OpenRaiser/Intern-Atlas huggingface dataset:
A Dynamic Service Description for Mobile Environments
Venue: arXiv cs.SE 2016
Year: 2016

With the increasing processing capability of mobile platforms and advancements in Internet of Things, modern mobile devices have shown a favorable prospect for on-the-go service provisioning. However, there is much to be done to realize this. A detailed, dynamic, and lightweight service description is an important requirement for automatic and efficient discovery, selection, and subsequent provisioning of services over mobile devices. Traditional approaches for service description are usually not directly adaptable to mobile environments owing to the latter's dynamic and distinct nature. In this paper, we propose a dynamic, lightweight, extensible, and detailed service description especially designed for mobile environments, considering crucial aspects such as isolated data source, collaborator partners, and hardware aspects along with the functional, non-functional, business, and contextual aspects. The description has been partitioned along these lines and various parts of the description are distributed between service registries and the mobile service providers. An up-to-date and light weight description has been achieved by this, without compromising on the overall consistency of the description. A prototype of the proposed system has been implemented with the intent of validating the feasibility of the approach. Further, the proposed approach is suitable for a heterogeneous environment comprising both wired and wireless systems.
Neuroevolution-Based Inverse Reinforcement Learning
Venue: arXiv cs.NE 2016
Year: 2016

The problem of Learning from Demonstration is targeted at learning to perform tasks based on observed examples. One approach to Learning from Demonstration is Inverse Reinforcement Learning, in which actions are observed to infer rewards. This work combines a feature based state evaluation approach to Inverse Reinforcement Learning with neuroevolution, a paradigm for modifying neural networks based on their performance on a given task. Neural networks are used to learn from a demonstrated expert policy and are evolved to generate a policy similar to the demonstration. The algorithm is discussed and evaluated against competitive feature-based Inverse Reinforcement Learning approaches. At the cost of execution time, neural networks allow for non-linear combinations of features in state evaluations. These valuations may correspond to state value or state reward. This results in better correspondence to observed examples as opposed to using linear combinations. This work also extends existing work on Bayesian Non-Parametric Feature Construction for Inverse Reinforcement Learning by using non-linear combinations of intermediate data to improve performance. The algorithm is observed to be specifically suitable for a linearly solvable non-deterministic Markov Decision Processes in which multiple rewards are sparsely scattered in state space. A conclusive performance hierarchy between evaluated algorithms is presented.
Network Topology Inference from Spectral Templates
Venue: arXiv cs.SI 2016
Year: 2016

We address the problem of identifying a graph structure from the observation of signals defined on its nodes. Fundamentally, the unknown graph encodes direct relationships between signal elements, which we aim to recover from observable indirect relationships generated by a diffusion process on the graph. The fresh look advocated here permeates benefits from convex optimization and stationarity of graph signals, in order to identify the graph shift operator (a matrix representation of the graph) given only its eigenvectors. These spectral templates can be obtained, e.g., from the sample covariance of independent graph signals diffused on the sought network. The novel idea is to find a graph shift that, while being consistent with the provided spectral information, endows the network with certain desired properties such as sparsity. To that end we develop efficient inference algorithms stemming from provably-tight convex relaxations of natural nonconvex criteria, particularizing the results for two shifts: the adjacency matrix and the normalized Laplacian. Algorithms and theoretical recovery conditions are developed not only when the templates are perfectly known, but also when the eigenvectors are noisy or when only a subset of them are given. Numerical tests showcase the effectiveness of the proposed algorithms in recovering social, brain, and amino-acid networks.
Testing Assignments to Constraint Satisfaction Problems
Venue: arXiv cs.DS 2016
Year: 2016

For a finite relational structure A, let CSP(A) denote the CSP instances whose constraint relations are taken from A. The resulting family of problems CSP(A) has been considered heavily in a variety of computational contexts. In this article, we consider this family from the perspective of property testing: given an instance of a CSP and query access to an assignment, one wants to decide whether the assignment satisfies the instance, or is far from so doing. While previous works on this scenario studied concrete templates or restricted classes of structures, this article presents comprehensive classification theorems. Our first contribution is a dichotomy theorem completely characterizing the structures A such that CSP(A) is constant-query testable: (i) If A has a majority polymorphism and a Maltsev polymorphism, then CSP(A) is constant-query testable with one-sided error. (ii) Else, testing CSP(A) requires a super-constant number of queries. Let $\exists$CSP(A) denote the extension of CSP(A) to instances which may include existentially quantified variables. Our second contribution is to classify all structures A in terms of the number of queries needed to test assignments to instances of $\exists$CSP(A), with one-sided error. More specifically, we show the following trichotomy: (i) If A has a majority polymorphism and a Maltsev polymorphism, then $\exists$CSP(A) is constant-query testable with one-sided error. (ii) Else, if A has a $(k + 1)$-ary near-unanimity polymorphism for some $k \geq 2$, and no Maltsev polymorphism then $\exists$CSP(A) is not constant-query testable (even with two-sided error) but is sublinear-query testable with one-sided error. (iii) Else, testing $\exists$CSP(A) with one-sided error requires a linear number of queries.
LTE/LTE-A Random Access for Massive Machine-Type Communications in Smart Cities
Venue: arXiv cs.NI 2016
Year: 2016

Massive Machine-Type Communications (MTC) over cellular networks is expected to be an integral part of wireless "Smart City" applications. The Long Term Evolution (LTE)/LTE-Advanced (LTE-A) technology is a major candidate for provisioning of MTC applications. However, due to the diverse characteristics of payload size, transmission periodicity, power efficiency, and quality of service (QoS) requirement, MTC poses huge challenges to LTE/LTE-A technologies. In particular, efficient management of massive random access is one of the most critical challenges. In case of massive random access attempts, the probability of preamble collision drastically increases, thus the performance of LTE/LTE-A random access degrades sharply. In this context, this article reviews the current state-of-the-art proposals to control massive random access of MTC devices in LTE/LTE-A networks. The proposals are compared in terms of five major metrics, namely, access delay, access success rate, power efficiency, QoS guarantee, and the effect on Human-Type Communications (HTC). To this end, we propose a novel collision resolution random access model for massive MTC over LTE/LTE-A. Our proposed model basically resolves the preamble collisions instead of avoidance, and targets to manage massive and bursty access attempts. Simulations of our proposed model show huge improvements in random access success rate compared to the standard slotted-Aloha-based models. The new model can also coexist with existing LTE/LTE-A Medium Access Control (MAC) protocol, and ensure high reliability and time-efficient network access.
Unique Parallel Decomposition for the Pi-calculus
Venue: arXiv cs.LO 2016
Year: 2016

A (fragment of a) process algebra satisfies unique parallel decomposition if the definable behaviours admit a unique decomposition into indecomposable parallel components. In this paper we prove that finite processes of the pi-calculus, i.e. processes that perform no infinite executions, satisfy this property modulo strong bisimilarity and weak bisimilarity. Our results are obtained by an application of a general technique for establishing unique parallel decomposition using decomposition orders.
Reversible Multiparty Sessions with Checkpoints
Venue: arXiv cs.LO 2016
Year: 2016

Reversible interactions model different scenarios, like biochemical systems and human as well as automatic negotiations. We abstract interactions via multiparty sessions enriched with named checkpoints. Computations can either go forward or roll back to some checkpoints, where possibly different choices may be taken. In this way communications can be undone and different conversations may be tried. Interactions are typed with global types, which control also rollbacks. Typeability of session participants in agreement with global types ensures session fidelity and progress of reversible communications.
Growing Graphs with Hyperedge Replacement Graph Grammars
Venue: arXiv cs.SI 2016
Year: 2016

Discovering the underlying structures present in large real world graphs is a fundamental scientific problem. In this paper we show that a graph's clique tree can be used to extract a hyperedge replacement grammar. If we store an ordering from the extraction process, the extracted graph grammar is guaranteed to generate an isomorphic copy of the original graph. Or, a stochastic application of the graph grammar rules can be used to quickly create random graphs. In experiments on large real world networks, we show that random graphs, generated from extracted graph grammars, exhibit a wide range of properties that are very similar to the original graphs. In addition to graph properties like degree or eigenvector centrality, what a graph "looks like" ultimately depends on small details in local graph substructures that are difficult to define at a global level. We show that our generative graph model is able to preserve these local substructures when generating new graphs and performs well on new and difficult tests of model robustness.
Gaze2Segment: A Pilot Study for Integrating Eye-Tracking Technology into Medical Image Segmentation
Venue: arXiv cs.CV 2016
Year: 2016

This study introduced a novel system, called Gaze2Segment, integrating biological and computer vision techniques to support radiologists' reading experience with an automatic image segmentation task. During diagnostic assessment of lung CT scans, the radiologists' gaze information were used to create a visual attention map. This map was then combined with a computer-derived saliency map, extracted from the gray-scale CT images. The visual attention map was used as an input for indicating roughly the location of a object of interest. With computer-derived saliency information, on the other hand, we aimed at finding foreground and background cues for the object of interest. At the final step, these cues were used to initiate a seed-based delineation process. Segmentation accuracy of the proposed Gaze2Segment was found to be 86% with dice similarity coefficient and 1.45 mm with Hausdorff distance. To the best of our knowledge, Gaze2Segment is the first true integration of eye-tracking technology into a medical image segmentation task without the need for any further user-interaction.
Floware: Balanced Flow Monitoring in Software Defined Networks
Venue: arXiv cs.NI 2016
Year: 2016

OpenFlow is a protocol implementing Software Defined Networking, a new networking paradigm, which segregates packet forwarding and accounting (performed on switches) from the routing decisions and advanced protocols (executed on a central controller). This segregation increases agility and flexibility of a networking infrastructure and reduces its operational expenses. OpenFlow controllers expose standard interfaces to facilitate variety of networking applications. In particular, a monitoring application can use these interfaces to push into the OpenFlow switches rules that collect traffic flow statistics at different aggregation levels. The aggregation level determines the monitoring accuracy and the induced network overhead. In this paper, we propose Floware an OpenFlow application that allows discovery and monitoring of active flows at any required aggregation level. Floware balances the monitoring overhead among many switches in order to reduce its negative effect on network performance. In addition, Floware integrates with monitoring systems based on legacy protocols such as NetFlow. We demonstrate the application with soft switches emulated in Mininet, the Floodlight controller, and the NetFlow Analyzer as a legacy network analysis and intrusion detection system. Evaluation results demonstrate the positive impact of balanced monitoring.
On the Regular Emptiness Problem of Subzero Automata
Venue: arXiv cs.FL 2016
Year: 2016

Subzero automata is a class of tree automata whose acceptance condition can express probabilistic constraints. Our main result is that the problem of determining if a subzero automaton accepts some regular tree is decidable.
The DoF Region of the Three-Receiver MIMO Broadcast Channel with Side Information and Its Relation to Index Coding Capacity
Venue: arXiv cs.IT 2016
Year: 2016

We consider the three-receiver Gaussian multiple-input multiple-output (MIMO) broadcast channel with an arbitrary number of antennas at each of the transmitter and the receivers. We investigate the degrees-of-freedom (DoF) region of the channel when each receiver requests a private message, and may know some of the messages requested by the other receivers as receiver message side information (RMSI). We establish the DoF region of the channel for all 16 possible non-isomorphic RMSI configurations by deriving tight inner and outer bounds on the region. To derive the inner bounds, we first propose a scheme for each RMSI configuration which exploits both the null space and the side information of the receivers. We then use these schemes in conjunction with time sharing for 15 RMSI configurations, and with time sharing and two-symbol extension for the remaining one. To derive the outer bounds, we construct enhanced versions of the channel for each RMSI configuration, and upper bound their DoF region. After establishing the DoF region, in the case where all the nodes have the same number of antennas, we introduce some common properties of the DoF region, and the capacity region of the index coding problem.
Data Collection and Wireless Communication in Internet of Things (IoT) Using Economic Analysis and Pricing Models: A Survey
Venue: arXiv cs.GT 2016
Year: 2016

This paper provides a state-of-the-art literature review on economic analysis and pricing models for data collection and wireless communication in Internet of Things (IoT). Wireless Sensor Networks (WSNs) are the main component of IoT which collect data from the environment and transmit the data to the sink nodes. For long service time and low maintenance cost, WSNs require adaptive and robust designs to address many issues, e.g., data collection, topology formation, packet forwarding, resource and power optimization, coverage optimization, efficient task allocation, and security. For these issues, sensors have to make optimal decisions from current capabilities and available strategies to achieve desirable goals. This paper reviews numerous applications of the economic and pricing models, known as intelligent rational decision-making methods, to develop adaptive algorithms and protocols for WSNs. Besides, we survey a variety of pricing strategies in providing incentives for phone users in crowdsensing applications to contribute their sensing data. Furthermore, we consider the use of some pricing models in Machine-to-Machine (M2M) communication. Finally, we highlight some important open research issues as well as future research directions of applying economic and pricing models to IoT.
Learning Mobile App Usage Routine through Learning Automata
Venue: arXiv cs.AI 2016
Year: 2016

Since its conception, smart app market has grown exponentially. Success in the app market depends on many factors among which the quality of the app is a significant contributor, such as energy use. Nevertheless, smartphones, as a subset of mobile computing devices. inherit the limited power resource constraint. Therefore, there is a challenge of maintaining the resource while increasing the target app quality. This paper introduces Learning Automata (LA) as an online learning method to learn and predict the app usage routines of the users. Such prediction can leverage the app cache functionality of the operating system and thus (i) decreases app launch time and (ii) preserve battery. Our algorithm, which is an online learning approach, temporally updates and improves the internal states of itself. In particular, it learns the transition probabilities between app launching. Each App launching instance updates the transition probabilities related to that App, and this will result in improving the prediction. We benefit from a real-world lifelogging dataset and our experimental results show considerable success with respect to the two baseline methods that are used currently for smartphone app prediction approaches.
The XML and Semantic Web Worlds: Technologies, Interoperability and Integration. A Survey of the State of the Art
Venue: arXiv cs.DB 2016
Year: 2016

In the context of the emergent Web of Data, a large number of organizations, institutes and companies (e.g., DBpedia, Geonames, PubMed ACM, IEEE, NASA, BBC) adopt the Linked Data practices and publish their data utilizing Semantic Web (SW) technologies. On the other hand, the dominant standard for information exchange in the Web today is XML. Many international standards (e.g., Dublin Core, MPEG-7, METS, TEI, IEEE LOM) have been expressed in XML Schema resulting to a large number of XML datasets. The SW and XML worlds and their developed infrastructures are based on different data models, semantics and query languages. Thus, it is crucial to provide interoperability and integration mechanisms to bridge the gap between the SW and XML worlds. In this chapter, we give an overview and a comparison of the technologies and the standards adopted by the XML and SW worlds. In addition, we outline the latest efforts from the W3C groups, including the latest working drafts and recommendations (e.g., OWL 2, SPARQL 1.1, XML Schema 1.1). Moreover, we present a survey of the research approaches which aim to provide interoperability and integration between the XML and SW worlds. Finally, we present the SPARQL2XQuery and XS2OWL Frameworks, which bridge the gap and create an interoperable environment between the two worlds. These Frameworks provide mechanisms for: (a) Query translation (SPARQL to XQuery translation); (b) Mapping specification and generation (Ontology to XML Schema mapping); and (c) Schema transformation (XML Schema to OWL transformation).
MSE-based Precoding for MIMO Downlinks in Heterogeneous Networks
Venue: arXiv cs.IT 2016
Year: 2016

Considering a heterogeneous network (HetNet) system consisting of a macro tier overlaid with a second tier of small cells (SCs), this paper studies the mean square error (MSE) based precoding design to be employed by the macro base station and the SC nodes for multiple-input multiple-output (MIMO) downlinks. First, a new sum-MSE of all users based minimization problem is proposed aiming to design a set of macro cell (MC) and SC transmit precoding matrices or vectors. To solve it, two different algorithms are presented. One is via a relaxed-constraints based alternating optimization (RAO) realized by efficient alternating optimization and relaxing non-convex constraints to convex ones. The other is via an unconstrained alternating optimization with normalization (UAON) implemented by introducing the constraints into the iterations with the normalization operation. Second, a separate MSE minimization based precoding is proposed by considering the signal and interference terms corresponding to the macro tier and the SCs separately. Simulation results show that the sum-MSE based RAO algorithm provides the best MSE performance among the proposed schemes under a number of system configurations. When the number of antennas at the macro-BS is sufficiently large, the MSE of the separate MSE-based precoding is found to approach that of RAO and surpass that of UAON. Together, this paper provides a suite of three new precoding techniques that is expected to meet the need in a broad range of HetNet environments with adequate balance between performance and complexity.
Deep Motif Dashboard: Visualizing and Understanding Genomic Sequences Using Deep Neural Networks
Venue: arXiv cs.LG 2016
Year: 2016

Deep neural network (DNN) models have recently obtained state-of-the-art prediction accuracy for the transcription factor binding (TFBS) site classification task. However, it remains unclear how these approaches identify meaningful DNA sequence signals and give insights as to why TFs bind to certain locations. In this paper, we propose a toolkit called the Deep Motif Dashboard (DeMo Dashboard) which provides a suite of visualization strategies to extract motifs, or sequence patterns from deep neural network models for TFBS classification. We demonstrate how to visualize and understand three important DNN models: convolutional, recurrent, and convolutional-recurrent networks. Our first visualization method is finding a test sequence's saliency map which uses first-order derivatives to describe the importance of each nucleotide in making the final prediction. Second, considering recurrent models make predictions in a temporal manner (from one end of a TFBS sequence to the other), we introduce temporal output scores, indicating the prediction score of a model over time for a sequential input. Lastly, a class-specific visualization strategy finds the optimal input sequence for a given TFBS positive class via stochastic gradient optimization. Our experimental results indicate that a convolutional-recurrent architecture performs the best among the three architectures. The visualization techniques indicate that CNN-RNN makes predictions by modeling both motifs as well as dependencies among them.