OpenRaiser/Intern-Atlas huggingface dataset:
Dynamic Clustering in Social Networks using Louvain and Infomap Method
Venue: arXiv cs.SI 2016
Year: 2016

Finding communities or clusters in social networks is a fa- mous topic in social network analysis. Most algorithms are limited to static snapshots, so they cannot handle dynamics within the underlying graph. In this paper we present a modification of the Louvain community detec- tion method to handle changes in the graph without rerunning the full algorithm. Also, we adapted the Louvain greedy approach to optimize the Infomap measure. The main idea is, to recalculate only a small area around the changes. Depending on the graph size and the amount of changes, this yields a massive runtime decrease. As validation data, we provide a graph generator, which produces spe- cific community structures, at given times and also intermediate steps to transform the graph from one to another specific graph. Experiments show that runtime decrease is possible without much loss of quality. These values depend on the reprocessed area inside the graph.
A hybrid approach based segmentation technique for brain tumor in MRI Images
Venue: arXiv cs.CV 2016
Year: 2016

Automatic image segmentation becomes very crucial for tumor detection in medical image processing.In general, manual and semi automatic segmentation techniques require more time and knowledge. However these drawbacks had overcome by automatic segmentation still there needs to develop more appropriate techniques for medical image segmentation. Therefore, we proposed hybrid approach based image segmentation using the combined features of region growing and threshold based segmentation techniques. It is followed by pre-processing stage to provide an accurate brain tumor extraction by the help of Magnetic Resonance Imaging (MRI). If the tumor has holes, the region growing segmentation algorithm cannot reveal but the proposed hybrid segmentation technique can be achieved and the result as well improved. Hence the result used to made assessment with the various performance measures as DICE, JACCARD similarity, accuracy, sensitivity and specificity. These similarity measures have been extensively used for evaluation with the ground truth of each processed image and its results are compared and analyzed.
Effective Capacity in Broadcast Channels with Arbitrary Inputs
Venue: arXiv cs.IT 2016
Year: 2016

We consider a broadcast scenario where one transmitter communicates with two receivers under quality-of-service constraints. The transmitter initially employs superposition coding strategies with arbitrarily distributed signals and sends data to both receivers. Regarding the channel state conditions, the receivers perform successive interference cancellation to decode their own data. We express the effective capacity region that provides the maximum allowable sustainable data arrival rate region at the transmitter buffer or buffers. Given an average transmission power limit, we provide a two-step approach to obtain the optimal power allocation policies that maximize the effective capacity region. Then, we characterize the optimal decoding regions at the receivers in the space spanned by the channel fading power values. We finally substantiate our results with numerical presentations.
Variational Autoencoders for Semi-supervised Text Classification
Venue: arXiv cs.CL 2016
Year: 2016

Although semi-supervised variational autoencoder (SemiVAE) works in image classification task, it fails in text classification task if using vanilla LSTM as its decoder. From a perspective of reinforcement learning, it is verified that the decoder's capability to distinguish between different categorical labels is essential. Therefore, Semi-supervised Sequential Variational Autoencoder (SSVAE) is proposed, which increases the capability by feeding label into its decoder RNN at each time-step. Two specific decoder structures are investigated and both of them are verified to be effective. Besides, in order to reduce the computational complexity in training, a novel optimization method is proposed, which estimates the gradient of the unlabeled objective function by sampling, along with two variance reduction techniques. Experimental results on Large Movie Review Dataset (IMDB) and AG's News corpus show that the proposed approach significantly improves the classification accuracy compared with pure-supervised classifiers, and achieves competitive performance against previous advanced methods. State-of-the-art results can be obtained by integrating other pretraining-based methods.
60 GHz Outdoor Urban Measurement Study of the Feasibility of Multi-Gbps mm-Wave Cellular Networks
Venue: arXiv cs.NI 2016
Year: 2016

Future 5G cellular networks are expected to exploit the abundant spectrum resources of the millimeter-wave (mm-wave) bands to satisfy demand for multi-Gbps mobile links anticipated by exponential data traffic growth. However, given the directional nature of mm-wave links, the feasibility of mm-wave mobile networks is critically dependent on efficient antenna beamsteering and a rich inventory of strong LOS (line-of-sight) and NLOS (non line-of-sight) paths from effective reflectors in the urban environment. In this paper we report results from detailed angular measurements of 60 GHz links at an example outdoor pico-cellular site in a mixed-use urban environment typical of European cities. Our work is the first to systematically analyze the beamsteering requirements of future mm-wave cellular networks based on real measurements. Our results reveal that the urban environment provides substantial opportunities for multi-Gbps mm-wave connectivity, but that the availability of strong LOS/NLOS links is highly location and orientation-specific. Our results also show that high speed mm-wave links are very sensitive to beam misalignment. This has important implications for practical mm-wave cellular network design: (i) high-precision beamsteering is required to maintain stable data rates even for quasi-stationary users; and (ii) providing seamless high speed service in mobility scenarios will be extremely challenging. Our results thus cast doubt on whether outdoor mm-wave cellular deployments will be feasible in practice, given the high network control overhead of meeting such stringent beamsteering requirements.
The red one!: On learning to refer to things based on their discriminative properties
Venue: arXiv cs.CL 2016
Year: 2016

As a first step towards agents learning to communicate about their visual environment, we propose a system that, given visual representations of a referent (cat) and a context (sofa), identifies their discriminative attributes, i.e., properties that distinguish them (has_tail). Moreover, despite the lack of direct supervision at the attribute level, the model learns to assign plausible attributes to objects (sofa-has_cushion). Finally, we present a preliminary experiment confirming the referential success of the predicted discriminative attributes.
A Hybrid Linear Logic for Constrained Transition Systems
Venue: arXiv cs.LO 2016
Year: 2016

Linear implication can represent state transitions, but real transition systems operate under temporal, stochastic or probabilistic constraints that are not directly representable in ordinary linear logic. We propose a general modal extension of intuitionistic linear logic where logical truth is indexed by constraints and hybrid connectives combine constraint reasoning with logical reasoning. The logic has a focused cut-free sequent calculus that can be used to internalize the rules of particular constrained transition systems; we illustrate this with an adequate encoding of the synchronous stochastic pi-calculus.
Tweeting the Mind and Instagramming the Heart: Exploring Differentiated Content Sharing on Social Media
Venue: arXiv cs.SI 2016
Year: 2016

Understanding the usage of multiple OSNs (Online Social Networks) has been of significant research interest as it helps in identifying the unique and distinguishing trait in each social media platform that contributes to its continued existence. The comparison between the OSNs is insightful when it is done based on the representative majority of the users holding active accounts on all the platforms. In this research, we collected a set of user profiles holding accounts on both Twitter and Instagram, these platforms being of prominence among a majority of users. An extensive textual and visual analysis on the media content posted by these users revealed that both these platforms are indeed perceived differently at a fundamental level with Instagram engaging more of the users' heart and Twitter capturing more of their mind. These differences got reflected in almost every microscopic analysis done upon the linguistic, topical and visual aspects.
Revisiting Active Perception
Venue: arXiv cs.CV 2016
Year: 2016

Despite the recent successes in robotics, artificial intelligence and computer vision, a complete artificial agent necessarily must include active perception. A multitude of ideas and methods for how to accomplish this have already appeared in the past, their broader utility perhaps impeded by insufficient computational power or costly hardware. The history of these ideas, perhaps selective due to our perspectives, is presented with the goal of organizing the past literature and highlighting the seminal contributions. We argue that those contributions are as relevant today as they were decades ago and, with the state of modern computational tools, are poised to find new life in the robotic perception systems of the next decade.
Learning to Blend Computer Game Levels
Venue: arXiv cs.AI 2016
Year: 2016

We present an approach to generate novel computer game levels that blend different game concepts in an unsupervised fashion. Our primary contribution is an analogical reasoning process to construct blends between level design models learned from gameplay videos. The models represent probabilistic relationships between elements in the game. An analogical reasoning process maps features between two models to produce blended models that can then generate new level chunks. As a proof-of-concept we train our system on the classic platformer game Super Mario Bros. due to its highly-regarded and well understood level design. We evaluate the extent to which the models represent stylistic level design knowledge and demonstrate the ability of our system to explain levels that were blended by human expert designers.
Best-of-K Bandits
Venue: arXiv cs.LG 2016
Year: 2016

This paper studies the Best-of-K Bandit game: At each time the player chooses a subset S among all N-choose-K possible options and observes reward max(X(i) : i in S) where X is a random vector drawn from a joint distribution. The objective is to identify the subset that achieves the highest expected reward with high probability using as few queries as possible. We present distribution-dependent lower bounds based on a particular construction which force a learner to consider all N-choose-K subsets, and match naive extensions of known upper bounds in the bandit setting obtained by treating each subset as a separate arm. Nevertheless, we present evidence that exhaustive search may be avoided for certain, favorable distributions because the influence of high-order order correlations may be dominated by lower order statistics. Finally, we present an algorithm and analysis for independent arms, which mitigates the surprising non-trivial information occlusion that occurs due to only observing the max in the subset. This may inform strategies for more general dependent measures, and we complement these result with independent-arm lower bounds.
Distributed Control for Charging Multiple Electric Vehicles with Overload Limitation
Venue: arXiv cs.SY 2016
Year: 2016

Severe pollution induced by traditional fossil fuels arouses great attention on the usage of plug-in electric vehicles (PEVs) and renewable energy. However, large-scale penetration of PEVs combined with other kinds of appliances tends to cause excessive or even disastrous burden on the power grid, especially during peak hours. This paper focuses on the scheduling of PEVs charging process among different charging stations and each station can be supplied by both renewable energy generators and a distribution network. The distribution network also powers some uncontrollable loads. In order to minimize the on-grid energy cost with local renewable energy and non-ideal storage while avoiding the overload risk of the distribution network, an online algorithm consisting of scheduling the charging of PEVs and energy management of charging stations is developed based on Lyapunov optimization and Lagrange dual decomposition techniques. The algorithm can satisfy the random charging requests from PEVs with provable performance. Simulation results with real data demonstrate that the proposed algorithm can decrease the time-average cost of stations while avoiding overload in the distribution network in the presence of random uncontrollable loads.
Lexical bundles in computational linguistics academic literature
Venue: arXiv cs.CL 2016
Year: 2016

In this study we analyzed a corpus of 8 million words academic literature from Computational lingustics' academic literature. the lexical bundles from this corpus are categorized based on structures and functions.
The Effects of Mobility on the Hit Performance of Cached D2D Networks
Venue: arXiv cs.NI 2016
Year: 2016

A device-to-device (D2D) wireless network is considered, where user devices also have the ability to cache content. In such networks, users are mobile and communication links can be spontaneously activated and dropped depending on the users' relative position. Receivers request files from transmitters, these files having a certain popularity and file-size distribution. In this work a new performance metric is introduced, namely the Service Success Probability, which captures the specificities of D2D networks. For the Poisson Point Process case for node distribution and the SNR coverage model, explicit expressions are derived. Simulations support the analytical results and explain the influence of mobility and file-size distribution on the system performance, while providing intuition on how to appropriately cache content on mobile storage space. Of particular interest is the investigation on how different file-size distributions (Exponential, Uniform, or Heavy-Tailed) influence the performance.
A Performance Evaluation of Container Technologies on Internet of Things Devices
Venue: arXiv cs.DC 2016
Year: 2016

The use of virtualization technologies in different contexts - such as Cloud Environments, Internet of Things (IoT), Software Defined Networking (SDN) - has rapidly increased during the last years. Among these technologies, container-based solutions own characteristics for deploying distributed and lightweight applications. This paper presents a performance evaluation of container technologies on constrained devices, in this case, on Raspberry Pi. The study shows that, overall, the overhead added by containers is negligible.
A ternary square-free sequence avoiding factors equivalent to $abcacba$
Venue: arXiv cs.FL 2016
Year: 2016

We solve a problem of Petrova, finalizing the classification of letter patterns avoidable by ternary square-free words; we show that there is a ternary square-free word avoiding letter pattern $xyzxzyx$. In fact, we: (1) characterize all the (two-way) infinite ternary square-free words avoiding letter pattern $xyzxzyx$ (2) characterize the lexicographically least (one-way) infinite ternary square-free word avoiding letter pattern $xyzxzyx$ (3) show that the number of ternary square-free words of length $n$ avoiding letter pattern $xyzxzyx$ grows exponentially with $n$.
Mechanisms for House Allocation with Existing Tenants under Dichotomous Preferences
Venue: arXiv cs.GT 2016
Year: 2016

We consider house allocation with existing tenants in which each agent has dichotomous preferences. We present strategyproof, polynomial-time, and (strongly) individually rational algorithms that satisfy the maximum number of agents. For the endowment only model, one of the algorithms also returns a core-stable allocation.
EMMA: A New Platform to Evaluate Hardware-based Mobile Malware Analyses
Venue: arXiv cs.CR 2016
Year: 2016

Hardware-based malware detectors (HMDs) are a key emerging technology to build trustworthy computing platforms, especially mobile platforms. Quantifying the efficacy of HMDs against malicious adversaries is thus an important problem. The challenge lies in that real-world malware typically adapts to defenses, evades being run in experimental settings, and hides behind benign applications. Thus, realizing the potential of HMDs as a line of defense - that has a small and battery-efficient code base - requires a rigorous foundation for evaluating HMDs. To this end, we introduce EMMA - a platform to evaluate the efficacy of HMDs for mobile platforms. EMMA deconstructs malware into atomic, orthogonal actions and introduces a systematic way of pitting different HMDs against a diverse subset of malware hidden inside benign applications. EMMA drives both malware and benign programs with real user-inputs to yield an HMD's effective operating range - i.e., the malware actions a particular HMD is capable of detecting. We show that small atomic actions, such as stealing a Contact or SMS, have surprisingly large hardware footprints, and use this insight to design HMD algorithms that are less intrusive than prior work and yet perform 24.7% better. Finally, EMMA brings up a surprising new result - obfuscation techniques used by malware to evade static analyses makes them more detectable using HMDs.
On Energy Cooperation in Energy Harvesting Underlay Cognitive Radio Network
Venue: arXiv cs.IT 2016
Year: 2016

In this paper, we consider an energy harvesting cognitive radio network (EH-CRN), where a primary and a secondary user coexist in underlay mode. Both the transmitters have energy harvesting capability and are equipped with finite capacity battery to store the harvested energy. In addition, the secondary user (SU) has an independent energy transfer unit such that it can transfer some portion of it's harvested energy to the primary user (PU). We obtain an optimal transmit power and energy transfer policy for single-slot and a suboptimal policy for multi-slot scenario maximizing the number of bits transmitted by SU under the primary sum-rate constraint in an offline setting. For both cases, the effect of energy cooperation on the system performance is studied and it is observed that energy cooperation results in higher SU throughput.
Hierarchical Linearly-Solvable Markov Decision Problems
Venue: arXiv cs.AI 2016
Year: 2016

We present a hierarchical reinforcement learning framework that formulates each task in the hierarchy as a special type of Markov decision process for which the Bellman equation is linear and has analytical solution. Problems of this type, called linearly-solvable MDPs (LMDPs) have interesting properties that can be exploited in a hierarchical setting, such as efficient learning of the optimal value function or task compositionality. The proposed hierarchical approach can also be seen as a novel alternative to solving LMDPs with large state spaces. We derive a hierarchical version of the so-called Z-learning algorithm that learns different tasks simultaneously and show empirically that it significantly outperforms the state-of-the-art learning methods in two classical hierarchical reinforcement learning domains: the taxi domain and an autonomous guided vehicle task.
Cognitive Green Radio for Energy-Aware Communications
Venue: arXiv cs.IT 2016
Year: 2016

In 5G networks, the number of connected devices, data rate and data volume per area, as well as the variety of QoS requirements, will attain unprecedented scales. The achievement of these goals will rely on new technologies and disruptive changes in network architecture and node design. Energy efficiency is believed to play a key role in complementing the 5G technologies and optimizing their deployment, dynamic configuration and management [1]. Within the framework of green communications and networks, especially for next-generation green cellular radio access networks, the GREAT (Green Cognitive Radio for Energy-Aware wireless communication Technologies evolution) initiative, a CominLabs Excellence Center (Laboratoire d'Excellence) and Université Européenne de Bretagne (UEB)project, has mainly addressed the fundamental issues of energy efficiency from various perspectives and angles, leveraging on cognitive techniques, at networking level as well as at thephysical layer level.
Output Regulation for Systems on Matrix Lie-group
Venue: arXiv cs.SY 2016
Year: 2016

This paper deals with the problem of output regulation for systems defined on matrix Lie-Groups. Reference trajectories to be tracked are supposed to be generated by an exosystem, defined on the same Lie-Group of the controlled system, and only partial relative error measurements are supposed to be available. These measurements are assumed to be invariant and associated to a group action on a homogeneous space of the state space. In the spirit of the internal model principle the proposed control structure embeds a copy of the exosystem kinematic. This control problem is motivated by many real applications fields in aerospace, robotics, projective geometry, to name a few, in which systems are defined on matrix Lie-groups and references in the associated homogenous spaces.
Optimized Sensor Collaboration for Estimation of Temporally Correlated Parameters
Venue: arXiv cs.IT 2016
Year: 2016

In this paper, we aim to design the optimal sensor collaboration strategy for the estimation of time-varying parameters, where collaboration refers to the act of sharing measurements with neighboring sensors prior to transmission to a fusion center. We begin by addressing the sensor collaboration problem for the estimation of uncorrelated parameters. We show that the resulting collaboration problem can be transformed into a special nonconvex optimization problem, where a difference of convex functions carries all the nonconvexity. This specific problem structure enables the use of a convex-concave procedure to obtain a near-optimal solution. When the parameters of interest are temporally correlated, a penalized version of the convex-concave procedure becomes well suited for designing the optimal collaboration scheme. In order to improve computational efficiency, we further propose a fast algorithm that scales gracefully with problem size via the alternating direction method of multipliers. Numerical results are provided to demonstrate the effectiveness of our approach and the impact of parameter correlation and temporal dynamics of sensor networks on estimation performance.
Information Flow in Logical Environments
Venue: arXiv cs.LO 2016
Year: 2016

This paper describes information flow within logical environments. The theory of information flow, the logic of distributed systems, was first defined by Barwise and Seligman (Information Flow: The Logic of Distributed Systems. 1997). Logical environments are a semantic-oriented version of institutions. The theory of institutions, which was initiated by Goguen and Burstall (Institutions: Abstract Model Theory for Specification and Programming. 1992), is abstract model theory. Information flow is the flow of information in channels over distributed systems. The semantic integration of distributed systems, be they ontologies, databases or other information resources, can be defined in terms of the channel theory of information flow. As originally defined, the theory of information flow uses only a specific logical environment in order to discuss information flow. This paper shows how information flow can be defined in an arbitrary logical environment.