OpenRaiser/Intern-Atlas huggingface dataset:
REST: Real-to-Synthetic Transform for Illumination Invariant Camera Localization
Venue: arXiv cs.CV 2018
Year: 2018

Accurate camera localization is an essential part of tracking systems. However, localization results are greatly affected by illumination. Including data collected under various lighting conditions can improve the robustness of the localization algorithm to lighting variation. However, this is very tedious and time consuming. By using synthesized images it is possible to easily accumulate a large variety of views under varying illumination and weather conditions. Despite continuously improving processing power and rendering algorithms, synthesized images do not perfectly match real images of the same scene, i.e. there exists a gap between real and synthesized images that also affects the accuracy of camera localization. To reduce the impact of this gap, we introduce "REal-to-Synthetic Transform (REST)." REST is an autoencoder-like network that converts real features to their synthetic counterpart. The converted features can then be matched against the accumulated database for robust camera localization. In our experiments REST improved feature matching accuracy under variable lighting conditions by approximately 30%. Moreover, our system outperforms state of the art CNN-based camera localization methods trained with synthetic images. We believe our method could be used to initialize local tracking and to simplify data accumulation for lighting robust localization.
A clustering approach to infer Wikipedia contributors' profile
Venue: arXiv cs.HC 2018
Year: 2018

In online communities, recent studies have strongly improved our knowledge about the different types or profiles of contributors, from casual to very involved ones, through focused people. However they do so by using very complex methodologies (qualitative-quantitative mix, with a high workload to manually codify/characterize the edits), making their replication for the practitioners limited. These studies are on the English Wikipedia only. The objective of this paper is to highlight different profiles of contributors with clustering techniques. The originality is to show how using only the edits, and their distribution over time, allows to build these contributors profiles with a good accuracy and stability amongst languages. The methodology is validated with both Romanian and Danish wikis. The highlighted profiles are identifiable early in the history of involvement, suggesting that light monitoring of newcomers may be sufficient to adapt the interaction with them and increase the retention rate.
Clipping free attacks against artificial neural networks
Venue: arXiv cs.LG 2018
Year: 2018

During the last years, a remarkable breakthrough has been made in AI domain thanks to artificial deep neural networks that achieved a great success in many machine learning tasks in computer vision, natural language processing, speech recognition, malware detection and so on. However, they are highly vulnerable to easily crafted adversarial examples. Many investigations have pointed out this fact and different approaches have been proposed to generate attacks while adding a limited perturbation to the original data. The most robust known method so far is the so called C&W attack [1]. Nonetheless, a countermeasure known as feature squeezing coupled with ensemble defense showed that most of these attacks can be destroyed [6]. In this paper, we present a new method we call Centered Initial Attack (CIA) whose advantage is twofold : first, it insures by construction the maximum perturbation to be smaller than a threshold fixed beforehand, without the clipping process that degrades the quality of attacks. Second, it is robust against recently introduced defenses such as feature squeezing, JPEG encoding and even against a voting ensemble of defenses. While its application is not limited to images, we illustrate this using five of the current best classifiers on ImageNet dataset among which two are adversarialy retrained on purpose to be robust against attacks. With a fixed maximum perturbation of only 1.5% on any pixel, around 80% of attacks (targeted) fool the voting ensemble defense and nearly 100% when the perturbation is only 6%. While this shows how it is difficult to defend against CIA attacks, the last section of the paper gives some guidelines to limit their impact.
SEAT: A Taxonomy to Characterize Automation in Software Engineering
Venue: arXiv cs.SE 2018
Year: 2018

Reducing cost and time required to build high quality software is a major goal for software developers. Building tools and techniques that can help achieve such a goal is the chief aim for Automated Software Engineering (ASE) researchers. However, in order to be effective an ASE researcher or professional must understand the characteristics of both successful and not-so-successful ASE tools, and the constituent techniques employed by such ASE tools. In this paper we present such a characterization of ASE tools and major constituent techniques from different areas of computer science and engineering that have been employed by such ASE tools. To develop the characterization we carried out an extensive systematic literature review over about 1175 ASE research articles. One of our key goal was to identify useful relationships among ASE tools, their constituent techniques and the software development life cycle activities that these tools targeted. In terms of changes in popularity of constituent techniques with time we did not observe any clear trend. We organized the results of our characterization as a taxonomy called SEAT (Software Engineering Automation Taxonomy). A salient feature of SEAT is that it focuses on automation of activities from all phases of SDLC. Such a taxonomy, among other applications, shall enable synthesizing new automation tools for different SDLC activities. Recomposing existing systems to achieve better features will also be possible. Further, the taxonomy has been realized as a graph database using neo4j(an open source graph database), which can be queried using an SQL like language. The graph database allowed us to uncover hidden relationships by way of exhaustive search for connections and paths between different nodes (i.e. concepts). We demonstrate the efficacy of SEAT by discussing few practical use cases.
Forecasting Cyber Attacks with Imbalanced Data Sets and Different Time Granularities
Venue: arXiv cs.CR 2018
Year: 2018

If cyber incidents are predicted a reasonable amount of time before they occur, defensive actions to prevent their destructive effects could be planned. Unfortunately, most of the time we do not have enough observables of the malicious activities before they are already under way. Therefore, this work suggests to use unconventional signals extracted from various data sources with different time granularities to predict cyber incidents for target entities. A Bayesian network is used to predict cyber attacks where the unconventional signals are used as indicative random variables. This work also develops a novel minority class over sampling technique to improve cyber attack prediction on imbalanced data sets. The results show that depending on the selected time granularity, the unconventional signals are able to predict cyber attacks for the anonimyzed target organization even though the signals are not explicitly related to that organization. Furthermore, the minority over sampling approach developed achieves better performance compared to the existing filtering techniques in the literature.
Source Code Optimization using Equivalent Mutants
Venue: arXiv cs.SE 2018
Year: 2018

A mutant is a program obtained by syntactically modifying a program's source code; an equivalent mutant is a mutant, which is functionally equivalent to the original program. Mutants are primarily used in \emph{mutation testing}, and when deriving a test suite, obtaining an equivalent mutant is considered to be highly negative, although these equivalent mutants could be used for other purposes. We present an approach that considers equivalent mutants valuable, and utilizes them for source code optimization. Source code optimization enhances a program's source code preserving its behavior. We showcase a procedure to achieve source code optimization based on equivalent mutants and discuss proper mutation operators. Experimental evaluation with Java and C programs demonstrates the applicability of the proposed approach. An algorithmic approach for source code optimization using equivalent mutants is proposed. It is showcased that whenever applicable, the approach can outperform traditional compiler optimizations.
Dushnik-Miller dimension of TD-Delaunay complexes
Venue: arXiv cs.DM 2018
Year: 2018

TD-Delaunay graphs, where TD stands for triangular distance, is a variation of the classical Delaunay triangulations obtained from a specific convex distance function. Bonichon et. al. noticed that every triangulation is the TD-Delaunay graph of a set of points in $\mathbb{R}^2$, and conversely every TD-Delaunay graph is planar. It seems natural to study the generalization of this property in higher dimensions. Such a generalization is obtained by defining an analogue of the triangular distance for $\mathbb{R}^d$. It is easy to see that TD-Delaunay complexes of $\mathbb{R}^{d-1}$ are of Dushnik-Miller dimension $d$. The converse holds for $d=2$ or $3$ and it was conjectured independently by Mary and Evans et. al. to hold for larger $d$. Here we disprove the conjecture already for $d = 4$.
Comparison of angular spread for 6 and 60 GHz based on 3GPP standard
Venue: arXiv cs.IT 2018
Year: 2018

In an urban environment, a multipath propagation is one of the basic phenomena affecting a quality of received signals. This causes dispersions in time and angular domains. Basic parameters describing these dispersions are the rms delay spread and rms angle spread, respectively. The delay spread is related to a frequency of the transmitted signal and the nature of the propagation environment. In this paper, we show a mutual relationship between the time and angular dispersions in the received signal. The obtained simulation results present a comparison of the described dispersions for two different frequencies. In this case, the multi-elliptical propagation model and standard model developed by 3GPP are the basis for the simulation analysis of new communication system solutions.
Homa: A Receiver-Driven Low-Latency Transport Protocol Using Network Priorities (Complete Version)
Venue: arXiv cs.NI 2018
Year: 2018

Homa is a new transport protocol for datacenter networks. It provides exceptionally low latency, especially for workloads with a high volume of very short messages, and it also supports large messages and high network utilization. Homa uses in-network priority queues to ensure low latency for short messages; priority allocation is managed dynamically by each receiver and integrated with a receiver-driven flow control mechanism. Homa also uses controlled overcommitment of receiver downlinks to ensure efficient bandwidth utilization at high load. Our implementation of Homa delivers 99th percentile round-trip times less than 15μs for short messages on a 10 Gbps network running at 80% load. These latencies are almost 100x lower than the best published measurements of an implementation. In simulations, Homa's latency is roughly equal to pFabric and significantly better than pHost, PIAS, and NDP for almost all message sizes and workloads. Homa can also sustain higher network loads than pFabric, pHost, or PIAS.
Correlation properties of signal at mobile receiver for different propagation environments
Venue: arXiv cs.IT 2018
Year: 2018

An issue of the parameter selection in various branches of a multi-antenna receiver system determines its effectiveness. A significant effect on these parameters are correlation properties of received signals. In this paper, the assessment of the signal correlation properties for different environmental conditions is presented. The obtained results showed that depending on the receiver speed, the adaptive selection of the delays in the different RAKE receiver branches provide minimization of the correlation between the signals. Particularly low levels of the signal correlation could be obtained in complex propagation environments such as urban and bad urban.
BER measurements in the evaluation of operation correctness of VSAT modem traffic interfaces
Venue: arXiv cs.IT 2018
Year: 2018

This paper presents using bit error rate (BER) measurements to evaluate operation correctness of traffic (input-output) interfaces in modem of very small aperture terminal (VSAT). Such functional tests are carried out, for example, when purchasing communication equipment for armed forces. Generally, available standards do not describe measurement procedures in this area. In this case, accredited laboratories should develop dedicated assessment methodologies. In this paper, we show the methodology for the VSAT modems, which is based on the BER measurements.
Metric Learning with Dynamically Generated Pairwise Constraints for Ear Recognition
Venue: arXiv cs.CV 2018
Year: 2018

Ear recognition task is known as predicting whether two ear images belong to the same person or not. In this paper, we present a novel metric learning method for ear recognition. This method is formulated as a pairwise constrained optimization problem. In each training cycle, this method selects the nearest similar and dissimilar neighbors of each sample to construct the pairwise constraints, and then solve the optimization problem by the iterated Bregman projections. Experiments are conducted on AMI, USTB II and WPUT databases. The results show that the proposed approach can achieve promising recognition rates in ear recognition, and its training process is much more efficient than the other competing metric learning methods.
Unsupervised Separation of Transliterable and Native Words for Malayalam
Venue: arXiv cs.CL 2018
Year: 2018

Differentiating intrinsic language words from transliterable words is a key step aiding text processing tasks involving different natural languages. We consider the problem of unsupervised separation of transliterable words from native words for text in Malayalam language. Outlining a key observation on the diversity of characters beyond the word stem, we develop an optimization method to score words based on their nativeness. Our method relies on the usage of probability distributions over character n-grams that are refined in step with the nativeness scorings in an iterative optimization formulation. Using an empirical evaluation, we illustrate that our method, DTIM, provides significant improvements in nativeness scoring for Malayalam, establishing DTIM as the preferred method for the task.
A Quality Model for Actionable Analytics in Rapid Software Development
Venue: arXiv cs.SE 2018
Year: 2018

Background: Accessing relevant data on the product, process, and usage perspectives of software as well as integrating and analyzing such data is crucial for getting reliable and timely actionable insights aimed at continuously managing software quality in Rapid Software Development (RSD). In this context, several software analytics tools have been developed in recent years. However, there is a lack of explainable software analytics that software practitioners trust. Aims: We aimed at creating a quality model (called Q-Rapids quality model) for actionable analytics in RSD, implementing it, and evaluating its understandability and relevance. Method: We performed workshops at four companies in order to determine relevant metrics as well as product and process factors. We also elicited how these metrics and factors are used and interpreted by practitioners when making decisions in RSD. We specified the Q-Rapids quality model by comparing and integrating the results of the four workshops. Then we implemented the Q-Rapids tool to support the usage of the Q-Rapids quality model as well as the gathering, integration, and analysis of the required data. Afterwards we installed the Q-Rapids tool in the four companies and performed semi-structured interviews with eight product owners to evaluate the understandability and relevance of the Q-Rapids quality model. Results: The participants of the evaluation perceived the metrics as well as the product and process factors of the Q-Rapids quality model as understandable. Also, they considered the Q-Rapids quality model relevant for identifying product and process deficiencies (e.g., blocking code situations). Conclusions: By means of heterogeneous data sources, the Q-Rapids quality model enables detecting problems that take more time to find manually and adds transparency among the perspectives of system, process, and usage.
On shrinking horizon move-blocking predictive control
Venue: arXiv cs.SY 2018
Year: 2018

This manuscript contains technical details of recent results developed by the authors on shrinking horizon predictive control with a move-blocking strategy.
Comparing Published Scientific Journal Articles to Their Pre-Print Versions -- Extended Version
Venue: arXiv cs.DL 2018
Year: 2018

Academic publishers claim that they add value to scholarly communications by coordinating reviews and contributing and enhancing text during publication. These contributions come at a considerable cost: U.S. academic libraries paid $1.7 billion for serial subscriptions in 2008 alone. Library budgets, in contrast, are flat and not able to keep pace with serial price inflation. We have investigated the publishers' value proposition by conducting a comparative study of pre-print papers from two distinct science, technology, and medicine (STM) corpora and their final published counterparts. This comparison had two working assumptions: 1) if the publishers' argument is valid, the text of a pre-print paper should vary measurably from its corresponding final published version, and 2) by applying standard similarity measures, we should be able to detect and quantify such differences. Our analysis revealed that the text contents of the scientific papers generally changed very little from their pre-print to final published versions. These findings contribute empirical indicators to discussions of the added value of commercial publishers and therefore should influence libraries' economic decisions regarding access to scholarly publications.
Bounded Context Switching for Valence Systems
Venue: arXiv cs.LO 2018
Year: 2018

We study valence systems, finite-control programs over infinite-state memories modeled in terms of graph monoids. Our contribution is a notion of bounded context switching (BCS). Valence systems generalize pushdowns, concurrent pushdowns, and Petri nets. In these settings, our definition conservatively generalizes existing notions. The main finding is that reachability within a bounded number of context switches is in NP, independent of the memory (the graph monoid). Our proof is genuinely algebraic, and therefore contributes a new way to think about BCS. In addition, we exhibit a class of storage mechanisms for which BCS reachability belongs to P.
Parameterized Intractability of Even Set and Shortest Vector Problem from Gap-ETH
Venue: arXiv cs.CC 2018
Year: 2018

The $k$-Even Set problem is a parameterized variant of the Minimum Distance Problem of linear codes over $\mathbb F_2$, which can be stated as follows: given a generator matrix $\mathbf A$ and an integer $k$, determine whether the code generated by $\mathbf A$ has distance at most $k$. Here, $k$ is the parameter of the problem. The question of whether $k$-Even Set is fixed parameter tractable (FPT) has been repeatedly raised in literature and has earned its place in Downey and Fellows' book (2013) as one of the "most infamous" open problems in the field of Parameterized Complexity. In this work, we show that $k$-Even Set does not admit FPT algorithms under the (randomized) Gap Exponential Time Hypothesis (Gap-ETH) [Dinur'16, Manurangsi-Raghavendra'16]. In fact, our result rules out not only exact FPT algorithms, but also any constant factor FPT approximation algorithms for the problem. Furthermore, our result holds even under the following weaker assumption, which is also known as the Parameterized Inapproximability Hypothesis (PIH) [Lokshtanov et al.'17]: no (randomized) FPT algorithm can distinguish a satisfiable 2CSP instance from one which is only $0.99$-satisfiable (where the parameter is the number of variables). We also consider the parameterized $k$-Shortest Vector Problem (SVP), in which we are given a lattice whose basis vectors are integral and an integer $k$, and the goal is to determine whether the norm of the shortest vector (in the $\ell_p$ norm for some fixed $p$) is at most $k$. Similar to $k$-Even Set, this problem is also a long-standing open problem in the field of Parameterized Complexity. We show that, for any $p > 1$, $k$-SVP is hard to approximate (in FPT time) to some constant factor, assuming PIH. Furthermore, for the case of $p = 2$, the inapproximability factor can be amplified to any constant.
Demystifying Core Ranking in Pinterest Image Search
Venue: arXiv cs.IR 2018
Year: 2018

Pinterest Image Search Engine helps hundreds of millions of users discover interesting content everyday. This motivates us to improve the image search quality by evolving our ranking techniques. In this work, we share how we practically design and deploy various ranking pipelines into Pinterest image search ecosystem. Specifically, we focus on introducing our novel research and study on three aspects: training data, user/image featurization and ranking models. Extensive offline and online studies compared the performance of different models and demonstrated the efficiency and effectiveness of our final launched ranking models.
Automation of Processor Verification Using Recurrent Neural Networks
Venue: arXiv cs.OH 2018
Year: 2018

When considering simulation-based verification of processors, the current trend is to generate stimuli using pseudorandom generators (PRGs), apply them to the processor inputs and monitor the achieved coverage of its functionality in order to determine verification completeness. Stimuli can have different forms, for example, they can be represented by bit vectors applied to the input ports of the processor or by programs that are loaded directly into the program memory. In this paper, we propose a new technique dynamically altering constraints for PRG via recurrent neural network, which receives a coverage feedback from the simulation of design under verification. For the demonstration purposes we used processors provided by Codasip as their coverage state space is reasonably big and differs for various kinds of processors. Nevertheless, techniques presented in this paper are widely applicable. The results of experiments show that not only the coverage closure is achieved much sooner, but we are able to isolate a small set of stimuli with high coverage that can be used for running regression tests.
The Impact of the Object-Oriented Software Evolution on Software Metrics: The Iris Approach
Venue: arXiv cs.SE 2018
Year: 2018

The Object-Oriented (OO) software system evolves over the time to meet the new requirements. Based on the initial release of software, the continuous modification of software code leads to software evolution. Software needs to evolve over the time to meet the new user's requirements. Software companies often develop variant software of the original one depends on customers' needs. The main hypothesis of this paper states that the software when it evolves over the time, its code continues to grow, change and become more complex. This paper proposes an automatic approach (Iris) to examine the proposed hypothesis. Originality of this approach is the exploiting of the software variants to study the impact of software evolution on the software metrics. This paper presents the results of experiments conducted on three releases of drawing shapes software, sixteen releases of rhino software, eight releases of mobile media software and ten releases of ArgoUML software. Based on the extracted software metrics, It has been found that Iris hypothesis is supported by the computed metrics.