OpenRaiser/Intern-Atlas huggingface dataset:
ToARist: An Augmented Reality Tourism App created through User-Centred Design
Venue: arXiv cs.HC 2018
Year: 2018

Through Augmented Reality (AR), virtual graphics can transform the physical world. This offers benefits to mobile tourism, where points of interest (POIs) can be annotated on a smartphone screen. Although several of these applications exist, usability issues can discourage adoption. User-centred design (UCD) solicits frequent feedback, often contributing to usable products. While AR mock-ups have been constructed through UCD, we develop a novel and functional tourism app. We solicit requirements through a synthesis of domain analysis, tourist observation and semi-structured interviews. Through four rounds of iterative development, users test and refine the app. The final product, dubbed ToARist, is evaluated by 20 participants, who engage in a tourism task around a UK city. Users regard the system as usable, but find technical issues can disrupt AR. We finish by reflecting on our design and critiquing the challenges of a strict user-centred methodology.
"Privacy is the Boring Bit": User Perceptions and Behaviour in the Internet-of-Things
Venue: arXiv cs.CY 2018
Year: 2018

In opinion polls, the public frequently claim to value their privacy. However, individuals often seem to overlook the principle, contributing to a disparity labelled the `Privacy Paradox'. The growth of the Internet-of-Things (IoT) is frequently claimed to place privacy at risk. However, the Paradox remains underexplored in the IoT. In addressing this, we first conduct an online survey (N = 170) to compare public opinions of IoT and less-novel devices. Although we find users perceive privacy risks, many still decide to purchase smart devices. With the IoT rated less usable/familiar, we assert that it constrains protective behaviour. To explore this hypothesis, we perform contextualised interviews (N = 40) with the public. In these dialogues, owners discuss their opinions and actions with a personal device. We find the Paradox is significantly more prevalent in the IoT, frequently justified by a lack of awareness. We finish by highlighting the qualitative comments of users, and suggesting practical solutions to their issues. This is the first work, to our knowledge, to evaluate the Privacy Paradox over a broad range of technologies.
Adapting the Predator-Prey Game Theoretic Environment to Army Tactical Edge Scenarios with Computational Multiagent Systems
Venue: arXiv cs.MA 2018
Year: 2018

The historical origins of the game theoretic predator-prey pursuit problem can be traced back to Benda, et al., 1985 [1]. Their work adapted the predator-prey ecology problem into a pursuit environment which focused on the dynamics of cooperative behavior between predator agents. Modifications to the predator-prey ecology problem [2] have been implemented to understand how variations to predator [3] and prey [3-5] attributes, including communication [6], can modify dynamic interactions between entities that emerge within that environment [7-9]. Furthermore, the predator-prey pursuit environment has become a testbed for simulation experiments with computational multiagent systems [10-12]. This article extends the theoretical contributions of previous work by providing 1) additional variations to predator and prey attributes for simulated multiagent systems in the pursuit problem, and 2) military-relevant predator-prey environments simulating highly dynamic, tactical edge scenarios that Soldiers might encounter on future battlefields. Through this exploration of simulated tactical edge scenarios with computational multiagent systems, Soldiers will have a greater chance to achieve overmatch on the battlefields of tomorrow.
Neural Chinese Word Segmentation with Dictionary Knowledge
Venue: arXiv cs.CL 2018
Year: 2018

Chinese word segmentation (CWS) is an important task for Chinese NLP. Recently, many neural network based methods have been proposed for CWS. However, these methods require a large number of labeled sentences for model training, and usually cannot utilize the useful information in Chinese dictionary. In this paper, we propose two methods to exploit the dictionary information for CWS. The first one is based on pseudo labeled data generation, and the second one is based on multi-task learning. The experimental results on two benchmark datasets validate that our approach can effectively improve the performance of Chinese word segmentation, especially when training data is insufficient.
Assessment of electrical and infrastructure recovery in Puerto Rico following hurricane Maria using a multisource time series of satellite imagery
Venue: arXiv cs.CV 2018
Year: 2018

Puerto Rico suffered severe damage from the category 5 hurricane (Maria) in September 2017. Total monetary damages are estimated to be ~92 billion USD, the third most costly tropical cyclone in US history. The response to this damage has been tempered and slow moving, with recent estimates placing 45% of the population without power three months after the storm. Consequently, we developed a unique data-fusion mapping approach called the Urban Development Index (UDI) and new open source tool, Comet Time Series (CometTS), to analyze the recovery of electricity and infrastructure in Puerto Rico. Our approach incorporates a combination of time series visualizations and change detection mapping to create depictions of power or infrastructure loss. It also provides a unique independent assessment of areas that are still struggling to recover. For this workflow, our time series approach combines nighttime imagery from the Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP VIIRS), multispectral imagery from two Landsat satellites, US Census data, and crowd-sourced building footprint labels. Based upon our approach we can identify and evaluate: 1) the recovery of electrical power compared to pre-storm levels, 2) the location of potentially damaged infrastructure that has yet to recover from the storm, and 3) the number of persons without power over time. As of May 31, 2018, declined levels of observed brightness across the island indicate that 13.9% +/- ~5.6% of persons still lack power and/or that 13.2% +/- ~5.3% of infrastructure has been lost. In comparison, the Puerto Rico Electric Power Authority states that less than 1% of their customers still are without power.
A Fast-Converged Acoustic Modeling for Korean Speech Recognition: A Preliminary Study on Time Delay Neural Network
Venue: arXiv cs.CL 2018
Year: 2018

In this paper, a time delay neural network (TDNN) based acoustic model is proposed to implement a fast-converged acoustic modeling for Korean speech recognition. The TDNN has an advantage in fast-convergence where the amount of training data is limited, due to subsampling which excludes duplicated weights. The TDNN showed an absolute improvement of 2.12% in terms of character error rate compared to feed forward neural network (FFNN) based modelling for Korean speech corpora. The proposed model converged 1.67 times faster than a FFNN-based model did.
Object Relation Detection Based on One-shot Learning
Venue: arXiv cs.CV 2018
Year: 2018

Detecting the relations among objects, such as "cat on sofa" and "person ride horse", is a crucial task in image understanding, and beneficial to bridging the semantic gap between images and natural language. Despite the remarkable progress of deep learning in detection and recognition of individual objects, it is still a challenging task to localize and recognize the relations between objects due to the complex combinatorial nature of various kinds of object relations. Inspired by the recent advances in one-shot learning, we propose a simple yet effective Semantics Induced Learner (SIL) model for solving this challenging task. Learning in one-shot manner can enable a detection model to adapt to a huge number of object relations with diverse appearance effectively and robustly. In addition, the SIL combines bottom-up and top-down attention mech- anisms, therefore enabling attention at the level of vision and semantics favorably. Within our proposed model, the bottom-up mechanism, which is based on Faster R-CNN, proposes objects regions, and the top-down mechanism selects and integrates visual features according to semantic information. Experiments demonstrate the effectiveness of our framework over other state-of-the-art methods on two large-scale data sets for object relation detection.
Noisy Private Information Retrieval: On Separability of Channel Coding and Information Retrieval
Venue: arXiv cs.IT 2018
Year: 2018

We consider the problem of noisy private information retrieval (NPIR) from $N$ non-communicating databases, each storing the same set of $M$ messages. In this model, the answer strings are not returned through noiseless bit pipes, but rather through \emph{noisy} memoryless channels. We aim at characterizing the PIR capacity for this model as a function of the statistical information measures of the noisy channels such as entropy and mutual information. We derive a general upper bound for the retrieval rate in the form of a max-min optimization. We use the achievable schemes for the PIR problem under asymmetric traffic constraints and random coding arguments to derive a general lower bound for the retrieval rate. The upper and lower bounds match for $M=2$ and $M=3$, for any $N$, and any noisy channel. The results imply that separation between channel coding and retrieval is optimal except for adapting the traffic ratio from the databases. We refer to this as \emph{almost separation}. Next, we consider the private information retrieval problem from multiple access channels (MAC-PIR). In MAC-PIR, the database responses reach the user through a multiple access channel (MAC) that mixes the responses together in a stochastic way. We show that for the additive MAC and the conjunction/disjunction MAC, channel coding and retrieval scheme are \emph{inseparable} unlike in NPIR. We show that the retrieval scheme depends on the properties of the MAC, in particular on the linearity aspect. For both cases, we provide schemes that achieve the full capacity without any loss due to the privacy constraint, which implies that the user can exploit the nature of the channel to improve privacy. Finally, we show that the full unconstrained capacity is not always attainable by determining the capacity of the selection channel.
Analysis and Modeling of Behavioral Changes in a News Service
Venue: arXiv cs.IR 2018
Year: 2018

Information is transmitted through websites, and immediate reactions to various kinds of information are required. Hence, efforts by users to select information themselves have increased, which is fueling further improvements in recommendation services that can reduce such burdens. On the other hand, filter bubbles that only provide biased information to users are generated due to redundant recommendations. In this research, we analyzed behavioral changes prior to recommendation by clustering, and we found that user attributes and cluster contents are different among users with different behavioral changes. The proportion of users under forty and women was relatively large in the diversity-increasing group. We also proposed an article selection model to clarify the influence of recommendation systems on behavioral changes. We compared our proposed model with the target data, verified it, and evaluated the effect of recommendation systems on user behavior. Our simulation results showed that diversity usually decreases, but collaborative filtering can suppress the diversity decrease more effectively than non-recommendations. We also found that the category that users are interested in the most is easily strengthened and is one factor that leads to less diversity, and a recommendation method that can suppress the strengthening of the category that users are interested in the most will be effective for developing a recommendation system that can suppress diversity decreasing.
Verification of Immediate Observation Population Protocols
Venue: arXiv cs.LO 2018
Year: 2018

Population protocols (Angluin et al., PODC, 2004) are a formal model of sensor networks consisting of identical mobile devices. Two devices can interact and thereby change their states. Computations are infinite sequences of interactions satisfying a strong fairness constraint. A population protocol is well-specified if for every initial configuration $C$ of devices, and every computation starting at $C$, all devices eventually agree on a consensus value depending only on $C$. If a protocol is well-specified, then it is said to compute the predicate that assigns to each initial configuration its consensus value. In a previous paper we have shown that the problem whether a given protocol is well-specified and the problem whether it computes a given predicate are decidable. However, in the same paper we prove that both problems are at least as hard as the reachability problem for Petri nets. Since all known algorithms for Petri net reachability have non-primitive recursive complexity, in this paper we restrict attention to immediate observation (IO) population protocols, a class introduced and studied in (Angluin et al., PODC, 2006). We show that both problems are solvable in exponential space for IO protocols. This is the first syntactically defined, interesting class of protocols for which an algorithm not requiring Petri net reachability is found.
Human Trajectories Characteristics
Venue: arXiv cs.SI 2018
Year: 2018

Communication devices (mobile networks, social media platforms) are produced digital traces for their users either voluntarily or not. This type of collective data can give powerful indications on their effect on urban systems design and development. For understanding the collective human behavior of urban city, the modeling techniques could be used. In this study the most important feature of human mobility is considered, which is the radius of gyration . This parameter is used to measure how (far /frequent) the individuals are shift inside specific observed region.
Improvement in the UAV position estimation with low-cost GPS, INS and vision-based system: Application to a quadrotor UAV
Venue: arXiv cs.SY 2018
Year: 2018

In this paper, we develop a position estimation system for Unmanned Aerial Vehicles formed by hardware and software. It is based on low-cost devices: GPS, commercial autopilot sensors and dense optical flow algorithm implemented in an onboard microcomputer. Comparative tests were conducted using our approach and the conventional one, where only fusion of GPS and inertial sensors are used. Experiments were conducted using a quadrotor in two flying modes: hovering and trajectory tracking in outdoor environments. Results demonstrate the effectiveness of the proposed approach in comparison with the conventional approaches presented in the vast majority of commercial drones.
Probably approximately correct learning of Horn envelopes from queries
Venue: arXiv cs.AI 2018
Year: 2018

We propose an algorithm for learning the Horn envelope of an arbitrary domain using an expert, or an oracle, capable of answering certain types of queries about this domain. Attribute exploration from formal concept analysis is a procedure that solves this problem, but the number of queries it may ask is exponential in the size of the resulting Horn formula in the worst case. We recall a well-known polynomial-time algorithm for learning Horn formulas with membership and equivalence queries and modify it to obtain a polynomial-time probably approximately correct algorithm for learning the Horn envelope of an arbitrary domain.
Real-Time Index Authentication for Event-Oriented Surveillance Video Query using Blockchain
Venue: arXiv cs.DC 2018
Year: 2018

Information from surveillance video is essential for situational awareness (SAW). Nowadays, a prohibitively large amount of surveillance data is being generated continuously by ubiquitously distributed video sensors. It is very challenging to immediately identify the objects of interest or zoom in suspicious actions from thousands of video frames. Making the big data indexable is critical to tackle this problem. It is ideal to generate pattern indexes in a real-time, on-site manner on the video streaming instead of depending on the batch processing at the cloud centers. The modern edge-fog-cloud computing paradigm allows implementation of time sensitive tasks at the edge of the network. The on-site edge devices collect the information sensed in format of frames and extracts useful features. The near-site fog nodes conduct the contextualization and classification of the features. The remote cloud center is in charge of more data intensive and computing intensive tasks. However, exchanging the index information among devices in different layers raises security concerns where an adversary can capture or tamper with features to mislead the surveillance system. In this paper, a blockchain enabled scheme is proposed to protect the index data through an encrypted secure channel between the edge and fog nodes. It reduces the chance of attacks on the small edge and fog devices. The feasibility of the proposal is validated through intensive experimental analysis.
An Adaptable Maturity Strategy for Information Security
Venue: arXiv cs.CR 2018
Year: 2018

The lack of security in information systems has caused numerous financial and moral losses to several organizations. The organizations have a series of information security measures recommended by literature and international standards. However, the implementation of policies, actions, and adjustment to such standards is not simple and must be addressed by specific needs identified by the Information Security Governance in each organization. There are many challenges in effectively establishing, maintaining, and measuring information security in a way that adds value. Those challenges demonstrate a need for further investigations which address the problem. This paper presents a strategy to measure the maturity in information security aiming, also, to assist in the application and prioritization of information security actions in the corporate environment. For this, a survey was used as the main methodological instrument, reaching 157 distinct companies. As a result, it was possible to classify the ISO/IEC 27001 and 27002 controls in four stages according to the importance given by the companies. The COBIT maturity levels and a risk analysis matrix were also used. Finally, the adaptable strategy was successfully tested in a company
Learning Neuron Non-Linearities with Kernel-Based Deep Neural Networks
Venue: arXiv cs.LG 2018
Year: 2018

The effectiveness of deep neural architectures has been widely supported in terms of both experimental and foundational principles. There is also clear evidence that the activation function (e.g. the rectifier and the LSTM units) plays a crucial role in the complexity of learning. Based on this remark, this paper discusses an optimal selection of the neuron non-linearity in a functional framework that is inspired from classic regularization arguments. It is shown that the best activation function is represented by a kernel expansion in the training set, that can be effectively approximated over an opportune set of points modeling 1-D clusters. The idea can be naturally extended to recurrent networks, where the expressiveness of kernel-based activation functions turns out to be a crucial ingredient to capture long-term dependencies. We give experimental evidence of this property by a set of challenging experiments, where we compare the results with neural architectures based on state of the art LSTM cells.
Joint Power and Time Allocation for NOMA-MEC Offloading
Venue: arXiv cs.IT 2018
Year: 2018

This paper considers non-orthogonal multiple access (NOMA) assisted mobile edge computing (MEC), where the power and time allocation is jointly optimized to reduce the energy consumption of offloading. Closed-form expressions for the optimal power and time allocation solutions are obtained and used to establish the conditions for determining whether conventional orthogonal multiple access (OMA), pure NOMA or hybrid NOMA should be used for MEC offloading.
Saliency Map Estimation for Omni-Directional Image Considering Prior Distributions
Venue: arXiv cs.CV 2018
Year: 2018

In recent years, the deep learning techniques have been applied to the estimation of saliency maps, which represent probability density functions of fixations when people look at the images. Although the methods of saliency-map estimation have been actively studied for 2-dimensional planer images, the methods for omni-directional images to be utilized in virtual environments had not been studied, until a competition of saliency-map estimation for the omni-directional images was held in ICME2017. In this paper, novel methods for estimating saliency maps for the omni-directional images are proposed considering the properties of prior distributions for fixations in the planar images and the omni-directional images.
Can Blockchain Protect Internet-of-Things?
Venue: arXiv cs.CR 2018
Year: 2018

In the Internet-of-Things, the number of connected devices is expected to be extremely huge, i.e., more than a couple of ten billion. It is however well-known that the security for the Internet-of-Things is still open problem. In particular, it is difficult to certify the identification of connected devices and to prevent the illegal spoofing. It is because the conventional security technologies have advanced for mainly protecting logical network and not for physical network like the Internet-of-Things. In order to protect the Internet-of-Things with advanced security technologies, we propose a new concept (datachain layer) which is a well-designed combination of physical chip identification and blockchain. With a proposed solution of the physical chip identification, the physical addresses of connected devices are uniquely connected to the logical addresses to be protected by blockchain.
Learning to Listen, Read, and Follow: Score Following as a Reinforcement Learning Game
Venue: arXiv cs.AI 2018
Year: 2018

Score following is the process of tracking a musical performance (audio) with respect to a known symbolic representation (a score). We start this paper by formulating score following as a multimodal Markov Decision Process, the mathematical foundation for sequential decision making. Given this formal definition, we address the score following task with state-of-the-art deep reinforcement learning (RL) algorithms such as synchronous advantage actor critic (A2C). In particular, we design multimodal RL agents that simultaneously learn to listen to music, read the scores from images of sheet music, and follow the audio along in the sheet, in an end-to-end fashion. All this behavior is learned entirely from scratch, based on a weak and potentially delayed reward signal that indicates to the agent how close it is to the correct position in the score. Besides discussing the theoretical advantages of this learning paradigm, we show in experiments that it is in fact superior compared to previously proposed methods for score following in raw sheet music images.