OpenRaiser/Intern-Atlas huggingface dataset:
Human Task Monitoring and Contextual Analysis for Domain Specific Business Processes
Venue: arXiv cs.SE 2016
Year: 2016

Monitoring the execution of business processes and activities composing them is an essential capability of Business Process Management (BPM) Suites. Human tasks are a particular type of business activities, and the understanding of their execution is essential in effectively managing both the processes and human resources. This paper proposes a monitoring framework with a capability to monitor and analyze the human tasks in a domain specific setting and contextually correlate the task execution patterns to the workload distribution on human users. The framework uses the notion of concept probes that match the business concepts used in definition of business processes. The proposed human task monitoring and contextual analysis (HTMCA) component considers multiple artifacts involved in the execution of a human task, rather than focusing only on classic activity/task metrics retrieved from BPM engines.This approach aspires to provide two main advantages to organizations using it. Firstly, it enhances the understanding of the workload of human users that participate in people-intensive business processes under various roles. Secondly, it gives organizations tools and insight for fine-tuning their user performance taking into account the specific context of their business various artifacts domains. The proposed framework builds on previous work that lays the basis of vendor-independent, concept-centric BPM monitoring, and provides the critical missing element of human task understanding. This has the potential to significantly benefit any BPM deployment and the validation work is in advanced stages of building a full prototype that demonstrates this value in a realistic industrial setting.
Output Feedback Control of a Class of Uncertain Systems Under Control-derivative dependent disturbances
Venue: arXiv cs.SY 2016
Year: 2016

This paper addresses the design of robust dynamic output feedback control for highly uncertain systems in which the unknown disturbance might be excited by the derivative of the control input. This context appears in many industrial problems such as the speed control of the hydraulic turbines and the frequency stabilization in micro grids to cite but few examples. A key feature that has to be carefully addressed in this context is that too agressive feedback might lead to the loss of controllability and/or a significant drop in the closed-loop performance. The paper formulates the problem, underlines its relevance and gives a rigorous solution in which a dynamic output feedback is given together with a realistic set of sufficient conditions on the controller's parameters that enable to assess the behavior of the closed-loop under different circumstances. A numerical example is given to illustrate the relevance of the proposed successful design characterization.
Bidirectional LSTM-CRF for Clinical Concept Extraction
Venue: arXiv cs.CL 2016
Year: 2016

Extraction of concepts present in patient clinical records is an essential step in clinical research. The 2010 i2b2/VA Workshop on Natural Language Processing Challenges for clinical records presented concept extraction (CE) task, with aim to identify concepts (such as treatments, tests, problems) and classify them into predefined categories. State-of-the-art CE approaches heavily rely on hand crafted features and domain specific resources which are hard to collect and tune. For this reason, this paper employs bidirectional LSTM with CRF decoding initialized with general purpose off-the-shelf word embeddings for CE. The experimental results achieved on 2010 i2b2/VA reference standard corpora using bidirectional LSTM CRF ranks closely with top ranked systems.
A Bayesian Approach to Estimation of Speaker Normalization Parameters
Venue: arXiv cs.SD 2016
Year: 2016

In this work, a Bayesian approach to speaker normalization is proposed to compensate for the degradation in performance of a speaker independent speech recognition system. The speaker normalization method proposed herein uses the technique of vocal tract length normalization (VTLN). The VTLN parameters are estimated using a novel Bayesian approach which utilizes the Gibbs sampler, a special type of Markov Chain Monte Carlo method. Additionally the hyperparameters are estimated using maximum likelihood approach. This model is used assuming that human vocal tract can be modeled as a tube of uniform cross section. It captures the variation in length of the vocal tract of different speakers more effectively, than the linear model used in literature. The work has also investigated different methods like minimization of Mean Square Error (MSE) and Mean Absolute Error (MAE) for the estimation of VTLN parameters. Both single pass and two pass approaches are then used to build a VTLN based speech recognizer. Experimental results on recognition of vowels and Hindi phrases from a medium vocabulary indicate that the Bayesian method improves the performance by a considerable margin.
Relation between Functional Complexity, Scalability and Energy Efficiency in WSNs
Venue: arXiv cs.NI 2016
Year: 2016

In order to understand the underlying mechanisms that lead to certain network properties (i.e. scalability, energy efficiency) we apply a complex systems science approach to analyze clustering in Wireless Sensor Networks (WSN). We represent different implementations of clustering in WSNs with a functional topology graph. Different characteristics of the functional topology provide insight into the relationships between system parts that result in certain properties of the whole system. Moreover, we employ a complexity metric - functional complexity (C_F) - to explain how local interactions give rise to the global behavior of the network. Our analysis shows that higher values of C_F indicate higher scalability and lower energy efficiency.
Security Testbed for the Internet of Things
Venue: arXiv cs.CR 2016
Year: 2016

The Internet of Things (IoT) is a global ecosystem of information and communication technologies aimed at connecting any type of object (thing), at any time and in any place, to each other and to the Internet. One of the major problems associated with the IoT is maintaining security; the heterogeneous nature of such deployments poses a challenge to many aspects of security, including security testing and analysis. In addition, there is no existing mechanism that performs security testing for IoT devices in different contexts. In this paper, we propose an innovative security testbed framework targeted at IoT devices. The security testbed supports both standard and context-based security testing, with a set of security tests conducted under the different environmental conditions in which IoT devices operate. The requirements and architectural design of the proposed testbed are discussed, and the testbed operation is demonstrated in several testing scenarios.
"RollRoller" Novel Spherical Mobile Robot Basic Dynamical Analysis and Motion Simulations
Venue: arXiv cs.RO 2016
Year: 2016

This paper introduces novel air actuated spherical robot called "RollRoller". The RollRoller robot consists of two essential parts: tubes covered with a shell as a frame and mechanical controlling parts to correspond movements. The RollRoller is proposed to be high potential alternative for exploration and rescue missions robots because robot utilizing its locomotion via all possible deriving methods (gravity, torque and angular momentum forces). In beginning , characteristic and role of each of component and features were explained. Next, to determine the uniqueness of this robot, the known and other extra possible motions are shown by proposing their own algorithmic movements. To illustrate main motion of this robot was inherent to mathematical models, the forward direction dynamical behavior on flat surface was derived. Additionally, Matlab Simulink was used to plot the results to validate the behavior for both fractional and non-fractional terrains. Lastly, after designing the model of robot in Solidworks Program, Adams/View visualization software ( the robot simulated form ) was utilized to proof the Matlab Simulink results and to show the more detailed and complete form of locomotion including the forward direction and circular locomotion in proposed robot.
Learning to Learn Neural Networks
Venue: arXiv cs.LG 2016
Year: 2016

Meta-learning consists in learning learning algorithms. We use a Long Short Term Memory (LSTM) based network to learn to compute on-line updates of the parameters of another neural network. These parameters are stored in the cell state of the LSTM. Our framework allows to compare learned algorithms to hand-made algorithms within the traditional train and test methodology. In an experiment, we learn a learning algorithm for a one-hidden layer Multi-Layer Perceptron (MLP) on non-linearly separable datasets. The learned algorithm is able to update parameters of both layers and generalise well on similar datasets.
Privacy-preserving schemes for Ad Hoc Social Networks: A survey
Venue: arXiv cs.CR 2016
Year: 2016

In this paper, we review the state of the art of privacy-preserving schemes for ad hoc social networks, including, mobile social networks (MSNs) and vehicular social networks (VSNs). Specifically, we select and in-detail examine thirty-three privacy preserving schemes developed for or applied in the context of ad hoc social networks. These schemes are published between 2008 and 2016. Based on this existing privacy preservation schemes, we survey privacy preservation models, including location privacy, identity privacy, anonymity, traceability, interest privacy, backward privacy, and content oriented privacy. The recent important attacks of leaking privacy, countermeasures, and game theoretic approaches in VSNs and MSNs are summarized in form of tables. In addition, an overview of recommendations for further research is also provided. With this survey, readers can have a more thorough understanding of research trends in privacy-preserving schemes for ad hoc social networks
Compositional bisimulation metric reasoning with Probabilistic Process Calculi
Venue: arXiv cs.LO 2016
Year: 2016

We study which standard operators of probabilistic process calculi allow for compositional reasoning with respect to bisimulation metric semantics. We argue that uniform continuity (generalizing the earlier proposed property of non-expansiveness) captures the essential nature of compositional reasoning and allows now also to reason compositionally about recursive processes. We characterize the distance between probabilistic processes composed by standard process algebra operators. Combining these results, we demonstrate how compositional reasoning about systems specified by continuous process algebra operators allows for metric assume-guarantee like performance validation.
Accelerating BLAS on Custom Architecture through Algorithm-Architecture Co-design
Venue: arXiv cs.AR 2016
Year: 2016

Basic Linear Algebra Subprograms (BLAS) play key role in high performance and scientific computing applications. Experimentally, yesteryear multicore and General Purpose Graphics Processing Units (GPGPUs) are capable of achieving up to 15 to 57% of the theoretical peak performance at 65W to 240W respectively for compute bound operations like Double/Single Precision General Matrix Multiplication (XGEMM). For bandwidth bound operations like Single/Double precision Matrix-vector Multiplication (XGEMV) the performance is merely 5 to 7% of the theoretical peak performance in multicores and GPGPUs respectively. Achieving performance in BLAS requires moving away from conventional wisdom and evolving towards customized accelerator tailored for BLAS through algorithm-architecture co-design. In this paper, we present acceleration of Level-1 (vector operations), Level-2 (matrix-vector operations), and Level-3 (matrix-matrix operations) BLAS through algorithm architecture co-design on a Coarse-grained Reconfigurable Architecture (CGRA). We choose REDEFINE CGRA as a platform for our experiments since REDEFINE can be adapted to support domain of interest through tailor-made Custom Function Units (CFUs). For efficient sequential realization of BLAS, we present design of a Processing Element (PE) and perform micro-architectural enhancements in the PE to achieve up-to 74% of the theoretical peak performance of PE in DGEMM, 40% in DGEMV and 20% in double precision inner product (DDOT). We attach this PE to REDEFINE CGRA as a CFU and show the scalability of our solution. Finally, we show performance improvement of 3-140x in PE over commercially available Intel micro-architectures, ClearSpeed CSX700, FPGA, and Nvidia GPGPUs.
Dynamic accessibility using Big Data: The role of the changing conditions of network congestion and destination attractiveness
Venue: arXiv cs.CY 2016
Year: 2016

This paper analyses urban accessibility using a fully dynamic approach.
Emergence of Digital Twins
Venue: arXiv cs.CY 2016
Year: 2016

Multiple forms of digital transformation are imminent. Digital Twins represent one concept. It is gaining momentum because it may offer real-time transparency. Rapid diffusion of digital duplicates faces hurdles due to lack of semantic interoperability between architectures, standards and ontologies. The technologies necessary for automated discovery are in short supply. Progression of the field depends on convergence of information technology, operational technology and protocol-agnostic telecommunications. Making sense of the data, ability to curate data and perform data analytics at the edge (or mist rather than in the fog or cloud) is key to value. Delivering engines to the edge are crucial for analytics at the edge when latency is critical. The confluence of these and other factors may chart the future path for Digital Twins. The number of unknown unknowns and the known unknowns in this process makes it imperative to create global infrastructures and organize groups to pursue the development of fundamental building blocks and new ideas through research.
Representation of dynamic spatial phenomena: The daily car accessibility in London
Venue: arXiv cs.CY 2016
Year: 2016

This paper uses traditional maps in animations to show traffic congestion effects in London
Multi-view metric learning for multi-instance image classification
Venue: arXiv cs.CV 2016
Year: 2016

It is critical and meaningful to make image classification since it can help human in image retrieval and recognition, object detection, etc. In this paper, three-sides efforts are made to accomplish the task. First, visual features with bag-of-words representation, not single vector, are extracted to characterize the image. To improve the performance, the idea of multi-view learning is implemented and three kinds of features are provided, each one corresponds to a single view. The information from three views is complementary to each other, which can be unified together. Then a new distance function is designed for bags by computing the weighted sum of the distances between instances. The technique of metric learning is explored to construct a data-dependent distance metric to measure the relationships between instances, meanwhile between bags and images, more accurately. Last, a novel approach, called MVML, is proposed, which optimizes the joint probability that every image is similar with its nearest image. MVML learns multiple distance metrics, each one models a single view, to unifies the information from multiple views. The method can be solved by alternate optimization iteratively. Gradient ascent and positive semi-definite projection are utilized in the iterations. Distance comparisons verified that the new bag distance function is prior to previous functions. In model evaluation, numerical experiments show that MVML with multiple views performs better than single view condition, which demonstrates that our model can assemble the complementary information efficiently and measure the distance between images more precisely. Experiments on influence of parameters and instance number validate the consistency of the method.
The Broadcaster-Repacking Problem
Venue: arXiv cs.DS 2016
Year: 2016

The Federal Communications Commission's (FCC's) ongoing Incentive Auction will, if successful, transfer billions of dollars of radio spectrum from television broadcasters to mobile-network operators. Hundreds of broadcasters may go off the air. Most of those remaining on the air, including hundreds of Canadian broadcasters not bidding, will have to move to new channels to continue broadcasting. The auction can only end if all these broadcasters will fit into the spectrum remaining for television. Whether a given set of broadcasters fits is the broadcaster-repacking problem. The FCC must calculate its solutions thousands of times per round of bidding. Speed is essential. By reducing the broadcaster-repacking problem to the maximum independent set problem, we show that the former is $\mathcal{NP}$-complete. This reduction also allows us to expand on sparsity-exploiting heuristics in the literature, which have made the FCC's repacking-problem instances tractable. We conclude by relating the heuristics to satisfiability and integer programming reductions. These provide a basis for implementing algorithms in off-the-shelf software to solve the broadcaster-repacking problem.
A Convex Programming-based Algorithm for Mean Payoff Stochastic Games with Perfect Information
Venue: arXiv cs.DS 2016
Year: 2016

We consider two-person zero-sum stochastic mean payoff games with perfect information, or BWR-games, given by a digraph $G = (V, E)$, with local rewards $r: E \to \ZZ$, and three types of positions: black $V_B$, white $V_W$, and random $V_R$ forming a partition of $V$. It is a long-standing open question whether a polynomial time algorithm for BWR-games exists, even when $|V_R|=0$. In fact, a pseudo-polynomial algorithm for BWR-games would already imply their polynomial solvability. In this short note, we show that BWR-games can be solved via convex programming in pseudo-polynomial time if the number of random positions is a constant.
A Hybrid Hoare Logic for Gene Network Models
Venue: arXiv cs.LO 2016
Year: 2016

The main difficulty when modelling gene networks is the identification of the parameters that govern their dynamics. It is particularly difficult for models in which time is continuous: parameters have real values which cannot be enumerated. The widespread idea is to infer new constraints that reduce the range of possible values. Here we present a new work based on a particular class of Hybrid automata (inspired by Thomas discrete models) where discrete parameters are replaced by signed celerities. We propose a new approach involving Hoare logic and weakest precondition calculus (a la Dijkstra) that generates constraints on the parameter values. Indeed, once proper specifications are extracted from biological traces with duration information (found in the literature or biological experiments), they play a role similar to imperative programs in the classical Hoare logic. We illustrate our hybrid Hoare logic on a small model controlling the lacI repressor of the lactose operon.
Deterministic Distributed (Delta + o(Δ))-Edge-Coloring, and Vertex-Coloring of Graphs with Bounded Diversity
Venue: arXiv cs.DC 2016
Year: 2016

We consider coloring problems in the distributed message-passing setting. The previously-known deterministic algorithms for edge-coloring employed at least (2Delta - 1) colors, even though any graph admits an edge-coloring with Delta + 1 colors [V64]. Moreover, the previously-known deterministic algorithms that employed at most O(Delta) colors required superlogarithmic time [B15,BE10,BE11,FHK15]. In the current paper we devise deterministic edge-coloring algorithms that employ only Delta + o(Delta) colors, for a very wide family of graphs. Specifically, as long as the arboricity is a = O(Delta^{1 - ε}), for a constant epsilon > 0, our algorithm computes such a coloring within {polylogarithmic} deterministic time. We also devise significantly improved deterministic edge-coloring algorithms for {general graphs} for a very wide range of parameters. Specifically, for any value $χ$ in the range [4Delta, 2^{o(log Delta)} \cdot Delta], our χ-edge-coloring algorithm has smaller running time than the best previously-known χ-edge-coloring algorithms. Our algorithms are actually much more general, since edge-coloring is equivalent to {vertex-coloring of line graphs.} Our method is applicable to vertex-coloring of the family of graphs with {bounded diversity} that contains line graphs, line graphs of hypergraphs, and many other graphs. Our results are obtained using a novel technique that connects vertices or edges in a certain way that reduces clique size. The resulting structures, which we call {connectors}, can be colored more efficiently than the original graph. Moreover, the color classes constitute simpler subgraphs that can be colored even more efficiently using appropriate connectors. Hence, we recurse until we obtain sufficiently simple structures that are colored directly. We introduce several types of connectors that are useful for various scenarios.
The impacts of congestion on automobile accessibility. What happens in large European cities?
Venue: arXiv cs.CY 2016
Year: 2016

This paper analyze how congestion impact in accessibility in eight european cities
Model-Driven Design of Clinical Guidance Systems
Venue: arXiv cs.CY 2016
Year: 2016

Clinical guidance systems have been widely adopted to help medical staffs to avoid preventable medical errors such as delay in diagnosis, treatment or untended deviations from best practice guidelines. However, because patient condition changes rapidly and medical staffs are usually overloaded in acute care setting, how to ensure the correctness of the system reaction to those rapid changes and managing interaction with physicians remains challenging. In this paper, we propose a domain-specific model-driven design approach to address these challenges for designing clinical guidance systems. Firstly, we translate the relevant medical knowledge described in generic literature and different best practice guidelines into a set of executable and indirectly verifiable finite state machine models. We introduce an organ-centric paradigm to construct clinical models, and also develop a physician model to track physician-system interactions and deviations. Secondly, for verification of the compositional system model, we translate the model into timed automata, based on which, we formalize a set of clinical and system safety requirements as computation tree logic(CTL) formulas and use the UPPAAL model checking tool to formally verify those requirements. In this way, the correctness of the model can be mathematically proved. Finally, we can automatically generate the executable code from the verified model using the corresponding code generation tools for finite state machine. For evaluation, we apply the approach to the design of clinical guidance systems for cardiac arrest. The generated code can be deployed and interact with existing guidance systems.
Enhanced Object Detection via Fusion With Prior Beliefs from Image Classification
Venue: arXiv cs.CV 2016
Year: 2016

In this paper, we introduce a novel fusion method that can enhance object detection performance by fusing decisions from two different types of computer vision tasks: object detection and image classification. In the proposed work, the class label of an image obtained from image classification is viewed as prior knowledge about existence or non-existence of certain objects. The prior knowledge is then fused with the decisions of object detection to improve detection accuracy by mitigating false positives of an object detector that are strongly contradicted with the prior knowledge. A recently introduced novel fusion approach called dynamic belief fusion (DBF) is used to fuse the detector output with the classification prior. Experimental results show that the detection performance of all the detection algorithms used in the proposed work is improved on benchmark datasets via the proposed fusion framework.