OpenRaiser/Intern-Atlas huggingface dataset:
Working in Pairs: Understanding the Effects of Worker Interactions in Crowdwork
Venue: arXiv cs.HC 2018
Year: 2018

Crowdsourcing has gained popularity as a tool to harness human brain power to help solve problems that are difficult for computers. Previous work in crowdsourcing often assumes that workers complete crowdwork independently. In this paper, we relax the independent property of crowdwork and explore how introducing direct, synchronous, and free-style interactions between workers would affect crowdwork. In particular, motivated by the concept of peer instruction in educational settings, we study the effects of peer communication in crowdsourcing environments. In the crowdsourcing setting with peer communication, pairs of workers are asked to complete the same task together by first generating their initial answers to the task independently and then freely discussing the tasks with each other and updating their answers after the discussion. We experimentally examine the effects of peer communication in crowdwork on various common types of tasks on crowdsourcing platforms, including image labeling, optical character recognition (OCR), audio transcription, and nutrition analysis. Our experiment results show that the work quality is significantly improved in tasks with peer communication compared to tasks where workers complete the work independently. However, participating in tasks with peer communication has limited effects on influencing worker's independent performance in tasks of the same type in the future.
Understanding and Formalizing Accountability for Cyber-Physical Systems
Venue: arXiv cs.SE 2018
Year: 2018

Accountability is the property of a system that enables the uncovering of causes for events and helps understand who or what is responsible for these events. Definitions and interpretations of accountability differ; however, they are typically expressed in natural language that obscures design decisions and the impact on the overall system. This paper presents a formal model to express the accountability properties of cyber-physical systems. To illustrate the usefulness of our approach, we demonstrate how three different interpretations of accountability can be expressed using the proposed model and describe the implementation implications through a case study. This formal model can be used to highlight context specific-elements of accountability mechanisms, define their capabilities, and express different notions of accountability. In addition, it makes design decisions explicit and facilitates discussion, analysis and comparison of different approaches.
Consistency-aware Shading Orders Selective Fusion for Intrinsic Image Decomposition
Venue: arXiv cs.CV 2018
Year: 2018

We address the problem of decomposing a single image into reflectance and shading. The difficulty comes from the fact that the components of image---the surface albedo, the direct illumination, and the ambient illumination---are coupled heavily in observed image. We propose to infer the shading by ordering pixels by their relative brightness, without knowing the absolute values of the image components beforehand. The pairwise shading orders are estimated in two ways: brightness order and low-order fittings of local shading field. The brightness order is a non-local measure, which can be applied to any pair of pixels including those whose reflectance and shading are both different. The low-order fittings are used for pixel pairs within local regions of smooth shading. Together, they can capture both global order structure and local variations of the shading. We propose a Consistency-aware Selective Fusion (CSF) to integrate the pairwise orders into a globally consistent order. The iterative selection process solves the conflicts between the pairwise orders obtained by different estimation methods. Inconsistent or unreliable pairwise orders will be automatically excluded from the fusion to avoid polluting the global order. Experiments on the MIT Intrinsic Image dataset show that the proposed model is effective at recovering the shading including deep shadows. Our model also works well on natural images from the IIW dataset, the UIUC Shadow dataset and the NYU-Depth dataset, where the colors of direct lights and ambient lights are quite different.
Convolutional Neural Network Pruning to Accelerate Membrane Segmentation in Electron Microscopy
Venue: arXiv cs.CV 2018
Year: 2018

Biological membranes are one of the most basic structures and regions of interest in cell biology. In the study of membranes, segment extraction is a well-known and difficult problem because of impeding noise, directional and thickness variability, etc. Recent advances in electron microscopy membrane segmentation are able to cope with such difficulties by training convolutional neural networks. However, because of the massive amount of features that have to be extracted while propagating forward, the practical usability diminishes, even with state-of-the-art GPU's. A significant part of these network features typically contains redundancy through correlation and sparsity. In this work, we propose a pruning method for convolutional neural networks that ensures the training loss increase is minimized. We show that the pruned networks, after retraining, are more efficient in terms of time and memory, without significantly affecting the network accuracy. This way, we manage to obtain real-time membrane segmentation performance, for our specific electron microscopy setup.
Building an Emulation Environment for Cyber Security Analyses of Complex Networked Systems
Venue: arXiv cs.CR 2018
Year: 2018

Computer networks are undergoing a phenomenal growth, driven by the rapidly increasing number of nodes constituting the networks. At the same time, the number of security threats on Internet and intranet networks is constantly growing, and the testing and experimentation of cyber defense solutions requires the availability of separate, test environments that best emulate the complexity of a real system. Such environments support the deployment and monitoring of complex mission-driven network scenarios, thus enabling the study of cyber defense strategies under real and controllable traffic and attack scenarios. In this paper, we propose a methodology that makes use of a combination of techniques of network and security assessment, and the use of cloud technologies to build an emulation environment with adjustable degree of affinity with respect to actual reference networks or planned systems. As a byproduct, starting from a specific study case, we collected a dataset consisting of complete network traces comprising benign and malicious traffic, which is feature-rich and publicly available.
Heuristics-based Query Reordering for Federated Queries in SPARQL 1.1 and SPARQL-LD
Venue: arXiv cs.DB 2018
Year: 2018

The federated query extension of SPARQL 1.1 allows executing queries distributed over different SPARQL endpoints. SPARQL-LD is a recent extension of SPARQL 1.1 which enables to directly query any HTTP web source containing RDF data, like web pages embedded with RDFa, JSON-LD or Microformats, without requiring the declaration of named graphs. This makes possible to query a large number of data sources (including SPARQL endpoints, online resources, or even Web APIs returning RDF data) through a single one concise query. However, not optimal formulation of SPARQL 1.1 and SPARQL-LD queries can lead to a large number of calls to remote resources which in turn can lead to extremely high query execution times. In this paper, we address this problem and propose a set of query reordering methods which make use of heuristics to reorder a set of SERVICE graph patterns based on their restrictiveness, without requiring the gathering and use of statistics from the remote sources. Such a query optimization approach is widely applicable since it can be exploited on top of existing SPARQL 1.1 and SPARQL-LD implementations. Evaluation results show that query reordering can highly decrease the query-execution time, while a method that considers the number and type of unbound variables and joins achieves the optimal query plan in 88% of the cases.
Action and intention recognition of pedestrians in urban traffic
Venue: arXiv cs.CV 2018
Year: 2018

Action and intention recognition of pedestrians in urban settings are challenging problems for Advanced Driver Assistance Systems as well as future autonomous vehicles to maintain smooth and safe traffic. This work investigates a number of feature extraction methods in combination with several machine learning algorithms to build knowledge on how to automatically detect the action and intention of pedestrians in urban traffic. We focus on the motion and head orientation to predict whether the pedestrian is about to cross the street or not. The work is based on the Joint Attention for Autonomous Driving (JAAD) dataset, which contains 346 videoclips of various traffic scenarios captured with cameras mounted in the windshield of a car. An accuracy of 72% for head orientation estimation and 85% for motion detection is obtained in our experiments.
LincoSim: a web based HPC-cloud platform for automatic virtual towing tank analysis
Venue: arXiv cs.DC 2018
Year: 2018

In this work, we present a new web based HPC-cloud platform for automatic virtual towing tank analysis. It is well known that the design project of a new hull requires a continuous integration of shape hypothesis and hydrodynamics verifications using analytical tools, 3D computational methods, experimental facilities and sea keeping trial tests. The complexity and the cost of such design tools increase considerably moving from analytical tools to sea keeping trials. In order to perform a meaningful trade-off between costs and high quality data acquiring during the last decade the usage of 3D computational models has grown pushed also by well-known technological factors. Nevertheless, in the past, there were several limiting factors on the wide diffusion of 3D computational models to perform virtual towing tank data acquiring, from hardware and software costs to the specific technological skills needed. In this work we propose an innovative high-level approach which is embodied in the so-called LincoSim web application in which a hypothetical designer user can carry out the simulation only starting from its own geometry and a set of meaningful physical parameters. LincoSim automatically manages and hides to the user all the necessary details of CFD modelling and of HPC infrastructure usage allowing them to access, visualize and analyze the outputs from the same single access point made up from the web browser. In addition to the web interface, the platform includes a back-end server which implements a Cloud logic and can be connected to multiple HPC machines for computing. LincoSim is currently set up with finite volume Open-FOAM CFD engine. A preliminary validation campaign has been performed to assess the robustness and the reliability of the tool and is proposed as a novel approach for the development of Computer Aided Engineering (CAE) applications.
Blockchain-based Supply Chain Traceability: Token Recipes model Manufacturing Processes
Venue: arXiv cs.CY 2018
Year: 2018

Growing consumer awareness as well as manufacturers' internal quality requirements lead to novel demands on supply chain traceability. Existing centralized solutions suffer from isolated data storage and lacking trust when multiple parties are involved. Decentralized blockchain-based approaches attempt to overcome these shortcomings by creating digital representations of physical goods to facilitate tracking across multiple entities. However, they currently do not capture the transformation of goods in manufacturing processes. Therefore, the relation between ingredients and product is lost, limiting the ability to trace a product's provenance. We propose a blockchain-based supply chain traceability system using smart contracts. In such contracts, manufacturers define the composition of products in the form of recipes. Each ingredient of the recipe is a non-fungible token that corresponds to a batch of physical goods. When the recipe is applied, its ingredients are consumed and a new token is produced. This mechanism preserves the traceability of product transformations. The system is implemented for the Ethereum Virtual Machine and is applicable to any blockchain configuration that supports it. Our evaluation reveals that the gas costs scale linearly with the number of products considered in the system. This leads to the conclusion that the solution can handle complex use cases.
Deep Neural Network inference with reduced word length
Venue: arXiv cs.LG 2018
Year: 2018

Deep neural networks (DNN) are powerful models for many pattern recognition tasks, yet their high computational complexity and memory requirement limit them to applications on high-performance computing platforms. In this paper, we propose a new method to evaluate DNNs trained with 32bit floating point (float32) accuracy using only low precision integer arithmetics in combination with binary shift and clipping operations. Because hardware implementation of these operations is much simpler than high precision floating point calculation, our method can be used for an efficient DNN inference on dedicated hardware. In experiments on MNIST, we demonstrate that DNNs trained with float32 can be evaluated using a combination of 2bit integer arithmetics and a few float32 calculations in each layer or only 3bit integer arithmetics in combination with binary shift and clipping without significant performance degradation.
Automatic Full Compilation of Julia Programs and ML Models to Cloud TPUs
Venue: arXiv cs.PL 2018
Year: 2018

Google's Cloud TPUs are a promising new hardware architecture for machine learning workloads. They have powered many of Google's milestone machine learning achievements in recent years. Google has now made TPUs available for general use on their cloud platform and as of very recently has opened them up further to allow use by non-TensorFlow frontends. We describe a method and implementation for offloading suitable sections of Julia programs to TPUs via this new API and the Google XLA compiler. Our method is able to completely fuse the forward pass of a VGG19 model expressed as a Julia program into a single TPU executable to be offloaded to the device. Our method composes well with existing compiler-based automatic differentiation techniques on Julia code, and we are thus able to also automatically obtain the VGG19 backwards pass and similarly offload it to the TPU. Targeting TPUs using our compiler, we are able to evaluate the VGG19 forward pass on a batch of 100 images in 0.23s which compares favorably to the 52.4s required for the original model on the CPU. Our implementation is less than 1000 lines of Julia, with no TPU specific changes made to the core Julia compiler or any other Julia packages.
Chord Recognition in Symbolic Music: A Segmental CRF Model, Segment-Level Features, and Comparative Evaluations on Classical and Popular Music
Venue: arXiv cs.SD 2018
Year: 2018

We present a new approach to harmonic analysis that is trained to segment music into a sequence of chord spans tagged with chord labels. Formulated as a semi-Markov Conditional Random Field (semi-CRF), this joint segmentation and labeling approach enables the use of a rich set of segment-level features, such as segment purity and chord coverage, that capture the extent to which the events in an entire segment of music are compatible with a candidate chord label. The new chord recognition model is evaluated extensively on three corpora of classical music and a newly created corpus of rock music. Experimental results show that the semi-CRF model performs substantially better than previous approaches when trained on a sufficient number of labeled examples and remains competitive when the amount of training data is limited.
Time-Aware and Corpus-Specific Entity Relatedness
Venue: arXiv cs.IR 2018
Year: 2018

Entity relatedness has emerged as an important feature in a plethora of applications such as information retrieval, entity recommendation and entity linking. Given an entity, for instance a person or an organization, entity relatedness measures can be exploited for generating a list of highly-related entities. However, the relation of an entity to some other entity depends on several factors, with time and context being two of the most important ones (where, in our case, context is determined by a particular corpus). For example, the entities related to the International Monetary Fund are different now compared to some years ago, while these entities also may highly differ in the context of a USA news portal compared to a Greek news portal. In this paper, we propose a simple but flexible model for entity relatedness which considers time and entity aware word embeddings by exploiting the underlying corpus. The proposed model does not require external knowledge and is language independent, which makes it widely useful in a variety of applications.
Approximating the Quadratic Transportation Metric in Near-Linear Time
Venue: arXiv cs.DS 2018
Year: 2018

Computing the quadratic transportation metric (also called the $2$-Wasserstein distance or root mean square distance) between two point clouds, or, more generally, two discrete distributions, is a fundamental problem in machine learning, statistics, computer graphics, and theoretical computer science. A long line of work has culminated in a sophisticated geometric algorithm due to Agarwal and Sharathkumar in 2014, which runs in time $\tilde{O}(n^{3/2})$, where $n$ is the number of points. However, obtaining faster algorithms has proven difficult since the $2$-Wasserstein distance is known to have poor sketching and embedding properties, which limits the effectiveness of geometric approaches. In this paper, we give an extremely simple deterministic algorithm with $\tilde{O}(n)$ runtime by using a completely different approach based on entropic regularization, approximate Sinkhorn scaling, and low-rank approximations of Gaussian kernel matrices. We give explicit dependence of our algorithm on the dimension and precision of the approximation.
Novel Adaptive Algorithms for Estimating Betweenness, Coverage and k-path Centralities
Venue: arXiv cs.DS 2018
Year: 2018

An important index widely used to analyze social and information networks is betweenness centrality. In this paper, first given a directed network $G$ and a vertex $r\in V(G)$, we present a novel adaptive algorithm for estimating betweenness score of $r$. Our algorithm first computes two subsets of the vertex set of $G$, called $\mathcal{RF}(r)$ and $\mathcal{RT}(r)$, that define the sample spaces of the start-points and the end-points of the samples. Then, it adaptively samples from $\mathcal{RF}(r)$ and $\mathcal{RT}(r)$ and stops as soon as some condition is satisfied. The stopping condition depends on the samples met so far, $|\mathcal{RF}(r)|$ and $|\mathcal{RT}(r)|$. We show that compared to the well-known existing methods, our algorithm gives a more efficient $(λ,δ)$-approximation. Then, we propose a novel algorithm for estimating $k$-path centrality of $r$. Our algorithm is based on computing two sets $\mathcal{RF}(r)$ and $\mathcal{D}(r)$. While $\mathcal{RF}(r)$ defines the sample space of the source vertices of the sampled paths, $\mathcal{D}(r)$ defines the sample space of the other vertices of the paths. We show that in order to give a $(λ,δ)$-approximation of the $k$-path score of $r$, our algorithm requires considerably less samples. Moreover, it processes each sample faster and with less memory. Finally, we empirically evaluate our proposed algorithms and show their superior performance. Also, we show that they can be used to efficiently compute centrality scores of a set of vertices.
Resource-Constrained Simultaneous Detection and Labeling of Objects in High-Resolution Satellite Images
Venue: arXiv cs.CV 2018
Year: 2018

We describe a strategy for detection and classification of man-made objects in large high-resolution satellite photos under computational resource constraints. We detect and classify candidate objects by using five pipelines of convolutional neural network processing (CNN), run in parallel. Each pipeline has its own unique strategy for fine tunning parameters, proposal region filtering, and dealing with image scales. The conflicting region proposals are merged based on region confidence and not just based on overlap areas, which improves the quality of the final bounding-box regions selected. We demonstrate this strategy using the recent xView challenge, which is a complex benchmark with more than 1,100 high-resolution images, spanning 800,000 aerial objects around the world covering a total area of 1,400 square kilometers at 0.3 meter ground sample distance. To tackle the resource-constrained problem posed by the xView challenge, where inferences are restricted to be on CPU with 8GB memory limit, we used lightweight CNN's trained with the single shot detector algorithm. Our approach was competitive on sequestered sets; it was ranked third.
Immercity: a curation content application in Virtual and Augmented reality
Venue: arXiv cs.GR 2018
Year: 2018

When working with emergent and appealing technologies as Virtual Reality, Mixed Reality and Augmented Reality, the issue of definitions appear very often. Indeed, our experience with various publics allows us to notice that technology definitions pose ambiguity and representation problems for informed as well as novice users. In this paper we present Immercity, a content curation system designed in the context of a collaboration between the University of Montpellier and CapGemi-ni, to deliver a technology watch. It is also used as a testbed for our experiences with Virtual, Mixed and Augmented reality to explore new interaction techniques and devices, artificial intelligence integration, visual affordances, performance , etc. But another, very interesting goal appeared: use Immercity to communicate about Virtual, Mixed and Augmented Reality by using them as a support.
Extending the centerpoint theorem to multiple points
Venue: arXiv cs.CG 2018
Year: 2018

The centerpoint theorem is a well-known and widely used result in discrete geometry. It states that for any point set $P$ of $n$ points in $\mathbb{R}^d$, there is a point $c$, not necessarily from $P$, such that each halfspace containing $c$ contains at least $\frac{n}{d+1}$ points of $P$. Such a point $c$ is called a centerpoint, and it can be viewed as a generalization of a median to higher dimensions. In other words, a centerpoint can be interpreted as a good representative for the point set $P$. But what if we allow more than one representative? For example in one-dimensional data sets, often certain quantiles are chosen as representatives instead of the median. We present a possible extension of the concept of quantiles to higher dimensions. The idea is to find a set $Q$ of (few) points such that every halfspace that contains one point of $Q$ contains a large fraction of the points of $P$ and every halfspace that contains more of $Q$ contains an even larger fraction of $P$. This setting is comparable to the well-studied concepts of weak $\varepsilon$-nets and weak $\varepsilon$-approximations, where it is stronger than the former but weaker than the latter.