OpenRaiser/Intern-Atlas huggingface dataset:
Jolie Good Buildings: Internet of things for smart building infrastructure supporting concurrent apps utilizing distributed microservices
Venue: arXiv cs.SE 2016
Year: 2016

A large percentage of buildings, domestic or special-purpose, is expected to become increasingly "smarter" in the future, due to the immense benefits in terms of energy saving, safety, flexibility, and comfort, that relevant new technologies offer. However, concerning the hardware, software, or platform levels, no clearly dominant standard frameworks currently exist. Here, we will present a prototype platform for supporting multiple concurrent applications for smart buildings, which is utilizing an advanced sensor network as well as a distributed micro services architecture, centrally featuring the Jolie language. The architecture and benefits of our system are discussed, as well as a prototype containing a number of nodes and a user interface, deployed in a real-world academic building environment. Our results illustrate the promising nature of our approach, as well as open avenues for future work towards it wider and larger scale applicability.
On Prefix Normal Words and Prefix Normal Forms
Venue: arXiv cs.DM 2016
Year: 2016

A $1$-prefix normal word is a binary word with the property that no factor has more $1$s than the prefix of the same length; a $0$-prefix normal word is defined analogously. These words arise in the context of indexed binary jumbled pattern matching, where the aim is to decide whether a word has a factor with a given number of $1$s and $0$s (a given Parikh vector). Each binary word has an associated set of Parikh vectors of the factors of the word. Using prefix normal words, we provide a characterization of the equivalence class of binary words having the same set of Parikh vectors of their factors. We prove that the language of prefix normal words is not context-free and is strictly contained in the language of pre-necklaces, which are prefixes of powers of Lyndon words. We give enumeration results on $\textit{pnw}(n)$, the number of prefix normal words of length $n$, showing that, for sufficiently large $n$, \[ 2^{n-4 \sqrt{n \lg n}} \le \textit{pnw}(n) \le 2^{n - \lg n + 1}. \] For fixed density (number of $1$s), we show that the ordinary generating function of the number of prefix normal words of length $n$ and density $d$ is a rational function. Finally, we give experimental results on $\textit{pnw}(n)$, discuss further properties, and state open problems.
Tool Support for Continuous Quality Control
Venue: arXiv cs.SE 2016
Year: 2016

Over time, software systems suffer gradual quality decay and therefore costs can rise if organizations fail to take proactive countermeasures. Quality control is the first step to avoiding this cost trap. Continuous quality assessments help users identify quality problems early, when their removal is still inexpensive; they also aid decision making by providing an integrated view of a software system's current status. As a side effect, continuous and timely feedback helps developers and maintenance personnel improve their skills and thereby decreases the likelihood of future quality defects. To make regular quality control feasible, it must be highly automated, and assessment results must be presented in an aggregated manner to avoid overwhelming users with data. This article offers an overview of tools that aim to address these issues. The authors also discuss their own flexible, open-source toolkit, which supports the creation of dashboards for quality control.
Who's that Actor? Automatic Labelling of Actors in TV series starting from IMDB Images
Venue: arXiv cs.CV 2016
Year: 2016

In this work, we aim at automatically labeling actors in a TV series. Rather than relying on transcripts and subtitles, as has been demonstrated in the past, we show how to achieve this goal starting from a set of example images of each of the main actors involved, collected from the Internet Movie Database (IMDB). The problem then becomes one of domain adaptation: actors' IMDB photos are typically taken at awards ceremonies and are quite different from their appearances in TV series. In each series as well, there is considerable change in actor appearance due to makeup, lighting, ageing, etc. To bridge this gap, we propose a graph-matching based self-labelling algorithm, which we coin HSL (Hungarian Self Labeling). Further, we propose a new edge cost to be used in this context, as well as an extension that is more robust to outliers, where prototypical faces for each of the actors are selected based on a hierarchical clustering procedure. We conduct experiments with 15 episodes from 3 different TV series and demonstrate automatic annotation with an accuracy of 90% and up.
On Mixing in Pairwise Markov Random Fields with Application to Social Networks
Venue: arXiv cs.DM 2016
Year: 2016

We consider pairwise Markov random fields which have a number of important applications in statistical physics, image processing and machine learning such as Ising model and labeling problem to name a couple. Our own motivation comes from the need to produce synthetic models for social networks with attributes. First, we give conditions for rapid mixing of the associated Glauber dynamics and consider interesting particular cases. Then, for pairwise Markov random fields with submodular energy functions we construct monotone perfect simulation.
What is a Fog Node A Tutorial on Current Concepts towards a Common Definition
Venue: arXiv cs.NI 2016
Year: 2016

Fog computing has emerged as a promising technology that can bring the cloud applications closer to the physical IoT devices at the network edge. While it is widely known what cloud computing is, and how data centers can build the cloud infrastructure and how applications can make use of this infrastructure, there is no common picture on what fog computing and a fog node, as its main building block, really is. One of the first attempts to define a fog node was made by Cisco, qualifying a fog computing system as a mini-cloud, located at the edge of the network and implemented through a variety of edge devices, interconnected by a variety, mostly wireless, communication technologies. Thus, a fog node would be the infrastructure implementing the said mini-cloud. Other proposals have their own definition of what a fog node is, usually in relation to a specific edge device, a specific use case or an application. In this paper, we first survey the state of the art in technologies for fog computing nodes as building blocks of fog computing, paying special attention to the contributions that analyze the role edge devices play in the fog node definition. We summarize and compare the concepts, lessons learned from their implementation, and show how a conceptual framework is emerging towards a unifying fog node definition. We focus on core functionalities of a fog node as well as in the accompanying opportunities and challenges towards their practical realization in the near future.
Dictionary Learning with Equiprobable Matching Pursuit
Venue: arXiv cs.LG 2016
Year: 2016

Sparse signal representations based on linear combinations of learned atoms have been used to obtain state-of-the-art results in several practical signal processing applications. Approximation methods are needed to process high-dimensional signals in this way because the problem to calculate optimal atoms for sparse coding is NP-hard. Here we study greedy algorithms for unsupervised learning of dictionaries of shift-invariant atoms and propose a new method where each atom is selected with the same probability on average, which corresponds to the homeostatic regulation of a recurrent convolutional neural network. Equiprobable selection can be used with several greedy algorithms for dictionary learning to ensure that all atoms adapt during training and that no particular atom is more likely to take part in the linear combination on average. We demonstrate via simulation experiments that dictionary learning with equiprobable selection results in higher entropy of the sparse representation and lower reconstruction and denoising errors, both in the case of ordinary matching pursuit and orthogonal matching pursuit with shift-invariant dictionaries. Furthermore, we show that the computational costs of the matching pursuits are lower with equiprobable selection, leading to faster and more accurate dictionary learning algorithms.
An End-to-End Architecture for Keyword Spotting and Voice Activity Detection
Venue: arXiv cs.CL 2016
Year: 2016

We propose a single neural network architecture for two tasks: on-line keyword spotting and voice activity detection. We develop novel inference algorithms for an end-to-end Recurrent Neural Network trained with the Connectionist Temporal Classification loss function which allow our model to achieve high accuracy on both keyword spotting and voice activity detection without retraining. In contrast to prior voice activity detection models, our architecture does not require aligned training data and uses the same parameters as the keyword spotting model. This allows us to deploy a high quality voice activity detector with no additional memory or maintenance requirements.
Development of a multi-sensor perceptual system for mobile robot and EKF-based localization
Venue: arXiv cs.RO 2016
Year: 2016

This paper presents the design and implementation of a perceptual system for the mobile robot using modern sensors and multi-point communication channels. The data extracted from the perceptual system is processed by a sensor fusion model to obtain meaningful information for the robot localization and control. Due to the uncertainties of acquiring data, an extended Kalman filter was applied to get optimal states of the system. Several experiments have been conducted and the results confirmed the functioning operation of the perceptual system and the efficiency of the Kalman filter approach.
Fast Wavenet Generation Algorithm
Venue: arXiv cs.SD 2016
Year: 2016

This paper presents an efficient implementation of the Wavenet generation process called Fast Wavenet. Compared to a naive implementation that has complexity O(2^L) (L denotes the number of layers in the network), our proposed approach removes redundant convolution operations by caching previous calculations, thereby reducing the complexity to O(L) time. Timing experiments show significant advantages of our fast implementation over a naive one. While this method is presented for Wavenet, the same scheme can be applied anytime one wants to perform autoregressive generation or online prediction using a model with dilated convolution layers. The code for our method is publicly available.
Dispersing Points on Intervals
Venue: arXiv cs.CG 2016
Year: 2016

We consider a problem of dispersing points on disjoint intervals on a line. Given n pairwise disjoint intervals sorted on a line, we want to find a point in each interval such that the minimum pairwise distance of these points is maximized. Based on a greedy strategy, we present a linear time algorithm for the problem. Further, we also solve in linear time the cycle version of the problem where the intervals are given on a cycle.
Flexible Scheduling of Distributed Analytic Applications
Venue: arXiv cs.DC 2016
Year: 2016

This work addresses the problem of scheduling user-defined analytic applications, which we define as high-level compositions of frameworks, their components, and the logic necessary to carry out work. The key idea in our application definition, is to distinguish classes of components, including rigid and elastic types: the first being required for an application to make progress, the latter contributing to reduced execution times. We show that the problem of scheduling such applications poses new challenges, which existing approaches address inefficiently. Thus, we present the design and evaluation of a novel, flexible heuristic to schedule analytic applications, that aims at high system responsiveness, by allocating resources efficiently. Our algorithm is evaluated using trace-driven simulations, with large-scale real system traces: our flexible scheduler outperforms a baseline approach across a variety of metrics, including application turnaround times, and resource allocation efficiency. We also present the design and evaluation of a full-fledged system, which we have called Zoe, that incorporates the ideas presented in this paper, and report concrete improvements in terms of efficiency and performance, with respect to prior generations of our system.
Is a picture worth a thousand words? A Deep Multi-Modal Fusion Architecture for Product Classification in e-commerce
Venue: arXiv cs.CV 2016
Year: 2016

Classifying products into categories precisely and efficiently is a major challenge in modern e-commerce. The high traffic of new products uploaded daily and the dynamic nature of the categories raise the need for machine learning models that can reduce the cost and time of human editors. In this paper, we propose a decision level fusion approach for multi-modal product classification using text and image inputs. We train input specific state-of-the-art deep neural networks for each input source, show the potential of forging them together into a multi-modal architecture and train a novel policy network that learns to choose between them. Finally, we demonstrate that our multi-modal network improves the top-1 accuracy % over both networks on a real-world large-scale product classification dataset that we collected fromWalmart.com. While we focus on image-text fusion that characterizes e-commerce domains, our algorithms can be easily applied to other modalities such as audio, video, physical sensors, etc.
Moore: Interval Arithmetic in Modern C++
Venue: arXiv cs.MS 2016
Year: 2016

We present the library Moore, which implements Interval Arithmetic in modern C++. This library is based on a new feature in the C++ language called concepts, which reduces the problems caused by template meta programming, and leads to a new approach for implementing interval arithmetic libraries in C++.
An Inductive Proof Method for Simulation-based Compiler Correctness
Venue: arXiv cs.PL 2016
Year: 2016

We study induction on the program structure as a proof method for bisimulation-based compiler correctness. We consider a first-order language with mutually recursive function definitions, system calls, and an environment semantics. The proof method relies on a generalization of compatibility of function definition with the bisimulation. We use the inductive method to show correctness of a form of dead code elimination. This is an interesting case study because the transformation removes function, variable, and parameter definitions from the program. While such transformations require modification of the simulation in a coinductive proof, the inductive method deals with them naturally. All our results are formalized in Coq.
Getting Closer to the Essence of Music: The Con Espressione Manifesto
Venue: arXiv cs.SD 2016
Year: 2016

This text offers a personal and very subjective view on the current situation of Music Information Research (MIR). Motivated by the desire to build systems with a somewhat deeper understanding of music than the ones we currently have, I try to sketch a number of challenges for the next decade of MIR research, grouped around six simple truths about music that are probably generally agreed on, but often ignored in everyday research.
Symbolic Representation for Analog Realization of A Family of Fractional Order Controller Structures via Continued Fraction Expansion
Venue: arXiv cs.SY 2016
Year: 2016

This paper uses the Continued Fraction Expansion (CFE) method for analog realization of fractional order differ-integrator and few special classes of fractional order (FO) controllers viz. Fractional Order Proportional-Integral-Derivative (FOPID) controller, FO[PD] controller and FO lead-lag compensator. Contemporary researchers have given several formulations for rational approximation of fractional order elements. However, approximation of the controllers studied in this paper, due to having fractional power of a rational transfer function, is not available in analog domain; although its digital realization already exists. This motivates us for applying CFE based analog realization technique for complicated FO controller structures to get equivalent rational transfer functions in terms of the controller tuning parameters. The symbolic expressions for rationalized transfer function in terms of the controller tuning parameters are especially important as ready references, without the need of running CFE algorithm every time and also helps in the synthesis of analog circuits for such FO controllers.
Exploration for Multi-task Reinforcement Learning with Deep Generative Models
Venue: arXiv cs.AI 2016
Year: 2016

Exploration in multi-task reinforcement learning is critical in training agents to deduce the underlying MDP. Many of the existing exploration frameworks such as $E^3$, $R_{max}$, Thompson sampling assume a single stationary MDP and are not suitable for system identification in the multi-task setting. We present a novel method to facilitate exploration in multi-task reinforcement learning using deep generative models. We supplement our method with a low dimensional energy model to learn the underlying MDP distribution and provide a resilient and adaptive exploration signal to the agent. We evaluate our method on a new set of environments and provide intuitive interpretation of our results.
C-RNN-GAN: Continuous recurrent neural networks with adversarial training
Venue: arXiv cs.AI 2016
Year: 2016

Generative adversarial networks have been proposed as a way of efficiently training deep generative neural networks. We propose a generative adversarial model that works on continuous sequential data, and apply it by training it on a collection of classical music. We conclude that it generates music that sounds better and better as the model is trained, report statistics on generated music, and let the reader judge the quality by downloading the generated songs.
Photographic home styles in Congress: a computer vision approach
Venue: arXiv cs.SI 2016
Year: 2016

While members of Congress now routinely communicate with constituents using images on a variety of internet platforms, little is known about how images are used as a means of strategic political communication. This is due primarily to computational limitations which have prevented large-scale, systematic analyses of image features. New developments in computer vision, however, are bringing the systematic study of images within reach. Here, we develop a framework for understanding visual political communication by extending Fenno's analysis of home style (Fenno 1978) to images and introduce "photographic" home styles. Using approximately 192,000 photographs collected from MCs Facebook profiles, we build machine learning software with convolutional neural networks and conduct an image manipulation experiment to explore how the race of people that MCs pose with shape photographic home styles. We find evidence that electoral pressures shape photographic home styles and demonstrate that Democratic and Republican members of Congress use images in very different ways.
Efficient Likelihood Bayesian Constrained Local Model
Venue: arXiv cs.CV 2016
Year: 2016

The constrained local model (CLM) proposes a paradigm that the locations of a set of local landmark detectors are constrained to lie in a subspace, spanned by a shape point distribution model (PDM). Fitting the model to an object involves two steps. A response map, which represents the likelihood of the location of a landmark, is first computed for each landmark using local-texture detectors. Then, an optimal PDM is determined by jointly maximizing all the response maps simultaneously, with a global shape constraint. This global optimization can be considered as a Bayesian inference problem, where the posterior distribution of the shape parameters, as well as the pose parameters, can be inferred using maximum a posteriori (MAP). In this paper, we present a cascaded face-alignment approach, which employs random-forest regressors to estimate the positions of each landmark, as a likelihood term, efficiently in the CLM model. Interpretation from CLM framework, this algorithm is named as an efficient likelihood Bayesian constrained local model (elBCLM). Furthermore, in each stage of the regressors, the PDM non-rigid parameters of previous stage can work as shape clues for training each stage regressors. Experimental results on benchmarks show our approach achieve about 3 to 5 times speed-up compared with CLM models and improve around 10% on fitting quality compare with the same setting regression models.
Sequential Person Recognition in Photo Albums with a Recurrent Network
Venue: arXiv cs.CV 2016
Year: 2016

Recognizing the identities of people in everyday photos is still a very challenging problem for machine vision, due to non-frontal faces, changes in clothing, location, lighting and similar. Recent studies have shown that rich relational information between people in the same photo can help in recognizing their identities. In this work, we propose to model the relational information between people as a sequence prediction task. At the core of our work is a novel recurrent network architecture, in which relational information between instances' labels and appearance are modeled jointly. In addition to relational cues, scene context is incorporated in our sequence prediction model with no additional cost. In this sense, our approach is a unified framework for modeling both contextual cues and visual appearance of person instances. Our model is trained end-to-end with a sequence of annotated instances in a photo as inputs, and a sequence of corresponding labels as targets. We demonstrate that this simple but elegant formulation achieves state-of-the-art performance on the newly released People In Photo Albums (PIPA) dataset.
Sovereignty of the Apps: There's more to Relevance than Downloads
Venue: arXiv cs.CY 2016
Year: 2016

The value of mobile apps is traditionally measured by metrics such as the number of downloads, installations, or user ratings. A problem with these measures is that they reflect actual usage at most indirectly. Indeed, analytic companies have suggested that retention rates, i.e., the number of days users continue to interact with an installed app are low. We conduct the first independent and large-scale study of retention rates and usage behavior trends in the wild. We study their impact on a large-scale database of app-usage data from a community of 339,842 users and more than 213,667 apps. Our analysis shows that, on average, applications lose 70% of their users in the first week, while very popular applications (top 100) lose only 45%. It also reveals, however, that many applications have more complex usage behavior patterns due to seasonality, marketing, or other factors. To capture such effects, we develop a novel app-usage behavior trend measure which provides instantaneous information about the "hotness" of an application. We identify typical trends in app popularity and classify applications into archetypes. From these, we can distinguish, for instance, trendsetters from copycat apps. In our results, roughly 40% of all apps never gain more than a handful of users. Less than 0.4% of the remaining 60% are constantly popular, 1% flop after an initial steep rise, and 7% continuously rise in popularity. We conclude by demonstrating that usage behavior trend information can be used to develop better mobile app recommendations. With the proposed usage-based measures (retention and trend), we are able to shift sovereignty in app recommendations back to where it really matters: actual usage statistics, in contrast to download count and user ratings which are prone to manipulation by people.