OpenRaiser/Intern-Atlas huggingface dataset:
Optimal Geo-Indistinguishable Mechanisms for Location Privacy
Venue: arXiv cs.CR 2014
Year: 2014

We consider the geo-indistinguishability approach to location privacy, and the trade-off with respect to utility. We show that, given a desired degree of geo-indistinguishability, it is possible to construct a mechanism that minimizes the service quality loss, using linear programming techniques. In addition we show that, under certain conditions, such mechanism also provides optimal privacy in the sense of Shokri et al. Furthermore, we propose a method to reduce the number of constraints of the linear program from cubic to quadratic, maintaining the privacy guarantees and without affecting significantly the utility of the generated mechanism. This reduces considerably the time required to solve the linear program, thus enlarging significantly the location sets for which the optimal mechanisms can be computed.
Assessing the Reach and Impact of Game-Based Learning Approaches to Cultural Competency and Behavioural Change
Venue: arXiv cs.AI 2014
Year: 2014

As digital games continue to be explored as solutions to educational and behavioural challenges, the need for evaluation methodologies which support both the unique nature of the format and the need for comparison with other approaches continues to increase. In this workshop paper, a range of challenges are described related specifically to the case of cultural learning using digital games, in terms of how it may best be assessed, understood, and sustained through an iterative process supported by research. An evaluation framework is proposed, identifying metrics for reach and impact and their associated challenges, as well as presenting ethical considerations and the means to utilize evaluation outcomes within an iterative cycle, and to provide feedback to learners. Presenting as a case study a serious game from the Mobile Assistance for Social Inclusion and Empowerment of Immigrants with Persuasive Learning Technologies and Social Networks (MASELTOV) project, the use of the framework in the context of an integrative project is discussed, with emphasis on the need to view game-based learning as a blended component of the cultural learning process, rather than a standalone solution. The particular case of mobile gaming is also considered within this case study, providing a platform by which to deliver and update content in response to evaluation outcomes. Discussion reflects upon the general challenges related to the assessment of cultural learning, and behavioural change in more general terms, suggesting future work should address the need to provide sustainable, research-driven platforms for game-based learning content.
A Tight Lower Bound on Certificate Complexity in Terms of Block Sensitivity and Sensitivity
Venue: arXiv cs.CC 2014
Year: 2014

Sensitivity, certificate complexity and block sensitivity are widely used Boolean function complexity measures. A longstanding open problem, proposed by Nisan and Szegedy, is whether sensitivity and block sensitivity are polynomially related. Motivated by the constructions of functions which achieve the largest known separations, we study the relation between 1-certificate complexity and 0-sensitivity and 0-block sensitivity. Previously the best known lower bound was $C_1(f)\geq \frac{bs_0(f)}{2 s_0(f)}$, achieved by Kenyon and Kutin. We improve this to $C_1(f)\geq \frac{3 bs_0(f)}{2 s_0(f)}$. While this improvement is only by a constant factor, this is quite important, as it precludes achieving a superquadratic separation between $bs(f)$ and $s(f)$ by iterating functions which reach this bound. In addition, this bound is tight, as it matches the construction of Ambainis and Sun up to an additive constant.
Statistical Constraints
Venue: arXiv cs.AI 2014
Year: 2014

We introduce statistical constraints, a declarative modelling tool that links statistics and constraint programming. We discuss two statistical constraints and some associated filtering algorithms. Finally, we illustrate applications to standard problems encountered in statistics and to a novel inspection scheduling problem in which the aim is to find inspection plans with desirable statistical properties.
Distribution-Independent Reliable Learning
Venue: arXiv cs.LG 2014
Year: 2014

We study several questions in the reliable agnostic learning framework of Kalai et al. (2009), which captures learning tasks in which one type of error is costlier than others. A positive reliable classifier is one that makes no false positive errors. The goal in the positive reliable agnostic framework is to output a hypothesis with the following properties: (i) its false positive error rate is at most $ε$, (ii) its false negative error rate is at most $ε$ more than that of the best positive reliable classifier from the class. A closely related notion is fully reliable agnostic learning, which considers partial classifiers that are allowed to predict "unknown" on some inputs. The best fully reliable partial classifier is one that makes no errors and minimizes the probability of predicting "unknown", and the goal in fully reliable learning is to output a hypothesis that is almost as good as the best fully reliable partial classifier from a class. For distribution-independent learning, the best known algorithms for PAC learning typically utilize polynomial threshold representations, while the state of the art agnostic learning algorithms use point-wise polynomial approximations. We show that one-sided polynomial approximations, an intermediate notion between polynomial threshold representations and point-wise polynomial approximations, suffice for learning in the reliable agnostic settings. We then show that majorities can be fully reliably learned and disjunctions of majorities can be positive reliably learned, through constructions of appropriate one-sided polynomial approximations. Our fully reliable algorithm for majorities provides the first evidence that fully reliable learning may be strictly easier than agnostic learning. Our algorithms also satisfy strong attribute-efficiency properties, and provide smooth tradeoffs between sample complexity and running time.
Pareto-depth for Multiple-query Image Retrieval
Venue: arXiv cs.IR 2014
Year: 2014

Most content-based image retrieval systems consider either one single query, or multiple queries that include the same object or represent the same semantic information. In this paper we consider the content-based image retrieval problem for multiple query images corresponding to different image semantics. We propose a novel multiple-query information retrieval algorithm that combines the Pareto front method (PFM) with efficient manifold ranking (EMR). We show that our proposed algorithm outperforms state of the art multiple-query retrieval algorithms on real-world image databases. We attribute this performance improvement to concavity properties of the Pareto fronts, and prove a theoretical result that characterizes the asymptotic concavity of the fronts.
Collision free autonomous navigation and formation building for non-holonomic ground robots
Venue: arXiv cs.RO 2014
Year: 2014

The primary objective of a safe navigation algorithm is to guide the object from its current position to the target position while avoiding any collision with the en-route obstacles, and the appropriate obstacle avoidance strategies are the key factors to ensure safe navigation tasks in dynamic environments. In this report, three different obstacle avoidance strategies for safe navigation in dynamic environments have been presented. The biologically-inspired navigation algorithm (BINA) is efficient in terms of avoidance time. The equidistant based navigation algorithm (ENA) is able to achieve navigation task with in uncertain dynamic environments. The navigation algorithm algorithm based on an integrated environment representation (NAIER) allows the object to seek a safe path through obstacles in unknown dynamic environment in a human-like fashion. The performances and features of the proposed navigation algorithms are confirmed by extensive simulation results and experiments with a real non-holonomic mobile robot. The algorithms have been implemented on two real control systems: intelligent wheelchair and robotic hospital bed. The performance of the proposed algorithms with SAM and Flexbed demonstrate their capabilities to achieve navigation tasks in complicated real time scenarios. The proposed algorithms are easy to be implemented in real time and costly efficient. An extra study on networked multi-robots formation building algorithm is presented in this paper. A constructive and easy-to-implement decentralised control is proposed for a formation building of a group of random positioned objects. Furthermore, the problem of formation building with anonymous objects is addressed. This randomised decentralised navigation algorithm achieves the convergence to a desired configuration with probability 1.
VHDL Modeling of Intrusion Detection & Prevention System (IDPS) A Neural Network Approach
Venue: arXiv cs.CR 2014
Year: 2014

The rapid development and expansion of World Wide Web and network systems have changed the computing world in the last decade and also equipped the intruders and hackers with new facilities for their destructive purposes. The cost of temporary or permanent damages caused by unauthorized access of the intruders to computer systems has urged different organizations to increasingly implement various systems to monitor data flow in their network. The systems are generally known as Intrusion Detection System (IDS).Our objective is to implement an artificial network approach to the design of intrusion detection and prevention system and finally convert the designed model to a VHDL (Very High Speed Integrated Circuit Hardware Descriptive Language) code. This feature enables the system to suggest proper actions against possible attacks. The promising results of the present study show the potential applicability of ANNs for developing practical IDSs.
Toward automatic censorship detection in microblogs
Venue: arXiv cs.SI 2014
Year: 2014

Social media is an area where users often experience censorship through a variety of means such as the restriction of search terms or active and retroactive deletion of messages. In this paper we examine the feasibility of automatically detecting censorship of microblogs. We use a network growing model to simulate discussion over a microblog follow network and compare two censorship strategies to simulate varying levels of message deletion. Using topological features extracted from the resulting graphs, a classifier is trained to detect whether or not a given communication graph has been censored. The results show that censorship detection is feasible under empirically measured levels of message deletion. The proposed framework can enable automated censorship measurement and tracking, which, when combined with aggregated citizen reports of censorship, can allow users to make informed decisions about online communication habits.
Important Molecular Descriptors Selection Using Self Tuned Reweighted Sampling Method for Prediction of Antituberculosis Activity
Venue: arXiv cs.LG 2014
Year: 2014

In this paper, a new descriptor selection method for selecting an optimal combination of important descriptors of sulfonamide derivatives data, named self tuned reweighted sampling (STRS), is developed. descriptors are defined as the descriptors with large absolute coefficients in a multivariate linear regression model such as partial least squares(PLS). In this study, the absolute values of regression coefficients of PLS model are used as an index for evaluating the importance of each descriptor Then, based on the importance level of each descriptor, STRS sequentially selects N subsets of descriptors from N Monte Carlo (MC) sampling runs in an iterative and competitive manner. In each sampling run, a fixed ratio (e.g. 80%) of samples is first randomly selected to establish a regresson model. Next, based on the regression coefficients, a two-step procedure including rapidly decreasing function (RDF) based enforced descriptor selection and self tuned sampling (STS) based competitive descriptor selection is adopted to select the important descriptorss. After running the loops, a number of subsets of descriptors are obtained and root mean squared error of cross validation (RMSECV) of PLS models established with subsets of descriptors is computed. The subset of descriptors with the lowest RMSECV is considered as the optimal descriptor subset. The performance of the proposed algorithm is evaluated by sulfanomide derivative dataset. The results reveal an good characteristic of STRS that it can usually locate an optimal combination of some important descriptors which are interpretable to the biologically of interest. Additionally, our study shows that better prediction is obtained by STRS when compared to full descriptor set PLS modeling, Monte Carlo uninformative variable elimination (MC-UVE).
On the Equivalence of Two Security Notions for Hierarchical Key Assignment Schemes in the Unconditional Setting
Venue: arXiv cs.CR 2014
Year: 2014

The access control problem in a hierarchy can be solved by using a hierarchical key assignment scheme, where each class is assigned an encryption key and some private information. A formal security analysis for hierarchical key assignment schemes has been traditionally considered in two different settings, i.e., the unconditionally secure and the computationally secure setting, and with respect to two different notions: security against key recovery (KR-security) and security with respect to key indistinguishability (KI-security), with the latter notion being cryptographically stronger. Recently, Freire, Paterson and Poettering proposed strong key indistinguishability (SKI-security) as a new security notion in the computationally secure setting, arguing that SKI-security is strictly stronger than KI-security in such a setting. In this paper we consider the unconditionally secure setting for hierarchical key assignment schemes. In such a setting the security of the schemes is not based on specific unproven computational assumptions, i.e., it relies on the theoretical impossibility of breaking them, despite the computational power of an adversary coalition. We prove that, in this setting, SKI-security is not stronger than KI-security, i.e., the two notions are fully equivalent from an information-theoretic point of view.
What Is It Like to Be a Brain Simulation?
Venue: arXiv cs.AI 2014
Year: 2014

We frame the question of what kind of subjective experience a brain simulation would have in contrast to a biological brain. We discuss the brain prosthesis thought experiment. We evaluate how the experience of the brain simulation might differ from the biological, according to a number of hypotheses about experience and the properties of simulation. Then, we identify finer questions relating to the original inquiry, and answer them from both a general physicalist, and panexperientialist perspective.
Uncertainty Principle in Control Theory, Part I: Analysis of Performance Limitations
Venue: arXiv cs.SY 2014
Year: 2014

This paper investigates performance limitations and tradeoffs in the control design for linear time-invariant systems. It is shown that control specifications in time domain and in frequency domain are always mutually exclusive determined by uncertainty relations. The uncertainty principle from quantum mechanics and harmonic analysis therefore embeds itself inherently in control theory. The relations among transient specifications, system bandwidth and control energy are obtained within the framework of uncertainty principle. If the control system is provided with a large bandwidth or great control energy, then it can ensure transient specifications as good as it can be. Such a control system could be approximated by prolate spheroidal wave functions. The obtained results are also applicable to filter design due to the duality of filtering and control.
Energy Efficient Joint Source and Channel Sensing in Cognitive Radio Sensor Networks
Venue: arXiv cs.IT 2014
Year: 2014

A novel concept of Joint Source and Channel Sensing (JSCS) is introduced in the context of Cognitive Radio Sensor Networks (CRSN). Every sensor node has two basic tasks: application-oriented source sensing and ambient-oriented channel sensing. The former is to collect the application-specific source information and deliver it to the access point within some limit of distortion, while the latter is to find the vacant channels and provide spectrum access opportunities for the sensed source information. With in-depth exploration, we find that these two tasks are actually interrelated when taking into account the energy constraints. The main focus of this paper is to minimize the total power consumed by these two tasks while bounding the distortion of the application-specific source information. Firstly, we present a specific slotted sensing and transmission scheme, and establish the multi-task power consumption model. Secondly, we jointly analyze the interplay between these two sensing tasks, and then propose a proper sensing and power allocation scheme to minimize the total power consumption. Finally, Simulation results are given to validate the proposed scheme.
VM Power Prediction in Distributed Systems for Maximizing Renewable Energy Usage
Venue: arXiv cs.DC 2014
Year: 2014

In the context of GreenPAD project it is important to predict the energy consumption of individual (and mixture of) VMs / workload for optimal scheduling (running those VMs which require higher energy when there is more green energy available and vice-versa) in order to maximize green energy utilization. For this we execute the following experiments on an Openstack cloud testbed consisting of Fujitsu servers: VM energy measurement for different configurations (flavor + workload) and VM energy prediction for a new configuration. The automation framework for running these experiments uses bash scripts which call tools like 'stress' (simulating workloads), 'collected' (resource usage) and 'IPMI' (power measurement). We propose a linear model for predicting the power usage of the VMs based on regression. We first collect the resource usage (using collected) and the associated power usage (using IPMI) for different VM configurations and use this to build a (multi-) regression model (between resource usage and VM energy consumption). Then we use the information about the resource usage patterns of the new workload to predict the power usage. For predicting power for mix of workloads we execute (build a regression model based on) experiments with random workloads. We observe the highest energy usage for CPU-intensive workloads followed by memory-intensive workloads.
A new model for Context-Oriented Programs
Venue: arXiv cs.PL 2014
Year: 2014

Context-oriented programming (COP) is a new technique for programming that allows changing the context in which commands execute as a program executes. Compared to object-oriented programming (aspect-oriented programming), COP is more flexible (modular and structured). This paper presents a precise syntax-directed operational semantics for context-oriented programming with layers, as realized by COP languages like ContextJ* and ContextL. Our language model is built on Java enriched with layer concepts and activation and deactivation of layer scopes. The paper also presents a static type system that guarantees that typed programs do not get stuck. Using the means of the proposed semantics, the mathematical correctness of the type system is presented in the paper.
On Estimation Error Outage for Scalar Gauss-Markov Signals Sent Over Fading Channels
Venue: arXiv cs.IT 2014
Year: 2014

Measurements of a scalar linear Gauss-Markov process are sent over a fading channel. The fading channel is modeled as independent and identically distributed random variables with known realization at the receiver. The optimal estimator at the receiver is the Kalman filter. In contrast to the classical Kalman filter theory, given a random channel, the Kalman gain and the error covariance become random. Then the probability distribution function of expected estimation error and its outage probability can be chosen for estimation quality assessment. In this paper and in order to get the estimation error outage, we provide means to characterize the stationary probability density function of the random expected estimation error. Furthermore and for the particular case of the i.i.d. Rayleigh fading channels, upper and lower bounds for the outage probability are derived which provide insight and simpler means for design purposes. We also show that the bounds are tight for the high SNR regime, and that the outage probability decreases linearly with the inverse of the average channel SNR.
Near Optimal Bayesian Active Learning for Decision Making
Venue: arXiv cs.LG 2014
Year: 2014

How should we gather information to make effective decisions? We address Bayesian active learning and experimental design problems, where we sequentially select tests to reduce uncertainty about a set of hypotheses. Instead of minimizing uncertainty per se, we consider a set of overlapping decision regions of these hypotheses. Our goal is to drive uncertainty into a single decision region as quickly as possible. We identify necessary and sufficient conditions for correctly identifying a decision region that contains all hypotheses consistent with observations. We develop a novel Hyperedge Cutting (HEC) algorithm for this problem, and prove that is competitive with the intractable optimal policy. Our efficient implementation of the algorithm relies on computing subsets of the complete homogeneous symmetric polynomials. Finally, we demonstrate its effectiveness on two practical applications: approximate comparison-based learning and active localization using a robot manipulator.
Incremental Learning of Event Definitions with Inductive Logic Programming
Venue: arXiv cs.LG 2014
Year: 2014

Event recognition systems rely on properly engineered knowledge bases of event definitions to infer occurrences of events in time. The manual development of such knowledge is a tedious and error-prone task, thus event-based applications may benefit from automated knowledge construction techniques, such as Inductive Logic Programming (ILP), which combines machine learning with the declarative and formal semantics of First-Order Logic. However, learning temporal logical formalisms, which are typically utilized by logic-based Event Recognition systems is a challenging task, which most ILP systems cannot fully undertake. In addition, event-based data is usually massive and collected at different times and under various circumstances. Ideally, systems that learn from temporal data should be able to operate in an incremental mode, that is, revise prior constructed knowledge in the face of new evidence. Most ILP systems are batch learners, in the sense that in order to account for new evidence they have no alternative but to forget past knowledge and learn from scratch. Given the increased inherent complexity of ILP and the volumes of real-life temporal data, this results to algorithms that scale poorly. In this work we present an incremental method for learning and revising event-based knowledge, in the form of Event Calculus programs. The proposed algorithm relies on abductive-inductive learning and comprises a scalable clause refinement methodology, based on a compressive summarization of clause coverage in a stream of examples. We present an empirical evaluation of our approach on real and synthetic data from activity recognition and city transport applications.
Open science in machine learning
Venue: arXiv cs.LG 2014
Year: 2014

We present OpenML and mldata, open science platforms that provides easy access to machine learning data, software and results to encourage further study and application. They go beyond the more traditional repositories for data sets and software packages in that they allow researchers to also easily share the results they obtained in experiments and to compare their solutions with those of others.