OpenRaiser/Intern-Atlas huggingface dataset:
Graph Embedding Techniques, Applications, and Performance: A Survey
Venue: arXiv cs.SI 2017
Year: 2017

Graphs, such as social networks, word co-occurrence networks, and communication networks, occur naturally in various real-world applications. Analyzing them yields insight into the structure of society, language, and different patterns of communication. Many approaches have been proposed to perform the analysis. Recently, methods which use the representation of graph nodes in vector space have gained traction from the research community. In this survey, we provide a comprehensive and structured analysis of various graph embedding techniques proposed in the literature. We first introduce the embedding task and its challenges such as scalability, choice of dimensionality, and features to be preserved, and their possible solutions. We then present three categories of approaches based on factorization methods, random walks, and deep learning, with examples of representative algorithms in each category and analysis of their performance on various tasks. We evaluate these state-of-the-art methods on a few common datasets and compare their performance against one another. Our analysis concludes by suggesting some potential applications and future directions. We finally present the open-source Python library we developed, named GEM (Graph Embedding Methods, available at https://github.com/palash1992/GEM), which provides all presented algorithms within a unified interface to foster and facilitate research on the topic.
The Blank Stare: Retrieving Unique Eye Tracking Signatures Independent of Visual Stimuli
Venue: arXiv cs.HC 2017
Year: 2017

Using Low Cost Portable Eye Tracking for Biometric Identification Or Verification: Eye tracking technologies have in recent years become available outside of specialised labs, and are starting to become integrated in tablets and virtual reality headsets. This offers new opportunities for use in common office- and home environments, such as for biometric recognition (identification or verification), alone or in combination with other technologies. This paper exposes two fundamentally different approaches that have been suggested, based on spatial and temporal signatures respectively. While deploying different stimulation paradigms for recording, it also proposes an alternative way to analyze spatial domain signatures using Fourier transformation. Empirical data recorded from two subjects over two weeks, three months apart, are found to support previous results. Further, variations and stability of some of the proposed signatures are analyzed over the extended timeframe and under slightly varying conditions.
On a class of optimization-based robust estimators
Venue: arXiv cs.SY 2017
Year: 2017

We consider in this paper the problem of estimating a parameter matrix from observations which are affected by two types of noise components: (i) a sparse noise sequence which, whenever nonzero can have arbitrarily large amplitude (ii) and a dense and bounded noise sequence of "moderate" amount. This is termed a robust regression problem. To tackle it, a quite general optimization-based framework is proposed and analyzed. When only the sparse noise is present, a sufficient bound is derived on the number of nonzero elements in the sparse noise sequence that can be accommodated by the estimator while still returning the true parameter matrix. While almost all the restricted isometry-based bounds from the literature are not verifiable, our bound can be easily computed through solving a convex optimization problem. Moreover, empirical evidence tends to suggest that it is generally tight. If in addition to the sparse noise sequence, the training data are affected by a bounded dense noise, we derive an upper bound on the estimation error.
Formalising opencypher Graph Queries in Relational Algebra
Venue: arXiv cs.DB 2017
Year: 2017

Graph database systems are increasingly adapted for storing and processing heterogeneous network-like datasets. However, due to the novelty of such systems, no standard data model or query language has yet emerged. Consequently, migrating datasets or applications even between related technologies often requires a large amount of manual work or ad-hoc solutions, thus subjecting the users to the possibility of vendor lock-in. To avoid this threat, vendors are working on supporting existing standard languages (e.g. SQL) or creating standardised languages. In this paper, we present a formal specification for openCypher, a high-level declarative graph query language with an ongoing standardisation effort. We introduce relational graph algebra, which extends relational operators by adapting graph-specific operators and define a mapping from core openCypher constructs to this algebra. We propose an algorithm that allows systematic compilation of openCypher queries.
Learning Product Automata
Venue: arXiv cs.SE 2017
Year: 2017

In this paper we give an optimization for active learning algorithms, applicable to learning Moore machines where the output comprises several observables. These machines can be decomposed themselves by projecting on each observable, resulting in smaller components. These components can then be learnt with fewer queries. This is in particular interesting for learning software, where compositional methods are important for guaranteeing scalability.
Video Processing for Barycenter Trajectory Identification in Diving
Venue: arXiv cs.CV 2017
Year: 2017

The aim of this paper is to show a procedure for identify the barycentre of a diver by means of video processing. This procedure is aimed to introduce quantitative analysis tools and diving performance measurement and therefore in diving training. Sport performance analysis is a trend that is growing exponentially for all level athletes: it has been applied extensively in some sports such as cycling. Sport performance analysis has been applied mainly for high level athletes; in order to be used also for middle or low level athletes the proposed technique has to be flexible and low cost. Video processing is suitable to fulfil both these requirements. In diving, the first analysis that has to be done is the barycentre trajectory tracking.
Teaching Concurrent Software Design: A Case Study Using Android
Venue: arXiv cs.SE 2017
Year: 2017

In this article, we explore various parallel and distributed computing topics from a user-centric software engineering perspective. Specifically, in the context of mobile application development, we study the basic building blocks of interactive applications in the form of events, timers, and asynchronous activities, along with related software modeling, architecture, and design topics.
Hardness Results for Structured Linear Systems
Venue: arXiv cs.CC 2017
Year: 2017

We show that if the nearly-linear time solvers for Laplacian matrices and their generalizations can be extended to solve just slightly larger families of linear systems, then they can be used to quickly solve all systems of linear equations over the reals. This result can be viewed either positively or negatively: either we will develop nearly-linear time algorithms for solving all systems of linear equations over the reals, or progress on the families we can solve in nearly-linear time will soon halt.
Why Do Men Get More Attention? Exploring Factors Behind Success in an Online Design Community
Venue: arXiv cs.SI 2017
Year: 2017

Online platforms are an increasingly popular tool for people to produce, promote or sell their work. However recent studies indicate that social disparities and biases present in the real world might transfer to online platforms and could be exacerbated by seemingly harmless design choices on the site (e.g., recommendation systems or publicly visible success measures). In this paper we analyze an exclusive online community of teams of design professionals called Dribbble and investigate apparent differences in outcomes by gender. Overall, we find that men produce more work, and are able to show it to a larger audience thus receiving more likes. Some of this effect can be explained by the fact that women have different skills and design different images. Most importantly however, women and men position themselves differently in the Dribbble community. Our investigation of users' position in the social network shows that women have more clustered and gender homophilous following relations, which leads them to have smaller and more closely knit social networks. Overall, our study demonstrates that looking behind the apparent patterns of gender inequalities in online markets with the help of social networks and product differentiation helps us to better understand gender differences in success and failure.
On the Achievable Rates of Decentralized Equalization in Massive MU-MIMO Systems
Venue: arXiv cs.IT 2017
Year: 2017

Massive multi-user (MU) multiple-input multiple-output (MIMO) promises significant gains in spectral efficiency compared to traditional, small-scale MIMO technology. Linear equalization algorithms, such as zero forcing (ZF) or minimum mean-square error (MMSE)-based methods, typically rely on centralized processing at the base station (BS), which results in (i) excessively high interconnect and chip input/output data rates, and (ii) high computational complexity. In this paper, we investigate the achievable rates of decentralized equalization that mitigates both of these issues. We consider two distinct BS architectures that partition the antenna array into clusters, each associated with independent radio-frequency chains and signal processing hardware, and the results of each cluster are fused in a feedforward network. For both architectures, we consider ZF, MMSE, and a novel, non-linear equalization algorithm that builds upon approximate message passing (AMP), and we theoretically analyze the achievable rates of these methods. Our results demonstrate that decentralized equalization with our AMP-based methods incurs no or only a negligible loss in terms of achievable rates compared to that of centralized solutions.
Light Field Video Capture Using a Learning-Based Hybrid Imaging System
Venue: arXiv cs.CV 2017
Year: 2017

Light field cameras have many advantages over traditional cameras, as they allow the user to change various camera settings after capture. However, capturing light fields requires a huge bandwidth to record the data: a modern light field camera can only take three images per second. This prevents current consumer light field cameras from capturing light field videos. Temporal interpolation at such extreme scale (10x, from 3 fps to 30 fps) is infeasible as too much information will be entirely missing between adjacent frames. Instead, we develop a hybrid imaging system, adding another standard video camera to capture the temporal information. Given a 3 fps light field sequence and a standard 30 fps 2D video, our system can then generate a full light field video at 30 fps. We adopt a learning-based approach, which can be decomposed into two steps: spatio-temporal flow estimation and appearance estimation. The flow estimation propagates the angular information from the light field sequence to the 2D video, so we can warp input images to the target view. The appearance estimation then combines these warped images to output the final pixels. The whole process is trained end-to-end using convolutional neural networks. Experimental results demonstrate that our algorithm outperforms current video interpolation methods, enabling consumer light field videography, and making applications such as refocusing and parallax view generation achievable on videos for the first time.
Assisting Service Providers In Peer-to-peer Marketplaces: Maximizing Gain Over Flexible Attributes
Venue: arXiv cs.DB 2017
Year: 2017

Peer to peer marketplaces such as AirBnB enable transactional exchange of services directly between people. In such platforms, those providing a service (hosts in AirBnB) are faced with various choices. For example in AirBnB, although some amenities in a property (attributes of the property) are fixed, others are relatively flexible and can be provided without significant effort. Providing an amenity is usually associated with a cost. Naturally different sets of amenities may have a different "gains" for a host. Consequently, given a limited budget, deciding which amenities (attributes) to offer is challenging. In this paper, we formally introduce and define the problem of Gain Maximization over Flexible Attributes (GMFA). We first prove that the problem is NP-hard and show that identifying an approximate algorithm with a constant approximate ratio is unlikely. We then provide a practically efficient exact algorithm to the GMFA problem for the general class of monotonic gain functions, which quantify the benefit of sets of attributes. As the next part of our contribution, we focus on the design of a practical gain function for GMFA. We introduce the notion of frequent-item based count (FBC), which utilizes the existing tuples in the database to define the notion of gain, and propose an efficient algorithm for computing it. We present the results of a comprehensive experimental evaluation of the proposed techniques on real dataset from AirBnB and demonstrate the practical relevance and utility of our proposal.
Geometry of Optimization and Implicit Regularization in Deep Learning
Venue: arXiv cs.LG 2017
Year: 2017

We argue that the optimization plays a crucial role in generalization of deep learning models through implicit regularization. We do this by demonstrating that generalization ability is not controlled by network size but rather by some other implicit control. We then demonstrate how changing the empirical optimization procedure can improve generalization, even if actual optimization quality is not affected. We do so by studying the geometry of the parameter space of deep networks, and devising an optimization algorithm attuned to this geometry.
An Anthropic Argument against the Future Existence of Superintelligent Artificial Intelligence
Venue: arXiv cs.AI 2017
Year: 2017

This paper uses anthropic reasoning to argue for a reduced likelihood that superintelligent AI will come into existence in the future. To make this argument, a new principle is introduced: the Super-Strong Self-Sampling Assumption (SSSSA), building on the Self-Sampling Assumption (SSA) and the Strong Self-Sampling Assumption (SSSA). SSA uses as its sample the relevant observers, whereas SSSA goes further by using observer-moments. SSSSA goes further still and weights each sample proportionally, according to the size of a mind in cognitive terms. SSSSA is required for human observer-samples to be typical, given by how much non-human animals outnumber humans. Given SSSSA, the assumption that humans experience typical observer-samples relies on a future where superintelligent AI does not dominate, which in turn reduces the likelihood of it being created at all.
Scalable System Scheduling for HPC and Big Data
Venue: arXiv cs.DC 2017
Year: 2017

In the rapidly expanding field of parallel processing, job schedulers are the "operating systems" of modern big data architectures and supercomputing systems. Job schedulers allocate computing resources and control the execution of processes on those resources. Historically, job schedulers were the domain of supercomputers, and job schedulers were designed to run massive, long-running computations over days and weeks. More recently, big data workloads have created a need for a new class of computations consisting of many short computations taking seconds or minutes that process enormous quantities of data. For both supercomputers and big data systems, the efficiency of the job scheduler represents a fundamental limit on the efficiency of the system. Detailed measurement and modeling of the performance of schedulers are critical for maximizing the performance of a large-scale computing system. This paper presents a detailed feature analysis of 15 supercomputing and big data schedulers. For big data workloads, the scheduler latency is the most important performance characteristic of the scheduler. A theoretical model of the latency of these schedulers is developed and used to design experiments targeted at measuring scheduler latency. Detailed benchmarking of four of the most popular schedulers (Slurm, Son of Grid Engine, Mesos, and Hadoop YARN) are conducted. The theoretical model is compared with data and demonstrates that scheduler performance can be characterized by two key parameters: the marginal latency of the scheduler $t_s$ and a nonlinear exponent $α_s$. For all four schedulers, the utilization of the computing system decreases to < 10\% for computations lasting only a few seconds. Multilevel schedulers that transparently aggregate short computations can improve utilization for these short computations to > 90\% for all four of the schedulers that were tested.
A Mathematical Theory of Human Machine Teaming
Venue: arXiv cs.RO 2017
Year: 2017

We begin with a disquieting paradox: human machine teaming (HMT) often produces results worse than either the human or machine would produce alone. Critically, this failure is not a result of inferior human modeling or a suboptimal autonomy: even with perfect knowledge of human intention and perfect autonomy performance, prevailing teaming architectures still fail under trivial stressors~\cite{trautman-smc-2015}. This failure is instead a result of deficiencies at the \emph{decision fusion level}. Accordingly, \emph{efforts aimed solely at improving human prediction or improving autonomous performance will not produce acceptable HMTs: we can no longer model humans, machines and adversaries as distinct entities.} We thus argue for a strong but essential condition: HMTs should perform no worse than either member of the team alone, and this performance bound should be independent of environment complexity, human-machine interfacing, accuracy of the human model, or reliability of autonomy or human decision making.
CHAM: action recognition using convolutional hierarchical attention model
Venue: arXiv cs.CV 2017
Year: 2017

Recently, the soft attention mechanism, which was originally proposed in language processing, has been applied in computer vision tasks like image captioning. This paper presents improvements to the soft attention model by combining a convolutional LSTM with a hierarchical system architecture to recognize action categories in videos. We call this model the Convolutional Hierarchical Attention Model (CHAM). The model applies a convolutional operation inside the LSTM cell and an attention map generation process to recognize actions. The hierarchical architecture of this model is able to explicitly reason on multi-granularities of action categories. The proposed architecture achieved improved results on three publicly available datasets: the UCF sports dataset, the Olympic sports dataset and the HMDB51 dataset.
Private Information Retrieval from MDS Coded Databases with Colluding Servers under Several Variant Models
Venue: arXiv cs.IT 2017
Year: 2017

Private information retrieval (PIR) gets renewed attentions due to its information-theoretic reformulation and its application in distributed storage system (DSS). The general PIR model considers a coded database containing $N$ servers storing $M$ files. Each file is stored independently via the same arbitrary $(N,K)$-MDS code. A user wants to retrieve a specific file from the database privately against an arbitrary set of $T$ colluding servers. A key problem is to analyze the PIR capacity, defined as the maximal number of bits privately retrieved per one downloaded bit. Several extensions for the general model appear by bringing in various additional constraints. In this paper, we propose a general PIR scheme for several variant PIR models including: PIR with robust servers, PIR with Byzantine servers, the multi-file PIR model and PIR with arbitrary collusion patterns.
Low-Complexity Decoding for Symmetric, Neighboring and Consecutive Side-information Index Coding Problems
Venue: arXiv cs.IT 2017
Year: 2017

The capacity of symmetric, neighboring and consecutive side-information single unicast index coding problems (SNC-SUICP) with number of messages equal to the number of receivers was given by Maleki, Cadambe and Jafar. For these index coding problems, an optimal index code construction by using Vandermonde matrices was proposed. This construction requires all the side-information at the receivers to decode their wanted messages and also requires large field size. In an earlier work, we constructed binary matrices of size $m \times n (m\geq n)$ such that any $n$ adjacent rows of the matrix are linearly independent over every field. Calling these matrices as Adjacent Independent Row (AIR) matrices using which we gave an optimal scalar linear index code for the one-sided SNC-SUICP for any given number of messages and one-sided side-information. By using Vandermonde matrices or AIR matrices, every receiver needs to solve $K-D$ equations with $K-D$ unknowns to obtain its wanted message, where $K$ is the number of messages and $D$ is the size of the side-information. In this paper, we analyze some of the combinatorial properties of the AIR matrices. By using these properties, we present a low-complexity decoding which helps to identify a reduced set of side-information for each users with which the decoding can be carried out. By this method every receiver is able to decode its wanted message symbol by simply adding some index code symbols (broadcast symbols). We explicitly give both the reduced set of side-information and the broadcast messages to be used by each receiver to decode its wanted message. For a given pair or receivers our decoding identifies which one will perform better than the other when the broadcast channel is noisy.
Mīmā\d{m}sā deontic logic: proof theory and applications
Venue: arXiv cs.LO 2017
Year: 2017

Starting with the deontic principles in M\=ımā\d{m}sā texts we introduce a new deontic logic. We use general proof-theoretic methods to obtain a cut-free sequent calculus for this logic, resulting in decidability, complexity results and neighbourhood semantics. The latter is used to analyse a well known example of conflicting obligations from the Vedas.
Connected Health in Multiple Sclerosis: a mobile applications review
Venue: arXiv cs.HC 2017
Year: 2017

Multiple Sclerosis (MS) is an unpredictable, often disabling disease that can adversely affect any body function; this often requires persons with MS to be active patients who are able to self-manage. There are currently thousands of health applications available but it is unknown how many concern MS. We conducted a systematic review of all MS apps present in the most popular app stores (iTunes and Google Play store) on June 2016 to identify all relevant MS apps. After discarding non-MS related apps and duplicates, only a total of 25 MS apps were identified. App description contents and features were explored to assess target audience, functionalities, and developing entities. The vast majority of apps were focused on disease and treatment information with disease management being a close second. This is the first study that reviews MS apps and it highlights an interesting gap in the current repertoire of MS mHealth resources.