OpenRaiser/Intern-Atlas huggingface dataset:
Additional Representations for Improving Synthetic Aperture Sonar Classification Using Convolutional Neural Networks
Venue: arXiv cs.CV 2018
Year: 2018

Object classification in synthetic aperture sonar (SAS) imagery is usually a data starved and class imbalanced problem. There are few objects of interest present among much benign seafloor. Despite these problems, current classification techniques discard a large portion of the collected SAS information. In particular, a beamformed SAS image, which we call a single-look complex (SLC) image, contains complex pixels composed of real and imaginary parts. For human consumption, the SLC is converted to a magnitude-phase representation and the phase information is discarded. Even more problematic, the magnitude information usually exhibits a large dynamic range (>80dB) and must be dynamic range compressed for human display. Often it is this dynamic range compressed representation, originally designed for human consumption, which is fed into a classifier. Consequently, the classification process is completely void of the phase information. In this work, we show improvements in classification performance using the phase information from the SLC as well as information from an alternate source: photographs. We perform statistical testing to demonstrate the validity of our results.
A Case Study on the Impact of Similarity Measure on Information Retrieval based Software Engineering Tasks
Venue: arXiv cs.SE 2018
Year: 2018

Information Retrieval (IR) plays a pivotal role in diverse Software Engineering (SE) tasks, e.g., bug localization and triaging, code retrieval, requirements analysis, etc. The choice of similarity measure is the core component of an IR technique. The performance of any IR method critically depends on selecting an appropriate similarity measure for the given application domain. Since different SE tasks operate on different document types like bug reports, software descriptions, source code, etc. that often contain non-standard domain-specific vocabulary, it is essential to understand which similarity measures work best for different SE documents. This paper presents two case studies on the effect of different similarity measure on various SE documents w.r.t. two tasks: (i) project recommendation: finding similar GitHub projects and (ii) bug localization: retrieving buggy source file(s) correspond to a bug report. These tasks contain a diverse combination of textual (i.e. description, readme) and code (i.e. source code, API, import package) artifacts. We observe that the performance of IR models varies when applied to different artifact types. We find that, in general, the context-aware models achieve better performance on textual artifacts. In contrast, simple keyword-based bag-of-words models perform better on code artifacts. On the other hand, the probabilistic ranking model BM25 performs better on a mixture of text and code artifacts. We further investigate how such an informed choice of similarity measure impacts the performance of SE tools. In particular, we analyze two previously proposed tools for project recommendation and bug localization tasks, which leverage diverse software artifacts, and observe that an informed choice of similarity measure indeed leads to improved performance of the existing SE tools.
Towards Learning Fine-Grained Disentangled Representations from Speech
Venue: arXiv cs.SD 2018
Year: 2018

Learning disentangled representations of high-dimensional data is currently an active research area. However, compared to the field of computer vision, less work has been done for speech processing. In this paper, we provide a review of two representative efforts on this topic and propose the novel concept of fine-grained disentangled speech representation learning.
Exploiting Effective Representations for Chinese Sentiment Analysis Using a Multi-Channel Convolutional Neural Network
Venue: arXiv cs.CL 2018
Year: 2018

Effective representation of a text is critical for various natural language processing tasks. For the particular task of Chinese sentiment analysis, it is important to understand and choose an effective representation of a text from different forms of Chinese representations such as word, character and pinyin. This paper presents a systematic study of the effect of these representations for Chinese sentiment analysis by proposing a multi-channel convolutional neural network (MCCNN), where each channel corresponds to a representation. Experimental results show that: (1) Word wins on the dataset of low OOV rate while character wins otherwise; (2) Using these representations in combination generally improves the performance; (3) The representations based on MCCNN outperform conventional ngram features using SVM; (4) The proposed MCCNN model achieves the competitive performance against the state-of-the-art model fastText for Chinese sentiment analysis.
LED Arrays of Laser Printers as Sources of Valuable Emissions for Electromagnetic Penetration Process
Venue: arXiv cs.CR 2018
Year: 2018

Protection of information against electromagnetic eavesdropping is an important issue. Information may be derivable from the shape of an unintended electromagnetic signal. The resulting electromagnetic emanations can be correlated with processing of classified information. The problem extends to computer printers. This article presents a technical analysis of LED arrays used in monochrome computer printers and their contribution to unintentional electromagnetic emanations. We analysed two printers from different manufacturers, designated $A$ and $B$. The forms of useful signals and their dependence on parameters of printing data are presented. Analyses were based on realistic type sizes and distribution of glyphs. Pictures were reconstructed from received radio frequency (RF) emanations. We observed differences in legibility of information receivable at a distance that we attribute to different ways used by printer designers to control the LED arrays, particularly the difference between relatively high voltage single-ended waveforms and lower-voltage differential signals. To decode the compromising emanations required knowledge of---or guessing---printer operating parameters including resolution, printing speed, and paper size. The optimal RF bandwidth for detecting individual pixels has been determined. Measurements were carried out across differences in construction and control of the LED arrays in tested printers, and the levels of RF emissions compared for selected operating modes (fast, high quality, or toner saving mode) of the printing device.
Arithmetic Word Problem Solver using Frame Identification
Venue: arXiv cs.CL 2018
Year: 2018

Automatic Word problem solving has always posed a great challenge for the NLP community. Usually a word problem is a narrative comprising of a few sentences and a question is asked about a quantity referred in the sentences. Solving word problem involves reasoning across sentences, identification of operations, their order, relevant quantities and discarding irrelevant quantities. In this paper, we present a novel approach for automatic arithmetic word problem solving. Our approach starts with frame identification. Each frame can either be classified as a state or an action frame. The frame identification is dependent on the verb in a sentence. Every frame is unique and is identified by its slots. The slots are filled using dependency parsed output of a sentence. The slots are entity holder, entity, quantity of the entity, recipient, additional information like place, time. The slots and frames helps to identify the type of question asked and the entity referred. Action frames act on state frame(s) which causes a change in quantities of the state frames. The frames are then used to build a graph where any change in quantities can be propagated to the neighboring nodes. Most of the current solvers can only answer questions related to the quantity, while our system can answer different kinds of questions like `who', `what' other than the quantity related questions `how many'. There are three major contributions of this paper. 1. Frame Annotated Corpus (with a frame annotation tool) 2. Frame Identification Module 3. A new easily understandable Framework for word problem solving
Hunting for Tractable Languages for Judgment Aggregation
Venue: arXiv cs.AI 2018
Year: 2018

Judgment aggregation is a general framework for collective decision making that can be used to model many different settings. Due to its general nature, the worst case complexity of essentially all relevant problems in this framework is very high. However, these intractability results are mainly due to the fact that the language to represent the aggregation domain is overly expressive. We initiate an investigation of representation languages for judgment aggregation that strike a balance between (1) being limited enough to yield computational tractability results and (2) being expressive enough to model relevant applications. In particular, we consider the languages of Krom formulas, (definite) Horn formulas, and Boolean circuits in decomposable negation normal form (DNNF). We illustrate the use of the positive complexity results that we obtain for these languages with a concrete application: voting on how to spend a budget (i.e., participatory budgeting).
On Minimizing Energy Consumption for D2D Clustered Caching Networks
Venue: arXiv cs.DC 2018
Year: 2018

We formulate and solve the energy minimization problem for a clustered device-to-device (D2D) network with cache-enabled mobile devices. Devices are distributed according to a Poisson cluster process (PCP) and are assumed to have a surplus memory which is exploited to proactively cache files from a library. Devices can retrieve the requested files from their caches, from neighboring devices in their proximity (cluster), or from the base station as a last resort. We minimize the energy consumption of the proposed network under a random prob- abilistic caching scheme, where files are independently cached according to a specific probability distribution. A closed-form expression for the D2D coverage probability is obtained. The energy consumption problem is then formulated as a function of the caching distribution, and the optimal probabilistic caching distribution is obtained. Results reveal that the proposed caching distribution reduces energy consumption up to 33% as compared to caching popular files scheme.
Necessary Field Size and Probability for MDP and Complete MDP Convolutional Codes
Venue: arXiv cs.IT 2018
Year: 2018

It has been shown that maximum distance profile (MDP) convolutional codes have optimal recovery rate for windows of a certain length, when transmitting over an erasure channel. In addition, the subclass of complete MDP convolutional codes has the ability to reduce the waiting time during decoding. In this paper, we derive upper bounds on the necessary field size for the existence of MDP and complete MDP convolutional codes and show that these bounds improve the already existing ones. Moreover, we derive lower bounds for the probability that a random code is MDP respective complete MDP.
Generalized budgeted submodular set function maximization
Venue: arXiv cs.DS 2018
Year: 2018

In this paper we consider a generalization of the well-known budgeted maximum coverage problem. We are given a ground set of elements and a set of bins. The goal is to find a subset of elements along with an associated set of bins, such that the overall cost is at most a given budget, and the profit is maximized. Each bin has its own cost and the cost of each element depends on its associated bin. The profit is measured by a monotone submodular function over the elements. We first present an algorithm that guarantees an approximation factor of $\frac{1}{2}\left(1-\frac{1}{e^α}\right)$, where $α\leq 1$ is the approximation factor of an algorithm for a sub-problem. We give two polynomial-time algorithms to solve this sub-problem. The first one gives us $α=1- ε$ if the costs satisfies a specific condition, which is fulfilled in several relevant cases, including the unitary costs case and the problem of maximizing a monotone submodular function under a knapsack constraint. The second one guarantees $α=1-\frac{1}{e}-ε$ for the general case. The gap between our approximation guarantees and the known inapproximability bounds is $\frac{1}{2}$. We extend our algorithm to a bi-criterion approximation algorithm in which we are allowed to spend an extra budget up to a factor $β\geq 1$ to guarantee a $\frac{1}{2}\left(1-\frac{1}{e^{αβ}}\right)$-approximation. If we set $β=\frac{1}α\ln \left(\frac{1}{2ε}\right)$, the algorithm achieves an approximation factor of $\frac{1}{2}-ε$, for any arbitrarily small $ε>0$.
One Billion Apples' Secret Sauce: Recipe for the Apple Wireless Direct Link Ad hoc Protocol
Venue: arXiv cs.NI 2018
Year: 2018

Apple Wireless Direct Link (AWDL) is a proprietary and undocumented IEEE 802.11-based ad hoc protocol. Apple first introduced AWDL around 2014 and has since integrated it into its entire product line, including iPhone and Mac. While we have found that AWDL drives popular applications such as AirPlay and AirDrop on more than one billion end-user devices, neither the protocol itself nor potential security and Wi-Fi coexistence issues have been studied. In this paper, we present the operation of the protocol as the result of binary and runtime analysis. In short, each AWDL node announces a sequence of Availability Windows (AWs) indicating its readiness to communicate with other AWDL nodes. An elected master node synchronizes these sequences. Outside the AWs, nodes can tune their Wi-Fi radio to a different channel to communicate with an access point, or could turn it off to save energy. Based on our analysis, we conduct experiments to study the master election process, synchronization accuracy, channel hopping dynamics, and achievable throughput. We conduct a preliminary security assessment and publish an open source Wireshark dissector for AWDL to nourish future work.
Do females create higher impact research? Scopus citations and Mendeley readers for articles from five countries
Venue: arXiv cs.DL 2018
Year: 2018

There are known gender imbalances in participation in scientific fields, from female dominance of nursing to male dominance of mathematics. It is not clear whether there is also a citation imbalance, with some claiming that male-authored research tends to be more cited. No previous study has assessed gender differences in the readers of academic research on a large scale, however. In response, this article assesses whether there are gender differences in the average citations and/or Mendeley readers of academic publications. Field normalised logged Scopus citations and Mendeley readers from mid-2018 for articles published in 2014 were investigated for articles with first authors from India, Spain, Turkey, the UK and the USA in up to 251 fields with at least 50 male and female authors. Although female-authored research is less cited in Turkey (-4.0%) and India (-3.6%), it is marginally more cited in Spain (0.4%), the UK (0.4%), and the USA (0.2%). Female-authored research has fewer Mendeley readers in India (-1.1%) but more in Spain (1.4%), Turkey (1.1%), the UK (2.7%) and the USA (3.0%). Thus, whilst there may be little practical gender difference in citation impact in countries with mature science systems, the higher female readership impact suggests a wider audience for female-authored research. The results show that the conclusions from a gender analysis depend on the field normalisation method. A theoretically informed decision must therefore be made about which normalisation to use. The results also suggest that arithmetic mean-based field normalisation is favourable to males.
A survey of data transfer and storage techniques in prevalent cryptocurrencies and suggested improvements
Venue: arXiv cs.DC 2018
Year: 2018

This thesis focuses on aspects related to the functioning of the gossip networks underlying three relatively popular cryptocurrencies: Ethereum, Nano and IOTA. We look at topics such as automatic discovery of peers when a new node joins the network, bandwidth usage of a node, message passing protocols and storage schemas and optimizations for the shared ledger. We believe this is a topic that is often overlooked in works about blockchains and cryptocurrencies. Vulnerabilities and inefficiencies attain a higher significance than ones in a regular open source project because of the rather direct financial implications of these projects. Barring Bitcoin, a network that has been around for nearly 10 years, no other project has substantial documentation for its operational details other than scattered and sparse pages in the source code repositories. Almost all of the content described here has been extracted by studying the source code of the reference implementations of these projects. We evaluate the use of Invertible Bloom Lookup Tables and the Graphene protocol to decrease block propagation times and bandwidth usage of certain messages. We perform realistic simulations that show significant improvements. We provide a complete implementation of Graphene in Geth, Ethereum's main node software and test this implementation against the main Ethereum blockchain. We also crawled the chosen cryptocurrency networks for publicly visible nodes and provide an Autonomous System-level breakdown of these nodes with the end goal of estimating the ease of performing attacks such as BGP hijacks and their impact. Code written for implementing Graphene in Geth, performing various simulations and for other miscellaneous tasks has been uploaded to Github at https://github.com/sunfinite/masters-thesis.
Monitoring compliance with governmental and institutional open access policies across Spanish universities
Venue: arXiv cs.DL 2018
Year: 2018

Universities and research centers in Spain are subject to a national open access (OA) mandate and to their own OA institutional policies, if any, but compliance with these requirements has not been fully monitored yet. We studied the degree of OA archiving of publications of 28 universities within the period 2012-2014. Of these, 12 have an institutional OA mandate, 9 do not require but request or encourage OA of scholarly outputs, and 7 do not have a formal OA statement but are well known for their support of the OA movement. The potential OA rate was calculated according to the publisher open access policies indicated in Sherpa/Romeo directory. The universities showed an asymmetric distribution of 1% to 63% of articles archived in repositories that matched those indexed by the Web of Science in the same period, of which 1% to 35% were OA and the rest were closed access. For articles on work carried out with public funding and subject to the Spanish Science law, the percentage was similar or slightly higher. However, the analysis of potential OA showed that the figure could have reached 80% in some cases. This means that the real proportion of articles in OA is far below what it could potentially be.
Power Minimization Based Joint Task Scheduling and Resource Allocation in Downlink C-RAN
Venue: arXiv cs.IT 2018
Year: 2018

In this paper, we consider the network power minimization problem in a downlink cloud radio access network (C-RAN), taking into account the power consumed at the baseband unit (BBU) for computation and the power consumed at the remote radio heads and fronthaul links for transmission. The power minimization problem for transmission is a fast time-scale issue whereas the power minimization problem for computation is a slow time-scale issue. Therefore, the joint network power minimization problem is a mixed time-scale problem. To tackle the time-scale challenge, we introduce large system analysis to turn the original fast time-scale problem into a slow time-scale one that only depends on the statistical channel information. In addition, we propose a bound improving branch-and-bound algorithm and a combinational algorithm to find the optimal and suboptimal solutions to the power minimization problem for computation, respectively, and propose an iterative coordinate descent algorithm to find the solutions to the power minimization problem for transmission. Finally, a distributed algorithm based on hierarchical decomposition is proposed to solve the joint network power minimization problem. In summary, this work provides a framework to investigate how execution efficiency and computing capability at BBU as well as delay constraint of tasks can affect the network power minimization problem in C-RANs.
Deep Learning Based Speed Estimation for Constraining Strapdown Inertial Navigation on Smartphones
Venue: arXiv cs.CV 2018
Year: 2018

Strapdown inertial navigation systems are sensitive to the quality of the data provided by the accelerometer and gyroscope. Low-grade IMUs in handheld smart-devices pose a problem for inertial odometry on these devices. We propose a scheme for constraining the inertial odometry problem by complementing non-linear state estimation by a CNN-based deep-learning model for inferring the momentary speed based on a window of IMU samples. We show the feasibility of the model using a wide range of data from an iPhone, and present proof-of-concept results for how the model can be combined with an inertial navigation system for three-dimensional inertial navigation.
Dynamic all scores matrices for LCS score
Venue: arXiv cs.DS 2018
Year: 2018

The problem of aligning two strings A,B in order to determine their similarity is fundamental in the field of pattern matching. An important concept in this domain is the "all scores matrix" that encodes the local alignment comparison of two strings. Namely, let K denote the all scores matrix containing the alignment score of every substring of B with A, and let J denote the all scores matrix containing the alignment score of every suffix of B with every prefix of A. In this paper we consider the problem of maintaining an all scores matrix where the scoring function is the LCS score, while supporting single character prepend and append operations to A and N. Our algorithms exploit the sparsity parameters L=LCS(A,B) and Delta = |B|-L. For the matrix K we propose an algorithm that supports incremental operations to both ends of A in O(Delta) time. Whilst for the matrix J we propose an algorithm that supports a single type of incremental operation, either a prepend operation to A or an append operation to B, in O(L) time. This structure can also be extended to support both operations simultaneously in O(L log log L) time.
Ensemble Kalman Inversion: A Derivative-Free Technique For Machine Learning Tasks
Venue: arXiv cs.LG 2018
Year: 2018

The standard probabilistic perspective on machine learning gives rise to empirical risk-minimization tasks that are frequently solved by stochastic gradient descent (SGD) and variants thereof. We present a formulation of these tasks as classical inverse or filtering problems and, furthermore, we propose an efficient, gradient-free algorithm for finding a solution to these problems using ensemble Kalman inversion (EKI). Applications of our approach include offline and online supervised learning with deep neural networks, as well as graph-based semi-supervised learning. The essence of the EKI procedure is an ensemble based approximate gradient descent in which derivatives are replaced by differences from within the ensemble. We suggest several modifications to the basic method, derived from empirically successful heuristics developed in the context of SGD. Numerical results demonstrate wide applicability and robustness of the proposed algorithm.
The effective entropy of next/previous larger/smaller value queries
Venue: arXiv cs.DS 2018
Year: 2018

We study the problem of storing the minimum number of bits required to answer next/previous larger/smaller value queries on an array $A$ of $n$ numbers, without storing $A$. We show that these queries can be answered by storing at most $3.701 n$ bits. Our result improves the result of Jo and Satti [TCS 2016] that gives an upper bound of $4.088n$ bits for this problem.