OpenRaiser/Intern-Atlas huggingface dataset:
Ethical Implications of IT-enabled Information Flows Conceived as Intermediaries or Mediators
Venue: arXiv cs.CY 2016
Year: 2016

This paper contributes to a better understanding of ethical concerns regarding the deployment of complex public sector IT systems and the information flows they instigate. The paper aims to reveal how different views on IT and IT-enabled information flows allow us to see differently their social implications and to construe different ethical questions. This is achieved by i) defining two opposing views on IT-enabled information flows as 'intermediaries' and 'mediators'; ii) by analysing the controversial case of My School - a web portal that provides performance data of 9,500 Australian schools - that introduces new information flows in the education sector; and iii) by revealing and explaining how some unintended negative social implications emerge and how the articulation of ethical concerns depends on the view on My School-enabled information flows. The paper concludes with theoretical and practical implications, with particular emphasis on responsibilities of all involved, setting up foundations for an important area of future IS research.
A design science exploration of a visual-spatial learning system with feedback
Venue: arXiv cs.CY 2016
Year: 2016

Our paper is research in progress that is research investigating the use of games technology to enhance the learning of a physical skill. The Microsoft Kinect is a system designed for gaming with the capability to track the movement of users. Our research explored whether such a system could be used to provide feedback when teaching sign vocabulary. Whilst there are technologies available for teaching sign language, currently none provide feedback on the accuracy of the users' attempts at making signs. In this paper we report how the three-dimensional dsplay capability of the technology can enhance the users' experience. Also, when using tracking to identify errors in physical movements, how and when should feedback be given. A design science approach was undertaken to find a solution to this real world problem. The design and implementation of the solution provides interesting insights into how technology can not only emulate but also improve upon traditional learning of physical skills.
Business Intelligence and Supply Chain Agility
Venue: arXiv cs.CY 2016
Year: 2016

Supply Chain Agility is important for organisations to stay competitive in today's dynamic business environment. There is increasing interest in deploying Business Intelligence (BI) in the Supply Chain Management (SCM) context to improve Supply Chain (SC) Agility. However, there is limited research exploring BI contributions to SC Agility. In this research-in-progress paper we propose a model based on a conceptual analysis of the literature showing how BI can help organisations achieve SC Agility by supporting the key areas of SCM (Plan, Source, Make, Deliver and Return). In the next stage of this project, we will conduct a series of case studies investigating how organisations use BI when managing their SC activities and how BI contributes to SC Agility. The result of the study will help organizations deploy BI effectively to support SCM and improve SC Agility.
The challenges and opportunities of delivering wireless high speed broadband services in Rural and Remote Australia: A Case Study of Western Downs Region (WDR)
Venue: arXiv cs.CY 2016
Year: 2016

This paper critically assesses wireless broadband internet infrastructure, in the rural and remote communities of WDR in terms of supply, demand and utilisation. Only 8 of 20 towns have ADSL/ADSL2+, and only 3 towns have 4G mobile network coverage. Conversely all of the towns have 2G/3G mobile network coverage but have problems with speed, reliability of service and capacity to handle data traffic loads at peak times. Satellite broadband internet for remote areas is also patchy at best. Satisfaction with existing wireless broadband internet services is highly variable across rural and remote communities in WDR. Finally we provide suggestions to improve broadband internet access for rural and remote communities. Public and private investment and sharing of wired and wireless broadband internet infrastructure is needed to provide the backhaul networks and 4G mobile and fixed wireless services to ensure high speed, reliable and affordable broadband services for rural and remote communities.
Quantitative Analysis of Desirability in User Experience
Venue: arXiv cs.CY 2016
Year: 2016

The multi-dimensional nature of user experience warrants rigorous assessment of the interactive experience in systems. User experience assessments are based on product evaluations and subsequent analysis of the collected data using quantitative and qualitative techniques. The quality of user experience assessments are dependent on the effectiveness of the techniques deployed. This paper presents the results of a quantitative analysis of desirability aspects of the user experience in a comparative product evaluation study. The data collection was conducted using 118 item Microsoft Product Reaction Cards (PRC) tool followed by data analysis based on the Surface Measure of Overall Performance (SMOP) approach. The results of this study suggest that the incorporation of SMOP as an approach for PRC data analysis derive conclusive evidence of desirability in user experience. The significance of the paper is that it presents a novel analysis method incorporating product reaction cards and surface measure of overall performance approach for an effective quantitative analysis which can be used in academic research and industrial practice.
An Analysis of Personal Information Privacy Concerns Using Q-Methodology
Venue: arXiv cs.CY 2016
Year: 2016

Information privacy has gained increased attention in recent years. This paper focuses on a particular aspect of privacy, i.e., personal information privacy. In this paper a conceptual framework is developed based Westin's theory of Personal Information Privacy (PIP). Concourse theory and Q-methodology was used alongside the literature and the New Zealand Privacy Act 1993 to develop a Q-sort questionnaire. The resulting 29 statements were then sorted by 12 students (majoring in IS Security). The results indicate that for some, privacy priorities may be stable across contexts, and for others this differs, suggesting that current views of privacy (e.g. Westin's theory) may need revising for the modern digital age. The Q-sort methodology also identified three types, each representing distinct collective perspectives on personal information privacy. These types are discussed along with implications and suggestions for future research.
On the complexity of probabilistic trials for hidden satisfiability problems
Venue: arXiv cs.CC 2016
Year: 2016

What is the minimum amount of information and time needed to solve 2SAT? When the instance is known, it can be solved in polynomial time, but is this also possible without knowing the instance? Bei, Chen and Zhang (STOC '13) considered a model where the input is accessed by proposing possible assignments to a special oracle. This oracle, on encountering some constraint unsatisfied by the proposal, returns only the constraint index. It turns out that, in this model, even 1SAT cannot be solved in polynomial time unless P=NP. Hence, we consider a model in which the input is accessed by proposing probability distributions over assignments to the variables. The oracle then returns the index of the constraint that is most likely to be violated by this distribution. We show that the information obtained this way is sufficient to solve 1SAT in polynomial time, even when the clauses can be repeated. For 2SAT, as long as there are no repeated clauses, in polynomial time we can even learn an equivalent formula for the hidden instance and hence also solve it. Furthermore, we extend these results to the quantum regime. We show that in this setting 1QSAT can be solved in polynomial time up to constant precision, and 2QSAT can be learnt in polynomial time up to inverse polynomial precision.
Location Based Performance Model for Indoor mmWave Wearable Communication
Venue: arXiv cs.IT 2016
Year: 2016

Simultaneous use of high-end wearable wireless devices like smart glasses is challenging in a dense indoor environment due to the high nature of interference. In this scenario, the millimeter wave (mmWave) band offers promising potential for achieving gigabits per second throughput. Here we propose a novel system model for analyzing system performance of mmWave based communication among wearables. The proposed model accounts for the non-isotropy of the indoor environment and the effects of reflections that are predominant for indoor mmWave signals. The effect of human body blockages are modeled and the system performance is shown to hugely vary depending on the user location, body orientation and the density of the network. Closed form expressions for spatially averaged signal to interference plus noise ratio distribution are also derived as a function of the location and orientation of a reference user.
Human Centred Object Co-Segmentation
Venue: arXiv cs.CV 2016
Year: 2016

Co-segmentation is the automatic extraction of the common semantic regions given a set of images. Different from previous approaches mainly based on object visuals, in this paper, we propose a human centred object co-segmentation approach, which uses the human as another strong evidence. In order to discover the rich internal structure of the objects reflecting their human-object interactions and visual similarities, we propose an unsupervised fully connected CRF auto-encoder incorporating the rich object features and a novel human-object interaction representation. We propose an efficient learning and inference algorithm to allow the full connectivity of the CRF with the auto-encoder, that establishes pairwise relations on all pairs of the object proposals in the dataset. Moreover, the auto-encoder learns the parameters from the data itself rather than supervised learning or manually assigned parameters in the conventional CRF. In the extensive experiments on four datasets, we show that our approach is able to extract the common objects more accurately than the state-of-the-art co-segmentation algorithms.
MITRE at SemEval-2016 Task 6: Transfer Learning for Stance Detection
Venue: arXiv cs.AI 2016
Year: 2016

We describe MITRE's submission to the SemEval-2016 Task 6, Detecting Stance in Tweets. This effort achieved the top score in Task A on supervised stance detection, producing an average F1 score of 67.8 when assessing whether a tweet author was in favor or against a topic. We employed a recurrent neural network initialized with features learned via distant supervision on two large unlabeled datasets. We trained embeddings of words and phrases with the word2vec skip-gram method, then used those features to learn sentence representations via a hashtag prediction auxiliary task. These sentence vectors were then fine-tuned for stance detection on several hundred labeled examples. The result was a high performing system that used transfer learning to maximize the value of the available training data.
A multilayer reactive system for robots interacting with children with autism
Venue: arXiv cs.RO 2016
Year: 2016

There is a lack of autonomy on traditional Robot-Assisted Therapy systems interacting with children with autism. To overcome this limitation a supervised autonomous robot controller is being built. In this paper we present a multilayer reactive system within such controller. The goal of this Reactive system is to allow the robot to appropriately react to the child's behavior creating the illusion of being alive.
Massive Machine-type Communications in 5G: Physical and MAC-layer solutions
Venue: arXiv cs.IT 2016
Year: 2016

Machine-type communications (MTC) are expected to play an essential role within future 5G systems. In the FP7 project METIS, MTC has been further classified into "massive Machine-Type Communication" (mMTC) and "ultra-reliable Machine-Type Communication" (uMTC). While mMTC is about wireless connectivity to tens of billions of machine-type terminals, uMTC is about availability, low latency, and high reliability. The main challenge in mMTC is scalable and efficient connectivity for a massive number of devices sending very short packets, which is not done adequately in cellular systems designed for human-type communications. Furthermore, mMTC solutions need to enable wide area coverage and deep indoor penetration while having low cost and being energy efficient. In this article, we introduce the physical (PHY) and medium access control (MAC) layer solutions developed within METIS to address this challenge.
DDoS Attacks with Randomized Traffic Innovation: Botnet Identification Challenges and Strategies
Venue: arXiv cs.IT 2016
Year: 2016

Distributed Denial-of-Service (DDoS) attacks are usually launched through the $botnet$, an "army" of compromised nodes hidden in the network. Inferential tools for DDoS mitigation should accordingly enable an early and reliable discrimination of the normal users from the compromised ones. Unfortunately, the recent emergence of attacks performed at the application layer has multiplied the number of possibilities that a botnet can exploit to conceal its malicious activities. New challenges arise, which cannot be addressed by simply borrowing the tools that have been successfully applied so far to earlier DDoS paradigms. In this work, we offer basically three contributions: $i)$ we introduce an abstract model for the aforementioned class of attacks, where the botnet emulates normal traffic by continually learning admissible patterns from the environment; $ii)$ we devise an inference algorithm that is shown to provide a consistent (i.e., converging to the true solution as time progresses) estimate of the botnet possibly hidden in the network; and $iii)$ we verify the validity of the proposed inferential strategy over $real$ network traces.
It ain't necessarily so: Basic sequent systems for negative modalities
Venue: arXiv cs.LO 2016
Year: 2016

We look at non-classical negations and their corresponding adjustment connectives from a modal viewpoint, over complete distributive lattices, and apply a very general mechanism in order to offer adequate analytic proof systems to logics that are based on them. Defining non-classical negations within usual modal semantics automatically allows one to treat equivalent formulas as synonymous, and to have a natural justification for a global version of the contraposition rule. From that perspective, our study offers a particularly useful environment in which negative modalities and their companions may be used for dealing with inconsistency and indeterminacy. After investigating modal logics based on arbitrary frames, we extend the results to serial frames, reflexive frames, functional frames, and symmetric frames. In each case we also investigate when and how classical negation may thereby be defined.
Designing Commercial Therapeutic Robots for Privacy Preserving Systems and Ethical Research Practices within the Home
Venue: arXiv cs.HC 2016
Year: 2016

The migration of robots from the laboratory into sensitive home settings as commercially available therapeutic agents represents a significant transition for information privacy and ethical imperatives. We present new privacy paradigms and apply the Fair Information Practices (FIPs) to investigate concerns unique to the placement of therapeutic robots in private home contexts. We then explore the importance and utility of research ethics as operationalized by existing human subjects research frameworks to guide the consideration of therapeutic robotic users -- a step vital to the continued research and development of these platforms. Together, privacy and research ethics frameworks provide two complementary approaches to protect users and ensure responsible yet robust information sharing for technology development. We make recommendations for the implementation of these principles -- paying particular attention to specific principles that apply to vulnerable individuals (i.e., children, disabled, or elderly persons)--to promote the adoption and continued improvement of long-term, responsible, and research-enabled robotics in private settings.
On Probabilistic Shaping of Quadrature Amplitude Modulation for the Nonlinear Fiber Channel
Venue: arXiv cs.IT 2016
Year: 2016

Different aspects of probabilistic shaping for a multi-span optical communication system are studied. First, a numerical analysis of the additive white Gaussian noise (AWGN) channel investigates the effect of using a small number of input probability mass functions (PMFs) for a range of signal-to-noise ratios (SNRs), instead of optimizing the constellation shaping for each SNR. It is shown that if a small penalty of at most 0.1 dB SNR to the full shaping gain is acceptable, just two shaped PMFs are required per quadrature amplitude modulation (QAM) over a large SNR range. For a multi-span wavelength division multiplexing (WDM) optical fiber system with 64QAM input, it is shown that just one PMF is required to achieve large gains over uniform input for distances from 1,400 km to 3,000 km. Using recently developed theoretical models that extend the Gaussian noise (GN) model and full-field split-step simulations, we illustrate the ramifications of probabilistic shaping on the effective SNR after fiber propagation. Our results show that, for a fixed average optical launch power, a shaping gain is obtained for the noise contributions from fiber amplifiers and modulation-independent nonlinear interference (NLI), whereas shaping simultaneously causes a penalty as it leads to an increased NLI. However, this nonlinear shaping loss is found to have a relatively minor impact, and optimizing the shaped PMF with a modulation-dependent GN model confirms that the PMF found for AWGN is also a good choice for a multi-span fiber system.
Modeling Missing Data in Clinical Time Series with RNNs
Venue: arXiv cs.LG 2016
Year: 2016

We demonstrate a simple strategy to cope with missing data in sequential inputs, addressing the task of multilabel classification of diagnoses given clinical time series. Collected from the pediatric intensive care unit (PICU) at Children's Hospital Los Angeles, our data consists of multivariate time series of observations. The measurements are irregularly spaced, leading to missingness patterns in temporally discretized sequences. While these artifacts are typically handled by imputation, we achieve superior predictive performance by treating the artifacts as features. Unlike linear models, recurrent neural networks can realize this improvement using only simple binary indicators of missingness. For linear models, we show an alternative strategy to capture this signal. Training models on missingness patterns only, we show that for some diseases, what tests are run can be as predictive as the results themselves.
Myopic Policies for Non-Preemptive Scheduling of Jobs with Decaying Value
Venue: arXiv cs.SY 2016
Year: 2016

In many scheduling applications, minimizing delays is of high importance. One adverse effect of such delays is that the reward for completion of a job may decay over time. Indeed in healthcare settings, delays in access to care can result in worse outcomes, such as an increase in mortality risk. Motivated by managing hospital operations in disaster scenarios, as well as other applications in perishable inventory control and information services, we consider non-preemptive scheduling of jobs whose internal value decays over time. Because solving for the optimal scheduling policy is computationally intractable, we focus our attention on the performance of three intuitive heuristics: (1) a policy which maximizes the expected immediate reward, (2) a policy which maximizes the expected immediate reward rate, and (3) a policy which prioritizes jobs with imminent deadlines. We provide performance guarantees for all three policies and show that many of these performance bounds are tight. In addition, we provide numerical experiments and simulations to compare how the policies perform in a variety of scenarios. Our theoretical and numerical results allow us to establish rules-of-thumb for applying these heuristics in a variety of situations, including patient scheduling scenarios.
Using Virtual Humans to Understand Real Ones
Venue: arXiv cs.HC 2016
Year: 2016

Human interactions are characterized by explicit as well as implicit channels of communication. While the explicit channel transmits overt messages, the implicit ones transmit hidden messages about the communicator (e.g., his/her intentions and attitudes). There is a growing consensus that providing a computer with the ability to manipulate implicit affective cues should allow for a more meaningful and natural way of studying particular non-verbal signals of human-human communications by human-computer interactions. In this pilot study, we created a non-dynamic human-computer interaction while manipulating three specific non-verbal channels of communication: gaze pattern, facial expression, and gesture. Participants rated the virtual agent on affective dimensional scales (pleasure, arousal, and dominance) while their physiological signal (electrodermal activity, EDA) was captured during the interaction. Assessment of the behavioral data revealed a significant and complex three-way interaction between gaze, gesture, and facial configuration on the dimension of pleasure, as well as a main effect of gesture on the dimension of dominance. These results suggest a complex relationship between different non-verbal cues and the social context in which they are interpreted. Qualifying considerations as well as possible next steps are further discussed in light of these exploratory findings.
Path-Following Algorithms for Beamforming and Signal Splitting in RF Energy Harvesting Networks
Venue: arXiv cs.IT 2016
Year: 2016

We consider the joint design of transmit beamforming and receive signal-splitting ratios in the downlink of a wireless network with simultaneous radio-frequency (RF) information and energy transfer. Under constraints on the signal-to-interference-plus-noise ratio (SINR) at each user and the total transmit power at the base station, the design objective is to maximize either the sum harvested energy or the minimum harvested energy. We develop a computationally efficient path-following method to solve these challenging nonconvex optimization problems. We mathematically show that the proposed algorithms iteratively progress and converge to locally optimal solutions. Simulation results further show that these locally optimal solutions are the same as the globally optimal solutions for the considered practical network settings.