OpenRaiser/Intern-Atlas huggingface dataset:
Better partitions of protein graphs for subsystem quantum chemistry
Venue: arXiv cs.DS 2016
Year: 2016

Determining the interaction strength between proteins and small molecules is key to analyzing their biological function. Quantum-mechanical calculations such as \emph{Density Functional Theory} (DFT) give accurate and theoretically well-founded results. With common implementations the running time of DFT calculations increases quadratically with molecule size. Thus, numerous subsystem-based approaches have been developed to accelerate quantum-chemical calculations. These approaches partition the protein into different fragments, which are treated separately. Interactions between different fragments are approximated and introduce inaccuracies in the calculated interaction energies. To minimize these inaccuracies, we represent the amino acids and their interactions as a weighted graph in order to apply graph partitioning. None of the existing graph partitioning work can be directly used, though, due to the unique constraints in partitioning such protein graphs. We therefore present and evaluate several algorithms, partially building upon established concepts, but adapted to handle the new constraints. For the special case of partitioning a protein along the main chain, we also present an efficient dynamic programming algorithm that yields provably optimal results. In the general scenario our algorithms usually improve the previous approach significantly and take at most a few seconds.
Super-Resolution From Binary Measurements With Unknown Threshold
Venue: arXiv cs.IT 2016
Year: 2016

We address the problem of super-resolution of point sources from binary measurements, where random projections of the blurred measurement of the actual signal are encoded using only the sign information. The threshold used for binary quantization is not known to the decoder. We develop an algorithm that solves convex programs iteratively and achieves signal recovery. The proposed algorithm, which we refer to as the binary super-resolution (BSR) algorithm, recovers point sources with reasonable accuracy, albeit up to a scale factor. We show through simulations that the BSR algorithm is successful in recovering the locations and the amplitudes of the point sources, even in the presence of significant amount of blurring. We also propose a framework for handling noisy measurements and demonstrate that BSR gives a reliable reconstruction (correspondingly, reconstruction signal-to-noise ratio (SNR) of about 22 dB) for a measurement SNR of 15 dB.
De-identification of Patient Notes with Recurrent Neural Networks
Venue: arXiv cs.CL 2016
Year: 2016

Objective: Patient notes in electronic health records (EHRs) may contain critical information for medical investigations. However, the vast majority of medical investigators can only access de-identified notes, in order to protect the confidentiality of patients. In the United States, the Health Insurance Portability and Accountability Act (HIPAA) defines 18 types of protected health information (PHI) that needs to be removed to de-identify patient notes. Manual de-identification is impractical given the size of EHR databases, the limited number of researchers with access to the non-de-identified notes, and the frequent mistakes of human annotators. A reliable automated de-identification system would consequently be of high value. Materials and Methods: We introduce the first de-identification system based on artificial neural networks (ANNs), which requires no handcrafted features or rules, unlike existing systems. We compare the performance of the system with state-of-the-art systems on two datasets: the i2b2 2014 de-identification challenge dataset, which is the largest publicly available de-identification dataset, and the MIMIC de-identification dataset, which we assembled and is twice as large as the i2b2 2014 dataset. Results: Our ANN model outperforms the state-of-the-art systems. It yields an F1-score of 97.85 on the i2b2 2014 dataset, with a recall 97.38 and a precision of 97.32, and an F1-score of 99.23 on the MIMIC de-identification dataset, with a recall 99.25 and a precision of 99.06. Conclusion: Our findings support the use of ANNs for de-identification of patient notes, as they show better performance than previously published systems while requiring no feature engineering.
Distributed Machine Learning in Materials that Couple Sensing, Actuation, Computation and Communication
Venue: arXiv cs.LG 2016
Year: 2016

This paper reviews machine learning applications and approaches to detection, classification and control of intelligent materials and structures with embedded distributed computation elements. The purpose of this survey is to identify desired tasks to be performed in each type of material or structure (e.g., damage detection in composites), identify and compare common approaches to learning such tasks, and investigate models and training paradigms used. Machine learning approaches and common temporal features used in the domains of structural health monitoring, morphable aircraft, wearable computing and robotic skins are explored. As the ultimate goal of this research is to incorporate the approaches described in this survey into a robotic material paradigm, the potential for adapting the computational models used in these applications, and corresponding training algorithms, to an amorphous network of computing nodes is considered. Distributed versions of support vector machines, graphical models and mixture models developed in the field of wireless sensor networks are reviewed. Potential areas of investigation, including possible architectures for incorporating machine learning into robotic nodes, training approaches, and the possibility of using deep learning approaches for automatic feature extraction, are discussed.
Information Security Strategy in Organisations: Review, Discussion and Future Research Directions
Venue: arXiv cs.CY 2016
Year: 2016

Dependence on information, including for some of the world's largest organisations such as governments and multi-national corporations, has grown rapidly in recent years. However, reports of information security breaches and their associated consequences continue to indicate that attacks are still escalating on organisations when conducting these information-based activities. Clearly, more research is needed to better understand how organisations should formulate strategy to secure their information. Through a thematic review of academic security literature, we (1) analyse the antecedent conditions that motivate the potential adoption of a comprehensive information security strategy, (2) the current perspectives of strategy and (3) the yields and benefits that could be enjoyed post-adoption. Our contributions include a definition of information security strategy. We argue for a paradigm shift to extend from internally-focussed protection of organisation-wide information towards a strategic view that considers the inter-organisational level. Our findings are then used to suggest future research directions.
IT Managers' Perception and Response to Digital Disruption: An Exploratory Study
Venue: arXiv cs.CY 2016
Year: 2016

Digital disruption introduces technology-enabled changes at a pace and scale that fundamentally shifts established ways in which we live and work. Given the impact of digital disruption can span the individual, organisational, industry and societal levels, it has received growing attention in Information Systems. A less researched area of digital disruption, however, is the perspective of IT managers in organisations. This paper draws on i) situational awareness theory to identify IT managers' perceptions of digital disruption, and ii) disruptive innovation theory to identify IT managers' responses to digital disruption. A case study of senior IT managers in an Australian university identifies that IT managers perceive digital disruption from the technological, learning and sense-making perspectives. IT managers' first-order responses to digital disruption were shown to address resource, process and value-based issues while second-order responses focused on the need to build dynamic capabilities in order to be prepared for future digital disruption.
Using stories to bridge the chasm between perspectives: How metaphors and genres are used to share meaning
Venue: arXiv cs.CY 2016
Year: 2016

Natural language, although complex in structure, contains considerable detail. All instances of language serve the purpose of making sense of experience and the intent of actors. Language conveys actor's personal reference to goals, responsibility, and values. In this paper we consider how actors from distinct perspectives communicate when they have share common goals. We have observed the planning of a community and cultural event where actors are variously responsible for management and for artistic merit. Specifically, we consider actor's use of language as a tool to span perspectives and how functional discourse analysis tools and techniques enable a deeper interpretive understanding of the layers of discourse when derived from a rich context. We will also illustrate patterns we have found in the use of discourse and actor's ability to bridge the reasoning and logical gap between distinct perspectives through actor's reference to metaphors and genres.
An SME's Adoption of a Cloud Based Integrated Management System (IMS) When Certifying Against Management System Standards (MSS)
Venue: arXiv cs.CY 2016
Year: 2016

This case study introduces a four step approach used by a Singapore small and medium enterprise (SME) in implementing a cloud computing based integrated management system (IMS) to meet the ISO 9001, ISO 14001, and OHSAS 18001 certification requirements. The objectives of this case study are to study: (1) the challenges encountered by an SME during the IMS integration process at each of the four levels of integration (2) the extent which Frugal IT Innovation and Technology Acceptance Model (TAM3) concepts apply to our four step approach. The four step approach was assessed against a framework of four integration levels of the management systems (MS), and also the applicability of two IS theories (Frugal IT Innovation and TAM3). Data was collected via: (1) direct observations (2) participant observations; (3) interviews with key personnel involved in the project; and (4) analysis of documents pertaining to the project. This case study provides an exemplary model on using IS theories and public cloud technologies to develop an effective IMS that other SMEs can learn from.
Applying Adapted Big Five Teamwork Theory to Agile Software Development
Venue: arXiv cs.CY 2016
Year: 2016

Teamwork is a central tenet of agile software development and various teamwork theories partially explain teamwork in that context. Big Five teamwork theory is one of the most influential teamwork theories, but prior research shows that the team leadership concept in this theory it is not applicable to agile software development. This paper applies an adapted form of Big Five teamwork theory to cases of agile software development. Three independent cases were drawn from a single organisation. This ensured a measure of control over contextual factors affecting the teamwork. The findings show that the adapted form of Big Five teamwork theory, including a shared team leadership concept, is fully applicable to some forms of agile software development, but not all. For practitioners, insights as to which agile practices support teamwork are provided.
Word Sense Disambiguation using a Bidirectional LSTM
Venue: arXiv cs.CL 2016
Year: 2016

In this paper we present a clean, yet effective, model for word sense disambiguation. Our approach leverage a bidirectional long short-term memory network which is shared between all words. This enables the model to share statistical strength and to scale well with vocabulary size. The model is trained end-to-end, directly from the raw text to sense labels, and makes effective use of word order. We evaluate our approach on two standard datasets, using identical hyperparameter settings, which are in turn tuned on a third set of held out data. We employ no external resources (e.g. knowledge graphs, part-of-speech tagging, etc), language specific features, or hand crafted rules, but still achieve statistically equivalent results to the best state-of-the-art systems, that employ no such limitations.
Alternative Technique to Asymmetry Analysis-Based Overlapping for Foot Ulcer Examination: Scalable Scanning
Venue: arXiv cs.CV 2016
Year: 2016

Asymmetry analysis based on the overlapping of thermal images proved able to detect inflammation and, predict foot ulceration. This technique involves three main steps: segmentation, geometric transformation, and overlapping. However, the overlapping technique, which consists of subtracting the intensity levels of the right foot from those of the left foot, can also detect false abnormal areas if the projections of the left and right feet are not the same. In this paper, we present an alternative technique to asymmetry analysis-based overlapping. The proposed technique, scalable scanning, allows for an effective comparison even if the shapes and sizes of the feet projections appear differently in the image. The tested results show that asymmetry analysis- based scalable scanning provides fewer false abnormal areas than does asymmetry analysis -based overlapping.
Beyond the Ultra-Dense Barrier - Paradigm shifts on the road beyond 1000x wireless capacity
Venue: arXiv cs.NI 2016
Year: 2016

It has become increasingly clear that the current design paradigm for mobile broadband systems is not a scalable and economically feasible way to solve the expected future 'capacity crunch', in particular in indoor locations with large user densities. 'Moore's law', e.g. state-of-the art signal processing and advanced antenna techniques now being researched, as well as more millimeter wave spectrum indeed provide more capacity, but are not the answer to the 3-4 orders of magnitude more capacity at today's cost, that is needed.We argue that solving the engineering problem of providing high data rates alone is not sufficient. Instead we need to solve the techno-economic problem to find both business models and scalable technical solutions that provide extreme area capacity for a given cost and energy consumption. In this paper we will show that achieving very high capacities is indeed feasible in indoor environments.
External Lexical Information for Multilingual Part-of-Speech Tagging
Venue: arXiv cs.CL 2016
Year: 2016

Morphosyntactic lexicons and word vector representations have both proven useful for improving the accuracy of statistical part-of-speech taggers. Here we compare the performances of four systems on datasets covering 16 languages, two of these systems being feature-based (MEMMs and CRFs) and two of them being neural-based (bi-LSTMs). We show that, on average, all four approaches perform similarly and reach state-of-the-art results. Yet better performances are obtained with our feature-based models on lexically richer datasets (e.g. for morphologically rich languages), whereas neural-based results are higher on datasets with less lexical variability (e.g. for English). These conclusions hold in particular for the MEMM models relying on our system MElt, which benefited from newly designed features. This shows that, under certain conditions, feature-based approaches enriched with morphosyntactic lexicons are competitive with respect to neural methods.
IT Governance in Actor-Network Mode of Collaboration : Cost Management Process Based on Game Theory
Venue: arXiv cs.CY 2016
Year: 2016

IT governance, like global governance of projects, requires cooperation between several actors. In general, such cooperation builds a collaboration network between entities. Many works in the literature interested in collaboration network, but no one of them were focused directly on how to build a network in an IT governance perspective. In this paper, we investigate how game theory can be exploited to provide a formal implementation of Cost Management Process, while highlighting Actor-Network as a framework of collaboration and its incentive stage as a key step for network construction. Our objective is to propose an approach of network establishment, by inciting actors through cost savings. For that, we use Shapley Value to answer the question: For the sake of IT governance, which coalitions are likely to form in order to ensure best cost-saving objectives in ANT mode of collaboration? A graphical tool is developed also to visualize and simulate networks evolution.
Approximating the rectilinear crossing number
Venue: arXiv cs.CG 2016
Year: 2016

A straight-line drawing of a graph $G$ is a mapping which assigns to each vertex a point in the plane and to each edge a straight-line segment connecting the corresponding two points. The rectilinear crossing number of a graph $G$, $\overline{cr}(G)$, is the minimum number of crossing edges in any straight-line drawing of $G$. Determining or estimating $\overline{cr}(G)$ appears to be a difficult problem, and deciding if $\overline{cr}(G)\leq k$ is known to be NP-hard. In fact, the asymptotic behavior of $\overline{cr}(K_n)$ is still unknown. In this paper, we present a deterministic $n^{2+o(1)}$-time algorithm that finds a straight-line drawing of any $n$-vertex graph $G$ with $\overline{cr}(G) + o(n^4)$ crossing edges. Together with the well-known Crossing Lemma due to Ajtai et al. and Leighton, this result implies that for any dense $n$-vertex graph $G$, one can efficiently find a straight-line drawing of $G$ with $(1 + o(1))\overline{cr}(G)$ crossing edges.
Neural Associative Memory for Dual-Sequence Modeling
Venue: arXiv cs.NE 2016
Year: 2016

Many important NLP problems can be posed as dual-sequence or sequence-to-sequence modeling tasks. Recent advances in building end-to-end neural architectures have been highly successful in solving such tasks. In this work we propose a new architecture for dual-sequence modeling that is based on associative memory. We derive AM-RNNs, a recurrent associative memory (AM) which augments generic recurrent neural networks (RNN). This architecture is extended to the Dual AM-RNN which operates on two AMs at once. Our models achieve very competitive results on textual entailment. A qualitative analysis demonstrates that long range dependencies between source and target-sequence can be bridged effectively using Dual AM-RNNs. However, an initial experiment on auto-encoding reveals that these benefits are not exploited by the system when learning to solve sequence-to-sequence tasks which indicates that additional supervision or regularization is needed.
Photo Stylistic Brush: Robust Style Transfer via Superpixel-Based Bipartite Graph
Venue: arXiv cs.CV 2016
Year: 2016

With the rapid development of social network and multimedia technology, customized image and video stylization has been widely used for various social-media applications. In this paper, we explore the problem of exemplar-based photo style transfer, which provides a flexible and convenient way to invoke fantastic visual impression. Rather than investigating some fixed artistic patterns to represent certain styles as was done in some previous works, our work emphasizes styles related to a series of visual effects in the photograph, e.g. color, tone, and contrast. We propose a photo stylistic brush, an automatic robust style transfer approach based on Superpixel-based BIpartite Graph (SuperBIG). A two-step bipartite graph algorithm with different granularity levels is employed to aggregate pixels into superpixels and find their correspondences. In the first step, with the extracted hierarchical features, a bipartite graph is constructed to describe the content similarity for pixel partition to produce superpixels. In the second step, superpixels in the input/reference image are rematched to form a new superpixel-based bipartite graph, and superpixel-level correspondences are generated by a bipartite matching. Finally, the refined correspondence guides SuperBIG to perform the transformation in a decorrelated color space. Extensive experimental results demonstrate the effectiveness and robustness of the proposed method for transferring various styles of exemplar images, even for some challenging cases, such as night images.
Atomic RMI 2: Highly Parallel Pessimistic Distributed Transactional Memory
Venue: arXiv cs.DC 2016
Year: 2016

Distributed Transactional Memory (DTM) is an emerging approach to distributed synchronization based on the application of the transaction abstraction to distributed computation. DTM comes in several system models, but the control flow model (CF) is particularly powerful, since it allows transactions to delegate computation to remote nodes as well as access shared data. However, there are no existing CF DTM systems that perform on par with state-of-the-art systems operating in other models. Hence, we introduce a CF DTM synchronization algorithm, OptSVA-CF. It supports fine-grained pessimistic concurrency control, so it avoids aborts, and thus avoids problems with irrevocable operations. Furthermore, it uses early release and asynchrony to parallelize concurrent transactions to a high degree, while retaining strong safety properties. We implement it as Atomic RMI 2, in effect producing a CF DTM system that, as our evaluation shows, can outperform a state-of-the-art non-CF DTM such as HyFlow2.
A weakly universal cellular automaton on the tessellation $\{8,3\}$
Venue: arXiv cs.DM 2016
Year: 2016

In this paper, we construct a weakly universal cellular automaton on the tessellation $\{8,3\}$ which is not rotation invariant but which is truly planar.
Using a Distributional Semantic Vector Space with a Knowledge Base for Reasoning in Uncertain Conditions
Venue: arXiv cs.AI 2016
Year: 2016

The inherent inflexibility and incompleteness of commonsense knowledge bases (KB) has limited their usefulness. We describe a system called Displacer for performing KB queries extended with the analogical capabilities of the word2vec distributional semantic vector space (DSVS). This allows the system to answer queries with information which was not contained in the original KB in any form. By performing analogous queries on semantically related terms and mapping their answers back into the context of the original query using displacement vectors, we are able to give approximate answers to many questions which, if posed to the KB alone, would return no results. We also show how the hand-curated knowledge in a KB can be used to increase the accuracy of a DSVS in solving analogy problems. In these ways, a KB and a DSVS can make up for each other's weaknesses.
ENTRA: Whole-Systems Energy Transparency
Venue: arXiv cs.AR 2016
Year: 2016

Promoting energy efficiency to a first class system design goal is an important research challenge. Although more energy-efficient hardware can be designed, it is software that controls the hardware; for a given system the potential for energy savings is likely to be much greater at the higher levels of abstraction in the system stack. Thus the greatest savings are expected from energy-aware software development, which is the vision of the EU ENTRA project. This article presents the concept of energy transparency as a foundation for energy-aware software development. We show how energy modelling of hardware is combined with static analysis to allow the programmer to understand the energy consumption of a program without executing it, thus enabling exploration of the design space taking energy into consideration. The paper concludes by summarising the current and future challenges identified in the ENTRA project.