Miracle-dz/UniFAR-FTUs huggingface dataset:
POLE-LIKE STREET FURNITURE DECOMPOSTION IN MOBILE LASER SCANNING DATA

Automatic semantic interpretation of street furniture has become a popular topic in recent years. Current studies detect street furniture as connected components of points above the street level. Street furniture classification based on properties of such components suffers from large intra class variability of shapes and cannot deal with mixed classes like traffic signs attached to light poles. In this paper, we focus on the decomposition of point clouds of pole-like street furniture. A novel street furniture decomposition method is proposed, which consists of three steps: (i) acquirement of prior-knowledge, (ii) pole extraction, (iii) components separation. For the pole extraction, a novel global pole extraction approach is proposed to handle 3 different cases of street furniture. In the evaluation of results, which involves the decomposition of 27 different instances of street furniture, we demonstrate that our method decomposes mixed classes street furniture into poles and different components with respect to different functionalities.
Identifying Users' Gender via Social Representations

Gender prediction has evoked great research interests due to its potential applications like targeted advertisement and personalized search. Most of existing studies rely on the content texts. However, the text information is hard to access. This makes it diﬃcult to extract text features. In this paper, we propose a novel framework which only involves the users’ ids for gender prediction. The key idea is to represent users in the embedding connection space. We present two strategies to modify the word embedding technique for user embedding. The ﬁrst is to sequentialize users’ ids to get the order of social context. The second is to embed users into a large-sized sliding window of contexts. We conduct extensive experiments on two real data sets from Sina Weibo. Results show that our method is signiﬁcantly better than the state-of-the-art graph embedding baselines. Its accuracy also outperforms that of the content based approaches.
A Review on Image Processing Techniques forSynthetic Aperture Radar (SAR) Images

Synthetic Aperture Radar (SAR) has been deeply used for sea ice monitoring in polar regions. A computer aided analysis of SAR sea ice imagery is extremely difficult due to several imaging parameters and environmental factors. Image processing and neural network techniques are used to improve the performance of detecting and classifying sea ice in SAR images. This paper gives an overview of image processing techniques of SAR images and its recent progress. The primary objective of this paper is to summarize some of the well known methods used in various stages of image processing system.
Invetter: Locating Insecure Input Validations in Android Services

Android integrates an increasing number of features into system services to manage sensitive resources, such as location, medical and social network information. To prevent untrusted apps from abusing the services, Android implements a comprehensive set of access controls to ensure proper usage of sensitive resources. Unlike explicit permission-based access controls that are discussed extensively in the past, our paper focuses on the widespread yet undocumented input validation problem. As we show in the paper, there are in fact more input validations acting as security checks than permission checks, rendering them a critical foundation for Android framework. Unfortunately, these validations are unstructured, ill-defined, and fragmented, making it challenging to analyze. To this end, we design and implement a tool, called Invetter, that combines machine learning and static analysis to locate sensitive input validations that are problematic in system services. By applying Invetter to 4 different AOSP codebases and 4 vendor-customized images, we locate 103 candidate insecure validations. Among the true positives, we are able to confirm that at least 20 of them are truly exploitable vulnerabilities by constructing various attacks such as privilege escalation and private information leakage.
On the Complexity of Verifying Timed Golog Programs over Description Logic Actions (Extended Version)

Golog programs allow to model complex behaviour of agents by combining primitive actions defined in a Situation Calculus theory using imperative and non-deterministic programming language constructs. In general, verifying temporal properties of Golog programs is undecidable. One way to establish decidability is to restrict the logic used by the program to a Description Logic (DL), for which recently some complexity upper bounds for verification problem have been established. However, so far it was open whether these results are tight, and lightweight DLs such as EL have not been studied at all. Furthermore, these results only apply to a setting where actions do not consume time, and the properties to be verified only refer to the timeline in a qualitative way. In a lot of applications, this is an unrealistic assumption. In this work, we study the verification problem for timed Golog programs, in which actions can be assigned differing durations, and temporal properties are specified in a metric branching time logic. This allows to annotate temporal properties with time intervals over which they are evaluated, to specify for example that some property should hold for at least n time units, or should become specified within some specified time window. We establish tight complexity bounds of the verification problem for both expressive and lightweight DLs. Our lower bounds already apply to a very limited fragment of the verification problem, and close open complexity bounds for the non-metrical cases studied before.
Spatial variability of phytoplankton pigment distributions in the Subtropical South Pacific Ocean: comparison between in situ and predicted data

Abstract. In the frame of the BIOSOPE cruise in 2004, the spatial distribution and structure of phytoplankton pigments was investigated along a transect crossing the ultra-oligotrophic South Pacific Subtropical Gyre (SPSG) between the Marquesas Archipelago (141° W–8° S) and the Chilean upwelling (73° W–34° S). A High Performance Liquid Chromatography (HPLC) method was improved in order to be able to accurately quantify pigments over such a large range of trophic levels, and especially from strongly oligotrophic conditions. Seven diagnostic pigments were associated to three phytoplankton size classes (pico-, nano and microphytoplankton). The total chlorophyll-α concentrations [TChlα] in surface waters were the lowest measured in the centre of the gyre, reaching 0.017 mg m−3. Pigment concentrations at the Deep Chlorophyll Maximum (DCM) were generally 10 fold the surface values. Results were compared to predictions from a global parameterisation based on remotely sensed surface [TChlα]. The agreement between the in situ and predicted data for such contrasting phytoplankton assemblages was generally good: throughout the oligotrophic gyre system, picophytoplankton (prochlorophytes and cyanophytes) and nanophytoplankton were the dominant classes. Relative bacteriochlorophyll-α concentrations varied around 2%. The transition zone between the Marquesas and the SPSG was also well predicted by the model. However, some regional characteristics have been observed where measured and modelled data differ. Amongst these features is the extreme depth of the DCM (180 m) towards the centre of the gyre, the presence of a deep nanoflagellate population beneath the DCM or the presence of a prochlorophyte-enriched population in the formation area of the high salinity South Pacific Tropical Water. A coastal site sampled in the eutrophic upwelling zone, characterised by recently upwelled water, was significantly and unusually enriched in picoeucaryotes, in contrast with an offshore upwelling site where a more typical senescent diatom population prevailed.
One-loop topological expansion for spin glasses in the large connectivity limit

We apply for the first time a new one-loop topological expansion around the Bethe solution to the spin-glass model with a field in the high connectivity limit, following the methodological scheme proposed in a recent work. The results are completely equivalent to the well-known ones, found by standard field-theoretical expansion around the fully connected model (Bray and Roberts 1980, and following works). However this method has the advantage that the starting point is the original Hamiltonian of the model, with no need to define an associated field theory, nor to know the initial values of the couplings, and the computations have a clear and simple physical meaning. Moreover this new method can also be applied in the case of zero temperature, when the Bethe model has a transition in field, contrary to the fully connected model that is always in the spin-glass phase. Sharing with finite-dimensional model the finite connectivity properties, the Bethe lattice is clearly a better starting point for an expansion with respect to the fully connected model. The present work is a first step towards the generalization of this new expansion to more difficult and interesting cases as the zero-temperature limit, where the expansion could lead to different results with respect to the standard one.
Performance Evaluation on Intellectual Property Rights Policy System of the Renewable Energy in China

With the intensive consumption and price rising of traditional fossil fuels, the development and utilization of renewable energy has attracted worldwide attention. Meanwhile, due to enhanced energy constraints and increasingly grim greenhouse gases, many countries all over the world have been developing renewable energy technologies to promote sustainable growth economically. Intellectual property rights (IPR) policy linked to renewable energy provides institutional guarantee for the development of renewable energy industry and technology. This study works to test the performance of IPR policies of renewable energy industry in China. We employed Cobb-Douglas production function and built on a quantitative policy indicator measurement system over the period from 2004 to 2013 to evaluate the impact of IPR policy of renewable energy industry on innovation and market development in China, which provides an all-round study on the IPR system by clearing up all IPR policy system that is related to the renewable energy industry. The results of Multiple Regression Models indicated that the current IPR policy of renewable energy industry are disconnected from China’s reality because the innovation ability of the renewable energy industry has not been improved rapidly and that both the market transformation ability and market prospect of new energy technology patents are weak.
Recursively preconditioned hierarchical interpolative factorization for elliptic partial differential equations

The hierarchical interpolative factorization for elliptic partial differential equations is a fast algorithm for approximate sparse matrix inversion in linear or quasilinear time. Its accuracy can degrade, however, when applied to strongly ill-conditioned problems. Here, we propose a simple modification that can significantly improve the accuracy at no additional asymptotic cost: applying a block Jacobi preconditioner before each level of skeletonization. This dramatically limits the impact of the underlying system conditioning and enables the construction of robust and highly efficient preconditioners even at quite modest compression tolerances. Numerical examples demonstrate the performance of the new approach.
Constructing Positive Definite Elastic Kernels with Application to Time Series Classification

This paper proposes some extensions to the work on kernels dedicated to string alignment (biological sequence alignment) based on the summing up of scores obtained by local alignments with gaps. The extensions we propose allow to construct, from classical time-warp distances, what we called summative time-warp kernels that are positive definite if some simple sufficient conditions are satisfied. Furthermore, from the same formalism, we derive a time-warp inner product that extends the usual euclidean inner product, providing the capability to handle discrete sequences or time series of variable lengths in an Hilbert space. The classification experiment we conducted, using either first near neighbor classifier or Support Vector Machine classifier leads to conclude that the positive definite elastic kernels we propose outperform the distance substituting kernels for the classical elastic distances we tested. In a similar way, the kernel based on the distance induced by the time-warp inner product outperforms significantly on the considered task the kernel based on the euclidean distance. Index Terms —Elastic distance, Time warp kernel, Time warp inner product, Definiteness, Time series classification, SVM.
A system dynamics approach to integrating sustainable land use with transportation system based on access management

The coordination planning between land use and transportation system is an important premise of solving urban transportation problems and realizing land use integration. This study investigates the interactive and feedback relationship between land use and transportation system from the perspective of access management. By integrating the land use and traffic data from Las Vegas Metropolitan area with the system dynamics model, the causal relationship and causal loop diagrams (CLDs) are introduced to analyze the cause-and-effect relationship and quantitative relationship between the factors of the combined system of land use and transportation, and then sub-models partition and system simulation are performed. The systems dynamics model is established by analyzing the relationship between a series of access management techniques, traffic characteristics, and land use features. The results show that system dynamics model can be used as an effective alternative to model the symbiosis relationship of land use and transportation system for urban planning and construction.Introduction
Performance Improvement in Binarization for Fingerprint Recognition

Fingerprint recognition have gain great attentiveness in the world of biometric. The performance of this system is become worst if the image quality is not good. Pre-processing steps enhance the quality of an image and give the suitable outcome. Image binarization is one of the fundamental pre-processing steps in fingerprint recognition. The purpose of image binarization is dividing an image into foreground and background using thresholding technique. This paper describes the use of global and local thresholding techniques. As well as present the comparative analysis of global, local thresholding and proposed adaptive local thresholding algorithm. The implementation is done using java with FVC2000 and FingerDOS databases of fingerprint and evaluates the best performance of the binarization algorithm using comparing the different criteria like execution time, quality of an image, and window size of neighborhood.
Generation of Aesthetic Emotions guided by Perceptual Features

We present an experimental prototype that aims the study of aesthetics-related features from visual and auditory domains to express a set of 13 emotions. In the visual domain features are unfolded with a chance of occurring according to their perceptual relevance, whereas in the auditory domain there is a previous categorization of emotions. In the end this will result in a series of digital abstract faces expressing certain emotional states.
Re Protein Hydrolysates Prepared from the Viscera of Skipjack Tuna ( Katsuwonus pelmamis ) : Antioxidative Activity and Functional Properties

Protein hydrolysates from skipjack tuna viscera prepared using Alcalase 2.4 L (VPH) with different degree of hydrolysis (DH: 10%, 20% and 30%) were prepared and determined for their antioxidative activity. VPH with 20% DH had the highest DPPH, ABTS radical scavenging activities and ferrous chelating activity (P<0.05). However, ferric reducing antioxidant activity of hydrolysates increased as DH increased (P<0.05). When VPH with 20% DH was determined for its pH and thermal stability, ABTS radical scavenging activity remained constant or slightly decreased in a wide pH range (2-11) and during heating (100C) for 180 min. Functional properties of VPH (20% DH) at different concentrations were also investigated. The emulsifying and foaming properties were governed by its concentrations used. Hydrolysis by Alcalase at 20% DH increased protein solubility to above 91% over a wide pH range (3-9). Therefore, VPH could be used as a food additive possessing both antioxidant activity and functional properties.
How Shell and Horn make a Unicorn: Experimenting with Visual Blending in Emoji

Emoji are becoming increasingly popular, both among users and brands. Their impact is such that some authors even mention a possible language shift towards visuality. We present a Visual Blending-based system for emoji generation, which is capable of representing concepts introduced by the user. Our approach combines data from ConceptNet, EmojiNet and Twitter’s Twemoji datasets to explore Visual Blending in emoji generation. In order to assess the quality of the system, a user study was conducted. The experimental results show that the system is able to produce new emoji that represent the concepts introduced. According to the participants, the blends are not only visually appealing but also unexpected.
Assessing Permafrost Degradation and Land Cover Changes (1986–2009) using Remote Sensing Data over Umiujaq, Sub‐Arctic Québec

Recent land cover changes in the Umiujaq region of northern Québec, Canada, have been quantified in order to estimate changes in the extent of discontinuous permafrost that strongly affect the forest‐tundra ecotone. Changes in the areas covered by different vegetation types, thermokarst lakes and degradation of lithalsas have been investigated over an area of 60 km2, extending from widespread discontinuous permafrost in the north to areas of scattered permafrost in the south, and from Hudson Bay in the west to the Lac Guillaume‐Delisle graben 10 km further east. We used high‐resolution remote sensing images (QuickBird 2004, GeoEye 2009) and four Landsat scenes (1986, 1990, 2001, 2008) as well as ground‐based data (vegetation, active layer thickness, snow parameters) collected between 2009 and 2011. Two change detection methods applied to estimate the land cover changes between 1986 and 2009 showed an overall increase in vegetation extent between 1986 and 2009, and a 21 per cent increase in tall vegetation (spruce and tall shrubs) between 2004 and 2009 at the expense of low vegetation (lichens, prostrate shrubs, herbaceous vegetation). Thermokarst lakes and lithalsas in ten sub‐areas were mapped manually from satellite imagery. The area covered by water decreased by 24 per cent between 2004 and 2009, often due to vegetation colonising the margins of lakes, and 93 of the observed lakes disappeared completely over that period. The area covered by lithalsas declined by 6 per cent. Our results demonstrate the viability of using high‐resolution satellite imagery to detect changes in the land surface that can serve as indicators of permafrost degradation in the sub‐Arctic. Copyright © 2015 John Wiley & Sons, Ltd.
Improving Community Detection with Linguistic Information

We present a general method for using documents produced by a group for which a social network can be constructed to improve community detection. We augment bag-ofwords document representations with “community words” based on the communities assigned by a a community detection algorithm, in counts weighted by the strength of evidence for community membership; we then run community detection again on the similarity graph of the modified documents. We show that combining the linguistic with network information improves significantly on systems using either kind of information alone.
MODELING OF DETONATION : ROLE OF VACANCIES IN THE INITIATION OF DETONATION IN AN ENERGETIC CRYSTAL

We have employed the method of molecular dynamics in a prelimi- nary study of the initiation of detonation in an energetic molecular crystal containing vacancy defects. The model was a two-dimensional filament of 8 x 240 diatomic molecules which were capable of undergoing exothermic dissociation. The reac- tion was initiated at one end of the filament by heating. The ensuing dynamics of detonation was followed by calculating the motions of the dissociating molecules as a function of time. The results showed that with this particular model, there was a tendency for the vacancies to diffuse by thermal motion, and that the net effect of the vacancies was the reduction of the caging effect on dissociation. This in turn produced a mea- surable effect on the initiation of detonation, the reaction rate, the energy and the stress profiles, and the shock propagation velocity. In order to study the effect of a cluster of vacancies on iniitiation, it will be necessary to modify the interatomic-intermolecular potentials so as to stabilize the vacancy configuration. A special feature of this investigation was that the computa- tional work was performed entirely on a personal computer driving a 32-bit coprocessor board. It was found that the use of such a system for doing large-scale modeling of scientific problems was both practical and cost-effective.
1 An Indexing Framework for eries on Probabilistic Graphs

Information in many applications, such as mobile wireless systems, social networks, and road networks, is captured by graphs. In many cases, such information is uncertain. We study the problem of querying a probabilistic graph, in which vertices are connected to each other probabilistically. In particular, we examine “source-to-target” queries (or ST-queries), such as computing the shortest path between two vertices. e major dierence with the deterministic seing is that query answers are enriched with probabilistic annotations. Evaluating ST-queries over probabilistic graphs is #P-hard, as it requires examining an exponential number of “possible worlds” – database instances generated from the probabilistic graph. Existing solutions to the ST-query problem, which sample possible worlds, have two downsides: (i) a possible world can be very large and (ii) many samples are needed for reasonable accuracy. To tackle these issues, we study the ProbTree, a data structure that stores a succinct, or indexed, version of the possible worlds of the graph. Existing ST-query solutions are executed on top of this structure, with the number of samples and sizes of the possible worlds reduced. We examine lossless and lossy methods for generating the ProbTree, which reect the trade-o between the accuracy and eciency of query evaluation. We analyze the correctness and complexity of these approaches. Our extensive experiments on real datasets show that the ProbTree is fast to generate and small in size. It also enhances the accuracy and eciency of existing ST-query algorithms signicantly.
Nested Compiled Inference for Hierarchical Reinforcement Learning

Probabilistic programming languages (PPLs) allow the representation of probability distributions as computer programs by means of stochastic language primitives and conditioning of variable values on observations [8, 7]. Probabilistic programs are generative models as they generate samples from a joint distribution p(x,y), where x are latent variables and y are output (or observed) data. Inference in PPLs amounts to computing the posterior p(x|y) conditioned on observed data. PPLs allow decoupling model specification from inference, which can be handled by techniques including Markov chain Monte Carlo [18], variational methods [19], expectation propagation [16], and sequential Monte Carlo [20]. Inference is often computationally expensive. Amortized inference [5] strategies are devised to store and reuse past inferences so that future inferences run faster.