OpenRaiser/Intern-Atlas huggingface dataset:
Power Beacon-Assisted Millimeter Wave Ad Hoc Networks
Venue: arXiv cs.IT 2017
Year: 2017

Deployment of low cost power beacons (PBs) is a promising solution for dedicated wireless power transfer (WPT) in future wireless networks. In this paper, we present a tractable model for PB-assisted millimeter wave (mmWave) wireless ad hoc networks, where each transmitter (TX) harvests energy from all PBs and then uses the harvested energy to transmit information to its desired receiver. Our model accounts for realistic aspects of WPT and mmWave transmissions, such as power circuit activation threshold, allowed maximum harvested power, maximum transmit power, beamforming and blockage. Using stochastic geometry, we obtain the Laplace transform of the aggregate received power at the TX to calculate the power coverage probability. We approximate and discretize the transmit power of each TX into a finite number of discrete power levels in log scale to compute the channel and total coverage probability. We compare our analytical predictions to simulations and observe good accuracy. The proposed model allows insights into effect of system parameters, such as transmit power of PBs, PB density, main lobe beam-width and power circuit activation threshold on the overall coverage probability. The results confirm that it is feasible and safe to power TXs in a mmWave ad hoc network using PBs.
Extroverts Tweet Differently from Introverts in Weibo
Venue: arXiv cs.CY 2017
Year: 2017

Being dominant factors driving the human actions, personalities can be excellent indicators in predicting the offline and online behavior of different individuals. However, because of the great expense and inevitable subjectivity in questionnaires and surveys, it is challenging for conventional studies to explore the connection between personality and behavior and gain insights in the context of large amount individuals. Considering the more and more important role of the online social media in daily communications, we argue that the footprint of massive individuals, like tweets in Weibo, can be the inspiring proxy to infer the personality and further understand its functions in shaping the online human behavior. In this study, a map from self-reports of personalities to online profiles of 293 active users in Weibo is established to train a competent machine learning model, which then successfully identifies over 7,000 users as extroverts or introverts. Systematical comparisons from perspectives of tempo-spatial patterns, online activities, emotion expressions and attitudes to virtual honor surprisingly disclose that the extrovert indeed behaves differently from the introvert in Weibo. Our findings provide solid evidence to justify the methodology of employing machine learning to objectively study personalities of massive individuals and shed lights on applications of probing personalities and corresponding behaviors solely through online profiles.
Scrum for cyber-physical systems: a process proposal
Venue: arXiv cs.SE 2017
Year: 2017

Agile development processes and especially Scrum are changing the state of the practice in software development. Many companies in the classical IT sector have adopted them to successfully tackle various challenges from the rapidly changing environments and increasingly complex software systems. Companies developing software for embedded or cyber-physical systems, however, are still hesitant to adopt such processes. Despite successful applications of Scrum and other agile methods for cyber-physical systems, there is still no complete process that maps their specific challenges to practices in Scrum. We propose to fill this gap by treating all design artefacts in such a development in the same way: In software development, the final design is already the product, in hardware and mechanics it is the starting point of production. We sketch the Scrum extension Scrum CPS by showing how Scrum could be used to develop all design artefacts for a cyber physical system. Hardware and mechanical parts that might not be available yet are simulated. With this approach, we can directly and iteratively build the final software and produce detailed models for the hardware and mechanics production in parallel. We plan to further detail Scrum CPS and apply it first in a series of student projects to gather more experience before testing it in an industrial case study.
On the Use of Default Parameter Settings in the Empirical Evaluation of Classification Algorithms
Venue: arXiv cs.LG 2017
Year: 2017

We demonstrate that, for a range of state-of-the-art machine learning algorithms, the differences in generalisation performance obtained using default parameter settings and using parameters tuned via cross-validation can be similar in magnitude to the differences in performance observed between state-of-the-art and uncompetitive learning systems. This means that fair and rigorous evaluation of new learning algorithms requires performance comparison against benchmark methods with best-practice model selection procedures, rather than using default parameter settings. We investigate the sensitivity of three key machine learning algorithms (support vector machine, random forest and rotation forest) to their default parameter settings, and provide guidance on determining sensible default parameter values for implementations of these algorithms. We also conduct an experimental comparison of these three algorithms on 121 classification problems and find that, perhaps surprisingly, rotation forest is significantly more accurate on average than both random forest and a support vector machine.
Guaranteed Sufficient Decrease for Variance Reduced Stochastic Gradient Descent
Venue: arXiv cs.LG 2017
Year: 2017

In this paper, we propose a novel sufficient decrease technique for variance reduced stochastic gradient descent methods such as SAG, SVRG and SAGA. In order to make sufficient decrease for stochastic optimization, we design a new sufficient decrease criterion, which yields sufficient decrease versions of variance reduction algorithms such as SVRG-SD and SAGA-SD as a byproduct. We introduce a coefficient to scale current iterate and satisfy the sufficient decrease property, which takes the decisions to shrink, expand or move in the opposite direction, and then give two specific update rules of the coefficient for Lasso and ridge regression. Moreover, we analyze the convergence properties of our algorithms for strongly convex problems, which show that both of our algorithms attain linear convergence rates. We also provide the convergence guarantees of our algorithms for non-strongly convex problems. Our experimental results further verify that our algorithms achieve significantly better performance than their counterparts.
On the Specification of Constraints for Dynamic Architectures
Venue: arXiv cs.SE 2017
Year: 2017

In dynamic architectures, component activation and connections between components may vary over time. With the emergence of mobile computing such architectures became increasingly important and several techniques emerged to support in their specification. These techniques usually allow for the specification of concrete architecture instances. Sometimes, however, it is desired to focus on the specification of constraints, rather than concrete architectures. Especially specifications of architecture patterns usually focus on a few, important constraints, leaving out the details of the concrete architecture implementing the pattern. With this article we introduce an approach to specify such constraints for dynamic architectures. To this end, we introduce the notion of configuration traces as an abstract model for dynamic architectures. Then, we introduce the notion of configuration trace assertions as a formal language based on linear temporal logic to specify constraints for such architectures. In addition, we also introduce the notion of configuration diagrams to specify interfaces and certain common activation and connection constraints in one single, graphical notation. The approach is well-suited to specify patterns for dynamic architectures and verify them by means of formal analyses. This is demonstrated by applying the approach to specify and verify the Blackboard pattern for dynamic architectures.
Energy Efficient Non-Cooperative Power Control in Small Cell Networks
Venue: arXiv cs.GT 2017
Year: 2017

In this paper, energy efficient power control for small cells underlaying a macro cellular network is investigated. We formulate the power control problem in self-organizing small cell networks as a non-cooperative game, and propose a distributed energy efficient power control scheme, which allows the small base stations (SBSs) to take individual decisions for attaining the Nash equilibrium (NE) with minimum information exchange. Specially, in the non-cooperative power control game, a non-convex optimization problem is formulated for each SBS to maximize their energy efficiency (EE). By exploiting the properties of parameter-free fractional programming and the concept of perspective function, the non-convex optimization problem for each SBS is transformed into an equivalent constrained convex optimization problem. Then, the constrained convex optimization problem is converted into an unconstrained convex optimization problem by exploiting the mixed penalty function method. The inequality constraints are eliminated by introducing the logarithmic barrier functions and the equality constraint is eliminated by introducing the quadratic penalty function. We also theoretically show the existence and the uniqueness of the NE in the non-cooperative power control game. Simulation results show remarkable improvements in terms of EE by using the proposed scheme.
Modulus consensus in discrete-time signed networks and properties of special recurrent inequalities
Venue: arXiv cs.SY 2017
Year: 2017

Recently the dynamics of signed networks, where the ties among the agents can be both positive (attractive) or negative (repulsive) have attracted substantial attention of the research community. Examples of such networks are models of opinion dynamics over signed graphs, recently introduced by Altafini (2012,2013) and extended to discrete-time case by Meng et al. (2014). It has been shown that under mild connectivity assumptions these protocols provide the convergence of opinions in absolute value, whereas their signs may differ. This "modulus consensus" may correspond to the polarization of the opinions (or bipartite consensus, including the usual consensus as a special case), or their convergence to zero. In this paper, we demonstrate that the phenomenon of modulus consensus in the discrete-time Altafini model is a manifestation of a more general and profound fact, regarding the solutions of a special recurrent inequality. Although such a recurrent inequality does not provide the uniqueness of a solution, it can be shown that, under some natural assumptions, each of its bounded solutions has a limit and, moreover, converges to consensus. A similar property has previously been established for special continuous-time differential inequalities (Proskurnikov, Cao, 2016). Besides analysis of signed networks, we link the consensus properties of recurrent inequalities to the convergence analysis of distributed optimization algorithms and the problems of Schur stability of substochastic matrices.
Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization
Venue: arXiv cs.CV 2017
Year: 2017

Gatys et al. recently introduced a neural algorithm that renders a content image in the style of another image, achieving so-called style transfer. However, their framework requires a slow iterative optimization process, which limits its practical application. Fast approximations with feed-forward neural networks have been proposed to speed up neural style transfer. Unfortunately, the speed improvement comes at a cost: the network is usually tied to a fixed set of styles and cannot adapt to arbitrary new styles. In this paper, we present a simple yet effective approach that for the first time enables arbitrary style transfer in real-time. At the heart of our method is a novel adaptive instance normalization (AdaIN) layer that aligns the mean and variance of the content features with those of the style features. Our method achieves speed comparable to the fastest existing approach, without the restriction to a pre-defined set of styles. In addition, our approach allows flexible user controls such as content-style trade-off, style interpolation, color & spatial controls, all using a single feed-forward neural network.
Black-Box Optimization in Machine Learning with Trust Region Based Derivative Free Algorithm
Venue: arXiv cs.LG 2017
Year: 2017

In this work, we utilize a Trust Region based Derivative Free Optimization (DFO-TR) method to directly maximize the Area Under Receiver Operating Characteristic Curve (AUC), which is a nonsmooth, noisy function. We show that AUC is a smooth function, in expectation, if the distributions of the positive and negative data points obey a jointly normal distribution. The practical performance of this algorithm is compared to three prominent Bayesian optimization methods and random search. The presented numerical results show that DFO-TR surpasses Bayesian optimization and random search on various black-box optimization problem, such as maximizing AUC and hyperparameter tuning.
The Use of Autoencoders for Discovering Patient Phenotypes
Venue: arXiv cs.LG 2017
Year: 2017

We use autoencoders to create low-dimensional embeddings of underlying patient phenotypes that we hypothesize are a governing factor in determining how different patients will react to different interventions. We compare the performance of autoencoders that take fixed length sequences of concatenated timesteps as input with a recurrent sequence-to-sequence autoencoder. We evaluate our methods on around 35,500 patients from the latest MIMIC III dataset from Beth Israel Deaconess Hospital.
Measuring, Predicting and Visualizing Short-Term Change in Word Representation and Usage in VKontakte Social Network
Venue: arXiv cs.SI 2017
Year: 2017

Language in social media is extremely dynamic: new words emerge, trend and disappear, while the meaning of existing words can fluctuate over time. Such dynamics are especially notable during a period of crisis. This work addresses several important tasks of measuring, visualizing and predicting short term text representation shift, i.e. the change in a word's contextual semantics, and contrasting such shift with surface level word dynamics, or concept drift, observed in social media streams. Unlike previous approaches on learning word representations from text, we study the relationship between short-term concept drift and representation shift on a large social media corpus - VKontakte posts in Russian collected during the Russia-Ukraine crisis in 2014-2015. Our novel contributions include quantitative and qualitative approaches to (1) measure short-term representation shift and contrast it with surface level concept drift; (2) build predictive models to forecast short-term shifts in meaning from previous meaning as well as from concept drift; and (3) visualize short-term representation shift for example keywords to demonstrate the practical use of our approach to discover and track meaning of newly emerging terms in social media. We show that short-term representation shift can be accurately predicted up to several weeks in advance. Our unique approach to modeling and visualizing word representation shifts in social media can be used to explore and characterize specific aspects of the streaming corpus during crisis events and potentially improve other downstream classification tasks including real-time event detection.
Epistemic Modeling with Justifications
Venue: arXiv cs.LO 2017
Year: 2017

Existing logical models do not fairly represent epistemic situations with fallible justifications, e.g., Russell's Prime Minister example, though such scenarios have long been at the center of epistemic studies. We introduce justification epistemic models, JEM, which can handle such scenarios. JEM makes justifications prime objects and draws a distinction between accepted and knowledge-producing justifications; belief and knowledge become derived notions. Furthermore, Kripke models can be viewed as special cases of JEMs with additional assumptions of evidence insensitivity and common knowledge of the model. We argue that JEM can be applied to a range of epistemic scenarios in CS, AI, Game Theory, etc.
Checking Properties along Multiple Reconfiguration Paths for Component-Based Systems
Venue: arXiv cs.SE 2017
Year: 2017

Reconfiguration paths express sequences of successive reconfiguration operations within a component-based approach allowing dynamic reconfigurations. We use constructs from regular expressions-pin particular, alternatives-to introduce multiple reconfiguration paths. We show how to put into action procedures allowing architectural, event, and temporal properties to be proved. Our method, related to finite state automata and using marking techniques, generalises what we did within previous work, where the regular expressions we processed were more restricted. But we can only deal with a subset of first-order logic formulas.
Whole-Building Fault Detection: A Scalable Approach Using Spectral Methods
Venue: arXiv cs.SY 2017
Year: 2017

In this paper, an extension to rules-based fault detection is demonstrated utilizing properties of the Koopman operator. The Koopman operator is an infinite-dimensional, linear operator that captures nonlinear, finite dimensional dynamics. The definition of the Koopman operator enables algorithms that can evaluate the magnitude and coincidence of time-series data. Using spectral properties of this operator, diagnostic rule signals generated from building management system (BMS) trend data can be decomposed into components that allow the capture of device behavior at varying time-scales and to a granular level. As it relates to the implementation of fault detection (FDD), this approach creates additional spatial and temporal characterizations of rule signals providing additional data structure and increasing effectiveness with which classification techniques can be applied to the analysis process. The approach permits a knowledge base to be applied in a similar manner to that of a rules-based approach, but the introduced extensions also facilitate the definition of new kinds of diagnostics and overall provide increased analysis potential.
Pseudorehearsal in value function approximation
Venue: arXiv cs.AI 2017
Year: 2017

Catastrophic forgetting is of special importance in reinforcement learning, as the data distribution is generally non-stationary over time. We study and compare several pseudorehearsal approaches for Q-learning with function approximation in a pole balancing task. We have found that pseudorehearsal seems to assist learning even in such very simple problems, given proper initialization of the rehearsal parameters.
Evolving Parsimonious Networks by Mixing Activation Functions
Venue: arXiv cs.NE 2017
Year: 2017

Neuroevolution methods evolve the weights of a neural network, and in some cases the topology, but little work has been done to analyze the effect of evolving the activation functions of individual nodes on network size, which is important when training networks with a small number of samples. In this work we extend the neuroevolution algorithm NEAT to evolve the activation function of neurons in addition to the topology and weights of the network. The size and performance of networks produced using NEAT with uniform activation in all nodes, or homogenous networks, is compared to networks which contain a mixture of activation functions, or heterogenous networks. For a number of regression and classification benchmarks it is shown that, (1) qualitatively different activation functions lead to different results in homogeneous networks, (2) the heterogeneous version of NEAT is able to select well performing activation functions, (3) producing heterogeneous networks that are significantly smaller than homogeneous networks.
Real-time Monitoring and Forecasting of Ecological Processes
Venue: arXiv cs.CY 2017
Year: 2017

The paper introduces a real-time monitoring and forecasting system for ecological phenomena. The process yields a collection of ecological parameters viewed as distributed time series, which are measured by means of wireless network of sensors. The acquired data are preliminary processed and modeled by using complex algorithms in view of prediction. There are three graphical user interfaces implemented within the monitoring and forecasting system: eko-View and eko-Greenhouse (which directly interacts with the process) and eko-Forecast (which estimates the future evolution of some ecological parameters). The monitoring system was effectively integrated in an industrial application dealing with automatic irrigation of a small greenhouse. The forecasting simulation results with real data and a comparative assessment of predictor performances are presented in the end.
Multi-Objective Learning and Mask-Based Post-Processing for Deep Neural Network Based Speech Enhancement
Venue: arXiv cs.SD 2017
Year: 2017

We propose a multi-objective framework to learn both secondary targets not directly related to the intended task of speech enhancement (SE) and the primary target of the clean log-power spectra (LPS) features to be used directly for constructing the enhanced speech signals. In deep neural network (DNN) based SE we introduce an auxiliary structure to learn secondary continuous features, such as mel-frequency cepstral coefficients (MFCCs), and categorical information, such as the ideal binary mask (IBM), and integrate it into the original DNN architecture for joint optimization of all the parameters. This joint estimation scheme imposes additional constraints not available in the direct prediction of LPS, and potentially improves the learning of the primary target. Furthermore, the learned secondary information as a byproduct can be used for other purposes, e.g., the IBM-based post-processing in this work. A series of experiments show that joint LPS and MFCC learning improves the SE performance, and IBM-based post-processing further enhances listening quality of the reconstructed speech.