OpenRaiser/Intern-Atlas huggingface dataset:
ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation
Venue: arXiv cs.CV 2016
Year: 2016

The ability to perform pixel-wise semantic segmentation in real-time is of paramount importance in mobile applications. Recent deep neural networks aimed at this task have the disadvantage of requiring a large number of floating point operations and have long run-times that hinder their usability. In this paper, we propose a novel deep neural network architecture named ENet (efficient neural network), created specifically for tasks requiring low latency operation. ENet is up to 18$\times$ faster, requires 75$\times$ less FLOPs, has 79$\times$ less parameters, and provides similar or better accuracy to existing models. We have tested it on CamVid, Cityscapes and SUN datasets and report on comparisons with existing state-of-the-art methods, and the trade-offs between accuracy and processing time of a network. We present performance measurements of the proposed architecture on embedded systems and suggest possible software improvements that could make ENet even faster.
Community detection in networks: Modularity optimization and maximum likelihood are equivalent
Venue: arXiv cs.SI 2016
Year: 2016

We demonstrate an exact equivalence between two widely used methods of community detection in networks, the method of modularity maximization in its generalized form which incorporates a resolution parameter controlling the size of the communities discovered, and the method of maximum likelihood applied to the special case of the stochastic block model known as the planted partition model, in which all communities in a network are assumed to have statistically similar properties. Among other things, this equivalence provides a mathematically principled derivation of the modularity function, clarifies the conditions and assumptions of its use, and gives an explicit formula for the optimal value of the resolution parameter.
Drawing the Almost Convex Set in an Integer Grid of Minimum Size
Venue: arXiv cs.CG 2016
Year: 2016

In 2001, Károlyi, Pach and Tóth introduced a family of point sets to solve an Erdős-Szekeres type problem; which have been used to solve several other Edős-Szekeres type problems. In this paper we refer to these sets as nested almost convex sets. A nested almost convex set $\mathcal{X}$ has the property that the interior of every triangle determined by three points in the same convex layer of $\mathcal{X}$, contains exactly one point of $\mathcal{X}$. In this paper, we introduce a characterization of nested almost convex sets. Our characterization implies that there exists at most one (up to order type) nested almost convex set of $n$ points. We use our characterization to obtain a linear time algorithm to construct nested almost convex sets of $n$ points, with integer coordinates of absolute values at most $O(n^{\log_2 5})$. Finally, we use our characterization to obtain an $O(n\log n)$-time algorithm to determine whether a set of points is a nested almost convex set.
Some Algorithmic Results on Restrained Domination in Graphs
Venue: arXiv cs.DM 2016
Year: 2016

A set $D\subseteq V$ of a graph $G=(V,E)$ is called a restrained dominating set of $G$ if every vertex not in $D$ is adjacent to a vertex in $D$ and to a vertex in $V \setminus D$. The \textsc{Minimum Restrained Domination} problem is to find a restrained dominating set of minimum cardinality. Given a graph $G$, and a positive integer $k$, the \textsc{Restrained Domination Decision} problem is to decide whether $G$ has a restrained dominating set of cardinality a most $k$. The \textsc{Restrained Domination Decision} problem is known to be NP-complete for chordal graphs. In this paper, we strengthen this NP-completeness result by showing that the \textsc{Restrained Domination Decision} problem remains NP-complete for doubly chordal graphs, a subclass of chordal graphs. We also propose a polynomial time algorithm to solve the \textsc{Minimum Restrained Domination} problem in block graphs, a subclass of doubly chordal graphs. The \textsc{Restrained Domination Decision} problem is also known to be NP-complete for split graphs. We propose a polynomial time algorithm to compute a minimum restrained dominating set of threshold graphs, a subclass of split graphs. In addition, we also propose polynomial time algorithms to solve the \textsc{Minimum Restrained Domination} problem in cographs and chain graphs. Finally, we give a new improved upper bound on the restrained domination number, cardinality of a minimum restrained dominating set in terms of number of vertices and minimum degree of graph. We also give a randomized algorithm to find a restrained dominating set whose cardinality satisfy our upper bound with a positive probability.
No Downlink Pilots are Needed in TDD Massive MIMO
Venue: arXiv cs.IT 2016
Year: 2016

We consider the Massive Multiple-Input Multiple-Output (MIMO) downlink with maximum-ratio and zero-forcing processing and time-division duplex (TDD) operation. To decode, the terminals must know their instantaneous effective channel gain. Conventionally, it is assumed that by virtue of channel hardening, this instantaneous gain is close to its average and hence that terminals can rely on knowledge of that average (also known as statistical channel information). However, in some propagation environments, such as keyhole channels, channel hardening does not hold. We propose a blind algorithm to estimate the effective channel gain at each user, that does not require any downlink pilots. We derive a capacity lower bound of each user for our proposed scheme, applicable to any propagation channel. Compared to the case of no downlink pilots (relying on channel hardening), and compared to training-based estimation using downlink pilots, our blind algorithm performs significantly better. The difference is especially pronounced in environments that do not offer channel hardening.
Deep Learning Convolutional Networks for Multiphoton Microscopy Vasculature Segmentation
Venue: arXiv cs.CV 2016
Year: 2016

Recently there has been an increasing trend to use deep learning frameworks for both 2D consumer images and for 3D medical images. However, there has been little effort to use deep frameworks for volumetric vascular segmentation. We wanted to address this by providing a freely available dataset of 12 annotated two-photon vasculature microscopy stacks. We demonstrated the use of deep learning framework consisting both 2D and 3D convolutional filters (ConvNet). Our hybrid 2D-3D architecture produced promising segmentation result. We derived the architectures from Lee et al. who used the ZNN framework initially designed for electron microscope image segmentation. We hope that by sharing our volumetric vasculature datasets, we will inspire other researchers to experiment with vasculature dataset and improve the used network architectures.
Structured Convolution Matrices for Energy-efficient Deep learning
Venue: arXiv cs.NE 2016
Year: 2016

We derive a relationship between network representation in energy-efficient neuromorphic architectures and block Toplitz convolutional matrices. Inspired by this connection, we develop deep convolutional networks using a family of structured convolutional matrices and achieve state-of-the-art trade-off between energy efficiency and classification accuracy for well-known image recognition tasks. We also put forward a novel method to train binary convolutional networks by utilising an existing connection between noisy-rectified linear units and binary activations.
How to manipulate truthful prior-dependent mechanisms?
Venue: arXiv cs.GT 2016
Year: 2016

In the standard formulation of mechanism design, a key assumption is that the designer has reliable information and technology to determine a prior distribution on types of the agents. In the meanwhile, as pointed out by the Wilson's Principle, a mechanism should reply as little as possible on the accuracy of prior type distribution. In this paper, we put forward a model to formalize and quantify this statement. In our model, each agent has a type distribution. In addition, the agent can commit to a fake distribution and bids consistently and credibly with respect to the fake distribution (i.e., plays Bayes equilibrium under the fake distributions). We study the equilibria of the induced distribution-committing games in several well-known mechanisms. Our results can be summarized as follows: (1) the game induced by Myerson's auction under our model is strategically equivalent to the first price auction under the standard model. As a consequence, they are revenue-equivalent as well. (2) the second-price auction yields weakly better revenue than several reserve-based and virtual-value-based auctions, under our fake distribution model. These results echo the recent literature on prior-independent mechanism design.
An Approach for Isolated Testing of Self-Organization Algorithms
Venue: arXiv cs.SE 2016
Year: 2016

We provide a systematic approach for testing self-organization (SO) algorithms. The main challenges for such a testing domain are the strongly ramified state space, the possible error masking, the interleaving of mechanisms, and the oracle problem resulting from the main characteristics of SO algorithms: their inherent non-deterministic behavior on the one hand, and their dynamic environment on the other. A key to success for our SO algorithm testing framework is automation, since it is rarely possible to cope with the ramified state space manually. The test automation is based on a model-based testing approach where probabilistic environment profiles are used to derive test cases that are performed and evaluated on isolated SO algorithms. Besides isolation, we are able to achieve representative test results with respect to a specific application. For illustration purposes, we apply the concepts of our framework to partitioning-based SO algorithms and provide an evaluation in the context of an existing smart-grid application.
Business Process Management: Saving the Planet?
Venue: arXiv cs.CY 2016
Year: 2016

Organisational and government concerns about environmental sustainability (ES) are on the increase. While a significant amount of research from a wide range of domains has addressed various ES challenges, intuitively, Business Process Management (BPM), with its focus on process improvement and process performance measurement, has much to offer the ES field. In this paper we aim to understand the BPM research contribution to ES, including specific Environmental Performance Indicators (EPI), and the BPM concepts that have been utilized in the ES context. To this end we conduct a systematic literature review to capture prior research focused on BPM and ES, coding the articles according to their contribution to EPIs and other ES concepts, while also contrasting their focus with main challenges identified in industry reports. Our study identifies which EPIs have been addressed in prior BPM research and highlights areas of future contribution.
A Framework for Techniques for Information Technology Enabled Innovation
Venue: arXiv cs.CY 2016
Year: 2016

Australia is seen as lagging in the innovation that is needed for corporate success and national productivity gains. There is an apparent lack of consistent and integrated advice to managers on how to undertake innovation. Thus, this study aims to develop and investigate a framework that relates innovation practices to the type of innovation outcome, in the context of Information Technology (IT) enabled innovations. An Innovation Practice Framework was developed based on the Knowledge-Innovation Matrix (KIM) proposed by Gregor and Hevner (2015). Eleven commonly used innovation techniques (practices) were identified and placed in one or more of the quadrants: invention, advancement, exaptation and exploitation. Interviews were conducted with key informants in nine organisations in the Australian Capital Territory. Results showed that the least used techniques were skunk works and crowdsourcing. The most used techniques were traditional market research, brainstorming and design thinking. The Innovation Practice Framework was given some support, with genius grants being related to invention outcomes, design thinking with exaptation, traditional R&D with advancement and managerial scanning with exploitation. The study contributes theoretically with the new Innovation Practice Framework and has the potential to be useful to managers in showing how benefits can be gained from a range of innovation practices. Further work is in progress.
Opportunities and challenges of mobile learning for promoting mathematical literacy
Venue: arXiv cs.CY 2016
Year: 2016

Mathematical literacy plays an important role in supporting individuals to fulfil their professional roles in modern society. The affordances of mobile technologies as well as the emergence of new theories in mobile learning have the potential to promote mathematical literacy. However, implementation of mobile learning in Indonesian society faces challenges related to perceived ethical and learning issues in curriculum-based educational settings. This study aims to investigate the preparedness of teachers in integrating mathematics subject content with mobile technologies, especially in promoting mathematical literacy. An exploratory study has been conducted using mixed methods by performing questionnaire survey and semi-structured interviews to understand teacher's knowledge towards mathematical literacy and identifying opportunities and challenges of mobile learning within instruction. Findings indicate that teachers mostly do not know about mathematical literacy, indicating that the concept of mathematical literacy needs to be promoted. Further, most schools prohibit the use of mobile devices in classrooms as they are wary of inappropriate use of mobile devices which may harm students' mental health and distract them from learning. Study finds this to be the most common cause for teachers' reluctance in using mobile learning.
Open data adoption in Australian government agencies: an exploratory study
Venue: arXiv cs.CY 2016
Year: 2016

Australia is among the leading countries that envisaged releasing unclassified public data under open license and reusable format with no further restriction on re/use. But, according to the Australian Information Commissioner John McMillan, Australia's progress on open data is 'patchy' and 'transitional'. He also evidenced that although a few agencies are proactive and have embraced the movements quite seriously, still there are "many obstacles that worked against effort to make government information and data discoverable and usable" (Hilvert 2013). Despondently, there is little empirical evidence that could explain what makes public departments not to release public data. Driven by the nature of the research, this study conducted an exploratory field study in Australia by interviewing eleven employees from six different government agencies. Applying content analysis technique, this study identifies six important antecedents to adoption of open data in public organisations, and proposes future research to test their relationships. As the main theoretical contribution, this study extends organisational behaviour toward technology diffusion. The findings of this study incite policymakers and managers to think about and prepare future strategies on open data developments.
Why customers participate in social commerce activities? - A laddering analysis
Venue: arXiv cs.CY 2016
Year: 2016

A new phenomenon emerging within virtual communities is a blurring between the social and commercial activities and motivations of participants. This paper explores motivations for participating in social commerce at a micro-business level between members of a virtual community of Malay lifestyle bloggers. The selected community was observed in order to understand the community and 21 participants were interviewed. We used laddering techniques to explore community attributes, the perceived consequences, and their links to the values of participants. We found that virtual community relationship was the main influential factor, and virtual community relationship contributed to the sense of social support as well as customers' trust in social commerce.
Constructing a Traditional Chinese Medicine Data Warehouse Application
Venue: arXiv cs.CY 2016
Year: 2016

The explosive growth in the development of Traditional Chinese Medicine (TCM) has resulted in the continued increase in clinical and research data. The lack of standardised terminology, flaws in data quality planning and management of TCM informatics are preventing clinical decision-making, drug discovery and education. This paper argues that the introduction of data warehousing technologies to enhance the effectiveness and durability in TCM is paramount. To showcase the role of data warehousing in the improvement of TCM, this paper presents a practical model for data warehousing with detailed explanation, which is based on the structured electronic records, for TCM clinical researches and medical knowledge discovery.
DefExt: A Semi Supervised Definition Extraction Tool
Venue: arXiv cs.CL 2016
Year: 2016

We present DefExt, an easy to use semi supervised Definition Extraction Tool. DefExt is designed to extract from a target corpus those textual fragments where a term is explicitly mentioned together with its core features, i.e. its definition. It works on the back of a Conditional Random Fields based sequential labeling algorithm and a bootstrapping approach. Bootstrapping enables the model to gradually become more aware of the idiosyncrasies of the target corpus. In this paper we describe the main components of the toolkit as well as experimental results stemming from both automatic and manual evaluation. We release DefExt as open source along with the necessary files to run it in any Unix machine. We also provide access to training and test data for immediate use.
DialPort: Connecting the Spoken Dialog Research Community to Real User Data
Venue: arXiv cs.AI 2016
Year: 2016

This paper describes a new spoken dialog portal that connects systems produced by the spoken dialog academic research community and gives them access to real users. We introduce a distributed, multi-modal, multi-agent prototype dialog framework that affords easy integration with various remote resources, ranging from end-to-end dialog systems to external knowledge APIs. To date, the DialPort portal has successfully connected to the multi-domain spoken dialog system at Cambridge University, the NOAA (National Oceanic and Atmospheric Administration) weather API and the Yelp API.
Fully Convolutional Networks for Dense Semantic Labelling of High-Resolution Aerial Imagery
Venue: arXiv cs.CV 2016
Year: 2016

The trend towards higher resolution remote sensing imagery facilitates a transition from land-use classification to object-level scene understanding. Rather than relying purely on spectral content, appearance-based image features come into play. In this work, deep convolutional neural networks (CNNs) are applied to semantic labelling of high-resolution remote sensing data. Recent advances in fully convolutional networks (FCNs) are adapted to overhead data and shown to be as effective as in other domains. A full-resolution labelling is inferred using a deep FCN with no downsampling, obviating the need for deconvolution or interpolation. To make better use of image features, a pre-trained CNN is fine-tuned on remote sensing data in a hybrid network context, resulting in superior results compared to a network trained from scratch. The proposed approach is applied to the problem of labelling high-resolution aerial imagery, where fine boundary detail is important. The dense labelling yields state-of-the-art accuracy for the ISPRS Vaihingen and Potsdam benchmark data sets.
Towards learning through robotic interaction alone: the joint guided search task
Venue: arXiv cs.RO 2016
Year: 2016

This work proposes a biologically inspired approach that focuses on attention systems that are able to inhibit or constrain what is relevant at any one moment. We propose a radically new approach to making progress in human-robot joint attention called "the joint guided search task". Visual guided search is the activity of the eye as it saccades from position to position recognizing objects in each fixation location until the target object is found. Our research focuses on the exchange of nonverbal behavior toward changing the fixation location while also performing object recognition. Our main goal is a very ambitious goal of sharing attention through probing synthetic foreground maps (i.e. what is being considered by the robotic agent) and the biological attention system of the human.
MHCL: IPv6 Multihop Host Configuration for Low-Power Wireless Networks
Venue: arXiv cs.NI 2016
Year: 2016

Standard routing protocols for Low power and Lossy Networks are typically designed to optimize bottom-up data flows, by maintaining a cycle-free network topology. The advantage of such topologies is low memory footprint to store routing information (only the parent's address needs to me known by each node). The disadvantage is that other communication patterns, like top-down and bidirectional data flows, are not easily implemented. In this work we propose MHCL: IPv6 Multihop Host Configuration for Low-Power Wireless Networks. MHCL employs hierarchical address allocation that explores cycle-free network topologies and aims to enable top-down data communication with low message overhead and memory footprint. We evaluated the performance of MHCL both analytically and through simulations. We implemented MHCL as a subroutine of RPL protocol on Contiki OS and showed that it significantly improves top-down message delivery in RPL, while using a constant amount of memory (i.e., independent of network size) and being efficient in terms of setup time and number of control messages.
Arbitrage-Free Combinatorial Market Making via Integer Programming
Venue: arXiv cs.GT 2016
Year: 2016

We present a new combinatorial market maker that operates arbitrage-free combinatorial prediction markets specified by integer programs. Although the problem of arbitrage-free pricing, while maintaining a bound on the subsidy provided by the market maker, is #P-hard in the worst case, we posit that the typical case might be amenable to modern integer programming (IP) solvers. At the crux of our method is the Frank-Wolfe (conditional gradient) algorithm which is used to implement a Bregman projection aligned with the market maker's cost function, using an IP solver as an oracle. We demonstrate the tractability and improved accuracy of our approach on real-world prediction market data from combinatorial bets placed on the 2010 NCAA Men's Division I Basketball Tournament, where the outcome space is of size 2^63. To our knowledge, this is the first implementation and empirical evaluation of an arbitrage-free combinatorial prediction market on this scale.
High Throughput Opportunistic Cooperative Device-to-Device Communications With Caching
Venue: arXiv cs.IT 2016
Year: 2016

To achieve the potential in providing high throughput for cellular networks by device-to-device (D2D) communications, the interference among D2D links should be carefully managed. In this paper, we propose an opportunistic cooperation strategy for D2D transmission by exploiting the caching capability at the users to control the interference among D2D links. We consider overlay inband D2D, divide the D2D users into clusters, and assign different frequency bands to cooperative and non-cooperative D2D links. To provide high opportunity for cooperative transmission, we introduce a caching policy. To maximize the network throughput, we jointly optimize the cluster size and bandwidth allocation, where the closed-form expression of the bandwidth allocation factor is obtained. Simulation results demonstrate that the proposed strategy can provide 400%-500% throughput gain over traditional D2D communications when the content popularity distribution is skewed, and can provide 60%-80% gain even when the content popularity distribution is uniform.