OpenRaiser/Intern-Atlas huggingface dataset:
Automatic Interpretation of Unordered Point Cloud Data for UAV Navigation in Construction
Venue: arXiv cs.RO 2017
Year: 2017

The objective of this work is to develop a data processing system that can automatically generate waypoints for navigation of an unmanned aerial vehicle (UAV) to inspect surfaces of structures like buildings and bridges. The input includes data recorded by two 2D laser scanners, orthogonally mounted on the UAV, and an inertial measurement unit (IMU). To achieve the goal, algorithms are developed to process the data collected. They are separated into three major groups: (i) the data registration and filtering to generate a 3D model of the structure and control the density of point clouds for data completeness enhancement; (ii) the surface and obstacle detection to assist the UAV in monitoring tasks; and (iii) the waypoint generation to set the flight path. Experiments on different data sets show that the developed system is able to reconstruct a 3D point cloud of the structure, extract its surfaces and objects, and generate waypoints for the UAV to accomplish inspection tasks.
Multi-cultural Wikipedia mining of geopolitics interactions leveraging reduced Google matrix analysis
Venue: arXiv cs.SI 2017
Year: 2017

Geopolitics focuses on political power in relation to geographic space. Interactions among world countries have been widely studied at various scales, observing economic exchanges, world history or international politics among others. This work exhibits the potential of Wikipedia mining for such studies. Indeed, Wikipedia stores valuable fine-grained dependencies among countries by linking webpages together for diverse types of interactions (not only related to economical, political or historical facts). We mine herein the Wikipedia networks of several language editions using the recently proposed method of reduced Google matrix analysis. This approach allows to establish direct and hidden links between a subset of nodes that belong to a much larger directed network. Our study concentrates on 40 major countries chosen worldwide. Our aim is to offer a multicultural perspective on their interactions by comparing networks extracted from five different Wikipedia language editions, emphasizing English, Russian and Arabic ones. We demonstrate that this approach allows to recover meaningful direct and hidden links among the 40 countries of interest.
Optimizing spatial throughput in device-to-device networks
Venue: arXiv cs.NI 2017
Year: 2017

Results are presented for optimizing device-to-device communications in cellular networks, while maintaining spectral efficiency of the base-station-to-device downlink channel. We build upon established and tested stochastic geometry models of signal-to-interference ratio in wireless networks based on the Poisson point process, which incorporate random propagation effects such as fading and shadowing. A key result is a simple formula, allowing one to optimize the device-to-device spatial throughput by suitably adjusting the proportion of active devices. These results can lead to further investigation as they can be immediately applied to more sophisticated models such as studying multi-tier network models to address coverage in closed access networks.
A Note on the Topologicity of Quantale-Valued Topological Spaces
Venue: arXiv cs.LO 2017
Year: 2017

For a quantale ${\sf{V}}$, the category $\sf V$-${\bf Top}$ of ${\sf{V}}$-valued topological spaces may be introduced as a full subcategory of those ${\sf{V}}$-valued closure spaces whose closure operation preserves finite joins. In generalization of Barr's characterization of topological spaces as the lax algebras of a lax extension of the ultrafilter monad from maps to relations of sets, for ${\sf{V}}$ completely distributive, ${\sf{V}}$-topological spaces have recently been shown to be characterizable by a lax extension of the ultrafilter monad to ${\sf{V}}$-valued relations. As a consequence, ${\sf{V}}$-$\bf Top$ is seen to be a topological category over $\bf Set$, provided that ${\sf{V}}$ is completely distributive. In this paper we give a choice-free proof that ${\sf{V}}$-$\bf Top$ is a topological category over $\bf Set$ under the considerably milder provision that ${\sf{V}}$ be a spatial coframe. When ${\sf{V}}$ is a continuous lattice, that provision yields complete distributivity of ${\sf{V}}$ in the constructive sense, hence also in the ordinary sense whenever the Axiom of Choice is granted.
Cutting-off Redundant Repeating Generations for Neural Abstractive Summarization
Venue: arXiv cs.CL 2017
Year: 2017

This paper tackles the reduction of redundant repeating generation that is often observed in RNN-based encoder-decoder models. Our basic idea is to jointly estimate the upper-bound frequency of each target vocabulary in the encoder and control the output words based on the estimation in the decoder. Our method shows significant improvement over a strong RNN-based encoder-decoder baseline and achieved its best results on an abstractive summarization benchmark.
Expanding Subjective Lexicons for Social Media Mining with Embedding Subspaces
Venue: arXiv cs.CL 2017
Year: 2017

Recent approaches for sentiment lexicon induction have capitalized on pre-trained word embeddings that capture latent semantic properties. However, embeddings obtained by optimizing performance of a given task (e.g. predicting contextual words) are sub-optimal for other applications. In this paper, we address this problem by exploiting task-specific representations, induced via embedding sub-space projection. This allows us to expand lexicons describing multiple semantic properties. For each property, our model jointly learns suitable representations and the concomitant predictor. Experiments conducted over multiple subjective lexicons, show that our model outperforms previous work and other baselines; even in low training data regimes. Furthermore, lexicon-based sentiment classifiers built on top of our lexicons outperform similar resources and yield performances comparable to those of supervised models.
Vertical stratification of forest canopy for segmentation of under-story trees within small-footprint airborne LiDAR point clouds
Venue: arXiv cs.CV 2017
Year: 2017

Airborne LiDAR point cloud representing a forest contains 3D data, from which vertical stand structure even of understory layers can be derived. This paper presents a tree segmentation approach for multi-story stands that stratifies the point cloud to canopy layers and segments individual tree crowns within each layer using a digital surface model based tree segmentation method. The novelty of the approach is the stratification procedure that separates the point cloud to an overstory and multiple understory tree canopy layers by analyzing vertical distributions of LiDAR points within overlapping locales. The procedure does not make a priori assumptions about the shape and size of the tree crowns and can, independent of the tree segmentation method, be utilized to vertically stratify tree crowns of forest canopies. We applied the proposed approach to the University of Kentucky Robinson Forest - a natural deciduous forest with complex and highly variable terrain and vegetation structure. The segmentation results showed that using the stratification procedure strongly improved detecting understory trees (from 46% to 68%) at the cost of introducing a fair number of over-segmented understory trees (increased from 1% to 16%), while barely affecting the overall segmentation quality of overstory trees. Results of vertical stratification of the canopy showed that the point density of understory canopy layers were suboptimal for performing a reasonable tree segmentation, suggesting that acquiring denser LiDAR point clouds would allow more improvements in segmenting understory trees. As shown by inspecting correlations of the results with forest structure, the segmentation approach is applicable to a variety of forest types.
Self-Taught Convolutional Neural Networks for Short Text Clustering
Venue: arXiv cs.IR 2017
Year: 2017

Short text clustering is a challenging problem due to its sparseness of text representation. Here we propose a flexible Self-Taught Convolutional neural network framework for Short Text Clustering (dubbed STC^2), which can flexibly and successfully incorporate more useful semantic features and learn non-biased deep text representation in an unsupervised manner. In our framework, the original raw text features are firstly embedded into compact binary codes by using one existing unsupervised dimensionality reduction methods. Then, word embeddings are explored and fed into convolutional neural networks to learn deep feature representations, meanwhile the output units are used to fit the pre-trained binary codes in the training process. Finally, we get the optimal clusters by employing K-means to cluster the learned representations. Extensive experimental results demonstrate that the proposed framework is effective, flexible and outperform several popular clustering methods when tested on three public short text datasets.
Video-based Person Re-identification with Accumulative Motion Context
Venue: arXiv cs.CV 2017
Year: 2017

Video based person re-identification plays a central role in realistic security and video surveillance. In this paper we propose a novel Accumulative Motion Context (AMOC) network for addressing this important problem, which effectively exploits the long-range motion context for robustly identifying the same person under challenging conditions. Given a video sequence of the same or different persons, the proposed AMOC network jointly learns appearance representation and motion context from a collection of adjacent frames using a two-stream convolutional architecture. Then AMOC accumulates clues from motion context by recurrent aggregation, allowing effective information flow among adjacent frames and capturing dynamic gist of the persons. The architecture of AMOC is end-to-end trainable and thus motion context can be adapted to complement appearance clues under unfavorable conditions (e.g. occlusions). Extensive experiments are conduced on three public benchmark datasets, i.e., the iLIDS-VID, PRID-2011 and MARS datasets, to investigate the performance of AMOC. The experimental results demonstrate that the proposed AMOC network outperforms state-of-the-arts for video-based re-identification significantly and confirm the advantage of exploiting long-range motion context for video based person re-identification, validating our motivation evidently.
A robust approach for tree segmentation in deciduous forests using small-footprint airborne LiDAR data
Venue: arXiv cs.CV 2017
Year: 2017

This paper presents a non-parametric approach for segmenting trees from airborne LiDAR data in deciduous forests. Based on the LiDAR point cloud, the approach collects crown information such as steepness and height on-the-fly to delineate crown boundaries, and most importantly, does not require a priori assumptions of crown shape and size. The approach segments trees iteratively starting from the tallest within a given area to the smallest until all trees have been segmented. To evaluate its performance, the approach was applied to the University of Kentucky Robinson Forest, a deciduous closed-canopy forest with complex terrain and vegetation conditions. The approach identified 94% of dominant and co-dominant trees with a false detection rate of 13%. About 62% of intermediate, overtopped, and dead trees were also detected with a false detection rate of 15%. The overall segmentation accuracy was 77%. Correlations of the segmentation scores of the proposed approach with local terrain and stand metrics was not significant, which is likely an indication of the robustness of the approach as results are not sensitive to the differences in terrain and stand structures.
Interference Minimization in 5G Heterogeneous Networks
Venue: arXiv cs.IT 2017
Year: 2017

In this paper, we focus on one of the representative 5G network scenarios, namely multi-tier heterogeneous cellular networks. User association is investigated in order to reduce the down-link co-channel interference. Firstly, in order to analyze the multi-tier heterogeneous cellular networks where the base stations in different tiers usually adopt different transmission powers, we propose a Transmission Power Normalization Model (TPNM), which is able to convert a multi-tier cellular network into a single-tier network, such that all base stations have the same normalized transmission power. Then using TPNM, the signal and interference received at any point in the complex multi-tier environment can be analyzed by considering the same point in the equivalent single-tier cellular network model, thus significantly simplifying the analysis. On this basis, we propose a new user association scheme in heterogeneous cellular networks, where the base station that leads to the smallest interference to other co-channel mobile stations is chosen from a set of candidate base stations that satisfy the quality-of-service (QoS) constraint for an intended mobile station. Numerical results show that the proposed user association scheme is able to significantly reduce the down-link interference compared with existing schemes while maintaining a reasonably good QoS.
Classification of Smartphone Users Using Internet Traffic
Venue: arXiv cs.LG 2017
Year: 2017

Today, smartphone devices are owned by a large portion of the population and have become a very popular platform for accessing the Internet. Smartphones provide the user with immediate access to information and services. However, they can easily expose the user to many privacy risks. Applications that are installed on the device and entities with access to the device's Internet traffic can reveal private information about the smartphone user and steal sensitive content stored on the device or transmitted by the device over the Internet. In this paper, we present a method to reveal various demographics and technical computer skills of smartphone users by their Internet traffic records, using machine learning classification models. We implement and evaluate the method on real life data of smartphone users and show that smartphone users can be classified by their gender, smoking habits, software programming experience, and other characteristics.
Sustainable Incentives for Mobile Crowdsensing: Auctions, Lotteries, and Trust and Reputation Systems
Venue: arXiv cs.GT 2017
Year: 2017

Proper incentive mechanisms are critical for mobile crowdsensing systems to motivate people to actively and persistently participate. This article provides an exposition of design principles of six incentive mechanisms, drawing special attention to the sustainability issue. We cover three primary classes of incentive mechanisms: auctions, lotteries, and trust and reputation systems, as well as three other frameworks of promising potential: bargaining games, contract theory, and market-driven mechanisms.
STRIPS Planning in Infinite Domains
Venue: arXiv cs.AI 2017
Year: 2017

Many robotic planning applications involve continuous actions with highly non-linear constraints, which cannot be modeled using modern planners that construct a propositional representation. We introduce STRIPStream: an extension of the STRIPS language which can model these domains by supporting the specification of blackbox generators to handle complex constraints. The outputs of these generators interact with actions through possibly infinite streams of objects and static predicates. We provide two algorithms which both reduce STRIPStream problems to a sequence of finite-domain planning problems. The representation and algorithms are entirely domain independent. We demonstrate our framework on simple illustrative domains, and then on a high-dimensional, continuous robotic task and motion planning domain.
Integrating sentiment and social structure to determine preference alignments: The Irish Marriage Referendum
Venue: arXiv cs.SI 2017
Year: 2017

We examine the relationship between social structure and sentiment through the analysis of a large collection of tweets about the Irish Marriage Referendum of 2015. We obtain the sentiment of every tweet with the hashtags #marref and #marriageref that was posted in the days leading to the referendum, and construct networks to aggregate sentiment and use it to study the interactions among users. Our results show that the sentiment of mention tweets posted by users is correlated with the sentiment of received mentions, and there are significantly more connections between users with similar sentiment scores than among users with opposite scores in the mention and follower networks. We combine the community structure of the two networks with the activity level of the users and sentiment scores to find groups of users who support voting `yes' or `no' in the referendum. There were numerous conversations between users on opposing sides of the debate in the absence of follower connections, which suggests that there were efforts by some users to establish dialogue and debate across ideological divisions. Our analysis shows that social structure can be integrated successfully with sentiment to analyse and understand the disposition of social media users. These results have potential applications in the integration of data and meta-data to study opinion dynamics, public opinion modelling, and polling.
The Geodesic Distance between $\mathcal{G}_I^0$ Models and its Application to Region Discrimination
Venue: arXiv cs.CV 2017
Year: 2017

The $\mathcal{G}_I^0$ distribution is able to characterize different regions in monopolarized SAR imagery. It is indexed by three parameters: the number of looks (which can be estimated in the whole image), a scale parameter and a texture parameter. This paper presents a new proposal for feature extraction and region discrimination in SAR imagery, using the geodesic distance as a measure of dissimilarity between $\mathcal{G}_I^0$ models. We derive geodesic distances between models that describe several practical situations, assuming the number of looks known, for same and different texture and for same and different scale. We then apply this new tool to the problems of (i)~identifying edges between regions with different texture, and (ii)~quantify the dissimilarity between pairs of samples in actual SAR data. We analyze the advantages of using the geodesic distance when compared to stochastic distances.
An affective computational model for machine consciousness
Venue: arXiv cs.AI 2017
Year: 2017

In the past, several models of consciousness have become popular and have led to the development of models for machine consciousness with varying degrees of success and challenges for simulation and implementations. Moreover, affective computing attributes that involve emotions, behavior and personality have not been the focus of models of consciousness as they lacked motivation for deployment in software applications and robots. The affective attributes are important factors for the future of machine consciousness with the rise of technologies that can assist humans. Personality and affection hence can give an additional flavor for the computational model of consciousness in humanoid robotics. Recent advances in areas of machine learning with a focus on deep learning can further help in developing aspects of machine consciousness in areas that can better replicate human sensory perceptions such as speech recognition and vision. With such advancements, one encounters further challenges in developing models that can synchronize different aspects of affective computing. In this paper, we review some existing models of consciousnesses and present an affective computational model that would enable the human touch and feel for robotic systems.
Truthful Facility Location with Additive Errors
Venue: arXiv cs.GT 2017
Year: 2017

We address the problem of locating facilities on the $[0,1]$ interval based on reports from strategic agents. The cost of each agent is her distance to the closest facility, and the global objective is to minimize either the maximum cost of an agent or the social cost. As opposed to the extensive literature on facility location which considers the multiplicative error, we focus on minimizing the worst-case additive error. Minimizing the additive error incentivizes mechanisms to adapt to the size of the instance. I.e., mechanisms can sacrifice little efficiency in small instances (location profiles in which all agents are relatively close to one another), in order to gain more [absolute] efficiency in large instances. We argue that this measure is better suited for many manifestations of the facility location problem in various domains. We present tight bounds for mechanisms locating a single facility in both deterministic and randomized cases. We further provide several extensions for locating multiple facilities.
Discrete Optimal Global Convergence of an Evolutionary Algorithm for Clusters under the Potential of Lennard Jones
Venue: arXiv cs.CE 2017
Year: 2017

A review of the properties that bond the particles under Lennard Jones Potential allow to states properties and conditions for building evolutive algorithms using the CB lattice with other different lattices. The new lattice is called CB lattice and it is based on small cubes. A set of propositions states convergence and optimal conditions over the CB lattice for an evolutionary algorithm. The evolutionary algorithm is a reload version of previous genetic algorithms based in phenotypes. The novelty using CB lattice, together with the other lattices, and ad-hoc cluster segmentation and enumeration, is to allow the combination of genotype (DNA coding for cluster using their particle's number) and phenotype (geometrical shapes using particle's coordinates in 3D). A parallel version of an evolutionary algorithm for determining the global optimality is depicted. The results presented are from a standalone program for a personal computer of the evolutionary algorithm, which can estimate all putative optimal Lennard Jones Clusters from 13 to 1612 particles. The novelty are the theoretical results for the evolutionary algorithm's efficiency, the strategies with phenotype or genotype, and the classification of the clusters based in an ad-hoc geometric algorithm for segmenting a cluster into its nucleus and layers. Also, the standalone program is not only capable to replicate the optimal Lennard Jones clusters in The Cambridge Cluster Database (CCD), but to find new ones.
Citation indices and dimensional homogeneity
Venue: arXiv cs.DL 2017
Year: 2017

The importance of dimensional analysis and dimensional homogeneity in bibliometric studies is always overlooked. In this paper, we look at this issue systematically and show that most h-type indices have the dimensions of [P], where [P] is the basic dimensional unit in bibliometrics which is the unit publication or paper. The newly introduced Euclidean index, based on the Euclidean length of the citation vector has the dimensions [P3/2]. An empirical example is used to illustrate the concepts.
Constraint Logic Programming over Infinite Domains with an Application to Proof
Venue: arXiv cs.PL 2017
Year: 2017

We present a CLP(FD)-based constraint solver able to deal with unbounded domains. It is based on constraint propagation, resorting to enumeration if all other methods fail. An important aspect is detecting when enumeration was complete and if this has an impact on the soundness of the result. We present a technique which guarantees soundness in the following way: if the constraint solver finds a solution it is guaranteed to be correct; if the constraint solver fails to find a solution it can either return the result "definitely false" in case it knows enumeration was exhaustive, or "unknown" in case it was aborted. The technique can deal with nested universal and existential quantifiers. It can easily be extended to set comprehensions and other operators introducing new quantified variables. We show applications in data validation and proof.