OpenRaiser/Intern-Atlas huggingface dataset:
Multi-instance Dynamic Ordinal Random Fields for Weakly-Supervised Pain Intensity Estimation
Venue: arXiv cs.CV 2016
Year: 2016

In this paper, we address the Multi-Instance-Learning (MIL) problem when bag labels are naturally represented as ordinal variables (Multi--Instance--Ordinal Regression). Moreover, we consider the case where bags are temporal sequences of ordinal instances. To model this, we propose the novel Multi-Instance Dynamic Ordinal Random Fields (MI-DORF). In this model, we treat instance-labels inside the bag as latent ordinal states. The MIL assumption is modelled by incorporating a high-order cardinality potential relating bag and instance-labels,into the energy function. We show the benefits of the proposed approach on the task of weakly-supervised pain intensity estimation from the UNBC Shoulder-Pain Database. In our experiments, the proposed approach significantly outperforms alternative non-ordinal methods that either ignore the MIL assumption, or do not model dynamic information in target data.
A Lightweight Approach to Performance Portability with targetDP
Venue: arXiv cs.DC 2016
Year: 2016

Leading HPC systems achieve their status through use of highly parallel devices such as NVIDIA GPUs or Intel Xeon Phi many-core CPUs. The concept of performance portability across such architectures, as well as traditional CPUs, is vital for the application programmer. In this paper we describe targetDP, a lightweight abstraction layer which allows grid-based applications to target data parallel hardware in a platform agnostic manner. We demonstrate the effectiveness of our pragmatic approach by presenting performance results for a complex fluid application (with which the model was co-designed), plus a separate lattice QCD particle physics code. For each application, a single source code base is seen to achieve portable performance, as assessed within the context of the Roofline model. TargetDP can be combined with MPI to allow use on systems containing multiple nodes: we demonstrate this through provision of scaling results on traditional and GPU-accelerated large scale supercomputers.
A Non-linear GPU Thread Map for Triangular Domains
Venue: arXiv cs.DC 2016
Year: 2016

There is a stage in the GPU computing pipeline where a grid of thread-blocks, in \textit{parallel space}, is mapped onto the problem domain, in \textit{data space}. Since the parallel space is restricted to a box type geometry, the mapping approach is typically a $k$-dimensional bounding box (BB) that covers a $p$-dimensional data space. Threads that fall inside the domain perform computations while threads that fall outside are discarded at runtime. In this work we study the case of mapping threads efficiently onto triangular domain problems and propose a block-space linear map $λ(ω)$, based on the properties of the lower triangular matrix, that reduces the number of unnnecessary threads from $\mathcal{O}(n^2)$ to $\mathcal{O}(n)$. Performance results for global memory accesses show an improvement of up to $18\%$ with respect to the \textit{bounding-box} approach, placing $λ(ω)$ on second place below the \textit{rectangular-box} approach and above the \textit{recursive-partition} and \textit{upper-triangular} approaches. For shared memory scenarios $λ(ω)$ was the fastest approach achieving $7\%$ of performance improvement while preserving thread locality. The results obtained in this work make $λ(ω)$ an interesting map for efficient GPU computing on parallel problems that define a triangular domain with or without neighborhood interactions. The extension to tetrahedral domains is analyzed, with applications to triplet-interaction n-body applications.
Towards Learning and Verifying Invariants of Cyber-Physical Systems by Code Mutation
Venue: arXiv cs.SE 2016
Year: 2016

Cyber-physical systems (CPS), which integrate algorithmic control with physical processes, often consist of physically distributed components communicating over a network. A malfunctioning or compromised component in such a CPS can lead to costly consequences, especially in the context of public infrastructure. In this short paper, we argue for the importance of constructing invariants (or models) of the physical behaviour exhibited by CPS, motivated by their applications to the control, monitoring, and attestation of components. To achieve this despite the inherent complexity of CPS, we propose a new technique for learning invariants that combines machine learning with ideas from mutation testing. We present a preliminary study on a water treatment system that suggests the efficacy of this approach, propose strategies for establishing confidence in the correctness of invariants, then summarise some research questions and the steps we are taking to investigate them.
Faster O(|V|^2|E|W)-Time Energy Algorithms for Optimal Strategy Synthesis in Mean Payoff Games
Venue: arXiv cs.DS 2016
Year: 2016

This study strengthens the links between Mean Payoff Games (\MPG{s}) and Energy Games (EG{s}). Firstly, we offer a faster $O(|V|^2|E|W)$ pseudo-polynomial time and $Θ(|V|+|E|)$ space deterministic algorithm for solving the Value Problem and Optimal Strategy Synthesis in \MPG{s}. This improves the best previously known estimates on the pseudo-polynomial time complexity to: \[ O(|E|\log |V|) + Θ\Big(\sum_{v\in V}\texttt{deg}_Γ(v)\cdot\ell_Γ(v)\Big) = O(|V|^2|E|W), \] where $\ell_Γ(v)$ counts the number of times that a certain energy-lifting operator $δ(\cdot, v)$ is applied to any $v\in V$, along a certain sequence of Value-Iterations on reweighted \EG{s}; and $\texttt{deg}_Γ(v)$ is the degree of $v$. This improves significantly over a previously known pseudo-polynomial time estimate, i.e. $Θ\big(|V|^2|E|W + \sum_{v\in V}\texttt{deg}_Γ(v)\cdot\ell_Γ(v)\big)$ \citep{CR15, CR16}, as the pseudo-polynomiality is now confined to depend solely on $\ell_Γ$. Secondly, we further explore on the relationship between Optimal Positional Strategies (OPSs) in \MPG{s} and Small Energy-Progress Measures (SEPMs) in reweighted \EG{s}. It is observed that the space of all OPSs, $\texttt{opt}_ΓΣ^M_0$, admits a unique complete decomposition in terms of extremal-SEPM{s} in reweighted EG{s}. This points out what we called the "Energy-Lattice $\mathcal{X}^*_Γ$ associated to $\texttt{opt}_ΓΣ^M_0$". Finally, it is offered a pseudo-polynomial total-time recursive procedure for enumerating (w/o repetitions) all the elements of $\mathcal{X}^*_Γ$, and for computing the corresponding partitioning of $\texttt{opt}_ΓΣ^M_0$.
Best-Buddies Similarity - Robust Template Matching using Mutual Nearest Neighbors
Venue: arXiv cs.CV 2016
Year: 2016

We propose a novel method for template matching in unconstrained environments. Its essence is the Best-Buddies Similarity (BBS), a useful, robust, and parameter-free similarity measure between two sets of points. BBS is based on counting the number of Best-Buddies Pairs (BBPs)--pairs of points in source and target sets, where each point is the nearest neighbor of the other. BBS has several key features that make it robust against complex geometric deformations and high levels of outliers, such as those arising from background clutter and occlusions. We study these properties, provide a statistical analysis that justifies them, and demonstrate the consistent success of BBS on a challenging real-world dataset while using different types of features.
Using Natural Language Processing to Screen Patients with Active Heart Failure: An Exploration for Hospital-wide Surveillance
Venue: arXiv cs.CL 2016
Year: 2016

In this paper, we proposed two different approaches, a rule-based approach and a machine-learning based approach, to identify active heart failure cases automatically by analyzing electronic health records (EHR). For the rule-based approach, we extracted cardiovascular data elements from clinical notes and matched patients to different colors according their heart failure condition by using rules provided by experts in heart failure. It achieved 69.4% accuracy and 0.729 F1-Score. For the machine learning approach, with bigram of clinical notes as features, we tried four different models while SVM with linear kernel achieved the best performance with 87.5% accuracy and 0.86 F1-Score. Also, from the classification comparison between the four different models, we believe that linear models fit better for this problem. Once we combine the machine-learning and rule-based algorithms, we will enable hospital-wide surveillance of active heart failure through increased accuracy and interpretability of the outputs.
An Information Extraction Approach to Prescreen Heart Failure Patients for Clinical Trials
Venue: arXiv cs.CL 2016
Year: 2016

To reduce the large amount of time spent screening, identifying, and recruiting patients into clinical trials, we need prescreening systems that are able to automate the data extraction and decision-making tasks that are typically relegated to clinical research study coordinators. However, a major obstacle is the vast amount of patient data available as unstructured free-form text in electronic health records. Here we propose an information extraction-based approach that first automatically converts unstructured text into a structured form. The structured data are then compared against a list of eligibility criteria using a rule-based system to determine which patients qualify for enrollment in a heart failure clinical trial. We show that we can achieve highly accurate results, with recall and precision values of 0.95 and 0.86, respectively. Our system allowed us to significantly reduce the time needed for prescreening patients from a few weeks to a few minutes. Our open-source information extraction modules are available for researchers and could be tested and validated in other cardiovascular trials. An approach such as the one we demonstrate here may decrease costs and expedite clinical trials, and could enhance the reproducibility of trials across institutions and populations.
An IoT application development using IoTSuite
Venue: arXiv cs.SE 2016
Year: 2016

Application development in the Internet of Things (IoT) is challenging because it involves dealing with issues that attribute to different life-cycle phases. First, the application logic has to be analyzed and then separated into a set of distributed tasks for an underlying network. Then, the tasks have to be implemented for the specific hardware. Moreover, we take different IoT applications and present development of these applications using IoTSuite. In this paper, we introduce a design and implementation of ToolSuite, a suite of tools, for reducing burden of each stage of IoT application development process. We take different class of IoT applications, largely found in the IoT literature, and demonstrate these IoT application development using IoTSuite. These applications have been tested on several IoT technologies such as Android, Raspberry PI, Arduino, and JavaSE-enabled devices, Messaging protocols such as MQTT, CoAP, WebSocket, Server technologies such as Node.js, Relational database such as MySQL, and Microsoft Azure Cloud services.
Discriminative Enhancement for Single Channel Audio Source Separation using Deep Neural Networks
Venue: arXiv cs.SD 2016
Year: 2016

The sources separated by most single channel audio source separation techniques are usually distorted and each separated source contains residual signals from the other sources. To tackle this problem, we propose to enhance the separated sources to decrease the distortion and interference between the separated sources using deep neural networks (DNNs). Two different DNNs are used in this work. The first DNN is used to separate the sources from the mixed signal. The second DNN is used to enhance the separated signals. To consider the interactions between the separated sources, we propose to use a single DNN to enhance all the separated sources together. To reduce the residual signals of one source from the other separated sources (interference), we train the DNN for enhancement discriminatively to maximize the dissimilarity between the predicted sources. The experimental results show that using discriminative enhancement decreases the distortion and interference between the separated sources.
Making a Case for Learning Motion Representations with Phase
Venue: arXiv cs.CV 2016
Year: 2016

This work advocates Eulerian motion representation learning over the current standard Lagrangian optical flow model. Eulerian motion is well captured by using phase, as obtained by decomposing the image through a complex-steerable pyramid. We discuss the gain of Eulerian motion in a set of practical use cases: (i) action recognition, (ii) motion prediction in static images, (iii) motion transfer in static images and, (iv) motion transfer in video. For each task we motivate the phase-based direction and provide a possible approach.
Performance of ternary double circulant, double twistulant, and self-dual codes
Venue: arXiv cs.IT 2016
Year: 2016

We study the performance of ternary isodual codes which are not self-dual and ternary self-dual codes, as measured by the decoding error probability in bounded distance decoding. We compare the performance of ternary double circulant and double twistulant codes which are not self-dual with ternary extremal self-dual codes. We also investigate the performance of ternary self-dual codes having large minimum weights.
Algorithms For Shaping a Particle Swarm With a Shared Control Input Using Boundary Interaction
Venue: arXiv cs.RO 2016
Year: 2016

Consider a swarm of particles controlled by global inputs. This paper presents algorithms for shaping such swarms in 2D using boundary walls. The range of configurations created by conforming a swarm to a boundary wall is limited. We describe the set of stable configurations of a swarm in two canonical workspaces, a circle and a square. To increase the diversity of configurations, we add boundary interaction to our model. We provide algorithms using friction with walls to place two robots at arbitrary locations in a rectangular workspace. Next, we extend this algorithm to place $n$ agents at desired locations. We conclude with efficient techniques to control the covariance of a swarm not possible without wall-friction. Simulations and hardware implementations with 100 robots validate these results. These methods may have particular relevance for current micro- and nano-robots controlled by global inputs.
RRDVCR: Real-Time Reliable Data Delivery Based on Virtual Coordinating Routing for Wireless Sensor Networks
Venue: arXiv cs.NI 2016
Year: 2016

Real-time industrial application requires routing protocol that guarantees data delivery with reliable, efficient and low end-to-end delay. Existing Routing(THVR) [13] is based velocity of Two-Hop Velocity and protocol relates two-hop velocity to delay to select the next forwarding node, that has overhead of exchanging control packets, and depleting the available energy in nodes. We propose a Real-Time Reliable Data delivery based on Virtual Coordinates Routing (RRDVCR) algorithm, based on the number of hops to the destination rather than geographic distance. Selection of forwarding node is based on packet progress offered by two-hops, link quality and available energy at the forwarding nodes. All these metric are co-related by dynamic co-relation factor. The proposed protocol uses selective acknowledgment scheme that results in lower overhead and energy consumption. Simulation results shows that there is about 22% and 9.5% decrease in energy consumption compared to SPEED [8] and THVR [13] respectively, 16% and 38% increase in packet delivery compared to THVR [13] and SPEED[8] respectively, and overhead is reduced by 50%.
Revisiting calculation of moments of number of comparisons used by the randomized quick sort algorithm
Venue: arXiv cs.DS 2016
Year: 2016

We revisit the method of Kirschenhofer, Prodinger and Tichy to calculate the moments of comparisons used by the quick sort algorithm. We reemphasize that this approach helps in calculating these quantities with less computation. We also point out that as observed by Knuth this method also gives moments for total path length of a binary search tree built over a random set of n keys.
A modular architecture for transparent computation in Recurrent Neural Networks
Venue: arXiv cs.NE 2016
Year: 2016

Computation is classically studied in terms of automata, formal languages and algorithms; yet, the relation between neural dynamics and symbolic representations and operations is still unclear in traditional eliminative connectionism. Therefore, we suggest a unique perspective on this central issue, to which we would like to refer as to transparent connectionism, by proposing accounts of how symbolic computation can be implemented in neural substrates. In this study we first introduce a new model of dynamics on a symbolic space, the versatile shift, showing that it supports the real-time simulation of a range of automata. We then show that the Goedelization of versatile shifts defines nonlinear dynamical automata, dynamical systems evolving on a vectorial space. Finally, we present a mapping between nonlinear dynamical automata and recurrent artificial neural networks. The mapping defines an architecture characterized by its granular modularity, where data, symbolic operations and their control are not only distinguishable in activation space, but also spatially localizable in the network itself, while maintaining a distributed encoding of symbolic representations. The resulting networks simulate automata in real-time and are programmed directly, in absence of network training. To discuss the unique characteristics of the architecture and their consequences, we present two examples: i) the design of a Central Pattern Generator from a finite-state locomotive controller, and ii) the creation of a network simulating a system of interactive automata that supports the parsing of garden-path sentences as investigated in psycholinguistics experiments.
A three-dimensional approach to Visual Speech Recognition using Discrete Cosine Transforms
Venue: arXiv cs.CV 2016
Year: 2016

Visual speech recognition aims to identify the sequence of phonemes from continuous speech. Unlike the traditional approach of using 2D image feature extraction methods to derive features of each video frame separately, this paper proposes a new approach using a 3D (spatio-temporal) Discrete Cosine Transform to extract features of each feasible sub-sequence of an input video which are subsequently classified individually using Support Vector Machines and combined to find the most likely phoneme sequence using a tailor-made Hidden Markov Model. The algorithm is trained and tested on the VidTimit database to recognise sequences of phonemes as well as visemes (visual speech units). Furthermore, the system is extended with the training on phoneme or viseme pairs (biphones) to counteract the human speech ambiguity of co-articulation. The test set accuracy for the recognition of phoneme sequences is 20%, and the accuracy of viseme sequences is 39%. Both results improve the best values reported in other papers by approximately 2%. The contribution of the result is three-fold: Firstly, this paper is the first to show that 3D feature extraction methods can be applied to continuous sequence recognition tasks despite the unknown start positions and durations of each phoneme. Secondly, the result confirms that 3D feature extraction methods improve the accuracy compared to 2D features extraction methods. Thirdly, the paper is the first to specifically compare an otherwise identical method with and without using biphones, verifying that the usage of biphones has a positive impact on the result.
Human Body Orientation Estimation using Convolutional Neural Network
Venue: arXiv cs.RO 2016
Year: 2016

Personal robots are expected to interact with the user by recognizing the user's face. However, in most of the service robot applications, the user needs to move himself/herself to allow the robot to see him/her face to face. To overcome such limitations, a method for estimating human body orientation is required. Previous studies used various components such as feature extractors and classification models to classify the orientation which resulted in low performance. For a more robust and accurate approach, we propose the light weight convolutional neural networks, an end to end system, for estimating human body orientation. Our body orientation estimation model achieved 81.58% and 94% accuracy with the benchmark dataset and our own dataset respectively. The proposed method can be used in a wide range of service robot applications which depend on the ability to estimate human body orientation. To show its usefulness in service robot applications, we designed a simple robot application which allows the robot to move towards the user's frontal plane. With this, we demonstrated an improved face detection rate.
In the Name of the Name: RDF literals, ER Attributes and the Potential to Rethink the Structures and Visualizations of Catalogs
Venue: arXiv cs.DL 2016
Year: 2016

The aim of this study is to contribute to the field of machine-processable bibliographic data that is suitable for the Semantic Web. We examine the Entity Relationship (ER) model, which has been selected by IFLA as a "conceptual framework" in order to model the FR family (FRBR, FRAD, and RDA), and the problems ER causes as we move towards the Semantic Web. Subsequently, while maintaining the semantics of the aforementioned standards but rejecting the ER as a conceptual framework for bibliographic data, this paper builds on the RDF (Resource Description Framework) potential and documents how both the RDF and Linked Data's rationale can affect the way we model bibliographic data. In this way, a new approach to bibliographic data emerges where the distinction between description and authorities is obsolete. Instead, the integration of the authorities with descriptive information becomes fundamental so that a network of correlations can be established between the entities and the names by which the entities are known. Naming is a vital issue for human cultures because names are not random sequences of characters or sounds that stand just as identifiers for the entities; they also have socio-cultural meanings and interpretations. Thus, instead of describing indivisible resources, we could describe entities that appear in a variety of names on various resources. In this study, a method is proposed to connect the names with the entities they represent and, in this way, to document the provenance of these names by connecting specific resources with specific names.
Equilibrium Graphs
Venue: arXiv cs.AI 2016
Year: 2016

In this paper we present an extension of Peirce's existential graphs to provide a diagrammatic representation of expressions in Quantified Equilibrium Logic (QEL). Using this formalisation, logical connectives are replaced by encircled regions (circles and squares) and quantified variables are represented as "identity" lines. Although the expressive power is equivalent to that of QEL, the new representation can be useful for illustrative or educational purposes.
The Effect of Class Imbalance and Order on Crowdsourced Relevance Judgments
Venue: arXiv cs.IR 2016
Year: 2016

In this paper we study the effect on crowd worker efficiency and effectiveness of the dominance of one class in the data they process. We aim at understanding if there is any positive or negative bias in workers seeing many negative examples in the identification of positive labels. To test our hypothesis, we design an experiment where crowd workers are asked to judge the relevance of documents presented in different orders. Our findings indicate that there is a significant improvement in the quality of relevance judgements when presenting relevant results before the non-relevant ones.