A Congruence Format for Name-passing Calculi

We define and use a SOS-based framework to specify the transition systems of calculi with name-passing properties. This setting uses proof-theoretic tools to take care of some of the difficulties specific to name-binding and make them easier to handle in proofs. The contribution of this paper is the presentation of a format that ensures that open bisimilarity is a congruence for calculi specified within this framework, extending the well-known tyft/tyxt format to the case of name-binding and name-passing. We apply this result to the π-calculus in both its late and early semantics.     

A Congruence Rule Format with Universal Quantification

We investigate the addition of universal quantification to the meta-theory of Structural Operational Semantics (SOS). We study the syntax and semantics of SOS rules extended with universal quantification and propose a congruence rule format for strong bisimilarity that supports this new feature.     

A semantic framework for open processes

We propose a general methodology for analysing the behaviour of open systems modelled as coordinators, i.e., open terms of suitable process calculi. A coordinator is understood as a process with holes or placeholders where other coordinators and components (i.e., closed terms) can be plugged in, thus influencing its behaviour. The operational semantics of coordinators is given by means of a symbolic transition system, where states are coordinators and transitions are labeled by spatial/modal formulae expressing the potential interaction that plugged components may enable. Behavioural equivalences for coordinators, like strong and weak bisimilarities, can be straightforwardly defined over such a transition system. Different from other approaches based on universal closures, i.e., where two coordinators are considered equivalent when all their closed instances are equivalent, our semantics preserves the openness of the system during its evolution, thus allowing dynamic instantiation to be accounted for in the semantics. To further support the adequacy of the construction, we show that our symbolic equivalences provide correct approximations of their universally closed counterparts, coinciding with them over closed components. For process calculi in suitable formats, we show how tractable symbolic semantics can be defined constructively using unification.     

Mining frequent closed rooted trees

Many knowledge representation mechanisms are based on tree-like structures, thus symbolizing the fact that certain pieces of information are related in one sense or another. There exists a well-studied process of closure-based data mining in the itemset framework: we consider the extension of this process into trees. We focus mostly on the case where labels on the nodes are nonexistent or unreliable, and discuss algorithms for closure-based mining that only rely on the root of the tree and the link structure. We provide a notion of intersection that leads to a deeper understanding of the notion of support-based closure, in terms of an actual closure operator. We describe combinatorial characterizations and some properties of ordered trees, discuss their applicability to unordered trees, and rely on them to design efficient algorithms for mining frequent closed subtrees both in the ordered and the unordered settings. Empirical validations and comparisons with alternative algorithms are provided.     

Discovering Typed Communities in Mobile Social Networks

Mobile social networks,which consist of mobile users who communicate with each other using cell phones,are reflections of people’s interactions in social lives.Discovering typed communities(e.g.,family communities or corporate communities) in mobile social networks is a very promising problem.For example,it can help mobile operators to determine the target users for precision marketing.In this paper we propose discovering typed communities in mobile social networks by utilizing the labels of relationships between users.We use the user logs stored by mobile operators,including communication and user movement records,to collectively label all the relationships in a network,by employing an undirected probabilistic graphical model,i.e.,conditional random fields.Then we use two methods to discover typed communities based on the results of relationship labeling:one is simply retaining or cutting relationships according to their labels,and the other is using sophisticated weighted community detection algorithms.The experimental results show that our proposed framework performs well in terms of the accuracy of typed community detection in mobile social networks.     

Label noise correction and application in crowdsourcing

The important task of correcting label noise is addressed infrequently in literature. The difficulty of developing a robust label correction algorithm leads to this silence concerning label correction. To break the silence, we propose two algorithms to correct label noise. One utilizes self-training to re-label noise, called Self-Training Correction (STC). Another is a clustering-based method, which groups instances together to infer their ground-truth labels, called Cluster-based Correction (CC). We also adapt an algorithm from previous work, a consensus-based method called Polishing that consults with an ensemble of classifiers to change the values of attributes and labels. We simplify Polishing such that it only alters labels of instances, and call it Polishing Labels (PL). We experimentally compare our novel methods with Polishing Labels by examining their improvements on the label qualities, model qualities, and AUC metrics of binary and multi-class data sets under different noise levels. Our experimental results demonstrate that CC significantly improves label qualities, model qualities, and AUC metrics consistently. We further investigate how these three noise correction algorithms improve the data quality, in terms of label accuracy, in the context of image labeling in crowdsourcing. First, we look at three consensus methods for inferring a ground-truth label from the multiple noisy labels obtained from crowdsourcing, i.e., Majority Voting (MV), Dawid Skene (DS), and KOS. We then apply the three noise correction methods to correct labels inferred by these consensus methods. Our experimental results show that the noise correction methods improve the labeling quality significantly. As an overall result of our experiments, we conclude that CC performs the best. Our research has illustrated the viability of implementing noise correction as another line of defense against labeling error, especially in a crowdsourcing setting. Furthermore, it presents the feasibility of the automation of an otherwise manual process of analyzing a data set, and correcting and cleaning the instances, an expensive and time-consuming task.     

Remote stabilization over fading channels

In this paper, we study the problem of remote mean square stabilization of a MIMO system when independent fading channels are dedicated to each actuator and sensor. We show that the stochastic variables responsible for the fading can be seen as a source of model uncertainty. This view leads to robust control analysis and synthesis problems with a deterministic nominal system and a stochastic, structured, model uncertainty. As a special case, we consider the stabilization over Erasure or drop-out channels. We show that the largest probability of erasure tolerable by the closed loop is obtained from solving a robust control synthesis problem. In more general terms, we establish that the set of plants which can be stabilized by linear controllers over fading channels is fundamentally limited by the channel generated uncertainty. We show that, the notion of mean square capacity, defined for a single channel in the loop, captures this limitation precisely and characterizes equivalence classes of channels within the class of memoryless fading channels.     

A comparison of statistical relational learning and graph neural networks for aggregate graph queries

Statistical relational learning (SRL) and graph neural networks (GNNs) are two powerful approaches for learning and inference over graphs. Typically, they are evaluated in terms of simple metrics such as accuracy over individual node labels. Complex aggregate graph queries (AGQ) involving multiple nodes, edges, and labels are common in the graph mining community and are used to estimate important network properties such as social cohesion and influence. While graph mining algorithms support AGQs, they typically do not take into account uncertainty, or when they do, make simplifying assumptions and do not build full probabilistic models. In this paper, we examine the performance of SRL and GNNs on AGQs over graphs with partially observed node labels. We show that, not surprisingly, inferring the unobserved node labels as a first step and then evaluating the queries on the fully observed graph can lead to sub-optimal estimates, and that a better approach is to compute these queries as an expectation under the joint distribution. We propose a sampling framework to tractably compute the expected values of AGQs. Motivated by the analysis of subgroup cohesion in social networks, we propose a suite of AGQs that estimate the community structure in graphs. In our empirical evaluation, we show that by estimating these queries as an expectation, SRL-based approaches yield up to a 50-fold reduction in average error when compared to existing GNN-based approaches.

     

Automatic localization and annotation of facial features using machine learning techniques

Content-based image retrieval (CBIR) systems traditionally find images within a database that are similar to query image using low level features, such as colour histograms. However, this requires a user to provide an image to the system. It is easier for a user to query the CBIR system using search terms which requires the image content to be described by semantic labels. However, finding a relationship between the image features and semantic labels is a challenging problem to solve. This paper aims to discover semantic labels for facial features for use in a face image retrieval system. Face image retrieval traditionally uses global face-image information to determine similarity between images. However little has been done in the field of face image retrieval to use local face-features and semantic labelling. Our work aims to develop a clustering method for the discovery of semantic labels of face-features. We also present a machine learning based face-feature localization mechanism which we show has promise in providing accurate localization.     

Surviving sensor node failures by MMU-less incremental checkpointing

For some critical safety applications, sensor nodes embed valuable information, and they should be able to operate unattended and unfailing for several months or years. One promising solution is to adopt a checkpointing that periodically saves the state of a sensor node, thereby maintaining node reliability and network availability. Thus, this study first shows the design and implementation of a full checkpointing for WSNs. However, checkpointing is expensive. Therefore, incremental checkpointing was previously proposed to eliminate the checkpoint overhead by relying on the page protection hardware to identify dirty pages. Because sensor nodes are resource-constrained and do not equip with the page protection hardware, previous incremental checkpointings cannot be directly applied. To address this issue, this paper proposes three incremental checkpointings for WSNs. These three methods differ in the granularity of the checkpoint memory data unit and module execution overhead. In addition, we designed an incremental checkpoint file format that simultaneously supports proposed three different incremental checkpointings and accommodates them with sensor network characteristics. We implemented the full and three incremental checkpointings on SOS in the mica2 sensor motes. A performance evaluation of the three incremental checkpointings is presented. We also discuss and evaluate a method for selecting the appropriate incremental checkpointing. To the best of our knowledge, this study is the first to design and implement incremental checkpointing in MMU-less WSNs.     

Label ranking by learning pairwise preferences

Preference learning is an emerging topic that appears in different guises in the recent literature. This work focuses on a particular learning scenario called label ranking, where the problem is to learn a mapping from instances to rankings over a finite number of labels. Our approach for learning such a mapping, called ranking by pairwise comparison (RPC), first induces a binary preference relation from suitable training data using a natural extension of pairwise classification. A ranking is then derived from the preference relation thus obtained by means of a ranking procedure, whereby different ranking methods can be used for minimizing different loss functions. In particular, we show that a simple (weighted) voting strategy minimizes risk with respect to the well-known Spearman rank correlation. We compare RPC to existing label ranking methods, which are based on scoring individual labels instead of comparing pairs of labels. Both empirically and theoretically, it is shown that RPC is superior in terms of computational efficiency, and at least competitive in terms of accuracy.     

Quadratic boosting

This paper presents a strategy to improve the AdaBoost algorithm with a quadratic combination of base classifiers. We observe that learning this combination is necessary to get better performance and is possible by constructing an intermediate learner operating on the combined linear and quadratic terms. This is not trivial, as the parameters of the base classifiers are not under direct control, obstructing the application of direct optimization. We propose a new method realizing iterative optimization indirectly. First we train a classifier by randomizing the labels of training examples. Subsequently, the input learner is called repeatedly with a systematic update of the labels of the training examples in each round. We show that the quadratic boosting algorithm converges under the condition that the given base learner minimizes the empirical error. We also give an upper bound on the VC-dimension of the new classifier. Our experimental results on 23 standard problems show that quadratic boosting compares favorably with AdaBoost on large data sets at the cost of training speed. The classification time of the two algorithms, however, is equivalent.     

A syntactic commutativity format for SOS

Considering operators defined using Structural Operational Semantics (SOS), commutativity axioms are intuitive properties that hold for many of them. Proving this intuition is usually a laborious task, requiring several pages of boring and standard proof. To save this effort, we propose a syntactic SOS format which guarantees commutativity for a set of composition operators.     

Clip art rendering of smooth isosurfaces

Clip art is a simplified illustration form consisting of layered filled polygons or closed curves used to convey 3D shape information in a 2D vector graphics format. This paper focuses on the problem of direct conversion of smooth surfaces, ranging from the free-form shapes of art and design to the mathematical structures of geometry and topology, into a clip art form suitable for illustration use in books, papers and presentations.We show how to represent silhouette, shadow, gleam and other surface feature curves as the intersection of implicit surfaces, and derive equations for their efficient interrogation via particle chains. We further describe how to sort, orient, identify and fill the closed regions that overlay to form clip art. We demonstrate the results with numerous renderings used to illustrate the paper itself.     

Efficient max-margin multi-label classification with applications to zero-shot learning

The goal in multi-label classification is to tag a data point with the subset of relevant labels from a pre-specified set. Given a set of L labels, a data point can be tagged with any of the 2 ^L possible subsets. The main challenge therefore lies in optimising over this exponentially large label space subject to label correlations. Our objective, in this paper, is to design efficient algorithms for multi-label classification when the labels are densely correlated. In particular, we are interested in the zero-shot learning scenario where the label correlations on the training set might be significantly different from those on the test set. We propose a max-margin formulation where we model prior label correlations but do not incorporate pairwise label interaction terms in the prediction function. We show that the problem complexity can be reduced from exponential to linear while modelling dense pairwise prior label correlations. By incorporating relevant correlation priors we can handle mismatches between the training and test set statistics. Our proposed formulation generalises the effective 1-vs-All method and we provide a principled interpretation of the 1-vs-All technique. We develop efficient optimisation algorithms for our proposed formulation. We adapt the Sequential Minimal Optimisation (SMO) algorithm to multi-label classification and show that, with some book-keeping, we can reduce the training time from being super-quadratic to almost linear in the number of labels. Furthermore, by effectively re-utilizing the kernel cache and jointly optimising over all variables, we can be orders of magnitude faster than the competing state-of-the-art algorithms. We also design a specialised algorithm for linear kernels based on dual co-ordinate ascent with shrinkage that lets us effortlessly train on a million points with a hundred labels.     

Overlapping community detection in labeled graphs

We present a new approach for the problem of finding overlapping communities in graphs and social networks. Our approach consists of a novel problem definition and three accompanying algorithms. We are particularly interested in graphs that have labels on their vertices, although our methods are also applicable to graphs with no labels. Our goal is to find k communities so that the total edge density over all k communities is maximized. In the case of labeled graphs, we require that each community is succinctly described by a set of labels. This requirement provides a better understanding for the discovered communities. The proposed problem formulation leads to the discovery of vertex-overlapping and dense communities that cover as many graph edges as possible. We capture these properties with a simple objective function, which we solve by adapting efficient approximation algorithms for the generalized maximum-coverage problem and the densest-subgraph problem. Our proposed algorithm is a generic greedy scheme. We experiment with three variants of the scheme, obtained by varying the greedy step of finding a dense subgraph. We validate our algorithms by comparing with other state-of-the-art community-detection methods on a variety of performance measures. Our experiments confirm that our algorithms achieve results of high quality in terms of the reported measures, and are practical in terms of performance.     

Robust interactive image segmentation using structure-aware labeling

Interactive image segmentation has remained an active research topic in image processing and graphics, since the user intention can be incorporated to enhance the performance. It can be employed to mobile devices which now allow user interaction as an input, enabling various applications. Most interactive segmentation methods assume that the initial labels are correctly and carefully assigned to some parts of regions to segment. Inaccurate labels, such as foreground labels in background regions for example, lead to incorrect segments, even by a small number of inaccurate labels, which is not appropriate for practical usage such as mobile application. In this paper, we present an interactive segmentation method that is robust to inaccurate initial labels (scribbles). To address this problem, we propose a structure-aware labeling method using occurrence and co-occurrence probability (OCP) of color values for each initial label in a unified framework. Occurrence probability captures a global distribution of all color values within each label, while co-occurrence one encodes a local distribution of color values around the label. We show that nonlocal regularization together with the OCP enables robust image segmentation to inaccurately assigned labels and alleviates a small-cut problem. We analyze theoretic relations of our approach to other segmentation methods. Intensive experiments with synthetic and manual labels show that our approach outperforms the state of the art.     

From Facial Expression Recognition to Interpersonal Relation Prediction

Interpersonal relation defines the association, e.g., warm, friendliness, and dominance, between two or more people. We investigate if such fine-grained and high-level relation traits can be characterized and quantified from face images in the wild. We address this challenging problem by first studying a deep network architecture for robust recognition of facial expressions. Unlike existing models that typically learn from facial expression labels alone, we devise an effective multitask network that is capable of learning from rich auxiliary attributes such as gender, age, and head pose, beyond just facial expression data. While conventional supervised training requires datasets with complete labels (e.g., all samples must be labeled with gender, age, and expression), we show that this requirement can be relaxed via a novel attribute propagation method. The approach further allows us to leverage the inherent correspondences between heterogeneous attribute sources despite the disparate distributions of different datasets. With the network we demonstrate state-of-the-art results on existing facial expression recognition benchmarks. To predict inter-personal relation, we use the expression recognition network as branches for a Siamese model. Extensive experiments show that our model is capable of mining mutual context of faces for accurate fine-grained interpersonal prediction.     

Generating priority rewrite systems for OSOS process languages

We propose an algorithm for generating a Priority Rewrite System (PRS) for an arbitrary process language in the OSOS format such that rewriting of process terms is sound for bisimulation and head normalising. The algorithm is inspired by a procedure which was developed by Aceto, Bloom and Vaandrager and presented in Turning SOS rules into equations [L. Aceto, B. Bloom, F.W. Vaandrager, Turning SOS rules into equations, Information and Computation 111 (1994) 1–52].For a subclass of OSOS process languages representing finite behaviours the PRSs that are generated by our algorithm are strongly normalising (terminating) and confluent, where termination is proved using the dependency pair and dependency graph techniques. Additionally, such PRSs are complete for bisimulation on closed process terms modulo associativity and commutativity of the choice operator of CCS. We illustrate the usefulness of our results, and the benefits of rewriting with priorities in general, with several examples.