OpenRaiser/Intern-Atlas huggingface dataset:
A Safe Regression Testing Technique for Web Services based on WSDL Specification
Venue: arXiv cs.SE 2015
Year: 2015

Specification-based regression testing of web services is an important activity which verifies the quality of web services. A major problem in web services is that only provider has the source code and both user and broker only have the XML based specification. So from the perspective of user and broker, specification based regression testing of web services is needed. The existing techniques are code based. Due to the dynamic behavior of web services, web services undergo maintenance and evolution process rapidly. Retesting of web services is required in order to verify the impact of changes. In this paper, we present an automated safe specification based regression testing approach that uses original and modified WSDL specifications for change identification. All the relevant test cases are selected as reusable hence our regression test selection approach is safe.
A density compensation-based path computing model for measuring semantic similarity
Venue: arXiv cs.AI 2015
Year: 2015

The shortest path between two concepts in a taxonomic ontology is commonly used to represent the semantic distance between concepts in the edge-based semantic similarity measures. In the past, the edge counting is considered to be the default method for the path computation, which is simple, intuitive and has low computational complexity. However, a large lexical taxonomy of such as WordNet has the irregular densities of links between concepts due to its broad domain but. The edge counting-based path computation is powerless for this non-uniformity problem. In this paper, we advocate that the path computation is able to be separated from the edge-based similarity measures and form various general computing models. Therefore, in order to solve the problem of non-uniformity of concept density in a large taxonomic ontology, we propose a new path computing model based on the compensation of local area density of concepts, which is equal to the number of direct hyponyms of the subsumers of concepts in their shortest path. This path model considers the local area density of concepts as an extension of the edge-based path and converts the local area density divided by their depth into the compensation for edge-based path with an adjustable parameter, which idea has been proven to be consistent with the information theory. This model is a general path computing model and can be applied in various edge-based similarity algorithms. The experiment results show that the proposed path model improves the average correlation between edge-based measures with human judgments on Miller and Charles benchmark from less than 0.8 to more than 0.85, and has a big advantage in efficiency than information content (IC) computation in a dynamic ontology, thereby successfully solving the non-uniformity problem of taxonomic ontology.
Cellular-Base-Station Assisted Device-to-Device Communications in TV White Space
Venue: arXiv cs.NI 2015
Year: 2015

This paper presents a systematic approach to exploit TV white space (TVWS) for device-to-device (D2D) communications with the aid of the existing cellular infrastructure. The goal is to build a location-specific TVWS database, which provides a look-up table service for any D2D link to determine its maximum permitted emission power (MPEP) in an unlicensed digital TV (DTV) band. To achieve this goal, the idea of mobile crowd sensing is firstly introduced to collect active spectrum measurements from massive personal mobile devices. Considering the incompleteness of crowd measurements, we formulate the problem of unknown measurements recovery as a matrix completion problem and apply a powerful fixed point continuation algorithm to reconstruct the unknown elements from the known elements. By joint exploitation of the big spectrum data in its vicinity, each cellular base station further implements a nonlinear support vector machine algorithm to perform irregular coverage boundary detection of a licensed DTV transmitter. With the knowledge of the detected coverage boundary, an opportunistic spatial reuse algorithm is developed for each D2D link to determine its MPEP. Simulation results show that the proposed approach can successfully enable D2D communications in TVWS while satisfying the interference constraint from the licensed DTV services. In addition, to our best knowledge, this is the first try to explore and exploit TVWS inside the DTV protection region resulted from the shadowing effect. Potential application scenarios include communications between internet of vehicles in the underground parking, D2D communications in hotspots such as subway, game stadiums, and airports, etc.
The decision problem for a three-sorted fragment of set theory with restricted quantification and finite enumerations
Venue: arXiv cs.LO 2015
Year: 2015

We solve the satisfiability problem for a three-sorted fragment of set theory (denoted $3LQST_0^R$), which admits a restricted form of quantification over individual and set variables and the finite enumeration operator $\{\text{-}, \text{-}, \ldots, \text{-}\}$ over individual variables, by showing that it enjoys a small model property, i.e., any satisfiable formula $ψ$ of $3LQST_0^R$ has a finite model whose size depends solely on the length of $ψ$ itself. Several set-theoretic constructs are expressible by $3LQST_0^R$-formulae, such as some variants of the power set operator and the unordered Cartesian product. In particular, concerning the unordered Cartesian product, we show that when finite enumerations are used to represent the construct, the resulting formula is exponentially shorter than the one that can be constructed without resorting to such terms.
Benchmarking Big Data Systems: State-of-the-Art and Future Directions
Venue: arXiv cs.PF 2015
Year: 2015

The great prosperity of big data systems such as Hadoop in recent years makes the benchmarking of these systems become crucial for both research and industry communities. The complexity, diversity, and rapid evolution of big data systems gives rise to various new challenges about how we design generators to produce data with the 4V properties (i.e. volume, velocity, variety and veracity), as well as implement application-specific but still comprehensive workloads. However, most of the existing big data benchmarks can be described as attempts to solve specific problems in benchmarking systems. This article investigates the state-of-the-art in benchmarking big data systems along with the future challenges to be addressed to realize a successful and efficient benchmark.
Reversible Digital Filters Total Parametric Sensitivity Optimization using Non-canonical Hypercomplex Number Systems
Venue: arXiv cs.NA 2015
Year: 2015

Digital filter construction method, which is optimal by parametric sensitivity, based on using of non-canonical hypercomplex number systems is proposed and investigated. It is shown that the use of non-canonical hypercomplex number system with greater number of non-zero structure constants in multiplication table can significantly improve the sensitivity of the digital filter.
Monitoring Bounded LTL Properties Using Interval Analysis
Venue: arXiv cs.LO 2015
Year: 2015

Verification of temporal logic properties plays a crucial role in proving the desired behaviors of hybrid systems. In this paper, we propose an interval method for verifying the properties described by a bounded linear temporal logic. We relax the problem to allow outputting an inconclusive result when verification process cannot succeed with a prescribed precision, and present an efficient and rigorous monitoring algorithm that demonstrates that the problem is decidable. This algorithm performs a forward simulation of a hybrid automaton, detects a set of time intervals in which the atomic propositions hold, and validates the property by propagating the time intervals. A continuous state at a certain time computed in each step is enclosed by an interval vector that is proven to contain a unique solution. In the experiments, we show that the proposed method provides a useful tool for formal analysis of nonlinear and complex hybrid systems.
Extending The Lossy Spring-Loaded Inverted Pendulum Model with a Slider-Crank Mechanism
Venue: arXiv cs.RO 2015
Year: 2015

Spring Loaded Inverted Pendulum (SLIP) model has a long history in describing running behavior in animals and humans as well as has been used as a design basis for robots capable of dynamic locomotion. Anchoring the SLIP for lossy physical systems resulted in newer models which are extended versions of original SLIP with viscous damping in the leg. However, such lossy models require an additional mechanism for pumping energy to the system to control the locomotion and to reach a limit-cycle. Some studies solved this problem by adding an actively controllable torque actuation at the hip joint and this actuation has been successively used in many robotic platforms, such as the popular RHex robot. However, hip torque actuation produces forces on the COM dominantly at forward direction with respect to ground, making height control challenging especially at slow speeds. The situation becomes more severe when the horizontal speed of the robot reaches zero, i.e. steady hoping without moving in horizontal direction, and the system reaches to singularity in which vertical degrees of freedom is completely lost. To this end, we propose an extension of the lossy SLIP model with a slider-crank mechanism, SLIP- SCM, that can generate a stable limit-cycle when the body is constrained to vertical direction. We propose an approximate analytical solution to the nonlinear system dynamics of SLIP- SCM model to characterize its behavior during the locomotion. Finally, we perform a fixed-point stability analysis on SLIP-SCM model using our approximate analytical solution and show that proposed model exhibits stable behavior in our range of interest.
A New Approach to Coding in Content Based MANETs
Venue: arXiv cs.NI 2015
Year: 2015

In content-based mobile ad hoc networks (CB-MANETs), random linear network coding (NC) can be used to reliably disseminate large files under intermittent connectivity. Conventional NC involves random unrestricted coding at intermediate nodes. This however is vulnerable to pollution attacks. To avoid attacks, a brute force approach is to restrict the mixing at the source. However, source restricted NC generally reduces the robustness of the code in the face of errors, losses and mobility induced intermittence. CB-MANETs introduce a new option. Caching is common in CB MANETs and a fully reassembled cached file can be viewed as a new source. Thus, NC packets can be mixed at all sources (including the originator and the intermediate caches) yet still providing protection from pollution. The hypothesis we wish to test in this paper is whether in CB-MANETs with sufficient caches of a file, the performance (in terms of robustness) of the restricted coding equals that of unrestricted coding. In this paper, we examine and compare unrestricted coding to full cache coding, source only coding, and no coding. As expected, we find that full cache coding remains competitive with unrestricted coding while maintaining full protection against pollution attacks.
Performance assessment and design of finite length LDPC codes for the Gaussian wiretap channel
Venue: arXiv cs.IT 2015
Year: 2015

In this work we study the reliability and secrecy performance achievable by practical LDPC codes over the Gaussian wiretap channel. While several works have already addressed this problem in asymptotic conditions, i.e., under the hypothesis of codewords of infinite length, only a few approaches exist for the finite length regime. We propose an approach to measure the performance of practical codes and compare it with that achievable in asymptotic conditions. Moreover, based on the secrecy metrics we adopt to achieve this target, we propose a code optimization algorithm which allows to design irregular LDPC codes able to approach the ultimate performance limits even at moderately small codeword lengths (in the order of 10000 bits).
Entropy and Syntropy in the Context of Five-Valued Logics
Venue: arXiv cs.AI 2015
Year: 2015

This paper presents a five-valued representation of bifuzzy sets. This representation is related to a five-valued logic that uses the following values: true, false, inconsistent, incomplete and ambiguous. In the framework of five-valued representation, formulae for similarity, entropy and syntropy of bifuzzy sets are constructed.
A Real-time Cargo Damage Management System via a Sorting Array Triangulation Technique
Venue: arXiv cs.AI 2015
Year: 2015

This report covers an intelligent decision support system (IDSS), which handles an efficient and effective way to rapidly inspect containerized cargos for defection. Defection is either cargo exposure to radiation, physical damages such as holes, punctured surfaces, iron surface oxidation, etc. The system uses a sorting array triangulation technique (SAT) and surface damage detection (SDD) to conduct the inspection. This new technique saves time and money on finding damaged goods during transportation such that, instead of running $n$ inspections on $n$ containers, only 3 inspections per triangulation or a ratio of $3:n$ is required, assuming $n > 3$ containers. The damaged stack in the array is virtually detected contiguous to an actually-damaged cargo by calculating nearby distances of such cargos, delivering reliable estimates for the whole local stack population. The estimated values on damaged, somewhat damaged and undamaged cargo stacks, are listed and profiled after being sorted by the program, thereby submitted to the manager for a final decision. The report describes the problem domain and the implementation of the simulator prototype, showing how the system operates via software, hardware with/without human agents, conducting real-time inspections and management per se.
A framework for the simulation of CR-VANET channel sensing, coordination and allocation
Venue: arXiv cs.NI 2015
Year: 2015

VANETs are considered as networks of critical future importance, with the main concern being the safety of travelers and infrastructure. A lot of methods for coordination and channel allocation in the context of VANETs are being introduced. As such, the need of a framework to reliably compare the relative performances of different channel sensing, allocation and coordination schemes which takes into account the movement of vehicles is felt. In this paper, we introduce a framework that can be used to define and compare such schemes in a variety of scenarios. Simulation results clearly show the robustness of our technique by eliminating the misdetections and reducing to a great extent the false alarms.
What May Visualization Processes Optimize?
Venue: arXiv cs.HC 2015
Year: 2015

In this paper, we present an abstract model of visualization and inference processes and describe an information-theoretic measure for optimizing such processes. In order to obtain such an abstraction, we first examined six classes of workflows in data analysis and visualization, and identified four levels of typical visualization components, namely disseminative, observational, analytical and model-developmental visualization. We noticed a common phenomenon at different levels of visualization, that is, the transformation of data spaces (referred to as alphabets) usually corresponds to the reduction of maximal entropy along a workflow. Based on this observation, we establish an information-theoretic measure of cost-benefit ratio that may be used as a cost function for optimizing a data visualization process. To demonstrate the validity of this measure, we examined a number of successful visualization processes in the literature, and showed that the information-theoretic measure can mathematically explain the advantages of such processes over possible alternatives.
Recovering Residual Forensic Data from Smartphone Interactions with Cloud Storage Providers
Venue: arXiv cs.CR 2015
Year: 2015

There is a growing demand for cloud storage services such as Dropbox, Box, Syncplicity and SugarSync. These public cloud storage services can store gigabytes of corporate and personal data in remote data centres around the world, which can then be synchronized to multiple devices. This creates an environment which is potentially conducive to security incidents, data breaches and other malicious activities. The forensic investigation of public cloud environments presents a number of new challenges for the digital forensics community. However, it is anticipated that end-devices such as smartphones, will retain data from these cloud storage services. This research investigates how forensic tools that are currently available to practitioners can be used to provide a practical solution for the problems related to investigating cloud storage environments. The research contribution is threefold. First, the findings from this research support the idea that end-devices which have been used to access cloud storage services can be used to provide a partial view of the evidence stored in the cloud service. Second, the research provides a comparison of the number of files which can be recovered from different versions of cloud storage applications. In doing so, it also supports the idea that amalgamating the files recovered from more than one device can result in the recovery of a more complete dataset. Third, the chapter contributes to the documentation and evidentiary discussion of the artefacts created from specific cloud storage applications and different versions of these applications on iOS and Android smartphones.
Efficient Learning of Ensembles with QuadBoost
Venue: arXiv cs.LG 2015
Year: 2015

We first present a general risk bound for ensembles that depends on the Lp norm of the weighted combination of voters which can be selected from a continuous set. We then propose a boosting method, called QuadBoost, which is strongly supported by the general risk bound and has very simple rules for assigning the voters' weights. Moreover, QuadBoost exhibits a rate of decrease of its empirical error which is slightly faster than the one achieved by AdaBoost. The experimental results confirm the expectation of the theory that QuadBoost is a very efficient method for learning ensembles.
On SAT Models Enumeration in Itemset Mining
Venue: arXiv cs.AI 2015
Year: 2015

Frequent itemset mining is an essential part of data analysis and data mining. Recent works propose interesting SAT-based encodings for the problem of discovering frequent itemsets. Our aim in this work is to define strategies for adapting SAT solvers to such encodings in order to improve models enumeration. In this context, we deeply study the effects of restart, branching heuristics and clauses learning. We then conduct an experimental evaluation on SAT-Based itemset mining instances to show how SAT solvers can be adapted to obtain an efficient SAT model enumerator.
Network Creation Games: Think Global - Act Local
Venue: arXiv cs.GT 2015
Year: 2015

We investigate a non-cooperative game-theoretic model for the formation of communication networks by selfish agents. Each agent aims for a central position at minimum cost for creating edges. In particular, the general model (Fabrikant et al., PODC'03) became popular for studying the structure of the Internet or social networks. Despite its significance, locality in this game was first studied only recently (Bilò et al., SPAA'14), where a worst case locality model was presented, which came with a high efficiency loss in terms of quality of equilibria. Our main contribution is a new and more optimistic view on locality: agents are limited in their knowledge and actions to their local view ranges, but can probe different strategies and finally choose the best. We study the influence of our locality notion on the hardness of computing best responses, convergence to equilibria, and quality of equilibria. Moreover, we compare the strength of local versus non-local strategy-changes. Our results address the gap between the original model and the worst case locality variant. On the bright side, our efficiency results are in line with observations from the original model, yet we have a non-constant lower bound on the price of anarchy.
Agent-Based Product Configuration: towards Generalized Consensus Seeking
Venue: arXiv cs.MA 2015
Year: 2015

This paper will present an evolution of a fuzzy agent based platform which performed products configuration. As a first step, we used the notion of consensus to establish robust results at the end of the configuration process. We implemented the concept of generalized consensus which implied the consideration of consensuses from the beginning, in this way robust data are treated during the entire process and the final result enables the designer to distinguish the robust components and flexible ones in a set of configurations.
On defining generalized rank weights
Venue: arXiv cs.IT 2015
Year: 2015

This paper investigates the generalized rank weights, with a definition implied by the study of the generalized rank weight enumerator. We study rank metric codes over $L$, where $L$ is a finite Galois extension of a field $K$. This is a generalization of the case where $K = \mathbb{F}_q$ and $L = \mathbb{F}_{q^m}$ of Gabidulin codes to arbitrary characteristic. We show equivalence to previous definitions, in particular the ones by Kurihara-Matsumoto-Uyematsu, Oggier-Sboui and Ducoat. As an application of the notion of generalized rank weights, we discuss codes that are degenerate with respect to the rank metric.
Compact Shape Trees: A Contribution to the Forest of Shape Correspondences and Matching Methods
Venue: arXiv cs.CV 2015
Year: 2015

We propose a novel technique, termed compact shape trees, for computing correspondences of single-boundary 2-D shapes in O(n2) time. Together with zero or more features defined at each of n sample points on the shape's boundary, the compact shape tree of a shape comprises the O(n) collection of vectors emanating from any of the sample points on the shape's boundary to the rest of the sample points on the boundary. As it turns out, compact shape trees have a number of elegant properties both in the spatial and frequency domains. In particular, via a simple vector-algebraic argument, we show that the O(n) collection of vectors in a compact shape tree possesses at least the same discriminatory power as the O(n2) collection of lines emanating from each sample point to every other sample point on a shape's boundary. In addition, we describe neat approaches for achieving scale and rotation invariance with compact shape trees in the spatial domain; by viewing compact shape trees as aperiodic discrete signals, we also prove scale and rotation invariance properties for them in the Fourier domain. Towards these, along the way, using concepts from differential geometry and the Calculus, we propose a novel theory for sampling 2-D shape boundaries in a scale and rotation invariant manner. Finally, we propose a number of shape recognition experiments to test the efficacy of our concept.
Stochastic Digital Backpropagation with Residual Memory Compensation
Venue: arXiv cs.IT 2015
Year: 2015

Stochastic digital backpropagation (SDBP) is an extension of digital backpropagation (DBP) and is based on the maximum a posteriori principle. SDBP takes into account noise from the optical amplifiers in addition to handling deterministic linear and nonlinear impairments. The decisions in SDBP are taken on a symbol-by-symbol (SBS) basis, ignoring any residual memory, which may be present due to non-optimal processing in SDBP. In this paper, we extend SDBP to account for memory between symbols. In particular, two different methods are proposed: a Viterbi algorithm (VA) and a decision directed approach. Symbol error rate (SER) for memory-based SDBP is significantly lower than the previously proposed SBS-SDBP. For inline dispersion-managed links, the VA-SDBP has up to 10 and 14 times lower SER than DBP for QPSK and 16-QAM, respectively.