OpenRaiser/Intern-Atlas huggingface dataset:
A study of research trends and issues in wireless ad hoc networks
Venue: arXiv cs.NI 2017
Year: 2017

Ad hoc network enables network creation on the fly without support of any predefined infrastructure. The spontaneous erection of networks in anytime and anywhere fashion enables development of various novel applications based on ad hoc networks. However, at the same ad hoc network presents several new challenges. Different research proposals have came forward to resolve these challenges. This chapter provides a survey of current issues, solutions and research trends in wireless ad hoc network. Even though various surveys are already available on the topic, rapid developments in recent years call for an updated account on this topic. The chapter has been organized as follows. In the first part of the chapter, various ad hoc network's issues arising at different layers of TCP/IP protocol stack are presented. An overview of research proposals to address each of these issues is also provided. The second part of the chapter investigates various emerging models of ad hoc networks, discusses their distinctive properties and highlights various research issues arising due to these properties. We specifically provide discussion on ad hoc grids, ad hoc clouds, wireless mesh networks and cognitive radio ad hoc networks. The chapter ends with presenting summary of the current research on ad hoc network, ignored research areas and directions for further research.
Multi-tier Drone Architecture for 5G/B5G Cellular Networks: Challenges, Trends, and Prospects
Venue: arXiv cs.NI 2017
Year: 2017

Drones (or unmanned aerial vehicles [UAVs]) are expected to be an important component of fifth generation (5G)/beyond 5G (B5G) cellular architectures that can potentially facilitate wireless broadcast or point-to-multipoint transmissions. The distinct features of various drones such as the maximum operational altitude, communication, coverage, computation, and endurance impel the use of a multi-tier architecture for future drone-cell networks. In this context, this article focuses on investigating the feasibility of multi-tier drone network architecture over traditional single-tier drone networks and identifying the scenarios in which drone networks can potentially complement the traditional RF-based terrestrial networks. We first identify the challenges associated with multi-tier drone networks as well as drone-assisted cellular networks. We then review the existing state-of-the-art innovations in drone networks and drone-assisted cellular networks. We then investigate the performance of a multi-tier drone network in terms of spectral efficiency of downlink transmission while illustrating the optimal intensity and altitude of drones in different tiers numerically. Our results demonstrate the specific network load conditions (i.e., ratio of user intensity and base station intensity) where deployment of drones can be beneficial (in terms of spectral efficiency of downlink transmission) for conventional terrestrial cellular networks.
On Computing Min-Degree Elimination Orderings
Venue: arXiv cs.DS 2017
Year: 2017

We study faster algorithms for producing the minimum degree ordering used to speed up Gaussian elimination. This ordering is based on viewing the non-zero elements of a symmetric positive definite matrix as edges of an undirected graph, and aims at reducing the additional non-zeros (fill) in the matrix by repeatedly removing the vertex of minimum degree. It is one of the most widely used primitives for pre-processing sparse matrices in scientific computing. Our result is in part motivated by the observation that sub-quadratic time algorithms for finding min-degree orderings are unlikely, assuming the strong exponential time hypothesis (SETH). This provides justification for the lack of provably efficient algorithms for generating such orderings, and leads us to study speedups via degree-restricted algorithms as well as approximations. Our two main results are: (1) an algorithm that produces a min-degree ordering whose maximum degree is bounded by $Δ$ in $O(m Δ\log^3{n})$ time, and (2) an algorithm that finds an $(1 + ε)$-approximate marginal min-degree ordering in $O(m \log^{5}n ε^{-2})$ time. Both of our algorithms rely on a host of randomization tools related to the $\ell_0$-estimator by [Cohen `97]. A key technical issue for the final nearly-linear time algorithm are the dependencies of the vertex removed on the randomness in the data structures. To address this, we provide a method for generating a pseudo-deterministic access sequence, which then allows the incorporation of data structures that only work under the oblivious adversary model.
Learning Deep Representations of Medical Images using Siamese CNNs with Application to Content-Based Image Retrieval
Venue: arXiv cs.CV 2017
Year: 2017

Deep neural networks have been investigated in learning latent representations of medical images, yet most of the studies limit their approach in a single supervised convolutional neural network (CNN), which usually rely heavily on a large scale annotated dataset for training. To learn image representations with less supervision involved, we propose a deep Siamese CNN (SCNN) architecture that can be trained with only binary image pair information. We evaluated the learned image representations on a task of content-based medical image retrieval using a publicly available multiclass diabetic retinopathy fundus image dataset. The experimental results show that our proposed deep SCNN is comparable to the state-of-the-art single supervised CNN, and requires much less supervision for training.
EMFET: E-mail Features Extraction Tool
Venue: arXiv cs.IR 2017
Year: 2017

EMFET is an open source and flexible tool that can be used to extract a large number of features from any email corpus with emails saved in EML format. The extracted features can be categorized into three main groups: header features, payload (body) features, and attachment features. The purpose of the tool is to help practitioners and researchers to build datasets that can be used for training machine learning models for spam detection. So far, 140 features can be extracted using EMFET. EMFET is extensible and easy to use. The source code of EMFET is publicly available at GitHub (https://github.com/WadeaHijjawi/EmailFeaturesExtraction)
A Digital Game Maturity Model (DGMM)
Venue: arXiv cs.SE 2017
Year: 2017

Game development is an interdisciplinary concept that embraces artistic, software engineering, management, and business disciplines. This research facilitates a better understanding of important dimensions of digital game development methodology. Game development is considered as one of the most complex tasks in software engineering. The increased popularity of digital games, the challenges faced by game development organizations in developing quality games, and high competition in the digital game industry demand a game development maturity assessment. Consequently, this study presents a Digital Game Maturity Model to evaluate the current development methodology in an organization. The framework of this model consists of assessment questionnaires, a performance scale, and a rating method. The main goal of the questionnaires is to collect information about current processes and practices. In general, this research contributes towards formulating a comprehensive and unified strategy for game development maturity evaluation. Two case studies were conducted and their assessment results reported. These demonstrate the level of maturity of current development practices in two organizations.
The Research Production of Nations and Departments: A Statistical Model for the Share of Publications
Venue: arXiv cs.DL 2017
Year: 2017

Policy makers and managers sometimes assess the share of research produced by a group (country, department, institution). This takes the form of the percentage of publications in a journal, field or broad area that has been published by the group. This quantity is affected by essentially random influences that obscure underlying changes over time and differences between groups. A model of research production is needed to help identify whether differences between two shares indicate underlying differences. This article introduces a simple production model for indicators that report the share of the world's output in a journal or subject category, assuming that every new article has the same probability to be authored by a given group. With this assumption, confidence limits can be calculated for the underlying production capability (i.e., probability to publish). The results of a time series analysis of national contributions to 36 large monodisciplinary journals 1996-2016 are broadly consistent with this hypothesis. Follow up tests of countries and institutions in 26 Scopus subject categories support the conclusions but highlight the importance of ensuring consistent subject category coverage.
Controlling Elections through Social Influence
Venue: arXiv cs.MA 2017
Year: 2017

Election control considers the problem of an adversary who attempts to tamper with a voting process, in order to either ensure that their favored candidate wins (constructive control) or another candidate loses (destructive control). As online social networks have become significant sources of information for potential voters, a new tool in an attacker's arsenal is to effect control by harnessing social influence, for example, by spreading fake news and other forms of misinformation through online social media. We consider the computational problem of election control via social influence, studying the conditions under which finding good adversarial strategies is computationally feasible. We consider two objectives for the adversary in both the constructive and destructive control settings: probability and margin of victory (POV and MOV, respectively). We present several strong negative results, showing, for example, that the problem of maximizing POV is inapproximable for any constant factor. On the other hand, we present approximation algorithms which provide somewhat weaker approximation guarantees, such as bicriteria approximations for the POV objective and constant-factor approximations for MOV. Finally, we present mixed integer programming formulations for these problems. Experimental results show that our approximation algorithms often find near-optimal control strategies, indicating that election control through social influence is a salient threat to election integrity.
IVE-GAN: Invariant Encoding Generative Adversarial Networks
Venue: arXiv cs.LG 2017
Year: 2017

Generative adversarial networks (GANs) are a powerful framework for generative tasks. However, they are difficult to train and tend to miss modes of the true data generation process. Although GANs can learn a rich representation of the covered modes of the data in their latent space, the framework misses an inverse mapping from data to this latent space. We propose Invariant Encoding Generative Adversarial Networks (IVE-GANs), a novel GAN framework that introduces such a mapping for individual samples from the data by utilizing features in the data which are invariant to certain transformations. Since the model maps individual samples to the latent space, it naturally encourages the generator to cover all modes. We demonstrate the effectiveness of our approach in terms of generative performance and learning rich representations on several datasets including common benchmark image generation tasks.
That's Enough: Asynchrony with Standard Choreography Primitives
Venue: arXiv cs.PL 2017
Year: 2017

Choreographies are widely used for the specification of concurrent and distributed software architectures. Since asynchronous communications are ubiquitous in real-world systems, previous works have proposed different approaches for the formal modelling of asynchrony in choreographies. Such approaches typically rely on ad-hoc syntactic terms or semantics for capturing the concept of messages in transit, yielding different formalisms that have to be studied separately. In this work, we take a different approach, and show that such extensions are not needed to reason about asynchronous communications in choreographies. Rather, we demonstrate how a standard choreography calculus already has all the needed expressive power to encode messages in transit (and thus asynchronous communications) through the primitives of process spawning and name mobility. The practical consequence of our results is that we can reason about real-world systems within a choreography formalism that is simpler than those hitherto proposed.
Prediction of the progression of subcortical brain structures in Alzheimer's disease from baseline
Venue: arXiv cs.CV 2017
Year: 2017

We propose a method to predict the subject-specific longitudinal progression of brain structures extracted from baseline MRI, and evaluate its performance on Alzheimer's disease data. The disease progression is modeled as a trajectory on a group of diffeomorphisms in the context of large deformation diffeomorphic metric mapping (LDDMM). We first exhibit the limited predictive abilities of geodesic regression extrapolation on this group. Building on the recent concept of parallel curves in shape manifolds, we then introduce a second predictive protocol which personalizes previously learned trajectories to new subjects, and investigate the relative performances of two parallel shifting paradigms. This design only requires the baseline imaging data. Finally, coefficients encoding the disease dynamics are obtained from longitudinal cognitive measurements for each subject, and exploited to refine our methodology which is demonstrated to successfully predict the follow-up visits.
Robust Collaborative Object Transportation Using Multiple MAVs
Venue: arXiv cs.RO 2017
Year: 2017

Collaborative object transportation using multiple Micro Aerial Vehicles (MAVs) with limited communication is a challenging problem. In this paper we address the problem of multiple MAVs mechanically coupled to a bulky object for transportation purposes without explicit communication between agents. The apparent physical properties of each agent are reshaped to achieve robustly stable transportation. Parametric uncertainties and unmodeled dynamics of each agent are quantified and techniques from robust control theory are employed to choose the physical parameters of each agent to guarantee stability. Extensive simulation analysis and experimental results show that the proposed method guarantees stability in worst case scenarios.
Microsoft Academic Automatic Document Searches: Accuracy for Journal Articles and Suitability for Citation Analysis
Venue: arXiv cs.DL 2017
Year: 2017

Microsoft Academic is a free academic search engine and citation index that is similar to Google Scholar but can be automatically queried. Its data is potentially useful for bibliometric analysis if it is possible to search effectively for individual journal articles. This article compares different methods to find journal articles in its index by searching for a combination of title, authors, publication year and journal name and uses the results for the widest published correlation analysis of Microsoft Academic citation counts for journal articles so far. Based on 126,312 articles from 323 Scopus subfields in 2012, the optimal strategy to find articles with DOIs is to search for them by title and filter out those with incorrect DOIs. This finds 90% of journal articles. For articles without DOIs, the optimal strategy is to search for them by title and then filter out matches with dissimilar metadata. This finds 89% of journal articles, with an additional 1% incorrect matches. The remaining articles seem to be mainly not indexed by Microsoft Academic or indexed with a different language version of their title. From the matches, Scopus citation counts and Microsoft Academic counts have an average Spearman correlation of 0.95, with the lowest for any single field being 0.63. Thus, Microsoft Academic citation counts are almost universally equivalent to Scopus citation counts for articles that are not recent but there are national biases in the results.
Server, server in the cloud. Who is the fairest in the crowd?
Venue: arXiv cs.CY 2017
Year: 2017

This paper follows the recent history of automated beauty competitions to discuss how machine learning techniques, in particular neural networks, alter the way attractiveness is handled and how this impacts the cultural landscape. We describe experiments performed to probe the behavior of two different convolutional neural network architectures in the classification of facial attractiveness in a large database of celebrity faces. As opposed to other easily definable facial features, attractiveness is difficult to detect robustly even for the best classification systems. Based on the observations from these experiments, we discuss several approaches to detect factors that come into play when a machine evaluates human features, and how bias can occur not only in data selection but in network architectures; in multiple forms on multiple levels throughout the process. The overall goal is to map out with mixed methods a novel condition: slippages produced by platform level machine learning systems that make judgements in domains considered dependent on high level human intelligence.
Deep Learning for Real-Time Crime Forecasting and its Ternarization
Venue: arXiv cs.LG 2017
Year: 2017

Real-time crime forecasting is important. However, accurate prediction of when and where the next crime will happen is difficult. No known physical model provides a reasonable approximation to such a complex system. Historical crime data are sparse in both space and time and the signal of interests is weak. In this work, we first present a proper representation of crime data. We then adapt the spatial temporal residual network on the well represented data to predict the distribution of crime in Los Angeles at the scale of hours in neighborhood-sized parcels. These experiments as well as comparisons with several existing approaches to prediction demonstrate the superiority of the proposed model in terms of accuracy. Finally, we present a ternarization technique to address the resource consumption issue for its deployment in real world. This work is an extension of our short conference proceeding paper [Wang et al, Arxiv 1707.03340].
Relating counting complexity to non-uniform probability measures
Venue: arXiv cs.CC 2017
Year: 2017

A standard method for designing randomized algorithms to approximately count the number of solutions of a problem in $\#$P, is by constructing a rapidly mixing Markov chain converging to the uniform distribution over this set of solutions. This construction is not always an easy task, and it is conjectured that it is not always possible. We want to investigate other possibilities for using Markov Chains in relation to counting, and whether we can relate algorithmic counting to other, non-uniform, probability distributions over the set we want to count. In this paper we present a family of probability distributions over the set of solutions of a problem in TotP, and show how they relate to counting; counting is equivalent to computing their normalizing factors. We analyse the complexity of sampling, of computing the normalizing factor, and of computing the size support of these distributions. The latter is also equivalent to counting. We also show how the above tasks relate to each other, and to other problems in complexity theory as well. In short, we prove that sampling and approximating the normalizing factor is easy. We do this by constructing a family of rapidly mixing Markov chains for which these distributions are stationary. At the same time we show that exactly computing the normalizing factor is TotP-hard. However the reduction proving the latter is not approximation preserving, which conforms with the fact that TotP-hard problems are inapproximable if NP $\neq$ RP. The problem we consider is the Size-of-Subtree, a TotP-complete problem under parsimonious reductions. Therefore the results presented here extend to any problem in TotP. TotP is the Karp-closure of self-reducible problems in $\#$P, having decision version in P.
Towards an executable semantics of automobile RTOS standard and its application to conformance verification
Venue: arXiv cs.SE 2017
Year: 2017

The automobile Real-Time Operating System (RTOS) is hard to design and implement due to its real time features and increasing complexity. Some automobile RTOS standards are released aiming at unifying the software architecture of vehicle systems. Most of the standards are presented informally in natural languages, which may lead to not only ambiguities in specifications but also difficulties in conformance verification. This paper proposes a rewriting-based approach for formalising the automobile RTOS standard. Taking the OSEK/VDX standard as an example, an executional formal semantics of the automobile RTOS kernel, which focuses on the real time features, is defined using $\mathbb{K}$, a rewriting-based framework. We also report some ambiguous definitions of the OSEK/VDX standard, which we find in the process of formalisation. The $\mathbb{K}$ semantics of the OSEK/VDX standard is applied to conformance verification, which is used to check the conformance of not only the automobile operating system kernel but the applications.
On Code Rates of Fractional Repetition Codes
Venue: arXiv cs.IT 2017
Year: 2017

In \textit{Distributed Storage Systems} (DSSs), usually, data is stored using replicated packets on different chunk servers. Recently a new paradigm of \textit{Fractional Repetition} (FR) codes have been introduced, in which, data is replicated in a smart way on distributed servers using a \textit{Maximum Distance Separable} (MDS) code. In this work, for a non-uniform FR code, bounds on the FR code rate and DSS code rate are studied. Using matrix representation of an FR code, some universally good FR codes have been obtained.
Enhanced Uplink Resource Allocation in Non-Orthogonal Multiple Access Systems
Venue: arXiv cs.IT 2017
Year: 2017

Non-orthogonal multiple access (NOMA) is envisioned to be one of the most beneficial technologies for next generation wireless networks due to its enhanced performance compared to other conventional radio access techniques. Although the principle of NOMA allows multiple users to use the same frequency resource, due to decoding complication, information of users in practical systems cannot be decoded successfully if many of them use the same channel. Consequently, assigned spectrum of a system needs to be split into multiple subchannels in order to multiplex that among many users. Uplink resource allocation for such systems is more complicated compared to the downlink ones due to the individual users' power constraints and discrete nature of subchannel assignment. In this paper, we propose an uplink subchannel and power allocation scheme for such systems. Due to the NP-hard and non-convex nature of the problem, the complete solution, that optimizes both subchannel assignment and power allocation jointly, is intractable. Consequently, we solve the problem in two steps. First, based on the assumption that the maximal power level of a user is subdivided equally among its allocated subchannels, we apply many-to-many matching model to solve the subchannel-user mapping problem. Then, in order to enhance the performance of the system further, we apply iterative water-filling and geometric programming two power allocation techniques to allocate power in each allocated subchannel-user slot optimally. Extensive simulation has been conducted to verify the effectiveness of the proposed scheme. The results demonstrate that the proposed scheme always outperforms all existing works in this context under all possible scenarios.