OpenRaiser/Intern-Atlas huggingface dataset:
The SPHERE Challenge: Activity Recognition with Multimodal Sensor Data
Venue: arXiv cs.CY 2016
Year: 2016

This paper outlines the Sensor Platform for HEalthcare in Residential Environment (SPHERE) project and details the SPHERE challenge that will take place in conjunction with European Conference on Machine Learning and Principles and Practice of Knowledge Discovery (ECML-PKDD) between March and July 2016. The SPHERE challenge is an activity recognition competition where predictions are made from video, accelerometer and environmental sensors. Monetary prizes will be awarded to the top three entrants, with Euro 1,000 being awarded to the winner, Euro 600 being awarded to the first runner up, and Euro 400 being awarded to the second runner up.
Hybrid Collaborative Filtering with Autoencoders
Venue: arXiv cs.IR 2016
Year: 2016

Collaborative Filtering aims at exploiting the feedback of users to provide personalised recommendations. Such algorithms look for latent variables in a large sparse matrix of ratings. They can be enhanced by adding side information to tackle the well-known cold start problem. While Neu-ral Networks have tremendous success in image and speech recognition, they have received less attention in Collaborative Filtering. This is all the more surprising that Neural Networks are able to discover latent variables in large and heterogeneous datasets. In this paper, we introduce a Collaborative Filtering Neural network architecture aka CFN which computes a non-linear Matrix Factorization from sparse rating inputs and side information. We show experimentally on the MovieLens and Douban dataset that CFN outper-forms the state of the art and benefits from side information. We provide an implementation of the algorithm as a reusable plugin for Torch, a popular Neural Network framework.
MOT16: A Benchmark for Multi-Object Tracking
Venue: arXiv cs.CV 2016
Year: 2016

Standardized benchmarks are crucial for the majority of computer vision applications. Although leaderboards and ranking tables should not be over-claimed, benchmarks often provide the most objective measure of performance and are therefore important guides for reseach. Recently, a new benchmark for Multiple Object Tracking, MOTChallenge, was launched with the goal of collecting existing and new data and creating a framework for the standardized evaluation of multiple object tracking methods. The first release of the benchmark focuses on multiple people tracking, since pedestrians are by far the most studied object in the tracking community. This paper accompanies a new release of the MOTChallenge benchmark. Unlike the initial release, all videos of MOT16 have been carefully annotated following a consistent protocol. Moreover, it not only offers a significant increase in the number of labeled boxes, but also provides multiple object classes beside pedestrians and the level of visibility for every single object of interest.
Automatic segmentation of lizard spots using an active contour model
Venue: arXiv cs.CV 2016
Year: 2016

Animal biometrics is a challenging task. In the literature, many algorithms have been used, e.g. penguin chest recognition, elephant ears recognition and leopard stripes pattern recognition, but to use technology to a large extent in this area of research, still a lot of work has to be done. One important target in animal biometrics is to automate the segmentation process, so in this paper we propose a segmentation algorithm for extracting the spots of Diploglossus millepunctatus, an endangered lizard species. The automatic segmentation is achieved with a combination of preprocessing, active contours and morphology. The parameters of each stage of the segmentation algorithm are found using an optimization procedure, which is guided by the ground truth. The results show that automatic segmentation of spots is possible. A 78.37 % of correct segmentation in average is reached.
PCANet: An energy perspective
Venue: arXiv cs.CV 2016
Year: 2016

The principal component analysis network (PCANet), which is one of the recently proposed deep learning architectures, achieves the state-of-the-art classification accuracy in various databases. However, the explanation of the PCANet is lacked. In this paper, we try to explain why PCANet works well from energy perspective point of view based on a set of experiments. The impact of various parameters on the error rate of PCANet is analyzed in depth. It was found that this error rate is correlated with the logarithm of energy of image. The proposed energy explanation approach can be used as a testing method for checking if every step of the constructed networks is necessary.
Training Input-Output Recurrent Neural Networks through Spectral Methods
Venue: arXiv cs.LG 2016
Year: 2016

We consider the problem of training input-output recurrent neural networks (RNN) for sequence labeling tasks. We propose a novel spectral approach for learning the network parameters. It is based on decomposition of the cross-moment tensor between the output and a non-linear transformation of the input, based on score functions. We guarantee consistent learning with polynomial sample and computational complexity under transparent conditions such as non-degeneracy of model parameters, polynomial activations for the neurons, and a Markovian evolution of the input sequence. We also extend our results to Bidirectional RNN which uses both previous and future information to output the label at each time point, and is employed in many NLP tasks such as POS tagging.
Yes-no Bloom filter: A way of representing sets with fewer false positives
Venue: arXiv cs.DS 2016
Year: 2016

The Bloom filter (BF) is a space efficient randomized data structure particularly suitable to represent a set supporting approximate membership queries. BFs have been extensively used in many applications especially in networking due to their simplicity and flexibility. The performances of BFs mainly depends on query overhead, space requirements and false positives. The aim of this paper is to focus on false positives. Inspired by the recent application of the BF in a novel multicast forwarding fabric for information centric networks, this paper proposes the yes-no BF, a new way of representing a set, based on the BF, but with significantly lower false positives and no false negatives. Although it requires slightly more processing at the stage of its formation, it offers the same processing requirements for membership queries as the BF. After introducing the yes-no BF, we show through simulations, that it has better false positive performance than the BF.
Using Newton's method to model a spatial light distribution of a LED with attached secondary optics
Venue: arXiv cs.OH 2016
Year: 2016

In design of optical systems based on LED (Light emitting diode) technology, a crucial task is to handle the unstructured data describing properties of optical elements in standard formats. This leads to the problem of data fitting within an appropriate model. Newton's method is used as an upgrade of previously developed most promising discrete optimization heuristics showing improvement of both performance and quality of solutions. Experiment also indicates that a combination of an algorithm that finds promising initial solutions as a preprocessor to Newton's method may be a winning idea, at least on some datasets of instances.
A Survey of the State of the Art in Data Mining and Integration Query Languages
Venue: arXiv cs.DB 2016
Year: 2016

The major aim of this survey is to identify the strengths and weaknesses of a representative set of Data-Mining and Integration (DMI) query languages. We describe a set of properties of DMI-related languages that we use for a systematic evaluation of these languages. In addition, we introduce a scoring system that we use to quantify our opinion on how well a DMI-related language supports a property. The languages surveyed in this paper include: DMQL, MineSQL, MSQL, M2MQL, dmFSQL, OLEDB for DM, MINE RULE, and Oracle Data Mining. This survey may help researchers to propose a DMI language that is beyond the state-of-the-art, or it may help practitioners to select an existing language that fits well a purpose.
Kulfi: Robust Traffic Engineering Using Semi-Oblivious Routing
Venue: arXiv cs.NI 2016
Year: 2016

Wide-area network traffic engineering enables network operators to reduce congestion and improve utilization by balancing load across multiple paths. Current approaches to traffic engineering can be modeled in terms of a routing component that computes forwarding paths, and a load balancing component that maps incoming flows onto those paths dynamically, adjusting sending rates to fit current conditions. Unfortunately, existing systems rely on simple strategies for one or both of these components, which leads to poor performance or requires making frequent updates to forwarding paths, significantly increasing management complexity. This paper explores a different approach based on semi-oblivious routing, a natural extension of oblivious routing in which the system computes a diverse set of paths independent of demands, but also dynamically adapts sending rates as conditions change. Semi-oblivious routing has a number of important advantages over competing approaches including low overhead, nearly optimal performance, and built-in protection against unexpected bursts of traffic and failures. Through in-depth simulations and a deployment on SDN hardware, we show that these benefits are robust, and hold across a wide range of topologies, demands, resource budgets, and failure scenarios.
Principles of Layered Attestation
Venue: arXiv cs.CR 2016
Year: 2016

Systems designed with measurement and attestation in mind are often layered, with the lower layers measuring the layers above them. Attestations of such systems, which we call layered attestations, must bundle together the results of a diverse set of application-specific measurements of various parts of the system. Some methods of layered attestation are more trustworthy than others, so it is important for system designers to understand the trust consequences of different system configurations. This paper presents a formal framework for reasoning about layered attestations, and provides generic reusable principles for achieving trustworthy results.
FastCap: An Efficient and Fair Algorithm for Power Capping in Many-Core Systems
Venue: arXiv cs.PF 2016
Year: 2016

Future servers will incorporate many active lowpower modes for different system components, such as cores and memory. Though these modes provide flexibility for power management via Dynamic Voltage and Frequency Scaling (DVFS), they must be operated in a coordinated manner. Such coordinated control creates a combinatorial space of possible power mode configurations. Given the rapid growth of the number of cores, it is becoming increasingly challenging to quickly select the configuration that maximizes the performance under a given power budget. Prior power capping techniques do not scale well to large numbers of cores, and none of those works has considered memory DVFS. In this paper, we present FastCap, our optimization approach for system-wide power capping, using both CPU and memory DVFS. Based on a queuing model, FastCap formulates power capping as a non-linear optimization problem where we seek to maximize the system performance under a power budget, while promoting fairness across applications. Our FastCap algorithm solves the optimization online and efficiently (low complexity on the number of cores), using a small set of performance counters as input. To evaluate FastCap, we simulate it for a many-core server running different types of workloads. Our results show that FastCap caps power draw accurately, while producing better application performance and fairness than many existing CPU power capping methods (even after they are extended to use of memory DVFS as well).
Ecology-Based DoS Attack in Cognitive Radio Networks
Venue: arXiv cs.CR 2016
Year: 2016

Cognitive radio technology, which is designed to enhance spectrum utilization, depends on the success of opportunistic access, where secondary users (SUs) exploit spectrum void unoccupied by primary users (PUs) for transmissions. We note that the system behaviors are very similar to the interactions among different species coexisting in an ecosystem. However, SUs of a selfish nature or of misleading information may make concurrent transmissions with PUs for additional incentives, and thus disrupt the entire ecosystem. By exploiting this vulnerability, this paper proposes a novel distributed denial-of-service (DoS) attack where invasive species, i.e., malicious users (MUs), induce originally normal-behaved SUs to execute concurrent transmissions with PUs and thus collapse the cognitive radio network. We adopt stochastic geometry to model the spatial distributions of PUs, SUs, and MUs for the analysis of the mutual interference among them. The access strategy of each SU in the spectrum sharing ecosystem, which evolves with the experienced payoffs and interference, is modeled by an evolutionary game. Based on the evolutionary stable strategy concept, we could efficiently identify the fragile operating region at which normal-behaved SUs are eventually evolved to conduct concurrent transmissions and thus to cause the ruin of the network.
A Unified View of Localized Kernel Learning
Venue: arXiv cs.LG 2016
Year: 2016

Multiple Kernel Learning, or MKL, extends (kernelized) SVM by attempting to learn not only a classifier/regressor but also the best kernel for the training task, usually from a combination of existing kernel functions. Most MKL methods seek the combined kernel that performs best over every training example, sacrificing performance in some areas to seek a global optimum. Localized kernel learning (LKL) overcomes this limitation by allowing the training algorithm to match a component kernel to the examples that can exploit it best. Several approaches to the localized kernel learning problem have been explored in the last several years. We unify many of these approaches under one simple system and design a new algorithm with improved performance. We also develop enhanced versions of existing algorithms, with an eye on scalability and performance.
In the Search of Optimal Concurrency
Venue: arXiv cs.DC 2016
Year: 2016

Implementing a concurrent data structure typically begins with defining its sequential specification. However, when used \emph{as is}, a nontrivial sequential data structure, such as a linked list, a search tree, or a hash table, may expose incorrect behavior: lost updates, inconsistent responses, etc. To ensure correctness, portions of the sequential code operating on the shared data must be "protected" from data races using synchronization primitives and, thus, certain schedules of the steps of concurrent operations must be rejected. But can we ensure that we do not "overuse" synchronization, i.e., that we reject a concurrent schedule only if it violates correctness? In this paper, we treat this question formally by introducing the notion of a \emph{concurrency-optimal} implementation. A program's concurrency is defined here as its ability to accept concurrent schedules, i.e., interleavings of steps of its sequential implementation. An implementation is concurrency-optimal if it accepts all interleavings that do not violate the program's correctness. We explore the concurrency properties of \emph{search} data structures which can be represented in the form of directed acyclic graphs exporting insert, delete and search operations. We prove, for the first time, that \emph{pessimistic} e.g., based on conservative locking) and \emph{optimistic serializable} e.g., based on serializable transactional memory) implementations of search data-structures are incomparable in terms of concurrency. Specifically, there exist simple interleavings of sequential code that cannot be accepted by \emph{any} pessimistic (and \emph{resp.}, serializable optimistic) implementation, but accepted by a serializable optimistic one (and \emph{resp.}, pessimistic). Thus, neither of these two implementation classes is concurrency-optimal.
Inter-User Interference Coordination in Full-Duplex Systems Based on Geographical Context Information
Venue: arXiv cs.IT 2016
Year: 2016

We propose a coordination scheme to minimize the interference between users in a cellular network with full-duplex base stations and half-duplex user devices. Our scheme exploits signal attenuation from obstacles between the users by (i) extracting spatially isolated regions from a radio map and (ii) assigning simultaneous co-channel uplink and downlink transmissions to users in these regions such that inter-user interference is minimized. While adding low computational complexity and insignificant signaling overhead to existing deployments, evaluating our solution with real coverage data shows impressive gains compared to conventional half-duplex and full-duplex operation.
Methods for Stochastic Collection and Replenishment (SCAR) optimisation for persistent autonomy
Venue: arXiv cs.RO 2016
Year: 2016

Consideration of resources such as fuel, battery charge, and storage space, is a crucial requirement for the successful persistent operation of autonomous systems. The Stochastic Collection and Replenishment (SCAR) scenario is motivated by mining and agricultural scenarios where a dedicated replenishment agent transports a resource between a centralised replenishment point to agents using the resource in the field. The agents in the field typically operate within fixed areas (for example, benches in mining applications, and fields or orchards in agricultural scenarios), and the motion of the replenishment agent may be restricted by a road network. Existing research has typically approached the problem of scheduling the actions of the dedicated replenishment agent from a short-term and deterministic angle. This paper introduces a method of incorporating uncertainty in the schedule optimisation through a novel prediction framework, and a branch and bound optimisation method which uses the prediction framework to minimise the downtime of the agents. The prediction framework makes use of several Gaussian approximations to quickly calculate the risk-weighted cost of a schedule. The anytime nature of the branch and bound method is exploited within an MPC-like framework to outperform existing optimisation methods while providing reasonable calculation times in large scenarios.
Computational Aspects of Private Bayesian Persuasion
Venue: arXiv cs.GT 2016
Year: 2016

We study computational questions in a game-theoretic model that, in particular, aims to capture advertising/persuasion applications such as viral marketing. Specifically, we consider a multi-agent Bayesian persuasion model where an informed sender (marketer) tries to persuade a group of agents (consumers) to adopt a certain product. The quality of the product is known to the sender, but it is unknown to the agents. The sender is allowed to commit to a signaling policy where she sends a private signal---say, a viral marketing ad---to every agent. This work studies the computation aspects of finding a signaling policy that maximizes the sender's revenue. We show that if the sender's utility is a submodular function of the set of agents that adopt the product, then we can efficiently find a signaling policy whose revenue is at least (1-1/e) times the optimal. We also prove that approximating the sender's optimal revenue by a factor better than (1-1/e) is NP-hard and, hence, the developed approximation guarantee is essentially tight. When the senders' utility is a function of the number of agents that adopt the product (i.e., the utility function is anonymous), we show that an optimal signaling policy can be computed in polynomial time. Our results are based on an interesting connection between the Bayesian persuasion problem and the evaluation of the concave closure of a set function.
A Novel Sufficient Condition for Generalized Orthogonal Matching Pursuit
Venue: arXiv cs.IT 2016
Year: 2016

Generalized orthogonal matching pursuit (gOMP), also called orthogonal multi-matching pursuit, is an extension of OMP in the sense that $N\geq1$ indices are identified per iteration. In this paper, we show that if the restricted isometry constant (RIC) $δ_{NK+1}$ of a sensing matrix $\A$ satisfies $δ_{NK+1} < 1/\sqrt {K/N+1}$, then under a condition on the signal-to-noise ratio, gOMP identifies at least one index in the support of any $K$-sparse signal $\x$ from $\y=\A\x+\v$ at each iteration, where $\v$ is a noise vector. Surprisingly, this condition does not require $N\leq K$ which is needed in Wang, \textit{et al} 2012 and Liu, \textit{et al} 2012. Thus, $N$ can have more choices. When $N=1$, it reduces to be a sufficient condition for OMP, which is less restrictive than that proposed in Wang 2015. Moreover, in the noise-free case, it is a sufficient condition for accurately recovering $\x$ in $K$ iterations which is less restrictive than the best known one. In particular, it reduces to the sharp condition proposed in Mo 2015 when $N=1$.
Rapidly Mixing Markov Chains: A Comparison of Techniques (A Survey)
Venue: arXiv cs.DS 2016
Year: 2016

We survey existing techniques to bound the mixing time of Markov chains. The mixing time is related to a geometric parameter called conductance which is a measure of edge-expansion. Bounds on conductance are typically obtained by a technique called "canonical paths" where the idea is to find a set of paths, one between every source-destination pair, such that no edge is heavily congested. However, the canonical paths approach cannot always show rapid mixing of a rapidly mixing chain. This drawback disappears if we allow the flow between a pair of states to be spread along multiple paths. We prove that for a large class of Markov chains canonical paths does capture rapid mixing. Allowing multiple paths to route the flow still does help a great deal in proofs, as illustrated by a result of Morris & Sinclair (FOCS'99) on the rapid mixing of a Markov chain for sampling 0/1 knapsack solutions. A different approach to prove rapid mixing is "Coupling". Path Coupling is a variant discovered by Bubley & Dyer (FOCS'97) that often tremendously reduces the complexity of designing good Couplings. We present several applications of Path Coupling in proofs of rapid mixing. These invariably lead to much better bounds on mixing time than known using conductance, and moreover Coupling based proofs are typically simpler. This motivates the question of whether Coupling can be made to work whenever the chain is rapidly mixing. This question was answered in the negative by Kumar & Ramesh (FOCS'99), who showed that no Coupling strategy can prove the rapid mixing of the Jerrum-Sinclair chain for sampling perfect and near-perfect matchings.
Computing Shortest Paths Using A*, Landmarks, and Polygon Inequalities (Abstract)
Venue: arXiv cs.DS 2016
Year: 2016

We introduce a new heuristic for the A* algorithm that references a data structure much smaller than the one required by the ALT heuristic. This heuristic's benefits are permitted by a new approach for computing lower bounds using generalized polygon inequalities, leveraging distance information from two landmarks as opposed to the common single landmark paradigm. In this paper, we demonstrate that this heuristic stores a reduced amount of preprocessing information in comparison to previous landmark algorithms while performing faster search queries.