OpenRaiser/Intern-Atlas huggingface dataset:
LCrowdV: Generating Labeled Videos for Simulation-based Crowd Behavior Learning
Venue: arXiv cs.CV 2016
Year: 2016

We present a novel procedural framework to generate an arbitrary number of labeled crowd videos (LCrowdV). The resulting crowd video datasets are used to design accurate algorithms or training models for crowded scene understanding. Our overall approach is composed of two components: a procedural simulation framework for generating crowd movements and behaviors, and a procedural rendering framework to generate different videos or images. Each video or image is automatically labeled based on the environment, number of pedestrians, density, behavior, flow, lighting conditions, viewpoint, noise, etc. Furthermore, we can increase the realism by combining synthetically-generated behaviors with real-world background videos. We demonstrate the benefits of LCrowdV over prior lableled crowd datasets by improving the accuracy of pedestrian detection and crowd behavior classification algorithms. LCrowdV would be released on the WWW.
Decision making via semi-supervised machine learning techniques
Venue: arXiv cs.LG 2016
Year: 2016

Semi-supervised learning (SSL) is a class of supervised learning tasks and techniques that also exploits the unlabeled data for training. SSL significantly reduces labeling related costs and is able to handle large data sets. The primary objective is the extraction of robust inference rules. Decision support systems (DSSs) who utilize SSL have significant advantages. Only a small amount of labelled data is required for the initialization. Then, new (unlabeled) data can be utilized and improve system's performance. Thus, the DSS is continuously adopted to new conditions, with minimum effort. Techniques which are cost effective and easily adopted to dynamic systems, can be beneficial for many practical applications. Such applications fields are: (a) industrial assembly lines monitoring, (b) sea border surveillance, (c) elders' falls detection, (d) transportation tunnels inspection, (e) concrete foundation piles defect recognition, (f) commercial sector companies financial assessment and (g) image advanced filtering for cultural heritage applications.
IoT Cloud-based Distribution System State Estimation: Virtual Objects and Context-Awareness
Venue: arXiv cs.NI 2016
Year: 2016

This paper presents an IoT cloud-based state estimation system for distribution networks in which the PMUs (Phasor Measurement Units) are virtualized with respect to the physical devices. In the considered system only application level entities are put in the cloud, whereas virtualized PMUs are running in the communication network edge (i.e. closer to the physical objects) in order to have a certain degree of local logic, which allows to implement a bandwidth-efficient and smart data transmission to the involved applications in the cloud. The major contributions of the paper are the following: we demonstrate that a cloud-based architecture is capable of achieving the QoS level required by the specific state estimation application; we show that implementing a certain local logic for data transmission in the cloud, the result of the state estimation is not degraded with respect to the case of an estimation that takes place frequently at fixed intervals; we show the results in terms of latency and reduced network load for a reference smart grid network.
An IoT Architecture for Wide Area Measurement Systems: a Virtualized PMU Based Approach
Venue: arXiv cs.NI 2016
Year: 2016

Internet of Things (IoT) technologies are pervading different application domains by relying on sensing and actuating devices that share, process and present meaningful real-world information. One of the most important of these domains is certainly the Smart Grid (SG), where the use of advanced measurement and control equipment and the diffusion of communication technologies are making an adaptive, reliable, and efficient management of the energy possible, with several new applications. In this scenario, this paper focuses on one of the major IoT features, which is the virtualization, and proposes an IoT solution for wide area measurement systems where virtualized phasor measurement resources are introduced. Such a solution is intended to make a programmable and smart environment fostering interoperability, reusability and flexibility of SG services. The performance of the system is evaluated for both the traffic generated and the latency to understand which scenarios can benefit from its deployment.
Adaptive PMU-based Distribution System State Estimation exploiting the Cloud-based IoT paradigm
Venue: arXiv cs.NI 2016
Year: 2016

This paper presents an adaptive Distribution System State Estimation (DSSE) which relies on a Cloud-based IoT paradigm. The methodology is adaptive in terms of the rate of execution of the estimation process which varies depending on the indications of the distributed measurement system. The system is composed, in particular, of Phasor Measurement Units (PMUs). PMUs are virtualized with respect to the physical devices and the corresponding virtualizing modules run in the communication network edge (i.e. closer to the physical objects). PMUs are set at a higher measurement rate, while the estimation process works at a given slower rate, for example once per second, in normal operative conditions. A local decision algorithm implemented in the virtualized module, monitors the measured quantities in order to detect and address possible unexpected dynamics. In particular, different metrics can be applied: the variations and the trend of variation of the rms voltage values, but also the Rate Of Change Of Frequency (ROCOF) of the monitored signals can be used to trigger rate variation in the DSSE.In case dynamics are detected, the measurement data is sent to the DSSE at higher rates and the estimation process runs consequently on a finer time scale. In the considered system only application level entities are located in the Cloud, thus allowing to obtain a bandwidth-efficient and smart data transmission. The results obtained on a 13-bus systems prove the goodness of the proposed methodologies.
Tighter bounds lead to improved classifiers
Venue: arXiv cs.LG 2016
Year: 2016

The standard approach to supervised classification involves the minimization of a log-loss as an upper bound to the classification error. While this is a tight bound early on in the optimization, it overemphasizes the influence of incorrectly classified examples far from the decision boundary. Updating the upper bound during the optimization leads to improved classification rates while transforming the learning into a sequence of minimization problems. In addition, in the context where the classifier is part of a larger system, this modification makes it possible to link the performance of the classifier to that of the whole system, allowing the seamless introduction of external constraints.
Kinodynamic Motion Planning: A Novel Type Of Nonlinear, Passive Damping Forces And Advantages
Venue: arXiv cs.RO 2016
Year: 2016

This article extends the capabilities of the harmonic potential field approach to planning to cover both the kinematic and dynamic aspects of a robot motion. The suggested approach converts the gradient guidance field from a harmonic potential to a control signal by augmenting it with a novel type of damping forces called nonlinear, anisotropic, damping forces. The combination of the two provides a signal that can both guide a robot and effectively manage its dynamics. The kinodynamic planning signal inherits the guidance capabilities of the harmonic gradient field. It can also be easily configured to efficiently suppress the inertia-induced transients in the robot trajectory without compromising the speed of operation. The approach works with dissipative systems as well as systems acted on by external forces without needing the full knowledge of the system dynamics. Theoretical developments and simulation results are provided in this article.
The Design of Arbitrage-Free Data Pricing Schemes
Venue: arXiv cs.DB 2016
Year: 2016

Motivated by a growing market that involves buying and selling data over the web, we study pricing schemes that assign value to queries issued over a database. Previous work studied pricing mechanisms that compute the price of a query by extending a data seller's explicit prices on certain queries, or investigated the properties that a pricing function should exhibit without detailing a generic construction. In this work, we present a formal framework for pricing queries over data that allows the construction of general families of pricing functions, with the main goal of avoiding arbitrage. We consider two types of pricing schemes: instance-independent schemes, where the price depends only on the structure of the query, and answer-dependent schemes, where the price also depends on the query output. Our main result is a complete characterization of the structure of pricing functions in both settings, by relating it to properties of a function over a lattice. We use our characterization, together with information-theoretic methods, to construct a variety of arbitrage-free pricing functions. Finally, we discuss various tradeoffs in the design space and present techniques for efficient computation of the proposed pricing functions.
Discovering topically- and temporally-coherent events in interaction networks
Venue: arXiv cs.SI 2016
Year: 2016

With the increasing use of online communication platforms, such as email, twitter, and messaging applications, we are faced with a growing amount of data that combine content (what is said), time (when), and user (by whom) information. An important computational challenge is to analyze these data, discover meaningful patterns, and understand what is happening. We consider the problem of mining online communication data and finding top-k temporal events. We define a temporal event to be a coherent topic that is discussed frequently, in a relatively short time span, while the information ow of the event respects the underlying network structure. We construct our model for detecting temporal events in two steps. We first introduce the notion of interaction meta-graph, which connects associated interactions. Using this notion, we define a temporal event to be a subset of interactions that (i) are topically and temporally close and (ii) correspond to a tree that captures the information ow. Finding the best temporal event leads to budget version of the prize-collecting Steiner-tree (PCST) problem, which we solve using three different methods: a greedy approach, a dynamic-programming algorithm, and an adaptation to an existing approximation algorithm. The problem of finding the top- k events among a set of candidate events maps to maximum set-cover problem, and thus, solved by greedy. We compare and analyze our algorithms in both synthetic and real datasets, such as twitter and email communication. The results show that our methods are able to detect meaningful temporal events.
The Guarded Lambda-Calculus: Programming and Reasoning with Guarded Recursion for Coinductive Types
Venue: arXiv cs.LO 2016
Year: 2016

We present the guarded lambda-calculus, an extension of the simply typed lambda-calculus with guarded recursive and coinductive types. The use of guarded recursive types ensures the productivity of well-typed programs. Guarded recursive types may be transformed into coinductive types by a type-former inspired by modal logic and Atkey-McBride clock quantification, allowing the typing of acausal functions. We give a call-by-name operational semantics for the calculus, and define adequate denotational semantics in the topos of trees. The adequacy proof entails that the evaluation of a program always terminates. We introduce a program logic with Löb induction for reasoning about the contextual equivalence of programs. We demonstrate the expressiveness of the calculus by showing the definability of solutions to Rutten's behavioural differential equations.
A weakly universal cellular automaton in the heptagrid
Venue: arXiv cs.DM 2016
Year: 2016

In this paper, we construct a weakly universal cellular automaton in the heptagrid, the tessellation $\{7,3\}$ which is not rotation invariant but which is truly planar. This result, under these conditions, cannot be improved for the tessellations $\{p,3\}$.
Comments on "sub-KBT micro-electromechanical irreversible logic gate"
Venue: arXiv cs.ET 2016
Year: 2016

In a recent article, Nature Communications 7 (2016) 12068, the authors claimed that they demonstrated sub-kBT energy dissipation at elementary logic operations. However, the argumentation is invalid because it neglects the dominant source of energy dissipation, namely, the charging energy of the capacitance of the input electrode, which totally dissipates during the full (0-1-0) cycle of logic values. The neglected dissipation phenomenon is identical with the mechanism that leads to the lower physical limit of dissipation (70-100 kBT) in today's microprocessors (CMOS logic) and in any other system with thermally activated errors thus the same limit holds for the new scheme, too.
Supplementary Appendix for: Constrained Perturbation Regularization Approach for Signal Estimation Using Random Matrix Theory
Venue: arXiv cs.IT 2016
Year: 2016

In this supplementary appendix we provide proofs and additional extensive simulations that complement the analysis of the main paper (constrained perturbation regularization approach for signal estimation using random matrix theory).
A Joint Typicality Approach to Algebraic Network Information Theory
Venue: arXiv cs.IT 2016
Year: 2016

This paper presents a joint typicality framework for encoding and decoding nested linear codes for multi-user networks. This framework provides a new perspective on compute-forward within the context of discrete memoryless networks. In particular, it establishes an achievable rate region for computing the weighted sum of nested linear codewords over a discrete memoryless multiple-access channel (MAC). When specialized to the Gaussian MAC, this rate region recovers and improves upon the lattice-based compute-forward rate region of Nazer and Gastpar, thus providing a unified approach for discrete memoryless and Gaussian networks. Furthermore, this framework can be used to shed light on the joint decoding rate region for compute-forward, which is considered an open problem. Specifically, this work establishes an achievable rate region for simultaneously decoding two linear combinations of nested linear codewords from K senders.
Exploring Prediction Uncertainty in Machine Translation Quality Estimation
Venue: arXiv cs.CL 2016
Year: 2016

Machine Translation Quality Estimation is a notoriously difficult task, which lessens its usefulness in real-world translation environments. Such scenarios can be improved if quality predictions are accompanied by a measure of uncertainty. However, models in this task are traditionally evaluated only in terms of point estimate metrics, which do not take prediction uncertainty into account. We investigate probabilistic methods for Quality Estimation that can provide well-calibrated uncertainty estimates and evaluate them in terms of their full posterior predictive distributions. We also show how this posterior information can be useful in an asymmetric risk scenario, which aims to capture typical situations in translation workflows.
Sparse Graphical Representation based Discriminant Analysis for Heterogeneous Face Recognition
Venue: arXiv cs.CV 2016
Year: 2016

Face images captured in heterogeneous environments, e.g., sketches generated by the artists or composite-generation software, photos taken by common cameras and infrared images captured by corresponding infrared imaging devices, usually subject to large texture (i.e., style) differences. This results in heavily degraded performance of conventional face recognition methods in comparison with the performance on images captured in homogeneous environments. In this paper, we propose a novel sparse graphical representation based discriminant analysis (SGR-DA) approach to address aforementioned face recognition in heterogeneous scenarios. An adaptive sparse graphical representation scheme is designed to represent heterogeneous face images, where a Markov networks model is constructed to generate adaptive sparse vectors. To handle the complex facial structure and further improve the discriminability, a spatial partition-based discriminant analysis framework is presented to refine the adaptive sparse vectors for face matching. We conducted experiments on six commonly used heterogeneous face datasets and experimental results illustrate that our proposed SGR-DA approach achieves superior performance in comparison with state-of-the-art methods.
TensiStrength: Stress and relaxation magnitude detection for social media texts
Venue: arXiv cs.CL 2016
Year: 2016

Computer systems need to be able to react to stress in order to perform optimally on some tasks. This article describes TensiStrength, a system to detect the strength of stress and relaxation expressed in social media text messages. TensiStrength uses a lexical approach and a set of rules to detect direct and indirect expressions of stress or relaxation, particularly in the context of transportation. It is slightly more effective than a comparable sentiment analysis program, although their similar performances occur despite differences on almost half of the tweets gathered. The effectiveness of TensiStrength depends on the nature of the tweets classified, with tweets that are rich in stress-related terms being particularly problematic. Although generic machine learning methods can give better performance than TensiStrength overall, they exploit topic-related terms in a way that may be undesirable in practical applications and that may not work as well in more focused contexts. In conclusion, TensiStrength and generic machine learning approaches work well enough to be practical choices for intelligent applications that need to take advantage of stress information, and the decision about which to use depends on the nature of the texts analysed and the purpose of the task.
MPI Derived Datatypes: Performance Expectations and Status Quo
Venue: arXiv cs.DC 2016
Year: 2016

We examine natural expectations on communication performance using MPI derived datatypes in comparison to the baseline, "raw" performance of communicating simple, non-contiguous data layouts. We show that common MPI libraries sometimes violate these datatype performance expectations, and discuss reasons why this happens, but also show cases where MPI libraries perform well. Our findings are in many ways surprising and disappointing. First, the performance of derived datatypes is sometimes worse than the semantically equivalent packing and unpacking using the corresponding MPI functionality. Second, the communication performance equivalence stated in the MPI standard between a single contiguous datatype and the repetition of its constituent datatype does not hold universally. Third, the heuristics that are typically employed by MPI libraries at type-commit time are insufficient to enforce natural performance guidelines, and better type normalization heuristics may have a significant performance impact. We show cases where all the MPI type constructors are necessary to achieve the expected performance for certain data layouts. We describe our benchmarking approach to verify the datatype performance guidelines, and present extensive verification results for different MPI libraries.
Network Simplification for Secure AF Relaying
Venue: arXiv cs.IT 2016
Year: 2016

We consider a class of Gaussian layered networks where a source communicates with a destination through L intermediate relay layers with N nodes in each layer in the presence of a single eavesdropper which can overhear the transmissions of the nodes in the last layer. For such networks we address the question: what fraction of maximum secure achievable rate can be maintained if only a fraction of available relay nodes are used in each layer? In particular, we provide upper bounds on additive and multiplicative gaps between the optimal secure AF when all N relays in each layer are used and when only k, 1 <= k < N, relays are used in each layer. We show that asymptotically (in source power), the additive gap increases at most logarithmically with ratio N/k and L, and the corresponding multiplicative gap increases at most quadratically with ratio N/k and L. To the best of our knowledge, this work offers the first characterization of the performance of network simplification in layered amplify-and-forward relay networks in the presence of an eavesdropper.
Throwing fuel on the embers: Probability or Dichotomy, Cognitive or Linguistic?
Venue: arXiv cs.CL 2016
Year: 2016

Prof. Robert Berwick's abstract for his forthcoming invited talk at the ACL2016 workshop on Cognitive Aspects of Computational Language Learning revives an ancient debate. Entitled "Why take a chance?", Berwick seems to refer implicitly to Chomsky's critique of the statistical approach of Harris as well as the currently dominant paradigms in CoNLL. Berwick avoids Chomsky's use of "innate" but states that "the debate over the existence of sophisticated mental grammars was settled with Chomsky's Logical Structure of Linguistic Theory (1957/1975)", acknowledging that "this debate has often been revived". This paper agrees with the view that this debate has long since been settled, but with the opposite outcome! Given the embers have not yet died away, and the questions remain fundamental, perhaps it is appropriate to refuel the debate, so I would like to join Bob in throwing fuel on this fire by reviewing the evidence against the Chomskian position!
Evaluating Unsupervised Dutch Word Embeddings as a Linguistic Resource
Venue: arXiv cs.CL 2016
Year: 2016

Word embeddings have recently seen a strong increase in interest as a result of strong performance gains on a variety of tasks. However, most of this research also underlined the importance of benchmark datasets, and the difficulty of constructing these for a variety of language-specific tasks. Still, many of the datasets used in these tasks could prove to be fruitful linguistic resources, allowing for unique observations into language use and variability. In this paper we demonstrate the performance of multiple types of embeddings, created with both count and prediction-based architectures on a variety of corpora, in two language-specific tasks: relation evaluation, and dialect identification. For the latter, we compare unsupervised methods with a traditional, hand-crafted dictionary. With this research, we provide the embeddings themselves, the relation evaluation task benchmark for use in further research, and demonstrate how the benchmarked embeddings prove a useful unsupervised linguistic resource, effectively used in a downstream task.
STAM: A Framework for Spatio-Temporal Affordance Maps
Venue: arXiv cs.RO 2016
Year: 2016

Affordances have been introduced in literature as action opportunities that objects offer, and used in robotics to semantically represent their interconnection. However, when considering an environment instead of an object, the problem becomes more complex due to the dynamism of its state. To tackle this issue, we introduce the concept of Spatio-Temporal Affordances (STA) and Spatio-Temporal Affordance Map (STAM). Using this formalism, we encode action semantics related to the environment to improve task execution capabilities of an autonomous robot. We experimentally validate our approach to support the execution of robot tasks by showing that affordances encode accurate semantics of the environment.
Probabilistic Programming and PyMC3
Venue: arXiv cs.OH 2016
Year: 2016

In recent years sports analytics has gotten more and more popular. We propose a model for Rugby data - in particular to model the 2014 Six Nations tournament. We propose a Bayesian hierarchical model to estimate the characteristics that bring a team to lose or win a game, and predict the score of particular matches. This is intended to be a brief introduction to Probabilistic Programming in Python and in particular the powerful library called PyMC3.