OpenRaiser/Intern-Atlas huggingface dataset:
Parameterized Neural Network Language Models for Information Retrieval
Venue: arXiv cs.IR 2015
Year: 2015

Information Retrieval (IR) models need to deal with two difficult issues, vocabulary mismatch and term dependencies. Vocabulary mismatch corresponds to the difficulty of retrieving relevant documents that do not contain exact query terms but semantically related terms. Term dependencies refers to the need of considering the relationship between the words of the query when estimating the relevance of a document. A multitude of solutions has been proposed to solve each of these two problems, but no principled model solve both. In parallel, in the last few years, language models based on neural networks have been used to cope with complex natural language processing tasks like emotion and paraphrase detection. Although they present good abilities to cope with both term dependencies and vocabulary mismatch problems, thanks to the distributed representation of words they are based upon, such models could not be used readily in IR, where the estimation of one language model per document (or query) is required. This is both computationally unfeasible and prone to over-fitting. Based on a recent work that proposed to learn a generic language model that can be modified through a set of document-specific parameters, we explore use of new neural network models that are adapted to ad-hoc IR tasks. Within the language model IR framework, we propose and study the use of a generic language model as well as a document-specific language model. Both can be used as a smoothing component, but the latter is more adapted to the document at hand and has the potential of being used as a full document language model. We experiment with such models and analyze their results on TREC-1 to 8 datasets.
Model of Multilayer Knowledge Diffusion for Competence Development in an Organization
Venue: arXiv cs.SI 2015
Year: 2015

Growing role of intellectual capital within organizations is affecting new strategies related to knowledge management and competence development. Among different aspects related to this field, knowledge diffusion has become one of interesting areas from both practitioner and researchers perspective. Several models were proposed with main goal to simulate diffusion and to explain the nature of these processes. Existing models are focused on knowledge diffusion and they assume diffusion within a single layer using knowledge representation. From the organizational perspective connecting several types of knowledge and modelling changes of competence can bring additional value. In the article we extended existing approaches by using multilayer diffusion model and focused on analysis of competence development process. The proposed model describes competence development process in a new way through horizontal and vertical knowledge diffusion in multilayer network. In the network, agents collaborate and interchange various kind of knowledge through different layers and this mutual activities affect the competences in a positive or negative way. Taking under consideration workers cognitive and social abilities and the previous level of competence the new competence level can be estimated. The model is developed to support competence management in different organizations.
Wireless Powered Communications: Performance Analysis and Optimization
Venue: arXiv cs.IT 2015
Year: 2015

This paper investigates the average throughput of a wireless powered communications system, where an energy constrained source, powered by a dedicated power beacon (PB), communicates with a destination. It is assumed that the PB is capable of performing channel estimation, digital beamforming, and spectrum sensing as a communication device. Considering a time splitting approach, the source first harvests energy from the PB equipped with multiple antennas, and then transmits information to the destination. Assuming Nakagami-m fading channels, analytical expressions for the average throughput are derived for two different transmission modes, namely, delay tolerant and delay intolerant. In addition, closed-form solutions for the optimal time split, which maximize the average throughput are obtained in some special cases, i.e., high transmit power regime and large number of antennas. Finally, the impact of co-channel interference is studied. Numerical and simulation results have shown that increasing the number of transmit antennas at the PB is an effective tool to improve the average throughput and the interference can be potentially exploited to enhance the average throughput, since it can be utilized as an extra source of energy. Also, the impact of fading severity level of the energy transfer link on the average throughput is not significant, especially if the number of PB antennas is large. Finally, it is observed that the source position has a great impact on the average throughput.
Source Localization in Networks: Trees and Beyond
Venue: arXiv cs.SI 2015
Year: 2015

Information diffusion in networks can be used to model many real-world phenomena, including rumor spreading on online social networks, epidemics in human beings, and malware on the Internet. Informally speaking, the source localization problem is to identify a node in the network that provides the best explanation of the observed diffusion. Despite significant efforts and successes over last few years, theoretical guarantees of source localization algorithms were established only for tree networks due to the complexity of the problem. This paper presents a new source localization algorithm, called the Short-Fat Tree (SFT) algorithm. Loosely speaking, the algorithm selects the node such that the breadth-first search (BFS) tree from the node has the minimum depth but the maximum number of leaf nodes. Performance guarantees of SFT under the independent cascade (IC) model are established for both tree networks and the Erdos-Renyi (ER) random graph. On tree networks, SFT is the maximum a posterior (MAP) estimator. On the ER random graph, the following fundamental limits have been obtained: $(i)$ when the infection duration $<\frac{2}{3}t_u,$ SFT identifies the source with probability one asymptotically, where $t_u=\left\lceil\frac{\log n}{\log μ}\right\rceil+2$ and $μ$ is the average node degree, $(ii)$ when the infection duration $>t_u,$ the probability of identifying the source approaches zero asymptotically under any algorithm; and $(iii)$ when infection duration $<t_u,$ the BFS tree starting from the source is a fat tree. Numerical experiments on tree networks, the ER random graphs and real world networks with different evaluation metrics show that the SFT algorithm outperforms existing algorithms.
Resource Allocation Techniques for Wireless Powered Communication Networks with Energy Storage Constraint
Venue: arXiv cs.IT 2015
Year: 2015

This paper studies multi-user wireless powered communication networks, where energy constrained users charge their energy storages by scavenging energy of the radio frequency signals radiated from a hybrid access point (H-AP). The energy is then utilized for the users' uplink information transmission to the H-AP in time division multiple access mode. In this system, we aim to maximize the uplink sum rate performance by jointly optimizing energy and time resource allocation for multiple users in both infinite capacity and finite capacity energy storage cases. First, when the users are equipped with the infinite capacity energy storages, we derive the optimal downlink energy transmission policy at the H-AP. Based on this result, analytical resource allocation solutions are obtained. Next, we propose the optimal energy and time allocation algorithm for the case where each user has finite capacity energy storage. Simulation results confirm that the proposed algorithms offer 30% average sum rate performance gain over conventional schemes.
Practical Accounting in Content-Centric Networking (extended version)
Venue: arXiv cs.NI 2015
Year: 2015

Content-Centric Networking (CCN) is a new class of network architectures designed to address some key limitations of the current IP-based Internet. One of its main features is in-network content caching, which allows requests for content to be served by routers. Despite improved bandwidth utilization and lower latency for popular content retrieval, in-network content caching offers producers no means of collecting information about content that is requested and later served from network caches. Such information is often needed for accounting purposes. In this paper, we design some secure accounting schemes that vary in the degree of consumer, router, and producer involvement. Next, we identify and analyze performance and security tradeoffs, and show that specific per-consumer accounting is impossible in the presence of router caches and without application-specific support. We then recommend accounting strategies that entail a few simple requirements for CCN architectures. Finally, our experimental results show that forms of native and secure CCN accounting are both more viable and practical than application-specific approaches with little modification to the existing architecture and protocol.
Cybernetic modeling of Industrial Control Systems: Towards threat analysis of critical infrastructure
Venue: arXiv cs.CR 2015
Year: 2015

Industrial Control Systems (ICS) encompassing resources for process automation are subjected to a wide variety of security threats. The threat landscape is arising due to increased adoption of Commercial-of-the-shelf (COTS) products as well as the convergence of Internet and legacy systems. Prevalent security approaches for protection of critical infrastructure are scattered among various subsystems and modules of ICS networks. This demands a new state-of-the-art cybernetic model of ICS networks, which can help in threat analysis by providing a comprehensive view of the relationships and interactions between the subsystems. Towards this direction, the principles of the Viable System Model (VSM) are applied to introduce a conceptual recursive model of secure ICS networks that can drive cyber security decisions.
Approximation and Heuristic Algorithms for Computing Backbones in Asymmetric Ad-Hoc Networks
Venue: arXiv cs.NI 2015
Year: 2015

We consider the problem of dominating set-based virtual backbone used for routing in asymmetric wireless ad-hoc networks. These networks have non-uniform transmission ranges and are modeled using the well-established disk graphs. The corresponding graph theoretic problem seeks a strongly connected dominating-absorbent set of minimum cardinality in a digraph. A subset of nodes in a digraph is a strongly connected dominating-absorbent set if the subgraph induced by these nodes is strongly connected and each node in the graph is either in the set or has both an in-neighbor and an out-neighbor in it. Distributed algorithms for this problem are of practical significance due to the dynamic nature of ad-hoc networks. We present a first distributed approximation algorithm, with a constant approximation factor and O(Diam) running time, where Diam is the diameter of the graph. Moreover we present a simple heuristic algorithm and conduct an extensive simulation study showing that our heuristic outperforms previously known approaches for the problem.
Hierarchical Representation of Prosody for Statistical Speech Synthesis
Venue: arXiv cs.CL 2015
Year: 2015

Prominences and boundaries are the essential constituents of prosodic structure in speech. They provide for means to chunk the speech stream into linguistically relevant units by providing them with relative saliences and demarcating them within coherent utterance structures. Prominences and boundaries have both been widely used in both basic research on prosody as well as in text-to-speech synthesis. However, there are no representation schemes that would provide for both estimating and modelling them in a unified fashion. Here we present an unsupervised unified account for estimating and representing prosodic prominences and boundaries using a scale-space analysis based on continuous wavelet transform. The methods are evaluated and compared to earlier work using the Boston University Radio News corpus. The results show that the proposed method is comparable with the best published supervised annotation methods.
Assisting Composition of Email Responses: a Topic Prediction Approach
Venue: arXiv cs.CL 2015
Year: 2015

We propose an approach for helping agents compose email replies to customer requests. To enable that, we use LDA to extract latent topics from a collection of email exchanges. We then use these latent topics to label our data, obtaining a so-called "silver standard" topic labelling. We exploit this labelled set to train a classifier to: (i) predict the topic distribution of the entire agent's email response, based on features of the customer's email; and (ii) predict the topic distribution of the next sentence in the agent's reply, based on the customer's email features and on features of the agent's current sentence. The experimental results on a large email collection from a contact center in the tele- com domain show that the proposed ap- proach is effective in predicting the best topic of the agent's next sentence. In 80% of the cases, the correct topic is present among the top five recommended topics (out of fifty possible ones). This shows the potential of this method to be applied in an interactive setting, where the agent is presented a small list of likely topics to choose from for the next sentence.
DeepLogo: Hitting Logo Recognition with the Deep Neural Network Hammer
Venue: arXiv cs.CV 2015
Year: 2015

Recently, there has been a flurry of industrial activity around logo recognition, such as Ditto's service for marketers to track their brands in user-generated images, and LogoGrab's mobile app platform for logo recognition. However, relatively little academic or open-source logo recognition progress has been made in the last four years. Meanwhile, deep convolutional neural networks (DCNNs) have revolutionized a broad range of object recognition applications. In this work, we apply DCNNs to logo recognition. We propose several DCNN architectures, with which we surpass published state-of-art accuracy on a popular logo recognition dataset.
Data-Efficient Learning of Feedback Policies from Image Pixels using Deep Dynamical Models
Venue: arXiv cs.AI 2015
Year: 2015

Data-efficient reinforcement learning (RL) in continuous state-action spaces using very high-dimensional observations remains a key challenge in developing fully autonomous systems. We consider a particularly important instance of this challenge, the pixels-to-torques problem, where an RL agent learns a closed-loop control policy ("torques") from pixel information only. We introduce a data-efficient, model-based reinforcement learning algorithm that learns such a closed-loop policy directly from pixel information. The key ingredient is a deep dynamical model for learning a low-dimensional feature embedding of images jointly with a predictive model in this low-dimensional feature space. Joint learning is crucial for long-term predictions, which lie at the core of the adaptive nonlinear model predictive control strategy that we use for closed-loop control. Compared to state-of-the-art RL methods for continuous states and actions, our approach learns quickly, scales to high-dimensional state spaces, is lightweight and an important step toward fully autonomous end-to-end learning from pixels to torques.
On the Maximal Shortest Path in a Connected Component in V2V
Venue: arXiv cs.PF 2015
Year: 2015

In this work, a VANET (Vehicular Ad-hoc NETwork) is considered to operate on a simple lane, without infrastructure. The arrivals of vehicles are assumed to be general with any traffic and speed assumptions. The vehicles communicate through the shortest path. In this paper, we study the probability distribution of the number of hops on the maximal shortest path in a connected component of vehicles. The general formulation is given for any assumption of road traffic. Then, it is applied to calculate the z-transform of this distribution for medium and dense networks in the Poisson case. Our model is validated with the Madrid road traces of the Universitat Politècnica de Catalunya. These results may be useful for example when evaluating diffusion protocols through the shortest path in a VANET, where not only the mean but also the other moments are needed to derive accurate results.
Inband device-to-device relays in cellular networks
Venue: arXiv cs.NI 2015
Year: 2015

A new design for two-hop opportunistic relaying in cellular networks is proposed, with the objective of throughput improvement. We propose using idle UEs in a cellular system with better channel to the base station to relay traffic for active UEs. One of the key ideas proposed is the use of (only) uplink spectrum for the Access links, and corresponding interference management schemes for managing interference between Access and Backhaul links. The proposed algorithms and architecture show a median throughput gain of 110% for downlink and 40% for uplink in system simulations performed according to the 3GPP methodology.
From Rough Consensus to Automated Reasoning
Venue: arXiv cs.NI 2015
Year: 2015

Unwritten languages today often have no official grammar, and are rather governed by "unspoken rules". Similarly, we argue that the young discipline of networking is still a practice that lacks a deep understanding of the rules that govern it. This situation results in a tremendous loss of time and efforts. First, since the rules are unspoken, they are not systematically reused. Second, since there is no grammar, it is impossible to assert if a sentence is correct. Comparing two networking approaches or solutions is sometimes a synonym of endless religious debates. Drawing the proper conclusion from this claim, we advocate that networking research should spend more efforts on better understanding its rules as a first step to automatically reuse them. To illustrate our claim, we focus in this paper on one specific networking problem, and show how different instances of this same problem were solved in parallel, resulting in different solutions, and no explicit knowledge reuse.
Simulating Dense Small Cell Networks
Venue: arXiv cs.NI 2015
Year: 2015

Through massive deployment of additional small cell infrastructure, Dense Small cell Networks (DSNs) are expected to help meet the foreseen increase in traffic demand on cellular networks. Performance assessment of architectural and protocol solutions tailored to DSNs will require system and network level simulators that can appropriately model the complex interference environment found in those networks. This paper identifies the main features of DSN simulators, and guides the reader in the selection of an appropriate simulator for their desired investigations. We extend our discussion with a comparison of representative DSN simulators.
Service Provider DevOps network capabilities and tools
Venue: arXiv cs.NI 2015
Year: 2015

This report provides an understanding of how the UNIFY Service Provider (SP)-DevOps concept can be applied and integrated with a combined cloud and transport network NFV architecture. Specifically, the report contains technical descriptions of a set of novel SP-DevOps tools and support functions that facilitate observability, troubleshooting, verification, and VNF development processes. The tools and support functions are described in detail together with their architectural mapping, giving a wider understanding of the SP-DevOps concept as a whole, and how SP-DevOps tools can be used for supporting orchestration and programmability in the UNIFY NFV framework. The concept is further exemplified in a case study for deployment and scaling of an Elastic Firewall.
Human languages order information efficiently
Venue: arXiv cs.CL 2015
Year: 2015

Most languages use the relative order between words to encode meaning relations. Languages differ, however, in what orders they use and how these orders are mapped onto different meanings. We test the hypothesis that, despite these differences, human languages might constitute different `solutions' to common pressures of language use. Using Monte Carlo simulations over data from five languages, we find that their word orders are efficient for processing in terms of both dependency length and local lexical probability. This suggests that biases originating in how the brain understands language strongly constrain how human languages change over generations.
A Model for Interactive Scores with Temporal Constraints and Conditional Branching
Venue: arXiv cs.LO 2015
Year: 2015

Interactive Scores (IS) are a formalism for the design and performance of interactive multimedia scenarios. IS provide temporal relations (TR), but they cannot represent conditional branching and TRs simultaneously. We propose an extension to Allombert et al.'s IS model by including a condition on the TRs. We found out that in order to have a coherent model in all possible scenarios, durations must be flexible; however, sometimes it is possible to have fixed durations. To show the relevance of our model, we modeled an existing multimedia installation called Mariona. In Mariona there is choice, random durations and loops. Whether we can represent all the TRs available in Allombert et al.'s model into ours, or we have to choose between a timed conditional branching model and a pure temporal model before writing a scenario, still remains as an open question.
Artificial Intelligence and Asymmetric Information Theory
Venue: arXiv cs.AI 2015
Year: 2015

When human agents come together to make decisions, it is often the case that one human agent has more information than the other. This phenomenon is called information asymmetry and this distorts the market. Often if one human agent intends to manipulate a decision in its favor the human agent can signal wrong or right information. Alternatively, one human agent can screen for information to reduce the impact of asymmetric information on decisions. With the advent of artificial intelligence, signaling and screening have been made easier. This paper studies the impact of artificial intelligence on the theory of asymmetric information. It is surmised that artificial intelligent agents reduce the degree of information asymmetry and thus the market where these agents are deployed become more efficient. It is also postulated that the more artificial intelligent agents there are deployed in the market the less is the volume of trades in the market. This is because for many trades to happen the asymmetry of information on goods and services to be traded should exist, creating a sense of arbitrage.
Spectrum Sharing Opportunities of Full-Duplex Systems using Improper Gaussian Signaling
Venue: arXiv cs.IT 2015
Year: 2015

Sharing the licensed spectrum of full-duplex (FD) primary users (PU) brings strict limitations on the underlay cognitive radio operation. Particularly, the self interference may overwhelm the PU receiver and limit the opportunity of secondary users (SU) to access the spectrum. Improper Gaussian signaling (IGS) has demonstrated its superiority in improving the performance of interference channel systems. Throughout this paper, we assume a FD PU pair that uses proper Gaussian signaling (PGS), and a half-duplex SU pair that uses IGS. The objective is to maximize the SU instantaneous achievable rate while meeting the PU quality-of-service. To this end, we propose a simplified algorithm that optimizes the SU signal parameters, i.e, the transmit power and the circularity coefficient, which is a measure of the degree of impropriety of the SU signal, to achieve the design objective. Numerical results show the merits of adopting IGS compared with PGS for the SU especially with the existence of week PU direct channels and/or strong SU interference channels.