OpenRaiser/Intern-Atlas huggingface dataset:
The QWERTY effect on the web: How typing shapes the meaning of words in online human-computer interaction
Venue: arXiv cs.HC 2016
Year: 2016

The QWERTY effect postulates that the keyboard layout influences word meanings by linking positivity to the use of the right hand and negativity to the use of the left hand. For example, previous research has established that words with more right hand letters are rated more positively than words with more left hand letters by human subjects in small scale experiments. In this paper, we perform large scale investigations of the QWERTY effect on the web. Using data from eleven web platforms related to products, movies, books, and videos, we conduct observational tests whether a hand-meaning relationship can be found in decoding text on the web. Furthermore, we investigate whether encoding text on the web exhibits the QWERTY effect as well, by analyzing the relationship between the text of online reviews and their star ratings in four additional datasets. Overall, we find robust evidence for the QWERTY effect both at the point of text interpretation (decoding) and at the point of text creation (encoding). We also find under which conditions the effect might not hold. Our findings have implications for any algorithmic method aiming to evaluate the meaning of words on the web, including for example semantic or sentiment analysis, and show the existence of "dactilar onomatopoeias" that shape the dynamics of word-meaning associations. To the best of our knowledge, this is the first work to reveal the extent to which the QWERTY effect exists in large scale human-computer interaction on the web.
Group secret key agreement over state-dependent wireless broadcast channels
Venue: arXiv cs.CR 2016
Year: 2016

We consider a group of $m$ trusted and authenticated nodes that aim to create a shared secret key $K$ over a wireless channel in the presence of an eavesdropper Eve. We assume that there exists a state dependent wireless broadcast channel from one of the honest nodes to the rest of them including Eve. All of the trusted nodes can also discuss over a cost-free, noiseless and unlimited rate public channel which is also overheard by Eve. For this setup, we develop an information-theoretically secure secret key agreement protocol. We show the optimality of this protocol for "linear deterministic" wireless broadcast channels. This model generalizes the packet erasure model studied in literature for wireless broadcast channels. For "state-dependent Gaussian" wireless broadcast channels, we propose an achievability scheme based on a multi-layer wiretap code. Finding the best achievable secret key generation rate leads to solving a non-convex power allocation problem. We show that using a dynamic programming algorithm, one can obtain the best power allocation for this problem. Moreover, we prove the optimality of the proposed achievability scheme for the regime of high-SNR and large-dynamic range over the channel states in the (generalized) degrees of freedom sense.
Incentivizing Sharing in Realtime D2D Streaming Networks: A Mean Field Game Perspective
Venue: arXiv cs.GT 2016
Year: 2016

We consider the problem of streaming live content to a cluster of co-located wireless devices that have both an expensive unicast base-station-to-device (B2D) interface, as well as an inexpensive broadcast device-to-device (D2D) interface, which can be used simultaneously. Our setting is a streaming system that uses a block-by-block random linear coding approach to achieve a target percentage of on-time deliveries with minimal B2D usage. Our goal is to design an incentive framework that would promote such cooperation across devices, while ensuring good quality of service. Based on ideas drawn from truth-telling auctions, we design a mechanism that achieves this goal via appropriate transfers (monetary payments or rebates) in a setting with a large number of devices, and with peer arrivals and departures. Here, we show that a Mean Field Game can be used to accurately approximate our system. Furthermore, the complexity of calculating the best responses under this regime is low. We implement the proposed system on an Android testbed, and illustrate its efficient performance using real world experiments.
Machine Learning for Visual Navigation of Unmanned Ground Vehicles
Venue: arXiv cs.CV 2016
Year: 2016

The use of visual information for the navigation of unmanned ground vehicles in a cross-country environment recently received great attention. However, until now, the use of textural information has been somewhat less effective than color or laser range information. This manuscript reviews the recent achievements in cross-country scene segmentation and addresses their shortcomings. It then describes a problem related to classification of high dimensional texture features. Finally, it compares three machine learning algorithms aimed at resolving this problem. The experimental results for each machine learning algorithm with the discussion of comparisons are given at the end of the manuscript.
Multiuser Resource Allocation for Mobile-Edge Computation Offloading
Venue: arXiv cs.IT 2016
Year: 2016

Mobile-edge computation offloading (MECO) offloads intensive mobile computation to clouds located at the edges of cellular networks. Thereby, MECO is envisioned as a promising technique for prolonging the battery lives and enhancing the computation capacities of mobiles. In this paper, we consider resource allocation in a MECO system comprising multiple users that time share a single edge cloud and have different computation loads. The optimal resource allocation is formulated as a convex optimization problem for minimizing the weighted sum mobile energy consumption under constraint on computation latency and for both the cases of infinite and finite edge cloud computation capacities. The optimal policy is proved to have a threshold-based structure with respect to a derived offloading priority function, which yields priorities for users according to their channel gains and local computing energy consumption. As a result, users with priorities above and below a given threshold perform complete and minimum offloading, respectively. Computing the threshold requires iterative computation. To reduce the complexity, a sub-optimal resource-allocation algorithm is proposed and shown by simulation to have close-to-optimal performance.
Practical Recovery Solution for Information Loss in Real-Time Network Environment
Venue: arXiv cs.NI 2016
Year: 2016

Feedback mechanism based algorithms are frequently used to solve network optimization problems. These schemes involve users and network exchanging information (e.g. requests for bandwidth allocation and pricing) to achieve convergence towards an optimal solution. However, in the implementation, these algorithms do not guarantee that messages will be delivered to the destination when network congestion occurs. This in turn often results in packet drops, which may cause information loss, and this condition may lead to algorithm failing to converge. To prevent this failure, we propose least square (LS) estimation algorithm to recover the missing information when packets are dropped from the network. The simulation results involving several scenarios demonstrate that LS estimation can provide the convergence for feedback mechanism based algorithm.
UAV-Assisted Heterogeneous Networks for Capacity Enhancement
Venue: arXiv cs.NI 2016
Year: 2016

Modern day wireless networks have tremendously evolved driven by a sharp increase in user demands, continuously requesting more data and services. This puts significant strain on infrastructure based macro cellular networks due to the inefficiency in handling these traffic demands, cost effectively. A viable solution is the use of unmanned aerial vehicles (UAVs) as intermediate aerial nodes between the macro and small cell tiers for improving coverage and boosting capacity. This letter investigates the problem of user demand based UAV assignment over geographical areas subject to high traffic demands. A neural based cost function approach is formulated in which UAVs are matched to a particular geographical area. It is shown that leveraging multiple UAVs not only provides long range connectivity but also better load balancing and traffic offload. Simulation study demonstrate that the proposed approach yields significant improvements in terms of 5th percentile spectral efficiency up to 38\% and reduced delays up to 37.5\% compared to a ground-based network baseline without UAVs.
Chordal networks of polynomial ideals
Venue: arXiv cs.SC 2016
Year: 2016

We introduce a novel representation of structured polynomial ideals, which we refer to as chordal networks. The sparsity structure of a polynomial system is often described by a graph that captures the interactions among the variables. Chordal networks provide a computationally convenient decomposition into simpler (triangular) polynomial sets, while preserving the underlying graphical structure. We show that many interesting families of polynomial ideals admit compact chordal network representations (of size linear in the number of variables), even though the number of components is exponentially large. Chordal networks can be computed for arbitrary polynomial systems using a refinement of the chordal elimination algorithm from [Cifuentes-Parrilo-2016]. Furthermore, they can be effectively used to obtain several properties of the variety, such as its dimension, cardinality, and equidimensional components, as well as an efficient probabilistic test for radical ideal membership. We apply our methods to examples from algebraic statistics and vector addition systems; for these instances, algorithms based on chordal networks outperform existing techniques by orders of magnitude.
Users Polarization on Facebook and Youtube
Venue: arXiv cs.SI 2016
Year: 2016

On social media algorithms for content promotion, accounting for users preferences, might limit the exposure to unsolicited contents. In this work, we study how the same contents (videos) are consumed on different platforms -- i.e. Facebook and YouTube -- over a sample of $12M$ of users. Our findings show that the same content lead to the formation of echo chambers, irrespective of the online social network and thus of the algorithm for content promotion. Finally, we show that the users' commenting patterns are accurate early predictors for the formation of echo-chambers.
An Experimental Study of Dynamic Dominators
Venue: arXiv cs.DS 2016
Year: 2016

Motivated by recent applications of dominator computations, we consider the problem of dynamically maintaining the dominators of flow graphs through a sequence of insertions and deletions of edges. Our main theoretical contribution is a simple incremental algorithm that maintains the dominator tree of a flow graph with $n$ vertices through a sequence of $k$ edge insertions in $O(m\min\{n,k\}+kn)$ time, where $m$ is the total number of edges after all insertions. Moreover, we can test in constant time if a vertex $u$ dominates a vertex $v$, for any pair of query vertices $u$ and $v$. Next, we present a new decremental algorithm to update a dominator tree through a sequence of edge deletions. Although our new decremental algorithm is not asymptotically faster than repeated applications of a static algorithm, i.e., it runs in $O(mk)$ time for $k$ edge deletions, it performs well in practice. By combining our new incremental and decremental algorithms we obtain a fully dynamic algorithm that maintains the dominator tree through intermixed sequence of insertions and deletions of edges. Finally, we present efficient implementations of our new algorithms as well as of existing algorithms, and conduct an extensive experimental study on real-world graphs taken from a variety of application areas.
Construction of a Pragmatic Base Line for Journal Classifications and Maps Based on Aggregated Journal-Journal Citation Relations
Venue: arXiv cs.DL 2016
Year: 2016

A number of journal classification systems have been developed in bibliometrics since the launch of the Citation Indices by the Institute of Scientific Information (ISI) in the 1960s. These systems are used to normalize citation counts with respect to field-specific citation patterns. The best known system is the so-called "Web-of-Science Subject Categories" (WCs). In other systems papers are classified by algorithmic solutions. Using the Journal Citation Reports 2014 of the Science Citation Index and the Social Science Citation Index (n of journals = 11,149), we examine options for developing a new system based on journal classifications into subject categories using aggregated journal-journal citation data. Combining routines in VOSviewer and Pajek, a tree-like classification is developed. At each level one can generate a map of science for all the journals subsumed under a category. Nine major fields are distinguished at the top level. Further decomposition of the social sciences is pursued for the sake of example with a focus on journals in information science (LIS) and science studies (STS). The new classification system improves on alternative options by avoiding the problem of randomness in each run that has made algorithmic solutions hitherto irreproducible. Limitations of the new system are discussed (e.g. the classification of multi-disciplinary journals). The system's usefulness for field-normalization in bibliometrics should be explored in future studies.
Knowledge Extraction and Knowledge Integration governed by Łukasiewicz Logics
Venue: arXiv cs.AI 2016
Year: 2016

The development of machine learning in particular and artificial intelligent in general has been strongly conditioned by the lack of an appropriate interface layer between deduction, abduction and induction. In this work we extend traditional algebraic specification methods in this direction. Here we assume that such interface for AI emerges from an adequate Neural-Symbolic integration. This integration is made for universe of discourse described on a Topos governed by a many-valued Łukasiewicz logic. Sentences are integrated in a symbolic knowledge base describing the problem domain, codified using a graphic-based language, wherein every logic connective is defined by a neuron in an artificial network. This allows the integration of first-order formulas into a network architecture as background knowledge, and simplifies symbolic rule extraction from trained networks. For the train of such neural networks we changed the Levenderg-Marquardt algorithm, restricting the knowledge dissemination in the network structure using soft crystallization. This procedure reduces neural network plasticity without drastically damaging the learning performance, allowing the emergence of symbolic patterns. This makes the descriptive power of produced neural networks similar to the descriptive power of Łukasiewicz logic language, reducing the information lost on translation between symbolic and connectionist structures. We tested this method on the extraction of knowledge from specified structures. For it, we present the notion of fuzzy state automata, and we use automata behaviour to infer its structure. We use this type of automata on the generation of models for relations specified as symbolic background knowledge.
An Integrated Method of Data Hiding and Compression of Medical Images
Venue: arXiv cs.CR 2016
Year: 2016

A new technique for embedding data into an image coupled with compression has been proposed in this paper. A fast and efficient coding algorithms are needed for effective storage and transmission, due to the popularity of telemedicine and the use of digital medical images. Medical images are produced and transferred between hospitals for review by physicians who are geographically apart. Such image data need to be stored for future reference of patients as well. This necessitates compact storage of medical images before being transmitted over Internet. Moreover, as the patient information is also embedded within the medical images, it is very important to maintain the confidentiality of patient data. Hence, this article aims at hiding patient information as well, within the medical image followed by joint compression. The hidden data and the host image are absolutely recoverable from the embedded image without any loss.
Self-Triggered Control for Multi-Agent Systems with Quantized Communication or Sensing
Venue: arXiv cs.SY 2016
Year: 2016

The consensus problem for multi-agent systems with quantized communication or sensing is considered. Centralized and distributed self-triggered rules are proposed to reduce the overall need of communication and system updates. It is proved that these self-triggered rules realize consensus exponentially if the network topologies have a spanning tree and the quantization function is uniform. Numerical simulations are provided to show the effectiveness of the theoretical results.
An Extremal Series of Eulerian Synchronizing Automata
Venue: arXiv cs.FL 2016
Year: 2016

We present an infinite series of $n$-state Eulerian automata whose reset words have length at least $(n^2-3)/2$. This improves the current lower bound on the length of shortest reset words in Eulerian automata. We conjecture that $(n^2-3)/2$ also forms an upper bound for this class and we experimentally verify it for small automata by an exhaustive computation.
A Study on the Electrode Protection System of an 100t Ultra High Power Electric Furnace
Venue: arXiv cs.SY 2016
Year: 2016

In this paper a method to design the electrode protection system using arc current and voltage and pressure inside the hydraulic cylinder of electrode lift device of an 100t ultra high power furnace (UHP furnace) and confident factor(CF) and Lyapnov function, and then we validated our method through the field experimentation.
Multicarrier PAPR Reduction by Iteratively Shifting and Concentrating the Probability Measure
Venue: arXiv cs.IT 2016
Year: 2016

The peak power problem in multicarrier waveforms is well-known and imposes substantial limitations on wireless communications. As the quest for investigation of enabling technologies for the next generation of wireless communication systems 5G is at its peak, the problem is re-emerging in a much broader range of technologies. However, despite numerous publications on the topic, there is no well-established structure available for the problem, which motivates a boost in research. In this paper, a novel peak power reduction algorithm is proposed which offers a substantial Peak-to-Average Power Ratio (PAPR) reduction and exhibits high potential for further refinements. Mathematical tractability of the algorithm is expected to be of particular importance to this end. A remarkable early observation is a PAPR reduction of about 4.5 dB for 64 subcarriers with a rate loss of 0.5 bits per complex data symbol in an OFDM scheme, which is half the rate loss that other methods require in this class of algorithms.
Ds-bounds for cyclic codes: new bounds for abelian codes
Venue: arXiv cs.IT 2016
Year: 2016

In this paper we develop a technique to extend any bound for cyclic codes constructed from its defining sets (ds-bounds) to abelian (or multivariate) codes. We use this technique to improve the searching of new bounds for abelian codes.
Systems Computing Challenges in the Internet of Things
Venue: arXiv cs.CY 2016
Year: 2016

A recent McKinsey report estimates the economic impact of the Internet of Things (IoT) to be between $3.9 to $11 trillion dollars by 20251 . IoT has the potential to have a profound impact on our daily lives, including technologies for the home, for health, for transportation, and for managing our natural resources. The Internet was largely driven by information and ideas generated by people, but advances in sensing and hardware have enabled computers to more easily observe the physical world. Coupling this additional layer of information with advances in machine learning brings dramatic new capabilities including the ability to capture and process tremendous amounts of data; to predict behaviors, activities, and the future in uncanny ways; and to manipulate the physical world in response. This trend will fundamentally change how people interact with physical objects and the environment. Success in developing value-added capabilities around IoT requires a broad approach that includes expertise in sensing and hardware, machine learning, networked systems, human-computer interaction, security, and privacy. Strategies for making IoT practical and spurring its ultimate adoption also require a multifaceted approach that often transcends technology, such as with concerns over data security, privacy, public policy, and regulatory issues. In this paper we argue that existing best practices in building robust and secure systems are insufficient to address the new challenges that IoT systems will present. We provide recommendations regarding investments in research areas that will help address inadequacies in existing systems, practices, tools, and policies.
Inherit Differential Privacy in Distributed Setting: Multiparty Randomized Function Computation
Venue: arXiv cs.CR 2016
Year: 2016

How to achieve differential privacy in the distributed setting, where the dataset is distributed among the distrustful parties, is an important problem. We consider in what condition can a protocol inherit the differential privacy property of a function it computes. The heart of the problem is the secure multiparty computation of randomized function. A notion \emph{obliviousness} is introduced, which captures the key security problems when computing a randomized function from a deterministic one in the distributed setting. By this observation, a sufficient and necessary condition about computing a randomized function from a deterministic one is given. The above result can not only be used to determine whether a protocol computing differentially private function is secure, but also be used to construct secure one. Then we prove that the differential privacy property of a function can be inherited by the protocol computing it if the protocol privately computes it. A composition theorem of differentially private protocols is also presented. We also construct some protocols to generate random variate in the distributed setting, such as the uniform random variates and the inversion method. By using these fundamental protocols, we construct protocols of the Gaussian mechanism, the Laplace mechanism and the Exponential mechanism. Importantly, all these protocols satisfy obliviousness and so can be proved to be secure in a simulation based manner. We also provide a complexity bound of computing randomized function in the distribute setting. Finally, to show that our results are fundamental and powerful to multiparty differential privacy, we construct a differentially private empirical risk minimization protocol.
Global stabilization of linear systems with bounds on the feedback and its successive derivatives
Venue: arXiv cs.SY 2016
Year: 2016

We address the global stabilization of linear time-invariant (LTI) systems when the magnitude of the control input and its successive time derivatives, up to an order $p\in\mathbb N$, are bounded by prescribed values. We propose a static state feedback that solves this problem for any admissible LTI systems, namely for stabilizable systems whose internal dynamics has no eigenvalue with positive real part. This generalizes previous work done for single-input chains of integrators and rotating dynamics.