OpenRaiser/Intern-Atlas huggingface dataset:
Adaptive Online Sequential ELM for Concept Drift Tackling
Venue: arXiv cs.AI 2016
Year: 2016

A machine learning method needs to adapt to over time changes in the environment. Such changes are known as concept drift. In this paper, we propose concept drift tackling method as an enhancement of Online Sequential Extreme Learning Machine (OS-ELM) and Constructive Enhancement OS-ELM (CEOS-ELM) by adding adaptive capability for classification and regression problem. The scheme is named as adaptive OS-ELM (AOS-ELM). It is a single classifier scheme that works well to handle real drift, virtual drift, and hybrid drift. The AOS-ELM also works well for sudden drift and recurrent context change type. The scheme is a simple unified method implemented in simple lines of code. We evaluated AOS-ELM on regression and classification problem by using concept drift public data set (SEA and STAGGER) and other public data sets such as MNIST, USPS, and IDS. Experiments show that our method gives higher kappa value compared to the multiclassifier ELM ensemble. Even though AOS-ELM in practice does not need hidden nodes increase, we address some issues related to the increasing of the hidden nodes such as error condition and rank values. We propose taking the rank of the pseudoinverse matrix as an indicator parameter to detect underfitting condition.
Polynomial-time Tensor Decompositions with Sum-of-Squares
Venue: arXiv cs.DS 2016
Year: 2016

We give new algorithms based on the sum-of-squares method for tensor decomposition. Our results improve the best known running times from quasi-polynomial to polynomial for several problems, including decomposing random overcomplete 3-tensors and learning overcomplete dictionaries with constant relative sparsity. We also give the first robust analysis for decomposing overcomplete 4-tensors in the smoothed analysis model. A key ingredient of our analysis is to establish small spectral gaps in moment matrices derived from solutions to sum-of-squares relaxations. To enable this analysis we augment sum-of-squares relaxations with spectral analogs of maximum entropy constraints.
Towards an Autonomous System Monitor for Mitigating Correlation Attacks in the Tor Network
Venue: arXiv cs.CR 2016
Year: 2016

After carefully considering the scalability problem in Tor and exhaustively evaluating related works on AS-level adversaries, the author proposes ASmoniTor, which is an autonomous system monitor for mitigating correlation attacks in the Tor network. In contrast to prior works, which often released offline packets, including the source code of a modified Tor client and a snapshot of the Internet topology, ASmoniTor is an online system that assists end users with mitigating the threat of AS-level adversaries in a near real-time fashion. For Tor clients proposed in previous works, users need to compile the source code on their machine and continually update the snapshot of the Internet topology in order to obtain accurate AS-path inferences. On the contrary, ASmoniTor is an online platform that can be utilized easily by not only technical users, but also by users without a technical background, because they only need to access it via Tor and input two parameters to execute an AS-aware path selection algorithm. With ASmoniTor, the author makes three key technical contributions to the research against AS-level adversaries in the Tor network. First, ASmoniTor does not require the users to initiate complicated source code compilations. Second, it helps to reduce errors in AS-path inferences by letting users input a set of suspected ASes obtained directly from their own traceroute measurements. Third, the Internet topology database at the back-end of ASmoniTor is periodically updated to assure near real-time AS-path inferences between Tor exit nodes and the most likely visited websites. Finally, in addition to its convenience, ASmoniTor gives users full control over the information they want to input, thus preserving their privacy.
Energy Efficiency Optimization of Channel Access Probabilities in IEEE 802.15.6 UWB WBANs
Venue: arXiv cs.NI 2016
Year: 2016

Energy efficiency is essential for Wireless Body Area Network (WBAN) applications because of the battery-operated nodes. Other requirements such as throughput, delay, quality of service, and security levels also need to be considered in optimizing the network design. In this paper, we study the case in which the nodes access the medium probabilistically and we formulate an energy efficiency optimization problem under the rate and access probability constraints for IEEE 802.15.6 Impulse Radio Ultra-wideband (IR-UWB) WBANs. The proposed algorithm, dubbed Energy Efficiency Optimization of Channel Access Probabilities (EECAP), determines the optimal access probability and payload frame size for each node. The simulation results show that our algorithm rapidly converges to the optimal solution. We also provide detailed insights on the relationship between the optimal access probabilities and other network parameters such as the link distance, the number of nodes, and the minimum rate constraints.
Validating Simplified Processor Models in Architectural Studies
Venue: arXiv cs.AR 2016
Year: 2016

Cycle-accurate software simulation of multicores with complex microarchitectures is often excruciatingly slow. People use simplified core models to gain simulation speed. However, a persistent question is to what extent the results derived from a simplified core model can be used to characterize the behavior of a real machine. We propose a new methodology of validating simplified simulation models, which focuses on the trends of metric values across benchmarks and architectures, instead of errors of absolute metric values. To illustrate this methodology, we conduct a case study using an FPGA-accelerated cycle-accurate full system simulator. We evaluated three cache replacement polices on a 10-stage in-order core model, and then re-conducted all the experiments by substituting a 1-IPC core model for the 10-stage core model. We found that the 1-IPC core model generally produces qualitatively the same results as the accurate core model except for a few mismatches. We argue that most observed mismatches were either indistinguishable from experimental noise or corresponded to the cases where the policy differences even in the accurate model showed inconclusive results. We think it is fair to use simplified core models to study a feature once the influence of the simplification is understood. Additional studies on branch predictors and scaling properties of multithread benchmarks reinforce our argument. However, the validation of a simplified model requires a detailed cycle-accurate model!
There's No Comparison: Reference-less Evaluation Metrics in Grammatical Error Correction
Venue: arXiv cs.CL 2016
Year: 2016

Current methods for automatically evaluating grammatical error correction (GEC) systems rely on gold-standard references. However, these methods suffer from penalizing grammatical edits that are correct but not in the gold standard. We show that reference-less grammaticality metrics correlate very strongly with human judgments and are competitive with the leading reference-based evaluation metrics. By interpolating both methods, we achieve state-of-the-art correlation with human judgments. Finally, we show that GEC metrics are much more reliable when they are calculated at the sentence level instead of the corpus level. We have set up a CodaLab site for benchmarking GEC output using a common dataset and different evaluation metrics.
Automated Detection of Individual Micro-calcifications from Mammograms using a Multi-stage Cascade Approach
Venue: arXiv cs.CV 2016
Year: 2016

In mammography, the efficacy of computer-aided detection methods depends, in part, on the robust localisation of micro-calcifications ($μ$C). Currently, the most effective methods are based on three steps: 1) detection of individual $μ$C candidates, 2) clustering of individual $μ$C candidates, and 3) classification of $μ$C clusters. Where the second step is motivated both to reduce the number of false positive detections from the first step and on the evidence that malignancy depends on a relatively large number of $μ$C detections within a certain area. In this paper, we propose a novel approach to $μ$C detection, consisting of the detection \emph{and} classification of individual $μ$C candidates, using shape and appearance features, using a cascade of boosting classifiers. The final step in our approach then clusters the remaining individual $μ$C candidates. The main advantage of this approach lies in its ability to reject a significant number of false positive $μ$C candidates compared to previously proposed methods. Specifically, on the INbreast dataset, we show that our approach has a true positive rate (TPR) for individual $μ$Cs of 40\% at one false positive per image (FPI) and a TPR of 80\% at 10 FPI. These results are significantly more accurate than the current state of the art, which has a TPR of less than 1\% at one FPI and a TPR of 10\% at 10 FPI. Our results are competitive with the state of the art at the subsequent stage of detecting clusters of $μ$Cs.
CONE: Zero-Calibration Accurate Confidence Estimation for Indoor Localization Systems
Venue: arXiv cs.OH 2016
Year: 2016

Accurate estimation of the confidence of an indoor localization system is crucial for a number of applications including crowd-sensing applications, map-matching services, and probabilistic location fusion techniques; all of which lead to an enhanced user experience. Current approaches for quantifying the output accuracy of a localization system in real-time either do not provide a distance metric, require an extensive training process, and/or are tailored to a specific localization system. In this paper, we present the design, implementation, and evaluation of CONE: a novel calibration-free accurate confidence estimation system that can work in real-time with any location determination system. CONE builds on a sound theoretical model that allows it to trade the required user confidence with tight bound on the estimated confidence radius. We also introduce a new metric for evaluating confidence estimation systems that can capture new aspects of their performance. Evaluation of CONE on Android phones in a typical testbed using the iBeacons BLE technology with a side-by-side comparison with traditional confidence estimation techniques shows that CONE can achieve a consistent median absolute error difference accuracy of less than 2.7m while estimating the user position more than 80% of the time within the confidence circle. This is significantly better than the state-of-the-art confidence estimation systems that are tailored to the specific localization system in use. Moreover, CONE does not require any calibration and therefore provides a scalable and ubiquitous confidence estimation system for pervasive applications.
Universal Clustering via Crowdsourcing
Venue: arXiv cs.HC 2016
Year: 2016

Consider unsupervised clustering of objects drawn from a discrete set, through the use of human intelligence available in crowdsourcing platforms. This paper defines and studies the problem of universal clustering using responses of crowd workers, without knowledge of worker reliability or task difficulty. We model stochastic worker response distributions by incorporating traits of memory for similar objects and traits of distance among differing objects. We are particularly interested in two limiting worker types---temporary workers who retain no memory of responses and long-term workers with memory. We first define clustering algorithms for these limiting cases and then integrate them into an algorithm for the unified worker model. We prove asymptotic consistency of the algorithms and establish sufficient conditions on the sample complexity of the algorithm. Converse arguments establish necessary conditions on sample complexity, proving that the defined algorithms are asymptotically order-optimal in cost.
Causally consistent dynamic slicing
Venue: arXiv cs.PL 2016
Year: 2016

We offer a lattice-theoretic account of dynamic slicing for π-calculus, building on prior work in the sequential setting. For any run of a concurrent program, we exhibit a Galois connection relating forward slices of the start configuration to backward slices of the end configuration. We prove that, up to lattice isomorphism, the same Galois connection arises for any causally equivalent execution, allowing an efficient concurrent implementation of slicing via a standard interleaving semantics. Our approach has been formalised in the dependently-typed language Agda.
Application of Ontologies in Cloud Computing: The State-Of-The-Art
Venue: arXiv cs.DC 2016
Year: 2016

This paper presents a systematic survey on existing literature and seminal works relevant to the application of ontologies in different aspects of Cloud computing. Our hypothesis is that ontologies along with their reasoning capabilities can have significant impact on improving various aspects of the Cloud computing phenomena. Ontologies can promote intelligent decision support mechanisms for various Cloud based services. They can also provide effective interoperability among the Cloud based systems and resources. This survey can promote a comprehensive understanding on the roles and significance of ontologies within the overall domain of Cloud Computing. Also, this project can potentially form the basis of new research area and possibilities for both ontology and Cloud computing communities.
Lopsidependency in the Moser-Tardos framework: Beyond the Lopsided Lovasz Local Lemma
Venue: arXiv cs.DS 2016
Year: 2016

The Lopsided Lovász Local Lemma (LLLL) is a powerful probabilistic principle which has been used in a variety of combinatorial constructions. While originally a general statement about probability spaces, it has recently been transformed into a variety of polynomial-time algorithms. The resampling algorithm of Moser & Tardos (2010) is the most well-known example of this. A variety of criteria have been shown for the LLLL; the strongest possible criterion was shown by Shearer, and other criteria which are easier to use computationally have been shown by Bissacot et al (2011), Pegden (2014), Kolipaka & Szegedy (2011), and Kolipaka, Szegedy, Xu (2012). We show a new criterion for the Moser-Tardos algorithm to converge. This criterion is stronger than the LLLL criterion; this is possible because it does not apply in the same generality as the original LLLL; yet, it is strong enough to cover many applications of the LLLL in combinatorics. We show a variety of new bounds and algorithms. A noteworthy application is for $k$-SAT, with bounded occurrences of variables. As shown in Gebauer, Szábo, and Tardos (2011), a $k$-SAT instance in which every variable appears $L \leq \frac{2^{k+1}}{e (k+1)}$ times, is satisfiable. Although this bound is asymptotically tight (in $k$), we improve it to $L \leq \frac{2^{k+1} (1 - 1/k)^k}{k-1} - \frac{2}{k}$ which can be significantly stronger when $k$ is small. We introduce a new parallel algorithm for the LLLL. While Moser & Tardos described a simple parallel algorithm for the Lovász Local Lemma, and described a simple sequential algorithm for a form of the Lopsided Lemma, they were not able to combine the two. Our new algorithm applies in nearly all settings in which the sequential algorithm works --- this includes settings covered by our new stronger LLLL criterion.
ResearchDoom and CocoDoom: Learning Computer Vision with Games
Venue: arXiv cs.CV 2016
Year: 2016

In this short note we introduce ResearchDoom, an implementation of the Doom first-person shooter that can extract detailed metadata from the game. We also introduce the CocoDoom dataset, a collection of pre-recorded data extracted from Doom gaming sessions along with annotations in the MS Coco format. ResearchDoom and CocoDoom can be used to train and evaluate a variety of computer vision methods such as object recognition, detection and segmentation at the level of instances and categories, tracking, ego-motion estimation, monocular depth estimation and scene segmentation. The code and data are available at http://www.robots.ox.ac.uk/~vgg/research/researchdoom.
Supervisory Control of Fuzzy Discrete Event Systems for Simulation Equivalence
Venue: arXiv cs.SY 2016
Year: 2016

The supervisory control theory of fuzzy discrete event systems (FDESs) for fuzzy language equivalence has been developed. However, in a way, language equivalence has limited expressiveness. So if the given specification can not be expressed by language equivalence, then the control for language equivalence does not work. In this paper, we further establish the supervisory control theory of FDESs for fuzzy simulation equivalence whose expressiveness is stronger than that of fuzzy language equivalence. First, we formalize the notions of fuzzy simulation and fuzzy simulation equivalence between two FDESs. Then we present a method for deciding whether there is a fuzzy simulation or not. In addition, we also show several basic properties of fuzzy simulation relations. Afterwards, we put forward the notion of fuzzy simulation-based controllability, and particularly show that it serves as a necessary and sufficient condition for the existence of the fuzzy supervisors of FDESs. Moreover, we study the "range" control problem of FDESs. Some examples are given to illustrate the main results obtained.
Probabilistic Process Algebra to Unifying Quantum and Classical Computing in Closed Systems
Venue: arXiv cs.LO 2016
Year: 2016

We have unified quantum and classical computing in open quantum systems called qACP which is a quantum generalization of process algebra ACP. But, an axiomatization for quantum and classical processes with an assumption of closed quantum systems is still missing. For closed quantum systems, unitary operator, quantum measurement and quantum entanglement are three basic components for quantum computing. This leads to probability unavoidable. Along the solution of qACP to unify quantum and classical computing in open quantum systems, we unify quantum and classical computing with an assumption of closed systems under the framework of ACP-like probabilistic process algebra. This unification make it can be used widely in verification for quantum and classical computing mixed systems, such as most quantum communication protocols.
On the security defects of an image encryption scheme
Venue: arXiv cs.CR 2016
Year: 2016

This paper studies the security of a recently-proposed chaos-based image encryption scheme, and points out the following problems: 1) there exist a number of invalid keys and weak keys, and some keys are partially equivalent for encryption/decryption; 2) given one chosen plain-image, a subkey $K_{10}$ can be guessed with a smaller computational complexity than that of the simple brute-force attack; 3) given at most 128 chosen plain-images, a chosen-plaintext attack can possibly break the following part of the secret key: $\{K_i\bmod 128\}_{i=4}^{10}$, which works very well when $K_{10}$ is not too large; 4) when $K_{10}$ is relatively small, a known-plaintext attack can be carried out with only one known plain-image to recover some visual information of any other plain-images encrypted by the same key.
Enabling Medical Translation for Low-Resource Languages
Venue: arXiv cs.CL 2016
Year: 2016

We present research towards bridging the language gap between migrant workers in Qatar and medical staff. In particular, we present the first steps towards the development of a real-world Hindi-English machine translation system for doctor-patient communication. As this is a low-resource language pair, especially for speech and for the medical domain, our initial focus has been on gathering suitable training data from various sources. We applied a variety of methods ranging from fully automatic extraction from the Web to manual annotation of test data. Moreover, we developed a method for automatically augmenting the training data with synthetically generated variants, which yielded a very sizable improvement of more than 3 BLEU points absolute.
Design and Implementation of a Fleet Management System Using Novel GPS/GLONASS Tracker and Web-Based Software
Venue: arXiv cs.CY 2016
Year: 2016

Knowing where the vehicles are, what the drivers doing and monitoring every event in real time is the key parameters for a well-managed decision-making process. In this paper, a novel approach for control and monitoring of a fleet management system using three elements including GPS/GLONASS-based automatic vehicle locators (called Rad100), GPRS/SMS GSM cellular network and web-based software (called PayaRadyab) is proposed to show exact position of the desired vehicle on different maps and take detailed reports of the mission, travelled path, fuel consumption rate, speed limits, and other necessary information according to the customers requests. The most significant features of the proposed system are its global covering, high accuracy of positioning, easy operation by the user at any location, and easy energy management. In this study, I have designed and fabricated more than 50 Rad100 trackers and also programmed a web-based PayaRadyab software in which their performance and accuracy have been confirmed by the practical results in different conditions.
Lying Your Way to Better Traffic Engineering
Venue: arXiv cs.NI 2016
Year: 2016

To optimize the flow of traffic in IP networks, operators do traffic engineering (TE), i.e., tune routing-protocol parameters in response to traffic demands. TE in IP networks typically involves configuring static link weights and splitting traffic between the resulting shortest-paths via the Equal-Cost-MultiPath (ECMP) mechanism. Unfortunately, ECMP is a notoriously cumbersome and indirect means for optimizing traffic flow, often leading to poor network performance. Also, obtaining accurate knowledge of traffic demands as the input to TE is elusive, and traffic conditions can be highly variable, further complicating TE. We leverage recently proposed schemes for increasing ECMP's expressiveness via carefully disseminated bogus information ("lies") to design COYOTE, a readily deployable TE scheme for robust and efficient network utilization. COYOTE leverages new algorithmic ideas to configure (static) traffic splitting ratios that are optimized with respect to all (even adversarially chosen) traffic scenarios within the operator's "uncertainty bounds". Our experimental analyses show that COYOTE significantly outperforms today's prevalent TE schemes in a manner that is robust to traffic uncertainty and variation. We discuss experiments with a prototype implementation of COYOTE.
Inverse Diffusion Curves using Shape Optimization
Venue: arXiv cs.GR 2016
Year: 2016

The inverse diffusion curve problem focuses on automatic creation of diffusion curve images that resemble user provided color fields. This problem is challenging since the 1D curves have a nonlinear and global impact on resulting color fields via a partial differential equation (PDE). We introduce a new approach complementary to previous methods by optimizing curve geometry. In particular, we propose a novel iterative algorithm based on the theory of shape derivatives. The resulting diffusion curves are clean and well-shaped, and the final image closely approximates the input. Our method provides a user-controlled parameter to regularize curve complexity, and generalizes to handle input color fields represented in a variety of formats.
Modeling of Spatial Uncertainties in the Magnetic Reluctivity
Venue: arXiv cs.CE 2016
Year: 2016

In this paper a computationally efficient approach is suggested for the stochastic modeling of an inhomogeneous reluctivity of magnetic materials. These materials can be part of electrical machines, such as a single phase transformer (a benchmark example that is considered in this paper). The approach is based on the Karhunen-Loève expansion. The stochastic model is further used to study the statistics of the self inductance of the primary coil as a quantity of interest.