pinecone/core-2020-05-10-deduplication huggingface dataset:
An efficient algorithm for mining frequent sequence with constraint programming

The main advantage of Constraint Programming (CP) ap-
proaches for sequential pattern mining (SPM) is their modularity, which
includes the ability to add new constraints (regular expressions, length
restrictions, etc). The current best CP approach for SPM uses a global
constraint (module) that computes the projected database and enforces
the minimum frequency; it does this with a filtering algorithm similar to
the PrefixSpan method. However, the resulting system is not as scalable
as some of the most advanced mining systems like Zaki’s cSPADE. We
show how, using techniques from both data mining and CP, one can use a
generic constraint solver and yet outperform existing specialized systems.
This is mainly due to two improvements in the module that computes
the projected frequencies: first, computing the projected database can be
sped up by pre-computing the positions at which a symbol can become
unsupported by a sequence, thereby avoiding to scan the full sequence
each time; and second by taking inspiration from the trailing used in CP
solvers to devise a backtracking-aware data structure that allows fast
incremental storing and restoring of the projected database. Detailed
experiments show how this approach outperforms existing CP as well as
specialized systems for SPM, and that the gain in efficiency translates
directly into increased efficiency for other settings such as mining with
regular expressions.status: publishe
A Bayesian Network Model for Interesting Itemsets

Mining itemsets that are the most interesting under a statistical model of
the underlying data is a commonly used and well-studied technique for
exploratory data analysis, with the most recent interestingness models
exhibiting state of the art performance. Continuing this highly promising line
of work, we propose the first, to the best of our knowledge, generative model
over itemsets, in the form of a Bayesian network, and an associated novel
measure of interestingness. Our model is able to efficiently infer interesting
itemsets directly from the transaction database using structural EM, in which
the E-step employs the greedy approximation to weighted set cover. Our approach
is theoretically simple, straightforward to implement, trivially parallelizable
and retrieves itemsets whose quality is comparable to, if not better than,
existing state of the art algorithms as we demonstrate on several real-world
datasets.Comment: Supplementary material attached as Ancillary File; in PKDD 2016:
  European Conference on Machine Learning and Knowledge Discovery in Database
M-Flash: Fast Billion-scale Graph Computation Using a Bimodal Block
  Processing Model

Recent graph computation approaches have demonstrated that a single PC can
perform efficiently on billion-scale graphs. While these approaches achieve
scalability by optimizing I/O operations, they do not fully exploit the
capabilities of modern hard drives and processors. To overcome their
performance, in this work, we introduce the Bimodal Block Processing (BBP), an
innovation that is able to boost the graph computation by minimizing the I/O
cost even further. With this strategy, we achieved the following contributions:
(1) M-Flash, the fastest graph computation framework to date; (2) a flexible
and simple programming model to easily implement popular and essential graph
algorithms, including the first single-machine billion-scale eigensolver; and
(3) extensive experiments on real graphs with up to 6.6 billion edges,
demonstrating M-Flash's consistent and significant speedup.Comment: Hugo Gualdron, Robson Cordeiro, Jose Rodrigues-Jr, Duen Chau, Minsuk
  Kahng, U Kang (2016) M-Flash: Fast Billion-scale Graph Computation Using a
  Bimodal Block Processing Model, In: ECML-PKDD16, pages 623-640, LNCS,
  Springe
The Clustering of Expressive Timing Within a Phrase in Classical Piano Performances by Gaussian Mixture Models

In computational musicology research, clustering is a common approach to the analysis of expression. Our research uses mathematical model selection criteria to evaluate the performance of clustered and non-clustered models applied to intra-phrase tempo variations in classical piano performances. By engaging different standardisation methods for the tempo variations and engaging different types of covariance matrices, multiple pieces of performances are used for evaluating the performance of candidate models. The results of tests suggest that the clustered models perform better than the non-clustered models and the original tempo data should be standardised by the mean of tempo within a phrase
Expertise and Trust-Aware Social Web Service Recommendation

With the increasing number of Web services, the personalized recommendation of Web services has become more and more important. Fortunately, the social network popularity nowadays brings a good alternative for social recommendation to avoid the data sparsity problem that is not treated very well in the collaborative filtering approach. Since the social network provides a big data about the users, the trust concept has become necessary to filter this abundance and to foster the successful interactions between the users. In this paper, we firstly propose a trusted friend detection mechanism in a social network. The dynamic of the users’ interactions over time and the similarity of their interests have been considered. Secondly, we propose a Web service social recommendation mechanism which considers the expertise of the trusted friends according to their past invocation histories and the active user’s query. The experiments of each mechanism produced satisfactory results
Finding anomalies in SCADA logs using rare sequential pattern mining

Pattern mining is a branch of data mining used to discover hidden patterns or correlations among data. We use rare sequential pattern mining to find anomalies in critical infrastructure control networks such as <i>supervisory control and data acquisition</i> (SCADA) networks. As anomalous events occur rarely in a system and SCADA systems’ topology and actions do not change often, we argue that some anomalies can be detected using rare sequential pattern mining. This anomaly detection would be useful for intrusion detection or erroneous behaviour of a system. Although research into rare itemsets mining previously exists, neither research into rare sequential pattern mining nor its applicability to SCADA system anomaly detection has previously been completed. Moreover, since there is no consideration to events order, the applicability to intrusion detection in SCADA is minimal. By ensuring the events’ order is maintained, in this paper, we propose a novel Rare Sequential Pattern Mining (RSPM) technique which is a useful anomaly detection system for SCADA. We compared our algorithm with a rare itemset mining algorithm and found anomalous events in SCADA logs
Local subgroup discovery for eliciting and understanding new structure-odor relationships

Publication à la conférence Discovery Science 2016International audienceFrom a molecule to the brain perception, olfaction is a complex phenomenon that remains to be fully understood in neuroscience. A challenge is to establish comprehensive rules between the physicochem-ical properties of the molecules (e.g., weight, atom counts) and specific and small subsets of olfactory qualities (e.g., fruity, woody). This problem is particularly difficult as the current knowledge states that molecular properties only account for 30% of the identity of an odor: predictive models are found lacking in providing universal rules. However, descriptive approaches enable to elicit local hypotheses, validated by domain experts, to understand the olfactory percept. Based on a new quality measure tailored for multi-labeled data with skewed distributions, our approach extracts the top-k unredundant subgroups interpreted as descriptive rules description → {subset of labels}. Our experiments on benchmark and olfaction datasets demonstrate the capabilities of our approach with direct applications for the perfume and flavor industries
A framework for interpolating scattered data using space-filling curves

The analysis of spatial data occurs in many disciplines and covers a wide variety activities. Available techniques for such analysis include spatial interpolation which is useful for tasks such as visualization and imputation. This paper proposes a novel approach to interpolation using space-filling curves. Two simple interpolation methods are described and their ability to interpolate is compared to several interpolation techniques including natural neighbour interpolation. The proposed approach requires a Monte-Carlo step that requires a large number of iterations. However experiments demonstrate that the number of iterations will  not change appreciably with larger datasets
Things Bayes can't do

The problem of forecasting conditional probabilities of the next event given
the past is considered in a general probabilistic setting. Given an arbitrary
(large, uncountable) set C of predictors, we would like to construct a single
predictor that performs asymptotically as well as the best predictor in C, on
any data. Here we show that there are sets C for which such predictors exist,
but none of them is a Bayesian predictor with a prior concentrated on C. In
other words, there is a predictor with sublinear regret, but every Bayesian
predictor must have a linear regret. This negative finding is in sharp contrast
with previous results that establish the opposite for the case when one of the
predictors in $C$ achieves asymptotically vanishing error. In such a case, if
there is a predictor that achieves asymptotically vanishing error for any
measure in C, then there is a Bayesian predictor that also has this property,
and whose prior is concentrated on (a countable subset of) C
Probabilistic evaluation of process model matching techniques

Process model matching refers to the automatic identification of corresponding activities between two process models. It represents\ud
the basis for many advanced process model analysis techniques such as\ud
the  identification  of  similar  process  parts  or  process  model  search.  A\ud
central  problem  is  how  to  evaluate  the  performance  of  process  model\ud
matching techniques. Often, not even humans can agree on a set of correct  correspondences.  Current  evaluation  methods,  however,  require  a\ud
binary  gold  standard,  which  clearly  defines  which  correspondences  are\ud
correct. The disadvantage of this evaluation method is that it does not\ud
take the true complexity of the matching problem into account and does\ud
not fairly assess the capabilities of a matching technique. In this paper,\ud
we propose a novel evaluation method for process model matching techniques. In particular, we build on the assessment of multiple annotators\ud
to define probabilistic notions of precision and recall. We use the dataset\ud
and the results of the Process Model Matching Contest 2015 to assess and\ud
compare our evaluation method. We found that our probabilistic evaluation method assigns different ranks to the matching techniques from the\ud
contest and allows to gain more detailed insights into their performance
UMLtoGraphDB: Mapping Conceptual Schemas to Graph Databases

International audienceThe need to store and manipulate large volume of (unstructured) data has led to the development of several NoSQL databases for better scalability. Graph databases are a particular kind of NoSQL databases that have proven their efficiency to store and query highly interconnected data, and have become a promising solution for multiple applications. While the mapping of conceptual schemas to relational databases is a well-studied field of research, there are only few solutions that target conceptual modeling for NoSQL databases and even less focusing on graph databases. This is specially true when dealing with the mapping of business rules and constraints in the conceptual schema. In this article we describe a mapping from UML/OCL conceptual schemas to Blueprints, an abstraction layer on top of a variety of graph databases, and Gremlin, a graph traversal language, via an intermediate Graph metamodel. Tool support is fully available
NOSQL design for analytical workloads: Variability matters

Big Data has recently gained popularity and has strongly questioned relational databases as universal storage systems, especially in the presence of analytical workloads. As result, co-relational alternatives, commonly known as NOSQL (Not Only SQL) databases, are extensively used for Big Data. As the primary focus of NOSQL is on performance, NOSQL databases are directly designed at the physical level, and consequently the resulting schema is tailored to the dataset and access patterns of the problem in hand. However, we believe that NOSQL design can also benefit from traditional design approaches. In this paper we present a method to design databases for analytical workloads. Starting from the conceptual model and adopting the classical 3-phase design used for relational databases, we propose a novel design method considering the new features brought by NOSQL and encompassing relational and co-relational design altogether.Peer ReviewedPostprint (author's final draft
4D Match Trees for Non-rigid Surface Alignment

This paper presents a method for dense 4D temporal alignment of partial reconstructions of non-rigid surfaces observed from single or multiple moving cameras of complex scenes. 4D Match Trees are introduced for robust global alignment of non-rigid shape based on the similarity between images across sequences and views. Wide-timeframe sparse correspondence between arbitrary pairs of images is established using a segmentation-based feature detector (SFD) which is demonstrated to give improved matching of non-rigid shape. Sparse SFD correspondence allows the similarity between any pair of image frames to be estimated for moving cameras and multiple views. This enables the 4D Match Tree to be constructed which minimises the observed change in non-rigid shape for global alignment across all images. Dense 4D temporal correspondence across all frames is then estimated by traversing the 4D Match tree using optical flow initialised from the sparse feature matches. The approach is evaluated on single and multiple view images sequences for alignment of partial surface reconstructions of dynamic objects in complex indoor and outdoor scenes to obtain a temporally consistent 4D representation. Comparison to previous 2D and 3D scene flow demonstrates that 4D Match Trees achieve reduced errors due to drift and improved robustness to large non-rigid deformations
SSD: Single Shot MultiBox Detector

We present a method for detecting objects in images using a single deep
neural network. Our approach, named SSD, discretizes the output space of
bounding boxes into a set of default boxes over different aspect ratios and
scales per feature map location. At prediction time, the network generates
scores for the presence of each object category in each default box and
produces adjustments to the box to better match the object shape. Additionally,
the network combines predictions from multiple feature maps with different
resolutions to naturally handle objects of various sizes. Our SSD model is
simple relative to methods that require object proposals because it completely
eliminates proposal generation and subsequent pixel or feature resampling stage
and encapsulates all computation in a single network. This makes SSD easy to
train and straightforward to integrate into systems that require a detection
component. Experimental results on the PASCAL VOC, MS COCO, and ILSVRC datasets
confirm that SSD has comparable accuracy to methods that utilize an additional
object proposal step and is much faster, while providing a unified framework
for both training and inference. Compared to other single stage methods, SSD
has much better accuracy, even with a smaller input image size. For $300\times
300$ input, SSD achieves 72.1% mAP on VOC2007 test at 58 FPS on a Nvidia Titan
X and for $500\times 500$ input, SSD achieves 75.1% mAP, outperforming a
comparable state of the art Faster R-CNN model. Code is available at
https://github.com/weiliu89/caffe/tree/ssd .Comment: ECCV 201
Convolutional Oriented Boundaries

We present Convolutional Oriented Boundaries (COB), which produces multiscale
oriented contours and region hierarchies starting from generic image
classification Convolutional Neural Networks (CNNs). COB is computationally
efficient, because it requires a single CNN forward pass for contour detection
and it uses a novel sparse boundary representation for hierarchical
segmentation; it gives a significant leap in performance over the
state-of-the-art, and it generalizes very well to unseen categories and
datasets. Particularly, we show that learning to estimate not only contour
strength but also orientation provides more accurate results. We perform
extensive experiments on BSDS, PASCAL Context, PASCAL Segmentation, and
MS-COCO, showing that COB provides state-of-the-art contours, region
hierarchies, and object proposals in all datasets.Comment: ECCV 2016 Camera Read
Grounding of Textual Phrases in Images by Reconstruction

Grounding (i.e. localizing) arbitrary, free-form textual phrases in visual
content is a challenging problem with many applications for human-computer
interaction and image-text reference resolution. Few datasets provide the
ground truth spatial localization of phrases, thus it is desirable to learn
from data with no or little grounding supervision. We propose a novel approach
which learns grounding by reconstructing a given phrase using an attention
mechanism, which can be either latent or optimized directly. During training
our approach encodes the phrase using a recurrent network language model and
then learns to attend to the relevant image region in order to reconstruct the
input phrase. At test time, the correct attention, i.e., the grounding, is
evaluated. If grounding supervision is available it can be directly applied via
a loss over the attention mechanism. We demonstrate the effectiveness of our
approach on the Flickr 30k Entities and ReferItGame datasets with different
levels of supervision, ranging from no supervision over partial supervision to
full supervision. Our supervised variant improves by a large margin over the
state-of-the-art on both datasets.Comment: published at ECCV 2016 (oral); updated to final versio
SSHMT: Semi-supervised Hierarchical Merge Tree for Electron Microscopy
  Image Segmentation

Region-based methods have proven necessary for improving segmentation
accuracy of neuronal structures in electron microscopy (EM) images. Most
region-based segmentation methods use a scoring function to determine region
merging. Such functions are usually learned with supervised algorithms that
demand considerable ground truth data, which are costly to collect. We propose
a semi-supervised approach that reduces this demand. Based on a merge tree
structure, we develop a differentiable unsupervised loss term that enforces
consistent predictions from the learned function. We then propose a Bayesian
model that combines the supervised and the unsupervised information for
probabilistic learning. The experimental results on three EM data sets
demonstrate that by using a subset of only 3% to 7% of the entire ground truth
data, our approach consistently performs close to the state-of-the-art
supervised method with the full labeled data set, and significantly outperforms
the supervised method with the same labeled subset.Comment: Accepted by ECCV 201
MOON: A Mixed Objective Optimization Network for the Recognition of
  Facial Attributes

Attribute recognition, particularly facial, extracts many labels for each
image. While some multi-task vision problems can be decomposed into separate
tasks and stages, e.g., training independent models for each task, for a
growing set of problems joint optimization across all tasks has been shown to
improve performance. We show that for deep convolutional neural network (DCNN)
facial attribute extraction, multi-task optimization is better. Unfortunately,
it can be difficult to apply joint optimization to DCNNs when training data is
imbalanced, and re-balancing multi-label data directly is structurally
infeasible, since adding/removing data to balance one label will change the
sampling of the other labels. This paper addresses the multi-label imbalance
problem by introducing a novel mixed objective optimization network (MOON) with
a loss function that mixes multiple task objectives with domain adaptive
re-weighting of propagated loss. Experiments demonstrate that not only does
MOON advance the state of the art in facial attribute recognition, but it also
outperforms independently trained DCNNs using the same data. When using facial
attributes for the LFW face recognition task, we show that our balanced (domain
adapted) network outperforms the unbalanced trained network.Comment: Post-print of manuscript accepted to the European Conference on
  Computer Vision (ECCV) 2016
  http://link.springer.com/chapter/10.1007%2F978-3-319-46454-1_
Beyond Correlation Filters: Learning Continuous Convolution Operators
  for Visual Tracking

Discriminative Correlation Filters (DCF) have demonstrated excellent
performance for visual object tracking. The key to their success is the ability
to efficiently exploit available negative data by including all shifted
versions of a training sample. However, the underlying DCF formulation is
restricted to single-resolution feature maps, significantly limiting its
potential. In this paper, we go beyond the conventional DCF framework and
introduce a novel formulation for training continuous convolution filters. We
employ an implicit interpolation model to pose the learning problem in the
continuous spatial domain. Our proposed formulation enables efficient
integration of multi-resolution deep feature maps, leading to superior results
on three object tracking benchmarks: OTB-2015 (+5.1% in mean OP), Temple-Color
(+4.6% in mean OP), and VOT2015 (20% relative reduction in failure rate).
Additionally, our approach is capable of sub-pixel localization, crucial for
the task of accurate feature point tracking. We also demonstrate the
effectiveness of our learning formulation in extensive feature point tracking
experiments. Code and supplementary material are available at
http://www.cvl.isy.liu.se/research/objrec/visualtracking/conttrack/index.html.Comment: Accepted at ECCV 201
General Automatic Human Shape and Motion Capture Using Volumetric Contour Cues

Markerless motion capture algorithms require a 3D body with properly personalized skeleton dimension and/or body shape and appearance to successfully track a person. Unfortunately, many tracking methods consider model personalization a different problem and use manual or semi-automatic model initialization, which greatly reduces applicability. In this paper, we propose a fully automatic algorithm that jointly creates a rigged actor model commonly used for animation - skeleton, volumetric shape, appearance, and optionally a body surface - and estimates the actor's motion from multi-view video input only. The approach is rigorously designed to work on footage of general outdoor scenes recorded with very few cameras and without background subtraction. Our method uses a new image formation model with analytic visibility and analytically differentiable alignment energy. For reconstruction, 3D body shape is approximated as Gaussian density field. For pose and shape estimation, we minimize a new edge-based alignment energy inspired by volume raycasting in an absorbing medium. We further propose a new statistical human body model that represents the body surface, volumetric Gaussian density, as well as variability in skeleton shape. Given any multi-view sequence, our method jointly optimizes the pose and shape parameters of this model fully automatically in a spatiotemporal way