OpenRaiser/Intern-Atlas huggingface dataset:
Discovering Neighbor Devices in Computer Network: Development of CDP and LLDP Simulation Modules for OMNeT++
Venue: arXiv cs.NI 2017
Year: 2017

The purpose of data-link layer discovery protocols is to provide the network administrator with the current information (i.e., various Layer 2 and 3 parameters) about neighbor devices. These protocols are invaluable for network monitoring, maintenance, and troubleshooting. However, they start to play an important role in the operation of data-centers and other high-availability networks. This paper outlines design, implementation and deployment of Cisco Discovery Protocol and Link Layer Discovery Protocol simulation modules in OMNeT++ simulator.
Complete End-To-End Low Cost Solution To a 3D Scanning System with Integrated Turntable
Venue: arXiv cs.CV 2017
Year: 2017

3D reconstruction is a technique used in computer vision which has a wide range of applications in areas like object recognition, city modelling, virtual reality, physical simulations, video games and special effects. Previously, to perform a 3D reconstruction, specialized hardwares were required. Such systems were often very expensive and was only available for industrial or research purpose. With the rise of the availability of high-quality low cost 3D sensors, it is now possible to design inexpensive complete 3D scanning systems. The objective of this work was to design an acquisition and processing system that can perform 3D scanning and reconstruction of objects seamlessly. In addition, the goal of this work also included making the 3D scanning process fully automated by building and integrating a turntable alongside the software. This means the user can perform a full 3D scan only by a press of a few buttons from our dedicated graphical user interface. Three main steps were followed to go from acquisition of point clouds to the finished reconstructed 3D model. First, our system acquires point cloud data of a person/object using inexpensive camera sensor. Second, align and convert the acquired point cloud data into a watertight mesh of good quality. Third, export the reconstructed model to a 3D printer to obtain a proper 3D print of the model.
A Geometric Approach to Harmonic Color Palette Design
Venue: arXiv cs.CV 2017
Year: 2017

We address the problem of finding harmonic colors, this problem has many applications, from fashion to industrial design. In order to solve this problem we consider that colors follow normal distributions in tone (chroma and lightness) and hue. The proposed approach relies in the CIE standard for representing colors and evaluate proximity. Other approaches to this problem use a set of rules. Experimental results show that lines with specific parameters angles of inclination, and distance from the reference point are preferred over others, and that uncertain line patterns outperform non-linear patterns.
Embedded Binarized Neural Networks
Venue: arXiv cs.CV 2017
Year: 2017

We study embedded Binarized Neural Networks (eBNNs) with the aim of allowing current binarized neural networks (BNNs) in the literature to perform feedforward inference efficiently on small embedded devices. We focus on minimizing the required memory footprint, given that these devices often have memory as small as tens of kilobytes (KB). Beyond minimizing the memory required to store weights, as in a BNN, we show that it is essential to minimize the memory used for temporaries which hold intermediate results between layers in feedforward inference. To accomplish this, eBNN reorders the computation of inference while preserving the original BNN structure, and uses just a single floating-point temporary for the entire neural network. All intermediate results from a layer are stored as binary values, as opposed to floating-points used in current BNN implementations, leading to a 32x reduction in required temporary space. We provide empirical evidence that our proposed eBNN approach allows efficient inference (10s of ms) on devices with severely limited memory (10s of KB). For example, eBNN achieves 95\% accuracy on the MNIST dataset running on an Intel Curie with only 15 KB of usable memory with an inference runtime of under 50 ms per sample. To ease the development of applications in embedded contexts, we make our source code available that allows users to train and discover eBNN models for a learning task at hand, which fit within the memory constraint of the target device.
Monocular Navigation in Large Scale Dynamic Environments
Venue: arXiv cs.CV 2017
Year: 2017

We present a processing technique for a robust reconstruction of motion properties for single points in large scale, dynamic environments. We assume that the acquisition camera is moving and that there are other independently moving agents in a large environment, like road scenarios. The separation of direction and magnitude of the reconstructed motion allows for robust reconstruction of the dynamic state of the objects in situations, where conventional binocular systems fail due to a small signal (disparity) from the images due to a constant detection error, and where structure from motion approaches fail due to unobserved motion of other agents between the camera frames. We present the mathematical framework and the sensitivity analysis for the resulting system.
Rank-Select Indices Without Tears
Venue: arXiv cs.DS 2017
Year: 2017

A rank-select index for a sequence $B=(b_1,\ldots,b_n)$ of $n$ bits is a data structure that, if provided with an operation to access $Θ(\log n)$ arbitrary consecutive bits of $B$ in constant time (thus $B$ is stored outside of the data structure), can compute $\mathit{rank}_B(j)=\sum_{i=1}^j b_i$ for given $j\in\{0,\ldots,n\}$ and $\mathit{select}_B(k)=\min\{j:\mathit{rank}_B(j)\ge k\}$ for given $k\in\{1,\ldots,\sum_{i=1}^n b_i\}$. We describe a new rank-select index that, like previous rank-select indices, occupies $O(n\log\log n/\log n)$ bits and executes $\mathit{rank}$ and $\mathit{select}$ queries in constant time. Its derivation is intended to be particularly easy to follow and largely free of tedious low-level detail, its operations are given by straight-line code, and we show that it can be constructed in $O(n/\log n)$ time.
Deep Learning the Physics of Transport Phenomena
Venue: arXiv cs.LG 2017
Year: 2017

We have developed a new data-driven paradigm for the rapid inference, modeling and simulation of the physics of transport phenomena by deep learning. Using conditional generative adversarial networks (cGAN), we train models for the direct generation of solutions to steady state heat conduction and incompressible fluid flow purely on observation without knowledge of the underlying governing equations. Rather than using iterative numerical methods to approximate the solution of the constitutive equations, cGANs learn to directly generate the solutions to these phenomena, given arbitrary boundary conditions and domain, with high test accuracy (MAE$<$1\%) and state-of-the-art computational performance. The cGAN framework can be used to learn causal models directly from experimental observations where the underlying physical model is complex or unknown.
3D angle-of-arrival positioning using von Mises-Fisher distribution
Venue: arXiv cs.SY 2017
Year: 2017

We propose modeling an angle-of-arrival (AOA) positioning measurement as a von Mises-Fisher (VMF) distributed unit vector instead of the conventional normally distributed azimuth and elevation measurements. Describing the 2-dimensional AOA measurement with three numbers removes discontinuities and reduces nonlinearity at the poles of the azimuth-elevation coordinate system. Our computer simulations show that the proposed VMF measurement noise model based filters outperform the normal distribution based algorithms in accuracy in a scenario where close-to-pole measurements occur frequently.
Sequential Composition in the Presence of Intermediate Termination (Extended Abstract)
Venue: arXiv cs.LO 2017
Year: 2017

The standard operational semantics of the sequential composition operator gives rise to unbounded branching and forgetfulness when transparent process expressions are put in sequence. Due to transparency, the correspondence between context-free and pushdown processes fails modulo bisimilarity, and it is not clear how to specify an always terminating half counter. We propose a revised operational semantics for the sequential composition operator in the context of intermediate termination. With the revised operational semantics, we eliminate transparency, allowing us to establish a close correspondence between context-free processes and pushdown processes. Moreover, we prove the reactive Turing powerfulness of TCP with iteration and nesting with the revised operational semantics for sequential composition.
BlockSci: Design and applications of a blockchain analysis platform
Venue: arXiv cs.CR 2017
Year: 2017

Analysis of blockchain data is useful for both scientific research and commercial applications. We present BlockSci, an open-source software platform for blockchain analysis. BlockSci is versatile in its support for different blockchains and analysis tasks. It incorporates an in-memory, analytical (rather than transactional) database, making it several hundred times faster than existing tools. We describe BlockSci's design and present four analyses that illustrate its capabilities. This is a working paper that accompanies the first public release of BlockSci, available at https://github.com/citp/BlockSci. We seek input from the community to further develop the software and explore other potential applications.
Extreme Sparse Multinomial Logistic Regression: A Fast and Robust Framework for Hyperspectral Image Classification
Venue: arXiv cs.CV 2017
Year: 2017

Although the sparse multinomial logistic regression (SMLR) has provided a useful tool for sparse classification, it suffers from inefficacy in dealing with high dimensional features and manually set initial regressor values. This has significantly constrained its applications for hyperspectral image (HSI) classification. In order to tackle these two drawbacks, an extreme sparse multinomial logistic regression (ESMLR) is proposed for effective classification of HSI. First, the HSI dataset is projected to a new feature space with randomly generated weight and bias. Second, an optimization model is established by the Lagrange multiplier method and the dual principle to automatically determine a good initial regressor for SMLR via minimizing the training error and the regressor value. Furthermore, the extended multi-attribute profiles (EMAPs) are utilized for extracting both the spectral and spatial features. A combinational linear multiple features learning (MFL) method is proposed to further enhance the features extracted by ESMLR and EMAPs. Finally, the logistic regression via the variable splitting and the augmented Lagrangian (LORSAL) is adopted in the proposed framework for reducing the computational time. Experiments are conducted on two well-known HSI datasets, namely the Indian Pines dataset and the Pavia University dataset, which have shown the fast and robust performance of the proposed ESMLR framework.
Sorting with GPUs: A Survey
Venue: arXiv cs.DC 2017
Year: 2017

Sorting is a fundamental operation in computer science and is a bottleneck in many important fields. Sorting is critical to database applications, online search and indexing,biomedical computing, and many other applications. The explosive growth in computational power and availability of GPU coprocessors has allowed sort operations on GPUs to be done much faster than any equivalently priced CPU. Current trends in GPU computing shows that this explosive growth in GPU capabilities is likely to continue for some time. As such, there is a need to develop algorithms to effectively harness the power of GPUs for crucial applications such as sorting.
Spatial Modulation for More Spatial Multiplexing: RF-Chain-Limited Generalized Spatial Modulation Aided MmWave MIMO with Hybrid Precoding
Venue: arXiv cs.IT 2017
Year: 2017

The application of hybrid precoding in millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems has been proved effective for reducing the number of radio frequency (RF) chains. However, the maximum number of independent data streams is conventionally restricted by the number of RF chains, which leads to limiting the spatial multiplexing gain. To further improve the achievable spectral efficiency (SE), in this paper we propose a novel generalized spatial modulation (GenSM) aided mmWave MIMO system to convey an extra data stream via the index of the active antennas group, while no extra RF chain is required. Moreover, we also propose a hybrid analog and digital precoding scheme for SE maximization. More specifically, a closed-form lower bound is firstly derived to quantify the achievable SE of the proposed system. By utilizing this lower bound as the cost function, a two-step algorithm is proposed to optimize the hybrid precoder. The proposed algorithm not only utilizes the concavity of the cost function over the digital power allocation vector, but also invokes the convex $\ell_\infty$ relaxation to handle the non-convex constraint imposed by analog precoding. Finally, the proposed scheme is shown via simulations to outperform state-of-the-art mmWave MIMO schemes in terms of achievable SE.
FAST: Frequency-Aware Spatio-Textual Indexing for In-Memory Continuous Filter Query Processing
Venue: arXiv cs.DB 2017
Year: 2017

Many applications need to process massive streams of spatio-textual data in real-time against continuous spatio-textual queries. For example, in location-aware ad targeting publish/subscribe systems, it is required to disseminate millions of ads and promotions to millions of users based on the locations and textual profiles of users. In this paper, we study indexing of continuous spatio-textual queries. There exist several related spatio-textual indexes that typically integrate a spatial index with a textual index. However, these indexes usually have a high demand for main-memory and assume that the entire vocabulary of keywords is known in advance. Also, these indexes do not successfully capture the variations in the frequencies of keywords across different spatial regions and treat frequent and infrequent keywords in the same way. Moreover, existing indexes do not adapt to the changes in workload over space and time. For example, some keywords may be trending at certain times in certain locations and this may change as time passes. This affects the indexing and searching performance of existing indexes significantly. In this paper, we introduce FAST, a Frequency-Aware Spatio-Textual index for continuous spatio-textual queries. FAST is a main-memory index that requires up to one third of the memory needed by the state-of-the-art index. FAST does not assume prior knowledge of the entire vocabulary of indexed objects. FAST adaptively accounts for the difference in the frequencies of keywords within their corresponding spatial regions to automatically choose the best indexing approach that optimizes the insertion and search times. Extensive experimental evaluation using real and synthetic datasets demonstrates that FAST is up to 3x faster in search time and 5x faster in insertion time than the state-of-the-art indexes.
Game Theory Models for the Verification of the Collective Behaviour of Autonomous Cars
Venue: arXiv cs.MA 2017
Year: 2017

The collective of autonomous cars is expected to generate almost optimal traffic. In this position paper we discuss the multi-agent models and the verification results of the collective behaviour of autonomous cars. We argue that non-cooperative autonomous adaptation cannot guarantee optimal behaviour. The conjecture is that intention aware adaptation with a constraint on simultaneous decision making has the potential to avoid unwanted behaviour. The online routing game model is expected to be the basis to formally prove this conjecture.
Completion of High Order Tensor Data with Missing Entries via Tensor-train Decomposition
Venue: arXiv cs.NA 2017
Year: 2017

In this paper, we aim at the completion problem of high order tensor data with missing entries. The existing tensor factorization and completion methods suffer from the curse of dimensionality when the order of tensor N>>3. To overcome this problem, we propose an efficient algorithm called TT-WOPT (Tensor-train Weighted OPTimization) to find the latent core tensors of tensor data and recover the missing entries. Tensor-train decomposition, which has the powerful representation ability with linear scalability to tensor order, is employed in our algorithm. The experimental results on synthetic data and natural image completion demonstrate that our method significantly outperforms the other related methods. Especially when the missing rate of data is very high, e.g., 85% to 99%, our algorithm can achieve much better performance than other state-of-the-art algorithms.
Object-Oriented Knowledge Extraction using Universal Exploiters
Venue: arXiv cs.AI 2017
Year: 2017

This paper contains analysis and extension of exploiters-based knowledge extraction methods, which allow generation of new knowledge, based on the basic ones. The main achievement of the paper is useful features of some universal exploiters proof, which allow extending set of basic classes and set of basic relations by finite set of new classes of objects and relations among them, which allow creating of complete lattice. Proposed approach gives an opportunity to compute quantity of new classes, which can be generated using it, and quantity of different types, which each of obtained classes describes; constructing of defined hierarchy of classes with determined subsumption relation; avoidance of some problems of inheritance and more efficient restoring of basic knowledge within the database.
Survey and Systematization of Secure Device Pairing
Venue: arXiv cs.CR 2017
Year: 2017

Secure Device Pairing (SDP) schemes have been developed to facilitate secure communications among smart devices, both personal mobile devices and Internet of Things (IoT) devices. Comparison and assessment of SDP schemes is troublesome, because each scheme makes different assumptions about out-of-band channels and adversary models, and are driven by their particular use-cases. A conceptual model that facilitates meaningful comparison among SDP schemes is missing. We provide such a model. In this article, we survey and analyze a wide range of SDP schemes that are described in the literature, including a number that have been adopted as standards. A system model and consistent terminology for SDP schemes are built on the foundation of this survey, which are then used to classify existing SDP schemes into a taxonomy that, for the first time, enables their meaningful comparison and analysis.The existing SDP schemes are analyzed using this model, revealing common systemic security weaknesses among the surveyed SDP schemes that should become priority areas for future SDP research, such as improving the integration of privacy requirements into the design of SDP schemes. Our results allow SDP scheme designers to create schemes that are more easily comparable with one another, and to assist the prevention of persisting the weaknesses common to the current generation of SDP schemes.
Method to Detect Eye Position Noise from Video-Oculography when Detection of Pupil or Corneal Reflection Position Fails
Venue: arXiv cs.CV 2017
Year: 2017

We present software to detect noise in eye position signals from video-based eye-tracking systems that depend on accurate pupil and corneal reflection position estimation. When such systems transiently fail to properly detect the pupil or the corneal reflection due to occlusion from eyelids, eye lashes or various shadows, the estimated gaze position is false. This produces an artifactual signal in the position trace that is rapidly, irregularly oscillating between true and false gaze positions. We refer to this noise as RIONEPS (Rapid Irregularly Oscillating Noise of the Eye Position Signal). Our method for detecting these periods automatically is based on an estimate of the relative inefficiency of the eye position signal. We look for RIONEPS in the horizontal and vertical traces separately, and although we typically use it offline, it is suitable to adaptation for real time use. This method requires a threshold to be set, and although we provide some guidance, thresholds will have to be estimated empirically.
On-Disk Data Processing: Issues and Future Directions
Venue: arXiv cs.DC 2017
Year: 2017

In this paper, we present a survey of "on-disk" data processing (ODDP). ODDP, which is a form of near-data processing, refers to the computing arrangement where the secondary storage drives have the data processing capability. Proposed ODDP schemes vary widely in terms of the data processing capability, target applications, architecture and the kind of storage drive employed. Some ODDP schemes provide only a specific but heavily used operation like sort whereas some provide a full range of operations. Recently, with the advent of Solid State Drives, powerful and extensive ODDP solutions have been proposed. In this paper, we present a thorough review of architectures developed for different on-disk processing approaches along with current and future challenges and also identify the future directions which ODDP can take.
Cycles in adversarial regularized learning
Venue: arXiv cs.GT 2017
Year: 2017

Regularized learning is a fundamental technique in online optimization, machine learning and many other fields of computer science. A natural question that arises in these settings is how regularized learning algorithms behave when faced against each other. We study a natural formulation of this problem by coupling regularized learning dynamics in zero-sum games. We show that the system's behavior is Poincaré recurrent, implying that almost every trajectory revisits any (arbitrarily small) neighborhood of its starting point infinitely often. This cycling behavior is robust to the agents' choice of regularization mechanism (each agent could be using a different regularizer), to positive-affine transformations of the agents' utilities, and it also persists in the case of networked competition, i.e., for zero-sum polymatrix games.
Semantic Preserving Embeddings for Generalized Graphs
Venue: arXiv cs.AI 2017
Year: 2017

A new approach to the study of Generalized Graphs as semantic data structures using machine learning techniques is presented. We show how vector representations maintaining semantic characteristics of the original data can be obtained from a given graph using neural encoding architectures and considering the topological properties of the graph. Semantic features of these new representations are tested by using some machine learning tasks and new directions on efficient link discovery, entitity retrieval and long distance query methodologies on large relational datasets are investigated using real datasets. ---- En este trabajo se presenta un nuevo enfoque en el contexto del aprendizaje automático multi-relacional para el estudio de Grafos Generalizados. Se muestra cómo se pueden obtener representaciones vectoriales que mantienen características semánticas del grafo original utilizando codificadores neuronales y considerando las propiedades topológicas del grafo. Además, se evalúan las características semánticas capturadas por estas nuevas representaciones y se investigan nuevas metodologías eficientes relacionadas con Link Discovery, Entity Retrieval y consultas a larga distancia en grandes conjuntos de datos relacionales haciendo uso de bases de datos reales.