OpenRaiser/Intern-Atlas huggingface dataset:
Tsallis Regularized Optimal Transport and Ecological Inference
Venue: arXiv cs.LG 2016
Year: 2016

Optimal transport is a powerful framework for computing distances between probability distributions. We unify the two main approaches to optimal transport, namely Monge-Kantorovitch and Sinkhorn-Cuturi, into what we define as Tsallis regularized optimal transport (\trot). \trot~interpolates a rich family of distortions from Wasserstein to Kullback-Leibler, encompassing as well Pearson, Neyman and Hellinger divergences, to name a few. We show that metric properties known for Sinkhorn-Cuturi generalize to \trot, and provide efficient algorithms for finding the optimal transportation plan with formal convergence proofs. We also present the first application of optimal transport to the problem of ecological inference, that is, the reconstruction of joint distributions from their marginals, a problem of large interest in the social sciences. \trot~provides a convenient framework for ecological inference by allowing to compute the joint distribution --- that is, the optimal transportation plan itself --- when side information is available, which is \textit{e.g.} typically what census represents in political science. Experiments on data from the 2012 US presidential elections display the potential of \trot~in delivering a faithful reconstruction of the joint distribution of ethnic groups and voter preferences.
cesium: Open-Source Platform for Time-Series Inference
Venue: arXiv cs.MS 2016
Year: 2016

Inference on time series data is a common requirement in many scientific disciplines and internet of things (IoT) applications, yet there are few resources available to domain scientists to easily, robustly, and repeatably build such complex inference workflows: traditional statistical models of time series are often too rigid to explain complex time domain behavior, while popular machine learning packages require already-featurized dataset inputs. Moreover, the software engineering tasks required to instantiate the computational platform are daunting. cesium is an end-to-end time series analysis framework, consisting of a Python library as well as a web front-end interface, that allows researchers to featurize raw data and apply modern machine learning techniques in a simple, reproducible, and extensible way. Users can apply out-of-the-box feature engineering workflows as well as save and replay their own analyses. Any steps taken in the front end can also be exported to a Jupyter notebook, so users can iterate between possible models within the front end and then fine-tune their analysis using the additional capabilities of the back-end library. The open-source packages make us of many use modern Python toolkits, including xarray, dask, Celery, Flask, and scikit-learn.
Column Networks for Collective Classification
Venue: arXiv cs.LG 2016
Year: 2016

Relational learning deals with data that are characterized by relational structures. An important task is collective classification, which is to jointly classify networked objects. While it holds a great promise to produce a better accuracy than non-collective classifiers, collective classification is computational challenging and has not leveraged on the recent breakthroughs of deep learning. We present Column Network (CLN), a novel deep learning model for collective classification in multi-relational domains. CLN has many desirable theoretical properties: (i) it encodes multi-relations between any two instances; (ii) it is deep and compact, allowing complex functions to be approximated at the network level with a small set of free parameters; (iii) local and relational features are learned simultaneously; (iv) long-range, higher-order dependencies between instances are supported naturally; and (v) crucially, learning and inference are efficient, linear in the size of the network and the number of relations. We evaluate CLN on multiple real-world applications: (a) delay prediction in software projects, (b) PubMed Diabetes publication classification and (c) film genre classification. In all applications, CLN demonstrates a higher accuracy than state-of-the-art rivals.
SCTP User Message Interleaving Integration and Validation
Venue: arXiv cs.NI 2016
Year: 2016

The Stream Control Transmission Protocol (SCTP) is a connection and message oriented transport protocol. It supports multiple uni-directional streams in each direction allowing user message sequence preservation within each stream. This minimizes the re-sequencing delay at the receiver side in case of message loss. The base protocol, although being optimized for small messages, supports arbitrary large user messages by using fragmentation and reassembly at the cost of adding delays at the sender side. To overcome this limitation, a protocol extension called User Message Interleaving is currently being specified by the Internet Engineering Task Force (IETF). This paper describes the new extension, its integration and validation in the context of the INET framework.
On the Equivalency of Reliability and Security Metrics for Wireline Networks
Venue: arXiv cs.IT 2016
Year: 2016

In this paper, we show the equivalency of weak and strong secrecy conditions for a large class of secure network coding problems. When we restrict to linear operations, we show the equivalency of "perfect secrecy and zero-error constraints" with "weak secrecy and $ε$-error constraints".
Passivity-Based Distributed Optimization with Communication Delays Using PI Consensus Algorithm
Venue: arXiv cs.SY 2016
Year: 2016

In this paper, we address a class of distributed optimization problems in the presence of inter-agent communication delays based on passivity. We first focus on unconstrained distributed optimization and provide a passivity-based perspective for distributed optimization algorithms. This perspective allows us to handle communication delays while using scattering transformation. Moreover, we extend the results to constrained distributed optimization, where it is shown that the problem is solved by just adding one more feedback loop of a passive system to the solution of the unconstrained ones. We also show that delays can be incorporated in the same way as the unconstrained problems. Finally, the algorithm is applied to a visual human localization problem using a pedestrian detection algorithm.
Controllability Gramian Spectra of Random Networks
Venue: arXiv cs.SY 2016
Year: 2016

We propose a theoretical framework to study the eigenvalue spectra of the controllability Gramian of systems with random state matrices, such as networked systems with a random graph structure. Using random matrix theory, we provide expressions for the moments of the eigenvalue distribution of the controllability Gramian. These moments can then be used to derive useful properties of the eigenvalue distribution of the Gramian (in some cases, even closed-form expressions for the distribution). We illustrate this framework by considering system matrices derived from common random graph and matrix ensembles, such as the Wigner ensemble, the Gaussian Orthogonal Ensemble (GOE), and random regular graphs. Subsequently, we illustrate how the eigenvalue distribution of the Gramian can be used to draw conclusions about the energy required to control random system.
Long-Term Trends in the Public Perception of Artificial Intelligence
Venue: arXiv cs.CL 2016
Year: 2016

Analyses of text corpora over time can reveal trends in beliefs, interest, and sentiment about a topic. We focus on views expressed about artificial intelligence (AI) in the New York Times over a 30-year period. General interest, awareness, and discussion about AI has waxed and waned since the field was founded in 1956. We present a set of measures that captures levels of engagement, measures of pessimism and optimism, the prevalence of specific hopes and concerns, and topics that are linked to discussions about AI over decades. We find that discussion of AI has increased sharply since 2009, and that these discussions have been consistently more optimistic than pessimistic. However, when we examine specific concerns, we find that worries of loss of control of AI, ethical concerns for AI, and the negative impact of AI on work have grown in recent years. We also find that hopes for AI in healthcare and education have increased over time.
Itinerant routing in elastic optical networks
Venue: arXiv cs.NI 2016
Year: 2016

We state a new problem of \emph{itinerant routing} in elastic optical networks, which we define as the establishment and reconfiguration of an \emph{itinerant connection}. In an itinerant connection, one of the connection end nodes is allowed to change. Itinerant routing could also be considered a form of connection restoration, where a connection has to be restored to a different end node. With the introduction of new mobile-network architectures, the progressing integration of wireless and optical networks, the continuing growth of wireless high-bitrate services, and the eventual deployment of elastic optical networks, there is a need to support this itinerant routing in the elastic optical networks. We present and discuss two major use cases of the itinerant routing, and propose a \emph{novel reconfiguration algorithm}. Our algorithm does not deteriorate the network performance, and requires half as many new links to configure as the shortest-path reconfiguration algorithm. The performance evaluation was carried out with 46800 simulation runs using randomly-generated realistic transport networks.
A Formal Algebra for OLAP
Venue: arXiv cs.DB 2016
Year: 2016

Online Analytical Processing (OLAP) comprises tools and algorithms that allow querying multidimensional databases. It is based on the multidimensional model, where data can be seen as a cube, where each cell contains one or more measures can be aggregated along dimensions. Despite the extensive corpus of work in the field, a standard language for OLAP is still needed, since there is no well-defined, accepted semantics, for many of the usual OLAP operations. In this paper, we address this problem, and present a set of operations for manipulating a data cube. We clearly define the semantics of these operations, and prove that they can be composed, yielding a language powerful enough to express complex OLAP queries. We express these operations as a sequence of atomic transformations over a fixed multidimensional matrix, whose cells contain a sequence of measures. Each atomic transformation produces a new measure. When a sequence of transformations defines an OLAP operation, a flag is produced indicating which cells must be considered as input for the next operation. In this way, an elegant algebra is defined. Our main contribution, with respect to other similar efforts in the field is that, for the first time, a formal proof of the correctness of the operations is given, thus providing a clear semantics for them. We believe the present work will serve as a basis to build more solid practical tools for data analysis.
A Formal Solution to the Grain of Truth Problem
Venue: arXiv cs.AI 2016
Year: 2016

A Bayesian agent acting in a multi-agent environment learns to predict the other agents' policies if its prior assigns positive probability to them (in other words, its prior contains a \emph{grain of truth}). Finding a reasonably large class of policies that contains the Bayes-optimal policies with respect to this class is known as the \emph{grain of truth problem}. Only small classes are known to have a grain of truth and the literature contains several related impossibility results. In this paper we present a formal and general solution to the full grain of truth problem: we construct a class of policies that contains all computable policies as well as Bayes-optimal policies for every lower semicomputable prior over the class. When the environment is unknown, Bayes-optimal agents may fail to act optimally even asymptotically. However, agents based on Thompson sampling converge to play ε-Nash equilibria in arbitrary unknown computable multi-agent environments. While these results are purely theoretical, we show that they can be computationally approximated arbitrarily closely.
Block Compressed Sensing Based Distributed Device Detection for M2M Communications
Venue: arXiv cs.IT 2016
Year: 2016

In this work, we utilize the framework of compressed sensing (CS) for distributed device detection and resource allocation in large-scale machine-to-machine (M2M) communication networks. The devices deployed in the network are partitioned into clusters according to some pre-defined criteria. Moreover, the devices in each cluster are assigned a unique signature of a particular design that can be used to indicate their active status to the network. The proposed scheme in this work mainly consists of two essential steps: (i) The base station (BS) detects the active clusters and the number of active devices in each cluster using a novel block sketching algorithm, and then assigns a certain amount of resources accordingly. (ii) Each active device detects its ranking among all the active devices in its cluster using an enhanced greedy algorithm and accesses the corresponding resource for transmission based on the ranking. By exploiting the correlation in the device behaviors and the sparsity in the activation pattern of the M2M devices, the device detection problem is thus tackled as a CS support recovery procedure for a particular binary block-sparse signal $x\in\mathbb{B}^N$ -- with block sparsity $K_B$ and in-block sparsity $K_I$ over block size $d$. Theoretical analysis shows that the activation pattern of the M2M devices can be reliably reconstructed within an acquisition time of $\mathcal{O}(\max\{K_B\log N, K_BK_I\log d\})$, which achieves a better scaling and less computational complexity of $\mathcal{O}(N(K_I^2+\log N))$ compared with standard CS algorithms. Moreover, extensive simulations confirm the robustness of the proposed scheme in the detection process, especially in terms of higher detection probability and reduced access delay when compared with conventional schemes like LTE random access (RA) procedure and classic cluster-based access approaches.
DiNoDB: an Interactive-speed Query Engine for Ad-hoc Queries on Temporary Data
Venue: arXiv cs.DB 2016
Year: 2016

As data sets grow in size, analytics applications struggle to get instant insight into large datasets. Modern applications involve heavy batch processing jobs over large volumes of data and at the same time require efficient ad-hoc interactive analytics on temporary data. Existing solutions, however, typically focus on one of these two aspects, largely ignoring the need for synergy between the two. Consequently, interactive queries need to re-iterate costly passes through the entire dataset (e.g., data loading) that may provide meaningful return on investment only when data is queried over a long period of time. In this paper, we propose DiNoDB, an interactive-speed query engine for ad-hoc queries on temporary data. DiNoDB avoids the expensive loading and transformation phase that characterizes both traditional RDBMSs and current interactive analytics solutions. It is tailored to modern workflows found in machine learning and data exploration use cases, which often involve iterations of cycles of batch and interactive analytics on data that is typically useful for a narrow processing window. The key innovation of DiNoDB is to piggyback on the batch processing phase the creation of metadata that DiNoDB exploits to expedite the interactive queries. Our experimental analysis demonstrates that DiNoDB achieves very good performance for a wide range of ad-hoc queries compared to alternatives %such as Hive, Stado, SparkSQL and Impala.
Detection with Multimodal Dependent Data Using Low Dimensional Random Projections
Venue: arXiv cs.IT 2016
Year: 2016

Performing likelihood ratio based detection with high dimensional multimodal data is a challenging problem since the computation of the joint probability density functions (pdfs) in the presence of inter-modal dependence is difficult. While some computationally expensive approaches have been proposed for dependent multimodal data fusion (e.g., based on copula theory), a commonly used tractable approach is to compute the joint pdf as the product of marginal pdfs ignoring dependence. However, this method leads to poor performance when the data is strongly dependent. In this paper, we consider the problem of detection when dependence among multimodal data is modeled in a compressed domain where compression is obtained using low dimensional random projections. We employ a Gaussian approximation while modeling inter-modal dependence in the compressed domain which is computationally more efficient. We show that, under certain conditions, detection with multimodal dependent data in the compressed domain with a small number of compressed measurements yields enhanced performance compared to detection with high dimensional data via either the product approach or other suboptimal fusion approaches proposed in the literature.
Dense Wide-Baseline Scene Flow From Two Handheld Video Cameras
Venue: arXiv cs.CV 2016
Year: 2016

We propose a new technique for computing dense scene flow from two handheld videos with wide camera baselines and different photometric properties due to different sensors or camera settings like exposure and white balance. Our technique innovates in two ways over existing methods: (1) it supports independently moving cameras, and (2) it computes dense scene flow for wide-baseline scenarios.We achieve this by combining state-of-the-art wide-baseline correspondence finding with a variational scene flow formulation. First, we compute dense, wide-baseline correspondences using DAISY descriptors for matching between cameras and over time. We then detect and replace occluded pixels in the correspondence fields using a novel edge-preserving Laplacian correspondence completion technique. We finally refine the computed correspondence fields in a variational scene flow formulation. We show dense scene flow results computed from challenging datasets with independently moving, handheld cameras of varying camera settings.
Radon-Gabor Barcodes for Medical Image Retrieval
Venue: arXiv cs.CV 2016
Year: 2016

In recent years, with the explosion of digital images on the Web, content-based retrieval has emerged as a significant research area. Shapes, textures, edges and segments may play a key role in describing the content of an image. Radon and Gabor transforms are both powerful techniques that have been widely studied to extract shape-texture-based information. The combined Radon-Gabor features may be more robust against scale/rotation variations, presence of noise, and illumination changes. The objective of this paper is to harness the potentials of both Gabor and Radon transforms in order to introduce expressive binary features, called barcodes, for image annotation/tagging tasks. We propose two different techniques: Gabor-of-Radon-Image Barcodes (GRIBCs), and Guided-Radon-of-Gabor Barcodes (GRGBCs). For validation, we employ the IRMA x-ray dataset with 193 classes, containing 12,677 training images and 1,733 test images. A total error score as low as 322 and 330 were achieved for GRGBCs and GRIBCs, respectively. This corresponds to $\approx 81\%$ retrieval accuracy for the first hit.
Energy-Efficient Resource Allocation for SWIPT in Multiple Access Channels
Venue: arXiv cs.IT 2016
Year: 2016

In this paper, we study optimal resource allocation strategies for simultaneous information and power transfer (SWIPT) focusing on the system energy efficiency. We consider two-user multiple access channels in which energy harvesting (EH) and information decoding (ID) nodes are spatially separated. We formulate optimization problems that maximize system energy efficiency while taking harvested energy constraints into account. These are concave-linear fractional problems, and hence Karush-Kuhn-Tucker (KKT) conditions are necessary and sufficient to obtain globally optimal solution. Solving these optimization problems, we provide analytical expressions for optimal transmit power allocation among the source nodes, and identify the corresponding energy efficiency. We confirm the theoretical analysis via numerical results. Furthermore, we also characterize the effect of circuit power consumption on the system's efficiency as the harvested energy demand varies.
Should Terminology Principles be re-examined?
Venue: arXiv cs.AI 2016
Year: 2016

Operationalization of terminology for IT applications has revived the Wusterian approach. The conceptual dimension once more prevails after taking back seat to specialised lexicography. This is demonstrated by the emergence of ontology in terminology. While the Terminology Principles as defined in Felber manual and the ISO standards remain at the core of traditional terminology, their computational implementation raises some issues. In this article, while reiterating their importance, we will be re-examining these Principles from a dual perspective: that of logic in the mathematical sense of the term and that of epistemology as in the theory of knowledge. We will thus be clarifying and describing some of them so as to take into account advances in knowledge engineering (ontology) and formal systems (logic). The notion of ontoterminology, terminology whose conceptual system is a formal ontology, results from this approach.
Stacked-VLAN-Based Modeling of Hybrid ISP Traffic Control Schemes and Service Plans Exploiting Excess Bandwidth in Shared Access Networks
Venue: arXiv cs.NI 2016
Year: 2016

The current practice of shaping subscriber traffic using a token bucket filter by Internet service providers may result in a severe waste of network resources in shared access networks; except for a short period of time proportional to the size of a token bucket, it cannot allocate excess bandwidth among active subscribers even when there are only a few active subscribers. To better utilize the network resources in shared access networks, therefore, we recently proposed and analyzed the performance of access traffic control schemes, which can allocate excess bandwidth among active subscribers proportional to their token generation rates. Also, to exploit the excess bandwidth allocation enabled by the proposed traffic control schemes, we have been studying flexible yet practical service plans under a hybrid traffic control architecture, which are attractive to both an Internet service provider and its subscribers in terms of revenue and quality of service. In this paper we report the current status of our modeling of the hybrid traffic control schemes and service plans with OMNeT++/INET-HNRL based on IEEE standard 802.1Q stacked VLANs.
Development of a Fuzzy Expert System based Liveliness Detection Scheme for Biometric Authentication
Venue: arXiv cs.CV 2016
Year: 2016

Liveliness detection acts as a safe guard against spoofing attacks. Most of the researchers used vision based techniques to detect liveliness of the user, but they are highly sensitive to illumination effects. Therefore it is very hard to design a system, which will work robustly under all circumstances. Literature shows that most of the research utilize eye blink or mouth movement to detect the liveliness, while the other group used face texture to distinguish between real and imposter. The classification results of all these approaches decreases drastically in variable light conditions. Hence in this paper we are introducing fuzzy expert system which is sufficient enough to handle most of the cases comes in real time. We have used two testing parameters, (a) under bad illumination and (b) less movement in eyes and mouth in case of real user to evaluate the performance of the system. The system is behaving well in all, while in first case its False Rejection Rate (FRR) is 0.28, and in second case its FRR is 0.4.