OpenRaiser/Intern-Atlas huggingface dataset:
Spotting Separator Points at Line Terminals in Compressed Document Images for Text-line Segmentation
Venue: arXiv cs.CV 2017
Year: 2017

Line separators are used to segregate text-lines from one another in document image analysis. Finding the separator points at every line terminal in a document image would enable text-line segmentation. In particular, identifying the separators in handwritten text could be a thrilling exercise. Obviously it would be challenging to perform this in the compressed version of a document image and that is the proposed objective in this research. Such an effort would prevent the computational burden of decompressing a document for text-line segmentation. Since document images are generally compressed using run length encoding (RLE) technique as per the CCITT standards, the first column in the RLE will be a white column. The value (depth) in the white column is very low when a particular line is a text line and the depth could be larger at the point of text line separation. A longer consecutive sequence of such larger depth should indicate the gap between the text lines, which provides the separator region. In case of over separation and under separation issues, corrective actions such as deletion and insertion are suggested respectively. An extensive experimentation is conducted on the compressed images of the benchmark datasets of ICDAR13 and Alireza et al [17] to demonstrate the efficacy.
Analysing Soccer Games with Clustering and Conceptors
Venue: arXiv cs.LG 2017
Year: 2017

We present a new approach for identifying situations and behaviours, which we call "moves", from soccer games in the 2D simulation league. Being able to identify key situations and behaviours are useful capabilities for analysing soccer matches, anticipating opponent behaviours to aid selection of appropriate tactics, and also as a prerequisite for automatic learning of behaviours and policies. To support a wide set of strategies, our goal is to identify situations from data, in an unsupervised way without making use of pre-defined soccer specific concepts such as "pass" or "dribble". The recurrent neural networks we use in our approach act as a high-dimensional projection of the recent history of a situation on the field. Similar situations, i.e., with similar histories, are found by clustering of network states. The same networks are also used to learn so-called conceptors, that are lower-dimensional manifolds that describe trajectories through a high-dimensional state space that enable situation-specific predictions from the same neural network. With the proposed approach, we can segment games into sequences of situations that are learnt in an unsupervised way, and learn conceptors that are useful for the prediction of the near future of the respective situation.
The CLaC Discourse Parser at CoNLL-2015
Venue: arXiv cs.CL 2017
Year: 2017

This paper describes our submission (kosseim15) to the CoNLL-2015 shared task on shallow discourse parsing. We used the UIMA framework to develop our parser and used ClearTK to add machine learning functionality to the UIMA framework. Overall, our parser achieves a result of 17.3 F1 on the identification of discourse relations on the blind CoNLL-2015 test set, ranking in sixth place.
Agent-based computing from multi-agent systems to agent-based Models: a visual survey
Venue: arXiv cs.SI 2017
Year: 2017

Agent-Based Computing is a diverse research domain concerned with the building of intelligent software based on the concept of "agents". In this paper, we use Scientometric analysis to analyze all sub-domains of agent-based computing. Our data consists of 1,064 journal articles indexed in the ISI web of knowledge published during a twenty year period: 1990-2010. These were retrieved using a topic search with various keywords commonly used in sub-domains of agent-based computing. In our proposed approach, we have employed a combination of two applications for analysis, namely Network Workbench and CiteSpace - wherein Network Workbench allowed for the analysis of complex network aspects of the domain, detailed visualization-based analysis of the bibliographic data was performed using CiteSpace. Our results include the identification of the largest cluster based on keywords, the timeline of publication of index terms, the core journals and key subject categories. We also identify the core authors, top countries of origin of the manuscripts along with core research institutes. Finally, our results have interestingly revealed the strong presence of agent-based computing in a number of non-computing related scientific domains including Life Sciences, Ecological Sciences and Social Sciences.
LTE PHY Layer Vulnerability Analysis and Testing Using Open-Source SDR Tools
Venue: arXiv cs.NI 2017
Year: 2017

This paper provides a methodology to study the PHY layer vulnerability of wireless protocols in hostile radio environments. Our approach is based on testing the vulnerabilities of a system by analyzing the individual subsystems. By targeting an individual subsystem or a combination of subsystems at a time, we can infer the weakest part and revise it to improve the overall system performance. We apply our methodology to 4G LTE downlink by considering each control channel as a subsystem. We also develop open-source software enabling research and education using software-defined radios. We present experimental results with open-source LTE systems and shows how the different subsystems behave under targeted interference. The analysis for the LTE downlink shows that the synchronization signals (PSS/SSS) are very resilient to interference, whereas the downlink pilots or Cell-Specific Reference signals (CRS) are the most susceptible to a synchronized protocol-aware interferer. We also analyze the severity of control channel attacks for different LTE configurations. Our methodology and tools allow rapid evaluation of the PHY layer reliability in harsh signaling environments, which is an asset to improve current standards and develop new robust wireless protocols.
The Helsinki Neural Machine Translation System
Venue: arXiv cs.CL 2017
Year: 2017

We introduce the Helsinki Neural Machine Translation system (HNMT) and how it is applied in the news translation task at WMT 2017, where it ranked first in both the human and automatic evaluations for English--Finnish. We discuss the success of English--Finnish translations and the overall advantage of NMT over a strong SMT baseline. We also discuss our submissions for English--Latvian, English--Chinese and Chinese--English.
Neural Machine Translation with Extended Context
Venue: arXiv cs.CL 2017
Year: 2017

We investigate the use of extended context in attention-based neural machine translation. We base our experiments on translated movie subtitles and discuss the effect of increasing the segments beyond single translation units. We study the use of extended source language context as well as bilingual context extensions. The models learn to distinguish between information from different segments and are surprisingly robust with respect to translation quality. In this pilot study, we observe interesting cross-sentential attention patterns that improve textual coherence in translation at least in some selected cases.
On the topology effects in wireless sensor networks based prognostics and health management
Venue: arXiv cs.DC 2017
Year: 2017

In this work, we consider the usage of wireless sensor networks (WSN) to monitor an area of interest, in order to diagnose on real time its state. Each sensor node forwards information about relevant features towards the sink where the data is processed. Nevertheless, energy conservation is a key issue in the design of such networks and once a sensor exhausts its resources, it will be dropped from the network. This will lead to broken links and data loss. It is therefore important to keep the network running for as long as possible by preserving the energy held by the nodes. Indeed, saving the quality of service (QoS) of a wireless sensor network for a long period is very important in order to ensure accurate data. Then, the area diagnosing will be more accurate. From another side, packet transmission is the phase that consumes the highest amount of energy comparing to other activities in the network. Therefore, we can see that the network topology has an important impact on energy efficiency, and thus on data and diagnosis accuracies. In this paper, we study and compare four network topologies: distributed, hierarchical, centralized, and decentralized topology and show their impact on the resulting estimation of diagnostics. We have used six diagnostic algorithms, to evaluate both prognostic and health management with the variation of type of topology in WSN.
Jointly Optimal Routing and Caching for Arbitrary Network Topologies
Venue: arXiv cs.NI 2017
Year: 2017

We study a problem of fundamental importance to ICNs, namely, minimizing routing costs by jointly optimizing caching and routing decisions over an arbitrary network topology. We consider both source routing and hop-by-hop routing settings. The respective offline problems are NP-hard. Nevertheless, we show that there exist polynomial time approximation algorithms producing solutions within a constant approximation from the optimal. We also produce distributed, adaptive algorithms with the same approximation guarantees. We simulate our adaptive algorithms over a broad array of different topologies. Our algorithms reduce routing costs by several orders of magnitude compared to prior art, including algorithms optimizing caching under fixed routing.
Efficient Online Inference for Infinite Evolutionary Cluster models with Applications to Latent Social Event Discovery
Venue: arXiv cs.AI 2017
Year: 2017

The Recurrent Chinese Restaurant Process (RCRP) is a powerful statistical method for modeling evolving clusters in large scale social media data. With the RCRP, one can allow both the number of clusters and the cluster parameters in a model to change over time. However, application of the RCRP has largely been limited due to the non-conjugacy between the cluster evolutionary priors and the Multinomial likelihood. This non-conjugacy makes inference di cult and restricts the scalability of models which use the RCRP, leading to the RCRP being applied only in simple problems, such as those that can be approximated by a single Gaussian emission. In this paper, we provide a novel solution for the non-conjugacy issues for the RCRP and an example of how to leverage our solution for one speci c problem - the social event discovery problem. By utilizing Sequential Monte Carlo methods in inference, our approach can be massively paralleled and is highly scalable, to the extent it can work on tens of millions of documents. We are able to generate high quality topical and location distributions of the clusters that can be directly interpreted as real social events, and our experimental results suggest that the approaches proposed achieve much better predictive performance than techniques reported in prior work. We also demonstrate how the techniques we develop can be used in a much more general ways toward similar problems.
Product Matrix Minimum Storage Regenerating Codes with Flexible Number of Helpers
Venue: arXiv cs.IT 2017
Year: 2017

In coding for distributed storage systems, efficient data reconstruction and repair through accessing a predefined number of arbitrarily chosen storage nodes is guaranteed by regenerating codes. Traditionally, code parameters, specially the number of helper nodes participating in a repair process, are predetermined. However, depending on the state of the system and network traffic, it is desirable to adapt such parameters accordingly in order to minimize the cost of repair. In this work a class of regenerating codes with minimum storage is introduced that can simultaneously operate at the optimal repair bandwidth, for a wide range of exact repair mechanisms, based on different number of helper nodes.
Vector Space Model as Cognitive Space for Text Classification
Venue: arXiv cs.CL 2017
Year: 2017

In this era of digitization, knowing the user's sociolect aspects have become essential features to build the user specific recommendation systems. These sociolect aspects could be found by mining the user's language sharing in the form of text in social media and reviews. This paper describes about the experiment that was performed in PAN Author Profiling 2017 shared task. The objective of the task is to find the sociolect aspects of the users from their tweets. The sociolect aspects considered in this experiment are user's gender and native language information. Here user's tweets written in a different language from their native language are represented as Document - Term Matrix with document frequency as the constraint. Further classification is done using the Support Vector Machine by taking gender and native language as target classes. This experiment attains the average accuracy of 73.42% in gender prediction and 76.26% in the native language identification task.
3D Visibility Representations of 1-planar Graphs
Venue: arXiv cs.CG 2017
Year: 2017

We prove that every 1-planar graph G has a z-parallel visibility representation, i.e., a 3D visibility representation in which the vertices are isothetic disjoint rectangles parallel to the xy-plane, and the edges are unobstructed z-parallel visibilities between pairs of rectangles. In addition, the constructed representation is such that there is a plane that intersects all the rectangles, and this intersection defines a bar 1-visibility representation of G.
General Backpropagation Algorithm for Training Second-order Neural Networks
Venue: arXiv cs.LG 2017
Year: 2017

The artificial neural network is a popular framework in machine learning. To empower individual neurons, we recently suggested that the current type of neurons could be upgraded to 2nd order counterparts, in which the linear operation between inputs to a neuron and the associated weights is replaced with a nonlinear quadratic operation. A single 2nd order neurons already has a strong nonlinear modeling ability, such as implementing basic fuzzy logic operations. In this paper, we develop a general backpropagation (BP) algorithm to train the network consisting of 2nd-order neurons. The numerical studies are performed to verify of the generalized BP algorithm.
A Flow Model of Neural Networks
Venue: arXiv cs.LG 2017
Year: 2017

Based on a natural connection between ResNet and transport equation or its characteristic equation, we propose a continuous flow model for both ResNet and plain net. Through this continuous model, a ResNet can be explicitly constructed as a refinement of a plain net. The flow model provides an alternative perspective to understand phenomena in deep neural networks, such as why it is necessary and sufficient to use 2-layer blocks in ResNets, why deeper is better, and why ResNets are even deeper, and so on. It also opens a gate to bring in more tools from the huge area of differential equations.
Probabilistic Relation Induction in Vector Space Embeddings
Venue: arXiv cs.AI 2017
Year: 2017

Word embeddings have been found to capture a surprisingly rich amount of syntactic and semantic knowledge. However, it is not yet sufficiently well-understood how the relational knowledge that is implicitly encoded in word embeddings can be extracted in a reliable way. In this paper, we propose two probabilistic models to address this issue. The first model is based on the common relations-as-translations view, but is cast in a probabilistic setting. Our second model is based on the much weaker assumption that there is a linear relationship between the vector representations of related words. Compared to existing approaches, our models lead to more accurate predictions, and they are more explicit about what can and cannot be extracted from the word embedding.
Parallel solver for shifted systems in a hybrid CPU-GPU framework
Venue: arXiv cs.MS 2017
Year: 2017

This paper proposes a combination of a hybrid CPU--GPU and a pure GPU software implementation of a direct algorithm for solving shifted linear systems $(A - σI)X = B$ with large number of complex shifts $σ$ and multiple right-hand sides. Such problems often appear e.g. in control theory when evaluating the transfer function, or as a part of an algorithm performing interpolatory model reduction, as well as when computing pseudospectra and structured pseudospectra, or solving large linear systems of ordinary differential equations. The proposed algorithm first jointly reduces the general full $n\times n$ matrix $A$ and the $n\times m$ full right-hand side matrix $B$ to the controller Hessenberg canonical form that facilitates efficient solution: $A$ is transformed to a so-called $m$-Hessenberg form and $B$ is made upper-triangular. This is implemented as blocked highly parallel CPU--GPU hybrid algorithm; individual blocks are reduced by the CPU, and the necessary updates of the rest of the matrix are split among the cores of the CPU and the GPU. To enhance parallelization, the reduction and the updates are overlapped. In the next phase, the reduced $m$-Hessenberg--triangular systems are solved entirely on the GPU, with shifts divided into batches. The benefits of such load distribution are demonstrated by numerical experiments. In particular, we show that our proposed implementation provides an excellent basis for efficient implementations of computational methods in systems and control theory, from evaluation of transfer function to the interpolatory model reduction.
Verifying Quantum Programs: From Quipper to QPMC
Venue: arXiv cs.LO 2017
Year: 2017

In this paper we present a translation from the quantum programming language Quipper to the QPMC model checker, with the main aim of verifying Quipper programs. Quipper is an embedded functional programming language for quantum computation. It is above all a circuit description language, for this reason it uses the vector state formalism and its main purpose is to make circuit implementation easy providing high level operations for circuit manipulation. Quipper provides both an high-level circuit building interface and a simulator. QPMC is a model checker for quantum protocols based on the density matrix formalism. QPMC extends the probabilistic model checker IscasMC allowing to formally verify properties specified in the temporal logic QCTL on Quantum Markov Chains. We implemented and tested our translation on several quantum algorithms, including Grover's quantum search.
Aerial Rock Fragmentation Analysis in Low-Light Condition Using UAV Technology
Venue: arXiv cs.RO 2017
Year: 2017

In recent years, Unmanned Aerial Vehicle (UAV) technology has been introduced into the mining industry to conduct terrain surveying. This work investigates the application of UAVs with artificial lighting for measurement of rock fragmentation under poor lighting conditions, representing night shifts in surface mines or working conditions in underground mines. The study relies on indoor and outdoor experiments for rock fragmentation analysis using a quadrotor UAV. Comparison of the rock size distributions in both cases show that adequate artificial lighting enables similar accuracy to ideal lighting conditions.
Learning to Detect and Mitigate Cross-layer Attacks in Wireless Networks: Framework and Applications
Venue: arXiv cs.NI 2017
Year: 2017

Security threats such as jamming and route manipulation can have significant consequences on the performance of modern wireless networks. To increase the efficacy and stealthiness of such threats, a number of extremely challenging, cross-layer attacks have been recently unveiled. Although existing research has thoroughly addressed many single-layer attacks, the problem of detecting and mitigating cross-layer attacks still remains unsolved. For this reason, in this paper we propose a novel framework to analyze and address cross-layer attacks in wireless networks. Specifically, our framework consists of a detection and a mitigation component. The attack detection component is based on a Bayesian learning detection scheme that constructs a model of observed evidence to identify stealthy attack activities. The mitigation component comprises a scheme that achieves the desired trade-off between security and performance. We specialize and evaluate the proposed framework by considering a specific cross-layer attack that uses jamming as an auxiliary tool to achieve route manipulation. Simulations and experimental results obtained with a test-bed made up by USRP software-defined radios demonstrate the effectiveness of the proposed methodology.
Smart City Analytics: Ensemble-Learned Prediction of Citizen Home Care
Venue: arXiv cs.CY 2017
Year: 2017

We present an ensemble learning method that predicts large increases in the hours of home care received by citizens. The method is supervised, and uses different ensembles of either linear (logistic regression) or non-linear (random forests) classifiers. Experiments with data available from 2013 to 2017 for every citizen in Copenhagen receiving home care (27,775 citizens) show that prediction can achieve state of the art performance as reported in similar health related domains (AUC=0.715). We further find that competitive results can be obtained by using limited information for training, which is very useful when full records are not accessible or available. Smart city analytics does not necessarily require full city records. To our knowledge this preliminary study is the first to predict large increases in home care for smart city analytics.
A Hybrid Approach for Data Analytics for Internet of Things
Venue: arXiv cs.NI 2017
Year: 2017

The vision of the Internet of Things is to allow currently unconnected physical objects to be connected to the internet. There will be an extremely large number of internet connected devices that will be much more than the number of human being in the world all producing data. These data will be collected and delivered to the cloud for processing, especially with a view of finding meaningful information to then take action. However, ideally the data needs to be analysed locally to increase privacy, give quick responses to people and to reduce use of network and storage resources. To tackle these problems, distributed data analytics can be proposed to collect and analyse the data either in the edge or fog devices. In this paper, we explore a hybrid approach which means that both innetwork level and cloud level processing should work together to build effective IoT data analytics in order to overcome their respective weaknesses and use their specific strengths. Specifically, we collected raw data locally and extracted features by applying data fusion techniques on the data on resource constrained devices to reduce the data and then send the extracted features to the cloud for processing. We evaluated the accuracy and data consumption over network and thus show that it is feasible to increase privacy and maintain accuracy while reducing data communication demands.
Planning Based System for Child-Robot Interaction in Dynamic Play Environments
Venue: arXiv cs.RO 2017
Year: 2017

This paper describes the initial steps towards the design of a robotic system that intends to perform actions autonomously in a naturalistic play environment. At the same time it aims for social human-robot interaction~(HRI), focusing on children. We draw on existing theories of child development and on dimensional models of emotions to explore the design of a dynamic interaction framework for natural child-robot interaction. In this dynamic setting, the social HRI is defined by the ability of the system to take into consideration the socio-emotional state of the user and to plan appropriately by selecting appropriate strategies for execution. The robot needs a temporal planning system, which combines features of task-oriented actions and principles of social human robot interaction. We present initial results of an empirical study for the evaluation of the proposed framework in the context of a collaborative sorting game.