OpenRaiser/Intern-Atlas huggingface dataset:
Multitask Painting Categorization by Deep Multibranch Neural Network
Venue: arXiv cs.CV 2018
Year: 2018

In this work we propose a new deep multibranch neural network to solve the tasks of artist, style, and genre categorization in a multitask formulation. In order to gather clues from low-level texture details and, at the same time, exploit the coarse layout of the painting, the branches of the proposed networks are fed with crops at different resolutions. We propose and compare two different crop strategies: the first one is a random-crop strategy that permits to manage the tradeoff between accuracy and speed; the second one is a smart extractor based on Spatial Transformer Networks trained to extract the most representative subregions. Furthermore, inspired by the results obtained in other domains, we experiment the joint use of hand-crafted features directly computed on the input images along with neural ones. Experiments are performed on a new dataset originally sourced from wikiart.org and hosted by Kaggle, and made suitable for artist, style and genre multitask learning. The dataset here proposed, named MultitaskPainting100k, is composed by 100K paintings, 1508 artists, 125 styles and 41 genres. Our best method, tested on the MultitaskPainting100k dataset, achieves accuracy levels of 56.5%, 57.2%, and 63.6% on the tasks of artist, style and genre prediction respectively.
A standardized Project Gutenberg corpus for statistical analysis of natural language and quantitative linguistics
Venue: arXiv cs.CL 2018
Year: 2018

The use of Project Gutenberg (PG) as a text corpus has been extremely popular in statistical analysis of language for more than 25 years. However, in contrast to other major linguistic datasets of similar importance, no consensual full version of PG exists to date. In fact, most PG studies so far either consider only a small number of manually selected books, leading to potential biased subsets, or employ vastly different pre-processing strategies (often specified in insufficient details), raising concerns regarding the reproducibility of published results. In order to address these shortcomings, here we present the Standardized Project Gutenberg Corpus (SPGC), an open science approach to a curated version of the complete PG data containing more than 50,000 books and more than $3 \times 10^9$ word-tokens. Using different sources of annotated metadata, we not only provide a broad characterization of the content of PG, but also show different examples highlighting the potential of SPGC for investigating language variability across time, subjects, and authors. We publish our methodology in detail, the code to download and process the data, as well as the obtained corpus itself on 3 different levels of granularity (raw text, timeseries of word tokens, and counts of words). In this way, we provide a reproducible, pre-processed, full-size version of Project Gutenberg as a new scientific resource for corpus linguistics, natural language processing, and information retrieval.
Window detection in aerial texture images of the Berlin 3D CityGML Model
Venue: arXiv cs.CV 2018
Year: 2018

This article explores the usage of the state-of-art neural network Mask R-CNN to be used for window detection of texture files from the CityGML model of Berlin. As texture files are very irregular in terms of size, exposure settings and orientation, we use several parameter optimisation methods to improve the precision. Those textures are cropped from aerial photos, which implies that the angle of the facade, the exposure as well as contrast are calibrated towards the mean and not towards the single facade. The analysis of a single texture image with the human eye itself is challenging: A combination of window and facade estimation and perspective analysis is necessary in order to determine the facades and windows. We train and detect bounding boxes and masks from two data sets with image size 128 and 256. We explore various configuration optimisation methods and the relation of the Region Proposal Network, detected ROIs and the mask output. Our final results shows that the we can improve the average precision scores for both data set sizes, yet the initial AP score varies and leads to different resulting scores.
Unsupervised Event-based Learning of Optical Flow, Depth, and Egomotion
Venue: arXiv cs.CV 2018
Year: 2018

In this work, we propose a novel framework for unsupervised learning for event cameras that learns motion information from only the event stream. In particular, we propose an input representation of the events in the form of a discretized volume that maintains the temporal distribution of the events, which we pass through a neural network to predict the motion of the events. This motion is used to attempt to remove any motion blur in the event image. We then propose a loss function applied to the motion compensated event image that measures the motion blur in this image. We train two networks with this framework, one to predict optical flow, and one to predict egomotion and depths, and evaluate these networks on the Multi Vehicle Stereo Event Camera dataset, along with qualitative results from a variety of different scenes.
Towards Plan Transformations for Real-World Pick and Place Tasks
Venue: arXiv cs.RO 2018
Year: 2018

In this paper, we investigate the possibility of applying plan transformations to general manipulation plans in order to specialize them to the specific situation at hand. We present a framework for optimizing execution and achieving higher performance by autonomously transforming robot's behavior at runtime. We show that plans employed by robotic agents in real-world environments can be transformed, despite their control structures being very complex due to the specifics of acting in the real world. The evaluation is carried out on a plan of a PR2 robot performing pick and place tasks, to which we apply three example transformations, as well as on a large amount of experiments in a fast plan projection environment.
Statistical learning of geometric characteristics of wireless networks
Venue: arXiv cs.LG 2018
Year: 2018

Motivated by the prediction of cell loads in cellular networks, we formulate the following new, fundamental problem of statistical learning of geometric marks of point processes: An unknown marking function, depending on the geometry of point patterns, produces characteristics (marks) of the points. One aims at learning this function from the examples of marked point patterns in order to predict the marks of new point patterns. To approximate (interpolate) the marking function, in our baseline approach, we build a statistical regression model of the marks with respect some local point distance representation. In a more advanced approach, we use a global data representation via the scattering moments of random measures, which build informative and stable to deformations data representation, already proven useful in image analysis and related application domains. In this case, the regression of the scattering moments of the marked point patterns with respect to the non-marked ones is combined with the numerical solution of the inverse problem, where the marks are recovered from the estimated scattering moments. Considering some simple, generic marks, often appearing in the modeling of wireless networks, such as the shot-noise values, nearest neighbour distance, and some characteristics of the Voronoi cells, we show that the scattering moments can capture similar geometry information as the baseline approach, and can reach even better performance, especially for non-local marking functions. Our results motivate further development of statistical learning tools for stochastic geometry and analysis of wireless networks, in particular to predict cell loads in cellular networks from the locations of base stations and traffic demand.
Control Behavior Integrity for Distributed Cyber-Physical Systems
Venue: arXiv cs.CR 2018
Year: 2018

Cyber-physical control systems, such as industrial control systems (ICS), are increasingly targeted by cyberattacks. Such attacks can potentially cause tremendous damage, affect critical infrastructure or even jeopardize human life when the system does not behave as intended. Cyberattacks, however, are not new and decades of security research have developed plenty of solutions to thwart them. Unfortunately, many of these solutions cannot be easily applied to safety-critical cyber-physical systems. Further, the attack surface of ICS is quite different from what can be commonly assumed in classical IT systems. We present Scadman, a system with the goal to preserve the Control Behavior Integrity (CBI) of distributed cyber-physical systems. By observing the system-wide behavior, the correctness of individual controllers in the system can be verified. This allows Scadman to detect a wide range of attacks against controllers, like programmable logic controller (PLCs), including malware attacks, code-reuse and data-only attacks. We implemented and evaluated Scadman based on a real-world water treatment testbed for research and training on ICS security. Our results show that we can detect a wide range of attacks--including attacks that have previously been undetectable by typical state estimation techniques--while causing no false-positive warning for nominal threshold values.
Peer-to-Peer EnergyTrade: A Distributed Private Energy Trading Platform
Venue: arXiv cs.CR 2018
Year: 2018

Blockchain is increasingly being used as a distributed, anonymous, trustless framework for energy trading in smart grids. However, most of the existing solutions suffer from reliance on Trusted Third Parties (TTP), lack of privacy, and traffic and processing overheads. In our previous work, we have proposed a Secure Private Blockchain-based framework (SPB) for energy trading to address the aforementioned challenges. In this paper, we present a proof-on-concept implementation of SPB on the Ethereum private network to demonstrates SPB's applicability for energy trading. We benchmark SPB's performance against the relevant state-of-the-art. The implementation results demonstrate that SPB incurs lower overheads and monetary cost for end users to trade energy compared to existing solutions.
SfMLearner++: Learning Monocular Depth & Ego-Motion using Meaningful Geometric Constraints
Venue: arXiv cs.RO 2018
Year: 2018

Most geometric approaches to monocular Visual Odometry (VO) provide robust pose estimates, but sparse or semi-dense depth estimates. Off late, deep methods have shown good performance in generating dense depths and VO from monocular images by optimizing the photometric consistency between images. Despite being intuitive, a naive photometric loss does not ensure proper pixel correspondences between two views, which is the key factor for accurate depth and relative pose estimations. It is a well known fact that simply minimizing such an error is prone to failures. We propose a method using Epipolar constraints to make the learning more geometrically sound. We use the Essential matrix, obtained using Nister's Five Point Algorithm, for enforcing meaningful geometric constraints on the loss, rather than using it as labels for training. Our method, although simplistic but more geometrically meaningful, using lesser number of parameters, gives a comparable performance to state-of-the-art methods which use complex losses and large networks showing the effectiveness of using epipolar constraints. Such a geometrically constrained learning method performs successfully even in cases where simply minimizing the photometric error would fail.
A Survey of Hierarchy Identification in Social Networks
Venue: arXiv cs.CL 2018
Year: 2018

Humans are social by nature. Throughout history, people have formed communities and built relationships. Most relationships with coworkers, friends, and family are developed during face-to-face interactions. These relationships are established through explicit means of communications such as words and implicit such as intonation, body language, etc. By analyzing human interactions we can derive information about the relationships and influence among conversation participants. However, with the development of the Internet, people started to communicate through text in online social networks. Interestingly, they brought their communicational habits to the Internet. Many social network users form relationships with each other and establish communities with leaders and followers. Recognizing these hierarchical relationships is an important task because it will help to understand social networks and predict future trends, improve recommendations, better target advertisement, and improve national security by identifying leaders of anonymous terror groups. In this work, I provide an overview of current research in this area and present the state-of-the-art approaches to deal with the problem of identifying hierarchical relationships in social networks.
Statistical Location and Rotation-Aware Beam Search for Millimeter-Wave Networks
Venue: arXiv cs.NI 2018
Year: 2018

Beam training in dynamic millimeter-wave (mm-wave) networks with mobile devices is highly challenging as devices must scan a large angular domain to maintain alignment of their directional antennas under mobility. Device rotation is particularly challenging, as a handheld device may rotate significantly over a very short period of time, causing it to lose the connection to the Access Point (AP) unless the rotation is accompanied by immediate beam realignment. We study how to maintain the link to a mm-wave AP under rotation and without any input from inertial sensors, exploiting the fact that mm-wave devices will typically be multi-band. We present a model that maps Time-of-Flight measurements to rotation and propose a method to infer the rotation speed of the mobile terminal using only measurements from sub-6 GHz WiFi. We also use the same sub-6 GHz WiFi system to reduce the angle error estimate for link establishment, exploiting the spatial geometry of the deployed APs and a statistical model that maps the user position's spatial distribution to an angle error distribution. We leverage these findings to introduce SLASH, a Statistical Location and rotation-Aware beam SearcH algorithm that adaptively narrows the sector search space and accelerates both link establishment and maintenance between mm-wave devices. We evaluate SLASH with experiments conducted indoors with a sub-6 GHz WiFi Time-of-Flight positioning system and a 60-GHz testbed. SLASH can increase the data rate by more than 41% for link establishment and 67% for link maintenance with respect to prior work.
The collaboration behaviors of scientists in Italy: a field level analysis
Venue: arXiv cs.DL 2018
Year: 2018

The analysis of research collaboration by field is traditionally conducted beginning with the classification of the publications from the context of interest. In this work we propose an alternative approach based on the classification of the authors by field. The proposed method is more precise if the intended use is to provide a benchmark for the evaluation of individual propensity to collaborate. In the current study we apply the new methodology to all Italian university researchers in the hard sciences, measuring the propensity to collaborate for the various fields: in general, and specifically with intramural colleagues, extramural domestic and extramural foreign organizations. Using a simulation, we show that the results present substantial differences from those obtained through application of traditional approaches.
Research on Limited Buffer Scheduling Problems in Flexible Flow Shops with Setup Times
Venue: arXiv cs.AI 2018
Year: 2018

In order to solve the limited buffer scheduling problems in flexible flow shops with setup times, this paper proposes an improved whale optimization algorithm (IWOA) as a global optimization algorithm. Firstly, this paper presents a mathematic programming model for limited buffer in flexible flow shops with setup times, and applies the IWOA algorithm as the global optimization algorithm. Based on the whale optimization algorithm (WOA), the improved algorithm uses Levy flight, opposition-based learning strategy and simulated annealing to expand the search range, enhance the ability for jumping out of local extremum, and improve the continuous evolution of the algorithm. To verify the improvement of the proposed algorithm on the optimization ability of the standard WOA algorithm, the IWOA algorithm is tested by verification examples of small-scale and large-scale flexible flow shop scheduling problems, and the imperialist competitive algorithm (ICA), bat algorithm (BA), and whale optimization algorithm (WOA) are used for comparision. Based on the instance data of bus manufacturer, simulation tests are made on the four algorithms under variouis of practical evalucation scenarios. The simulation results show that the IWOA algorithm can better solve this type of limited buffer scheduling problem in flexible flow shops with setup times compared with the state of the art algorithms.
Not Just Age but Age and Quality of Information
Venue: arXiv cs.IT 2018
Year: 2018

A versatile scheduling problem to model a three-way tradeoff between delay/age, distortion, and energy is considered. The considered problem called the age and quality of information (AQI) is to select which packets to transmit at each time slot to minimize a linear combination of the distortion cost, the age/delay cost and the energy transmission cost in an online fashion. AQI generalizes multiple important problems such as age of information (AoI), the remote estimation problem with sampling constraint, the classical speed scaling problem among others. The worst case input model is considered, where the performance metric is the competitive ratio. A greedy algorithm is proposed that is shown to be 2-competitive, independent of all parameters of the problem. For the special case of AQI problem, a greedy online maximum weight matching based algorithm is also shown to be 2-competitive.
Automated detection of block falls in the north polar region of Mars
Venue: arXiv cs.CV 2018
Year: 2018

We developed a change detection method for the identification of ice block falls using NASA's HiRISE images of the north polar scarps on Mars. Our method is based on a Support Vector Machine (SVM), trained using Histograms of Oriented Gradients (HOG), and on blob detection. The SVM detects potential new blocks between a set of images; the blob detection, then, confirms the identification of a block inside the area indicated by the SVM and derives the shape of the block. The results from the automatic analysis were compared with block statistics from visual inspection. We tested our method in 6 areas consisting of 1000x1000 pixels, where several hundreds of blocks were identified. The results for the given test areas produced a true positive rate of ~75% for blocks with sizes larger than 0.7 m (i.e., approx. 3 times the available ground pixel size) and a false discovery rate of ~8.5%. Using blob detection we also recover the size of each block within 3 pixels of their actual size.
Subsurface structure analysis using computational interpretation and learning: A visual signal processing perspective
Venue: arXiv cs.CV 2018
Year: 2018

Understanding Earth's subsurface structures has been and continues to be an essential component of various applications such as environmental monitoring, carbon sequestration, and oil and gas exploration. By viewing the seismic volumes that are generated through the processing of recorded seismic traces, researchers were able to learn from applying advanced image processing and computer vision algorithms to effectively analyze and understand Earth's subsurface structures. In this paper, first, we summarize the recent advances in this direction that relied heavily on the fields of image processing and computer vision. Second, we discuss the challenges in seismic interpretation and provide insights and some directions to address such challenges using emerging machine learning algorithms.
Blockchain and Cryptocurrency: A comparative framework of the main Architectural Drivers
Venue: arXiv cs.CR 2018
Year: 2018

Blockchain is a decentralized transaction and data management solution, the technological weapon-of-choice behind the success of Bitcoin and other cryptocurrencies. As the number and variety of existing blockchain implementations continues to increase, adopters should focus on selecting the best one to support their decentralized applications (dApps), rather than developing new ones from scratch. In this paper we present a framework to aid software architects, developers, tool selectors and decision makers to adopt the right blockchain technology for their problem at hand. The framework exposes the correlation between technological decisions and architectural features, capturing the knowledge from existing industrial products, technical forums/blogs, experts' feedback and academic literature; plus our own experience using and developing blockchain-based applications. We validate our framework by applying it to dissect the most outstanding blockchain platforms, i.e., the ones behind the top 10 cryptocurrencies apart from Bitcoin. Then, we show how we applied it to a real-world case study in the insurtech domain.
Relevant Attributes in Formal Contexts
Venue: arXiv cs.AI 2018
Year: 2018

Computing conceptual structures, like formal concept lattices, is in the age of massive data sets a challenging task. There are various approaches to deal with this, e.g., random sampling, parallelization, or attribute extraction. A so far not investigated method in the realm of formal concept analysis is attribute selection, as done in machine learning. Building up on this we introduce a method for attribute selection in formal contexts. To this end, we propose the notion of relevant attributes which enables us to define a relative relevance function, reflecting both the order structure of the concept lattice as well as distribution of objects on it. Finally, we overcome computational challenges for computing the relative relevance through an approximation approach based on information entropy.
An Optical Flow-Based Approach for Minimally-Divergent Velocimetry Data Interpolation
Venue: arXiv cs.CV 2018
Year: 2018

Three-dimensional (3D) biomedical image sets are often acquired with in-plane pixel spacings that are far less than the out-of-plane spacings between images. The resultant anisotropy, which can be detrimental in many applications, can be decreased using image interpolation. Optical flow and/or other registration-based interpolators have proven useful in such interpolation roles in the past. When acquired images are comprised of signals that describe the flow velocity of fluids, additional information is available to guide the interpolation process. In this paper, we present an optical-flow based framework for image interpolation that also minimizes resultant divergence in the interpolated data.
Cluster validity index based on Jeffrey divergence
Venue: arXiv cs.LG 2018
Year: 2018

Cluster validity indexes are very important tools designed for two purposes: comparing the performance of clustering algorithms and determining the number of clusters that best fits the data. These indexes are in general constructed by combining a measure of compactness and a measure of separation. A classical measure of compactness is the variance. As for separation, the distance between cluster centers is used. However, such a distance does not always reflect the quality of the partition between clusters and sometimes gives misleading results. In this paper, we propose a new cluster validity index for which Jeffrey divergence is used to measure separation between clusters. Experimental results are conducted using different types of data and comparison with widely used cluster validity indexes demonstrates the outperformance of the proposed index.