OpenRaiser/Intern-Atlas huggingface dataset:
Ensemble-based cover song detection
Venue: arXiv cs.SD 2019
Year: 2019

Audio-based cover song detection has received much attention in the MIR community in the recent years. To date, the most popular formulation of the problem has been to compare the audio signals of two tracks and to make a binary decision based on this information only. However, leveraging additional signals might be key if one wants to solve the problem at an industrial scale. In this paper, we introduce an ensemble-based method that approaches the problem from a many-to-many perspective. Instead of considering pairs of tracks in isolation, we consider larger sets of potential versions for a given composition, and create and exploit the graph of relationships between these tracks. We show that this can result in a significant improvement in performance, in particular when the number of existing versions of a given composition is large.
Machine Learning on data with sPlot background subtraction
Venue: arXiv cs.LG 2019
Year: 2019

Data analysis in high energy physics often deals with data samples consisting of a mixture of signal and background events. The sPlot technique is a common method to subtract the contribution of the background by assigning weights to events. Part of the weights are by design negative. Negative weights lead to the divergence of some machine learning algorithms training due to absence of the lower bound in the loss function. In this paper we propose a mathematically rigorous way to train machine learning algorithms on data samples with background described by sPlot to obtain signal probabilities conditioned on observables, without encountering negative event weight at all. This allows usage of any out-of-the-box machine learning methods on such data.
Certified lattice reduction
Venue: arXiv cs.DS 2019
Year: 2019

Quadratic form reduction and lattice reduction are fundamental tools in computational number theory and in computer science, especially in cryptography. The celebrated Lenstra-Lenstra-Lovász reduction algorithm (so-called LLL) has been improved in many ways through the past decades and remains one of the central methods used for reducing integral lattice basis. In particular, its floating-point variants-where the rational arithmetic required by Gram-Schmidt orthogonalization is replaced by floating-point arithmetic-are now the fastest known. However, the systematic study of the reduction theory of real quadratic forms or, more generally, of real lattices is not widely represented in the literature. When the problem arises, the lattice is usually replaced by an integral approximation of (a multiple of) the original lattice, which is then reduced. While practically useful and proven in some special cases, this method doesn't offer any guarantee of success in general. In this work, we present an adaptive-precision version of a generalized LLL algorithm that covers this case in all generality. In particular, we replace floating-point arithmetic by Interval Arithmetic to certify the behavior of the algorithm. We conclude by giving a typical application of the result in algebraic number theory for the reduction of ideal lattices in number fields.
Evaluation of Machine Learning-based Anomaly Detection Algorithms on an Industrial Modbus/TCP Data Set
Venue: arXiv cs.CR 2019
Year: 2019

In the context of the Industrial Internet of Things, communication technology, originally used in home and office environments, is introduced into industrial applications. Commercial off-the-shelf products, as well as unified and well-established communication protocols make this technology easy to integrate and use. Furthermore, productivity is increased in comparison to classic industrial control by making systems easier to manage, set up and configure. Unfortunately, most attack surfaces of home and office environments are introduced into industrial applications as well, which usually have very few security mechanisms in place. Over the last years, several technologies tackling that issue have been researched. In this work, machine learning-based anomaly detection algorithms are employed to find malicious traffic in a synthetically generated data set of Modbus/TCP communication of a fictitious industrial scenario. The applied algorithms are Support Vector Machine (SVM), Random Forest, k-nearest neighbour and k-means clustering. Due to the synthetic data set, supervised learning is possible. Support Vector Machine and k-nearest neighbour perform well with different data sets, while k-nearest neighbour and k-means clustering do not perform satisfactorily.
SizeNet: Weakly Supervised Learning of Visual Size and Fit in Fashion Images
Venue: arXiv cs.CV 2019
Year: 2019

Finding clothes that fit is a hot topic in the e-commerce fashion industry. Most approaches addressing this problem are based on statistical methods relying on historical data of articles purchased and returned to the store. Such approaches suffer from the cold start problem for the thousands of articles appearing on the shopping platforms every day, for which no prior purchase history is available. We propose to employ visual data to infer size and fit characteristics of fashion articles. We introduce SizeNet, a weakly-supervised teacher-student training framework that leverages the power of statistical models combined with the rich visual information from article images to learn visual cues for size and fit characteristics, capable of tackling the challenging cold start problem. Detailed experiments are performed on thousands of textile garments, including dresses, trousers, knitwear, tops, etc. from hundreds of different brands.
Analyzing Turkish F and Turkish E keyboard layouts using learning curves
Venue: arXiv cs.HC 2019
Year: 2019

The F-layout was introduced in 1955 and eventually enforced as a national standard as a replacement to the popular QWERTY keyboard layout in Turkey. In a more recent work, another alternative (E-layout) was developed for Turkish language and argued to be faster and more comfortable than the F-layout. However, there has not been any empirical evidence favouring any of these layouts so far. To fill this research gap in the literature, we have employed a hybrid model and conducted both between-subjects and within-subjects user experiments with twelve freshmen majoring in computer engineering. The experimental results show that there is no significant difference between learning percentage of these two layouts but the completion time of typing a trial passage with the F-layout is significantly lower than the E-layout. The F-layout has also a significantly lower physical demand score, as revealed by the subjective assessments of participants. Based on our user survey data, we also discuss some possible reasons of F-keyboard limited prevalence among Turkish users.
Prediction of Construction Cost for Field Canals Improvement Projects in Egypt
Venue: arXiv cs.AI 2019
Year: 2019

Field canals improvement projects (FCIPs) are one of the ambitious projects constructed to save fresh water. To finance this project, Conceptual cost models are important to accurately predict preliminary costs at the early stages of the project. The first step is to develop a conceptual cost model to identify key cost drivers affecting the project. Therefore, input variables selection remains an important part of model development, as the poor variables selection can decrease model precision. The study discovered the most important drivers of FCIPs based on a qualitative approach and a quantitative approach. Subsequently, the study has developed a parametric cost model based on machine learning methods such as regression methods, artificial neural networks, fuzzy model and case-based reasoning.
MolSSI and BioExcel Workflow Workshop 2018 Report
Venue: arXiv cs.SE 2019
Year: 2019

Workflows in biomolecular science are very important as they are intricately intertwined with the scientific outcomes, as well as algorithmic and methodological innovations. The use and effectiveness of workflow tools to meet the needs of the biomolecular science community is varied. MolSSI co-organized a biomolecular workflows workshop in December 2018 with the goal of identifying specific software gaps and opportunities for improved workflow practices. This report captures presentations and discussion from that workshop. The workshop participants were primary tools developers, along with "neutral observers" and some biomolecular domain scientists. After contextualizing and motivating the workshop, the report covers the existing roles and emerging trends in how workflow systems are utilized. A few recurring observations are presented as recommendations for improving the use and effectiveness of workflow tools. The tools presented are discussed in Appendix B.
Cerberus: A Multi-headed Derenderer
Venue: arXiv cs.CV 2019
Year: 2019

To generalize to novel visual scenes with new viewpoints and new object poses, a visual system needs representations of the shapes of the parts of an object that are invariant to changes in viewpoint or pose. 3D graphics representations disentangle visual factors such as viewpoints and lighting from object structure in a natural way. It is possible to learn to invert the process that converts 3D graphics representations into 2D images, provided the 3D graphics representations are available as labels. When only the unlabeled images are available, however, learning to derender is much harder. We consider a simple model which is just a set of free floating parts. Each part has its own relation to the camera and its own triangular mesh which can be deformed to model the shape of the part. At test time, a neural network looks at a single image and extracts the shapes of the parts and their relations to the camera. Each part can be viewed as one head of a multi-headed derenderer. During training, the extracted parts are used as input to a differentiable 3D renderer and the reconstruction error is backpropagated to train the neural net. We make the learning task easier by encouraging the deformations of the part meshes to be invariant to changes in viewpoint and invariant to the changes in the relative positions of the parts that occur when the pose of an articulated body changes. Cerberus, our multi-headed derenderer, outperforms previous methods for extracting 3D parts from single images without part annotations, and it does quite well at extracting natural parts of human figures.
Controlling Reversibility in Reversing Petri Nets with Application to Wireless Communications
Venue: arXiv cs.LO 2019
Year: 2019

Petri nets are a formalism for modelling and reasoning about the behaviour of distributed systems. Recently, a reversible approach to Petri nets, Reversing Petri Nets (RPN), has been proposed, allowing transitions to be reversed spontaneously in or out of causal order. In this work we propose an approach for controlling the reversal of actions of an RPN, by associating transitions with conditions whose satisfaction/violation allows the execution of transitions in the forward/reversed direction, respectively. We illustrate the framework with a model of a novel, distributed algorithm for antenna selection in distributed antenna arrays.
Texture Selection for Automatic Music Genre Classification
Venue: arXiv cs.SD 2019
Year: 2019

Music Genre Classification is the problem of associating genre-related labels to digitized music tracks. It has applications in the organization of commercial and personal music collections. Often, music tracks are described as a set of timbre-inspired sound textures. In shallow-learning systems, the total number of sound textures per track is usually too high, and texture downsampling is necessary to make training tractable. Although previous work has solved this by linear downsampling, no extensive work has been done to evaluate how texture selection benefits genre classification in the context of the bag of frames track descriptions. In this paper, we evaluate the impact of frame selection on automatic music genre classification in a bag of frames scenario. We also present a novel texture selector based on K-Means aimed to identify diverse sound textures within each track. We evaluated texture selection in diverse datasets, four different feature sets, as well as its relationship to a univariate feature selection strategy. The results show that frame selection leads to significant improvement over the single vector baseline on datasets consisting of full-length tracks, regardless of the feature set. Results also indicate that the K-Means texture selector achieves significant improvements over the baseline, using fewer textures per track than the commonly used linear downsampling. The results also suggest that texture selection is complementary to the feature selection strategy evaluated. Our qualitative analysis indicates that texture variety within classes benefits model generalization. Our analysis shows that selecting specific audio excerpts can improve classification performance, and it can be done automatically.
Learning Portable Representations for High-Level Planning
Venue: arXiv cs.LG 2019
Year: 2019

We present a framework for autonomously learning a portable representation that describes a collection of low-level continuous environments. We show that these abstract representations can be learned in a task-independent egocentric space specific to the agent that, when grounded with problem-specific information, are provably sufficient for planning. We demonstrate transfer in two different domains, where an agent learns a portable, task-independent symbolic vocabulary, as well as rules expressed in that vocabulary, and then learns to instantiate those rules on a per-task basis. This reduces the number of samples required to learn a representation of a new task.
Leveraging Medical Visual Question Answering with Supporting Facts
Venue: arXiv cs.CV 2019
Year: 2019

In this working notes paper, we describe IBM Research AI (Almaden) team's participation in the ImageCLEF 2019 VQA-Med competition. The challenge consists of four question-answering tasks based on radiology images. The diversity of imaging modalities, organs and disease types combined with a small imbalanced training set made this a highly complex problem. To overcome these difficulties, we implemented a modular pipeline architecture that utilized transfer learning and multi-task learning. Our findings led to the development of a novel model called Supporting Facts Network (SFN). The main idea behind SFN is to cross-utilize information from upstream tasks to improve the accuracy on harder downstream ones. This approach significantly improved the scores achieved in the validation set (18 point improvement in F-1 score). Finally, we submitted four runs to the competition and were ranked seventh.
Blocksworld Revisited: Learning and Reasoning to Generate Event-Sequences from Image Pairs
Venue: arXiv cs.CV 2019
Year: 2019

The process of identifying changes or transformations in a scene along with the ability of reasoning about their causes and effects, is a key aspect of intelligence. In this work we go beyond recent advances in computational perception, and introduce a more challenging task, Image-based Event-Sequencing (IES). In IES, the task is to predict a sequence of actions required to rearrange objects from the configuration in an input source image to the one in the target image. IES also requires systems to possess inductive generalizability. Motivated from evidence in cognitive development, we compile the first IES dataset, the Blocksworld Image Reasoning Dataset (BIRD) which contains images of wooden blocks in different configurations, and the sequence of moves to rearrange one configuration to the other. We first explore the use of existing deep learning architectures and show that these end-to-end methods under-perform in inferring temporal event-sequences and fail at inductive generalization. We then propose a modular two-step approach: Visual Perception followed by Event-Sequencing, and demonstrate improved performance by combining learning and reasoning. Finally, by showing an extension of our approach on natural images, we seek to pave the way for future research on event sequencing for real world scenes.
On Evaluating the Effectiveness of the HoneyBot: A Case Study
Venue: arXiv cs.CR 2019
Year: 2019

In recent years, cyber-physical system (CPS) security as applied to robotic systems has become a popular research area. Mainly because robotics systems have traditionally emphasized the completion of a specific objective and lack security oriented design. Our previous work, HoneyBot \cite{celine}, presented the concept and prototype of the first software hybrid interaction honeypot specifically designed for networked robotic systems. The intuition behind HoneyBot was that it would be a remotely accessible robotic system that could simulate unsafe actions and physically perform safe actions to fool attackers. Unassuming attackers would think they were connected to an ordinary robotic system, believing their exploits were being successfully executed. All the while, the HoneyBot is logging all communications and exploits sent to be used for attacker attribution and threat model creation. In this paper, we present findings from the result of a user study performed to evaluate the effectiveness of the HoneyBot framework and architecture as it applies to real robotic systems. The user study consisted of 40 participants, was conducted over the course of several weeks, and drew from a wide range of participants aged between 18-60 with varying level of technical expertise. From the study we found that research subjects could not tell the difference between the simulated sensor values and the real sensor values coming from the HoneyBot, meaning the HoneyBot convincingly spoofed communications.
Putting Strong Linearizability in Context: Preserving Hyperproperties in Programs that Use Concurrent Objects
Venue: arXiv cs.DC 2019
Year: 2019

It has been observed that linearizability, the prevalent consistency condition for implementing concurrent objects, does not preserve some probability distributions. A stronger condition, called strong linearizability has been proposed, but its study has been somewhat ad-hoc. This paper investigates strong linearizability by casting it in the context of observational refinement of objects. We present a strengthening of observational refinement, which generalizes strong linearizability, obtaining several important implications. When a concrete concurrent object refining another, more abstract object - often sequential - the correctness of a program employing the concrete object can be verified by considering its behaviors when using the more abstract object. This means that trace properties of a program using the concrete object can be proved by considering the program with the abstract object. This, however, does not hold for hyperproperties, including many security properties and probability distributions of events. We define strong observational refinement, a strengthening of refinement that preserves hyperproperties, and prove that it is equivalent to the existence of forward simulations. We show that strong observational refinement generalizes strong linearizability. This implies that strong linearizability is also equivalent to forward simulation, and shows that strongly linearizable implementations can be composed both horizontally (i.e., locality) and vertically (i.e., with instantiation). For situations where strongly linearizable implementations do not exist (or are less efficient), we argue that reasoning about hyperproperties of programs can be simplified by strong observational refinement of abstract objects that are not necessarily sequential.
Automatic Ambiguity Detection
Venue: arXiv cs.CL 2019
Year: 2019

Most work on sense disambiguation presumes that one knows beforehand -- e.g. from a thesaurus -- a set of polysemous terms. But published lists invariably give only partial coverage. For example, the English word tan has several obvious senses, but one may overlook the abbreviation for tangent. In this paper, we present an algorithm for identifying interesting polysemous terms and measuring their degree of polysemy, given an unlabeled corpus. The algorithm involves: (i) collecting all terms within a k-term window of the target term; (ii) computing the inter-term distances of the contextual terms, and reducing the multi-dimensional distance space to two dimensions using standard methods; (iii) converting the two-dimensional representation into radial coordinates and using isotonic/antitonic regression to compute the degree to which the distribution deviates from a single-peak model. The amount of deviation is the proposed polysemy index
Deep Learning Moment Closure Approximations using Dynamic Boltzmann Distributions
Venue: arXiv cs.LG 2019
Year: 2019

The moments of spatial probabilistic systems are often given by an infinite hierarchy of coupled differential equations. Moment closure methods are used to approximate a subset of low order moments by terminating the hierarchy at some order and replacing higher order terms with functions of lower order ones. For a given system, it is not known beforehand which closure approximation is optimal, i.e. which higher order terms are relevant in the current regime. Further, the generalization of such approximations is typically poor, as higher order corrections may become relevant over long timescales. We have developed a method to learn moment closure approximations directly from data using dynamic Boltzmann distributions (DBDs). The dynamics of the distribution are parameterized using basis functions from finite element methods, such that the approach can be applied without knowing the true dynamics of the system under consideration. We use the hierarchical architecture of deep Boltzmann machines (DBMs) with multinomial latent variables to learn closure approximations for progressively higher order spatial correlations. The learning algorithm uses a centering transformation, allowing the dynamic DBM to be trained without the need for pre-training. We demonstrate the method for a Lotka-Volterra system on a lattice, a typical example in spatial chemical reaction networks. The approach can be applied broadly to learn deep generative models in applications where infinite systems of differential equations arise.
One SQL to Rule Them All
Venue: arXiv cs.DB 2019
Year: 2019

Real-time data analysis and management are increasingly critical for today`s businesses. SQL is the de facto lingua franca for these endeavors, yet support for robust streaming analysis and management with SQL remains limited. Many approaches restrict semantics to a reduced subset of features and/or require a suite of non-standard constructs. Additionally, use of event timestamps to provide native support for analyzing events according to when they actually occurred is not pervasive, and often comes with important limitations. We present a three-part proposal for integrating robust streaming into the SQL standard, namely: (1) time-varying relations as a foundation for classical tables as well as streaming data, (2) event time semantics, (3) a limited set of optional keyword extensions to control the materialization of time-varying query results. Motivated and illustrated using examples and lessons learned from implementations in Apache Calcite, Apache Flink, and Apache Beam, we show how with these minimal additions it is possible to utilize the complete suite of standard SQL semantics to perform robust stream processing.