OpenRaiser/Intern-Atlas huggingface dataset:
Use of visual programming in the university education by means of free software
Venue: arXiv cs.CY 2018
Year: 2018

The term visual programming has started to be used in Informatics so far, however, there are different views on its meaning. The separation of visual programming from development tools of interfaces provides not only the certainty for this technology, but also opens the systems of modeling and programming in the education unknown to a wide range. The use of visual programming which is based on the free software provides the new perspective algorithmic culture formation of future professionals of non-computer specialists. A wide application of visual programming in the educational process is the perspective direction of the cognitive activity intensification, laying the foundations of the algorithmic competence. Visual simulation systems which are related to the creation of the unified means of any software, when are used in training activities allow not only to create models, determining the nature, categories and connections, but also to perform the experimental calculation of the model's conduct for parametric input data, identify behaviour of the system. Therefore the detection problem of the free software that supports visual programming is relevant and for its solution the general features of such systems and ways of their use have been analyzed. These results suggest the significant potential use of visual programming technologies in the educational activities and that there exist the free software which implements this technology and only enhances its value. Visual programming is an advanced technology, but also has directions and prospects. Although it has not got a large scale in programming yet but it has the experience in the educational application. The results of the visual programming use in the algorithmic culture formation are undeniable, and thus the further use with improvement techniques will allow to develop the future students' algorithmic competence.
CaricatureShop: Personalized and Photorealistic Caricature Sketching
Venue: arXiv cs.CV 2018
Year: 2018

In this paper, we propose the first sketching system for interactively personalized and photorealistic face caricaturing. Input an image of a human face, the users can create caricature photos by manipulating its facial feature curves. Our system firstly performs exaggeration on the recovered 3D face model according to the edited sketches, which is conducted by assigning the laplacian of each vertex a scaling factor. To construct the mapping between 2D sketches and a vertex-wise scaling field, a novel deep learning architecture is developed. With the obtained 3D caricature model, two images are generated, one obtained by applying 2D warping guided by the underlying 3D mesh deformation and the other obtained by re-rendering the deformed 3D textured model. These two images are then seamlessly integrated to produce our final output. Due to the severely stretching of meshes, the rendered texture is of blurry appearances. A deep learning approach is exploited to infer the missing details for enhancing these blurry regions. Moreover, a relighting operation is invented to further improve the photorealism of the result. Both quantitative and qualitative experiment results validated the efficiency of our sketching system and the superiority of our proposed techniques against existing methods.
QUEST: Quadriletral Senary bit Pattern for Facial Expression Recognition
Venue: arXiv cs.CV 2018
Year: 2018

Facial expression has a significant role in analyzing human cognitive state. Deriving an accurate facial appearance representation is a critical task for an automatic facial expression recognition application. This paper provides a new feature descriptor named as Quadrilateral Senary bit Pattern for facial expression recognition. The QUEST pattern encoded the intensity changes by emphasizing the relationship between neighboring and reference pixels by dividing them into two quadrilaterals in a local neighborhood. Thus, the resultant gradient edges reveal the transitional variation information, that improves the classification rate by discriminating expression classes. Moreover, it also enhances the capability of the descriptor to deal with viewpoint variations and illumination changes. The trine relationship in a quadrilateral structure helps to extract the expressive edges and suppressing noise elements to enhance the robustness to noisy conditions. The QUEST pattern generates a six-bit compact code, which improves the efficiency of the FER system with more discriminability. The effectiveness of the proposed method is evaluated by conducting several experiments on four benchmark datasets: MMI, GEMEP-FERA, OULU-CASIA, and ISED. The experimental results show better performance of the proposed method as compared to existing state-art-the approaches.
Automatically Assessing Vulnerabilities Discovered by Compositional Analysis
Venue: arXiv cs.SE 2018
Year: 2018

Testing is the most widely employed method to find vulnerabilities in real-world software programs. Compositional analysis, based on symbolic execution, is an automated testing method to find vulnerabilities in medium- to large-scale programs consisting of many interacting components. However, existing compositional analysis frameworks do not assess the severity of reported vulnerabilities. In this paper, we present a framework to analyze vulnerabilities discovered by an existing compositional analysis tool and assign CVSS3 (Common Vulnerability Scoring System v3.0) scores to them, based on various heuristics such as interaction with related components, ease of reachability, complexity of design and likelihood of accepting unsanitized input. By analyzing vulnerabilities reported with CVSS3 scores in the past, we train simple machine learning models. By presenting our interactive framework to developers of popular open-source software and other security experts, we gather feedback on our trained models and further improve the features to increase the accuracy of our predictions. By providing qualitative (based on community feedback) and quantitative (based on prediction accuracy) evidence from 21 open-source programs, we show that our severity prediction framework can effectively assist developers with assessing vulnerabilities.
Skin disease identification from dermoscopy images using deep convolutional neural network
Venue: arXiv cs.CV 2018
Year: 2018

In this paper, a deep neural network based ensemble method is experimented for automatic identification of skin disease from dermoscopic images. The developed algorithm is applied on the task3 of the ISIC 2018 challenge dataset (Skin Lesion Analysis Towards Melanoma Detection).
PReMVOS: Proposal-generation, Refinement and Merging for Video Object Segmentation
Venue: arXiv cs.CV 2018
Year: 2018

We address semi-supervised video object segmentation, the task of automatically generating accurate and consistent pixel masks for objects in a video sequence, given the first-frame ground truth annotations. Towards this goal, we present the PReMVOS algorithm (Proposal-generation, Refinement and Merging for Video Object Segmentation). Our method separates this problem into two steps, first generating a set of accurate object segmentation mask proposals for each video frame and then selecting and merging these proposals into accurate and temporally consistent pixel-wise object tracks over a video sequence in a way which is designed to specifically tackle the difficult challenges involved with segmenting multiple objects across a video sequence. Our approach surpasses all previous state-of-the-art results on the DAVIS 2017 video object segmentation benchmark with a J & F mean score of 71.6 on the test-dev dataset, and achieves first place in both the DAVIS 2018 Video Object Segmentation Challenge and the YouTube-VOS 1st Large-scale Video Object Segmentation Challenge.
FluidDyn: a Python open-source framework for research and teaching in fluid dynamics
Venue: arXiv cs.OH 2018
Year: 2018

FluidDyn is a project to foster open-science and open-source in the fluid dynamics community. It is thought of as a research project to channel open-source dynamics, methods and tools to do science. We propose a set of Python packages forming a framework to study fluid dynamics with different methods, in particular laboratory experiments (package fluidlab), simulations (packages fluidfft, fluidsim and fluidfoam) and data processing (package fluidimage). In the present article, we give an overview of the specialized packages of the project and then focus on the base package called fluiddyn, which contains common code used in the specialized packages. Packages fluidfft and fluidsim are described with greater detail in two companion papers, Mohanan et al. (2018a,b). With the project FluidDyn, we demonstrate that specialized scientific code can be written with methods and good practices of the open-source community. The Mercurial repositories are available in Bitbucket (https://bitbucket.org/fluiddyn/). All codes are documented using Sphinx and Read the Docs, and tested with continuous integration run on Bitbucket, Pipelines and Travis. To improve the reuse potential, the codes are as modular as possible, leveraging the simple object-oriented programming model of Python. All codes are also written to be highly efficient, using C++, Cython and Pythran to speedup the performance of critical functions.
The Use Of The Cloud-based Learning Component With The MAXIMA System For Teaching Operations Research
Venue: arXiv cs.CY 2018
Year: 2018

The article highlights the problem of the use of c omputer mathematics (SCM) in today's high-tech environment, in particular, it describes the promising ways of introducing the cloud- oriented components based on SCM, which is a signif icant factor in increasing access to them as a means of educational and research activities in the field of mathematics and computer science disciplines. The role of SCM in training of bachelo rs of computer science and especially their use in operations research learning is revealed. The ma in characteristics of SCM MAXIMA and ways of access to it both locally and in the cloud-orien ted implementation are considered. The results of the expert evaluation of the cloud-based learning c omponent with the use of MAXIMA system for operations research learning are presented.
Learning to Generate and Reconstruct 3D Meshes with only 2D Supervision
Venue: arXiv cs.CV 2018
Year: 2018

We present a unified framework tackling two problems: class-specific 3D reconstruction from a single image, and generation of new 3D shape samples. These tasks have received considerable attention recently; however, existing approaches rely on 3D supervision, annotation of 2D images with keypoints or poses, and/or training with multiple views of each object instance. Our framework is very general: it can be trained in similar settings to these existing approaches, while also supporting weaker supervision scenarios. Importantly, it can be trained purely from 2D images, without ground-truth pose annotations, and with a single view per instance. We employ meshes as an output representation, instead of voxels used in most prior work. This allows us to exploit shading information during training, which previous 2D-supervised methods cannot. Thus, our method can learn to generate and reconstruct concave object classes. We evaluate our approach on synthetic data in various settings, showing that (i) it learns to disentangle shape from pose; (ii) using shading in the loss improves performance; (iii) our model is comparable or superior to state-of-the-art voxel-based approaches on quantitative metrics, while producing results that are visually more pleasing; (iv) it still performs well when given supervision weaker than in prior works.
CSIndexbr: Exploring the Brazilian Scientific Production in Computer Science
Venue: arXiv cs.DL 2018
Year: 2018

CSIndexbr is a web-based system that provides meaningful,open,and transparent data about Brazilian scientific production in Computer Science. Currently, the system collects full research papers published in the main track of selected conferences. The papers are retrieved from DBLP. In this article, we describe the main features and resources provided by CSIndexbr. We also comment on how other researchers can use the data provided by the system to analyze the Brazilian production in Computer Science.
Metric Sublinear Algorithms via Linear Sampling
Venue: arXiv cs.DS 2018
Year: 2018

In this work we provide a new technique to design fast approximation algorithms for graph problems where the points of the graph lie in a metric space. Specifically, we present a sampling approach for such metric graphs that, using a sublinear number of edge weight queries, provides a {\em linear sampling}, where each edge is (roughly speaking) sampled proportionally to its weight. For several natural problems, such as densest subgraph and max cut among others, we show that by sparsifying the graph using this sampling process, we can run a suitable approximation algorithm on the sparsified graph and the result remains a good approximation for the original problem. Our results have several interesting implications, such as providing the first sublinear time approximation algorithm for densest subgraph in a metric space, and improving the running time of estimating the average distance.
Handwritten Digit Recognition by Elastic Matching
Venue: arXiv cs.CV 2018
Year: 2018

A simple model of MNIST handwritten digit recognition is presented here. The model is an adaptation of a previous theory of face recognition. It realizes translation and rotation invariance in a principled way instead of being based on extensive learning from large masses of sample data. The presented recognition rates fall short of other publications, but due to its inspectability and conceptual and numerical simplicity, our system commends itself as a basis for further development.
Multipath Transmission Scheduling in Millimeter Wave Cloud Radio Access Networks
Venue: arXiv cs.NI 2018
Year: 2018

Millimeter wave (mmWave) communications provide great potential for next-generation cellular networks to meet the demands of fast-growing mobile data traffic with plentiful spectrum available. However, in a mmWave cellular system, the shadowing and blockage effects lead to the intermittent connectivity, and the handovers are more frequent. This paper investigates an ``all-mmWave'' cloud radio access network (cloud-RAN), in which both the fronthaul and the radio access links operate at mmWave. To address the intermittent transmissions, we allow the mobile users (MUs) to establish multiple connections to the central unit over the remote radio heads (RRHs). Specifically, we propose a multipath transmission framework by leveraging the ``all-mmWave'' cloud-RAN architecture, which makes decisions of the RRH association and the packet transmission scheduling according to the time-varying network statistics, such that a MU experiences the minimum queueing delay and packet drops. The joint RRH association and transmission scheduling problem is formulated as a Markov decision process (MDP). Due to the problem size, a low-complexity online learning scheme is put forward, which requires no a priori statistic information of network dynamics. Simulations show that our proposed scheme outperforms the state-of-art baselines, in terms of average queue length and average packet dropping rate.
Shortest path queries, graph partitioning and covering problems in worst and beyond worst case settings
Venue: arXiv cs.DS 2018
Year: 2018

In this thesis, we design algorithms for several NP-hard problems in both worst and beyond worst case settings. In the first part of the thesis, we apply the traditional worst case methodology and design approximation algorithms for the Hub Labeling problem; Hub Labeling is a preprocessing technique introduced to speed up shortest path queries. Before this work, Hub Labeling had been extensively studied mainly in the beyond worst case analysis setting, and in particular on graphs with low highway dimension. In this work, we significantly improve our theoretical understanding of the problem and design (worst-case) algorithms for various classes of graphs, such as general graphs, graphs with unique shortest paths and trees, as well as provide matching inapproximability lower bounds for the problem in its most general settings. Finally, we demonstrate a connection between computing a Hub Labeling on a tree and searching for a node in a tree. In the second part of the thesis, we turn to beyond worst case analysis and extensively study the stability model introduced by Bilu and Linial in an attempt to describe real-life instances of graph partitioning and clustering problems. Informally, an instance of a combinatorial optimization problem is stable if it has a unique optimal solution that remains the unique optimum under small multiplicative perturbations of the parameters of the input. Utilizing the power of convex relaxations for stable instances, we obtain several results for problems such as Edge/Node Multiway Cut, Independent Set (and its equivalent, in terms of exact solvability, Vertex Cover), clustering problems such as $k$-center and $k$-median and the symmetric Traveling Salesman problem. We also provide strong lower bounds for certain families of algorithms for covering problems, thus exhibiting potential barriers towards the design of improved algorithms in this framework.
A 36-Element Solution To Schneiders' Pyramid Hex-Meshing Problem And A Parity-Changing Template For Hex-Mesh Revision
Venue: arXiv cs.CG 2018
Year: 2018

In this paper, we present a solution that uses the least number of hexahedra to build a pyramid, which is the key block required for one type of automatic hex-meshing method to be successful. When the initial result of a hex-meshing program is not appropriate for specific applications, some templates are used for revision. The templates reported thus far are parity-preserving, which means that the parity of the number of hexahedra in a mesh is unchanged after a revision following the templates. We present a parity-changing template that makes the template set integral and more effective. These two findings are obtained by a program that we developed for this study, which is a tool for researchers to observe the characteristics of small hexahedral packings.
"Bilingual Expert" Can Find Translation Errors
Venue: arXiv cs.CL 2018
Year: 2018

Recent advances in statistical machine translation via the adoption of neural sequence-to-sequence models empower the end-to-end system to achieve state-of-the-art in many WMT benchmarks. The performance of such machine translation (MT) system is usually evaluated by automatic metric BLEU when the golden references are provided for validation. However, for model inference or production deployment, the golden references are prohibitively available or require expensive human annotation with bilingual expertise. In order to address the issue of quality evaluation (QE) without reference, we propose a general framework for automatic evaluation of translation output for most WMT quality evaluation tasks. We first build a conditional target language model with a novel bidirectional transformer, named neural bilingual expert model, which is pre-trained on large parallel corpora for feature extraction. For QE inference, the bilingual expert model can simultaneously produce the joint latent representation between the source and the translation, and real-valued measurements of possible erroneous tokens based on the prior knowledge learned from parallel data. Subsequently, the features will further be fed into a simple Bi-LSTM predictive model for quality evaluation. The experimental results show that our approach achieves the state-of-the-art performance in the quality estimation track of WMT 2017/2018.
Specification-Based Protocol Obfuscation
Venue: arXiv cs.CR 2018
Year: 2018

This paper proposes a new obfuscation technique of a communication protocol that is aimed at making the reverse engineering of the protocol more complex. The obfuscation is based on the transformation of protocol message format specification. The obfuscating transformations are applied to the Abstract Syntax Tree (AST) representation of the messages and mainly concern the ordering or aggregation of the AST nodes. The paper also presents the design of a framework that implements the proposed obfuscation technique by automatically generating, from the specification of the message format, a library performing the corresponding transformations. Finally, our framework is applied to two real application protocols (Modbus and HTTP) to illustrate the relevance and efficiency of the proposed approach. Various metrics recorded from the experiments show the significant increase of the complexity of the obfuscated protocol binary compared to the non-obfuscated code. It is also shown that the execution time and memory overheads remain acceptable for a practical deployment of the approach in operation.
Millimeter-Wave Propagation within a Computer Chip Package
Venue: arXiv cs.ET 2018
Year: 2018

Wireless Network-on-Chip (WNoC) appears as a promising alternative to conventional interconnect fabrics for chip-scale communications. The WNoC paradigm has been extensively analyzed from the physical, network and architecture perspectives assuming mmWave band operation. However, there has not been a comprehensive study at this band for realistic chip packages and, thus, the characteristics of such wireless channel remain not fully understood. This work addresses this issue by accurately modeling a flip-chip package and investigating the wave propagation inside it. Through parametric studies, a locally optimal configuration for 60 GHz WNoC is obtained, showing that chip-wide attenuation below 32.6 dB could be achieved with standard processes. Finally, the applicability of the methodology is discussed for higher bands and other integrated environments such as a Software-Defined Metamaterial (SDM).
Maker-Breaker domination game
Venue: arXiv cs.DM 2018
Year: 2018

We introduce the Maker-Breaker domination game, a two player game on a graph. At his turn, the first player, Dominator, select a vertex in order to dominate the graph while the other player, Staller, forbids a vertex to Dominator in order to prevent him to reach his goal. Both players play alternately without missing their turn. This game is a particular instance of the so-called Maker-Breaker games, that is studied here in a combinatorial context. In this paper, we first prove that deciding the winner of the Maker-Breaker domination game is PSPACE-complete, even for bipartite graphs and split graphs. It is then showed that the problem is polynomial for cographs and trees. In particular, we define a strategy for Dominator that is derived from a variation of the dominating set problem, called the pairing dominating set problem.