OpenRaiser/Intern-Atlas huggingface dataset:
Probabilistic Modelling of the Impact on Bus Punctuality of a Speed Limit Proposal in Edinburgh (Extended Version)
Venue: arXiv cs.CY 2015
Year: 2015

We propose a data-driven methodology for evaluating the impact of the introduction of a speed limit on the punctuality of bus services. In particular, we use high-frequency Automatic Vehicle Location data to parameterise a model that represents the movement of a bus along predefined patches of the route. We fit the probability distributions of the time spent in each patch to two classes of probability distributions: hyper-Erlang distributions, for which we use the tool HyperStar, and a variation of the three-parameter gamma distributions recommended by the Traffic Engineering Handbook. In both cases we obtain models that can be expressed using the framework of Probabilistic Timed Automata, allowing us to evaluate bus punctuality using the model checking tool UPPAAL. We conduct a case study involving a proposed speed limit in Edinburgh. This is an extended version of a paper presented at ValueTools 2015.
Tailoring the MontiArcAutomaton Component & Connector ADL for Generative Development
Venue: arXiv cs.SE 2015
Year: 2015

Component&connector (C&C) architecture description languages (ADLs) combine component-based software engineering and model-driven engineering to increase reuse and to abstract from implementation details. Applied to robotics application development, current C&C ADLs often require domain experts to provide component behavior descriptions as programming language artifacts or as models of a-priori mixed behavior modeling languages. They are limited to specific target platforms or require extensive handcrafting to transform platform-independent software architecture models into platform-specific implementations. We have developed the MontiArcAutomaton framework that combines structural extension of C&C concepts with integration of application-specific component behavior modeling languages, seamless transformation from logical into platform-specific software architectures, and a-posteriori black-box composition of code generators for different robotics platforms. This paper describes the roles and activities for tailoring MontiArcAutomaton to application-specific demands.
Transforming Platform-Independent to Platform-Specific Component and Connector Software Architecture Models
Venue: arXiv cs.SE 2015
Year: 2015

Combining component & connector architecture descriptionlanguageswithcomponentbehaviormodelinglanguages enables modeling great parts of software architectures platformindependently. Nontrivial systems typically contain components with programming language behavior descriptions to interface with APIs. These components tie the complete software architecture to a specific platform and thus hamper reuse. Previous work on software architecture reuse with multiple platforms either requires platform-specific handcrafting or the effort of explicit platform models. We present an automated approach to transform platform-independent, logical software architectures into architectures with platform-specific components. This approach introduces abstract components to the platform-independent architecture and refines the se with components specific to the target platform prior to code generation. Consequently, a single logical software architecture model can be reused with multiple target platforms, which increases architecture maturity and reduces the maintenance effort of multiple similar software architectures.
Binary CPMs with Improved Spectral Efficiency
Venue: arXiv cs.IT 2015
Year: 2015

We design new continuous phase modulation (CPM) formats which are based on the combination of a proper precoder with binary input and a ternary CPM. The proposed precoder constrains the signal phase evolution in order to increase the minimum Euclidean distance, and to limit the bandwidth expansion due to the use of a ternary CPM. The resulting schemes are highly spectrally efficient and outperform classical binary and quaternary formats in terms of coded and uncoded performance.
Identifying the Absorption Bump with Deep Learning
Venue: arXiv cs.CV 2015
Year: 2015

The pervasive interstellar dust grains provide significant insights to understand the formation and evolution of the stars, planetary systems, and the galaxies, and may harbor the building blocks of life. One of the most effective way to analyze the dust is via their interaction with the light from background sources. The observed extinction curves and spectral features carry the size and composition information of dust. The broad absorption bump at 2175 Angstrom is the most prominent feature in the extinction curves. Traditionally, statistical methods are applied to detect the existence of the absorption bump. These methods require heavy preprocessing and the co-existence of other reference features to alleviate the influence from the noises. In this paper, we apply Deep Learning techniques to detect the broad absorption bump. We demonstrate the key steps for training the selected models and their results. The success of Deep Learning based method inspires us to generalize a common methodology for broader science discovery problems. We present our on-going work to build the DeepDis system for such kind of applications.
Why are deep nets reversible: A simple theory, with implications for training
Venue: arXiv cs.LG 2015
Year: 2015

Generative models for deep learning are promising both to improve understanding of the model, and yield training methods requiring fewer labeled samples. Recent works use generative model approaches to produce the deep net's input given the value of a hidden layer several levels above. However, there is no accompanying "proof of correctness" for the generative model, showing that the feedforward deep net is the correct inference method for recovering the hidden layer given the input. Furthermore, these models are complicated. The current paper takes a more theoretical tack. It presents a very simple generative model for RELU deep nets, with the following characteristics: (i) The generative model is just the reverse of the feedforward net: if the forward transformation at a layer is $A$ then the reverse transformation is $A^T$. (This can be seen as an explanation of the old weight tying idea for denoising autoencoders.) (ii) Its correctness can be proven under a clean theoretical assumption: the edge weights in real-life deep nets behave like random numbers. Under this assumption ---which is experimentally tested on real-life nets like AlexNet--- it is formally proved that feed forward net is a correct inference method for recovering the hidden layer. The generative model suggests a simple modification for training: use the generative model to produce synthetic data with labels and include it in the training set. Experiments are shown to support this theory of random-like deep nets; and that it helps the training.
Transceiver Design to Maximize Sum Secrecy Rate in Full Duplex SWIPT Systems
Venue: arXiv cs.IT 2015
Year: 2015

This letter considers secrecy simultaneous wireless information and power transfer (SWIPT) in full duplex systems. In such a system, full duplex capable base station (FD-BS) is designed to transmit data to one downlink user and concurrently receive data from one uplink user, while one idle user harvests the radio-frequency (RF) signals energy to extend its lifetime. Moreover, to prevent eavesdropping, artificial noise (AN) is exploited by FD-BS to degrade the channel of the idle user, as well as to provide energy supply to the idle user. To maximize the sum of downlink secrecy rate and uplink secrecy rate, we jointly optimize the information covariance matrix, AN covariance matrix and receiver vector, under the constraints of the sum transmission power of FD-BS and the minimum harvested energy of the idle user. Since the problem is non-convex, the log-exponential reformulation and sequential parametric convex approximation (SPCA) method are used. Extensive simulation results are provided and demonstrate that our proposed full duplex scheme extremely outperforms the half duplex scheme.
A Distribution Adaptive Framework for Prediction Interval Estimation Using Nominal Variables
Venue: arXiv cs.LG 2015
Year: 2015

Proposed methods for prediction interval estimation so far focus on cases where input variables are numerical. In datasets with solely nominal input variables, we observe records with the exact same input $x^u$, but different real valued outputs due to the inherent noise in the system. Existing prediction interval estimation methods do not use representations that can accurately model such inherent noise in the case of nominal inputs. We propose a new prediction interval estimation method tailored for this type of data, which is prevalent in biology and medicine. We call this method Distribution Adaptive Prediction Interval Estimation given Nominal inputs (DAPIEN) and has four main phases. First, we select a distribution function that can best represent the inherent noise of the system for all unique inputs. Then we infer the parameters $θ_i$ (e.g. $θ_i=[mean_i, variance_i]$) of the selected distribution function for all unique input vectors $x^u_i$ and generate a new corresponding training set using pairs of $x^u_i, θ_i$. III). Then, we train a model to predict $θ$ given a new $x_u$. Finally, we calculate the prediction interval for a new sample using the inverse of the cumulative distribution function once the parameters $θ$ is predicted by the trained model. We compared DAPIEN to the commonly used Bootstrap method on three synthetic datasets. Our results show that DAPIEN provides tighter prediction intervals while preserving the requested coverage when compared to Bootstrap. This work can facilitate broader usage of regression methods in medicine and biology where it is necessary to provide tight prediction intervals while preserving coverage when input variables are nominal.
Author Evaluation Based on H-index and Citation Response
Venue: arXiv cs.DL 2015
Year: 2015

An accurate and fair assessment of the efficiency and impact of scientific work is, despite a lot of recent research effort, still an open problem. The measurement of quality and success of individual scientists and research groups can be approached from many different directions, none of which is universal. A reason for this is inherently different behavior of different scientists within the global research community. A complex evaluation of ones publication activities requires a careful consideration of a wide variety of factors. The well-known H-index is one of the most used bibliometric indices. Despite its many imperfections, its simplicity and ease of interpretation make it a popular scientometric method. This short paper uses the ideas behind the H-index ~to analyze communities of authors who cite publishing scientists. A new author evaluation measure named aH-index is proposed, and intuitive interpretations of its properties and semantics are presented. Preliminary experiments with authors with high H-index active in the area of computer science are presented to demonstrate the properties of the proposed measure.
Online learning in repeated auctions
Venue: arXiv cs.GT 2015
Year: 2015

Motivated by online advertising auctions, we consider repeated Vickrey auctions where goods of unknown value are sold sequentially and bidders only learn (potentially noisy) information about a good's value once it is purchased. We adopt an online learning approach with bandit feedback to model this problem and derive bidding strategies for two models: stochastic and adversarial. In the stochastic model, the observed values of the goods are random variables centered around the true value of the good. In this case, logarithmic regret is achievable when competing against well behaved adversaries. In the adversarial model, the goods need not be identical and we simply compare our performance against that of the best fixed bid in hindsight. We show that sublinear regret is also achievable in this case and prove matching minimax lower bounds. To our knowledge, this is the first complete set of strategies for bidders participating in auctions of this type.
Understanding Modern Banking Ledgers through Blockchain Technologies: Future of Transaction Processing and Smart Contracts on the Internet of Money
Venue: arXiv cs.CY 2015
Year: 2015

In this chapter we provide an overview of the concept of blockchain technology and its potential to disrupt the world of banking through facilitating global money remittance, smart contracts, automated banking ledgers and digital assets. In this regard, we first provide a brief overview of the core aspects of this technology, as well as the second-generation contract-based developments. From there we discuss key issues that must be considered in developing such ledger based technologies in a banking context.
Image Question Answering using Convolutional Neural Network with Dynamic Parameter Prediction
Venue: arXiv cs.CV 2015
Year: 2015

We tackle image question answering (ImageQA) problem by learning a convolutional neural network (CNN) with a dynamic parameter layer whose weights are determined adaptively based on questions. For the adaptive parameter prediction, we employ a separate parameter prediction network, which consists of gated recurrent unit (GRU) taking a question as its input and a fully-connected layer generating a set of candidate weights as its output. However, it is challenging to construct a parameter prediction network for a large number of parameters in the fully-connected dynamic parameter layer of the CNN. We reduce the complexity of this problem by incorporating a hashing technique, where the candidate weights given by the parameter prediction network are selected using a predefined hash function to determine individual weights in the dynamic parameter layer. The proposed network---joint network with the CNN for ImageQA and the parameter prediction network---is trained end-to-end through back-propagation, where its weights are initialized using a pre-trained CNN and GRU. The proposed algorithm illustrates the state-of-the-art performance on all available public ImageQA benchmarks.
From Pose to Activity: Surveying Datasets and Introducing CONVERSE
Venue: arXiv cs.CV 2015
Year: 2015

We present a review on the current state of publicly available datasets within the human action recognition community; highlighting the revival of pose based methods and recent progress of understanding person-person interaction modeling. We categorize datasets regarding several key properties for usage as a benchmark dataset; including the number of class labels, ground truths provided, and application domain they occupy. We also consider the level of abstraction of each dataset; grouping those that present actions, interactions and higher level semantic activities. The survey identifies key appearance and pose based datasets, noting a tendency for simplistic, emphasized, or scripted action classes that are often readily definable by a stable collection of sub-action gestures. There is a clear lack of datasets that provide closely related actions, those that are not implicitly identified via a series of poses and gestures, but rather a dynamic set of interactions. We therefore propose a novel dataset that represents complex conversational interactions between two individuals via 3D pose. 8 pairwise interactions describing 7 separate conversation based scenarios were collected using two Kinect depth sensors. The intention is to provide events that are constructed from numerous primitive actions, interactions and motions, over a period of time; providing a set of subtle action classes that are more representative of the real world, and a challenge to currently developed recognition methodologies. We believe this is among one of the first datasets devoted to conversational interaction classification using 3D pose features and the attributed papers show this task is indeed possible. The full dataset is made publicly available to the research community at www.csvision.swansea.ac.uk/converse.
A Framework for Evaluating the Retrieval Effectiveness of Search Engines
Venue: arXiv cs.IR 2015
Year: 2015

This chapter presents a theoretical framework for evaluating next generation search engines. We focus on search engines whose results presentation is enriched with additional information and does not merely present the usual list of 10 blue links, that is, of ten links to results, accompanied by a short description. While Web search is used as an example here, the framework can easily be applied to search engines in any other area. The framework not only addresses the results presentation, but also takes into account an extension of the general design of retrieval effectiveness tests. The chapter examines the ways in which this design might influence the results of such studies and how a reliable test is best designed.
Trees with small b-chromatic index
Venue: arXiv cs.DM 2015
Year: 2015

In a recent article [5], the authors claim that the distance between the b-chromatic index of a tree and a known upper bound is at most 1. At the same time, in [7] the authors claim to be able to construct a tree where this difference is bigger than 1. However, the given example was disconnected, i.e., actually consisted of a forest. Here, we slightly modify their construction in order to produce trees, thus getting that indeed the difference between the b-chromatic index of trees and the known upper bound can be arbitrarily large. We also point out the mistake made in [5].
Mechanisms Inducing Parallel Computation in a Model of Physarum polycephalum Transport Networks
Venue: arXiv cs.ET 2015
Year: 2015

P. polycephalum may be considered as a spatially represented parallel unconventional computing substrate, but how can this `computer' be programmed? In this paper we examine and catalogue individual low-level mechanisms which may be used to induce network formation and adaptation in a multi-agent model of P. polycephalum. These mechanisms include those intrinsic to the model (particle sensor angle, rotation angle, and scaling parameters) and those mediated by the environment (stimulus loca- tion, distance, angle, concentration, engulfment and consumption of nutrients, and the presence of simulated light irradiation, repellents and obstacles). The mechanisms in- duce a concurrent integration of chemoattractant and chemorepellent gradients diffusing within the 2D lattice upon which the agent population resides, stimulating growth, move- ment, morphological adaptation and network minimisation. Chemoattractant gradients, and their modulation by the engulfment and consumption of nutrients by the model population, represent an efficient outsourcing of spatial computation. The mechanisms may prove useful in understanding the search strategies and adaptation of distributed organisms within their environment, in understanding the minimal requirements for com- plex adaptive behaviours, and in developing methods of spatially programming parallel unconventional computers and robotic devices.
Preservation and decomposition theorems for bounded degree structures
Venue: arXiv cs.LO 2015
Year: 2015

We provide elementary algorithms for two preservation theorems for first-order sentences (FO) on the class âd of all finite structures of degree at most d: For each FO-sentence that is preserved under extensions (homomorphisms) on âd, a âd-equivalent existential (existential-positive) FO-sentence can be constructed in 5-fold (4-fold) exponential time. This is complemented by lower bounds showing that a 3-fold exponential blow-up of the computed existential (existential-positive) sentence is unavoidable. Both algorithms can be extended (while maintaining the upper and lower bounds on their time complexity) to input first-order sentences with modulo m counting quantifiers (FO+MODm). Furthermore, we show that for an input FO-formula, a âd-equivalent Feferman-Vaught decomposition can be computed in 3-fold exponential time. We also provide a matching lower bound.
Unitary-Group Invariant Kernels and Features from Transformed Unlabeled Data
Venue: arXiv cs.LG 2015
Year: 2015

The study of representations invariant to common transformations of the data is important to learning. Most techniques have focused on local approximate invariance implemented within expensive optimization frameworks lacking explicit theoretical guarantees. In this paper, we study kernels that are invariant to the unitary group while having theoretical guarantees in addressing practical issues such as (1) unavailability of transformed versions of labelled data and (2) not observing all transformations. We present a theoretically motivated alternate approach to the invariant kernel SVM. Unlike previous approaches to the invariant SVM, the proposed formulation solves both issues mentioned. We also present a kernel extension of a recent technique to extract linear unitary-group invariant features addressing both issues and extend some guarantees regarding invariance and stability. We present experiments on the UCI ML datasets to illustrate and validate our methods.
Algorithms for Communication Problems for Mobile Agents Exchanging Energy
Venue: arXiv cs.DS 2015
Year: 2015

We consider communication problems in the setting of mobile agents deployed in an edge-weighted network. The assumption of the paper is that each agent has some energy that it can transfer to any other agent when they meet (together with the information it holds). The paper deals with three communication problems: data delivery,convergecast and broadcast. These problems are posed for a centralized scheduler which has full knowledge of the instance. It is already known that, without energy exchange, all three problems are NP-complete even if the network is a line. Surprisingly, if we allow the agents to exchange energy, we show that all three problems are polynomially solvable on trees and have linear time algorithms on the line. On the other hand for general undirected and directed graphs we show that these problems, even if energy exchange is allowed, are still NP-complete.
Uncertainty-based Arbitration of Human-Machine Shared Control
Venue: arXiv cs.SY 2015
Year: 2015

Manufacturing requires consistent production rate and task success for sustainable operation. Some manufacturing tasks require a semi-autonomous approach, exploiting the combination of human adaptability and machine precision and speed, to be cost effective. The main contribution of this paper is a new approach to determine the level of autonomy for human-machine shared control based on the automation uncertainty. Moreover, the haptic feedback is scaled by the level of autonomy to indicate machine confidence to the operator. Experimentation results, with a human-robot peg-in-a-hole testbed, show more than 5 times improvement in the error tolerance for task completion with the shared control approach when compared to a purely autonomous method.
Stateless multicast switching in software defined networks
Venue: arXiv cs.NI 2015
Year: 2015

Multicast data delivery can significantly reduce traffic in operators' networks, but has been limited in deployment due to concerns such as the scalability of state management. This paper shows how multicast can be implemented in contemporary software defined networking (SDN) switches, with less state than existing unicast switching strategies, by utilising a Bloom Filter (BF) based switching technique. Furthermore, the proposed mechanism uses only proactive rule insertion, and thus, is not limited by congestion or delay incurred by reactive controller-aided rule insertion. We compare our solution against common switching mechanisms such as layer-2 switching and MPLS in realistic network topologies by modelling the TCAM state sizes in SDN switches. The results demonstrate that our approach has significantly smaller state size compared to existing mechanisms and thus is a multicast switching solution for next generation networks.