OpenRaiser/Intern-Atlas huggingface dataset:
The quantitative and qualitative content analysis of marketing literature for innovative information systems: the Aldrich Archive
Venue: arXiv cs.CY 2015
Year: 2015

The Aldrich Archive is a collection of technical and marketing material covering the period from 1977 to 2000; the physical documents are in the process of being digitised and made available on the internet. The Aldrich Archive includes contemporaneous case studies of end-user computer systems that were used for marketing purposes. This paper analyses these case studies of innovative information systems 1980 - 1990 using a quantitative and qualitative content analysis. The major aim of this research paper is to find out how innovative information systems were marketed in the decade from 1980 to 1990. The paper uses a double-step content analysis and does not focus on one method of content analysis only. The reason for choosing this approach is to combine the advantages of both quantitative and qualitative content analysis. The results of the quantitative content analysis indicated that the focus of the marketing material would be on information management / information supply. But the qualitative analysis revealed that the focus is on monetary advantages. The strong focus on monetary advantages of information technology seems typical for the 1980s and 1990s. In 1987, Robert Solow stated you can see the computer age everywhere but in the productivity statistics. This paradox caused a lot of discussion: since the introduction of the IT productivity paradox the business value of information technology has been the topic of many debates by practitioners as well as by academics.
An Electrical Structure-Based Approach to PMU Placement in the Electric Power Grid
Venue: arXiv cs.SY 2015
Year: 2015

The phasor measurement unit (PMU) placement problem is revisited by taking into account a stronger characterization of the electrical connectedness between various buses in the grid. To facilitate this study, the placement problem is approached from the perspective of the \emph{electrical structure} which, unlike previous work on PMU placement, accounts for the sensitivity between power injections and nodal phase angle differences between various buses in the power network. The problem is formulated as a binary integer program with the objective to minimize the number of PMUs for complete network observability in the absence of zero injection measurements. The implication of the proposed approach on static state estimation and fault detection algorithms incorporating PMU measurements is analyzed. Results show a significant improvement in the performance of estimation and detection schemes by employing the electrical structure-based PMU placement compared to its topological counterpart. In light of recent advances in the electrical structure of the grid, our study provides a more realistic perspective of PMU placement in the electric power grid.
Automatic Completion of Distributed Protocols with Symmetry
Venue: arXiv cs.FL 2015
Year: 2015

A distributed protocol is typically modeled as a set of communicating processes, where each process is described as an extended state machine along with fairness assumptions, and its correctness is specified using safety and liveness requirements. Designing correct distributed protocols is a challenging task. Aimed at simplifying this task, we allow the designer to leave some of the guards and updates to state variables in the description of extended state machines as unknown functions. The protocol completion problem then is to find interpretations for these unknown functions while guaranteeing correctness. In many distributed protocols, process behaviors are naturally symmetric, and thus, synthesized expressions are further required to obey symmetry constraints. Our counterexample-guided synthesis algorithm consists of repeatedly invoking two phases. In the first phase, candidates for unknown expressions are generated using the SMT solver Z3. This phase requires carefully orchestrating constraints to enforce the desired symmetry in read/write accesses. In the second phase, the resulting completed protocol is checked for correctness using a custom-built model checker that handles fairness assumptions, safety and liveness requirements, and exploits symmetry. When model checking fails, our tool examines a set of counterexamples to safety/liveness properties to generate constraints on unknown functions that must be satisfied by subsequent completions. For evaluation, we show that our prototype is able to automatically discover interesting missing details in distributed protocols for mutual exclusion, self stabilization, and cache coherence.
CCG Parsing and Multiword Expressions
Venue: arXiv cs.CL 2015
Year: 2015

This thesis presents a study about the integration of information about Multiword Expressions (MWEs) into parsing with Combinatory Categorial Grammar (CCG). We build on previous work which has shown the benefit of adding information about MWEs to syntactic parsing by implementing a similar pipeline with CCG parsing. More specifically, we collapse MWEs to one token in training and test data in CCGbank, a corpus which contains sentences annotated with CCG derivations. Our collapsing algorithm however can only deal with MWEs when they form a constituent in the data which is one of the limitations of our approach. We study the effect of collapsing training and test data. A parsing effect can be obtained if collapsed data help the parser in its decisions and a training effect can be obtained if training on the collapsed data improves results. We also collapse the gold standard and show that our model significantly outperforms the baseline model on our gold standard, which indicates that there is a training effect. We show that the baseline model performs significantly better on our gold standard when the data are collapsed before parsing than when the data are collapsed after parsing which indicates that there is a parsing effect. We show that these results can lead to improved performance on the non-collapsed standard benchmark although we fail to show that it does so significantly. We conclude that despite the limited settings, there are noticeable improvements from using MWEs in parsing. We discuss ways in which the incorporation of MWEs into parsing can be improved and hypothesize that this will lead to more substantial results. We finally show that turning the MWE recognition part of the pipeline into an experimental part is a useful thing to do as we obtain different results with different recognizers.
Exploring Nearest Neighbor Approaches for Image Captioning
Venue: arXiv cs.CV 2015
Year: 2015

We explore a variety of nearest neighbor baseline approaches for image captioning. These approaches find a set of nearest neighbor images in the training set from which a caption may be borrowed for the query image. We select a caption for the query image by finding the caption that best represents the "consensus" of the set of candidate captions gathered from the nearest neighbor images. When measured by automatic evaluation metrics on the MS COCO caption evaluation server, these approaches perform as well as many recent approaches that generate novel captions. However, human studies show that a method that generates novel captions is still preferred over the nearest neighbor approach.
Reproducible Evaluation of Pan-Tilt-Zoom Tracking
Venue: arXiv cs.CV 2015
Year: 2015

Tracking with a Pan-Tilt-Zoom (PTZ) camera has been a research topic in computer vision for many years. However, it is very difficult to assess the progress that has been made on this topic because there is no standard evaluation methodology. The difficulty in evaluating PTZ tracking algorithms arises from their dynamic nature. In contrast to other forms of tracking, PTZ tracking involves both locating the target in the image and controlling the motors of the camera to aim it so that the target stays in its field of view. This type of tracking can only be performed online. In this paper, we propose a new evaluation framework based on a virtual PTZ camera. With this framework, tracking scenarios do not change for each experiment and we are able to replicate online PTZ camera control and behavior including camera positioning delays, tracker processing delays, and numerical zoom. We tested our evaluation framework with the Camshift tracker to show its viability and to establish baseline results.
Contribution to Temporal Fault Tree Analysis without Modularization and Transformation into the State Space
Venue: arXiv cs.CE 2015
Year: 2015

Background: Fault tree analysis (FTA) is a well established method for qualitative as well as probabilistic reliability and safety analysis. As a Boolean model it does not support modelling of dynamic effects like sequence dependencies between fault events. This work describes a method that allows consideration of sequence dependencies without transformations into state-space. Concept: The new temporal fault tree analysis (TFTA) described in this work extends the Boolean FTA. The TFTA is based on a new temporal logic which adds a concept of time to the Boolean logic and algebra. This allows modelling of temporal relationships between events using two new temporal operators (PAND and SAND). With a set of temporal logic rules, a given temporal term may be simplified to its temporal disjunctive normal form (TDNF) which is similar to the Boolean DNF but includes event sequencies. In TDNF the top event's temporal system function may be reduced to a list of minimal cutset sequences (MCSS). These allow qualitative analyses similar to Boolean cutset analysis in normal FTA. Furthermore the TFTA may also be used for probabilistic analyses without using state-space models. Results: One significant aspect of the new TFTA described in this work is the possibility to take sequence dependencies into account for qualitative and probabilistic analyses without state-space transformations. Among others, this allows for modelling of event sequencies at all levels within a fault tree, a real qualitative analysis similar to the FTA's cutset analysis, and quantification of sequence dependencies within the same model.
Large System Analysis of Cognitive Radio Network via Partially-Projected Regularized Zero-Forcing Precoding
Venue: arXiv cs.IT 2015
Year: 2015

In this paper, we consider a cognitive radio (CR) network in which a secondary multiantenna base station (BS) attempts to communicate with multiple secondary users (SUs) using the radio frequency spectrum that is originally allocated to multiple primary users (PUs). Here, we employ partially-projected regularized zero-forcing (PP-RZF) precoding to control the amount of interference at the PUs and to minimize inter-SUs interference. The PP-RZF precoding partially projects the channels of the SUs into the null space of the channels from the secondary BS to the PUs. The regularization parameter and the projection control parameter are used to balance the transmissions to the PUs and the SUs. However, the search for the optimal parameters, which can maximize the ergodic sum-rate of the CR network, is a demanding process because it involves Monte-Carlo averaging. Then, we derive a deterministic expression for the ergodic sum-rate achieved by the PP-RZF precoding using recent advancements in large dimensional random matrix theory. The deterministic equivalent enables us to efficiently determine the two critical parameters in the PP-RZF precoding because no Monte-Carlo averaging is required. Several insights are also obtained through the analysis.
Scalable Parallel Numerical Constraint Solver Using Global Load Balancing
Venue: arXiv cs.DC 2015
Year: 2015

We present a scalable parallel solver for numerical constraint satisfaction problems (NCSPs). Our parallelization scheme consists of homogeneous worker solvers, each of which runs on an available core and communicates with others via the global load balancing (GLB) method. The parallel solver is implemented with X10 that provides an implementation of GLB as a library. In experiments, several NCSPs from the literature were solved and attained up to 516-fold speedup using 600 cores of the TSUBAME2.5 supercomputer.
Global Variational Method for Fingerprint Segmentation by Three-part Decomposition
Venue: arXiv cs.CV 2015
Year: 2015

Verifying an identity claim by fingerprint recognition is a commonplace experience for millions of people in their daily life, e.g. for unlocking a tablet computer or smartphone. The first processing step after fingerprint image acquisition is segmentation, i.e. dividing a fingerprint image into a foreground region which contains the relevant features for the comparison algorithm, and a background region. We propose a novel segmentation method by global three-part decomposition (G3PD). Based on global variational analysis, the G3PD method decomposes a fingerprint image into cartoon, texture and noise parts. After decomposition, the foreground region is obtained from the non-zero coefficients in the texture image using morphological processing. The segmentation performance of the G3PD method is compared to five state-of-the-art methods on a benchmark which comprises manually marked ground truth segmentation for 10560 images. Performance evaluations show that the G3PD method consistently outperforms existing methods in terms of segmentation accuracy.
Joint Representation Classification for Collective Face Recognition
Venue: arXiv cs.CV 2015
Year: 2015

Sparse representation based classification (SRC) is popularly used in many applications such as face recognition, and implemented in two steps: representation coding and classification. For a given set of testing images, SRC codes every image over the base images as a sparse representation then classifies it to the class with the least representation error. This scheme utilizes an individual representation rather than the collective one to classify such a set of images, doing so obviously ignores the correlation among the given images. In this paper, a joint representation classification (JRC) for collective face recognition is proposed. JRC takes the correlation of multiple images as well as a single representation into account. Under the assumption that the given face images are generally related to each other, JRC codes all the testing images over the base images simultaneously to facilitate recognition. To this end, the testing inputs are aligned into a matrix and the joint representation coding is formulated to a generalized $l_{2,q}-l_{2,p}$-minimization problem. To uniformly solve the induced optimization problems for any $q\in[1,2]$ and $p\in (0,2]$, an iterative quadratic method (IQM) is developed. IQM is proved to be a strict descent algorithm with convergence to the optimal solution. Moreover, a more practical IQM is proposed for large-scale case. Experimental results on three public databases show that the JRC with practical IQM no only saves much computational cost but also achieves better performance in collective face recognition than the state-of-the-arts.
Ensemble of Example-Dependent Cost-Sensitive Decision Trees
Venue: arXiv cs.LG 2015
Year: 2015

Several real-world classification problems are example-dependent cost-sensitive in nature, where the costs due to misclassification vary between examples and not only within classes. However, standard classification methods do not take these costs into account, and assume a constant cost of misclassification errors. In previous works, some methods that take into account the financial costs into the training of different algorithms have been proposed, with the example-dependent cost-sensitive decision tree algorithm being the one that gives the highest savings. In this paper we propose a new framework of ensembles of example-dependent cost-sensitive decision-trees. The framework consists in creating different example-dependent cost-sensitive decision trees on random subsamples of the training set, and then combining them using three different combination approaches. Moreover, we propose two new cost-sensitive combination approaches; cost-sensitive weighted voting and cost-sensitive stacking, the latter being based on the cost-sensitive logistic regression method. Finally, using five different databases, from four real-world applications: credit card fraud detection, churn modeling, credit scoring and direct marketing, we evaluate the proposed method against state-of-the-art example-dependent cost-sensitive techniques, namely, cost-proportionate sampling, Bayes minimum risk and cost-sensitive decision trees. The results show that the proposed algorithms have better results for all databases, in the sense of higher savings.
Compressed Nonnegative Matrix Factorization is Fast and Accurate
Venue: arXiv cs.LG 2015
Year: 2015

Nonnegative matrix factorization (NMF) has an established reputation as a useful data analysis technique in numerous applications. However, its usage in practical situations is undergoing challenges in recent years. The fundamental factor to this is the increasingly growing size of the datasets available and needed in the information sciences. To address this, in this work we propose to use structured random compression, that is, random projections that exploit the data structure, for two NMF variants: classical and separable. In separable NMF (SNMF) the left factors are a subset of the columns of the input matrix. We present suitable formulations for each problem, dealing with different representative algorithms within each one. We show that the resulting compressed techniques are faster than their uncompressed variants, vastly reduce memory demands, and do not encompass any significant deterioration in performance. The proposed structured random projections for SNMF allow to deal with arbitrarily shaped large matrices, beyond the standard limit of tall-and-skinny matrices, granting access to very efficient computations in this general setting. We accompany the algorithmic presentation with theoretical foundations and numerous and diverse examples, showing the suitability of the proposed approaches.
Euclidean Information Theory of Networks
Venue: arXiv cs.IT 2015
Year: 2015

In this paper, we extend the information theoretic framework that was developed in earlier work to multi-hop network settings. For a given network, we construct a novel deterministic model that quantifies the ability of the network in transmitting private and common messages across users. Based on this model, we formulate a linear optimization problem that explores the throughput of a multi-layer network, thereby offering the optimal strategy as to what kind of common messages should be generated in the network to maximize the throughput. With this deterministic model, we also investigate the role of feedback for multi-layer networks, from which we identify a variety of scenarios in which feedback can improve transmission efficiency. Our results provide fundamental guidelines as to how to coordinate cooperation between users to enable efficient information exchanges across them.
Have a Look at What I See
Venue: arXiv cs.CV 2015
Year: 2015

We propose a method for guiding a photographer to rotate her/his smartphone camera to obtain an image that overlaps with another image of the same scene. The other image is taken by another photographer from a different viewpoint. Our method is applicable even when the images do not have overlapping fields of view. Straightforward applications of our method include sharing attention to regions of interest for social purposes, or adding missing images to improve structure for motion results. Our solution uses additional images of the scene, which are often available since many people use their smartphone cameras regularly. These images may be available online from other photographers who are present at the scene. Our method avoids 3D scene reconstruction; it relies instead on a new representation that consists of the spatial orders of the scene points on two axes, x and y. This representation allows a sequence of points to be chosen efficiently and projected onto the photographers images, using epipolar point transfer. Overlaying these epipolar lines on the live preview of the camera produces a convenient interface to guide the user. The method was tested on challenging datasets of images and succeeded in guiding a photographer from one view to a non-overlapping destination view.
Computing Optimal Tolls in Routing Games without Knowing the Latency Functions
Venue: arXiv cs.GT 2015
Year: 2015

We consider the following question: in a nonatomic routing game, can the tolls that induce the minimum latency flow be computed without knowing the latency functions? Since the latency functions are unknown, we assume we are given an oracle that has access to the underlying routing game. A query to the oracle consists of tolls on edges, and the response is the resulting equilibrium flow. We show that in this model, it is impossible to obtain optimal tolls. However, if we augment the oracle so that it returns the total latency of the equilibrium flow induced by the tolls in addition to the flow itself, then the required tolls can be computed with a polynomial number of queries.
Towards Data-Driven Autonomics in Data Centers
Venue: arXiv cs.DC 2015
Year: 2015

Continued reliance on human operators for managing data centers is a major impediment for them from ever reaching extreme dimensions. Large computer systems in general, and data centers in particular, will ultimately be managed using predictive computational and executable models obtained through data-science tools, and at that point, the intervention of humans will be limited to setting high-level goals and policies rather than performing low-level operations. Data-driven autonomics, where management and control are based on holistic predictive models that are built and updated using generated data, opens one possible path towards limiting the role of operators in data centers. In this paper, we present a data-science study of a public Google dataset collected in a 12K-node cluster with the goal of building and evaluating a predictive model for node failures. We use BigQuery, the big data SQL platform from the Google Cloud suite, to process massive amounts of data and generate a rich feature set characterizing machine state over time. We describe how an ensemble classifier can be built out of many Random Forest classifiers each trained on these features, to predict if machines will fail in a future 24-hour window. Our evaluation reveals that if we limit false positive rates to 5%, we can achieve true positive rates between 27% and 88% with precision varying between 50% and 72%. We discuss the practicality of including our predictive model as the central component of a data-driven autonomic manager and operating it on-line with live data streams (rather than off-line on data logs). All of the scripts used for BigQuery and classification analyses are publicly available from the authors' website.
The Mean SIR of Large-Scale Wireless Networks: Its Closed-Form Expression and Main Applications
Venue: arXiv cs.IT 2015
Year: 2015

In a large-scale wireless ad hoc network in which all transmitters form a homogeneous of Poisson point process, the statistics of the signal-to-interference ratio (SIR) in prior work is only derived in closed-form for the case of Rayleigh fading channels. In this letter, the mean SIR is found in closed-form for general random channel (power) gain, transmission distance and power control models. According to the derived mean SIR, we first show that channel gain randomness actually benefits the mean SIR so that the upper bound on the mean spectrum efficiency increases. Then we show that stochastic power control and opportunistic scheduling that capture the randomness of channel gain and transmission distance can significantly not only enhance the mean SIR but reduce the outage probability. The mean-SIR-based throughput capacity is proposed and it can be maximized by a unique optimal intensity of transmitters if the derived supporting set of the intensity exists.