OpenRaiser/Intern-Atlas huggingface dataset:
On Dynamic Flow-Sensitive Floating-Label Systems
Venue: arXiv cs.CR 2015
Year: 2015

Flow-sensitive analysis for information-flow control (IFC) allows data structures to have mutable security labels, i.e., labels that can change over the course of the computation. This feature is often used to boost the permissiveness of the IFC monitor, by rejecting fewer runs of programs, and to reduce the burden of explicit label annotations. However, adding flow-sensitive constructs (e.g., references or files) to a dynamic IFC system is subtle and may also introduce high-bandwidth covert channels. In this work, we extend LIO---a language-based floating-label system---with flow-sensitive references. The key insight to safely manipulating the label of a reference is to not only consider the label on the data stored in the reference, i.e., the reference label, but also the label on the reference label itself. Taking this into consideration, we provide an upgrade primitive that can be used to change the label of a reference in a safe manner. We additionally provide a mechanism for automatic upgrades to eliminate the burden of determining when a reference should be upgraded. This approach naturally extends to a concurrent setting, which has not been previously considered by dynamic flow-sensitive systems. For both our sequential and concurrent calculi we prove non-interference by embedding the flow-sensitive system into the original, flow-insensitive LIO calculus---a surprising result on its own.
Requirements Problem and Solution Concepts for Adaptive Systems Engineering, and their Relationship to Mathematical Optimisation, Decision Analysis, and Expected Utility Theory
Venue: arXiv cs.SE 2015
Year: 2015

Requirements Engineering (RE) focuses on eliciting, modelling, and analyzing the requirements and environment of a system-to-be in order to design its specification. The design of the specification, usually called the Requirements Problem (RP), is a complex problem solving task, as it involves, for each new system-to-be, the discovery and exploration of, and decision making in, new and ill-defined problem and solution spaces. The default RP in RE is to design a specification of the system-to-be which (i) is consistent with given requirements and conditions of its environment, and (ii) together with environment conditions satisfies requirements. This paper (i) shows that the Requirements Problem for Adaptive Systems (RPAS) is different from, and is not a subclass of the default RP, (ii) gives a formal definition of RPAS, and (iii) discusses implications for future research.
Multi-Target Tracking with Time-Varying Clutter Rate and Detection Profile: Application to Time-lapse Cell Microscopy Sequences
Venue: arXiv cs.CV 2015
Year: 2015

Quantitative analysis of the dynamics of tiny cellular and sub-cellular structures, known as particles, in time-lapse cell microscopy sequences requires the development of a reliable multi-target tracking method capable of tracking numerous similar targets in the presence of high levels of noise, high target density, complex motion patterns and intricate interactions. In this paper, we propose a framework for tracking these structures based on the random finite set Bayesian filtering framework. We focus on challenging biological applications where image characteristics such as noise and background intensity change during the acquisition process. Under these conditions, detection methods usually fail to detect all particles and are often followed by missed detections and many spurious measurements with unknown and time-varying rates. To deal with this, we propose a bootstrap filter composed of an estimator and a tracker. The estimator adaptively estimates the required meta parameters for the tracker such as clutter rate and the detection probability of the targets, while the tracker estimates the state of the targets. Our results show that the proposed approach can outperform state-of-the-art particle trackers on both synthetic and real data in this regime.
A coalgebraic semantics for causality in Petri nets
Venue: arXiv cs.LO 2015
Year: 2015

In this paper we revisit some pioneering efforts to equip Petri nets with compact operational models for expressing causality. The models we propose have a bisimilarity relation and a minimal representative for each equivalence class, and they can be fully explained as coalgebras on a presheaf category on an index category of partial orders. First, we provide a set-theoretic model in the form of a a causal case graph, that is a labeled transition system where states and transitions represent markings and firings of the net, respectively, and are equipped with causal information. Most importantly, each state has a poset representing causal dependencies among past events. Our first result shows the correspondence with behavior structure semantics as proposed by Trakhtenbrot and Rabinovich. Causal case graphs may be infinitely-branching and have infinitely many states, but we show how they can be refined to get an equivalent finitely-branching model. In it, states are equipped with symmetries, which are essential for the existence of a minimal, often finite-state, model. The next step is constructing a coalgebraic model. We exploit the fact that events can be represented as names, and event generation as name generation. Thus we can apply the Fiore-Turi framework: we model causal relations as a suitable category of posets with action labels, and generation of new events with causal dependencies as an endofunctor on this category. Then we define a well-behaved category of coalgebras. Our coalgebraic model is still infinite-state, but we exploit the equivalence between coalgebras over a class of presheaves and History Dependent automata to derive a compact representation, which is equivalent to our set-theoretical compact model. Remarkably, state reduction is automatically performed along the equivalence.
On the Economic Efficiency of the Combinatorial Clock Auction
Venue: arXiv cs.GT 2015
Year: 2015

Since the 1990s spectrum auctions have been implemented world-wide. This has provided for a practical examination of an assortment of auction mechanisms and, amongst these, two simultaneous ascending price auctions have proved to be extremely successful. These are the simultaneous multiround ascending auction (SMRA) and the combinatorial clock auction (CCA). It has long been known that, for certain classes of valuation functions, the SMRA provides good theoretical guarantees on social welfare. However, no such guarantees were known for the CCA. In this paper, we show that CCA does provide strong guarantees on social welfare provided the price increment and stopping rule are well-chosen. This is very surprising in that the choice of price increment has been used primarily to adjust auction duration and the stopping rule has attracted little attention. The main result is a polylogarithmic approximation guarantee for social welfare when the maximum number of items demanded $\mathcal{C}$ by a bidder is fixed. Specifically, we show that either the revenue of the CCA is at least an $Ω\Big(\frac{1}{\mathcal{C}^{2}\log n\log^2m}\Big)$-fraction of the optimal welfare or the welfare of the CCA is at least an $Ω\Big(\frac{1}{\log n}\Big)$-fraction of the optimal welfare, where $n$ is the number of bidders and $m$ is the number of items. As a corollary, the welfare ratio -- the worst case ratio between the social welfare of the optimum allocation and the social welfare of the CCA allocation -- is at most $O(\mathcal{C}^2 \cdot \log n \cdot \log^2 m)$. We emphasize that this latter result requires no assumption on bidders valuation functions. Finally, we prove that such a dependence on $\mathcal{C}$ is necessary. In particular, we show that the welfare ratio of the CCA is at least $Ω\Big(\mathcal{C} \cdot \frac{\log m}{\log \log m}\Big)$.
Learning Weak Constraints in Answer Set Programming
Venue: arXiv cs.AI 2015
Year: 2015

This paper contributes to the area of inductive logic programming by presenting a new learning framework that allows the learning of weak constraints in Answer Set Programming (ASP). The framework, called Learning from Ordered Answer Sets, generalises our previous work on learning ASP programs without weak constraints, by considering a new notion of examples as ordered pairs of partial answer sets that exemplify which answer sets of a learned hypothesis (together with a given background knowledge) are preferred to others. In this new learning task inductive solutions are searched within a hypothesis space of normal rules, choice rules, and hard and weak constraints. We propose a new algorithm, ILASP2, which is sound and complete with respect to our new learning framework. We investigate its applicability to learning preferences in an interview scheduling problem and also demonstrate that when restricted to the task of learning ASP programs without weak constraints, ILASP2 can be much more efficient than our previously proposed system.
LDAExplore: Visualizing Topic Models Generated Using Latent Dirichlet Allocation
Venue: arXiv cs.IR 2015
Year: 2015

We present LDAExplore, a tool to visualize topic distributions in a given document corpus that are generated using Topic Modeling methods. Latent Dirichlet Allocation (LDA) is one of the basic methods that is predominantly used to generate topics. One of the problems with methods like LDA is that users who apply them may not understand the topics that are generated. Also, users may find it difficult to search correlated topics and correlated documents. LDAExplore, tries to alleviate these problems by visualizing topic and word distributions generated from the document corpus and allowing the user to interact with them. The system is designed for users, who have minimal knowledge of LDA or Topic Modelling methods. To evaluate our design, we run a pilot study which uses the abstracts of 322 Information Visualization papers, where every abstract is considered a document. The topics generated are then explored by users. The results show that users are able to find correlated documents and group them based on topics that are similar.
Improving the Performance of Multi-class Intrusion Detection Systems using Feature Reduction
Venue: arXiv cs.NI 2015
Year: 2015

Intrusion detection systems (IDS) are widely studied by researchers nowadays due to the dramatic growth in network-based technologies. Policy violations and unauthorized access is in turn increasing which makes intrusion detection systems of great importance. Existing approaches to improve intrusion detection systems focus on feature selection or reduction since some features are irrelevant or redundant which when removed improve the accuracy as well as the learning time. In this paper we propose a hybrid feature selection method using Correlation-based Feature Selection and Information Gain. In our work we apply adaptive boosting using naïve Bayes as the weak (base) classifier. The key point in our research is that we are able to improve the detection accuracy with a reduced number of features while precisely determining the attack. Experimental results showed that our proposed method achieved high accuracy compared to methods using only 5-class problem. Correlation is done using Greedy search strategy and naïve Bayes as the classifier on the reduced NSL-KDD dataset.
The SYSU System for the Interspeech 2015 Automatic Speaker Verification Spoofing and Countermeasures Challenge
Venue: arXiv cs.SD 2015
Year: 2015

Many existing speaker verification systems are reported to be vulnerable against different spoofing attacks, for example speaker-adapted speech synthesis, voice conversion, play back, etc. In order to detect these spoofed speech signals as a countermeasure, we propose a score level fusion approach with several different i-vector subsystems. We show that the acoustic level Mel-frequency cepstral coefficients (MFCC) features, the phase level modified group delay cepstral coefficients (MGDCC) and the phonetic level phoneme posterior probability (PPP) tandem features are effective for the countermeasure. Furthermore, feature level fusion of these features before i-vector modeling also enhance the performance. A polynomial kernel support vector machine is adopted as the supervised classifier. In order to enhance the generalizability of the countermeasure, we also adopted the cosine similarity and PLDA scoring as one-class classifications methods. By combining the proposed i-vector subsystems with the OpenSMILE baseline which covers the acoustic and prosodic information further improves the final performance. The proposed fusion system achieves 0.29% and 3.26% EER on the development and test set of the database provided by the INTERSPEECH 2015 automatic speaker verification spoofing and countermeasures challenge.
Exogenous Quantum Operator Logic Based on Density Operators
Venue: arXiv cs.LO 2015
Year: 2015

Although quantum logic by using exogenous approach has been proposed for reasoning about closed quantum systems, an improvement would be worth to study quantum logic based on density operators instead of unit vectors in the state logic point of view. In order to achieve this, we build an exogenous quantum operator logic(EQOL) based on density operators for reasoning about open quantum systems. We show that this logic is sound and complete. Just as the exogenous quantum propositional logic(EQPL), by applying exogenous approach, EQOL is extended from the classical propositional logic, and is used to describe the state logic based on density operators. As its applications, we confirm the entanglement property about Bell states by reasoning and logical argument, also verify the existence of eavesdropping about the basic BB84 protocol. As a novel type of mathematical formalism for open quantum systems, we introduce an exogenous quantum Markov chain(EQMC) where its quantum states are labelled using EQOL formulae. Then, an example is given to illustrate the termination verification problem of a generalized quantum loop program described using EQMC.
Semantics of templates in a compositional framework for building logics
Venue: arXiv cs.LO 2015
Year: 2015

There is a growing need for abstractions in logic specification languages such as FO(.) and ASP. One technique to achieve these abstractions are templates (sometimes called macros). While the semantics of templates are virtually always described through a syntactical rewriting scheme, we present an alternative view on templates as second order definitions. To extend the existing definition construct of FO(.) to second order, we introduce a powerful compositional framework for defining logics by modular integration of logic constructs specified as pairs of one syntactical and one semantical inductive rule. We use the framework to build a logic of nested second order definitions suitable to express templates. We show that under suitable restrictions, the view of templates as macros is semantically correct and that adding them does not extend the descriptive complexity of the base logic, which is in line with results of existing approaches.
Partition-based Distributed Kalman Filter with plug and play features
Venue: arXiv cs.SY 2015
Year: 2015

In this paper we propose a novel partition-based distributed state estimation scheme for non-overlapping subsystems based on Kalman filter. The estimation scheme is designed in order to account, in a rigorous fashion, for dynamic coupling terms between subsystems, and for the uncertainty related to the state estimates performed by the neighboring subsystems. The online implementation of the proposed estimation scheme is scalable, since it involves (i) small-scale matrix operations to be carried out by the estimator embedded in each subsystem and (ii) neighbor-to-neighbor transmission of a limited amount of data. We provide theoretical conditions ensuring the estimation convergence. Reconfigurability of the proposed estimation scheme is allowed in case of plug and play operations. Simulation tests are provided to illustrate the effectiveness of the proposed algorithm.
Do Open Source Software Developers Listen to Their Users?
Venue: arXiv cs.SE 2015
Year: 2015

In application software, the satisfaction of target users makes the software more acceptable. Open Source Software (OSS) systems have neither the physical nor the commercial boundaries of proprietary software, thus users from all over the world can interact with them. This free access is advantageous, as increasing numbers of users are able to access OSS; there are more chances of improvement. This study examines the way users feedback is handled by OSS developers. In our survey, we have also inquired whether OSS developers consult professional usability experts to improve their projects. According to the results, majority of OSS developers neither consider usability as their top priority nor do they consult usability experts.
A Framework for Process Assessment of Software Product Line
Venue: arXiv cs.SE 2015
Year: 2015

Software product line has emerged as an attractive phenomenon within organizations dealing with software development process. It involves assembly of products from existing core assets, commonly known as components, and continuous growth in the core assets as production proceeds. Organizations trying to incorporate the concept of software product line to reduce development time and cost require certain rules to be followed for successful development and management, they also require a direct procedure to evaluate the current maturity level of the process. In this work certain rules for developing and managing a software product line are put forward. Additionally, a fuzzy logic based software product line process assessment tool (SPLPAT) has been designed and implemented on the basis of developed rules for software product line process assessment. SPLPAT can be used to assess the process maturity level of software product line, and it provides an opportunity to handle imprecision and uncertainty present in software process variables. Four case studies were conducted to validate the framework, and results show that SPLPAT provides a direct mechanism to evaluate current software product line process maturity level within an organization. The results of the developed software product line process assessment approach were compared with the existing CMM-level of the organization in order to evaluate the reliability of the presented approach and to find out how effectively an organization can execute software product line process when it has already achieved a certain CMM level.
Implications of MBTI in Software Engineering Education
Venue: arXiv cs.SE 2015
Year: 2015

A number of approaches exist to aid the understanding of individual differences and their effects on teaching and learning. Educators have been using the Myers-Briggs Type Indicator (MBTI) to understand differences in learning styles and to develop teaching methods that cater for the various personality styles. Inspired by the MBTI, we developed a range of practices for effective teaching and learning in a software engineering course. Our aim is to reach every student, but in different ways, by devising various teaching approaches.
True Online Emphatic TD($λ$): Quick Reference and Implementation Guide
Venue: arXiv cs.LG 2015
Year: 2015

This document is a guide to the implementation of true online emphatic TD($λ$), a model-free temporal-difference algorithm for learning to make long-term predictions which combines the emphasis idea (Sutton, Mahmood & White 2015) and the true-online idea (van Seijen & Sutton 2014). The setting used here includes linear function approximation, the possibility of off-policy training, and all the generality of general value functions, as well as the emphasis algorithm's notion of "interest".
Economic Recommendation Systems
Venue: arXiv cs.GT 2015
Year: 2015

In the on-line Explore and Exploit literature, central to Machine Learning, a central planner is faced with a set of alternatives, each yielding some unknown reward. The planner's goal is to learn the optimal alternative as soon as possible, via experimentation. A typical assumption in this model is that the planner has full control over the experiment design and implementation. When experiments are implemented by a society of self-motivated agents the planner can only recommend experimentation but has no power to enforce it. Kremer et al (JPE, 2014) introduce the first study of explore and exploit schemes that account for agents' incentives. In their model it is implicitly assumed that agents do not see nor communicate with each other. Their main result is a characterization of an optimal explore and exploit scheme. In this work we extend Kremer et al (JPE, 2014) by adding a layer of a social network according to which agents can observe each other. It turns out that when observability is factored in the scheme proposed by Kremer et al (JPE, 2014) is no longer incentive compatible. In our main result we provide a tight bound on how many other agents can each agent observe and still have an incentive-compatible algorithm and asymptotically optimal outcome. More technically, for a setting with N agents where the number of nodes with degree greater than N^alpha is bounded by N^beta and 2*alpha+beta < 1 we construct incentive-compatible asymptotically optimal mechanism. The bound 2*alpha+beta < 1 is shown to be tight.
A Neural Prototype for a Virtual Chemical Spectrophotometer
Venue: arXiv cs.NE 2015
Year: 2015

A virtual chemical spectrophotometer for the simultaneous analysis of nickel (Ni) and cobalt (Co) was developed based on an artificial neural network (ANN). The developed ANN correlates the respective concentrations of Co and Ni given the absorbance profile of a Co-Ni mixture based on the Beer's Law. The virtual chemical spectrometer was trained using a 3-layer jump connection neural network model (NNM) with 126 input nodes corresponding to the 126 absorbance readings from 350 nm to 600 nm, 70 nodes in the hidden layer using a logistic activation function, and 2 nodes in the output layer with a logistic function. Test result shows that the NNM has correlation coefficients of 0.9953 and 0.9922 when predicting [Co] and [Ni], respectively. We observed, however, that the NNM has a duality property and that there exists a real-world practical application in solving the dual problem: Predict the Co-Ni mixture's absorbance profile given [Co] and [Ni]. It turns out that the dual problem is much harder to solve because the intended output has a much bigger cardinality than that of the input. Thus, we trained the dual ANN, a 3-layer jump connection nets with 2 input nodes corresponding to [Co] and [Ni], 70-logistic-activated nodes in the hidden layer, and 126 output nodes corresponding to the 126 absorbance readings from 250 nm to 600 nm. Test result shows that the dual NNM has correlation coefficients that range from 0.9050 through 0.9980 at 356 nm through 578 nm with the maximum coefficient observed at 480 nm. This means that the dual ANN can be used to predict the absorbance profile given the respective Co-Ni concentrations which can be of importance in creating academic models for a virtual chemical spectrophotometer.
Modeling Website Workload Using Neural Networks
Venue: arXiv cs.DC 2015
Year: 2015

In this article, artificial neural networks (ANN) are used for modeling the number of requests received by 1998 FIFA World Cup website. Modeling is done by means of time-series forecasting. The log traces of the website, available through the Internet Traffic Archive (ITA), are processed to obtain two time-series data sets that are used for finding the following measurements: requests/day and requests/second. These are modeled by training and simulating ANN. The method followed to collect and process the data, and perform the experiments have been detailed in this article. In total, 13 cases have been tried and their results have been presented, discussed, compared and summarized. Lastly, future works have also been mentioned.