OpenRaiser/Intern-Atlas huggingface dataset:
Decentralized Throughput Maximizing Policies for Deadline-Constrained Wireless Networks
Venue: arXiv cs.NI 2015
Year: 2015

We consider multi-hop wireless networks serving multiple flows in which only packets that meet hard end-to-end deadline constraints are useful, i.e., if a packet is not delivered to its destination node by its deadline, it is dropped from the network. We design decentralized scheduling policies for such multi-hop networks that attain the maximum throughput of useful packets. The resulting policy is decentralized in the sense that in order to make a transmission decision, a node only needs to know the "time-till-deadline" of the packets that are currently present at that node, and not the state of the entire network. The key to obtaining an easy-to-implement and highly decentralized policy is to replace the hard constraint on the number of simultaneous packet transmissions that can take place on the outgoing links of a node, by a time-average constraint on the number of transmissions. The policy thus obtained is guaranteed to provide maximum throughput. Analysis can be extended to the case of time-varying channel conditions in a straightforward manner. Simulations showing significant improvement over existing policies for deadline based scheduling, such as Earliest Deadline First, and supporting the theory, are presented.
Optimizing Quality of Experience of Dynamic Video Streaming over Fading Wireless Networks
Venue: arXiv cs.NI 2015
Year: 2015

We address the problem of video streaming packets from an Access Point (AP) to multiple clients over a shared wireless channel with fading. In such systems, each client maintains a buffer of packets from which to play the video, and an outage occurs in the streaming whenever the buffer is empty. Clients can switch to a lower-quality of video packet, or request packet transmission at a higher energy level in order to minimize the number of outages plus the number of outage periods and the number of low quality video packets streamed, while there is an average power constraint on the AP. We pose the problem of choosing the video quality and transmission power as a Constrained Markov Decision Process (CMDP). We show that the problem involving $N$ clients decomposes into $N$ MDPs, each involving only a single client, and furthermore that the optimal policy has a threshold structure, in which the decision to choose the video-quality and power-level of transmission depends solely on the buffer-level.
Facial Descriptors for Human Interaction Recognition In Still Images
Venue: arXiv cs.CV 2015
Year: 2015

This paper presents a novel approach in a rarely studied area of computer vision: Human interaction recognition in still images. We explore whether the facial regions and their spatial configurations contribute to the recognition of interactions. In this respect, our method involves extraction of several visual features from the facial regions, as well as incorporation of scene characteristics and deep features to the recognition. Extracted multiple features are utilized within a discriminative learning framework for recognizing interactions between people. Our designed facial descriptors are based on the observation that relative positions, size and locations of the faces are likely to be important for characterizing human interactions. Since there is no available dataset in this relatively new domain, a comprehensive new dataset which includes several images of human interactions is collected. Our experimental results show that faces and scene characteristics contain important information to recognize interactions between people.
Testing for common sense (violation) in airline pricing or how complexity asymmetry defeated you and the web
Venue: arXiv cs.CY 2015
Year: 2015

We have collected and analysed prices for more than 1.4 million flight tickets involving 63 destinations and 125 airlines and have found that common sense violation i.e., discrepancies between what consumers would expect and what truly holds for those prices, are far more frequent than one would think. For example, oftentimes the price of a single leg flight is higher than two-leg flights that include it under similar terms of travel (class, luggage allowance, etc.). This happened for up to 24.5% of available fares on a specific route in our dataset invalidating the common expectation that "further is more expensive". Likewise, we found several two-leg fares where buying each leg independently leads to lower overall cost than buying them together as a single ticket. This happened for up to 37% of available fares on a specific route invalidating the common expectation that "bundling saves money". Last, several single stop tickets in which the two legs were separated by 1-5 days (called multicity fares), were oftentimes found to be costing more than corresponding back-to-back fares with a small transit time. This was found to be occurring in up to 7.5% fares on a specific route invalidating that "a short transit is better than a longer one".
Class Association Rules Mining based Rough Set Method
Venue: arXiv cs.DB 2015
Year: 2015

This paper investigates the mining of class association rules with rough set approach. In data mining, an association occurs between two set of elements when one element set happen together with another. A class association rule set (CARs) is a subset of association rules with classes specified as their consequences. We present an efficient algorithm for mining the finest class rule set inspired form Apriori algorithm, where the support and confidence are computed based on the elementary set of lower approximation included in the property of rough set theory. Our proposed approach has been shown very effective, where the rough set approach for class association discovery is much simpler than the classic association method.
Learning from Synthetic Data Using a Stacked Multichannel Autoencoder
Venue: arXiv cs.CV 2015
Year: 2015

Learning from synthetic data has many important and practical applications. An example of application is photo-sketch recognition. Using synthetic data is challenging due to the differences in feature distributions between synthetic and real data, a phenomenon we term synthetic gap. In this paper, we investigate and formalize a general framework-Stacked Multichannel Autoencoder (SMCAE) that enables bridging the synthetic gap and learning from synthetic data more efficiently. In particular, we show that our SMCAE can not only transform and use synthetic data on the challenging face-sketch recognition task, but that it can also help simulate real images, which can be used for training classifiers for recognition. Preliminary experiments validate the effectiveness of the framework.
TransA: An Adaptive Approach for Knowledge Graph Embedding
Venue: arXiv cs.CL 2015
Year: 2015

Knowledge representation is a major topic in AI, and many studies attempt to represent entities and relations of knowledge base in a continuous vector space. Among these attempts, translation-based methods build entity and relation vectors by minimizing the translation loss from a head entity to a tail one. In spite of the success of these methods, translation-based methods also suffer from the oversimplified loss metric, and are not competitive enough to model various and complex entities/relations in knowledge bases. To address this issue, we propose \textbf{TransA}, an adaptive metric approach for embedding, utilizing the metric learning ideas to provide a more flexible embedding method. Experiments are conducted on the benchmark datasets and our proposed method makes significant and consistent improvements over the state-of-the-art baselines.
Hardware Impairments Aware Transceiver for Full-Duplex Massive MIMO Relaying
Venue: arXiv cs.IT 2015
Year: 2015

This paper studies the massive MIMO full-duplex relaying (MM-FDR), where multiple source-destination pairs communicate simultaneously with the help of a common full-duplex relay equipped with very large antenna arrays. Different from the traditional MM-FDR protocol, a general model where sources/destinations are allowed to equip with multiple antennas is considered. In contrast to the conventional MIMO system, massive MIMO must be built with low-cost components which are prone to hardware impairments. In this paper, the effect of hardware impairments is taken into consideration, and is modeled using transmit/receive distortion noises. We propose a low complexity hardware impairments aware transceiver scheme (named as HIA scheme) to mitigate the distortion noises by exploiting the statistical knowledge of channels and antenna arrays at sources and destinations. A joint degree of freedom and power optimization algorithm is presented to further optimize the spectral efficiency of HIA based MM-FDR. The results show that the HIA scheme can mitigate the "ceiling effect" appears in traditional MM-FDR protocol, if the numbers of antennas at sources and destinations can scale with that at the relay.
Efficient enumeration of solutions produced by closure operations
Venue: arXiv cs.CC 2015
Year: 2015

In this paper we address the problem of generating all elements obtained by the saturation of an initial set by some operations. More precisely, we prove that we can generate the closure by polymorphisms of a boolean relation with a polynomial delay. This implies for instance that we can compute with polynomial delay the closure of a family of sets by any set of "set operations" (e.g. by union, intersection, difference, symmetric difference$\dots$). To do so, we prove that for any set of operations $\mathcal{F}$, one can decide in polynomial time whether an elements belongs to the closure by $\mathcal{F}$ of a family of sets. When the relation is over a domain larger than two elements, our generic enumeration method fails for some cases, since the associated decision problem is $NP$-hard and we provide an alternative algorithm.
Measuring Verifiability in Online Information
Venue: arXiv cs.SI 2015
Year: 2015

The verifiability of online information is important, but difficult to assess systematically. We examine verifiability in the case of Wikipedia, one of the world's largest and most consulted online information sources. We extend prior work about quality of Wikipedia articles, knowledge production, and sources to consider the quality of Wikipedia references. We propose a multidimensional measure of verifiability that takes into account technical accuracy and practical accessibility of sources. We calculate article verifiability scores for a sample of 5,000 articles and 295,800 citations, and compare differently weighted models to illustrate effects of emphasizing particular elements of verifiability over others. We find that, while the quality of references in the overall sample is reasonably high, verifiability varies significantly by article, particularly when emphasizing the use of standard digital identifiers and taking into account the practical availability of referenced sources. We discuss the implications of these findings for measuring verifiability in online information more generally.
Computational evolution of decision-making strategies
Venue: arXiv cs.NE 2015
Year: 2015

Most research on adaptive decision-making takes a strategy-first approach, proposing a method of solving a problem and then examining whether it can be implemented in the brain and in what environments it succeeds. We present a method for studying strategy development based on computational evolution that takes the opposite approach, allowing strategies to develop in response to the decision-making environment via Darwinian evolution. We apply this approach to a dynamic decision-making problem where artificial agents make decisions about the source of incoming information. In doing so, we show that the complexity of the brains and strategies of evolved agents are a function of the environment in which they develop. More difficult environments lead to larger brains and more information use, resulting in strategies resembling a sequential sampling approach. Less difficult environments drive evolution toward smaller brains and less information use, resulting in simpler heuristic-like strategies.
Similar Handwritten Chinese Character Discrimination by Weakly Supervised Learning
Venue: arXiv cs.CV 2015
Year: 2015

Traditional approaches for handwritten Chinese character recognition suffer in classifying similar characters. In this paper, we propose to discriminate similar handwritten Chinese characters by using weakly supervised learning. Our approach learns a discriminative SVM for each similar pair which simultaneously localizes the discriminative region of similar character and makes the classification. For the first time, similar handwritten Chinese character recognition (SHCCR) is formulated as an optimization problem extended from SVM. We also propose a novel feature descriptor, Gradient Context, and apply bag-of-words model to represent regions with different scales. In our method, we do not need to select a sized-fixed sub-window to differentiate similar characters. The unconstrained property makes our method well adapted to high variance in the size and position of discriminative regions in similar handwritten Chinese characters. We evaluate our proposed approach over the CASIA Chinese character data set and the results show that our method outperforms the state of the art.
CloudSimNFV: Modeling and Simulation of Energy-Efficient NFV in Cloud Data Centers
Venue: arXiv cs.DC 2015
Year: 2015

Network Function Virtualization (NFV) takes advantage of hardware virtualization to undertake software processing for various functions, and complements the drawbacks of traditional network technology. To speed up NFV related research, we need a user friendly and easy to use research tool, which could support data center simulation, scheduling algorithms implementation and extension, and provide energy consumption simulation. As a cloud simulation toolkit, CloudSim has strong extendibility that could be extended to simulate NFV environment. This paper introduces a NFV cloud framework based on CloudSim and an energy consumption model based on multi-dimensional extension, implementing a toolkit named ClousimNFV to simulate the NFV scenario, proposing several scheduling algorithm based on for NFV applications. The toolkit validation and algorithm performance comparison are also given.
Fractional-order Generalized Principle of Self-Support (FOG PSS) in Control Systems Design
Venue: arXiv cs.SY 2015
Year: 2015

This paper reviews research that studies the principle of self-support (PSS) in some control systems and proposes a fractional-order generalized PSS framework for the first time. The existing PSS approach focuses on practical tracking problem of integer-order systems including robotic dynamics, high precision linear motor system, multi-axis high precision positioning system with unmeasurable variables, imprecise sensor information, uncertain parameters and external disturbances. More generally, by formulating the fractional PSS concept as a new generalized framework, we will focus in the possible fields on the fractional-order control problems such as practical tracking, $λ$-tracking, etc. of robot systems, multiple mobile agents, discrete dynamical systems, time delay systems and other uncertain nonlinear systems. Finally, the practical tracking of a first-order uncertain model of automobile is considered as a simple example to demonstrate the efficiency of the fractional-order generalized principle of self-support (FOGPSS) control strategy.
Early text classification: a Naive solution
Venue: arXiv cs.CL 2015
Year: 2015

Text classification is a widely studied problem, and it can be considered solved for some domains and under certain circumstances. There are scenarios, however, that have received little or no attention at all, despite its relevance and applicability. One of such scenarios is early text classification, where one needs to know the category of a document by using partial information only. A document is processed as a sequence of terms, and the goal is to devise a method that can make predictions as fast as possible. The importance of this variant of the text classification problem is evident in domains like sexual predator detection, where one wants to identify an offender as early as possible. This paper analyzes the suitability of the standard naive Bayes classifier for approaching this problem. Specifically, we assess its performance when classifying documents after seeing an increasingly number of terms. A simple modification to the standard naive Bayes implementation allows us to make predictions with partial information. To the best of our knowledge naive Bayes has not been used for this purpose before. Throughout an extensive experimental evaluation we show the effectiveness of the classifier for early text classification. What is more, we show that this simple solution is very competitive when compared with state of the art methodologies that are more elaborated. We foresee our work will pave the way for the development of more effective early text classification techniques based in the naive Bayes formulation.
On Large-Scale Retrieval: Binary or n-ary Coding?
Venue: arXiv cs.CV 2015
Year: 2015

The growing amount of data available in modern-day datasets makes the need to efficiently search and retrieve information. To make large-scale search feasible, Distance Estimation and Subset Indexing are the main approaches. Although binary coding has been popular for implementing both techniques, n-ary coding (known as Product Quantization) is also very effective for Distance Estimation. However, their relative performance has not been studied for Subset Indexing. We investigate whether binary or n-ary coding works better under different retrieval strategies. This leads to the design of a new n-ary coding method, "Linear Subspace Quantization (LSQ)" which, unlike other n-ary encoders, can be used as a similarity-preserving embedding. Experiments on image retrieval show that when Distance Estimation is used, n-ary LSQ outperforms other methods. However, when Subset Indexing is applied, interestingly, binary codings are more effective and binary LSQ achieves the best accuracy.
Fusing Multi-Stream Deep Networks for Video Classification
Venue: arXiv cs.CV 2015
Year: 2015

This paper studies deep network architectures to address the problem of video classification. A multi-stream framework is proposed to fully utilize the rich multimodal information in videos. Specifically, we first train three Convolutional Neural Networks to model spatial, short-term motion and audio clues respectively. Long Short Term Memory networks are then adopted to explore long-term temporal dynamics. With the outputs of the individual streams, we propose a simple and effective fusion method to generate the final predictions, where the optimal fusion weights are learned adaptively for each class, and the learning process is regularized by automatically estimated class relationships. Our contributions are two-fold. First, the proposed multi-stream framework is able to exploit multimodal features that are more comprehensive than those previously attempted. Second, we demonstrate that the adaptive fusion method using the class relationship as a regularizer outperforms traditional alternatives that estimate the weights in a "free" fashion. Our framework produces significantly better results than the state of the arts on two popular benchmarks, 92.2\% on UCF-101 (without using audio) and 84.9\% on Columbia Consumer Videos.
Comments on "Achievable Rates in Cognitive Radio Channels"
Venue: arXiv cs.IT 2015
Year: 2015

In a premier paper on the information-theoretic analysis of a two-user cognitive interference channel(CIC), Devroye et al. presented an achievable rate region for the two-user discrete memoryless CIC. The coding scheme proposed by Devroye et al. is correct but unfortunately some rate-terms in the derived achievable rate region are incorrect (in fact incomplete) because of occurring some mistakes in decoding and analysis of error probability. We correct and complete the wrong rate-terms and thereby show that the corrected achievable rate region includes the rate region presented in.
Hybrid Optimization Algorithm for Large-Scale QoS-Aware Service Composition
Venue: arXiv cs.AI 2015
Year: 2015

In this paper we present a hybrid approach for automatic composition of Web services that generates semantic input-output based compositions with optimal end-to-end QoS, minimizing the number of services of the resulting composition. The proposed approach has four main steps: 1) generation of the composition graph for a request; 2) computation of the optimal composition that minimizes a single objective QoS function; 3) multi-step optimizations to reduce the search space by identifying equivalent and dominated services; and 4) hybrid local-global search to extract the optimal QoS with the minimum number of services. An extensive validation with the datasets of the Web Service Challenge 2009-2010 and randomly generated datasets shows that: 1) the combination of local and global optimization is a general and powerful technique to extract optimal compositions in diverse scenarios; and 2) the hybrid strategy performs better than the state-of-the-art, obtaining solutions with less services and optimal QoS.
Exact Real Arithmetic with Perturbation Analysis and Proof of Correctness
Venue: arXiv cs.NA 2015
Year: 2015

In this article, we consider a simple representation for real numbers and propose top-down procedures to approximate various algebraic and transcendental operations with arbitrary precision. Detailed algorithms and proofs are provided to guarantee the correctness of the approximations. Moreover, we develop and apply a perturbation analysis method to show that our approximation procedures only recompute expressions when unavoidable. In the last decade, various theories have been developed and implemented to realize real computations with arbitrary precision. Proof of correctness for existing approaches typically consider basic algebraic operations, whereas detailed arguments about transcendental operations are not available. Another important observation is that in each approach some expressions might require iterative computations to guarantee the desired precision. However, no formal reasoning is provided to prove that such iterative calculations are essential in the approximation procedures. In our approximations of real functions, we explicitly relate the precision of the inputs to the guaranteed precision of the output, provide full proofs and a precise analysis of the necessity of iterations.
Web Services for Asynchronous, Distributed Optimization Using Conservative Signal Processing
Venue: arXiv cs.DC 2015
Year: 2015

This paper presents a systematic approach for implementing a class of nonlinear signal processing systems as a distributed web service, which in turn is used to solve optimization problems in a distributed, asynchronous fashion. As opposed to requiring a specialized server, the presented approach requires only the use of a commodity database back-end as a central resource, as might typically be used to serve data for websites having large numbers of concurrent users. In this sense the presented approach leverages not only the scalability and robustness of various database systems in sharing variables asynchronously between workers, but also critically it leverages the tools of signal processing in determining how the optimization algorithm might be organized and distributed among various heterogeneous workers. A publicly-accessible implementation is also presented, utilizing Firebase as a back-end server, and illustrating the use of the presented approach in solving various optimization problems commonly arising in the context of signal processing.