OpenRaiser/Intern-Atlas huggingface dataset:
Analyzing Families of Experiments in SE: A Systematic Mapping Study
Venue: arXiv cs.SE 2018
Year: 2018

Context: Families of experiments (i.e., groups of experiments with the same goal) are on the rise in Software Engineering (SE). Selecting unsuitable aggregation techniques to analyze families may undermine their potential to provide in-depth insights from experiments' results. Objectives: Identifying the techniques used to aggregate experiments' results within families in SE. Raising awareness of the importance of applying suitable aggregation techniques to reach reliable conclusions within families. Method: We conduct a systematic mapping study (SMS) to identify the aggregation techniques used to analyze families of experiments in SE. We outline the advantages and disadvantages of each aggregation technique according to mature experimental disciplines such as medicine and pharmacology. We provide preliminary recommendations to analyze and report families of experiments in view of families' common limitations with regard to joint data analysis. Results: Several aggregation techniques have been used to analyze SE families of experiments, including Narrative synthesis, Aggregated Data (AD), Individual Participant Data (IPD) mega-trial or stratified, and Aggregation of p-values. The rationale used to select aggregation techniques is rarely discussed within families. Families of experiments are commonly analyzed with unsuitable aggregation techniques according to the literature of mature experimental disciplines. Conclusion: Data analysis' reporting practices should be improved to increase the reliability and transparency of joint results. AD and IPD stratified appear to be suitable to analyze SE families of experiments.
Joint String Complexity for Markov Sources: Small Data Matters
Venue: arXiv cs.IT 2018
Year: 2018

String complexity is defined as the cardinality of a set of all distinct words (factors) of a given string. For two strings, we introduce the joint string complexity as the cardinality of a set of words that are common to both strings. String complexity finds a number of applications from capturing the richness of a language to finding similarities between two genome sequences. In this paper we analyze the joint string complexity when both strings are generated by Markov sources. We prove that the joint string complexity grows linearly (in terms of the string lengths) when both sources are statistically indistinguishable and sublinearly when sources are statistically not the same. Precise analysis of the joint string complexity turns out to be quite challenging requiring subtle singularity analysis and saddle point method over infinity many saddle points leading to novel oscillatory phenomena with single and double periodicities. To overcome these challenges, we apply powerful analytic techniques such as multivariate generating functions, multivariate depoissonization and Mellin transform, spectral matrix analysis, and complex asymptotic methods.
Guessing with a Bit of Help
Venue: arXiv cs.IT 2018
Year: 2018

What is the value of a single bit to a guesser? We study this problem in a setup where Alice wishes to guess an i.i.d. random vector, and can procure one bit of information from Bob, who observes this vector through a memoryless channel. We are interested in the guessing efficiency, which we define as the best possible multiplicative reduction in Alice's guessing-moments obtainable by observing Bob's bit. For the case of a uniform binary vector observed through a binary symmetric channel, we provide two lower bounds on the guessing efficiency by analyzing the performance of the Dictator and Majority functions, and two upper bounds via maximum entropy and Fourier-analytic / hypercontractivity arguments. We then extend our maximum entropy argument to give a lower bound on the guessing efficiency for a general channel with a binary uniform input, via the strong data-processing inequality constant of the reverse channel. We compute this bound for the binary erasure channel, and conjecture that Greedy Dictator functions achieve the guessing efficiency.
Asymptotic Performance Analysis of GSVD-NOMA Systems with a Large-Scale Antenna Array
Venue: arXiv cs.IT 2018
Year: 2018

This paper considers a multiple-input multiple-output (MIMO) downlink communication scenario with one base station and two users, where each user is equipped with m antennas and the base station is equipped with n antennas. To efficiently exploit the spectrum resources, we propose a transmission protocol which combines generalized singular value decomposition (GSVD) and non-orthogonal multiple access (NOMA). The average data rates achieved by the two users are adopted as performance metrics for evaluation of the proposed GSVD-NOMA scheme. In particular, we first characterize the limiting distribution of the squared generalized singular values of the two users' channel matrices for the asymptotic case where the numbers of transmit and receive antennas approach infinity. Then, we calculate the normalized average individual rates of the users in the considered asymptotic regime. Furthermore, we extend the proposed GSVD-NOMA scheme to the MIMO downlink communication scenario with more than two users by using a hybrid multiple access (MA) approach, where the base station first divides the users into different groups, then the proposed GSVD-NOMA scheme is implemented within each group, and different groups are allocated with orthogonal bandwidth resources. Finally, numerical results are provided to validate the effectiveness of the proposed GSVD-NOMA protocol, and the accuracy of the developed analytical results.
Hyperbolic Neural Networks
Venue: arXiv cs.LG 2018
Year: 2018

Hyperbolic spaces have recently gained momentum in the context of machine learning due to their high capacity and tree-likeliness properties. However, the representational power of hyperbolic geometry is not yet on par with Euclidean geometry, mostly because of the absence of corresponding hyperbolic neural network layers. This makes it hard to use hyperbolic embeddings in downstream tasks. Here, we bridge this gap in a principled manner by combining the formalism of Möbius gyrovector spaces with the Riemannian geometry of the Poincaré model of hyperbolic spaces. As a result, we derive hyperbolic versions of important deep learning tools: multinomial logistic regression, feed-forward and recurrent neural networks such as gated recurrent units. This allows to embed sequential data and perform classification in the hyperbolic space. Empirically, we show that, even if hyperbolic optimization tools are limited, hyperbolic sentence embeddings either outperform or are on par with their Euclidean variants on textual entailment and noisy-prefix recognition tasks.
Digital Transformation in Airport Ground Operations
Venue: arXiv cs.CY 2018
Year: 2018

How has digital transformation changed airport ground operations? Although the relevant peer-reviewed literature emphasizes the role of cost savings as a key driver behind digitalization of airport ground operations, the focus is on data-driven, customer-centric innovations. This paper argues that ground handling agents are deploying new technologies mainly to boost process efficiency and to cut costs. Our research shows that ground handling agents are embracing current trends to craft new business models and develop new revenue streams. In this paper, we examine the ground handling agent's value chain and identify areas that are strongly affected by digital transformation and those that are not. We discuss different business scenarios for digital technology and link them with relevant research, such as automated service data capturing, new digital services for passengers, big data, indoor navigation, and wearables in airport ground operations. We assess the maturity level of discussed technologies using NASA technology readiness levels.
Agilit{é} de d{é}veloppement des SI informatis{é}s et outils MDE : d{é}marche p{é}dagogique dans un cours de conception de syst{è}mes d'information informatis{é}s
Venue: arXiv cs.SE 2018
Year: 2018

In software development, business rules implemented by hand using programming code hinder agility of companies. Are our students in information systems aware of that? Do our lessons promote this realization ? We use model driven concepts (MDA, MDE) in order to demonstrate, with practical examples, that source code can be automatically generated as far as formal specification are sufficient and accurate. We have extended usual representation conventions of conceptual data modeling and developed a transformer tool. This make our students observing themselves that no source code need necessarily to be rewrite when a change of business rule occurs, but just an adaptation of the model and a new run of generation of code. In this way, they finally understand more easily the benefits of MDE tools.
A New Finitely Controllable Class of Tuple Generating Dependencies: The Triangularly-Guarded Class
Venue: arXiv cs.DB 2018
Year: 2018

In this paper we introduce a new class of tuple-generating dependencies (TGDs) called triangularly-guarded (TG) TGDs. We show that conjunctive query answering under this new class of TGDs is decidable since this new class of TGDs also satisfies the finite controllability (FC) property. We further show that this new class strictly contains some other decidable classes such as weak-acyclic, guarded, sticky and shy. In this sense, the class TG provides a unified representation of all these aforementioned classes of TGDs.
Using mobile phone sensor technology for mental health research: Integrated analysis to identify hidden challenges and potential solutions
Venue: arXiv cs.CY 2018
Year: 2018

Background: Mobile phone sensor technology has great potential in providing behavioral markers of mental health. However, this promise has not yet been brought to fruition. Objective: The objective of our study was to examine challenges involved in developing an app to extract behavioral markers of mental health from passive sensor data. Methods: Both technical challenges and acceptability of passive data collection for mental health research were assessed based on literature review and results obtained from a feasibility study. Socialise, a mobile phone app developed at the Black Dog Institute, was used to collect sensor data (Bluetooth, global positioning system, and battery status) and investigate views and experiences of a group of people with lived experience of mental health challenges (N=32). Results: On average, sensor data were obtained for 55% (Android) and 45% (iPhone OS) of scheduled scans. Battery life was reduced from 21.3 hours to 18.8 hours when scanning every 5 minutes with a reduction of 2.5 hours or 12%. Despite this relatively small reduction, most participants reported that the app had a noticeable effect on their battery life. In addition to battery life, the purpose of data collection, trust in the organization that collects data, and perceived impact on privacy were identified as main factors for acceptability. Conclusions: Based on the findings of the feasibility study and literature review, we recommend a commitment to open science and transparent reporting and stronger partnerships and communication with users. Sensing technology has the potential to greatly enhance the delivery and impact of mental health care. Realizing this requires all aspects of mobile phone sensor technology to be rigorously assessed.
A New Approach to the Statistical Analysis of Non-Central Complex Gaussian Quadratic Forms with Applications
Venue: arXiv cs.IT 2018
Year: 2018

This paper proposes a novel approach to the statistical characterization of non-central complex Gaussian quadratic forms (CGQFs). Its key strategy is the generation of an auxiliary random variable (RV) that converges in distribution to the original CGQF. Since the mean squared error between both is given in a simple closed-form formulation, the auxiliary RV can be particularized to achieve the required accuracy. The technique is valid for both definite and indefinite CGQFs and yields simple expressions of the probability density function (PDF) and the cumulative distribution function (CDF) that involve only elementary functions. This overcomes a major limitation of previous approaches, in which the complexity of the resulting PDF and CDF prevents from using them for subsequent calculations. To illustrate this end, the proposed method is applied to maximal ratio combining systems over correlated Rician channels, for which the outage probability and the average bit error rate are derived.
Towards the first adversarially robust neural network model on MNIST
Venue: arXiv cs.CV 2018
Year: 2018

Despite much effort, deep neural networks remain highly susceptible to tiny input perturbations and even for MNIST, one of the most common toy datasets in computer vision, no neural network model exists for which adversarial perturbations are large and make semantic sense to humans. We show that even the widely recognized and by far most successful defense by Madry et al. (1) overfits on the L-infinity metric (it's highly susceptible to L2 and L0 perturbations), (2) classifies unrecognizable images with high certainty, (3) performs not much better than simple input binarization and (4) features adversarial perturbations that make little sense to humans. These results suggest that MNIST is far from being solved in terms of adversarial robustness. We present a novel robust classification model that performs analysis by synthesis using learned class-conditional data distributions. We derive bounds on the robustness and go to great length to empirically evaluate our model using maximally effective adversarial attacks by (a) applying decision-based, score-based, gradient-based and transfer-based attacks for several different Lp norms, (b) by designing a new attack that exploits the structure of our defended model and (c) by devising a novel decision-based attack that seeks to minimize the number of perturbed pixels (L0). The results suggest that our approach yields state-of-the-art robustness on MNIST against L0, L2 and L-infinity perturbations and we demonstrate that most adversarial examples are strongly perturbed towards the perceptual boundary between the original and the adversarial class.
Concentric ESN: Assessing the Effect of Modularity in Cycle Reservoirs
Venue: arXiv cs.NE 2018
Year: 2018

The paper introduces concentric Echo State Network, an approach to design reservoir topologies that tries to bridge the gap between deterministically constructed simple cycle models and deep reservoir computing approaches. We show how to modularize the reservoir into simple unidirectional and concentric cycles with pairwise bidirectional jump connections between adjacent loops. We provide a preliminary experimental assessment showing how concentric reservoirs yield to superior predictive accuracy and memory capacity with respect to single cycle reservoirs and deep reservoir models.
Memory efficient distributed sliding super point cardinality estimation by GPU
Venue: arXiv cs.NI 2018
Year: 2018

Super point is a kind of special host in the network which contacts with huge of other hosts. Estimating its cardinality, the number of other hosts contacting with it, plays important roles in network management. But all of existing works focus on discrete time window super point cardinality estimation which has great latency and ignores many measuring periods. Sliding time window measures super point cardinality in a finer granularity than that of discrete time window but also more complex. This paper firstly introduces an algorithm to estimate super point cardinality under sliding time window from distributed edge routers. This algorithm's ability of sliding super point cardinality estimating comes from a novel method proposed in this paper which can record the time that a host appears. Based on this method, two sliding cardinality estimators, sliding rough estimator and sliding linear estimator, are devised for super points detection and their cardinalities estimation separately. When using these two estimators together, the algorithm consumes the smallest memory with the highest accuracy. This sliding super point cardinality algorithm can be deployed in distributed environment and acquire the global super points' cardinality by merging estimators of distributed nodes. Both of these estimators could process packets parallel which makes it becom possible to deal with high speed network in real time by GPU. Experiments on a real world traffic show that this algorithm have the highest accuracy and the smallest memory comparing with others when running under discrete time window. Under sliding time window, this algorithm also has the same performance as under discrete time window.
A hybrid approach of interpolations and CNN to obtain super-resolution
Venue: arXiv cs.CV 2018
Year: 2018

We propose a novel architecture that learns an end-to-end mapping function to improve the spatial resolution of the input natural images. The model is unique in forming a nonlinear combination of three traditional interpolation techniques using the convolutional neural network. Another proposed architecture uses a skip connection with nearest neighbor interpolation, achieving almost similar results. The architectures have been carefully designed to ensure that the reconstructed images lie precisely in the manifold of high-resolution images, thereby preserving the high-frequency components with fine details. We have compared with the state of the art and recent deep learning based natural image super-resolution techniques and found that our methods are able to preserve the sharp details in the image, while also obtaining comparable or better PSNR than them. Since our methods use only traditional interpolations and a shallow CNN with less number of smaller filters, the computational cost is kept low. We have reported the results of two proposed architectures on five standard datasets, for an upscale factor of 2. Our methods generalize well in most cases, which is evident from the better results obtained with increasingly complex datasets. For 4-times upscaling, we have designed similar architectures for comparing with other methods.
A Two-Stage Subspace Trust Region Approach for Deep Neural Network Training
Venue: arXiv cs.CV 2018
Year: 2018

In this paper, we develop a novel second-order method for training feed-forward neural nets. At each iteration, we construct a quadratic approximation to the cost function in a low-dimensional subspace. We minimize this approximation inside a trust region through a two-stage procedure: first inside the embedded positive curvature subspace, followed by a gradient descent step. This approach leads to a fast objective function decay, prevents convergence to saddle points, and alleviates the need for manually tuning parameters. We show the good performance of the proposed algorithm on benchmark datasets.
Navigating a maze differently - a user study
Venue: arXiv cs.HC 2018
Year: 2018

Navigating spaces is an embodied experience. Examples can vary from rescue workers trying to save people from natural disasters; a tourist finding their way to the nearest coffee shop, or a gamer solving a maze. Virtual reality allows these experiences to be simulated in a controlled virtual environment. However, virtual reality users remain anchored in the real world and the conventions by which the virtual environment is deployed influence user performance. There is currently a need to evaluate the degree of influence imposed by extrinsic factors and virtual reality hardware on its users. Traditionally, virtual reality experiences have been deployed using Head-Mounted Displays with powerful computers rendering the graphical content of the virtual environment; however, user input has been facilitated using an array of human interface devices including Keyboards, Mice, Trackballs, Touchscreens, Joysticks, Gamepads, Motion detecting cameras and Webcams. Some of these HIDs have also been introduced for non-immersive video games and general computing. Due to this fact, a subset of virtual reality users has greater familiarity than others in using these HIDs. Virtual reality experiences that utilize gamepads (controllers) to navigate virtual environments may introduce a bias towards usability among virtual reality users previously exposed to video-gaming. This article presents an evaluative user study conducted using our ubiquitous virtual reality framework with general audiences. Among our findings, we reveal a usability bias among virtual reality users who are predominantly video gamers. Beyond this, we found a statistical difference in user behavior between untethered immersive virtual reality experiences compared to untethered non-immersive virtual reality experiences.
Deployment of the Saddle Space Transformation in Tracking the Base of Support
Venue: arXiv cs.RO 2018
Year: 2018

Balance is the fundamental skill behind human locomotion, and its impairment is the principal indicator of self-perceived disability. Despite significant improvements in balance assessment, there is still large incidence of fall related injuries among elderlies. The Base of Support (BoS) is a popular method for bipedal stability assessment, but its accuracy depends on the accuracy the BoS geometry measurement. This work presents a method to ease the BoS tracking by the identification of a reference frame that allows to define postural models of the BoS geometry. Although we also propose a geometry based on the geometry determined from centre of pressure range of motion within the foot obtained from literature, this methodology can be used with other models (i.e., the feasible base of support). The model has been tested with 12 healthy subjects, which have been asked to explore their stability in six different postures. The results show that the model can accurate deform the geometry of the BoS to adapt its shape to the different postures, which can remove the necessity of force/torque sensors in some application. Potentially the proposed method can be also applied to describe any posture dependent attribute (e.g., gravitational forces), and it can be also applied to bipedal robots. Therefore, it constitutes a novel mathematical tool that can be deployed to develop both better sensors and models for bipeds. For example, it can be used with the Extrapolated CoM model to evaluate dynamic stability from the body kinematics.
AVID: Adversarial Visual Irregularity Detection
Venue: arXiv cs.CV 2018
Year: 2018

Real-time detection of irregularities in visual data is very invaluable and useful in many prospective applications including surveillance, patient monitoring systems, etc. With the surge of deep learning methods in the recent years, researchers have tried a wide spectrum of methods for different applications. However, for the case of irregularity or anomaly detection in videos, training an end-to-end model is still an open challenge, since often irregularity is not well-defined and there are not enough irregular samples to use during training. In this paper, inspired by the success of generative adversarial networks (GANs) for training deep models in unsupervised or self-supervised settings, we propose an end-to-end deep network for detection and fine localization of irregularities in videos (and images). Our proposed architecture is composed of two networks, which are trained in competing with each other while collaborating to find the irregularity. One network works as a pixel-level irregularity Inpainter, and the other works as a patch-level Detector. After an adversarial self-supervised training, in which I tries to fool D into accepting its inpainted output as regular (normal), the two networks collaborate to detect and fine-segment the irregularity in any given testing video. Our results on three different datasets show that our method can outperform the state-of-the-art and fine-segment the irregularity.