OpenRaiser/Intern-Atlas huggingface dataset:
Particle Swarm Optimized Power Consumption of Trilateration
Venue: arXiv cs.NI 2016
Year: 2016

Trilateration-based localization (TBL) has become a corner stone of modern technology. This study formulates the concern on how wireless sensor networks can take advantage of the computational intelligent techniques using both single- and multi-objective particle swarm optimization (PSO) with an overall aim of concurrently minimizing the required time for localization, minimizing energy consumed during localization, and maximizing the number of nodes fully localized through the adjustment of wireless sensor transmission ranges while using TBL process. A parameter-study of the applied PSO variants is performed, leading to results that show algorithmic improvements of up to 32% in the evaluated objectives.
Mean and variance of the LQG cost function
Venue: arXiv cs.SY 2016
Year: 2016

Linear Quadratic Gaussian (LQG) systems are well-understood and methods to minimize the expected cost are readily available. Less is known about the statistical properties of the resulting cost function. The contribution of this paper is a set of analytic expressions for the mean and variance of the LQG cost function. These expressions are derived using two different methods, one using solutions to Lyapunov equations and the other using only matrix exponentials. Both the discounted and the non-discounted cost function are considered, as well as the finite-time and the infinite-time cost function. The derived expressions are successfully applied to an example system to reduce the probability of the cost exceeding a given threshold.
Homogeneity of Cluster Ensembles
Venue: arXiv cs.LG 2016
Year: 2016

The expectation and the mean of partitions generated by a cluster ensemble are not unique in general. This issue poses challenges in statistical inference and cluster stability. In this contribution, we state sufficient conditions for uniqueness of expectation and mean. The proposed conditions show that a unique mean is neither exceptional nor generic. To cope with this issue, we introduce homogeneity as a measure of how likely is a unique mean for a sample of partitions. We show that homogeneity is related to cluster stability. This result points to a possible conflict between cluster stability and diversity in consensus clustering. To assess homogeneity in a practical setting, we propose an efficient way to compute a lower bound of homogeneity. Empirical results using the k-means algorithm suggest that uniqueness of the mean partition is not exceptional for real-world data. Moreover, for samples of high homogeneity, uniqueness can be enforced by increasing the number of data points or by removing outlier partitions. In a broader context, this contribution can be placed as a further step towards a statistical theory of partitions.
GitHub open source project recommendation system
Venue: arXiv cs.SI 2016
Year: 2016

Hosting platforms for software projects can form collaborative social networks and a prime example of this is GitHub which is arguably the most popular platform of this kind. An open source project recommendation system could be a major feature for a platform like GitHub, enabling its users to find relevant projects in a fast and simple manner. We perform network analysis on a constructed graph based on GitHub data and present a recommendation system that uses link prediction.
Learning to Communicate to Solve Riddles with Deep Distributed Recurrent Q-Networks
Venue: arXiv cs.AI 2016
Year: 2016

We propose deep distributed recurrent Q-networks (DDRQN), which enable teams of agents to learn to solve communication-based coordination tasks. In these tasks, the agents are not given any pre-designed communication protocol. Therefore, in order to successfully communicate, they must first automatically develop and agree upon their own communication protocol. We present empirical results on two multi-agent learning problems based on well-known riddles, demonstrating that DDRQN can successfully solve such tasks and discover elegant communication protocols to do so. To our knowledge, this is the first time deep reinforcement learning has succeeded in learning communication protocols. In addition, we present ablation experiments that confirm that each of the main components of the DDRQN architecture are critical to its success.
Predicting Clinical Events by Combining Static and Dynamic Information Using Recurrent Neural Networks
Venue: arXiv cs.LG 2016
Year: 2016

In clinical data sets we often find static information (e.g. patient gender, blood type, etc.) combined with sequences of data that are recorded during multiple hospital visits (e.g. medications prescribed, tests performed, etc.). Recurrent Neural Networks (RNNs) have proven to be very successful for modelling sequences of data in many areas of Machine Learning. In this work we present an approach based on RNNs, specifically designed for the clinical domain, that combines static and dynamic information in order to predict future events. We work with a database collected in the Charité Hospital in Berlin that contains complete information concerning patients that underwent a kidney transplantation. After the transplantation three main endpoints can occur: rejection of the kidney, loss of the kidney and death of the patient. Our goal is to predict, based on information recorded in the Electronic Health Record of each patient, whether any of those endpoints will occur within the next six or twelve months after each visit to the clinic. We compared different types of RNNs that we developed for this work, with a model based on a Feedforward Neural Network and a Logistic Regression model. We found that the RNN that we developed based on Gated Recurrent Units provides the best performance for this task. We also used the same models for a second task, i.e., next event prediction, and found that here the model based on a Feedforward Neural Network outperformed the other models. Our hypothesis is that long-term dependencies are not as relevant in this task.
Science on YouTube: What users find when they search for climate science and climate manipulation
Venue: arXiv cs.CY 2016
Year: 2016

Online video-sharing sites such as YouTube are very popular and also used by a lot of people to obtain knowledge and information, also on science, health and technology. Technically they could be valuable tools for the public communication of science and technology, but the users of YouTube are also confronted with conspiracy theories and erroneous and misleading information that deviates from scientific consensus views. This contribution details the results of a study that investigates what kind of information users find when they are searching for climate science and climate manipulation topics on YouTube and whether this information corresponds with or challenges scientific consensus views. An innovative methodological approach using the anonymization network Tor is introduced for drawing randomized samples of YouTube videos. This approach was used to select and examine a sample of 140 YouTube videos on climate topics.
Two-Bit Messages are Sufficient to Implement Atomic Read/Write Registers in Crash-prone Systems
Venue: arXiv cs.DC 2016
Year: 2016

Atomic registers are certainly the most basic objects of computing science. Their implementation on top of an n-process asynchronous message-passing system has received a lot of attention. It has been shown that t \textless{} n/2 (where t is the maximal number of processes that may crash) is a necessary and sufficient requirement to build an atomic register on top of a crash-prone asynchronous message-passing system. Considering such a context, this paper presents an algorithm which implements a single-writer multi-reader atomic register with four message types only, and where no message needs to carry control information in addition to its type. Hence, two bits are sufficient to capture all the control information carried by all the implementation messages. Moreover, the messages of two types need to carry a data value while the messages of the two other types carry no value at all. As far as we know, this algorithm is the first with such an optimality property on the size of control information carried by messages. It is also particularly efficient from a time complexity point of view.
The IMP game: Learnability, approximability and adversarial learning beyond $Σ^0_1$
Venue: arXiv cs.LO 2016
Year: 2016

We introduce a problem set-up we call the Iterated Matching Pennies (IMP) game and show that it is a powerful framework for the study of three problems: adversarial learnability, conventional (i.e., non-adversarial) learnability and approximability. Using it, we are able to derive the following theorems. (1) It is possible to learn by example all of $Σ^0_1 \cup Π^0_1$ as well as some supersets; (2) in adversarial learning (which we describe as a pursuit-evasion game), the pursuer has a winning strategy (in other words, $Σ^0_1$ can be learned adversarially, but $Π^0_1$ not); (3) some languages in $Π^0_1$ cannot be approximated by any language in $Σ^0_1$. We show corresponding results also for $Σ^0_i$ and $Π^0_i$ for arbitrary $i$.
Classification with Boosting of Extreme Learning Machine Over Arbitrarily Partitioned Data
Venue: arXiv cs.LG 2016
Year: 2016

Machine learning based computational intelligence methods are widely used to analyze large scale data sets in this age of big data. Extracting useful predictive modeling from these types of data sets is a challenging problem due to their high complexity. Analyzing large amount of streaming data that can be leveraged to derive business value is another complex problem to solve. With high levels of data availability (\textit{i.e. Big Data}) automatic classification of them has become an important and complex task. Hence, we explore the power of applying MapReduce based Distributed AdaBoosting of Extreme Learning Machine (ELM) to build a predictive bag of classification models. Accordingly, (i) data set ensembles are created; (ii) ELM algorithm is used to build weak learners (classifier functions); and (iii) builds a strong learner from a set of weak learners. We applied this training model to the benchmark knowledge discovery and data mining data sets.
Detection and Quantification of Flow Consistency in Business Process Models
Venue: arXiv cs.SE 2016
Year: 2016

Business process models abstract complex business processes by representing them as graphical models. Their layout, solely determined by the modeler, affects their understandability. To support the construction of understandable models it would be beneficial to systematically study this effect. However, this requires a basic set of measurable key visual features, depicting the layout properties that are meaningful to the human user. The aim of this research is thus twofold. First, to empirically identify key visual features of business process models which are perceived as meaningful to the user. Second, to show how such features can be quantified into computational metrics, which are applicable to business process models. We focus on one particular feature, consistency of flow direction, and show the challenges that arise when transforming it into a precise metric. We propose three different metrics addressing these challenges, each following a different view of flow consistency. We then report the results of an empirical evaluation, which indicates which metric is more effective in predicting the human perception of this feature. Moreover, two other automatic evaluations describing the performance and the computational capabilities of our metrics are reported as well.
Coinami: A Cryptocurrency with DNA Sequence Alignment as Proof-of-work
Venue: arXiv cs.CE 2016
Year: 2016

Rate of growth of the amount of data generated using the high throughput sequencing (HTS) platforms now exceeds the growth stipulated by Moore's Law. The HTS data is expected to surpass those of other "big data" domains such as astronomy, before the year 2025. In addition to sequencing genomes for research purposes, genome and exome sequencing in clinical settings will be a routine part of health care. The analysis of such large amounts of data, however, is not without computational challenges. This burden is even more increased due to the periodic updates to reference genomes, which typically require re-analysis of existing data. Here we propose Coin-Application Mediator Interface (Coinami) to distribute the workload for mapping reads to reference genomes using a volunteer grid computer approach similar to Berkeley Open Infrastructure for Network Computing (BOINC). However, since HTS read mapping requires substantial computational resources and fast analysis turnout is desired, Coinami uses the HTS read mapping as proof-of-work to generate valid blocks to main its own cryptocurrency system, which may help motivate volunteers to dedicate more resources. The Coinami protocol includes mechanisms to ensure that jobs performed by volunteers are correct, and provides genomic data privacy. The prototype implementation of Coinami is available at http://coinami.github.io/.
Complete Problems of Propositional Logic for the Exponential Hierarchy
Venue: arXiv cs.CC 2016
Year: 2016

Large complexity classes, like the exponential time hierarchy, received little attention in terms of finding complete problems. In this work a generalization of propositional logic is investigated which fills this gap with the introduction of Boolean higher-order quantifiers or equivalently Boolean Skolem functions. This builds on the important results of Wrathall and Stockmeyer regarding complete problems, namely QBF and QBF-k, for the polynomial hierarchy. Furthermore it generalizes the Dependency QBF problem introduced by Peterson, Reif and Azhar which is complete for NEXP, the first level of the exponential hierarchy. Also it turns out that the hardness results do not collapse at the consideration of conjunctive and disjunctive normal forms, in contrast to plain QBF.
Enhancing the Estimation of mm-Wave Large Array Channels by Exploiting Spatio-Temporal Correlation and Sparse Scattering
Venue: arXiv cs.IT 2016
Year: 2016

In order to cope with the large path-loss exponent of mm-Wave channels, a high beamforming gain is needed. This can be achieved with small hardware complexity and high hardware power efficiency by Hybrid Digital-Analog (HDA) beamforming, where a very large number $M\gg 1$ of antenna array elements requires only a relatively small $m\ll M$ number of A/D converters and modulators/demodulators. As such, the estimation of mm-Wave MIMO channels must deal with two specific problems: 1) high Doppler, due to the large carrier frequency; 2) impossibility of observing directly the M-dimensional channel vector at the antenna array elements, due to the mentioned HDA implementation. In this paper, we consider a novel scheme inspired by recent results on gridless multiple measurement vectors problem in compressed sensing, that is able to exploit the inherent mm-Wave channel sparsity in the angular domain in order to cope with both the above problems simultaneously. Our scheme uses past pilot-symbol observations in a window of length $T$ in order to estimate a low-dimensional subspace that approximately contains the channel vector at the current time. This subspace information can be used directly, in order to separate users in the spatial domain, or indirectly, in order to improve the estimate of the user channel vector from the current pilot-symbol observation.
Improving Data Quality in Intelligent Transportation Systems
Venue: arXiv cs.OH 2016
Year: 2016

Intelligent Transportation Systems (ITS) use data and information technology to improve the operation of our transportation network. ITS contributes to sustainable development by using technology to make the transportation system more efficient; improving our environment by reducing emissions, reducing the need for new construction and improving our daily lives through reduced congestion. A key component of ITS is traveler information. The Oregon Department of Transportation (ODOT) recently implemented a new traveler information system on selected freeways to provide drivers with travel time estimates that allow them to make more informed decisions about routing to their destinations. The ODOT project aims to improve traffic flow and promote efficient traffic movement, which can reduce emissions rates and improve air quality. The new ODOT system is based on travel data collected from a recently-increased set of sensors installed on its freeways. Our current project investigates novel data cleaning methodologies and the integration of those methodologies into the prediction of travel times. We use machine learning techniques on our archive to identify suspect data, and calculate revised travel times excluding this suspect data. We compare the resulting travel time predictions to ground-truth data, and to predictions based on simple, rule-based data cleaning. We report on the results of our study using qualitative and quantitative methods.
A New Spatio-Spectral Morphological Segmentation For Multi-Spectral Remote-Sensing Images
Venue: arXiv cs.CV 2016
Year: 2016

A general framework of spatio-spectral segmentation for multi-spectral images is introduced in this paper. The method is based on classification-driven stochastic watershed (WS) by Monte Carlo simulations, and it gives more regular and reliable contours than standard WS. The present approach is decomposed into several sequential steps. First, a dimensionality-reduction stage is performed using the factor-correspondence analysis method. In this context, a new way to select the factor axes (eigenvectors) according to their spatial information is introduced. Then, a spectral classification produces a spectral pre-segmentation of the image. Subsequently, a probability density function (pdf) of contours containing spatial and spectral information is estimated by simulation using a stochastic WS approach driven by the spectral classification. The pdf of the contours is finally segmented by a WS controlled by markers from a regularization of the initial classification.
Limiting Self-Propagating Malware Based on Connection Failure Behavior through Hyper-Compact Estimators
Venue: arXiv cs.NI 2016
Year: 2016

Self-propagating malware (e.g., an Internet worm) exploits security loopholes in software to infect servers and then use them to scan the Internet for more vulnerable servers. While the mechanisms of worm infection and their propagation models are well understood, defense against worms remains an open problem. One branch of defense research investigates the behavioral difference between worm-infected hosts and normal hosts to set them apart. One particular observation is that a worm-infected host, which scans the Internet with randomly selected addresses, has a much higher connection-failure rate than a normal host. Rate-limit algorithms have been proposed to control the spread of worms by traffic shaping based on connection failure rate. However, these rate-limit algorithms can work properly only if it is possible to measure failure rates of individual hosts efficiently and accurately. This paper points out a serious problem in the prior method. To address this problem, we first propose a solution based on a highly efficient double-bitmap data structure, which places only a small memory footprint on the routers, while providing good measurement of connection failure rates whose accuracy can be tuned by system parameters. Furthermore, we propose another solution based on shared register array data structure, achieving better memory efficiency and much larger estimation range than our double-bitmap solution.
Learning Efficient Algorithms with Hierarchical Attentive Memory
Venue: arXiv cs.LG 2016
Year: 2016

In this paper, we propose and investigate a novel memory architecture for neural networks called Hierarchical Attentive Memory (HAM). It is based on a binary tree with leaves corresponding to memory cells. This allows HAM to perform memory access in O(log n) complexity, which is a significant improvement over the standard attention mechanism that requires O(n) operations, where n is the size of the memory. We show that an LSTM network augmented with HAM can learn algorithms for problems like merging, sorting or binary searching from pure input-output examples. In particular, it learns to sort n numbers in time O(n log n) and generalizes well to input sequences much longer than the ones seen during the training. We also show that HAM can be trained to act like classic data structures: a stack, a FIFO queue and a priority queue.
On Christoffel and standard words and their derivatives
Venue: arXiv cs.DM 2016
Year: 2016

We introduce and study natural derivatives for Christoffel and finite standard words, as well as for characteristic Sturmian words. These derivatives, which are realized as inverse images under suitable morphisms, preserve the aforementioned classes of words. In the case of Christoffel words, the morphisms involved map $a$ to $a^{k+1}b$ (resp.,~$ab^{k}$) and $b$ to $a^{k}b$ (resp.,~$ab^{k+1}$) for a suitable $k>0$. As long as derivatives are longer than one letter, higher-order derivatives are naturally obtained. We define the depth of a Christoffel or standard word as the smallest order for which the derivative is a single letter. We give several combinatorial and arithmetic descriptions of the depth, and (tight) lower and upper bounds for it.
Performance Analysis of $l_0$ Norm Constrained Recursive Least Squares Algorithm
Venue: arXiv cs.IT 2016
Year: 2016

Performance analysis of $l_0$ norm constrained Recursive least Squares (RLS) algorithm is attempted in this paper. Though the performance pretty attractive compared to its various alternatives, no thorough study of theoretical analysis has been performed. Like the popular $l_0$ Least Mean Squares (LMS) algorithm, in $l_0$ RLS, a $l_0$ norm penalty is added to provide zero tap attractions on the instantaneous filter taps. A thorough theoretical performance analysis has been conducted in this paper with white Gaussian input data under assumptions suitable for many practical scenarios. An expression for steady state MSD is derived and analyzed for variations of different sets of predefined variables. Also a Taylor series expansion based approximate linear evolution of the instantaneous MSD has been performed. Finally numerical simulations are carried out to corroborate the theoretical analysis and are shown to match well for a wide range of parameters.
Asymptotically Optimal Gathering on a Grid
Venue: arXiv cs.DC 2016
Year: 2016

In this paper, we solve the local gathering problem of a swarm of $n$ indistinguishable, point-shaped robots on a two dimensional grid in asymptotically optimal time $\mathcal{O}(n)$ in the fully synchronous $\mathcal{FSYNC}$ time model. Given an arbitrarily distributed (yet connected) swarm of robots, the gathering problem on the grid is to locate all robots within a $2\times 2$-sized area that is not known beforehand. Two robots are connected if they are vertical or horizontal neighbors on the grid. The locality constraint means that no global control, no compass, no global communication and only local vision is available; hence, a robot can only see its grid neighbors up to a constant $L_1$-distance, which also limits its movements. A robot can move to one of its eight neighboring grid cells and if two or more robots move to the same location they are \emph{merged} to be only one robot. The locality constraint is the significant challenging issue here, since robot movements must not harm the (only globally checkable) swarm connectivity. For solving the gathering problem, we provide a synchronous algorithm -- executed by every robot -- which ensures that robots merge without breaking the swarm connectivity. In our model, robots can obtain a special state, which marks such a robot to be performing specific connectivity preserving movements in order to allow later merge operations of the swarm. Compared to the grid, for gathering in the Euclidean plane for the same robot and time model the best known upper bound is $\mathcal{O}(n^2)$.