OpenRaiser/Intern-Atlas huggingface dataset:
Walking on the Edge and Cosystolic Expansion
Venue: arXiv cs.CC 2016
Year: 2016

Random walks on regular bounded degree expander graphs have numerous applications. A key property of these walks is that they converge rapidly to the uniform distribution on the vertices. The recent study of expansion of high dimensional simplicial complexes, which are the high dimensional analogues of graphs, calls for the natural generalization of random walks to higher dimensions. In particular, a high order random walk on a $2$-dimensional simplicial complex moves at random between neighboring edges of the complex, where two edges are considered neighbors if they share a common triangle. We show that if a regular $2$-dimensional simplicial complex is a cosystolic expander and the underlying graph of the complex has a spectral gap larger than $1/2$, then the random walk on the edges of the complex converges rapidly to the uniform distribution on the edges.
Optically lightweight tracking of objects around a corner
Venue: arXiv cs.CV 2016
Year: 2016

The observation of objects located in inaccessible regions is a recurring challenge in a wide variety of important applications. Recent work has shown that indirect diffuse light reflections can be used to reconstruct objects and two-dimensional (2D) patterns around a corner. However, these prior methods always require some specialized setup involving either ultrafast detectors or narrowband light sources. Here we show that occluded objects can be tracked in real time using a standard 2D camera and a laser pointer. Unlike previous methods based on the backprojection approach, we formulate the problem in an analysis-by-synthesis sense. By repeatedly simulating light transport through the scene, we determine the set of object parameters that most closely fits the measured intensity distribution. We experimentally demonstrate that this approach is capable of following the translation of unknown objects, and translation and orientation of a known object, in real time.
Learning to Optimize
Venue: arXiv cs.LG 2016
Year: 2016

Algorithm design is a laborious process and often requires many iterations of ideation and validation. In this paper, we explore automating algorithm design and present a method to learn an optimization algorithm, which we believe to be the first method that can automatically discover a better algorithm. We approach this problem from a reinforcement learning perspective and represent any particular optimization algorithm as a policy. We learn an optimization algorithm using guided policy search and demonstrate that the resulting algorithm outperforms existing hand-engineered algorithms in terms of convergence speed and/or the final objective value.
Multitimescale method for approximating the path action relevant to non-equilibrium statistical physics
Venue: arXiv cs.NA 2016
Year: 2016

A path integral formalism has been proposed recently for non-equilibrium statistical physics applications by the author. In this contribution we outline an efficient method for its numerical evaluation. The method used is based on the multiscale MCMC method of Ceperley and co-workers in quantum applications. A significant new feature of the method proposed is that the time endpoint is not fixed and indeed the endpoint sample is the principle object of interest.
Efficient Deployment of Multiple Unmanned Aerial Vehicles for Optimal Wireless Coverage
Venue: arXiv cs.IT 2016
Year: 2016

In this paper, the efficient deployment of multiple unmanned aerial vehicles (UAVs) with directional antennas acting as wireless base stations that provide coverage for ground users is analyzed. First, the downlink coverage probability for UAVs as a function of the altitude and the antenna gain is derived. Next, using circle packing theory, the three-dimensional locations of the UAVs is determined in a way that the total coverage area is maximized while maximizing the coverage lifetime of the UAVs. Our results show that, in order to mitigate interference, the altitude of the UAVs must be properly adjusted based on the beamwidth of the directional antenna as well as coverage requirements. Furthermore, the minimum number of UAVs needed to guarantee a target coverage probability for a given geographical area is determined. Numerical results evaluate the various tradeoffs involved in various UAV deployment scenarios.
Can neural machine translation do simultaneous translation?
Venue: arXiv cs.CL 2016
Year: 2016

We investigate the potential of attention-based neural machine translation in simultaneous translation. We introduce a novel decoding algorithm, called simultaneous greedy decoding, that allows an existing neural machine translation model to begin translating before a full source sentence is received. This approach is unique from previous works on simultaneous translation in that segmentation and translation are done jointly to maximize the translation quality and that translating each segment is strongly conditioned on all the previous segments. This paper presents a first step toward building a full simultaneous translation system based on neural machine translation.
Formal refinement of extended state machines
Venue: arXiv cs.SE 2016
Year: 2016

In a traditional formal development process, e.g. using the B method, the informal user requirements are (manually) translated into a global abstract formal specification. This translation is especially difficult to achieve. The Event-B method was developed to incrementally and formally construct such a specification using stepwise refinement. Each increment takes into account new properties and system aspects. In this paper, we propose to couple a graphical notation called Algebraic State-Transition Diagrams (ASTD) with an Event-B specification in order to provide a better understanding of the software behaviour. The dynamic behaviour is captured by the ASTD, which is based on automata and process algebra operators, while the data model is described by means of an Event-B specification. We propose a methodology to incrementally refine such specification couplings, taking into account new refinement relations and consistency conditions between the control specification and the data specification. We compare the specifications obtained using each approach for readability and proof complexity. The advantages and drawbacks of the traditional approach and of our methodology are discussed. The whole process is illustrated by a railway CBTC-like case study. Our approach is supported by tools for translating ASTD's into B and Event-B into B.
Unifying Theories of Mobile Channels
Venue: arXiv cs.LO 2016
Year: 2016

In this paper we present the denotational semantics for channel mobility in the Unifying Theories of Programming (UTP) semantics framework. The basis for the model is the UTP theory of reactive processes (precisely, the UTP semantics for Communicating Sequential Processes (CSP)), which is slightly extended to allow the mobility of channels: the set of actions in which a process is authorised to participate, originally static or constant (set during the process's definition), is now made dynamic or variable: it can change during the process's execution. A channel is thus moved around by communicating it via other channels and then allowing the receiving process to extend its alphabet with the received channel. New healthiness conditions are stated to ensure an appropriate use of mobile channels.
Program Derivation by Correctness Enhacements
Venue: arXiv cs.LO 2016
Year: 2016

Relative correctness is the property of a program to be more-correct than another program with respect to a given specification. Among the many properties of relative correctness, that which we found most intriguing is the property that program P' refines program P if and only if P' is more-correct than P with respect to any specification. This inspires us to reconsider program derivation by successive refinements: each step of this process mandates that we transform a program P into a program P' that refines P, i.e. P' is more-correct than P with respect to any specification. This raises the question: why should we want to make P' more-correct than P with respect to any specification, when we only have to satisfy specification R? In this paper, we discuss a process of program derivation that replaces traditional sequence of refinement-based correctness-preserving transformations starting from specification R by a sequence of relative correctness-based correctness-enhancing transformations starting from abort.
Programming Language Features for Refinement
Venue: arXiv cs.PL 2016
Year: 2016

Algorithmic and data refinement are well studied topics that provide a mathematically rigorous approach to gradually introducing details in the implementation of software. Program refinements are performed in the context of some programming language, but mainstream languages lack features for recording the sequence of refinement steps in the program text. To experiment with the combination of refinement, automated verification, and language design, refinement features have been added to the verification-aware programming language Dafny. This paper describes those features and reflects on some initial usage thereof.
A Theory of Service Dependency
Venue: arXiv cs.LO 2016
Year: 2016

Service composition has become commonplace nowadays, in large part due to the increased complexity of software and supporting networks. Composition can be of many types, for instance sequential, prioritising, non-deterministic. However, a fundamental feature of the services to be composed consists in their dependencies with respect to each other. In this paper we propose a theory of service dependency, modelled around a dependency operator in the Action Systems formalism. We analyze its properties, composition behaviour, and refinement conditions with accompanying examples.
Adapting Sampling Interval of Sensor Networks Using On-Line Reinforcement Learning
Venue: arXiv cs.NI 2016
Year: 2016

Monitoring Wireless Sensor Networks (WSNs) are composed of sensor nodes that report temperature, relative humidity, and other environmental parameters. The time between two successive measurements is a critical parameter to set during the WSN configuration because it can impact the WSN's lifetime, the wireless medium contention and the quality of the reported data. As trends in monitored parameters can significantly vary between scenarios and within time, identifying a sampling interval suitable for several cases is also challenging. In this work, we propose a dynamic sampling rate adaptation scheme based on reinforcement learning, able to tune sensors' sampling interval on-the-fly, according to environmental conditions and application requirements. The primary goal is to set the sampling interval to the best value possible so as to avoid oversampling and save energy, while not missing environmental changes that can be relevant for the application. In simulations, our mechanism could reduce up to 73% the total number of transmissions compared to a fixed strategy and, simultaneously, keep the average quality of information provided by the WSN. The inherent flexibility of the reinforcement learning algorithm facilitates its use in several scenarios, so as to exploit the broad scope of the Internet of Things.
Order-preserving drawings of trees with approximately optimal height (and small width)
Venue: arXiv cs.CG 2016
Year: 2016

In this paper, we study how to draw trees so that they are planar, straight-line and respect a given order of edges around each node. We focus on minimizing the height, and show that we can always achieve a height of at most 2pw(T)+1, where pw(T) (the so-called pathwidth) is a known lower bound on the height. Hence we give an asymptotic 2-approximation algorithm. We also create a drawing whose height is at most 3pw(T ), but where the width can be bounded by the number of nodes. Finally we construct trees that require height 2pw(T)+1 in all planar order-preserving straight-line drawings.
Concept-Oriented Model: the Functional View
Venue: arXiv cs.DB 2016
Year: 2016

The plethora of existing data models and specific data modeling techniques is not only confusing but leads to complex, eclectic and inefficient designs of systems for data management and analytics. The main goal of this paper is to describe a unified approach to data modeling, called the concept-oriented model (COM), by using functions as a basis for its formalization. COM tries to answer the question what is data and to rethink basic assumptions underlying this and related notions. Its main goal is to unify major existing views on data (generality), using only a few main notions (simplicity) which are very close to how data is used in real life (naturalness).
A Formal Calculus for International Relations Computation and Evaluation
Venue: arXiv cs.CR 2016
Year: 2016

This publication presents a relation computation or calculus for international relations using a mathematical modeling. It examined trust for international relations and its calculus, which related to Bayesian inference, Dempster-Shafer theory and subjective logic. Based on an observation in the literature, we found no literature discussing the calculus method for the international relations. To bridge this research gap, we propose a relation algebra method for international relations computation. The proposed method will allow a relation computation which is previously subjective and incomputable. We also present three international relations as case studies to demonstrate the proposed method is a real-world scenario. The method will deliver the relation computation for the international relations that to support decision makers in a government such as foreign ministry, defense ministry, presidential or prime minister office. The Department of Defense (DoD) may use our method to determine a nation that can be identified as a friendly, neutral or hostile nation.
Multilingual Visual Sentiment Concept Matching
Venue: arXiv cs.CL 2016
Year: 2016

The impact of culture in visual emotion perception has recently captured the attention of multimedia research. In this study, we pro- vide powerful computational linguistics tools to explore, retrieve and browse a dataset of 16K multilingual affective visual concepts and 7.3M Flickr images. First, we design an effective crowdsourc- ing experiment to collect human judgements of sentiment connected to the visual concepts. We then use word embeddings to repre- sent these concepts in a low dimensional vector space, allowing us to expand the meaning around concepts, and thus enabling insight about commonalities and differences among different languages. We compare a variety of concept representations through a novel evaluation task based on the notion of visual semantic relatedness. Based on these representations, we design clustering schemes to group multilingual visual concepts, and evaluate them with novel metrics based on the crowdsourced sentiment annotations as well as visual semantic relatedness. The proposed clustering framework enables us to analyze the full multilingual dataset in-depth and also show an application on a facial data subset, exploring cultural in- sights of portrait-related affective visual concepts.
Semi-supervised structured output prediction by local linear regression and sub-gradient descent
Venue: arXiv cs.LG 2016
Year: 2016

We propose a novel semi-supervised structured output prediction method based on local linear regression in this paper. The existing semi-supervise structured output prediction methods learn a global predictor for all the data points in a data set, which ignores the differences of local distributions of the data set, and the effects to the structured output prediction. To solve this problem, we propose to learn the missing structured outputs and local predictors for neighborhoods of different data points jointly. Using the local linear regression strategy, in the neighborhood of each data point, we propose to learn a local linear predictor by minimizing both the complexity of the predictor and the upper bound of the structured prediction loss. The minimization problem is solved by sub-gradient descent algorithms. We conduct experiments over two benchmark data sets, and the results show the advantages of the proposed method.
NOUS: Construction and Querying of Dynamic Knowledge Graphs
Venue: arXiv cs.DB 2016
Year: 2016

The ability to construct domain specific knowledge graphs (KG) and perform question-answering or hypothesis generation is a transformative capability. Despite their value, automated construction of knowledge graphs remains an expensive technical challenge that is beyond the reach for most enterprises and academic institutions. We propose an end-to-end framework for developing custom knowledge graph driven analytics for arbitrary application domains. The uniqueness of our system lies A) in its combination of curated KGs along with knowledge extracted from unstructured text, B) support for advanced trending and explanatory questions on a dynamic KG, and C) the ability to answer queries where the answer is embedded across multiple data sources.
Active Long Term Memory Networks
Venue: arXiv cs.LG 2016
Year: 2016

Continual Learning in artificial neural networks suffers from interference and forgetting when different tasks are learned sequentially. This paper introduces the Active Long Term Memory Networks (A-LTM), a model of sequential multi-task deep learning that is able to maintain previously learned association between sensory input and behavioral output while acquiring knew knowledge. A-LTM exploits the non-convex nature of deep neural networks and actively maintains knowledge of previously learned, inactive tasks using a distillation loss. Distortions of the learned input-output map are penalized but hidden layers are free to transverse towards new local optima that are more favorable for the multi-task objective. We re-frame the McClelland's seminal Hippocampal theory with respect to Catastrophic Inference (CI) behavior exhibited by modern deep architectures trained with back-propagation and inhomogeneous sampling of latent factors across epochs. We present empirical results of non-trivial CI during continual learning in Deep Linear Networks trained on the same task, in Convolutional Neural Networks when the task shifts from predicting semantic to graphical factors and during domain adaptation from simple to complex environments. We present results of the A-LTM model's ability to maintain viewpoint recognition learned in the highly controlled iLab-20M dataset with 10 object categories and 88 camera viewpoints, while adapting to the unstructured domain of Imagenet with 1,000 object categories.
Angle-of-Attack Modulation in Trajectory Tracking for a Reusable Launch Vehicle
Venue: arXiv cs.SY 2016
Year: 2016

This paper deals with the problem of angle-of-attack modulation with the aim of enhancing transient performance of entry guidance during bank reversals, while compensating adverse effects of fast time-varying transient disturbances. An extended single-input/single-output system is developed in the velocity domain by means of a dynamic extension technique, and explicitly captures the trajectory dynamics of angle-of-attack modulation. A normal form for this extended system is derived for the sake of employing a feedback linearization controller. Further, the control characteristics of angle-of-attack modulation is found to be a non-minimum phase behavior under two common conditions in a near- equilibrium glide flight. Therefore, the issue of angle-of-attack modulation is formulated as robust output stabilization of the non-minimum phase system. A disturbance observer-based feedback linearization technique is used to design a robustly dynamical output-feedback controller for angle-of-attack modulation, and an internal-state feedback controller for bank-angle modulation is used to stabilize the unstable internal dynamics. Numerical simulations are conducted to demonstrate that the performance of the proposed method of angle-of-attack modulation is enhanced compared to the existing shuttle method.
The Relationship between Organization Culture and Knowledge sharing towards Business System Success
Venue: arXiv cs.CY 2016
Year: 2016

Understanding the business systems success factors has been a challenging process for both public and private organizations. Organizational culture is measured as a critical factor promoting knowledge sharing among employees. Based on the competing value framework (CVF), this research shows how various dimensions of organization culture influence knowledge sharing towards business systems success at the individual and organization level. A quantitative approach was applied to test the relationship between organizational cultures, knowledge sharing and business systems success in Saudi enterprise.
The Potential Impact of Digital Currencies on the Australian Economy
Venue: arXiv cs.CY 2016
Year: 2016

Crypto-currencies like Bitcoins are relatively recent phenomena on the online Internet landscape and an emerging force in the financial sector. While not conforming to traditional institutional practices, they are gaining increasing acceptance as viable commercial currencies. In this conceptual paper we discuss the potential impact of digital currency technology on the Australian economy, including the (i) payments sector, (ii) retail sector, and (iii) banking sector; and explore potential ways in which Australia can take advantage of digital currency technology to establish itself as a market leader in this field. The emergence of this new and potentially disruptive technology provides both opportunities as well as risks. In order to support innovation and the needs of the growing Australian digital currency industry it is important to define digital currencies and examine the impact regulatory frameworks could have on the further adoption and diffusion of the technology.
Point-wise mutual information-based video segmentation with high temporal consistency
Venue: arXiv cs.CV 2016
Year: 2016

In this paper, we tackle the problem of temporally consistent boundary detection and hierarchical segmentation in videos. While finding the best high-level reasoning of region assignments in videos is the focus of much recent research, temporal consistency in boundary detection has so far only rarely been tackled. We argue that temporally consistent boundaries are a key component to temporally consistent region assignment. The proposed method is based on the point-wise mutual information (PMI) of spatio-temporal voxels. Temporal consistency is established by an evaluation of PMI-based point affinities in the spectral domain over space and time. Thus, the proposed method is independent of any optical flow computation or previously learned motion models. The proposed low-level video segmentation method outperforms the learning-based state of the art in terms of standard region metrics.
Virtualising Process Assessments to Facilitate Continual Service Improvement in IT Service Management
Venue: arXiv cs.CY 2016
Year: 2016

The IT Service Management (ITSM) industry has defined processes as best practices in the widely-accepted IT Infrastructure Library (ITIL) framework. However, studies on the measurement of ITSM process improvement are scant. Our research addressed the dual problems of the lack of transparency and the need for efficiency in ITSM process assessment. Using the Design Science Research methodology, we developed a Software-mediated Process Assessment (SMPA) approach that enables assessment of ITSM processes. The SMPA approach includes process selection; online survey to collect assessment data; measurement of process capability; and reporting of process improvement recommendations. We implemented a decision support system (DSS) to automate the SMPA approach and evaluated it at two IT service providers. The evaluations indicated that the SMPA approach supports decision-making on process improvements. The findings provided design knowledge of virtualisation in ITSM process assessment and how this may facilitate continual service improvement.