OpenRaiser/Intern-Atlas huggingface dataset:
TDSelector: A Training Data Selection Method for Cross-Project Defect Prediction
Venue: arXiv cs.SE 2016
Year: 2016

In recent years, cross-project defect prediction (CPDP) attracted much attention and has been validated as a feasible way to address the problem of local data sparsity in newly created or inactive software projects. Unfortunately, the performance of CPDP is usually poor, and low quality training data selection has been regarded as a major obstacle to achieving better prediction results. To the best of our knowledge, most of existing approaches related to this topic are only based on instance similarity. Therefore, the objective of this work is to propose an improved training data selection method for CPDP that considers both similarity and the number of defects each training instance has (denoted by defects), which is referred to as TDSelector, and to demonstrate the effectiveness of the proposed method. Our experiments were conducted on 14 projects (including 15 data sets) collected from two public repositories. The results indicate that, in a specific CPDP scenario, the TDSelector-based bug predictor performs, on average, better than those based on the baseline methods, and the AUC (area under ROC curve) values are increased by up to 10.6 and 4.3%, respectively. Besides, an additional experiment shows that selecting those instances with more bugs directly as training data can further improve the performance of the bug predictor trained by our method.
Modeling documents with Generative Adversarial Networks
Venue: arXiv cs.LG 2016
Year: 2016

This paper describes a method for using Generative Adversarial Networks to learn distributed representations of natural language documents. We propose a model that is based on the recently proposed Energy-Based GAN, but instead uses a Denoising Autoencoder as the discriminator network. Document representations are extracted from the hidden layer of the discriminator and evaluated both quantitatively and qualitatively.
A Rigorous Framework for Specification, Analysis and Enforcement of Access Control Policies
Venue: arXiv cs.SE 2016
Year: 2016

Access control systems are widely used means for the protection of computing systems. They are defined in terms of access control policies regulating the accesses to system resources. In this paper, we introduce a formally-defined, fully-implemented framework for specification, analysis and enforcement of attribute-based access control policies. The framework rests on FACPL, a language with a compact, yet expressive, syntax for specification of real-world access control policies and with a rigorously defined denotational semantics. The framework enables the automatic verification of properties regarding both the authorisations enforced by single policies and the relationships among multiple policies. Effectiveness and performance of the analysis rely on a semantic-preserving representation of FACPL policies in terms of SMT formulae and on the use of efficient SMT solvers. Our analysis approach explicitly addresses some crucial aspects of policy evaluation, as e.g. missing attributes, erroneous values and obligations, which are instead overlooked in other proposals. The framework is supported by Java-based tools, among which an Eclipse- based IDE offering a tailored development and analysis environment for FACPL policies and a Java library for policy enforcement. We illustrate the framework and its formal ingredients by means of an e-Health case study, while its effectiveness is assessed by means of performance stress tests and experiments on a well-established benchmark.
Shape Estimation from Defocus Cue for Microscopy Images via Belief Propagation
Venue: arXiv cs.CV 2016
Year: 2016

In recent years, the usefulness of 3D shape estimation is being realized in microscopic or close-range imaging, as the 3D information can further be used in various applications. Due to limited depth of field at such small distances, the defocus blur induced in images can provide information about the 3D shape of the object. The task of `shape from defocus' (SFD), involves the problem of estimating good quality 3D shape estimates from images with depth-dependent defocus blur. While the research area of SFD is quite well-established, the approaches have largely demonstrated results on objects with bulk/coarse shape variation. However, in many cases, objects studied under microscopes often involve fine/detailed structures, which have not been explicitly considered in most methods. In addition, given that, in recent years, large data volumes are typically associated with microscopy related applications, it is also important for such SFD methods to be efficient. In this work, we provide an indication of the usefulness of the Belief Propagation (BP) approach in addressing these concerns for SFD. BP has been known to be an efficient combinatorial optimization approach, and has been empirically demonstrated to yield good quality solutions in low-level vision problems such as image restoration, stereo disparity estimation etc. For exploiting the efficiency of BP in SFD, we assume local space-invariance of the defocus blur, which enables the application of BP in a straightforward manner. Even with such an assumption, the ability of BP to provide good quality solutions while using non-convex priors, reflects in yielding plausible shape estimates in presence of fine structures on the objects under microscopy imaging.
GPU Accelerated Explicit Time Integration Methods for Electro-Quasistatic Fields
Venue: arXiv cs.CE 2016
Year: 2016

Electro-quasistatic field problems involving nonlinear materials are commonly discretized in space using finite elements. In this paper, it is proposed to solve the resulting system of ordinary differential equations by an explicit Runge-Kutta-Chebyshev time-integration scheme. This mitigates the need for Newton-Raphson iterations, as they are necessary within fully implicit time integration schemes. However, the electro-quasistatic system of ordinary differential equations has a Laplace-type mass matrix such that parts of the explicit time-integration scheme remain implicit. An iterative solver with constant preconditioner is shown to efficiently solve the resulting multiple right-hand side problem. This approach allows an efficient parallel implementation on a system featuring multiple graphic processing units.
Channel Measurements and Models for High-Speed Train Wireless Communication Systems in Tunnel Scenarios: A Survey
Venue: arXiv cs.NI 2016
Year: 2016

The rapid developments of high-speed trains (HSTs) introduce new challenges to HST wireless communication systems. Realistic HST channel models play a critical role in designing and evaluating HST communication systems. Due to the length limitation, bounding of tunnel itself, and waveguide effect, channel characteristics in tunnel scenarios are very different from those in other HST scenarios. Therefore, accurate tunnel channel models considering both large-scale and small-scale fading characteristics are essential for HST communication systems. Moreover, certain characteristics of tunnel channels have not been investigated sufficiently. This article provides a comprehensive review of the measurement campaigns in tunnels and presents some tunnel channel models using various modeling methods. Finally, future directions in HST tunnel channel measurements and modeling are discussed.
FolksoDrivenCloud: an annotation and process application for social collaborative networking
Venue: arXiv cs.CY 2016
Year: 2016

In this paper we present the FolksoDriven Cloud (FDC) built on Cloud and on Semantic technologies. Cloud computing has emerged in these recent years as the new paradigm for the provision of on-demand distributed computing resources. Semantic Web can be used for relationship between different data and descriptions of services to annotate provenance of repositories on ontologies. The FDC service is composed of a back-end which submits and monitors the documents, and a user front-end which allows users to schedule on-demand operations and to watch the progress of running processes. The impact of the proposed method is illustrated on a user since its inception.
Correctness of Hierarchical MCS Locks with Timeout
Venue: arXiv cs.DC 2016
Year: 2016

This manuscript serves as a correctness proof of the Hierarchical MCS locks with Timeout (HMCS-T) described in our paper titled "An Efficient Abortable-locking Protocol for Multi-level NUMA Systems" appearing in the proceedings of the 22nd ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming. HMCS-T is a very involved protocol. The system is stateful; the values of prior acquisition efforts affect the subsequent acquisition efforts. Also, the status of successors, predecessors, ancestors, and descendants affect steps followed by the protocol. The ability to make the protocol fully non-blocking leads to modifications to the \texttt{next} field, which causes deviation from the original MCS lock protocol both in acquisition and release. At several places, unconditional field updates are replaced with SWAP or CAS operations. We follow a multi-step approach to prove the correctness of HMCS-T. To demonstrate the correctness of the HMCS-T lock, we use the Spin model checking. Model checking causes a combinatorial explosion even to simulate a handful of threads. First, we understand the minimal, sufficient configurations necessary to prove safety properties of a single level of lock in the tree. We construct HMCS-T locks that represent these configurations. We model check these configurations, which proves the correctness of components of an HMCS-T lock. Finally, building upon these facts, we argue logically for the correctness of HMCS-T<n>.
On The Security Evaluation of Partial Password Implementations
Venue: arXiv cs.CR 2016
Year: 2016

A partial password is a mode of password-based authentication that is widely used, especially in the financial sector. It is based on a challenge-response protocol, where at each login attempt, a challenge requesting characters from randomly selected positions of a pre-shared secret is presented to the user. This model could be seen as a cheap way of preventing for example a malware or a key-logger installed on a user's device to learn the full password in a single step. Despite of the widespread adoption of this mechanism, especially by many UK banks, there is limited material in the open literature. Questions like how the security of the scheme varies with the sampling method employed to form the challenges or what are the existing server-side implementations are left unaddressed. In this paper, we study questions like how the security of this mechanism varies in relation to the number of challenge-response pairs available to an attacker under different ways of generating challenges. In addition, we discuss possible server-side implementations as "unofficially" listed in different online forums by information security ex- perts. To the best of our knowledge there is no formal academic literature in this direction and one of the aims of this paper is to motivate other researchers to study this topic.
SCALL: Software Component Allocator for Heterogeneous Embedded Systems
Venue: arXiv cs.SE 2016
Year: 2016

Allocation of software components on a heterogeneous computing platform involves hard decisions; handling different types of computing units with specific processing paradigms and a number of software components which require specific resources. An allocation method which explores the design space to provide a system architect with deployment alternatives in an early design phase can have a significant impact on the utilization of underlying platform. In this paper we present SCALL, an early prototype tool which uses heuristics and AHP for weighted multi-objective design space exploration to support systems architects in complex allocation decisions in early design phases.
Even $1 \times n$ Edge-Matching and Jigsaw Puzzles are Really Hard
Venue: arXiv cs.CC 2016
Year: 2016

We prove the computational intractability of rotating and placing $n$ square tiles into a $1 \times n$ array such that adjacent tiles are compatible--either equal edge colors, as in edge-matching puzzles, or matching tab/pocket shapes, as in jigsaw puzzles. Beyond basic NP-hardness, we prove that it is NP-hard even to approximately maximize the number of placed tiles (allowing blanks), while satisfying the compatibility constraint between nonblank tiles, within a factor of 0.9999999851. (On the other hand, there is an easy $1 \over 2$-approximation.) This is the first (correct) proof of inapproximability for edge-matching and jigsaw puzzles. Along the way, we prove NP-hardness of distinguishing, for a directed graph on $n$ nodes, between having a Hamiltonian path (length $n-1$) and having at most $0.999999284 (n-1)$ edges that form a vertex-disjoint union of paths. We use this gap hardness and gap-preserving reductions to establish similar gap hardness for $1 \times n$ jigsaw and edge-matching puzzles.
Abstracting Event-Driven Systems with Lifestate Rules
Venue: arXiv cs.PL 2016
Year: 2016

We present lifestate rules--an approach for abstracting event-driven object protocols. Developing applications against event-driven software frameworks is notoriously difficult. One reason why is that to create functioning applications, developers must know about and understand the complex protocols that abstract the internal behavior of the framework. Such protocols intertwine the proper registering of callbacks to receive control from the framework with appropriate application programming interface (API) calls to delegate back to it. Lifestate rules unify lifecycle and typestate constraints in one common specification language. Our primary contribution is a model of event-driven systems from which lifestate rules can be derived. We then apply specification mining techniques to learn lifestate specifications for Android framework types. In the end, our implementation is able to find several rules that characterize actual behavior of the Android framework.
Fronthaul-Aware Software-Defined Joint Resource Allocation and User Scheduling for 5G Networks
Venue: arXiv cs.NI 2016
Year: 2016

Software-defined networking (SDN) is the concept of decoupling the control and data planes to create a flexible and agile network, assisted by a central controller. However, the performance of SDN highly depends on the limitations in the fronthaul which are inadequately discussed in the existing literature. In this paper, a fronthaul-aware software-defined resource allocation mechanism is proposed for 5G wireless networks with in-band wireless fronthaul constraints. Considering the fronthaul capacity, the controller maximizes the time-averaged network throughput by enforcing a coarse correlated equilibrium (CCE) and incentivizing base stations (BSs) to locally optimize their decisions to ensure mobile users' (MUs) quality-of-service (QoS) requirements. By marrying tools from Lyapunov stochastic optimization and game theory, we propose a two-timescale approach where the controller gives recommendations, i.e., sub-carriers with low interference, in a long-timescale whereas BSs schedule their own MUs and allocate the available resources in every time slot. Numerical results show considerable throughput enhancements and delay reductions over a non-SDN network baseline.
Energy-Efficient Data Collection in Clustered Wireless Sensor Networks employing Distributed DCT
Venue: arXiv cs.NI 2016
Year: 2016

In this paper, a energy-efficient data collection method is proposed in which an integration between Discrete Cosine Transform (DCT) matrix and clustering in wireless sensor networks (WSNs) is exploited.Based on the fact that sensory data in WSNs is often highly correlated and is suitable to be transformed in DCT domain, we propose that each cluster from the networks only sends a small number of large DCT transformed coefficients to the base-station (BS) for data collection in two common ways, either directly or in multi-hop routing. All sensory data from the sensor network can be recovered based on the large coefficients received at the BS. We further analyze and formulate the communication cost as the power consumption for transmitting data in such networks based on stochastic problems. Some common clustering algorithms are applied and compared to verify the analysis and simulation results. Both noise and noiseless environments for the proposed method are considered.
In-Building Wideband Partition Loss Measurements at 2.5 GHz and 60 GHz
Venue: arXiv cs.NI 2016
Year: 2016

This paper contains measured data and empirical models for 2.5 & 60 GHz in-building propagation path loss and multipath delay spread. Path loss measurements were recorded using a broadband sliding correlator channel sounder which recorded over 39,000 Power Delay Profiles (PDPs) in 22 separate locations in a modern office building. Transmitters and receivers were separated by distances ranging from 3.5 to 27.4 meters, and were separated by a variety of obstructions, in order to create realistic environments for future single-cell-per-room wireless networks. Path loss data is coupled with site-specific information to provide insight into channel characteristics. These measurements and models may aid in the development of future in-building wireless networks in the unlicensed 2.4 GHz and 60 GHz bands.
Analysis of the noise in back-projection light field acquisition and its optimization
Venue: arXiv cs.CV 2016
Year: 2016

Light field reconstruction from images captured by focal plane sweeping can achieve high lateral resolution comparable to the modern camera sensor. This is impossible for the conventional micro-lenslet based light field capture systems. However, the severe defocus noise and the low depth resolution limit its applications. In this paper, we analyze the defocus noise and the depth resolution in the focal plane sweeping based light field reconstruction technique, and propose a method to reduce the defocus noise and improve the depth resolution. Both numerical and experimental results verify the proposed method.
The Joint Diffusion of a Digital Platform and its Complementary Goods: The Effects of Product Ratings and Observational Learning
Venue: arXiv cs.CY 2016
Year: 2016

The authors study the interdependent diffusion of an open source software (OSS) platform and its software complements. They quantify the role of OSS governance, quality signals such as product ratings, observational learning, and user actions upon adoption. To do so they extend the Bass Diffusion Model and apply it to a unique data set of 6 years of daily downloads of the Firefox browser and 52 of its add-ons. The study then re-casts the resulting differential equations into non-linear, discrete-time, state space forms; and estimate them using an MCMC approach to the Extended Kalman Filtern (EKF-MCMC). Unlike continuous-time filters, the EKF-MCMC approach avoids numerical integration, and so is more computational efficient, given the length of our time-series, high dimension of our state space and need to model heterogeneity. Results show, for example, that observational learning and add-on ratings increase the demand for Firefox add-ons; add-ons can increase the market potential of the Firefox platform; a slow add-on review process can diminish platform success; and OSS platforms (i.e. Chrome and Firefox) compete rather than complement each other.
Spectrum Allocation in Cognitive Networks
Venue: arXiv cs.NI 2016
Year: 2016

Cognitive Network is a technique which is used to improve the spectrum utilization. Current network scenario is experiencing the huge spectrum scarcity problem due to the fixed assignment policy so in this method great amount of spectrum remain unused. To overcome this limitation the spectrum allocation must be in dynamic manner. In this paper the spectrum allocation is discussed thoroughly. Interference is the most important factor that needs to be considered. It is caused by the environment (noise) or by other radio users. It limits the possibility of spectrum reuse. Channel assignment is one of the techniques used to control interference in the network. There exist a trade-off between network capacity and level of contention. In cognitive radio networks spectrum assignment or spectrum allocation or frequency assignment is used to avoid interference. It is the process of simultaneous selection of operating central frequency and bandwidth. In doing so, the process of sensing the spectrum becomes very crucial; it must be reliable, accurate and efficient. The accuracy of sensing affects the overall operation of cognitive networks. Accurate results not only lead to higher utilization of the spectrum but also preserve the privacy of primary user. Accuracy of sensing is highly affected by the natural causes like noise, shadowing, fading etc. There are many other challenges as well, like, hardware requirements, hidden node problem, security, sensing frequency and duration, decision fusion etc.
Generating Random Networks Without Short Cycles
Venue: arXiv cs.DS 2017
Year: 2017

Random graph generation is an important tool for studying large complex networks. Despite abundance of random graph models, constructing models with application-driven constraints is poorly understood. In order to advance state-of-the-art in this area, we focus on random graphs without short cycles as a stylized family of graphs, and propose the RandGraph algorithm for randomly generating them. For any constant k, when m=O(n^{1+1/[2k(k+3)]}), RandGraph generates an asymptotically uniform random graph with n vertices, m edges, and no cycle of length at most k using O(n^2m) operations. We also characterize the approximation error for finite values of n. To the best of our knowledge, this is the first polynomial-time algorithm for the problem. RandGraph works by sequentially adding $m$ edges to an empty graph with n vertices. Recently, such sequential algorithms have been successful for random sampling problems. Our main contributions to this line of research includes introducing a new approach for sequentially approximating edge-specific probabilities at each step of the algorithm, and providing a new method for analyzing such algorithms.
Steps Towards a Theory of Visual Information: Active Perception, Signal-to-Symbol Conversion and the Interplay Between Sensing and Control
Venue: arXiv cs.CV 2017
Year: 2017

This manuscript describes the elements of a theory of information tailored to control and decision tasks and specifically to visual data. The concept of Actionable Information is described, that relates to a notion of information championed by J. Gibson, and a notion of "complete information" that relates to the minimal sufficient statistics of a complete representation. It is shown that the "actionable information gap" between the two can be reduced by exercising control on the sensing process. Thus, senging, control and information are inextricably tied. This has consequences in the so-called "signal-to-symbol barrier" problem, as well as in the analysis and design of active sensing systems. It has ramifications in vision-based control, navigation, 3-D reconstruction and rendering, as well as detection, localization, recognition and categorization of objects and scenes in live video. This manuscript has been developed from a set of lecture notes for a summer course at the First International Computer Vision Summer School (ICVSS) in Scicli, Italy, in July of 2008. They were later expanded and amended for subsequent lectures in the same School in July 2009. Starting on November 1, 2009, they were further expanded for a special topics course, CS269, taught at UCLA in the Spring term of 2010.