OpenRaiser/Intern-Atlas huggingface dataset:
Decentralized Detection in Energy Harvesting Wireless Sensor Networks
Venue: arXiv cs.IT 2016
Year: 2016

We consider a decentralized hypothesis testing problem in which several peripheral energy harvesting sensors are arranged in parallel. Each sensor makes a noisy observation of a time varying phenomenon, and sends a message about the present hypothesis towards a fusion center at each time instance t. The fusion center, using the aggregate of the received messages during the time instance t, makes a decision about the state of the present hypothesis. We assume that each sensor is an energy harvesting device and is capable of harvesting all the energy it needs to communicate from its environment. Our contribution is to formulate and analyze the decentralized detection problem when the energy harvesting sensors are allowed to form a long term energy usage policy. Our analysis is based on a queuing-theoretic model for the battery. Then, by using numerical simulations, we show how the resulting performance differs from the energy unconstrained case.
Automatic Verification of Iterated Separating Conjunctions using Symbolic Execution
Venue: arXiv cs.PL 2016
Year: 2016

In permission logics such as separation logic, the iterated separating conjunction is a quantifier denoting access permission to an unbounded set of heap locations. In contrast to recursive predicates, iterated separating conjunctions do not prescribe a structure on the locations they range over, and so do not restrict how to traverse and modify these locations. This flexibility is important for the verification of random-access data structures such as arrays and data structures that can be traversed in multiple ways such as graphs. Despite its usefulness, no automatic program verifier natively supports iterated separating conjunctions; they are especially difficult to incorporate into symbolic execution engines, the prevalent technique for building verifiers for these logics. In this paper, we present the first symbolic execution technique to support general iterated separating conjunctions. We propose a novel representation of symbolic heaps and flexible support for logical specifications that quantify over heap locations. Our technique exhibits predictable and fast performance despite employing quantifiers at the SMT level, by carefully controlling quantifier instantiations. It is compatible with other features of permission logics such as fractional permissions, recursive predicates, and abstraction functions. Our technique is implemented as an extension of the Viper verification infrastructure.
Finding Relationships between Socio-Technical Aspects and Personality Traits by Mining Developer E-mails
Venue: arXiv cs.SE 2016
Year: 2016

Personality traits influence most, if not all, of the human activities, from those as natural as the way people walk, talk, dress and write to those most complex as the way they interact with others. Most importantly, personality influences the way people make decisions including, in the case of developers, the criteria they consider when selecting a software project they want to participate. Most of the works that study the influence of social, technical and human factors in software development projects have been focused on the impact of communications in software quality. For instance, on identifying predictors to detect files that may contain bugs before releasing an enhanced version of a software product. Only a few of these works focus on the analysis of personality traits of developers with commit permissions (committers) in Free/Libre and Open-Source Software projects and their relationship with the software artifacts they interact with. This paper presents an approach, based on the automatic recognition of personality traits from e-mails sent by committers in FLOSS projects, to uncover relationships between the social and technical aspects that occur during the software development process. Our experimental results suggest the existence of some relationships among personality traits projected by the committers through their e-mails and the social (communication) and technical activities they undertake. This work is a preliminary study aimed at supporting the setting up of efficient work teams in software development projects based on an appropriate mix of stakeholders taking into account their personality traits.
Probabilistic Relational Model Benchmark Generation
Venue: arXiv cs.LG 2016
Year: 2016

The validation of any database mining methodology goes through an evaluation process where benchmarks availability is essential. In this paper, we aim to randomly generate relational database benchmarks that allow to check probabilistic dependencies among the attributes. We are particularly interested in Probabilistic Relational Models (PRMs), which extend Bayesian Networks (BNs) to a relational data mining context and enable effective and robust reasoning over relational data. Even though a panoply of works have focused, separately , on the generation of random Bayesian networks and relational databases, no work has been identified for PRMs on that track. This paper provides an algorithmic approach for generating random PRMs from scratch to fill this gap. The proposed method allows to generate PRMs as well as synthetic relational data from a randomly generated relational schema and a random set of probabilistic dependencies. This can be of interest not only for machine learning researchers to evaluate their proposals in a common framework, but also for databases designers to evaluate the effectiveness of the components of a database management system.
Active Requirement Mining of Bounded-Time Temporal Properties of Cyber-Physical Systems
Venue: arXiv cs.SY 2016
Year: 2016

This paper uses active learning to solve the problem of mining bounded-time signal temporal requirements of cyber-physical systems or simply the requirement mining problem. By utilizing robustness degree, we formulates the requirement mining problem into two optimization problems, a parameter synthesis problem and a falsification problem. We then propose a new active learning algorithm called Gaussian Process Adaptive Confidence Bound (GP-ACB) to help solving the falsification problem. We show theoretically that the GP-ACB algorithm has a lower regret bound thus a larger convergence rate than some existing active learning algorithms, such as GP-UCB. We finally illustrate and apply our requirement mining algorithm on two case studies, the Ackley's function and a real world automatic transmission model. The case studies show that our mining algorithm with GP-ACB outperforms others, such as those based on Nelder-Mead, by an average of 30% to 40%. Our results demonstrate that there is a principled and efficient way of extracting requirements for complex cyber-physical systems.
A Nonlinear Weighted Total Variation Image Reconstruction Algorithm for Electrical Capacitance Tomography
Venue: arXiv cs.CV 2016
Year: 2016

A new iterative image reconstruction algorithm for electrical capacitance tomography (ECT) is proposed that is based on iterative soft thresholding of a total variation penalty and adaptive reweighted compressive sensing. This algorithm encourages sharp changes in the ECT image and overcomes the disadvantage of the $l_1$ minimization by equipping the total variation with an adaptive weighting depending on the reconstructed image. Moreover, the non-linear effect is also partially reduced due to the adoption of an updated sensitivity matrix. Simulation results show that the proposed algorithm recovers ECT images more precisely than existing state-of-the-art algorithms and therefore is suitable for the imaging of multiphase systems in industrial or medical applications.
Some Remarks on Conway and Iteration Theories
Venue: arXiv cs.LO 2016
Year: 2016

We present an axiomatization of Conway theories which yields,as a corollary, a very concise axiomatization of iteration theories satisfying the functorial implication for base morphisms.
Decentralized State Estimation via a Hybrid of Consensus and Covariance intersection
Venue: arXiv cs.SY 2016
Year: 2016

This paper presents a new recursive information consensus filter for decentralized dynamic-state estimation. No structure is assumed about the topology of the network and local estimators are assumed to have access only to local information. The network need not be connected at all times. Consensus over priors which might become correlated is performed through Covariance Intersection (CI) and consensus over new information is handled using weights based on a Metropolis Hastings Markov Chains. We establish bounds for estimation performance and show that our method produces unbiased conservative estimates that are better than CI. The performance of the proposed method is evaluated and compared with competing algorithms on an atmospheric dispersion problem.
Interactive and Scale Invariant Segmentation of the Rectum/Sigmoid via User-Defined Templates
Venue: arXiv cs.CV 2016
Year: 2016

Among all types of cancer, gynecological malignancies belong to the 4th most frequent type of cancer among women. Besides chemotherapy and external beam radiation, brachytherapy is the standard procedure for the treatment of these malignancies. In the progress of treatment planning, localization of the tumor as the target volume and adjacent organs of risks by segmentation is crucial to accomplish an optimal radiation distribution to the tumor while simultaneously preserving healthy tissue. Segmentation is performed manually and represents a time-consuming task in clinical daily routine. This study focuses on the segmentation of the rectum/sigmoid colon as an Organ-At-Risk in gynecological brachytherapy. The proposed segmentation method uses an interactive, graph-based segmentation scheme with a user-defined template. The scheme creates a directed two dimensional graph, followed by the minimal cost closed set computation on the graph, resulting in an outlining of the rectum. The graphs outline is dynamically adapted to the last calculated cut. Evaluation was performed by comparing manual segmentations of the rectum/sigmoid colon to results achieved with the proposed method. The comparison of the algorithmic to manual results yielded to a Dice Similarity Coefficient value of 83.85+/-4.08%, in comparison to 83.97+/-8.08% for the comparison of two manual segmentations of the same physician. Utilizing the proposed methodology resulted in a median time of 128 seconds per dataset, compared to 300 seconds needed for pure manual segmentation.
Compositional model checking of concurrent systems, with Petri nets
Venue: arXiv cs.LO 2016
Year: 2016

Compositionality and process equivalence are both standard concepts of process algebra. Compositionality means that the behaviour of a compound system relies only on the behaviour of its components, i.e. there is no emergent behaviour. Process equivalence means that the explicit statespace of a system takes a back seat to its interaction patterns: the information that an environment can obtain though interaction. Petri nets are a classical, yet widely used and understood, model of concurrency. Nevertheless, they have often been described as a non-compositional model, and tools tend to deal with monolithic, globally-specified models. This tutorial paper concentrates on Petri Nets with Boundaries (PNB): a compositional, graphical algebra of 1-safe nets, and its applications to reachability checking within the tool Penrose. The algorithms feature the use of compositionality and process equivalence, a powerful combination that can be harnessed to improve the performance of checking reachability and coverability in several common examples where Petri nets model realistic concurrent systems.
Mean-Field Semantics for a Process Calculus for Spatially-Explicit Ecological Models
Venue: arXiv cs.LO 2016
Year: 2016

We define a mean-field semantics for S-PALPS, a process calculus for spatially-explicit, individual-based modeling of ecological systems. The new semantics of S-PALPS allows an interpretation of the average behavior of a system as a set of recurrence equations. Recurrence equations are a useful approximation when dealing with a large number of individuals, as it is the case in epidemiological studies. As a case study, we compute a set of recurrence equations capturing the dynamics of an individual-based model of the transmission of dengue in Bello (Antioquia), Colombia.
Right Ideals of a Ring and Sublanguages of Science
Venue: arXiv cs.CL 2016
Year: 2016

Among Zellig Harris's numerous contributions to linguistics his theory of the sublanguages of science probably ranks among the most underrated. However, not only has this theory led to some exhaustive and meaningful applications in the study of the grammar of immunology language and its changes over time, but it also illustrates the nature of mathematical relations between chunks or subsets of a grammar and the language as a whole. This becomes most clear when dealing with the connection between metalanguage and language, as well as when reflecting on operators. This paper tries to justify the claim that the sublanguages of science stand in a particular algebraic relation to the rest of the language they are embedded in, namely, that of right ideals in a ring.
Modeling the Sequence of Brain Volumes by Local Mesh Models for Brain Decoding
Venue: arXiv cs.LG 2016
Year: 2016

We represent the sequence of fMRI (Functional Magnetic Resonance Imaging) brain volumes recorded during a cognitive stimulus by a graph which consists of a set of local meshes. The corresponding cognitive process, encoded in the brain, is then represented by these meshes each of which is estimated assuming a linear relationship among the voxel time series in a predefined locality. First, we define the concept of locality in two neighborhood systems, namely, the spatial and functional neighborhoods. Then, we construct spatially and functionally local meshes around each voxel, called seed voxel, by connecting it either to its spatial or functional p-nearest neighbors. The mesh formed around a voxel is a directed sub-graph with a star topology, where the direction of the edges is taken towards the seed voxel at the center of the mesh. We represent the time series recorded at each seed voxel in terms of linear combination of the time series of its p-nearest neighbors in the mesh. The relationships between a seed voxel and its neighbors are represented by the edge weights of each mesh, and are estimated by solving a linear regression equation. The estimated mesh edge weights lead to a better representation of information in the brain for encoding and decoding of the cognitive tasks. We test our model on a visual object recognition and emotional memory retrieval experiments using Support Vector Machines that are trained using the mesh edge weights as features. In the experimental analysis, we observe that the edge weights of the spatial and functional meshes perform better than the state-of-the-art brain decoding models.
Spectrum Pooling in MmWave Networks: Opportunities, Challenges, and Enablers
Venue: arXiv cs.IT 2016
Year: 2016

Motivated by the intrinsic characteristics of mmWave technologies, we discuss the possibility of an authorization regime that allows spectrum sharing between multiple operators, also referred to as spectrum pooling. In particular, considering user rate as the performance measure, we assess the benefit of coordination among the networks of different operators, study the impact of beamforming both at the base stations and at the user terminals, and analyze the pooling performance at different frequency carriers. We also discuss the enabling spectrum mechanisms, architectures, and protocols required to make spectrum pooling work in real networks. Our initial results show that, from a technical perspective, spectrum pooling at mmWave has the potential for a more efficient spectrum use than a traditional exclusive spectrum allocation to a single operator. However, further studies are needed in order to reach a thorough understanding of this matter, and we hope that this paper will help stimulate further research in this area.
Formal Specification and Analysis of Autonomous Systems under Partial Compliance
Venue: arXiv cs.RO 2016
Year: 2016

The widespread adoption of autonomous systems depends on providing guarantees of safety and functional correctness, at both design time and runtime. Information about the extent to which functional requirements can be met in combination with non-functional requirements (NFRs) -- i.e. requirements that can be partially complied with -- , under dynamic and uncertain environments, provides opportunities to enhance the safety and functional correctness of systems at design time. We present a technique to formally define system attributes that can change or be changed to deal with dynamic and uncertain environments (denominated weakened specifications) as a partially ordered lattice, and to automatically explore the system under different specifications, using probabilistic model checking, to find the likelihood of satisfying a requirement. The resulting probabilities form boundaries of "optimal specifications", analogous to Pareto frontiers in multi-objective optimization, informing the designer about the system's capabilities, such as resilience or robustness, when changing its attributes to deal with dynamic and uncertain environments. We illustrate the proposed technique through a domestic robotic assistant example.
The Domination Game: Proving the 3/5 Conjecture on Isolate-Free Forests
Venue: arXiv cs.DS 2016
Year: 2016

We analyze the domination game, where two players, Dominator and Staller, construct together a dominating set M in a given graph, by alternately selecting vertices into M. Each move must increase the size of the dominated set. The players have opposing goals: Dominator wishes M to be as small as possible, and Staller has the opposite goal. Kinnersley, West and Zamani conjectured that when both players play optimally on an isolate-free forest, there is a guaranteed upper bound for the size of the dominating set that depends only on the size n of the forest. This bound is 3n/5 when the first player is Dominator, and (3n+2)/5 when the first player is Staller. The conjecture was proved for specific families of forests by Kinnersley et al. and later extended by Bujtas. Here we prove it for all isolate-free forests, by supplying an algorithm for Dominator that guarantees the desired bound.
GeoGebra Tools with Proof Capabilities
Venue: arXiv cs.AI 2016
Year: 2016

We report about significant enhancements of the complex algebraic geometry theorem proving subsystem in GeoGebra for automated proofs in Euclidean geometry, concerning the extension of numerous GeoGebra tools with proof capabilities. As a result, a number of elementary theorems can be proven by using GeoGebra's intuitive user interface on various computer architectures including native Java and web based systems with JavaScript. We also provide a test suite for benchmarking our results with 200 test cases.
Decision Forests, Convolutional Networks and the Models in-Between
Venue: arXiv cs.CV 2016
Year: 2016

This paper investigates the connections between two state of the art classifiers: decision forests (DFs, including decision jungles) and convolutional neural networks (CNNs). Decision forests are computationally efficient thanks to their conditional computation property (computation is confined to only a small region of the tree, the nodes along a single branch). CNNs achieve state of the art accuracy, thanks to their representation learning capabilities. We present a systematic analysis of how to fuse conditional computation with representation learning and achieve a continuum of hybrid models with different ratios of accuracy vs. efficiency. We call this new family of hybrid models conditional networks. Conditional networks can be thought of as: i) decision trees augmented with data transformation operators, or ii) CNNs, with block-diagonal sparse weight matrices, and explicit data routing functions. Experimental validation is performed on the common task of image classification on both the CIFAR and Imagenet datasets. Compared to state of the art CNNs, our hybrid models yield the same accuracy with a fraction of the compute cost and much smaller number of parameters.
Performance Assessment of WhatsApp and IMO on Android Operating System (Lollipop and KitKat) during VoIP calls using 3G or WiFi
Venue: arXiv cs.PF 2016
Year: 2016

This paper assesses the performance of mobile messaging and VoIP connections. We investigate the CPU usage of WhatsApp and IMO under different scenarios. This analysis also enabled a comparison of the performance of these applications on two Android operating system (OS) versions: KitKat or Lollipop. Two models of smartphones were considered, viz. Galaxy Note 4 and Galaxy S4. The applications behavior was statistically investigated for both sending and receiving VoIP calls. Connections have been examined over 3G and WiFi. The handset model plays a decisive role in CPU usage of the application. t-tests showed that IMO has a better performance that WhatsApp whatever be the Android at a significance level 1%, on Galaxy Note 4. In contrast, WhatsApp requires less CPU than IMO on Galaxy S4 whatever be the OS and access (3G/WiFi). Galaxy Note 4 using WiFi always outperformed S4 in terms of processing efficiency.
Capacity Results for the Multicast Cognitive Interference Channel
Venue: arXiv cs.IT 2016
Year: 2016

The capacity region of the Multicast Cognitive Interference Channel (CIFC) is investigated. This channel consists of two independent transmitters that wish to multicast two different messages, each of them to a different set of users. In addition, one of the transmitters --commonly referred to as the cognitive transmitter-- has prior non-causal knowledge of both messages to be transmitted. This scenario combines difficulties and challenges arising in the Interference Channel, the Broadcast Channel and multicasting communications. Our aim concerns the derivation of optimal interference mitigation techniques in such a challenging communication setup. We investigate to this end the multi-primary CIFC and its dual multi-secondary CIFC under various interference regimes as an attempt to build a thorough understanding for the more general setting. It is shown that, for some regimes, well-known coding techniques for the conventional CIFC remain still optimal in the presence of multicasting. While in other regimes, evolved encoding and/or decoding strategies are crucial. A careful use of these coding schemes and new outer bounding techniques allows us to characterize the capacity region for several classes of discrete memoryless and Gaussian channels in different interference regimes.
Computing the Power Distribution in the IMF
Venue: arXiv cs.GT 2016
Year: 2016

The International Monetary Fund is one of the largest international organizations using a weighted voting system. The weights of its 188 members are determined by a fixed amount of basic votes plus some extra votes for so-called Special Drawing Rights (SDR). On January 26, 2016, the conditions for the SDRs were increased at the 14th General Quota Review, which drastically changed the corresponding voting weights. However, since the share of voting weights in general is not equal to the influence, of a committee member on the committees overall decision, so-called power indices were introduced. So far the power distribution of the IMF was only computed by either approximation procedures or smaller games than then entire Board of Governors consisting of 188 members. We improve existing algorithms, based on dynamic programming, for the computation of power indices and provide the exact results for the IMF Board of Governors before and after the increase of voting weights. Tuned low-level details of the algorithms allow the repeated routine with sparse computational resources and can of course be applied to other large voting bodies. It turned out that the Banzhaf power shares are rather sensitive to changes of the quota.