OpenRaiser/Intern-Atlas huggingface dataset:
Model based approach to study Defect Dependency in Large Scale Integrated Software Products
Venue: arXiv cs.SE 2017
Year: 2017

Large organizations have diverse product offerings to meet various business needs. To increase revenue, its common these days to offer software products as integrated product suite(s) rather than individual products. Creating and maintaining high quality software products within the integrated product suite requires rigorous product engineering methods. The sheer size of products and dependencies involved tend to raise unidentified defects that may become critical post product upgrades or after every release cycle. It is difficult to track such defects and its widespread across underlying sub-products. In this paper, we present a model based approach to study the defect dependency in large scale integrated software products to avoid surprise defects after product release. To validate the approach, we have applied it on some pilot projects in industry.
Ensembling Factored Neural Machine Translation Models for Automatic Post-Editing and Quality Estimation
Venue: arXiv cs.CL 2017
Year: 2017

This work presents a novel approach to Automatic Post-Editing (APE) and Word-Level Quality Estimation (QE) using ensembles of specialized Neural Machine Translation (NMT) systems. Word-level features that have proven effective for QE are included as input factors, expanding the representation of the original source and the machine translation hypothesis, which are used to generate an automatically post-edited hypothesis. We train a suite of NMT models that use different input representations, but share the same output space. These models are then ensembled together, and tuned for both the APE and the QE task. We thus attempt to connect the state-of-the-art approaches to APE and QE within a single framework. Our models achieve state-of-the-art results in both tasks, with the only difference in the tuning step which learns weights for each component of the ensemble.
Topic supervised non-negative matrix factorization
Venue: arXiv cs.CL 2017
Year: 2017

Topic models have been extensively used to organize and interpret the contents of large, unstructured corpora of text documents. Although topic models often perform well on traditional training vs. test set evaluations, it is often the case that the results of a topic model do not align with human interpretation. This interpretability fallacy is largely due to the unsupervised nature of topic models, which prohibits any user guidance on the results of a model. In this paper, we introduce a semi-supervised method called topic supervised non-negative matrix factorization (TS-NMF) that enables the user to provide labeled example documents to promote the discovery of more meaningful semantic structure of a corpus. In this way, the results of TS-NMF better match the intuition and desired labeling of the user. The core of TS-NMF relies on solving a non-convex optimization problem for which we derive an iterative algorithm that is shown to be monotonic and convergent to a local optimum. We demonstrate the practical utility of TS-NMF on the Reuters and PubMed corpora, and find that TS-NMF is especially useful for conceptual or broad topics, where topic key terms are not well understood. Although identifying an optimal latent structure for the data is not a primary objective of the proposed approach, we find that TS-NMF achieves higher weighted Jaccard similarity scores than the contemporary methods, (unsupervised) NMF and latent Dirichlet allocation, at supervision rates as low as 10% to 20%.
Verification of Magnitude and Phase Responses in Fixed-Point Digital Filters
Venue: arXiv cs.LO 2017
Year: 2017

In the digital signal processing (DSP) area, one of the most important tasks is digital filter design. Currently, this procedure is performed with the aid of computational tools, which generally assume filter coefficients represented with floating-point arithmetic. Nonetheless, during the implementation phase, which is often done in digital signal processors or field programmable gate arrays, the representation of the obtained coefficients can be carried out through integer or fixed-point arithmetic, which often results in unexpected behavior or even unstable filters. The present work addresses this issue and proposes a verification methodology based on the digital-system verifier (DSVerifier), with the goal of checking fixed-point digital filters w.r.t. implementation aspects. In particular, DSVerifier checks whether the number of bits used in coefficient representation will result in a filter with the same features specified during the design phase. Experimental results show that errors regarding frequency response and overflow are likely to be identified with the proposed methodology, which thus improves overall system's reliability.
State change modal method for numerical simulation of dynamic processes in a nuclear reactor
Venue: arXiv cs.CE 2017
Year: 2017

Modeling of dynamic processes in nuclear reactors is carried out, mainly, on the basis of the multigroup diffusion approximation for the neutron flux. The basic model includes a multidimensional set of coupled parabolic equations and ordinary differential equations. Dynamic processes are modeled by a successive change of the reactor states, which are characterized by given coefficients of the equations. In the modal method, the approximate solution is represented as an expansion on the first eigenfunctions of some spectral problem. The numerical-analytical method is based on the use of dominant time-eigenvalues of a multigroup diffusion model taking into account delayed neutrons. Numerical simulations of the dynamic process were performed in the framework of the two-group approximation for the VVER-1000 reactor test model. The last is characterized by the fact that some eigenvalues are complex.
Active learning in annotating micro-blogs dealing with e-reputation
Venue: arXiv cs.SI 2017
Year: 2017

Elections unleash strong political views on Twitter, but what do people really think about politics? Opinion and trend mining on micro blogs dealing with politics has recently attracted researchers in several fields including Information Retrieval and Machine Learning (ML). Since the performance of ML and Natural Language Processing (NLP) approaches are limited by the amount and quality of data available, one promising alternative for some tasks is the automatic propagation of expert annotations. This paper intends to develop a so-called active learning process for automatically annotating French language tweets that deal with the image (i.e., representation, web reputation) of politicians. Our main focus is on the methodology followed to build an original annotated dataset expressing opinion from two French politicians over time. We therefore review state of the art NLP-based ML algorithms to automatically annotate tweets using a manual initiation step as bootstrap. This paper focuses on key issues about active learning while building a large annotated data set from noise. This will be introduced by human annotators, abundance of data and the label distribution across data and entities. In turn, we show that Twitter characteristics such as the author's name or hashtags can be considered as the bearing point to not only improve automatic systems for Opinion Mining (OM) and Topic Classification but also to reduce noise in human annotations. However, a later thorough analysis shows that reducing noise might induce the loss of crucial information.
Local Feature Descriptor Learning with Adaptive Siamese Network
Venue: arXiv cs.LG 2017
Year: 2017

Although the recent progress in the deep neural network has led to the development of learnable local feature descriptors, there is no explicit answer for estimation of the necessary size of a neural network. Specifically, the local feature is represented in a low dimensional space, so the neural network should have more compact structure. The small networks required for local feature descriptor learning may be sensitive to initial conditions and learning parameters and more likely to become trapped in local minima. In order to address the above problem, we introduce an adaptive pruning Siamese Architecture based on neuron activation to learn local feature descriptors, making the network more computationally efficient with an improved recognition rate over more complex networks. Our experiments demonstrate that our learned local feature descriptors outperform the state-of-art methods in patch matching.
Characterizing Lifting and Lowering Activities with Insole FSR sensors in Industrial Exoskeletons
Venue: arXiv cs.RO 2017
Year: 2017

This paper presents an insole FSR (Force Sensing Resistor) to dynamically detect weight variations in an exoskeleton system. The proposed methodology is intended for tasks of lifting and lowering heavy weights with an industrial exoskeleton to substantially reduce spinal loads during these manual handling activities. Instead of extensively placing high-dense force sensors by covering the whole plantar area, as most of commercial applications do, we integrate only a few force sensors in specific plantar area, so that the sensory system is not restricted to an individual foot size and shape, and on the other hand has relatively lower material cost. Industrial exoskeletons are intended to assist workers when handling heavy goods. With this in mind, wearers are not able to use their hands to control the exoskeleton since they use them to handle the goods. Therefore, the exoskeleton controller is required to indirectly infer how much and when the wearer requires assistance for lifting or lowering a heavy weight. Our approach of dynamically detect and characterize the increment/decrement of weight, as well as the rising/falling edge, enables the exoskeleton's controller to trigger the request of assistive force to the actuators.
Evaluating the quality of tourist agendas customized to different travel styles
Venue: arXiv cs.AI 2017
Year: 2017

Many tourist applications provide a personalized tourist agenda with the list of recommended activities to the user. These applications must undoubtedly deal with the constraints and preferences that define the user interests. Among these preferences, we can find those that define the travel style of the user, such as the rhythm of the trip, the number of visits to include in the tour or the priority to visits of special interest for the user. In this paper, we deal with the task of creating a customized tourist agenda as a planning and scheduling application capable of conveniently scheduling the most appropriate goals (visits) so as to maximize the user satisfaction with the tourist route. This paper makes an analysis of the meaning of the travel style preferences and compares the quality of the solutions obtained by two different solvers, a PDDL-based planner and a Constraint Satisfaction Problem solver. We also define several quality metrics and perform extensive experiments in order to evaluate the results obtained with both solvers.
Adaptivity is exponentially powerful for testing monotonicity of halfspaces
Venue: arXiv cs.CC 2017
Year: 2017

We give a $\mathrm{poly}(\log n, 1/ε)$-query adaptive algorithm for testing whether an unknown Boolean function $f: \{-1,1\}^n \to \{-1,1\}$, which is promised to be a halfspace, is monotone versus $ε$-far from monotone. Since non-adaptive algorithms are known to require almost $Ω(n^{1/2})$ queries to test whether an unknown halfspace is monotone versus far from monotone, this shows that adaptivity enables an exponential improvement in the query complexity of monotonicity testing for halfspaces.
An improved kernel for the cycle contraction problem
Venue: arXiv cs.DS 2017
Year: 2017

The problem of modifying a given graph to satisfy certain properties has been one of the central topics in parameterized tractability study. In this paper, we study the cycle contraction problem, which makes a graph into a cycle by edge contractions. The problem has been studied {by Belmonte et al. [IPEC 2013]} who obtained a linear kernel with at most $6k+6$ vertices. We provide an improved kernel with at most $5k+4$ vertices for it in this paper.
Entropy, neutro-entropy and anti-entropy for neutrosophic information
Venue: arXiv cs.AI 2017
Year: 2017

This approach presents a multi-valued representation of the neutrosophic information. It highlights the link between the bifuzzy information and neutrosophic one. The constructed deca-valued structure shows the neutrosophic information complexity. This deca-valued structure led to construction of two new concepts for the neutrosophic information: neutro-entropy and anti-entropy. These two concepts are added to the two existing: entropy and non-entropy. Thus, we obtained the following triad: entropy, neutro-entropy and anti-entropy.
Tversky loss function for image segmentation using 3D fully convolutional deep networks
Venue: arXiv cs.CV 2017
Year: 2017

Fully convolutional deep neural networks carry out excellent potential for fast and accurate image segmentation. One of the main challenges in training these networks is data imbalance, which is particularly problematic in medical imaging applications such as lesion segmentation where the number of lesion voxels is often much lower than the number of non-lesion voxels. Training with unbalanced data can lead to predictions that are severely biased towards high precision but low recall (sensitivity), which is undesired especially in medical applications where false negatives are much less tolerable than false positives. Several methods have been proposed to deal with this problem including balanced sampling, two step training, sample re-weighting, and similarity loss functions. In this paper, we propose a generalized loss function based on the Tversky index to address the issue of data imbalance and achieve much better trade-off between precision and recall in training 3D fully convolutional deep neural networks. Experimental results in multiple sclerosis lesion segmentation on magnetic resonance images show improved F2 score, Dice coefficient, and the area under the precision-recall curve in test data. Based on these results we suggest Tversky loss function as a generalized framework to effectively train deep neural networks.
Detecting Large Concept Extensions for Conceptual Analysis
Venue: arXiv cs.CL 2017
Year: 2017

When performing a conceptual analysis of a concept, philosophers are interested in all forms of expression of a concept in a text---be it direct or indirect, explicit or implicit. In this paper, we experiment with topic-based methods of automating the detection of concept expressions in order to facilitate philosophical conceptual analysis. We propose six methods based on LDA, and evaluate them on a new corpus of court decision that we had annotated by experts and non-experts. Our results indicate that these methods can yield important improvements over the keyword heuristic, which is often used as a concept detection heuristic in many contexts. While more work remains to be done, this indicates that detecting concepts through topics can serve as a general-purpose method for at least some forms of concept expression that are not captured using naive keyword approaches.
Data set operations to hide decision tree rules
Venue: arXiv cs.AI 2017
Year: 2017

This paper focuses on preserving the privacy of sensitive patterns when inducing decision trees. We adopt a record augmentation approach for hiding sensitive classification rules in binary datasets. Such a hiding methodology is preferred over other heuristic solutions like output perturbation or cryptographic techniques - which restrict the usability of the data - since the raw data itself is readily available for public use. We show some key lemmas which are related to the hiding process and we also demonstrate the methodology with an example and an indicative experiment using a prototype hiding tool.
Quantifying the Benefits of Infrastructure Sharing
Venue: arXiv cs.GT 2017
Year: 2017

We analyze the benefits of network sharing between telecommunications operators. Sharing is seen as one way to speed the roll out of expensive technologies such as 5G since it allows the service providers to divide the cost of providing ubiquitous coverage. Our theoretical analysis focuses on scenarios with two service providers and compares the system dynamics when they are competing with the dynamics when they are cooperating. We show that sharing can be beneficial to a service provider even when it has the power to drive the other service provider out of the market, a byproduct of a non-convex cost function. A key element of this study is an analysis of the competitive equilibria for both cooperative and non-cooperative 2-person games in the presence of (non-convex) cost functions that involve a fixed cost component.
Deep learning with spatiotemporal consistency for nerve segmentation in ultrasound images
Venue: arXiv cs.CV 2017
Year: 2017

Ultrasound-Guided Regional Anesthesia (UGRA) has been gaining importance in the last few years, offering numerous advantages over alternative methods of nerve localization (neurostimulation or paraesthesia). However, nerve detection is one of the most tasks that anaesthetists can encounter in the UGRA procedure. Computer aided system that can detect automatically region of nerve, would help practitioner to concentrate more in anaesthetic delivery. In this paper we propose a new method based on deep learning combined with spatiotemporal information to robustly segment the nerve region. The proposed method is based on two phases, localisation and segmentation. The first phase, consists in using convolutional neural network combined with spatial and temporal consistency to detect the nerve zone. The second phase utilises active contour model to delineate the region of interest. Obtained results show the validity of the proposed approach and its robustness.
Gaussian Intersymbol Interference Channels With Mismatch
Venue: arXiv cs.IT 2017
Year: 2017

This paper considers the problem of channel coding over Gaussian intersymbol interference (ISI) channels with a given metric decoding rule. Specifically, it is assumed that the mismatched decoder has an incorrect assumption on the impulse response function. The mismatch capacity is the highest achievable rate for a given decoding rule. Existing lower bounds to the mismatch capacity for channels and decoding metrics with memory (as in our model) are presented only in the form of multi-letter expressions that have not been calculated in practice. Consequently, they provide little insight on the mismatch problem. In this paper, we derive computable single-letter lower bounds to the mismatch capacity, and discuss some implications of our results. Our achievable rates are based on two ensembles, the ensemble of codewords generated by an autoregressive process, and the ensemble of codewords drawn uniformly over a "type class" of real-valued sequences. Computation of our achievable rates demonstrates non-trivial behavior of the achievable rates as a function of the mismatched parameters. As a simple application of our technique, we derive also the random coding exponent associated with a mismatched decoder which assumes that there is no ISI at all. Finally, we compare our results with universal decoders which are designed outside the true class of channels that we consider in this paper.
Pedestrian Prediction by Planning using Deep Neural Networks
Venue: arXiv cs.CV 2017
Year: 2017

Accurate traffic participant prediction is the prerequisite for collision avoidance of autonomous vehicles. In this work, we predict pedestrians by emulating their own motion planning. From online observations, we infer a mixture density function for possible destinations. We use this result as the goal states of a planning stage that performs motion prediction based on common behavior patterns. The entire system is modeled as one monolithic neural network and trained via inverse reinforcement learning. Experimental validation on real world data shows the system's ability to predict both, destinations and trajectories accurately.
The HoTT reals coincide with the Escardó-Simpson reals
Venue: arXiv cs.LO 2017
Year: 2017

Escardó and Simpson defined a notion of interval object by a universal property in any category with binary products. The Homotopy Type Theory book defines a higher-inductive notion of reals, and suggests that the interval may satisfy this universal property. We show that this is indeed the case in the category of sets of any universe. We also show that the type of HoTT reals is the least Cauchy complete subset of the Dedekind reals containing the rationals.
Mixture-based Modeling of Spatially Correlated Interference in a Poisson Field of Interferers
Venue: arXiv cs.IT 2017
Year: 2017

As the interference in PPP based wireless networks exhibit spatial correlation, any joint analysis involving multiple spatial points either end up with numerical integrations over $\mathbb{R}^2$ or become analytically too intractable. To tackle these issues, we present an alternate approach which not only offers a simpler analytical structure, but also closely mimics the PPP characteristics. This approach at its core models the correlated interferences using a correlation framework constructed using random variable mixtures. Additionally, a correlation framework based on the more standard method of linear combination of random variables is also presented for comparison purpose. The performance of these models is studied by deriving the joint CCDF of SIRs at $N$ arbitrary points. The plots are found to tightly approximate the exact PPP-based results, with the tightness depending on the values of $λp$ (interferer intensity), $α$ (path loss exponent) and $N$. The applicability of the mixture-based model is also shown for a multi-antennae MRC receiver where only major derivation steps that simplifies the outage probability analysis are shown.
An Algorithm for Network and Data-aware Placement of Multi-Tier Applications in Cloud Data Centers
Venue: arXiv cs.DC 2017
Year: 2017

Today's Cloud applications are dominated by composite applications comprising multiple computing and data components with strong communication correlations among them. Although Cloud providers are deploying large number of computing and storage devices to address the ever increasing demand for computing and storage resources, network resource demands are emerging as one of the key areas of performance bottleneck. This paper addresses network-aware placement of virtual components (computing and data) of multi-tier applications in data centers and formally defines the placement as an optimization problem. The simultaneous placement of Virtual Machines and data blocks aims at reducing the network overhead of the data center network infrastructure. A greedy heuristic is proposed for the on-demand application components placement that localizes network traffic in the data center interconnect. Such optimization helps reducing communication overhead in upper layer network switches that will eventually reduce the overall traffic volume across the data center. This, in turn, will help reducing packet transmission delay, increasing network performance, and minimizing the energy consumption of network components. Experimental results demonstrate performance superiority of the proposed algorithm over other approaches where it outperforms the state-of-the-art network-aware application placement algorithm across all performance metrics by reducing the average network cost up to 67% and network usage at core switches up to 84%, as well as increasing the average number of application deployments up to 18%.