OpenRaiser/Intern-Atlas huggingface dataset:
A Scalable and Robust Framework for Intelligent Real-time Video Surveillance
Venue: arXiv cs.CV 2016
Year: 2016

In this paper, we present an intelligent, reliable and storage-efficient video surveillance system using Apache Storm and OpenCV. As a Storm topology, we have added multiple information extraction modules that only write important content to the disk. Our topology is extensible, capable of adding novel algorithms as per the use case without affecting the existing ones, since all the processing is independent of each other. This framework is also highly scalable and fault tolerant, which makes it a best option for organisations that need to monitor a large network of surveillance cameras.
Hybrid Analog and Digital Precoding: From Practical RF System Models to Information Theoretic Bounds
Venue: arXiv cs.IT 2016
Year: 2016

Hybrid analog-digital precoding is a key millimeter wave access technology, where an antenna array with reduced number of radio frequency (RF) chains is used with an RF precoding matrix to increase antenna gain at a reasonable cost. However, digital and RF precoder algorithms must be accompa- nied by a detailed system model of the RF precoder. In this work, we provide fundamental RF system models for these precoders, and show their impact on achievable rates. We show that hybrid precoding systems suffer from significant degradation, once the limitations of RF precoding network are accounted. We subsequently quantify this performance degradation, and use it as a reference for comparing the performance of different precoding methods. These results indicate that hybrid precoders must be redesigned (and their rates recomputed) to account for practical factors.
Matrix Semigroup Freeness Problems in $\mathrm{SL}(2,\mathbb{Z})$
Venue: arXiv cs.CC 2016
Year: 2016

In this paper we study decidability and complexity of decision problems on matrices from the special linear group $\mathrm{SL}(2,\mathbb{Z})$. In particular, we study the freeness problem: given a finite set of matrices $G$ generating a multiplicative semigroup $S$, decide whether each element of $S$ has at most one factorization over $G$. In other words, is $G$ a code? We show that the problem of deciding whether a matrix semigroup in $\mathrm{SL}(2,\mathbb{Z})$ is non-free is NP-hard. Then, we study questions about the number of factorizations of matrices in the matrix semigroup such as the finite freeness problem, the recurrent matrix problem, the unique factorizability problem, etc. Finally, we show that some factorization problems could be even harder in $\mathrm{SL}(2,\mathbb{Z})$, for example we show that to decide whether every prime matrix has at most $k$ factorizations is PSPACE-hard.
A Survey of Brain Inspired Technologies for Engineering
Venue: arXiv cs.AI 2016
Year: 2016

Cognitive engineering is a multi-disciplinary field and hence it is difficult to find a review article consolidating the leading developments in the field. The in-credible pace at which technology is advancing pushes the boundaries of what is achievable in cognitive engineering. There are also differing approaches to cognitive engineering brought about from the multi-disciplinary nature of the field and the vastness of possible applications. Thus research communities require more frequent reviews to keep up to date with the latest trends. In this paper we shall dis-cuss some of the approaches to cognitive engineering holistically to clarify the reasoning behind the different approaches and to highlight their strengths and weaknesses. We shall then show how developments from seemingly disjointed views could be integrated to achieve the same goal of creating cognitive machines. By reviewing the major contributions in the different fields and showing the potential for a combined approach, this work intends to assist the research community in devising more unified methods and techniques for developing cognitive machines.
Learning Runtime Parameters in Computer Systems with Delayed Experience Injection
Venue: arXiv cs.LG 2016
Year: 2016

Learning effective configurations in computer systems without hand-crafting models for every parameter is a long-standing problem. This paper investigates the use of deep reinforcement learning for runtime parameters of cloud databases under latency constraints. Cloud services serve up to thousands of concurrent requests per second and can adjust critical parameters by leveraging performance metrics. In this work, we use continuous deep reinforcement learning to learn optimal cache expirations for HTTP caching in content delivery networks. To this end, we introduce a technique for asynchronous experience management called delayed experience injection, which facilitates delayed reward and next-state computation in concurrent environments where measurements are not immediately available. Evaluation results show that our approach based on normalized advantage functions and asynchronous CPU-only training outperforms a statistical estimator.
Generalization of Quadratic Manifolds for Reduced Order Modeling of Nonlinear Structural Dynamics
Venue: arXiv cs.CE 2016
Year: 2016

In this paper, a generalization of a quadratic manifold approach for the reduction of geometrically nonlinear structural dynamics problems is presented. This generalization is constructed by a linearization of the static force with respect to the generalized coordinates, resulting in a shift of the quadratic behavior from the force to the manifold. In this framework, static derivatives emerge as natural extensions to modal derivatives for displacement fields other than the vibration modes, such as the Krylov subspace vectors. Here the dynamic problem is projected onto the tangent space of the quadratic manifold, allowing for a much less number of generalized coordinates compared to linear basis methods. The potential of the quadratic manifold approach is investigated in a numerical study, where several variations of the approach are compared on different examples, indicating a clear pattern where the proposed approach is applicable.
Parallel adaptive integration in high-performance functional Renormalization Group computations
Venue: arXiv cs.CE 2016
Year: 2016

The conceptual framework provided by the functional Renormalization Group (fRG) has become a formidable tool to study correlated electron systems on lattices which, in turn, provided great insights to our understanding of complex many-body phenomena, such as high- temperature superconductivity or topological states of matter. In this work we present one of the latest realizations of fRG which makes use of an adaptive numerical quadrature scheme specifically tailored to the described fRG scheme. The final result is an increase in performance thanks to improved parallelism and scalability.
End-to-End Answer Chunk Extraction and Ranking for Reading Comprehension
Venue: arXiv cs.CL 2016
Year: 2016

This paper proposes dynamic chunk reader (DCR), an end-to-end neural reading comprehension (RC) model that is able to extract and rank a set of answer candidates from a given document to answer questions. DCR is able to predict answers of variable lengths, whereas previous neural RC models primarily focused on predicting single tokens or entities. DCR encodes a document and an input question with recurrent neural networks, and then applies a word-by-word attention mechanism to acquire question-aware representations for the document, followed by the generation of chunk representations and a ranking module to propose the top-ranked chunk as the answer. Experimental results show that DCR achieves state-of-the-art exact match and F1 scores on the SQuAD dataset.
A GPU-Based Genetic Algorithm for the P-Median Problem
Venue: arXiv cs.DC 2016
Year: 2016

The p-median problem is a well-known NP-hard problem. Many heuristics have been proposed in the literature for this problem. In this paper, we exploit a GPGPU parallel computing platform to present a new genetic algorithm implemented in Cuda and based on a Pseudo Boolean formulation of the p-median problem. We have tested the effectiveness of our algorithm using a Tesla K40 (2880 Cuda cores) on 290 different benchmark instances obtained from OR-Library, discrete location problems benchmark library, and benchmarks introduced in recent publications. The algorithm succeeded in finding optimal solutions for all instances except for two OR-library instances, namely pmed30 and pmed40, where better than 99.9\% approximations were obtained.
A Simple Hash Class with Strong Randomness Properties in Graphs and Hypergraphs
Venue: arXiv cs.DS 2016
Year: 2016

We study randomness properties of graphs and hypergraphs generated by simple hash functions. Several hashing applications can be analyzed by studying the structure of $d$-uniform random ($d$-partite) hypergraphs obtained from a set $S$ of $n$ keys and $d$ randomly chosen hash functions $h_1,\dots,h_d$ by associating each key $x\in S$ with a hyperedge $\{h_1(x),\dots, h_d(x)\}$. Often it is assumed that $h_1,\dots,h_d$ exhibit a high degree of independence. We present a simple construction of a hash class whose hash functions have small constant evaluation time and can be stored in sublinear space. We devise general techniques to analyze the randomness properties of the graphs and hypergraphs generated by these hash functions, and we show that they can replace other, less efficient constructions in cuckoo hashing (with and without stash), the simulation of a uniform hash function, the construction of a perfect hash function, generalized cuckoo hashing and different load balancing scenarios.
The Durability and Fragility of Knowledge Infrastructures: Lessons Learned from Astronomy
Venue: arXiv cs.DL 2016
Year: 2016

Infrastructures are not inherently durable or fragile, yet all are fragile over the long term. Durability requires care and maintenance of individual components and the links between them. Astronomy is an ideal domain in which to study knowledge infrastructures, due to its long history, transparency, and accumulation of observational data over a period of centuries. Research reported here draws upon a long-term study of scientific data practices to ask questions about the durability and fragility of infrastructures for data in astronomy. Methods include interviews, ethnography, and document analysis. As astronomy has become a digital science, the community has invested in shared instruments, data standards, digital archives, metadata and discovery services, and other relatively durable infrastructure components. Several features of data practices in astronomy contribute to the fragility of that infrastructure. These include different archiving practices between ground- and space-based missions, between sky surveys and investigator-led projects, and between observational and simulated data. Infrastructure components are tightly coupled, based on international agreements. However, the durability of these infrastructures relies on much invisible work - cataloging, metadata, and other labor conducted by information professionals. Continual investments in care and maintenance of the human and technical components of these infrastructures are necessary for sustainability.
Learning recurrent representations for hierarchical behavior modeling
Venue: arXiv cs.AI 2016
Year: 2016

We propose a framework for detecting action patterns from motion sequences and modeling the sensory-motor relationship of animals, using a generative recurrent neural network. The network has a discriminative part (classifying actions) and a generative part (predicting motion), whose recurrent cells are laterally connected, allowing higher levels of the network to represent high level phenomena. We test our framework on two types of data, fruit fly behavior and online handwriting. Our results show that 1) taking advantage of unlabeled sequences, by predicting future motion, significantly improves action detection performance when training labels are scarce, 2) the network learns to represent high level phenomena such as writer identity and fly gender, without supervision, and 3) simulated motion trajectories, generated by treating motion prediction as input to the network, look realistic and may be used to qualitatively evaluate whether the model has learnt generative control rules.
An Experimental Comparison of Coded Modulation Strategies for 100 Gbit/s Transceivers
Venue: arXiv cs.IT 2016
Year: 2016

Coded modulation is a key technique to increase the spectral efficiency of coherent optical communication systems. Two popular strategies for coded modulation are turbo trellis-coded modulation (TTCM) and bit-interleaved coded modulation (BICM) based on low-density parity-check (LDPC) codes. Although BICM LDPC is suboptimal, its simplicity makes it very popular in practice. In this work, we compare the performance of TTCM and BICM LDPC using information-theoretic measures. Our information-theoretic results show that for the same overhead and modulation format only a very small penalty (less than 0.1 dB) is to be expected when an ideal BICM LDPC scheme is used. However, the results obtained for the coded modulation schemes implemented in this paper show that the TTCM outperforms BICM LDPC by a larger margin. For a 1000 km transmission at 100 Gbit/s, the observed gain was 0.4 dB.
Petroleum Refinery Multi-Antenna Propagation Measurements
Venue: arXiv cs.NI 2016
Year: 2016

This paper presents the results of the first multi- antenna propagation measurement campaign to be conducted at an operating petroleum refining facility. The measurement equipment transmits pseudo-random noise test signals from two antennas at a 2.47 GHz carrier with a signal bandwidth of approximately 25 MHz. The measurement data is analyzed to extract path loss exponent, shadowing distribution, fading distribution, coherence bandwidth and antenna correlation. The results reveal an environment where large scale attenuation is relatively mild, fading is severe and good performance is expected from both antenna and frequency diversity.
Detection of Single vs Multiple Antenna Transmission Systems Using Pilot Data
Venue: arXiv cs.IT 2016
Year: 2016

In this paper, we consider the problem of classifying the transmission system when it is not known a priori whether the transmission is via a single antenna or multiple antennas. The receiver is assumed to be employed with a known number of antennas. In a data frame transmitted by most multiple input multiple output (MIMO) systems, some pilot or training data is inserted for symbol timing synchronization and estimation of the channel. Our goal is to perform MIMO transmit antenna classification using this pilot data. More specifically, the problem of determining the transmission system is cast as a multiple hypothesis testing problem where the number of hypotheses is equal to the maximum number of transmit antennas. Under the assumption of receiver having the exact knowledge of pilot data used for timing synchronization and channel estimation, we consider maximum likelihood (ML) and correlation test statistics to classify the MIMO transmit system. When only probabilistic knowledge of pilot data is available at the receiver, a hybrid-maximum likelihood (HML) based test statistic is constructed using the expectation-maximization (EM) algorithm. The performance of the proposed algorithms is illustrated via simulations and comparative merits of different techniques in terms of the computational complexity and performance are discussed.
Self-Awareness of Cloud Applications
Venue: arXiv cs.SE 2016
Year: 2016

Cloud applications today deliver an increasingly larger portion of the Information and Communication Technology (ICT) services. To address the scale, growth, and reliability of cloud applications, self-aware management and scheduling are becoming commonplace. How are they used in practice? In this chapter, we propose a conceptual framework for analyzing state-of-the-art self-awareness approaches used in the context of cloud applications. We map important applications corresponding to popular and emerging application domains to this conceptual framework, and compare the practical characteristics, benefits, and drawbacks of self-awareness approaches. Last, we propose a roadmap for addressing open challenges in self-aware cloud and datacenter applications.
Comments on an image encryption scheme based on a chaotic Tent map
Venue: arXiv cs.CR 2016
Year: 2016

Recently an image encryption scheme based on a chaotic Tent map has been proposed by Li et al. This comment shows that this scheme is broken and no secure application can be found for it.
Bounds for the $l_1$-distance of $q$-ary lattices obtained via Constructions D, D$^{'}$ and $\overline{D}$
Venue: arXiv cs.IT 2016
Year: 2016

Lattices have been used in several problems in coding theory and cryptography. In this paper we approach $q$-ary lattices obtained via Constructions D, $\D'$ and $\overline{D}$. It is shown connections between Constructions D and $\D'$. Bounds for the minimum $l_1$-distance of lattices $Λ_{D}$, $Λ_{D'}$ and $Λ_{\overline{D}}$ and, under certain conditions, a generator matrix for $Λ_{D'}$ are presented. In addition, when the chain of codes used is closed under the zero-one addition, we derive explicit expressions for the minimum $l_1$-distances of the lattices $Λ_{D}$ and $Λ_{\overline{D}}$ attached to the distances of the codes used in these constructions.
Online Multi-view Clustering with Incomplete Views
Venue: arXiv cs.LG 2016
Year: 2016

In the era of big data, it is common to have data with multiple modalities or coming from multiple sources, known as "multi-view data". Multi-view clustering provides a natural way to generate clusters from such data. Since different views share some consistency and complementary information, previous works on multi-view clustering mainly focus on how to combine various numbers of views to improve clustering performance. However, in reality, each view may be incomplete, i.e., instances missing in the view. Furthermore, the size of data could be extremely huge. It is unrealistic to apply multi-view clustering in large real-world applications without considering the incompleteness of views and the memory requirement. None of previous works have addressed all these challenges simultaneously. In this paper, we propose an online multi-view clustering algorithm, OMVC, which deals with large-scale incomplete views. We model the multi-view clustering problem as a joint weighted nonnegative matrix factorization problem and process the multi-view data chunk by chunk to reduce the memory requirement. OMVC learns the latent feature matrices for all the views and pushes them towards a consensus. We further increase the robustness of the learned latent feature matrices in OMVC via lasso regularization. To minimize the influence of incompleteness, dynamic weight setting is introduced to give lower weights to the incoming missing instances in different views. More importantly, to reduce the computational time, we incorporate a faster projected gradient descent by utilizing the Hessian matrices in OMVC. Extensive experiments conducted on four real data demonstrate the effectiveness of the proposed OMVC method.
Optimized Thermal-Aware Job Scheduling and Control of Data Centers
Venue: arXiv cs.SY 2016
Year: 2016

Analyzing data centers with thermal-aware optimization techniques is a viable approach to reduce energy consumption of data centers. By taking into account thermal consequences of job placements among the servers of a data center, it is possible to reduce the amount of cooling necessary to keep the servers below a given safe temperature threshold. We set up an optimization problem to analyze and characterize the optimal setpoints for the workload distribution and the supply temperature of the cooling equipment. Furthermore under mild assumptions we design and analyze controllers that drive the data center to the optimal state without knowledge of the current total workload to be handled by the data center. The response of our controller is validated by simulations and convergence to the optimal setpoints is achieved under varying workload conditions.
An asymptotically optimal, online algorithm for weighted random sampling with replacement
Venue: arXiv cs.DS 2016
Year: 2016

This paper presents a novel algorithm solving the classic problem of generating a random sample of size s from population of size n with non-uniform probabilities. The sampling is done with replacement. The algorithm requires constant additional memory, and works in O(n) time (even when s >> n, in which case the algorithm produces a list containing, for every population member, the number of times it has been selected for sample). The algorithm works online, and as such is well-suited to processing streams. In addition, a novel method of mass-sampling from any discrete distribution using the algorithm is presented.
The new hybrid COAW method for solving multi-objective problems
Venue: arXiv cs.NE 2016
Year: 2016

In this article using Cuckoo Optimization Algorithm and simple additive weighting method the hybrid COAW algorithm is presented to solve multi-objective problems. Cuckoo algorithm is an efficient and structured method for solving nonlinear continuous problems. The created Pareto frontiers of the COAW proposed algorithm are exact and have good dispersion. This method has a high speed in finding the Pareto frontiers and identifies the beginning and end points of Pareto frontiers properly. In order to validation the proposed algorithm, several experimental problems were analyzed. The results of which indicate the proper effectiveness of COAW algorithm for solving multi-objective problems.
Knowledge-infused and Consistent Complex Event Processing over Real-time and Persistent Streams
Venue: arXiv cs.DB 2016
Year: 2016

Emerging applications in Internet of Things (IoT) and Cyber-Physical Systems (CPS) present novel challenges to Big Data platforms for performing online analytics. Ubiquitous sensors from IoT deployments are able to generate data streams at high velocity, that include information from a variety of domains, and accumulate to large volumes on disk. Complex Event Processing (CEP) is recognized as an important real-time computing paradigm for analyzing continuous data streams. However, existing work on CEP is largely limited to relational query processing, exposing two distinctive gaps for query specification and execution: (1) infusing the relational query model with higher level knowledge semantics, and (2) seamless query evaluation across temporal spaces that span past, present and future events. These allow accessible analytics over data streams having properties from different disciplines, and help span the velocity (real-time) and volume (persistent) dimensions. In this article, we introduce a Knowledge-infused CEP (X-CEP) framework that provides domain-aware knowledge query constructs along with temporal operators that allow end-to-end queries to span across real-time and persistent streams. We translate this query model to efficient query execution over online and offline data streams, proposing several optimizations to mitigate the overheads introduced by evaluating semantic predicates and in accessing high-volume historic data streams. The proposed X-CEP query model and execution approaches are implemented in our prototype semantic CEP engine, SCEPter. We validate our query model using domain-aware CEP queries from a real-world Smart Power Grid application, and experimentally analyze the benefits of our optimizations for executing these queries, using event streams from a campus-microgrid IoT deployment.