphere with constant positive curvature. We compare the proposed algorithm with other traditional manifold learning algorithms. Experimental results have shown that the proposed method outperforms other ML algorithms with a better neighborhood preserving rate.
Deep feature representation based on privileged knowledge transfer

Feature representation has been an elusive concept until neural networks become popular and exhibit the strong learning capability. However, as supervised labels provide a little meaningful information for feature representation, neural networks suffer from inadequate ability to acquire their representing knowledge by extracting patterns from raw data. To address this issue, in this paper, we propose a novel feature learning model by transferring the spatial relation information to the weights of neural networks. More specially, privileged knowledge stemmed from segmented image is fully utilized in distilling stage to perceive notable objects. The segmented image is regarded as a probability distribution of objects, the notable objects are fetched by maximizing lower bound of mutual information, instead of considering each image as an example of one-hot label. Through this way, spatial relation information is contributed to the feature learning besides class labels. This strategy, denoted as PKT-network, is applied to the image network training. Experimental results show that PKT-network performs excellently for multi-objects representation on Pascal VOC 2012 SegmentationClass (20 object classes) dataset and Microsoft COCO (80 object classes) dataset.
Enumerative feature subset based ranking system for learning to rank in presence of implicit user feedback

This paper proposed a new method for learning to rank documents using enumerative feature subsetting in the presence of the implicit user feedback of the various classes of users. The objective of this research was to provide an alternative method for learning ranking functions using important subsets of the LETOR (Learning to Rank) features based on the feedback of various classes of the users identified from active subsets of the features. This research, unlike other feature engineering approaches, do not force the learner to drop the inactive features permanently; instead, it allows to learn ranking function on currently active feature subsets while keeping inactive subsets of the features in the training process. The proposed model allows the search engine to dynamically utilize the implicit user feedback of the various classes of the users in learning ranking models repeatedly. The experiments performed on the LETOR MQ2008 dataset shows that the proposed model gives better NDCG (Normalized Discounted Cumulative Gain) scores for the subsets of users in the ensemble settings. Results also show that the variance of the predicted ranking can be controlled by controlling the hyper-parameters like the probability of the selection of a subset, feedback on the subsets and the weights of each subset used in training the low-level ranker. We have used cross-entropy pairwise learner RankNet and the maximum margin type svmRank as low-level rankers, but their objective functions are modified for the feature subsets. Results obtained by bagging and boosting based ensemble methods shows that the proposed method is flexible enough to model a family of the feedback weights on the individual models and can be used to provide personalized rank learning functions to the selected subsets of the users.
GraphA: Efficient Partitioning and Storage for Distributed Graph Computation

Distributed graph computation is central to applications ranging from language processing to social networks. However, natural graphs tend to have skewed power-law distrbutions where a small subset of the vertices have a large number of neighbors. Existing graph-parallel systems suffer from load imbalance, high communication cost, and inefficient processing. To address this problem, in this paper we present GraphA, an adaptive scheme for efficient large-scale graph computation. At the core of GraphA is an adaptive and uniform graph partitioning algorithm, which partitions the datasets by using an incremental number of mapping functions. GraphA further improves and leverages the ART index structure to realize fine-grained and low-cost graph storage. We have implemented GraphA both on Spark and on GraphLab. Extensive evaluation shows that GraphA significantly outperforms state-of-the-art graph-parallel systems (GraphX and PowerLyra) in ingress time, execution time and storage cost, for both real-world and synthetic graphs. GraphA achieves up to 7.1 x performance improvement over GraphX and 19.7 percent improvement over PowerLyra.
Online perceptual learning and natural language acquisition for autonomous robots

In this work, the problem of bootstrapping knowledge in language and vision for autonomous robots is addressed through novel techniques in grammar induction and word grounding to the perceptual world. In particular, we demonstrate a system, called OLAV, which is able, for the first time, to (1) learn to form discrete concepts from sensory data; (2) ground language (n-grams) to these concepts; (3) induce a grammar for the language being used to describe the perceptual world; and moreover to do all this incrementally, without storing all previous data. The learning is achieved in a loosely-supervised manner from raw linguistic and visual data. Moreover, the learnt model is transparent, rather than a black-box model and is thus open to human inspection. The visual data is collected using three different robotic platforms deployed in real-world and simulated environments and equipped with different sensing modalities, while the linguistic data is collected using online crowdsourcing tools and volunteers. The analysis performed on these robots demonstrates the effectiveness of the framework in learning visual concepts, language groundings and grammatical structure in these three online settings.
Leveraging learning innovations in cognitive computing with massive data sets: Using the offshore Panama papers leak to discover patterns

Exposing learners to cognitive computing concepts involves new learning strategies. Because cognitive computing is probabilistic, using massive sets of data is fundamental in understanding these concepts. One data set is the release of the Offshore Panama Papers Leaks Database (LeaksDB) in May 2016, in which researchers were able to access this graph database as part of the International Consortium of Investigative Journalists (ICIJ) Offshore Leaks investigation and to draw conclusions about companies, trusts, foundations, and funds incorporated in 21 tax havens. For the purpose of this research, GraphDB Server was installed and configured by faculty members from a mid-western university. In addition, DBPedia and GeoNames repositories were linked to LeaksDB, leading to the discovery of interesting patterns about these Geo facade relationships between Officers (persons or companies) and Countries.
Identifying major tasks and minor tasks within online reviews

Many e-commerce websites allow customers to provide reviews that reflect their experiences and opinions about products and services. Such published reviews, whether positive or negative, serve both the consumer and the business. Negative reviews can inform the merchant of issues that, when addressed, may improve the addressed aspect of the business and improve its online reputation. However, when the merchant fails to respond to customers’ concerns, the business faces potential loss of reputation. The Sentiminder system identifies major areas of customer concern, and specific concerns within each area. This helps the merchant to process a large body of reviews and find what needs to be addressed. In this paper we address the problems of quickly finding specific issues and specific comments that are consistently discussed in a negative way. Our technique drills down from the major task areas to more specific issues, assisting the user to accurately determine what issues need attention. The sentiment of reviews on the same topic can vary widely, so we maximize coherence over a variety of six different sentiment assessment techniques. We achieve from about 45% to 65% coherence. These suggestions are implemented in the Sentiminder, an online tool that creates schedules of optimal selections of tasks.
On Friedmann’s Subexponential Lower Bound for Zadeh’s Pivot Rule

The question whether the Simplex method admits a polynomial time pivot rule remains one of the most important open questions in discrete optimization. Zadeh's pivot rule had long been a promising candidate, before Friedmann (IPCO, 2011) presented a subexponential instance, based on a close relation to policy iteration algorithms for Markov decision processes (MDPs). We investigate Friedmann's lower bound construction and exhibit three flaws in his analysis: We show that (a) the initial policy for the policy iteration does not produce the required occurrence records and improving switches, (b) the specification of occurrence records is not entirely accurate, and (c) the sequence of improving switches described by Friedmann does not consistently follow Zadeh's pivot rule. In this paper, we resolve each of these issues. While the first two issues require only minor changes to the specifications of the initial policy and the occurrence records, the third issue requires a significantly more sophisticated ordering and associated tie-breaking rule that are in accordance with the Least-Entered pivot rule. Most importantly, our changes do not affect the macroscopic structure of Friedmann's MDP, and thus we are able to retain his original result.
An Approximation Algorithm for Sharing-Aware Virtual Machine Revenue Maximization

Cloud providers face the challenge of efficiently managing their infrastructure through minimizing resource consumption while allocating service requests such that their revenue is maximized. Solutions addressing this challenge should consider the sharing of memory pages among virtual machines (VMs) and the available capacity of each type of requested resources. We provide such solution by designing a greedy approximation algorithm for solving the sharing-aware virtual machine revenue maximization (SAVMRM) problem. The SAVMRM problem requires determining the set of VMs that can be instantiated on a given server such that the revenue derived from hosting the VMs is maximized. In addition, we model the SAVMRM problem as a multilinear binary program and optimally solve it, while accounting for page sharing and multiple resource constraints. We determine and analyze the approximability properties of our proposed greedy algorithm and evaluate it by performing extensive experiments using Google cluster workload traces. The experimental results show that under various scenarios, our proposed algorithm generates higher revenue than other VM allocation algorithms while achieving significant reduction of allocated memory.