OpenRaiser/Intern-Atlas huggingface dataset:
Tighter bounds for online bipartite matching
Venue: arXiv cs.DS 2018
Year: 2018

We study the online bipartite matching problem, introduced by Karp, Vazirani and Vazirani [1990]. For bipartite graphs with matchings of size $n$, it is known that the Ranking randomized algorithm matches at least $(1 - \frac{1}{e})n$ edges in expectation. It is also known that no online algorithm matches more than $(1 - \frac{1}{e})n + O(1)$ edges in expectation, when the input is chosen from a certain distribution that we refer to as $D_n$. This upper bound also applies to fractional matchings. We review the known proofs for this last statement. In passing we observe that the $O(1)$ additive term (in the upper bound for fractional matching) is $\frac{1}{2} - \frac{1}{2e} + O(\frac{1}{n})$, and that this term is tight: the online algorithm known as Balance indeed produces a fractional matching of this size. We provide a new proof that exactly characterizes the expected cardinality of the (integral) matching produced by Ranking when the input graph comes from the support of $D_n$. This expectation turns out to be $(1 - \frac{1}{e})n + 1 - \frac{2}{e} + O(\frac{1}{n!})$, and serves as an upper bound on the performance ratio of any online (integral) matching algorithm.
Dual Connectivity Support in 5G Networks: An SDN based approach
Venue: arXiv cs.NI 2018
Year: 2018

Dual Connectivity (DC) is one of the key techniques to harness the potential of heterogeneous cellular networks. However, 3rd Generation Partnership Project (3GPP) has introduced disparate mechanisms for DC support in different Radio Access Technologies (RATs), bringing complexity to the network nodes in a Multi-RAT Radio Access Network (RAN). Moreover, DC support requires an exchange of high volume of control information between these network nodes. To address these issues, in this paper an SDN based architecture for Multi-RAT RAN is proposed. The proposed architecture brings simplicity to the network interactions, allows flexibility in the network and helps the network in performing load balancing and mobility management functions effectively. We also demonstrate a reduction in the control signaling and an improvement in system performance compared to legacy architecture.
Never Use Labels: Signal Strength-Based Bayesian Device-Free Localization in Changing Environments
Venue: arXiv cs.NI 2018
Year: 2018

Device-free localization (DFL) methods use measured changes in the received signal strength (RSS) between many pairs of RF nodes to provide location estimates of a person inside the wireless network. Fundamental challenges for RSS DFL methods include having a model of RSS measurements as a function of a person's location, and maintaining an accurate model as the environment changes over time. Current methods rely on either labeled empty-area calibration or labeled fingerprints with a person at each location. Both need to be frequently recalibrated or retrained to stay current with changing environments. Other DFL methods only localize people in motion. In this paper, we address these challenges by, first, introducing a new mixture model for link RSS as a function of a person's location, and second, providing the framework to update model parameters without ever being provided labeled data from either empty-area or known-location classes. We develop two new Bayesian localization methods based on our mixture model and experimentally validate our system at three test sites with seven days of measurements. We demonstrate that our methods localize a person with non-degrading performance in changing environments, and, in addition, reduce localization error by 11-51% compared to other DFL methods.
Latent Variable Modeling for Generative Concept Representations and Deep Generative Models
Venue: arXiv cs.LG 2018
Year: 2018

Latent representations are the essence of deep generative models and determine their usefulness and power. For latent representations to be useful as generative concept representations, their latent space must support latent space interpolation, attribute vectors and concept vectors, among other things. We investigate and discuss latent variable modeling, including latent variable models, latent representations and latent spaces, particularly hierarchical latent representations and latent space vectors and geometry. Our focus is on that used in variational autoencoders and generative adversarial networks.
Accurate, Data-Efficient, Unconstrained Text Recognition with Convolutional Neural Networks
Venue: arXiv cs.CV 2018
Year: 2018

Unconstrained text recognition is an important computer vision task, featuring a wide variety of different sub-tasks, each with its own set of challenges. One of the biggest promises of deep neural networks has been the convergence and automation of feature extractors from input raw signals, allowing for the highest possible performance with minimum required domain knowledge. To this end, we propose a data-efficient, end-to-end neural network model for generic, unconstrained text recognition. In our proposed architecture we strive for simplicity and efficiency without sacrificing recognition accuracy. Our proposed architecture is a fully convolutional network without any recurrent connections trained with the CTC loss function. Thus it operates on arbitrary input sizes and produces strings of arbitrary length in a very efficient and parallelizable manner. We show the generality and superiority of our proposed text recognition architecture by achieving state of the art results on seven public benchmark datasets, covering a wide spectrum of text recognition tasks, namely: Handwriting Recognition, CAPTCHA recognition, OCR, License Plate Recognition, and Scene Text Recognition. Our proposed architecture has won the ICFHR2018 Competition on Automated Text Recognition on a READ Dataset.
Randomized Rumor Spreading in Ad Hoc Networks with Buffers
Venue: arXiv cs.SI 2018
Year: 2018

The randomized rumor spreading problem generates a big interest in the area of distributed algorithms due to its simplicity, robustness and wide range of applications. The two most popular communication paradigms used for spreading the rumor are Push and Pull algorithms. The former protocol allows nodes to send the rumor to a randomly selected neighbor at each step, while the latter is based on sending a request and downloading the rumor from a randomly selected neighbor, provided the neighbor has it. Previous analysis of these protocols assumed that every node could process all such push/pull operations within a single step, which could be unrealistic in practical situations. Therefore we propose a new framework for analysis rumor spreading accommodating buffers, in which a node can process only one push/pull message or push request at a time. We develop upper and lower bounds for randomized rumor spreading time in the new framework, and compare the results with analogous in the old framework without buffers.
Impact of Ground Truth Annotation Quality on Performance of Semantic Image Segmentation of Traffic Conditions
Venue: arXiv cs.CV 2018
Year: 2018

Preparation of high-quality datasets for the urban scene understanding is a labor-intensive task, especially, for datasets designed for the autonomous driving applications. The application of the coarse ground truth (GT) annotations of these datasets without detriment to the accuracy of semantic image segmentation (by the mean intersection over union - mIoU) could simplify and speedup the dataset preparation and model fine tuning before its practical application. Here the results of the comparative analysis for semantic segmentation accuracy obtained by PSPNet deep learning architecture are presented for fine and coarse annotated images from Cityscapes dataset. Two scenarios were investigated: scenario 1 - the fine GT images for training and prediction, and scenario 2 - the fine GT images for training and the coarse GT images for prediction. The obtained results demonstrated that for the most important classes the mean accuracy values of semantic image segmentation for coarse GT annotations are higher than for the fine GT ones, and the standard deviation values are vice versa. It means that for some applications some unimportant classes can be excluded and the model can be tuned further for some classes and specific regions on the coarse GT dataset without loss of the accuracy even. Moreover, this opens the perspectives to use deep neural networks for the preparation of such coarse GT datasets.
Private Information Retrieval from Non-Replicated Databases
Venue: arXiv cs.IT 2018
Year: 2018

We consider the problem of private information retrieval (PIR) of a single message out of $K$ messages from $N$ non-colluding and non-replicated databases. Different from the majority of the existing literature, which considers the case of replicated databases where all databases store the same content in the form of all $K$ messages, here, we consider the case of non-replicated databases under a special non-replication structure where each database stores $M$ out of $K$ messages and each message is stored across $R$ different databases. This generates an $R$-regular graph structure for the storage system where the vertices of the graph are the messages and the edges are the databases. We derive a general upper bound for $M=2$ that depends on the graph structure. We then specialize the problem to storage systems described by two special types of graph structures: cyclic graphs and \emph{fully-connected graphs}. We prove that the PIR capacity for the case of cyclic graphs is $\frac{2}{K+1}$, and the PIR capacity for the case of fully-connected graphs is $\min\{\frac{2}{K},\frac{1}{2}\}$. To that end, we propose novel achievable schemes for both graph structures that are capacity-achieving. The central insight in both schemes is to introduce dependency in the queries submitted to databases that do not contain the desired message, such that the requests can be compressed. In both cases, the results show severe degradation in PIR capacity due to non-replication.
Interest Point Detection based on Adaptive Ternary Coding
Venue: arXiv cs.CV 2018
Year: 2018

In this paper, an adaptive pixel ternary coding mechanism is proposed and a contrast invariant and noise resistant interest point detector is developed on the basis of this mechanism. Every pixel in a local region is adaptively encoded into one of the three statuses: bright, uncertain and dark. The blob significance of the local region is measured by the spatial distribution of the bright and dark pixels. Interest points are extracted from this blob significance measurement. By labeling the statuses of ternary bright, uncertain, and dark, the proposed detector shows more robustness to image noise and quantization errors. Moreover, the adaptive strategy for the ternary cording, which relies on two thresholds that automatically converge to the median of the local region in measurement, enables this coding to be insensitive to the image local contrast. As a result, the proposed detector is invariant to illumination changes. The state-of-the-art results are achieved on the standard datasets, and also in the face recognition application.
Responsive Equilibrium for Self-Adaptive Ubiquitous Interaction
Venue: arXiv cs.HC 2018
Year: 2018

This work attempts to unify two domains: the Game Theory for cooperative control systems and the Responsive Web Design, under the umbrella of crowdsourcing for information gain on Ubiquous Sytems related to different devices (as PC, Tablet, Mobile,...) This paper proposes a framework for adapting DOM objects components for a disaggregated system, which dynamically composes web pages for different kind of devices including ubiquitous/pervasive computing systems. It introduces the notions of responsive webdesign for non-cooperative Nash equilibrium proposing an algorithm (RE-SAUI) for the dynamic interface based on the game theory.
Transceiver Design for Ambient Backscatter Communication over Frequency-Selective Channels
Venue: arXiv cs.IT 2018
Year: 2018

Existing studies about ambient backscatter communication mostly assume flat-fading channels. However, frequency-selective channels widely exist in many practical scenarios. Therefore, this paper investigates ambient backscatter communication systems over frequency-selective channels. In particular, we propose an interference-free transceiver design to facilitate signal detection at the reader. Our design utilizes the cyclic prefix (CP) of orthogonal frequency-division multiplexing (OFDM) source symbols, which can cancel the signal interference and thus enhance the detection accuracy at the reader. Meanwhile, our design leads to no interference on the existing OFDM communication systems. Next we suggest a chi-square based detector for the reader and derive the optimal detection threshold. Simulations are then provided to corroborate our proposed studies.
Resource-Scalable CNN Synthesis for IoT Applications
Venue: arXiv cs.LG 2018
Year: 2018

State-of-the-art image recognition systems use sophisticated Convolutional Neural Networks (CNNs) that are designed and trained to identify numerous object classes. Such networks are fairly resource intensive to compute, prohibiting their deployment on resource-constrained embedded platforms. On one hand, the ability to classify an exhaustive list of categories is excessive for the demands of most IoT applications. On the other hand, designing a new custom-designed CNN for each new IoT application is impractical, due to the inherent difficulty in developing competitive models and time-to-market pressure. To address this problem, we investigate the question of: "Can one utilize an existing optimized CNN model to automatically build a competitive CNN for an IoT application whose objects of interest are a fraction of categories that the original CNN was designed to classify, such that the resource requirement is proportionally scaled down?" We use the term resource scalability to refer to this concept, and develop a methodology for automated synthesis of resource scalable CNNs from an existing optimized baseline CNN. The synthesized CNN has sufficient learning capacity for handling the given IoT application requirements, and yields competitive accuracy. The proposed approach is fast, and unlike the presently common practice of CNN design, does not require iterative rounds of training trial and error.
Visibility and Training in Open Source Software Adoption: A Case in Philippine Higher Education
Venue: arXiv cs.CY 2018
Year: 2018

Open Source Software (OSS) has been widely used in the educational environment largely due to its reduced cost of ownership. While OSS has evolved over the years, challenges exist in its implementation and wide adoption. Foremost among these detriments is the lack of available skills across industries. Since future users of this technology will settle in an organizational ecosystem where proprietary and OSS technologies coexist, it is vital to understand their learning environment where they initially acquire their technology skills. The University implements courses that champion the use of open source technologies within its curricula. However, other courses are also anchored on technologies that are proprietary. This study is based on the premise that training or the learning experience and visibility or the prevalence of OSS in the environment influences its adoption among students. Empirical evidences explore the relationship of visibility and training in the adoption of OSS from the perspectives of students in a Philippine university. A modified Technology Acceptance Model incorporating additional constructs is validated using Partial Least Squares Structural Equation Model. Results of the study confirms the applicability of TAM in this study, training has positive influence on both perceived ease of use and perceived usefulness and visibility has a positive influence on perceived ease of use towards initial acceptability of students of OSS. Educational implications of the study are discussed, limitations are acknowledged and research frontiers are recommended.
Isogeometric Mortar Coupling for Electromagnetic Problems
Venue: arXiv cs.CE 2018
Year: 2018

This paper discusses and analyses two domain decomposition approaches for electromagnetic problems that allow the combination of domains discretised by either Nédélec-type polynomial finite elements or spline-based isogeometric analysis. The first approach is a new isogeometric mortar method and the second one is based on a modal basis for the Lagrange multiplier space, called state-space concatenation in the engineering literature. Spectral correctness and in particular inf-sup stability of both approaches are analytically and numerically investigated. The new mortar method is shown to be unconditionally stable. Its construction of the discrete Lagrange multiplier space takes advantage of the high continuity of splines, and does not have an analogue for Nédélec finite elements. On the other hand, the approach with modal basis is easier to implement but relies on application knowledge to ensure stability and correctness.
Space Expansion of Feature Selection for Designing more Accurate Error Predictors
Venue: arXiv cs.LG 2018
Year: 2018

Approximate computing is being considered as a promising design paradigm to overcome the energy and performance challenges in computationally demanding applications. If the case where the accuracy can be configured, the quality level versus energy efficiency or delay also may be traded-off. For this technique to be used, one needs to make sure a satisfactory user experience. This requires employing error predictors to detect unacceptable approximation errors. In this work, we propose a scheduling-aware feature selection method which leverages the intermediate results of the hardware accelerator to improve the prediction accuracy. Additionally, it configures the error predictors according to the energy consumption and latency of the system. The approach enjoys the flexibility of the prediction time for a higher accuracy. The results on various benchmarks demonstrate significant improvements in the prediction accuracy compared to the prior works which used only the accelerator inputs for the prediction.
An ERP Implementation Method : Studying a Pharmaceutical Company
Venue: arXiv cs.SE 2018
Year: 2018

Analysing the development process for an ERP solution, in our case SAP, is one of the most critical processes in implementing standard software packages. Modelling of the proposed system can facilitate the development of enterprise systems not from scratch but through use of predefined parts who represents the best knowledge captured from numerous case studies. This aim at abstracting the specification of the required information system as well as modelling the process towards this goal. Modelling plays a central role in the organisation of the information systems development process and the information systems community has developed a large number of conceptual models, systems of concepts, for representing conceptual schemata. In the area of ERP systems, because of the characteristics that distinguishes them, conceptual modelling can help in all aspects of the development process, from goal elicitation to reuse of the captured knowledge, through the use of the appropriate modelling schemata. SAP offers a standardised software solution, thus making easier the alignment of SAP requirements to enterprise requirements in a goal form, and the correspondent business processes.
Detecting the Trend in Musical Taste over the Decade -- A Novel Feature Extraction Algorithm to Classify Musical Content with Simple Features
Venue: arXiv cs.IR 2018
Year: 2018

This work proposes a novel feature selection algorithm to classify Songs into different groups. Classification of musical content is often a non-trivial job and still relatively less explored area. The main idea conveyed in this article is to come up with a new feature selection scheme that does the classification job elegantly and with high accuracy but with simpler but wisely chosen small number of features thus being less prone to over-fitting. This uses a very basic general idea about the structure of the audio signal which is generally in the shape of a trapezium. So, using this general idea of the Musical Community we propose three frames to be considered and analyzed for feature extraction for each of the audio signal -- opening, stanzas and closing -- and it has been established with the help of a lot of experiments that this scheme leads to much efficient classification with less complex features in a low dimensional feature space thus is also a computationally less expensive method. Step by step analysis of feature extraction, feature ranking, dimensionality reduction using PCA has been carried in this article. Sequential Forward selection (SFS) algorithm is used to explore the most significant features both with the raw Fisher Discriminant Ratio (FDR) and also with the significant eigen-values after PCA. Also during classification extensive validation and cross validation has been done in a monte-carlo manner to ensure validity of the claims.
SMILK, linking natural language and data from the web
Venue: arXiv cs.IR 2018
Year: 2018

As part of the SMILK Joint Lab, we studied the use of Natural Language Processing to: (1) enrich knowledge bases and link data on the web, and conversely (2) use this linked data to contribute to the improvement of text analysis and the annotation of textual content, and to support knowledge extraction. The evaluation focused on brand-related information retrieval in the field of cosmetics. This article describes each step of our approach: the creation of ProVoc, an ontology to describe products and brands; the automatic population of a knowledge base mainly based on ProVoc from heterogeneous textual resources; and the evaluation of an application which that takes the form of a browser plugin providing additional knowledge to users browsing the web.
Sparse One-Time Grab Sampling of Inliers
Venue: arXiv cs.LG 2018
Year: 2018

Estimating structures in "big data" and clustering them are among the most fundamental problems in computer vision, pattern recognition, data mining, and many other other research fields. Over the past few decades, many studies have been conducted focusing on different aspects of these problems. One of the main approaches that is explored in the literature to tackle the problems of size and dimensionality is sampling subsets of the data in order to estimate the characteristics of the whole population, e.g. estimating the underlying clusters or structures in the data. In this paper, we propose a `one-time-grab' sampling algorithm\cite{jaberi2015swift,jaberi2018sparse}. This method can be used as the front end to any supervised or unsupervised clustering method. Rather than focusing on the strategy of maximizing the probability of sampling inliers, our goal is to minimize the number of samples needed to instantiate all underlying model instances. More specifically, our goal is to answer the following question: {\em `Given a very large population of points with $C$ embedded structures and gross outliers, what is the minimum number of points $r$ to be selected randomly in one grab in order to make sure with probability $P$ that at least $\varepsilon$ points are selected on each structure, where $\varepsilon$ is the number of degrees of freedom of each structure.'}