OpenRaiser/Intern-Atlas huggingface dataset:
Understanding User Instructions by Utilizing Open Knowledge for Service Robots
Venue: arXiv cs.RO 2016
Year: 2016

Understanding user instructions in natural language is an active research topic in AI and robotics. Typically, natural user instructions are high-level and can be reduced into low-level tasks expressed in common verbs (e.g., `take', `get', `put'). For robots understanding such instructions, one of the key challenges is to process high-level user instructions and achieve the specified tasks with robots' primitive actions. To address this, we propose novel algorithms by utilizing semantic roles of common verbs defined in semantic dictionaries and integrating multiple open knowledge to generate task plans. Specifically, we present a new method for matching and recovering semantics of user instructions and a novel task planner that exploits functional knowledge of robot's action model. To verify and evaluate our approach, we implemented a prototype system using knowledge from several open resources. Experiments on our system confirmed the correctness and efficiency of our algorithms. Notably, our system has been deployed in the KeJia robot, which participated the annual RoboCup@Home competitions in the past three years and achieved encouragingly high scores in the benchmark tests.
Analysis of buffering effects on hard real-time priority-preemptive wormhole networks
Venue: arXiv cs.NI 2016
Year: 2016

There are several approaches to analyse the worst-case response times of sporadic packets transmitted over priority-preemptive wormhole networks. In this paper, we provide an overview of the different approaches, discuss their strengths and weaknesses, and propose an approach that captures all effects considered by previous approaches while providing tight yet safe upper bounds for packet response times. We specifically address the problems created by buffering and backpressure in wormhole networks, which amplifies the problem of indirect interference in a way that has not been considered by the early analysis approaches. Didactic examples and large-scale experiments with synthetically generated packet flow sets provide evidence of the strength of the proposed approach.
MuFuRU: The Multi-Function Recurrent Unit
Venue: arXiv cs.NE 2016
Year: 2016

Recurrent neural networks such as the GRU and LSTM found wide adoption in natural language processing and achieve state-of-the-art results for many tasks. These models are characterized by a memory state that can be written to and read from by applying gated composition operations to the current input and the previous state. However, they only cover a small subset of potentially useful compositions. We propose Multi-Function Recurrent Units (MuFuRUs) that allow for arbitrary differentiable functions as composition operations. Furthermore, MuFuRUs allow for an input- and state-dependent choice of these composition operations that is learned. Our experiments demonstrate that the additional functionality helps in different sequence modeling tasks, including the evaluation of propositional logic formulae, language modeling and sentiment analysis.
Feature-based Recursive Observer Design for Homography Estimation
Venue: arXiv cs.CV 2016
Year: 2016

This paper presents a new algorithm for online estimation of a sequence of homographies applicable to image sequences obtained from robotic vehicles equipped with vision sensors. The approach taken exploits the underlying Special Linear group structure of the set of homographies along with gyroscope measurements and direct point-feature correspondences between images to develop temporal filter for the homography estimate. Theoretical analysis and experimental results are provided to demonstrate the robustness of the proposed algorithm. The experimental results show excellent performance even in the case of very fast camera motion (relative to frame rate), severe occlusion, and in the presence of specular reflections.
Efficient Robust Proper Learning of Log-concave Distributions
Venue: arXiv cs.DS 2016
Year: 2016

We study the {\em robust proper learning} of univariate log-concave distributions (over continuous and discrete domains). Given a set of samples drawn from an unknown target distribution, we want to compute a log-concave hypothesis distribution that is as close as possible to the target, in total variation distance. In this work, we give the first computationally efficient algorithm for this learning problem. Our algorithm achieves the information-theoretically optimal sample size (up to a constant factor), runs in polynomial time, and is robust to model misspecification with nearly-optimal error guarantees. Specifically, we give an algorithm that, on input $n=O(1/\eps^{5/2})$ samples from an unknown distribution $f$, runs in time $\widetilde{O}(n^{8/5})$, and outputs a log-concave hypothesis $h$ that (with high probability) satisfies $\dtv(h, f) = O(\opt)+\eps$, where $\opt$ is the minimum total variation distance between $f$ and the class of log-concave distributions. Our approach to the robust proper learning problem is quite flexible and may be applicable to many other univariate distribution families.
Performance Analysis of ZF Receivers with Imperfect CSI for Uplink Massive MIMO Systems
Venue: arXiv cs.IT 2016
Year: 2016

We consider the uplink of massive multipleinput multiple-output systems in a multicell environment. Since the base station (BS) estimates the channel state information (CSI) using the pilot signals transmitted from the users, each BS will have imperfect CSI in practice. Assuming zero-forcing method to eliminate the multi-user interference, we derive the exact analytical expressions for the probability density function (PDF) of the signal-to-interference-plus-noise ratio (SINR), the corresponding achievable rate, the outage probability, and the symbol error rate (SER) when the BS has imperfect CSI. An upper bound of the SER is also derived for an arbitrary number of antennas at the BS. Moreover, we derive the upper bound of the achievable rate for the case where the number of antennas at the BS goes to infinity, and the analysis is verified by presenting numerical results.
Policy Networks with Two-Stage Training for Dialogue Systems
Venue: arXiv cs.CL 2016
Year: 2016

In this paper, we propose to use deep policy networks which are trained with an advantage actor-critic method for statistically optimised dialogue systems. First, we show that, on summary state and action spaces, deep Reinforcement Learning (RL) outperforms Gaussian Processes methods. Summary state and action spaces lead to good performance but require pre-engineering effort, RL knowledge, and domain expertise. In order to remove the need to define such summary spaces, we show that deep RL can also be trained efficiently on the original state and action spaces. Dialogue systems based on partially observable Markov decision processes are known to require many dialogues to train, which makes them unappealing for practical deployment. We show that a deep RL method based on an actor-critic architecture can exploit a small amount of data very efficiently. Indeed, with only a few hundred dialogues collected with a handcrafted policy, the actor-critic deep learner is considerably bootstrapped from a combination of supervised and batch RL. In addition, convergence to an optimal policy is significantly sped up compared to other deep RL methods initialized on the data with batch RL. All experiments are performed on a restaurant domain derived from the Dialogue State Tracking Challenge 2 (DSTC2) dataset.
Fuzzy-Klassen Model for Development Disparities Analysis based on Gross Regional Domestic Product Sector of a Region
Venue: arXiv cs.AI 2016
Year: 2016

Analysis of regional development imbalances quadrant has a very important meaning in order to see the extent of achievement of the development of certain areas as well as the difference. Factors that could be used as a tool to measure the inequality of development is to look at the average growth and development contribution of each sector of Gross Regional Domestic Product (GRDP) based on the analyzed region and the reference region. This study discusses the development of a model to determine the regional development imbalances using fuzzy approach system, and the rules of typology Klassen. The model is then called fuzzy-Klassen. Implications Product Mamdani fuzzy system is used in the model as an inference engine to generate output after defuzzyfication process. Application of MATLAB is used as a tool of analysis in this study. The test a result of Kota Cilegon is shows that there are significant differences between traditional Klassen typology analyses with the results of the model developed. Fuzzy model-Klassen shows GRDP sector inequality Cilegon City is dominated by Quadrant I (K4), where status is the sector forward and grows exponentially. While the traditional Klassen typology, half of GRDP sector is dominated by Quadrant IV (K4) with a sector that is lagging relative status.
Stabilization Theory for Active Multi Port Networks
Venue: arXiv cs.SY 2016
Year: 2016

This paper proposes a theory for designing stable interconnection of linear active multi-port networks at the ports. Such interconnections can lead to unstable networks even if the original networks are stable with respect to bounded port excitations. Hence such a theory is necessary for realising interconnections of active multiport networks. Stabilization theory of linear feedback systems using stable coprime factorizations of transfer functions has been well known. This theory witnessed glorious developments in recent past culminating into the $H_{\infty}$ approach to design of feedback systems. However these important developments have seldom been utilized for network interconnections due to the difficulty of realizing feedback signal flow graph for multi-port networks with inputs and outputs as port sources and responses. This paper resolves this problem by developing the stabilization theory directly in terms of port connection description without formulation in terms of signal flow graph of the implicit feedback connection. The stable port interconnection results into an affine parametrized network function in which the free parameter is itself a stable network function and describes all stabilizing port compensations of a given network.
Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-related Applications
Venue: arXiv cs.CV 2016
Year: 2016

Facial expressions are an important way through which humans interact socially. Building a system capable of automatically recognizing facial expressions from images and video has been an intense field of study in recent years. Interpreting such expressions remains challenging and much research is needed about the way they relate to human affect. This paper presents a general overview of automatic RGB, 3D, thermal and multimodal facial expression analysis. We define a new taxonomy for the field, encompassing all steps from face detection to facial expression recognition, and describe and classify the state of the art methods accordingly. We also present the important datasets and the bench-marking of most influential methods. We conclude with a general discussion about trends, important questions and future lines of research.
WordNet2Vec: Corpora Agnostic Word Vectorization Method
Venue: arXiv cs.CL 2016
Year: 2016

A complex nature of big data resources demands new methods for structuring especially for textual content. WordNet is a good knowledge source for comprehensive abstraction of natural language as its good implementations exist for many languages. Since WordNet embeds natural language in the form of a complex network, a transformation mechanism WordNet2Vec is proposed in the paper. It creates vectors for each word from WordNet. These vectors encapsulate general position - role of a given word towards all other words in the natural language. Any list or set of such vectors contains knowledge about the context of its component within the whole language. Such word representation can be easily applied to many analytic tasks like classification or clustering. The usefulness of the WordNet2Vec method was demonstrated in sentiment analysis, i.e. classification with transfer learning for the real Amazon opinion textual dataset.
Asynchronous Distributed Hypothesis Testing in the Presence of Crash Failures
Venue: arXiv cs.DC 2016
Year: 2016

This paper addresses the problem of distributed hypothesis testing in multi-agent networks, where agents repeatedly collect local observations about an unknown state of the world, and try to collaboratively detect the true state through information exchange. We focus on the impact of failures and asynchrony (two fundamental factors in distributed systems) on the performance of consensus-based non-Bayesian learning. In particular, we consider the scenario where the networked agents may suffer crash faults, and messages delay can be arbitrarily long but finite. We identify the minimal global detectability of the network for non-Bayesian rule to succeed. In addition, we obtain a generalization of a celebrated result by Wolfowitz and Hajnal to submatrices, which might be of independent interest.
LANet: An Enriched Knowledgebase for Location-aware Activity Recommendation System
Venue: arXiv cs.SI 2016
Year: 2016

Accumulation of large amount of location-specific reviews on web due to escalating popularity of Location-based Social Networking platforms like Yelp, Foursquare, Brightkite etc. in recent years, has created the opportunity to discover location-specific activities and develop myriads of location-aware activity recommendation applications. The performance and popularity of such recommendation applications greatly depend on the richness and accuracy of the back-end knowledgebase, which intern is regulated by information relevancy and redundancy issues. Existing work on activity discovery have not made any attempt to ensure relevancy and non-redundancy of discovered knowledge (i.e., location-specific activities). Moreover, majority of these work have utilized body-worn sensors, images or human GPS traces and discovered generalized activities that do not convey any location-specific knowledge. In this thesis, we address the mentioned issues with serious concern and propose an effective solution to discover Location-specific Activity Network, in short LANet from location-aware reviews. The information network LANet serves as an accurate, enriched and unified knowledgebase of a Location-aware Activity Recommendation System. While building LANet, we also introduce novel ideas like, activity-based location similarity detection and measuring uniqueness, generality/speciality of an activity at a particular location to enrich the said knowledge base to a great extent. Experimental results show the information richness and accuracy of the proposed knowledge base which is comparable to human perception and accounts for our success in achieving the desired solution.
Intelligent audit code generation from free text in the context of neurosurgery
Venue: arXiv cs.CY 2016
Year: 2016

Clinical auditing requires codified data for aggregation and analysis of patterns. However in the medical domain obtaining structured data can be difficult as the most natural, expressive and comprehensive way to record a clinical encounter is through natural language. The task of creating structured data from naturally expressed information is known as information extraction. Specialised areas of medicine use their own language and data structures; the translation process has unique challenges, and often requires a fresh approach. This research is devoted to creating a novel semi-automated method for generating codified auditing data from clinical notes recorded in a neurosurgical department in an Australian teaching hospital. The method encapsulates specialist knowledge in rules that instantaneously make precise decisions for the majority of the matches, followed up by dictionary-based matching of the remaining text.
Social value and information quality in online health information search
Venue: arXiv cs.CY 2016
Year: 2016

This paper extends and validates a model of value-driven online healthcare information search in online shared contexts. Perceived value is an important factor behind users' decisions concerning search, consumption and reuse of products and services. The role of utilitarian, hedonic and epistemic value of information in user satisfaction and intention to repeat online search is well recognized, but little support has been found for social value affecting user satisfaction critical for such decisions. Therefore, a value-based model of online healthcare information search was extended adding detailed information quality measures. Our survey data collected from 143 college going students from more than 10 countries studying in South Korea demonstrated two novel results. At first, unlike existing studies, strong support was found for perceived social value affecting the user satisfaction. As a second substantial finding, the significance of social value could only be appreciated by using comprehensive constructs of information quality than simpler measures. Therefore, this study shows for social and shared HIS, users consider access, representation and context quality of healthcare information to be more important from social perspective than intrinsic (or content) quality. Therefore, developers and healthcare organizations interested in traction of their online healthcare information delivery systems (HIS), especially those using internet and social media networks, should focus on enhancing information quality of their systems accordingly.
Online self-disclosure and wellbeing of adolescents: A systematic literature review
Venue: arXiv cs.CY 2016
Year: 2016

The present research investigated the extent of online self-disclosure (SD) of adolescents through a systematic decadal literature review. This review identified three major areas of research focus, with studies categorized mainly as: (1) factors contributing to online SD of adolescents; (2) risks and consequences of online SD; and (3) future directions of research and practical interventions to address the problems. A detailed examination of the variables covered by the studies indicated that only a few aspects related to adolescents' online SD were addressed and these variables did not receive in-depth analysis. Some aspects of online SD received too much attention while others received none. Based on these findings, the present research argues that further research using sophisticated scientific research designs is needed to understand adolescents' online SD.
Strategy in a Digital World
Venue: arXiv cs.CY 2016
Year: 2016

Organizations are increasingly adopting digital strategies and investing heavily in digital technologies and initiatives. However, to date, there does not appear to be a clear understanding of digital strategies and their purpose, which forms the motivation for this research. This research-in-progress study aims to address this research gap by exploring 1) the various conceptions of digital strategy, and 2) the way in which digital strategies differ from conventional strategies, through interviews with three senior executives. We employed thematic analysis to analyse the interview data, which resulted in the identification of ten themes that were grouped under three theoretical constructs. We then explored the applicability of the six dimensions of strategy proposed by Hax (1990) in the digital context and proposed two additional dimensions. The contribution of this study is to provide a deeper understanding of digital strategy to support further academic research and provide guidance to practitioners.
Enhancing Public Service Delivery through Organisational Modeling
Venue: arXiv cs.CY 2016
Year: 2016

Government organisations utilise a Citizen's Charter in order to furnish the public with comprehensive details of their service offerings that defines how their overall organisational goals are achieved. Studies on the implementation of Citizen's Charter in developing countries indicated that important social factors were not considered. Reviewed literatures indicated that social factors could accelerate service delivery with the use of goal-oriented modelling (such as i* organisational modeling) as it represent conditions expected from social actors and their social dependencies. By modelling role dependencies among actors carrying out services in a business unit and applying vulnerability and criticality metrics, problem service areas were identified using the Citizen's Charter as a source of these methodologies. Improvements in these problem service areas were based on vulnerability and criticality levels of their corresponding actors. Recommendations to address these service areas include monitoring of key performance indicators of actors and task delegation when necessary.
Doctoral Consortium - Australasian Conference on Information Systems (ACIS) 2015
Venue: arXiv cs.CY 2016
Year: 2016

This submission contains all publications from the ACIS 2015 Doctoral Consortium held in Adelaide, Australia from November 30 - December 1, 2015.
metricDTW: local distance metric learning in Dynamic Time Warping
Venue: arXiv cs.LG 2016
Year: 2016

We propose to learn multiple local Mahalanobis distance metrics to perform k-nearest neighbor (kNN) classification of temporal sequences. Temporal sequences are first aligned by dynamic time warping (DTW); given the alignment path, similarity between two sequences is measured by the DTW distance, which is computed as the accumulated distance between matched temporal point pairs along the alignment path. Traditionally, Euclidean metric is used for distance computation between matched pairs, which ignores the data regularities and might not be optimal for applications at hand. Here we propose to learn multiple Mahalanobis metrics, such that DTW distance becomes the sum of Mahalanobis distances. We adapt the large margin nearest neighbor (LMNN) framework to our case, and formulate multiple metric learning as a linear programming problem. Extensive sequence classification results show that our proposed multiple metrics learning approach is effective, insensitive to the preceding alignment qualities, and reaches the state-of-the-art performances on UCR time series datasets.
Sparse Spectrum Sensing in Infrastructure-less Cognitive Radio Networks via Binary Consensus Algorithms
Venue: arXiv cs.IT 2016
Year: 2016

Compressive Sensing has been utilized in Cognitive Radio Networks (CRNs) to exploit the sparse nature of the occupation of the primary users. Also, distributed spectrum sensing has been proposed to tackle the wireless channel problems, like node or link failures, rather than the common (centralized approach) for spectrum sensing. In this paper, we propose a distributed spectrum sensing framework based on consensus algorithms where SU nodes exchange their binary decisions to take global decisions without a fusion center to coordinate the sensing process. Each SU will share its decision with its neighbors, and at every new iteration each SU will take a new decision based on its current decision and the decisions it receives from its neighbors; in the next iteration, each SU will share its new decision with its neighbors. We show via simulations that the detection performance can tend to the performance of majority rule Fusion Center based CRNs.