OpenRaiser/Intern-Atlas huggingface dataset:
Towards security defect prediction with AI
Venue: arXiv cs.CR 2018
Year: 2018

In this study, we investigate the limits of the current state of the art AI system for detecting buffer overflows and compare it with current static analysis tools. To do so, we developed a code generator, s-bAbI, capable of producing an arbitrarily large number of code samples of controlled complexity. We found that the static analysis engines we examined have good precision, but poor recall on this dataset, except for a sound static analyzer that has good precision and recall. We found that the state of the art AI system, a memory network modeled after Choi et al. [1], can achieve similar performance to the static analysis engines, but requires an exhaustive amount of training data in order to do so. Our work points towards future approaches that may solve these problems; namely, using representations of code that can capture appropriate scope information and using deep learning methods that are able to perform arithmetic operations.
A fixed-point model for semi-persistent scheduling of vehicular safety messages
Venue: arXiv cs.NI 2018
Year: 2018

In this paper, we focus on the performance analysis of a semi-persistent scheduling scheme for vehicular safety communications, motivated by the Mode 4 medium access control protocol in 3GPP Release 14 for Cellular-V2X. An analytical model is built and a fixed point method is used to calculate the collision probability and average delay in both fully connected and partially connected cases under the assumption of perfect PHY performance. We use Monte Carlo simulation to verify the results obtained in the analytical model. The simulation results show that our analytical model can give a good estimation of the collision probability and average delay. We verify that a trade-off between delay and collision probability can be achieved with a flexible resource block selection. Monte Carlo simulation results show that with the flexible resource selection scheme average delay can be shortened significantly with only a small compromise in collision probability.
Semi-Metrification of the Dynamic Time Warping Distance
Venue: arXiv cs.LG 2018
Year: 2018

The dynamic time warping (dtw) distance fails to satisfy the triangle inequality and the identity of indiscernibles. As a consequence, the dtw-distance is not warping-invariant, which in turn results in peculiarities in data mining applications. This article converts the dtw-distance to a semi-metric and shows that its canonical extension is warping-invariant. Empirical results indicate that the nearest-neighbor classifier in the proposed semi-metric space performs comparably to the same classifier in the standard dtw-space. To overcome the undesirable peculiarities of dtw-spaces, this result suggests to further explore the semi-metric space for data mining applications.
Use of Source Code Similarity Metrics in Software Defect Prediction
Venue: arXiv cs.SE 2018
Year: 2018

In recent years, defect prediction has received a great deal of attention in the empirical software engineering world. Predicting software defects before the maintenance phase is very important not only to decrease the maintenance costs but also increase the overall quality of a software product. There are different types of product, process, and developer based software metrics proposed so far to measure the defectiveness of a software system. This paper suggests to use a novel set of software metrics which are based on the similarities detected among the source code files in a software project. To find source code similarities among different files of a software system, plagiarism and clone detection techniques are used. Two simple similarity metrics are calculated for each file, considering its overall similarity to the defective and non defective files in the project. Using these similarity metrics, we predict whether a specific file is defective or not. Our experiments on 10 open source data sets show that depending on the amount of detected similarity, proposed metrics could achieve significantly better performance compared to the existing static code metrics in terms of the area under the curve (AUC).
Zero-Shot Adaptive Transfer for Conversational Language Understanding
Venue: arXiv cs.CL 2018
Year: 2018

Conversational agents such as Alexa and Google Assistant constantly need to increase their language understanding capabilities by adding new domains. A massive amount of labeled data is required for training each new domain. While domain adaptation approaches alleviate the annotation cost, prior approaches suffer from increased training time and suboptimal concept alignments. To tackle this, we introduce a novel Zero-Shot Adaptive Transfer method for slot tagging that utilizes the slot description for transferring reusable concepts across domains, and enjoys efficient training without any explicit concept alignments. Extensive experimentation over a dataset of 10 domains relevant to our commercial personal digital assistant shows that our model outperforms previous state-of-the-art systems by a large margin, and achieves an even higher improvement in the low data regime.
Modeling OWL with Rules: The ROWL Protege Plugin
Venue: arXiv cs.AI 2018
Year: 2018

In our experience, some ontology users find it much easier to convey logical statements using rules rather than OWL (or description logic) axioms. Based on recent theoretical developments on transformations between rules and description logics, we develop ROWL, a Protege plugin that allows users to enter OWL axioms by way of rules; the plugin then automatically converts these rules into OWL DL axioms if possible, and prompts the user in case such a conversion is not possible without weakening the semantics of the rule.
Space-Time Block Coding Based Beamforming for Beam Squint Compensation
Venue: arXiv cs.IT 2018
Year: 2018

In this paper, the beam squint problem, which causes significant variations in radiated beam gain over frequencies in millimeter wave communication system, is investigated. A constant modulus beamformer design, which is formulated to maximize the expected average beam gain within the bandwidth with limited variation over frequencies within the bandwidth, is proposed. A semidefinite relaxation (SDR) method is developed to solve the optimization problem under the constant modulus constraints. Depending on the eigenvalues of the optimal solution, either direct beamforming or transmit diversity based beamforming is employed for data transmissions. Through numerical results, the proposed transmission scheme can compensate for beam squint effectively and improve system throughput. Overall, a transmission scheme for beam squint compensation in wideband wireless communication systems is provided.
Semi-Supervised Training for Improving Data Efficiency in End-to-End Speech Synthesis
Venue: arXiv cs.CL 2018
Year: 2018

Although end-to-end text-to-speech (TTS) models such as Tacotron have shown excellent results, they typically require a sizable set of high-quality <text, audio> pairs for training, which are expensive to collect. In this paper, we propose a semi-supervised training framework to improve the data efficiency of Tacotron. The idea is to allow Tacotron to utilize textual and acoustic knowledge contained in large, publicly-available text and speech corpora. Importantly, these external data are unpaired and potentially noisy. Specifically, first we embed each word in the input text into word vectors and condition the Tacotron encoder on them. We then use an unpaired speech corpus to pre-train the Tacotron decoder in the acoustic domain. Finally, we fine-tune the model using available paired data. We demonstrate that the proposed framework enables Tacotron to generate intelligible speech using less than half an hour of paired training data.
On the Composability of Statistically Secure Random Oblivious Transfer
Venue: arXiv cs.CR 2018
Year: 2018

We show that stand-alone statistically secure random oblivious transfer protocols based on two-party stateless primitives are statistically universally composable. I.e. they are simulatable secure with an unlimited adversary, an unlimited simulator and an unlimited environment machine. Our result implies that several previous oblivious transfer protocols in the literature which were proven secure under weaker, non-composable definitions of security can actually be used in arbitrary statistically secure applications without lowering the security.
Asheetoxy: A Taxonomy for Classifying Negative Spreadsheet-related Phenomena
Venue: arXiv cs.SE 2018
Year: 2018

Spreadsheets (sometimes also called Excel programs) are powerful tools which play a business-critical role in many organizations. However, due to faulty spreadsheets many bad decisions have been taken in recent years. Since then, a number of researchers have been studying spreadsheet errors. However, one issue that hinders discussion among researchers and professionals is the lack of a commonly accepted taxonomy. Albeit a number of taxonomies for spreadsheet errors have been proposed in previous work, a major issue is that they use the term error that itself is already ambiguous. Furthermore, to apply most existing taxonomies, detailed knowledge about the underlying process and knowledge about the "brain state" of the acting spreadsheet users is required. Due to these limitations, known error-like phenomena in freely available spreadsheet corpora cannot be classified with these taxonomies. We propose Asheetoxy, a simple and phenomenon-oriented taxonomy that avoids the problematic term error altogether. An initial study with 7 participants indicates that even non-spreadsheet researchers similarly classify real-world spreadsheet phenomena using Asheetoxy.
Inadequate Risk Analysis Might Jeopardize The Functional Safety of Modern Systems
Venue: arXiv cs.SE 2018
Year: 2018

In the early 90s, researchers began to focus on security as an important property to address in combination with safety. Over the years, researchers have proposed approaches to harmonize activities within the safety and security disciplines. Despite the academic efforts to identify interdependencies and to propose combined approaches for safety and security, there is still a lack of integration between safety and security practices in the industrial context, as they have separate standards and independent processes often addressed and assessed by different organizational teams and authorities. Specifically, security concerns are generally not covered in any detail in safety standards potentially resulting in successfully safety-certified systems that still are open for security threats from e.g., malicious intents from internal and external personnel and hackers that may jeopardize safety. In recent years security has again received an increasing attention of being an important issue also in safety assurance, as the open interconnected nature of emerging systems makes them susceptible to security threats at a much higher degree than existing more confined products.This article presents initial ideas on how to extend safety work to include aspects of security during the context establishment and initial risk assessment procedures. The ambition of our proposal is to improve safety and increase efficiency and effectiveness of the safety work within the frames of the current safety standards, i.e., raised security awareness in compliance with the current safety standards. We believe that our proposal is useful to raise the security awareness in industrial contexts, although it is not a complete harmonization of safety and security disciplines, as it merely provides applicable guidance to increase security awareness in a safety context.
Gaussian Mixture Generative Adversarial Networks for Diverse Datasets, and the Unsupervised Clustering of Images
Venue: arXiv cs.LG 2018
Year: 2018

Generative Adversarial Networks (GANs) have been shown to produce realistically looking synthetic images with remarkable success, yet their performance seems less impressive when the training set is highly diverse. In order to provide a better fit to the target data distribution when the dataset includes many different classes, we propose a variant of the basic GAN model, called Gaussian Mixture GAN (GM-GAN), where the probability distribution over the latent space is a mixture of Gaussians. We also propose a supervised variant which is capable of conditional sample synthesis. In order to evaluate the model's performance, we propose a new scoring method which separately takes into account two (typically conflicting) measures - diversity vs. quality of the generated data. Through a series of empirical experiments, using both synthetic and real-world datasets, we quantitatively show that GM-GANs outperform baselines, both when evaluated using the commonly used Inception Score, and when evaluated using our own alternative scoring method. In addition, we qualitatively demonstrate how the \textit{unsupervised} variant of GM-GAN tends to map latent vectors sampled from different Gaussians in the latent space to samples of different classes in the data space. We show how this phenomenon can be exploited for the task of unsupervised clustering, and provide quantitative evaluation showing the superiority of our method for the unsupervised clustering of image datasets. Finally, we demonstrate a feature which further sets our model apart from other GAN models: the option to control the quality-diversity trade-off by altering, post-training, the probability distribution of the latent space. This allows one to sample higher quality and lower diversity samples, or vice versa, according to one's needs.
$β$-Stars or On Extending a Drawing of a Connected Subgraph
Venue: arXiv cs.CG 2018
Year: 2018

We consider the problem of extending the drawing of a subgraph of a given plane graph to a drawing of the entire graph using straight-line and polyline edges. We define the notion of star complexity of a polygon and show that a drawing $Γ_H$ of an induced connected subgraph $H$ can be extended with at most $\min\{ h/2, β+ \log_2(h) + 1\}$ bends per edge, where $β$ is the largest star complexity of a face of $Γ_H$ and $h$ is the size of the largest face of $H$. This result significantly improves the previously known upper bound of $72|V(H)|$ [5] for the case where $H$ is connected. We also show that our bound is worst case optimal up to a small additive constant. Additionally, we provide an indication of complexity of the problem of testing whether a star-shaped inner face can be extended to a straight-line drawing of the graph; this is in contrast to the fact that the same problem is solvable in linear time for the case of star-shaped outer face [9] and convex inner face [13].
Deep Chronnectome Learning via Full Bidirectional Long Short-Term Memory Networks for MCI Diagnosis
Venue: arXiv cs.CV 2018
Year: 2018

Brain functional connectivity (FC) extracted from resting-state fMRI (RS-fMRI) has become a popular approach for disease diagnosis, where discriminating subjects with mild cognitive impairment (MCI) from normal controls (NC) is still one of the most challenging problems. Dynamic functional connectivity (dFC), consisting of time-varying spatiotemporal dynamics, may characterize "chronnectome" diagnostic information for improving MCI classification. However, most of the current dFC studies are based on detecting discrete major brain status via spatial clustering, which ignores rich spatiotemporal dynamics contained in such chronnectome. We propose Deep Chronnectome Learning for exhaustively mining the comprehensive information, especially the hidden higher-level features, i.e., the dFC time series that may add critical diagnostic power for MCI classification. To this end, we devise a new Fully-connected Bidirectional Long Short-Term Memory Network (Full-BiLSTM) to effectively learn the periodic brain status changes using both past and future information for each brief time segment and then fuse them to form the final output. We have applied our method to a rigorously built large-scale multi-site database (i.e., with 164 data from NCs and 330 from MCIs, which can be further augmented by 25 folds). Our method outperforms other state-of-the-art approaches with an accuracy of 73.6% under solid cross-validations. We also made extensive comparisons among multiple variants of LSTM models. The results suggest high feasibility of our method with promising value also for other brain disorder diagnoses.
Learning End-to-end Autonomous Driving using Guided Auxiliary Supervision
Venue: arXiv cs.LG 2018
Year: 2018

Learning to drive faithfully in highly stochastic urban settings remains an open problem. To that end, we propose a Multi-task Learning from Demonstration (MT-LfD) framework which uses supervised auxiliary task prediction to guide the main task of predicting the driving commands. Our framework involves an end-to-end trainable network for imitating the expert demonstrator's driving commands. The network intermediately predicts visual affordances and action primitives through direct supervision which provide the aforementioned auxiliary supervised guidance. We demonstrate that such joint learning and supervised guidance facilitates hierarchical task decomposition, assisting the agent to learn faster, achieve better driving performance and increases transparency of the otherwise black-box end-to-end network. We run our experiments to validate the MT-LfD framework in CARLA, an open-source urban driving simulator. We introduce multiple non-player agents in CARLA and induce temporal noise in them for realistic stochasticity.
Spectral Collaborative Filtering
Venue: arXiv cs.IR 2018
Year: 2018

Despite the popularity of Collaborative Filtering (CF), CF-based methods are haunted by the \textit{cold-start} problem, which has a significantly negative impact on users' experiences with Recommender Systems (RS). In this paper, to overcome the aforementioned drawback, we first formulate the relationships between users and items as a bipartite graph. Then, we propose a new spectral convolution operation directly performing in the \textit{spectral domain}, where not only the proximity information of a graph but also the connectivity information hidden in the graph are revealed. With the proposed spectral convolution operation, we build a deep recommendation model called Spectral Collaborative Filtering (SpectralCF). Benefiting from the rich information of connectivity existing in the \textit{spectral domain}, SpectralCF is capable of discovering deep connections between users and items and therefore, alleviates the \textit{cold-start} problem for CF. To the best of our knowledge, SpectralCF is the first CF-based method directly learning from the \textit{spectral domains} of user-item bipartite graphs. We apply our method on several standard datasets. It is shown that SpectralCF significantly outperforms state-of-the-art models. Code and data are available at \url{https://github.com/lzheng21/SpectralCF}.
Orthogonal and Smooth Orthogonal Layouts of 1-Planar Graphs with Low Edge Complexity
Venue: arXiv cs.DS 2018
Year: 2018

While orthogonal drawings have a long history, smooth orthogonal drawings have been introduced only recently. So far, only planar drawings or drawings with an arbitrary number of crossings per edge have been studied. Recently, a lot of research effort in graph drawing has been directed towards the study of beyond-planar graphs such as 1-planar graphs, which admit a drawing where each edge is crossed at most once. In this paper, we consider graphs with a fixed embedding. For 1-planar graphs, we present algorithms that yield orthogonal drawings with optimal curve complexity and smooth orthogonal drawings with small curve complexity. For the subclass of outer-1-planar graphs, which can be drawn such that all vertices lie on the outer face, we achieve optimal curve complexity for both, orthogonal and smooth orthogonal drawings.
Fair Algorithms for Learning in Allocation Problems
Venue: arXiv cs.LG 2018
Year: 2018

Settings such as lending and policing can be modeled by a centralized agent allocating a resource (loans or police officers) amongst several groups, in order to maximize some objective (loans given that are repaid or criminals that are apprehended). Often in such problems fairness is also a concern. A natural notion of fairness, based on general principles of equality of opportunity, asks that conditional on an individual being a candidate for the resource, the probability of actually receiving it is approximately independent of the individual's group. In lending this means that equally creditworthy individuals in different racial groups have roughly equal chances of receiving a loan. In policing it means that two individuals committing the same crime in different districts would have roughly equal chances of being arrested. We formalize this fairness notion for allocation problems and investigate its algorithmic consequences. Our main technical results include an efficient learning algorithm that converges to an optimal fair allocation even when the frequency of candidates (creditworthy individuals or criminals) in each group is unknown. The algorithm operates in a censored feedback model in which only the number of candidates who received the resource in a given allocation can be observed, rather than the true number of candidates. This models the fact that we do not learn the creditworthiness of individuals we do not give loans to nor learn about crimes committed if the police presence in a district is low. As an application of our framework, we consider the predictive policing problem. The learning algorithm is trained on arrest data gathered from its own deployments on previous days, resulting in a potential feedback loop that our algorithm provably overcomes. We empirically investigate the performance of our algorithm on the Philadelphia Crime Incidents dataset.
Role of Trust in OAuth 2.0 and OpenID Connect
Venue: arXiv cs.CR 2018
Year: 2018

OAuth 2.0 is a framework for authorization. Being a framework, OAuth 2.0 allows extensions to build on top of it. OpenID Connect is one such extension which adds authentication layer using identity details. OAuth 2.0 define several roles that are required to complete the protocol. Both OAuth 2.0 and OpenID Connect involve interactions between these roles. These interactions require a pre-established trust or a trust establishment while protocol operate. This paper analyzes trust establishments between OAuth 2.0 roles and discuss important aspects of them. Such analysis is required for proper understanding of the protocols.
Do Language Models Understand Anything? On the Ability of LSTMs to Understand Negative Polarity Items
Venue: arXiv cs.CL 2018
Year: 2018

In this paper, we attempt to link the inner workings of a neural language model to linguistic theory, focusing on a complex phenomenon well discussed in formal linguis- tics: (negative) polarity items. We briefly discuss the leading hypotheses about the licensing contexts that allow negative polarity items and evaluate to what extent a neural language model has the ability to correctly process a subset of such constructions. We show that the model finds a relation between the licensing context and the negative polarity item and appears to be aware of the scope of this context, which we extract from a parse tree of the sentence. With this research, we hope to pave the way for other studies linking formal linguistics to deep learning.