OpenRaiser/Intern-Atlas huggingface dataset:
Fast Witness Extraction Using a Decision Oracle
Venue: arXiv cs.DS 2015
Year: 2015

The gist of many (NP-)hard combinatorial problems is to decide whether a universe of $n$ elements contains a witness consisting of $k$ elements that match some prescribed pattern. For some of these problems there are known advanced algebra-based FPT algorithms which solve the decision problem but do not return the witness. We investigate techniques for turning such a YES/NO-decision oracle into an algorithm for extracting a single witness, with an objective to obtain practical scalability for large values of $n$. By relying on techniques from combinatorial group testing, we demonstrate that a witness may be extracted with $O(k\log n)$ queries to either a deterministic or a randomized set inclusion oracle with one-sided probability of error. Furthermore, we demonstrate through implementation and experiments that the algebra-based FPT algorithms are practical, in particular in the setting of the $k$-path problem. Also discussed are engineering issues such as optimizing finite field arithmetic.
A Novel Approach For Finger Vein Verification Based on Self-Taught Learning
Venue: arXiv cs.CV 2015
Year: 2015

In this paper, we propose a method for user Finger Vein Authentication (FVA) as a biometric system. Using the discriminative features for classifying theses finger veins is one of the main tips that make difference in related works, Thus we propose to learn a set of representative features, based on autoencoders. We model the user finger vein using a Gaussian distribution. Experimental results show that our algorithm perform like a state-of-the-art on SDUMLA-HMT benchmark.
Cross-Layer Design of Wireless Multihop Networks over Stochastic Channels with Time-Varying Statistics
Venue: arXiv cs.SY 2015
Year: 2015

Network Utility Maximization (NUM) is often applied for the cross-layer design of wireless networks considering known wireless channels. However, realistic wireless channel capacities are stochastic bearing time-varying statistics, necessitating the redesign and solution of NUM problems to capture such effects. Based on NUM theory we develop a framework for scheduling, routing, congestion and power control in wireless multihop networks that considers stochastic Long or Short Term Fading wireless channels. Specifically, the wireless channel is modeled via stochastic differential equations alleviating several assumptions that exist in state-of-the-art channel modeling within the NUM framework such as the finite number of states or the stationarity. Our consideration of wireless channel modeling leads to a NUM problem formulation that accommodates non-convex and time-varying utilities. We consider both cases of non orthogonal and orthogonal access of users to the medium. In the first case, scheduling is performed via power control, while the latter separates scheduling and power control and the role of power control is to further increase users' optimal utility by exploiting random reductions of the stochastic channel power loss while also considering energy efficiency. Finally, numerical results evaluate the performance and operation of the proposed approach and study the impact of several involved parameters on convergence.
Mobile-Based Experience Sampling for Behaviour Research
Venue: arXiv cs.HC 2015
Year: 2015

The Experience Sampling Method (ESM) introduces in-situ sampling of human behaviour, and provides researchers and behavioural therapists with ecologically valid and timely assessments of a person's psychological state. This, in turn, opens up new opportunities for understanding behaviour at a scale and granularity that was not possible just a few years ago. The practical applications are many, such as the delivery of personalised and agile behaviour interventions. Mobile computing devices represent a revolutionary platform for improving ESM. They are an inseparable part of our daily lives, context-aware, and can interact with people at suitable moments. Furthermore, these devices are equipped with sensors, and can thus take part of the reporting burden off the participant, and collect data automatically. The goal of this survey is to discuss recent advancements in using mobile technologies for ESM (mESM), and present our vision of the future of mobile experience sampling.
Beat-Event Detection in Action Movie Franchises
Venue: arXiv cs.CV 2015
Year: 2015

While important advances were recently made towards temporally localizing and recognizing specific human actions or activities in videos, efficient detection and classification of long video chunks belonging to semantically defined categories such as "pursuit" or "romance" remains challenging.We introduce a new dataset, Action Movie Franchises, consisting of a collection of Hollywood action movie franchises. We define 11 non-exclusive semantic categories - called beat-categories - that are broad enough to cover most of the movie footage. The corresponding beat-events are annotated as groups of video shots, possibly overlapping.We propose an approach for localizing beat-events based on classifying shots into beat-categories and learning the temporal constraints between shots. We show that temporal constraints significantly improve the classification performance. We set up an evaluation protocol for beat-event localization as well as for shot classification, depending on whether movies from the same franchise are present or not in the training data.
On connectivity in a general random intersection graph
Venue: arXiv cs.DM 2015
Year: 2015

There has been growing interest in studies of general random intersection graphs. In this paper, we consider a general random intersection graph $\mathbb{G}(n,\overrightarrow{a}, \overrightarrow{K_n},P_n)$ defined on a set $\mathcal{V}_n$ comprising $n$ vertices, where $\overrightarrow{a}$ is a probability vector $(a_1,a_2,\ldots,a_m)$ and $\overrightarrow{K_n}$ is $(K_{1,n},K_{2,n},\ldots,K_{m,n})$. This graph has been studied in the literature including a most recent work by Yağan [arXiv:1508.02407]. Suppose there is a pool $\mathcal{P}_n$ consisting of $P_n$ distinct objects. The $n$ vertices in $\mathcal{V}_n$ are divided into $m$ groups $\mathcal{A}_1, \mathcal{A}_2, \ldots, \mathcal{A}_m$. Each vertex $v$ is independently assigned to exactly a group according to the probability distribution with $\mathbb{P}[v \in \mathcal{A}_i]= a_i$, where $i=1,2,\ldots,m$. Afterwards, each vertex in group $\mathcal{A}_i$ independently chooses $K_{i,n}$ objects uniformly at random from the object pool $\mathcal{P}_n$. Finally, an undirected edge is drawn between two vertices in $\mathcal{V}_n$ that share at least one object. This graph model $\mathbb{G}(n,\overrightarrow{a}, \overrightarrow{K_n},P_n)$ has applications in secure sensor networks and social networks. We investigate connectivity in this general random intersection graph $\mathbb{G}(n,\overrightarrow{a}, \overrightarrow{K_n},P_n)$ and present a sharp zero-one law. Our result is also compared with the zero-one law established by Yağan.
Building an IDE for the Calculational Derivation of Imperative Programs
Venue: arXiv cs.PL 2015
Year: 2015

In this paper, we describe an IDE called CAPS (Calculational Assistant for Programming from Specifications) for the interactive, calculational derivation of imperative programs. In building CAPS, our aim has been to make the IDE accessible to non-experts while retaining the overall flavor of the pen-and-paper calculational style. We discuss the overall architecture of the CAPS system, the main features of the IDE, the GUI design, and the trade-offs involved.
Towards Enabling Overture as a Platform for Formal Notation IDEs
Venue: arXiv cs.SE 2015
Year: 2015

Formal Methods tools will never have as many users as tools for popular programming languages and so the effort spent on constructing Integrated Development Environments (IDEs) will be orders of magnitudes lower than that of programming languages such as Java. This means newcomers to formal methods do not get the same user experience as with their favourite programming IDE. In order to improve this situation it is essential that efforts are combined so it is possible to reuse common features and thus not start from scratch every time. This paper presents the Overture platform where such a reuse philosophy is present. We give an overview of the platform itself as well as the extensibility principles that enable much of the reuse. The paper also contains several examples platform extensions, both in the form of new features and a new IDE supporting a new language.
Using a Machine Learning Approach to Implement and Evaluate Product Line Features
Venue: arXiv cs.SE 2015
Year: 2015

Bike-sharing systems are a means of smart transportation in urban environments with the benefit of a positive impact on urban mobility. In this paper we are interested in studying and modeling the behavior of features that permit the end user to access, with her/his web browser, the status of the Bike-Sharing system. In particular, we address features able to make a prediction on the system state. We propose to use a machine learning approach to analyze usage patterns and learn computational models of such features from logs of system usage. On the one hand, machine learning methodologies provide a powerful and general means to implement a wide choice of predictive features. On the other hand, trained machine learning models are provided with a measure of predictive performance that can be used as a metric to assess the cost-performance trade-off of the feature. This provides a principled way to assess the runtime behavior of different components before putting them into operation.
An Information-Theoretic Measure of Dependency Among Variables in Large Datasets
Venue: arXiv cs.IT 2015
Year: 2015

The maximal information coefficient (MIC), which measures the amount of dependence between two variables, is able to detect both linear and non-linear associations. However, computational cost grows rapidly as a function of the dataset size. In this paper, we develop a computationally efficient approximation to the MIC that replaces its dynamic programming step with a much simpler technique based on the uniform partitioning of data grid. A variety of experiments demonstrate the quality of our approximation.
Optimal Solutions for Adaptive Search Problems with Entropy Objectives
Venue: arXiv cs.IT 2015
Year: 2015

The problem of searching for an unknown object occurs in important applications ranging from security, medicine and defense. Sensors with the capability to process information rapidly require adaptive algorithms to control their search in response to noisy observations. In this paper, we discuss classes of dynamic, adaptive search problems, and formulate the resulting sensor control problems as stochastic control problems with imperfect information, based on previous work on noisy search problems. The structure of these problems, with objective functions related to information entropy, allows for a complete characterization of the optimal strategies and the optimal cost for the resulting finite-horizon stochastic control problems. We study the problem where an individual sensor is capable of searching over multiple sub-regions in a time, and provide a constructive algorithm for determining optimal policies in real time based on convex optimization. We also study the problem in which there are multiple sensors, each of which is only capable of detecting over one sub-region in a time, jointly searching for an object. Whereas this can be viewed as a special case of our multi-region results, we show that the computation of optimal policies can be decoupled into single-sensor individual scalar convex optimization problems, and provide simple symmetry conditions where the solutions can be determined analytically. We also consider the case where individual sensors can select the accuracy of their sensing modes with different costs, and derive optimal strategies for these problems in terms of the solutions of scalar convex optimization problems. We illustrate our results with experiments using multiple sensors searching for a single object.
Differential Positivity on Compact Sets
Venue: arXiv cs.SY 2015
Year: 2015

The paper studies differentially positive systems, that is, systems whose linearization along an arbitrary trajectory is positive. We illustrate the use of differential positivity on compact forward invariant sets for the characterization of bistable and periodic behaviors. Geometric conditions for differential positivity are provided. The introduction of compact sets simplifies the use of differential positivity in applications.
Learning Meta-Embeddings by Using Ensembles of Embedding Sets
Venue: arXiv cs.CL 2015
Year: 2015

Word embeddings -- distributed representations of words -- in deep learning are beneficial for many tasks in natural language processing (NLP). However, different embedding sets vary greatly in quality and characteristics of the captured semantics. Instead of relying on a more advanced algorithm for embedding learning, this paper proposes an ensemble approach of combining different public embedding sets with the aim of learning meta-embeddings. Experiments on word similarity and analogy tasks and on part-of-speech tagging show better performance of meta-embeddings compared to individual embedding sets. One advantage of meta-embeddings is the increased vocabulary coverage. We will release our meta-embeddings publicly.
Cascade Learning by Optimally Partitioning
Venue: arXiv cs.CV 2015
Year: 2015

Cascaded AdaBoost classifier is a well-known efficient object detection algorithm. The cascade structure has many parameters to be determined. Most of existing cascade learning algorithms are designed by assigning detection rate and false positive rate to each stage either dynamically or statically. Their objective functions are not directly related to minimum computation cost. These algorithms are not guaranteed to have optimal solution in the sense of minimizing computation cost. On the assumption that a strong classifier is given, in this paper we propose an optimal cascade learning algorithm (we call it iCascade) which iteratively partitions the strong classifiers into two parts until predefined number of stages are generated. iCascade searches the optimal number ri of weak classifiers of each stage i by directly minimizing the computation cost of the cascade. Theorems are provided to guarantee the existence of the unique optimal solution. Theorems are also given for the proposed efficient algorithm of searching optimal parameters ri. Once a new stage is added, the parameter ri for each stage decreases gradually as iteration proceeds, which we call decreasing phenomenon. Moreover, with the goal of minimizing computation cost, we develop an effective algorithm for setting the optimal threshold of each stage classifier. In addition, we prove in theory why more new weak classifiers are required compared to the last stage. Experimental results on face detection demonstrate the effectiveness and efficiency of the proposed algorithm.
Informational Bottlenecks in Two-Unicast Wireless Networks with Delayed CSIT
Venue: arXiv cs.IT 2015
Year: 2015

We study the impact of delayed channel state information at the transmitters (CSIT) in two-unicast wireless networks with a layered topology and arbitrary connectivity. We introduce a technique to obtain outer bounds to the degrees-of-freedom (DoF) region through the new graph-theoretic notion of bottleneck nodes. Such nodes act as informational bottlenecks only under the assumption of delayed CSIT, and imply asymmetric DoF bounds of the form $mD_1 + D_2 \leq m$. Combining this outer-bound technique with new achievability schemes, we characterize the sum DoF of a class of two-unicast wireless networks, which shows that, unlike in the case of instantaneous CSIT, the DoF of two-unicast networks with delayed CSIT can take an infinite set of values.
Exact Unification and Admissibility
Venue: arXiv cs.LO 2015
Year: 2015

A new hierarchy of "exact" unification types is introduced, motivated by the study of admissible rules for equational classes and non-classical logics. In this setting, unifiers of identities in an equational class are preordered, not by instantiation, but rather by inclusion over the corresponding sets of unified identities. Minimal complete sets of unifiers under this new preordering always have a smaller or equal cardinality than those provided by the standard instantiation preordering, and in significant cases a dramatic reduction may be observed. In particular, the classes of distributive lattices, idempotent semigroups, and MV-algebras, which all have nullary unification type, have unitary or finitary exact type. These results are obtained via an algebraic interpretation of exact unification, inspired by Ghilardi's algebraic approach to equational unification.
A Deep Pyramid Deformable Part Model for Face Detection
Venue: arXiv cs.CV 2015
Year: 2015

We present a face detection algorithm based on Deformable Part Models and deep pyramidal features. The proposed method called DP2MFD is able to detect faces of various sizes and poses in unconstrained conditions. It reduces the gap in training and testing of DPM on deep features by adding a normalization layer to the deep convolutional neural network (CNN). Extensive experiments on four publicly available unconstrained face detection datasets show that our method is able to capture the meaningful structure of faces and performs significantly better than many competitive face detection algorithms.
Compression and Recovery of Distributed Random Signals
Venue: arXiv cs.IT 2015
Year: 2015

We consider the case when a set of spatially distributed sensors make local observations which are noisy versions of a signal of interest. Each sensor transmits compressed information about its measurements to the fusion center which should recover the original signal within a prescribed accuracy. Such an information processing relates to a wireless sensor network (WSN) scenario. The key problem is to find models of the sensors and fusion center so that they will be optimal in the sense of minimization of the associated error under a certain criterion, such as the mean square error (MSE). We determine the models from the technique which is a combination of the maximum block improvement (MBI) method and the generic Karhunen-Loève transform (KLT). The resulting multi-compressor KLT-MBI algorithm is given in terms of pseudo-inverse matrices and, therefore, it is numerically stable and always exists. The proposed model provides compression, de-noising and reconstruction of distributed signals for the cases when known methods either are not applicable (because of singularity of associated matrices) or produce larger associated errors. Error analysis is provided.
Simulated Tempering and Swapping on Mean-Field Models
Venue: arXiv cs.DM 2015
Year: 2015

Simulated and parallel tempering are families of Markov Chain Monte Carlo algorithms where a temperature parameter is varied during the simulation to overcome bottlenecks to convergence due to multimodality. In this work we introduce and analyze the convergence for a set of new tempering distributions which we call \textit{entropy dampening}. For asymmetric exponential distributions and the mean field Ising model with and external field simulated tempering is known to converge slowly. We show that tempering with entropy dampening distributions mixes in polynomial time for these models. Examining slow mixing times of tempering more closely, we show that for the mean-field 3-state ferromagnetic Potts model, tempering converges slowly regardless of the temperature schedule chosen. On the other hand, tempering with entropy dampening distributions converges in polynomial time to stationarity. Finally we show that the slow mixing can be very expensive practically. In particular, the mixing time of simulated tempering is an exponential factor longer than the mixing time at the fixed temperature.
PHY-layer link quality indicators for wireless networks using matched-filters
Venue: arXiv cs.NI 2015
Year: 2015

We present a novel approach to accurate real-time estimation of wireless link quality using simple matched-filtering techniques. Our approach is based on the simple observation that there is a portion of each packet transmission from any given node that does not change from one packet to another; this includes preamble sequences used to synchronize the receiver and also address information in the packet header used for medium access control and routing. Our approach can be thought of as a generalized and simplified variant of standard signal processing techniques that are commonly used for preamble detection, automatic gain control, carrier sensing and other functions in many packet wireless networks. By using a combination of energy detection and correlation techniques, we show that we can effectively detect packet transmissions in real-time with low complexity, without decoding the packets themselves, and indeed, even without detailed knowledge of the packet format. We present extensive experimental results from a software-defined radio testbed to illustrate the effectiveness of this approach for 802.15.4 (Zigbee) networks even in the presence of strong interference signals and low SNR.
From Pretty Good To Great: Enhancing PGP using Bitcoin and the Blockchain
Venue: arXiv cs.CR 2015
Year: 2015

PGP is built upon a Distributed Web of Trust in which the trustworthiness of a user is established by others who can vouch through a digital signature for that particular identity. Preventing its wholesale adoption are a number of inherent weaknesses to include (but not limited to) the following: 1) Trust Relationships are built on a subjective honor system, 2) Only first degree relationships can be fully trusted, 3) Levels of trust are difficult to quantify with actual values, and 4) Issues with the Web of Trust itself (Certification and Endorsement). Although the security that PGP provides is proven to be reliable, it has largely failed to garner large scale adoption. In this paper, we propose several novel contributions to address the aforementioned issues with PGP and associated Web of Trust. To address the subjectivity of the Web of Trust, we provide a new certificate format based on Bitcoin which allows a user to verify a PGP certificate using Bitcoin identity-verification transactions - forming first degree trust relationships that are tied to actual values (i.e., number of Bitcoins transferred during transaction). Secondly, we present the design of a novel Distributed PGP key server that leverages the Bitcoin transaction blockchain to store and retrieve Bitcoin-Based PGP certificates. Lastly, we provide a web prototype application that demonstrates several of these capabilities in an actual environment.