OpenRaiser/Intern-Atlas huggingface dataset:
Service Provider DevOps
Venue: arXiv cs.NI 2017
Year: 2017

Although there is consensus that software defined networking and network functions virtualization overhaul service provisioning and deployment, the community still lacks a definite answer on how carrier-grade operations praxis needs to evolve. This article presents what lies beyond the first evolutionary steps in network management, identifies the challenges in service verification, observability, and troubleshooting, and explains how to address them using our Service Provider DevOps (SP-DevOps) framework. We compendiously cover the entire process from design goals to tool realization and employ an elastic version of an industry-standard use case to show how on-the-fly verification, software-defined monitoring, and automated troubleshooting of services reduce the cost of fault management actions. We assess SP-DevOps with respect to key attributes of software-defined telecommunication infrastructures both qualitatively and quantitatively, and demonstrate that SP-DevOps paves the way toward carrier-grade operations and management in the network virtualization era.
Fast Resampling of 3D Point Clouds via Graphs
Venue: arXiv cs.CV 2017
Year: 2017

To reduce cost in storing, processing and visualizing a large-scale point cloud, we consider a randomized resampling strategy to select a representative subset of points while preserving application-dependent features. The proposed strategy is based on graphs, which can represent underlying surfaces and lend themselves well to efficient computation. We use a general feature-extraction operator to represent application-dependent features and propose a general reconstruction error to evaluate the quality of resampling. We obtain a general form of optimal resampling distribution by minimizing the reconstruction error. The proposed optimal resampling distribution is guaranteed to be shift, rotation and scale-invariant in the 3D space. We next specify the feature-extraction operator to be a graph filter and study specific resampling strategies based on all-pass, low-pass, high-pass graph filtering and graph filter banks. We finally apply the proposed methods to three applications: large-scale visualization, accurate registration and robust shape modeling. The empirical performance validates the effectiveness and efficiency of the proposed resampling methods.
Occupancy Counting with Burst and Intermittent Signals in Smart Buildings
Venue: arXiv cs.NI 2017
Year: 2017

Zone-level occupancy counting is a critical technology for smart buildings and can be used for several applications such as building energy management, surveillance, and public safety. Existing occupancy counting techniques typically require installation of large number of occupancy monitoring sensors inside a building as well as an established network. In this study, in order to achieve occupancy counting, we consider the use of WiFi probe requests that are continuously transmitted from WiFi enabled smart devices. To this end, WiFi Pineapple equipment are used for passively capturing ambient probe requests from WiFi devices such as smart phones and tablets, where no connectivity to a WiFi network is required. This information is then used to localize users within coarsely defined occupancy zones, and subsequently to obtain occupancy count within each zone at different time scales. An interacting multi-model Kalman filter technique is developed to improve occupancy counting accuracy. Our numerical results using WiFi data collected at a university building show that utilization of WiFi probe requests can be considered a viable solution tool for zone-level occupancy counting in smart buildings.
Interplay Between Transmission Delay, Average Data Rate, and Performance in Output Feedback Control over Digital Communication Channels
Venue: arXiv cs.SY 2017
Year: 2017

The performance of a noisy linear time-invariant (LTI) plant, controlled over a noiseless digital channel with transmission delay, is investigated in this paper. The rate-limited channel connects the single measurement output of the plant to its single control input through a causal, but otherwise arbitrary, coder-controller pair. An infomation-theoretic approach is utilized to analyze the minimal average data rate required to attain the quadratic performance when the channel imposes a known constant delay on the transmitted data. This infimum average data rate is shown to be lower bounded by minimizing the directed information rate across a set of LTI filters and an additive white Gaussian noise (AWGN) channel. It is demonstrated that the presence of time delay in the channel increases the data rate needed to achieve a certain level of performance. The applicability of the results is verified through a numerical example. In particular, we show by simulations that when the optimal filters are used but the AWGN channel (used in the lower bound) is replaced by a simple scalar uniform quantizer, the resulting operational data rates are at most around 0.3 bits above the lower bounds.
Several Classes of Permutation Trinomials over $\mathbb F_{5^n}$ From Niho Exponents
Venue: arXiv cs.IT 2017
Year: 2017

The construction of permutation trinomials over finite fields attracts people's interest recently due to their simple form and some additional properties. Motivated by some results on the construction of permutation trinomials with Niho exponents, by constructing some new fractional polynomials that permute the set of the $(q+1)$-th roots of unity in $\mathbb F_{q^2}$, we present several classes of permutation trinomials with Niho exponents over $\mathbb F_{q^2}$, where $q=5^k$.
A Unified Optimization View on Generalized Matching Pursuit and Frank-Wolfe
Venue: arXiv cs.LG 2017
Year: 2017

Two of the most fundamental prototypes of greedy optimization are the matching pursuit and Frank-Wolfe algorithms. In this paper, we take a unified view on both classes of methods, leading to the first explicit convergence rates of matching pursuit methods in an optimization sense, for general sets of atoms. We derive sublinear ($1/t$) convergence for both classes on general smooth objectives, and linear convergence on strongly convex objectives, as well as a clear correspondence of algorithm variants. Our presented algorithms and rates are affine invariant, and do not need any incoherence or sparsity assumptions.
Systèmes du LIA à DEFT'13
Venue: arXiv cs.CL 2017
Year: 2017

The 2013 Défi de Fouille de Textes (DEFT) campaign is interested in two types of language analysis tasks, the document classification and the information extraction in the specialized domain of cuisine recipes. We present the systems that the LIA has used in DEFT 2013. Our systems show interesting results, even though the complexity of the proposed tasks.
Supporting the use of user generated content in journalistic practice
Venue: arXiv cs.HC 2017
Year: 2017

Social media and user-generated content (UGC) are increasingly important features of journalistic work in a number of different ways. However, their use presents major challenges, not least because information posted on social media is not always reliable and therefore its veracity needs to be checked before it can be considered as fit for use in the reporting of news. We report on the results of a series of in-depth ethnographic studies of journalist work practices undertaken as part of the requirements gathering for a prototype of a social media verification 'dashboard' and its subsequent evaluation. We conclude with some reflections upon the broader implications of our findings for the design of tools to support journalistic work.
Context-Aware Prediction of Derivational Word-forms
Venue: arXiv cs.CL 2017
Year: 2017

Derivational morphology is a fundamental and complex characteristic of language. In this paper we propose the new task of predicting the derivational form of a given base-form lemma that is appropriate for a given context. We present an encoder--decoder style neural network to produce a derived form character-by-character, based on its corresponding character-level representation of the base form and the context. We demonstrate that our model is able to generate valid context-sensitive derivations from known base forms, but is less accurate under a lexicon agnostic setting.
Faster Mutation Analysis via Equivalence Modulo States
Venue: arXiv cs.SE 2017
Year: 2017

Mutation analysis has many applications, such as asserting the quality of test suites and localizing faults. One important bottleneck of mutation analysis is scalability. The latest work explores the possibility of reducing the redundant execution via split-stream execution. However, split-stream execution is only able to remove redundant execution before the first mutated statement. In this paper we try to also reduce some of the redundant execution after the execution of the first mutated statement. We observe that, although many mutated statements are not equivalent, the execution result of those mutated statements may still be equivalent to the result of the original statement. In other words, the statements are equivalent modulo the current state. In this paper we propose a fast mutation analysis approach, AccMut. AccMut automatically detects the equivalence modulo states among a statement and its mutations, then groups the statements into equivalence classes modulo states, and uses only one process to represent each class. In this way, we can significantly reduce the number of split processes. Our experiments show that our approach can further accelerate mutation analysis on top of split-stream execution with a speedup of 2.56x on average.
Risk-based Triggering of Bio-inspired Self-Preservation to Protect Robots from Threats
Venue: arXiv cs.RO 2017
Year: 2017

Safety in autonomous systems has been mostly studied from a human-centered perspective. Besides the loads they may carry, autonomous systems are also valuable property, and self-preservation mechanisms are needed to protect them in the presence of external threats, including malicious robots and antagonistic humans. We present a biologically inspired risk-based triggering mechanism to initiate self-preservation strategies. This mechanism considers environmental and internal system factors to measure the overall risk at any moment in time, to decide whether behaviours such as fleeing or hiding are necessary, or whether the system should continue on its task. We integrated our risk-based triggering mechanism into a delivery rover that is being attacked by a drone and evaluated its effectiveness through systematic testing in a simulated environment in Robot Operating System (ROS) and Gazebo, with a variety of different randomly generated conditions. We compared the use of the triggering mechanism and different configurations of self-preservation behaviours to not having any of these. Our results show that triggering self-preservation increases the distance between the drone and the rover for many of these configurations, and, in some instances, the drone does not catch up with the rover. Our study demonstrates the benefits of embedding risk awareness and self-preservation into autonomous systems to increase their robustness, and the value of using bio-inspired engineering to find solutions in this area.
Style Transfer Generative Adversarial Networks: Learning to Play Chess Differently
Venue: arXiv cs.LG 2017
Year: 2017

The idea of style transfer has largely only been explored in image-based tasks, which we attribute in part to the specific nature of loss functions used for style transfer. We propose a general formulation of style transfer as an extension of generative adversarial networks, by using a discriminator to regularize a generator with an otherwise separate loss function. We apply our approach to the task of learning to play chess in the style of a specific player, and present empirical evidence for the viability of our approach.
Tackling Error Propagation through Reinforcement Learning: A Case of Greedy Dependency Parsing
Venue: arXiv cs.CL 2017
Year: 2017

Error propagation is a common problem in NLP. Reinforcement learning explores erroneous states during training and can therefore be more robust when mistakes are made early in a process. In this paper, we apply reinforcement learning to greedy dependency parsing which is known to suffer from error propagation. Reinforcement learning improves accuracy of both labeled and unlabeled dependencies of the Stanford Neural Dependency Parser, a high performance greedy parser, while maintaining its efficiency. We investigate the portion of errors which are the result of error propagation and confirm that reinforcement learning reduces the occurrence of error propagation.
Fast Algorithms for Designing Multiple Unimodular Waveforms With Good Correlation Properties
Venue: arXiv cs.IT 2017
Year: 2017

In this paper, we develop new fast and efficient algorithms for designing single/multiple unimodular waveforms/codes with good auto- and cross-correlation or weighted correlation properties, which are highly desired in radar and communication systems. The waveform design is based on the minimization of the integrated sidelobe level (ISL) and weighted ISL (WISL) of waveforms. As the corresponding optimization problems can quickly grow to large scale with increasing the code length and number of waveforms, the main issue turns to be the development of fast large-scale optimization techniques. The difficulty is also that the corresponding optimization problems are non-convex, but the required accuracy is high. Therefore, we formulate the ISL and WISL minimization problems as non-convex quartic optimization problems in frequency domain, and then simplify them into quadratic problems by utilizing the majorization-minimization technique, which is one of the basic techniques for addressing large-scale and/or non-convex optimization problems. While designing our fast algorithms, we find out and use inherent algebraic structures in the objective functions to rewrite them into quartic forms, and in the case of WISL minimization, to derive additionally an alternative quartic form which allows to apply the quartic-quadratic transformation. Our algorithms are applicable to large-scale unimodular waveform design problems as they are proved to have lower or comparable computational burden (analyzed theoretically) and faster convergence speed (confirmed by comprehensive simulations) than the state-of-the-art algorithms. In addition, the waveforms designed by our algorithms demonstrate better correlation properties compared to their counterparts.
Triaging Content Severity in Online Mental Health Forums
Venue: arXiv cs.CL 2017
Year: 2017

Mental health forums are online communities where people express their issues and seek help from moderators and other users. In such forums, there are often posts with severe content indicating that the user is in acute distress and there is a risk of attempted self-harm. Moderators need to respond to these severe posts in a timely manner to prevent potential self-harm. However, the large volume of daily posted content makes it difficult for the moderators to locate and respond to these critical posts. We present a framework for triaging user content into four severity categories which are defined based on indications of self-harm ideation. Our models are based on a feature-rich classification framework which includes lexical, psycholinguistic, contextual and topic modeling features. Our approaches improve the state of the art in triaging the content severity in mental health forums by large margins (up to 17% improvement over the F-1 scores). Using the proposed model, we analyze the mental state of users and we show that overall, long-term users of the forum demonstrate a decreased severity of risk over time. Our analysis on the interaction of the moderators with the users further indicates that without an automatic way to identify critical content, it is indeed challenging for the moderators to provide timely response to the users in need.
Diffusive Mobile Molecular Communications Over Time-Variant Channels
Venue: arXiv cs.IT 2017
Year: 2017

This letter introduces a formalism for modeling time-variant channels for diffusive molecular communication systems. In particular, we consider a fluid environment where one transmitter nano-machine and one receiver nano-machine are subjected to Brownian motion in addition to the diffusive motion of the information molecules used for communication. Due to the stochastic movements of the transmitter and receiver nano-machines, the statistics of the channel impulse response change over time. We show that the time-variant behaviour of the channel can be accurately captured by appropriately modifying the diffusion coefficient of the information molecules. Furthermore, we derive an analytical expression for evaluation of the expected error probability of a simple detector for the considered system. The accuracy of the proposed analytical expression is verified via particle-based simulation of the Brownian motion.
DRYVR:Data-driven verification and compositional reasoning for automotive systems
Venue: arXiv cs.SY 2017
Year: 2017

We present the DRYVR framework for verifying hybrid control systems that are described by a combination of a black-box simulator for trajectories and a white-box transition graph specifying mode switches. The framework includes (a) a probabilistic algorithm for learning sensitivity of the continuous trajectories from simulation data, (b) a bounded reachability analysis algorithm that uses the learned sensitivity, and (c) reasoning techniques based on simulation relations and sequential composition, that enable verification of complex systems under long switching sequences, from the reachability analysis of a simpler system under shorter sequences. We demonstrate the utility of the framework by verifying a suite of automotive benchmarks that include powertrain control, automatic transmission, and several autonomous and ADAS features like automatic emergency braking, lane-merge, and auto-passing controllers.
Solving DCOPs with Distributed Large Neighborhood Search
Venue: arXiv cs.AI 2017
Year: 2017

The field of Distributed Constraint Optimization has gained momentum in recent years, thanks to its ability to address various applications related to multi-agent cooperation. Nevertheless, solving Distributed Constraint Optimization Problems (DCOPs) optimally is NP-hard. Therefore, in large-scale, complex applications, incomplete DCOP algorithms are necessary. Current incomplete DCOP algorithms suffer of one or more of the following limitations: they (a) find local minima without providing quality guarantees; (b) provide loose quality assessment; or (c) are unable to benefit from the structure of the problem, such as domain-dependent knowledge and hard constraints. Therefore, capitalizing on strategies from the centralized constraint solving community, we propose a Distributed Large Neighborhood Search (D-LNS) framework to solve DCOPs. The proposed framework (with its novel repair phase) provides guarantees on solution quality, refining upper and lower bounds during the iterative process, and can exploit domain-dependent structures. Our experimental results show that D-LNS outperforms other incomplete DCOP algorithms on both structured and unstructured problem instances.
Regularizing Face Verification Nets For Pain Intensity Regression
Venue: arXiv cs.CV 2017
Year: 2017

Limited labeled data are available for the research of estimating facial expression intensities. For instance, the ability to train deep networks for automated pain assessment is limited by small datasets with labels of patient-reported pain intensities. Fortunately, fine-tuning from a data-extensive pre-trained domain, such as face verification, can alleviate this problem. In this paper, we propose a network that fine-tunes a state-of-the-art face verification network using a regularized regression loss and additional data with expression labels. In this way, the expression intensity regression task can benefit from the rich feature representations trained on a huge amount of data for face verification. The proposed regularized deep regressor is applied to estimate the pain expression intensity and verified on the widely-used UNBC-McMaster Shoulder-Pain dataset, achieving the state-of-the-art performance. A weighted evaluation metric is also proposed to address the imbalance issue of different pain intensities.
Realization of Ontology Web Search Engine
Venue: arXiv cs.AI 2017
Year: 2017

This paper describes the realization of the Ontology Web Search Engine. The Ontology Web Search Engine is realizable as independent project and as a part of other projects. The main purpose of this paper is to present the Ontology Web Search Engine realization details as the part of the Semantic Web Expert System and to present the results of the Ontology Web Search Engine functioning. It is expected that the Semantic Web Expert System will be able to process ontologies from the Web, generate rules from these ontologies and develop its knowledge base.
How Software Developers Mitigate Collaboration Friction with Chatbots
Venue: arXiv cs.HC 2017
Year: 2017

Modern software developers rely on an extensive set of social media tools and communication channels. The adoption of team communication platforms has led to the emergence of conversation-based tools and integrations, many of which are chatbots. Understanding how software developers manage their complex constellation of collaborators in conjunction with the practices and tools they use can bring valuable insights into socio-technical collaborative work in software development and other knowledge work domains. In this paper, we explore how chatbots can help reduce the friction points software developers face when working collaboratively. Using a socio-technical model for collaborative work, we identify three main areas for conflict: friction stemming from team interactions with each other, an individual's interactions with technology, and team interactions with technology. Finally, we provide a set of open questions for discussion within the research community.
CT Image Denoising with Perceptive Deep Neural Networks
Venue: arXiv cs.CV 2017
Year: 2017

Increasing use of CT in modern medical practice has raised concerns over associated radiation dose. Reduction of radiation dose associated with CT can increase noise and artifacts, which can adversely affect diagnostic confidence. Denoising of low-dose CT images on the other hand can help improve diagnostic confidence, which however is a challenging problem due to its ill-posed nature, since one noisy image patch may correspond to many different output patches. In the past decade, machine learning based approaches have made quite impressive progress in this direction. However, most of those methods, including the recently popularized deep learning techniques, aim for minimizing mean-squared-error (MSE) between a denoised CT image and the ground truth, which results in losing important structural details due to over-smoothing, although the PSNR based performance measure looks great. In this work, we introduce a new perceptual similarity measure as the objective function for a deep convolutional neural network to facilitate CT image denoising. Instead of directly computing MSE for pixel-to-pixel intensity loss, we compare the perceptual features of a denoised output against those of the ground truth in a feature space. Therefore, our proposed method is capable of not only reducing the image noise levels, but also keeping the critical structural information at the same time. Promising results have been obtained in our experiments with a large number of CT images.