OpenRaiser/Intern-Atlas huggingface dataset:
Dynamic Adaptive Streaming using Index-Based Learning Algorithms
Venue: arXiv cs.NI 2016
Year: 2016

We provide a unified framework using which we design scalable dynamic adaptive video streaming algorithms based on index based policies (dubbed DAS-IP) to maximize the Quality of Experience (QoE) provided to clients using video streaming services. Due to the distributed nature of our algorithm, it is easily implementable. We begin by considering the simplest set-up of a one-hop wireless network in which an Access Point (AP) transmits video packets to multiple clients over a shared unreliable channel. The video file meant for each client has been fragmented into several packets, and the server maintains multiple copies (each of different quality) of the same video file. Clients maintain individual packet buffers in order to mitigate the effect of uncertainty on video iterruption. Streaming experience, or the Quality of Experience (QoE) of a client depends on several factors: i) starvation/outage probability, i.e., average time duration for which the client does not play video because the buffer is empty, ii) average video quality, iii) average number of starvation periods, iv) temporal variations in video quality etc. We pose the problem of making dynamic streaming decisions in order to maximize the total QoE as a Constrained Markov Decision Process (CMDP). A consideration of the associated dual MDP suggests us that the problem is vastly simplified if the AP is allowed to charge a price per unit bandwidth usage from the clients. More concretely, a "client-by-client" QoE optimization leads to the networkwide QoE maximization, and thus provides us a decentralized streaming algorithm. This enables the clients to themselves decide the optimal streaming choices in each time-slot, and yields us a much desired client-level adaptation algorithm. The optimal policy has an appealing simple threshold structure.
Security of Rechargeable Energy-Harvesting Transmitters in Wireless Networks
Venue: arXiv cs.NI 2016
Year: 2016

In this letter, we investigate the security of a single-antenna rechargeable source node in the presence of a multi-antenna rechargeable cooperative jammer and a potential single-antenna eavesdropper. The batteries at the legitimate transmitting nodes (i.e. the source node and the jamming node) are assumed to be limited in capacity and are modeled as queueing systems. We investigate the impact of the energy arrival rates at the batteries on the achievable secrecy rates. In our energy-constrained network, we propose an efficient scheme to enhance the system's security by optimizing the transmission times of the source node. The jammer uses a subset of its antennas (and transmit radio-frequency chains) to create a beamformer which maximizes the system's secrecy rate while completely canceling the artificial noise at the legitimate destination. Our numerical results demonstrate the significant average secrecy rate gain of our proposed scheme.
Optimal Control-Based UAV Path Planning with Dynamically-Constrained TSP with Neighborhoods
Venue: arXiv cs.RO 2016
Year: 2016

This paper addresses path planning of an unmanned aerial vehicle (UAV) with remote sensing capabilities (or wireless communication capabilities). The goal of the path planning is to find a minimum-flight-time closed tour of the UAV visiting all executable areas of given remote sensing and communication tasks; in order to incorporate the nonlinear vehicle dynamics, this problem is regarded as a dynamically-constrained traveling salesman problem with neighborhoods. To obtain a close-to-optimal solution for the path planning in a tractable manner, a sampling-based roadmap algorithm that embeds an optimal control-based path generation process is proposed. The algorithm improves the computational efficiency by reducing numerical computations required for optimizing inefficient local paths, and by extracting additional information from a roadmap of a fixed number of samples. Comparative numerical simulations validate the efficiency of the presented algorithm in reducing computation time and improving the solution quality compared to previous roadmap-based planning methods.
Parsing Images of Overlapping Organisms with Deep Singling-Out Networks
Venue: arXiv cs.CV 2016
Year: 2016

This work is motivated by the mostly unsolved task of parsing biological images with multiple overlapping articulated model organisms (such as worms or larvae). We present a general approach that separates the two main challenges associated with such data, individual object shape estimation and object groups disentangling. At the core of the approach is a deep feed-forward singling-out network (SON) that is trained to map each local patch to a vectorial descriptor that is sensitive to the characteristics (e.g. shape) of a central object, while being invariant to the variability of all other surrounding elements. Given a SON, a local image patch can be matched to a gallery of isolated elements using their SON-descriptors, thus producing a hypothesis about the shape of the central element in that patch. The image-level optimization based on integer programming can then pick a subset of the hypotheses to explain (parse) the whole image and disentangle groups of organisms. While sharing many similarities with existing "analysis-by-synthesis" approaches, our method avoids the need for stochastic search in the high-dimensional configuration space and numerous rendering operations at test-time. We show that our approach can parse microscopy images of three popular model organisms (the C.Elegans roundworms, the Drosophila larvae, and the E.Coli bacteria) even under significant crowding and overlaps between organisms. We speculate that the overall approach is applicable to a wider class of image parsing problems concerned with crowded articulated objects, for which rendering training images is possible.
Managing Commercial HVAC Systems: What do Building Operators Really Need?
Venue: arXiv cs.CY 2016
Year: 2016

Buildings form an essential part of modern life; people spend a significant amount of their time in them, and they consume large amounts of energy. A variety of systems provide services such as lighting, air conditioning and security which are managed using Building Management Systems (BMS) by building operators. To better understand the capability of current BMS and characterize common practices of building operators, we investigated their use across five institutions in the US. We interviewed ten operators and discovered that BMS do not address a number of key concerns for the management of buildings. Our analysis is rooted in the everyday work of building operators and highlights a number of design suggestions to help improve the user experience and management of BMS, ultimately leading to improvements in productivity, as well as buildings comfort and energy efficiency.
Improving Tweet Representations using Temporal and User Context
Venue: arXiv cs.CL 2016
Year: 2016

In this work we propose a novel representation learning model which computes semantic representations for tweets accurately. Our model systematically exploits the chronologically adjacent tweets ('context') from users' Twitter timelines for this task. Further, we make our model user-aware so that it can do well in modeling the target tweet by exploiting the rich knowledge about the user such as the way the user writes the post and also summarizing the topics on which the user writes. We empirically demonstrate that the proposed models outperform the state-of-the-art models in predicting the user profile attributes like spouse, education and job by 19.66%, 2.27% and 2.22% respectively.
Comparative study of space filling curves for cache oblivious TU Decomposition
Venue: arXiv cs.SC 2016
Year: 2016

We examine several matrix layouts based on space-filling curves that allow for a cache-oblivious adaptation of parallel TU decomposition for rectangular matrices over finite fields. The TU algorithm of \cite{Dumas} requires index conversion routines for which the cost to encode and decode the chosen curve is significant. Using a detailed analysis of the number of bit operations required for the encoding and decoding procedures, and filtering the cost of lookup tables that represent the recursive decomposition of the Hilbert curve, we show that the Morton-hybrid order incurs the least cost for index conversion routines that are required throughout the matrix decomposition as compared to the Hilbert, Peano, or Morton orders. The motivation lies in that cache efficient parallel adaptations for which the natural sequential evaluation order demonstrates lower cache miss rate result in overall faster performance on parallel machines with private or shared caches, on GPU's, or even cloud computing platforms. We report on preliminary experiments that demonstrate how the TURBO algorithm in Morton-hybrid layout attains orders of magnitude improvement in performance as the input matrices increase in size. For example, when $N = 2^{13}$, the row major TURBO algorithm concludes within about 38.6 hours, whilst the Morton-hybrid algorithm with truncation size equal to $64$ concludes within 10.6 hours.
Crowd collectiveness measure via graph-based node clique learning
Venue: arXiv cs.CV 2016
Year: 2016

Collectiveness motions of crowd systems have attracted a great deal of attentions in recently years. In this paper, we try to measure the collectiveness of a crowd system by the proposed node clique learning method. The proposed method is a graph based method, and investigates the influence from one node to other nodes. A node is represented by a set of nodes which named a clique, which is obtained by spreading information from this node to other nodes in graph. Then only nodes with sufficient information are selected as the clique of this node. The motion coherence between two nodes is defined by node cliques comparing. The collectiveness of a node and the collectiveness of the crowd system are defined by the nodes coherence. Self-driven particle (SDP) model and the crowd motion database are used to test the ability of the proposed method in measuring collectiveness.
A Scalable Document-based Architecture for Text Analysis
Venue: arXiv cs.DB 2016
Year: 2016

Analyzing textual data is a very challenging task because of the huge volume of data generated daily. Fundamental issues in text analysis include the lack of structure in document datasets, the need for various preprocessing steps %(e.g., stem or lemma extraction, part-of-speech tagging, named entities recognition...), and performance and scaling issues. Existing text analysis architectures partly solve these issues, providing restrictive data schemas, addressing only one aspect of text preprocessing and focusing on one single task when dealing with performance optimization. %As a result, no definite solution is currently available. Thus, we propose in this paper a new generic text analysis architecture, where document structure is flexible, many preprocessing techniques are integrated and textual datasets are indexed for efficient access. We implement our conceptual architecture using both a relational and a document-oriented database. Our experiments demonstrate the feasibility of our approach and the superiority of the document-oriented logical and physical implementation.
iCrawl: Improving the Freshness of Web Collections by Integrating Social Web and Focused Web Crawling
Venue: arXiv cs.DL 2016
Year: 2016

Researchers in the Digital Humanities and journalists need to monitor, collect and analyze fresh online content regarding current events such as the Ebola outbreak or the Ukraine crisis on demand. However, existing focused crawling approaches only consider topical aspects while ignoring temporal aspects and therefore cannot achieve thematically coherent and fresh Web collections. Especially Social Media provide a rich source of fresh content, which is not used by state-of-the-art focused crawlers. In this paper we address the issues of enabling the collection of fresh and relevant Web and Social Web content for a topic of interest through seamless integration of Web and Social Media in a novel integrated focused crawler. The crawler collects Web and Social Media content in a single system and exploits the stream of fresh Social Media content for guiding the crawler.
Regular Separability of Parikh Automata
Venue: arXiv cs.FL 2016
Year: 2016

We investigate a subclass of languages recognized by vector addition systems, namely languages of nondeterministic Parikh automata. While the regularity problem (is the language of a given automaton regular?) is undecidable for this model, we show surprising decidability of the regular separability problem: given two Parikh automata, is there a regular language that contains one of them and is disjoint from the other?
Practical LTE and Wi-Fi Coexistence Techniques beyond LBT
Venue: arXiv cs.NI 2016
Year: 2016

Coexistence with Wi-Fi is the key issue for unlicensed band LTE. The main coexistence mechanism is Listen-Before-Talk, whereby radio frequency energy is sensed over a short period of time and compared to a threshold. Given the default energy thresholds, the energy sensing range is actually much less than the cell range. Both technologies can experience collisions due to transmission being below energy detection threshold. Currently Wi-Fi is agnostic of LTE presence in the unlicensed spectrum. To improve coexistence a communications channel via relaying is proposed to be used by the unlicensed band LTE, to announce its presence on an unlicensed channel. Legacy Wi-Fi APs may be programmed to interpret and respond by firmware upgrade at the AP to enhance its channel selection algorithm. Higher performance for both networks is demonstrated via more effective radio frequency channel selection and adaptive energy detection thresholding.
Hybrid Transactional Replication: State-Machine and Deferred-Update Replication Combined
Venue: arXiv cs.DC 2016
Year: 2016

We propose Hybrid Transactional Replication (HTR), a novel replication scheme for highly dependable services. It combines two schemes: a transaction is executed either optimistically by only one service replica in the deferred update mode (DU), or deterministically by all replicas in the state machine mode (SM); the choice is made by an oracle. The DU mode allows for parallelism and thus takes advantage of multicore hardware. In contrast to DU, the SM mode guarantees abort-free execution, so it is suitable for irrevocable operations and transactions generating high contention. For expressiveness, transactions can be discarded or retried on demand. We formally prove that the higher flexibility of the scheme does not come at the cost of weaker guarantees for clients: HTR satisfies strong consistency guarantees akin to those provided by other popular transactional replication schemes such as Deferred Update Replication. We developed HTR-enabled Paxos STM, an object-based distributed transactional memory system, and evaluated it thoroughly under various workloads. We show the benefits of using a novel oracle, which relies on machine learning techniques for automatic adaptation to changing conditions. In our tests, the ML-based oracle provides up to 50% improvement in throughput when compared to the system running with DU-only or SM-only oracles. Our approach is inspired by a well known algorithm used in the context of the multi-armed bandit problem.
Fine Asymptotics for Universal One-to-One Compression of Parametric Sources
Venue: arXiv cs.IT 2016
Year: 2016

Universal source coding at short blocklengths is considered for an exponential family of distributions. The \emph{Type Size} code has previously been shown to be optimal up to the third-order rate for universal compression of all memoryless sources over finite alphabets. The Type Size code assigns sequences ordered based on their type class sizes to binary strings ordered lexicographically. To generalize this type class approach for parametric sources, a natural scheme is to define two sequences to be in the same type class if and only if they are equiprobable under any model in the parametric class. This natural approach, however, is shown to be suboptimal. A variation of the Type Size code is introduced, where type classes are defined based on neighborhoods of minimal sufficient statistics. Asymptotics of the overflow rate of this variation are derived and a converse result establishes its optimality up to the third-order term. These results are derived for parametric families of $i.i.d.$ sources as well as Markov sources.
A sub-optimal sampling based method for path planning
Venue: arXiv cs.RO 2016
Year: 2016

In this paper a search algorithm is proposed to find a sub optimal path for a non-holonomic system. For this purpose the algorithm starts sampling the front part of the vehicle and moves towards the destination with a cost function. The bicycle model is used to define the non-holonomic system and a stability analysis with different integration methods is performed on the dynamics of the system. A proper integration method is chosen with a reasonably large step size in order to decrease the computation time. When the tree is close enough to destination the algorithm returns the path and in order to connect the tree to destination point an optimal control problem using single shooting method is defined. To test the algorithm different scenarios are tested and the simulation results show the success of the algorithm.
Efficiently Computing Piecewise Flat Embeddings for Data Clustering and Image Segmentation
Venue: arXiv cs.CV 2016
Year: 2016

Image segmentation is a popular area of research in computer vision that has many applications in automated image processing. A recent technique called piecewise flat embeddings (PFE) has been proposed for use in image segmentation; PFE transforms image pixel data into a lower dimensional representation where similar pixels are pulled close together and dissimilar pixels are pushed apart. This technique has shown promising results, but its original formulation is not computationally feasible for large images. We propose two improvements to the algorithm for computing PFE: first, we reformulate portions of the algorithm to enable various linear algebra operations to be performed in parallel; second, we propose utilizing an iterative linear solver (preconditioned conjugate gradient) to quickly solve a linear least-squares problem that occurs in the inner loop of a nested iteration. With these two computational improvements, we show on a publicly available image database that PFE can be sped up by an order of magnitude without sacrificing segmentation performance. Our results make this technique more practical for use on large data sets, not only for image segmentation, but for general data clustering problems.
Deep Motion Features for Visual Tracking
Venue: arXiv cs.CV 2016
Year: 2016

Robust visual tracking is a challenging computer vision problem, with many real-world applications. Most existing approaches employ hand-crafted appearance features, such as HOG or Color Names. Recently, deep RGB features extracted from convolutional neural networks have been successfully applied for tracking. Despite their success, these features only capture appearance information. On the other hand, motion cues provide discriminative and complementary information that can improve tracking performance. Contrary to visual tracking, deep motion features have been successfully applied for action recognition and video classification tasks. Typically, the motion features are learned by training a CNN on optical flow images extracted from large amounts of labeled videos. This paper presents an investigation of the impact of deep motion features in a tracking-by-detection framework. We further show that hand-crafted, deep RGB, and deep motion features contain complementary information. To the best of our knowledge, we are the first to propose fusing appearance information with deep motion features for visual tracking. Comprehensive experiments clearly suggest that our fusion approach with deep motion features outperforms standard methods relying on appearance information alone.
Physical-Layer Security for Spectrum Sharing Systems
Venue: arXiv cs.IT 2016
Year: 2016

In this paper, we examine the physical-layer security for a spectrum sharing system consisting of multiple source-destination pairs, which dynamically access their shared spectrum for data transmissions in the presence of an eavesdropper. We propose a source cooperation (SC) aided opportunistic jamming framework for protecting the transmission confidentiality of the spectrum sharing system against eavesdropping. Specifically, when a source node is allowed to access the shared spectrum for data transmissions, another source is opportunistically selected in the spectrum sharing system to transmit an artificial noise for disrupting the eavesdropper without affecting the legitimate transmissions. We present two specific SC aided opportunistic jamming schemes, namely the SC aided random jammer selection (RJS) and optimal jammer selection (OJS), which are referred to as the SC-RJS and SC-OJS, respectively. We also consider the conventional non-cooperation as a baseline. We derive closed-form intercept probability expressions for the non-cooperation, SC-RJS and SC-OJS schemes, based on which their secrecy diversity gains are determined through an asymptotic intercept probability analysis in the high signal-to-noise ratio (SNR) region. It is proved that the conventional non-cooperation exhibits a secrecy diversity of zero, whereas the proposed SC-RJS and SC-OJS achieve a higher secrecy diversity of one. This also surprisingly means that no additional secrecy diversity gain is achieved by the optimal jammer selection compared to the random selection strategy. In addition, numerical results show that the intercept probability performance of the SC-OJS is always better than that of the SC-RJS and non-cooperation, even when the legitimate channel is worse than the eavesdropping channel.
Stream Fusion, to Completeness
Venue: arXiv cs.PL 2016
Year: 2016

Stream processing is mainstream (again): Widely-used stream libraries are now available for virtually all modern OO and functional languages, from Java to C# to Scala to OCaml to Haskell. Yet expressivity and performance are still lacking. For instance, the popular, well-optimized Java 8 streams do not support the zip operator and are still an order of magnitude slower than hand-written loops. We present the first approach that represents the full generality of stream processing and eliminates overheads, via the use of staging. It is based on an unusually rich semantic model of stream interaction. We support any combination of zipping, nesting (or flat-mapping), sub-ranging, filtering, mapping-of finite or infinite streams. Our model captures idiosyncrasies that a programmer uses in optimizing stream pipelines, such as rate differences and the choice of a "for" vs. "while" loops. Our approach delivers hand-written-like code, but automatically. It explicitly avoids the reliance on black-box optimizers and sufficiently-smart compilers, offering highest, guaranteed and portable performance. Our approach relies on high-level concepts that are then readily mapped into an implementation. Accordingly, we have two distinct implementations: an OCaml stream library, staged via MetaOCaml, and a Scala library for the JVM, staged via LMS. In both cases, we derive libraries richer and simultaneously many tens of times faster than past work. We greatly exceed in performance the standard stream libraries available in Java, Scala and OCaml, including the well-optimized Java 8 streams.