OpenRaiser/Intern-Atlas huggingface dataset:
Concealing IMSI in 5G Network Using Identity Based Encryption
Venue: arXiv cs.CR 2017
Year: 2017

Subscription privacy of a user has been a historical concern with all the previous generation mobile networks, namely, GSM, UMTS,and LTE. While a little improvement have been achieved in securing the privacy of the long-term identity of a subscriber, the so called IMSI catchers are still in existence even in the LTE and advanced LTE networks. Proposals have been published to tackle this problem in 5G based on pseudonyms, and different public-key technologies. This paper looks into the problem of concealing long-term identity of a subscriber and presents a technique based on identity based encryption (IBE) to tackle it. The proposed solution can be extended to a mutual authentication and key agreement protocol between a serving network (SN) and a user equipment (UE). This mutual authentication and key agreement protocol does not need to connect with the home network (HN) on every run. A qualitative comparison of the advantages and disadvantages of different techniques show that our solution is competitive for securing the long-term identity privacy of a user in the 5G network.
Probabilistic Generative Adversarial Networks
Venue: arXiv cs.LG 2017
Year: 2017

We introduce the Probabilistic Generative Adversarial Network (PGAN), a new GAN variant based on a new kind of objective function. The central idea is to integrate a probabilistic model (a Gaussian Mixture Model, in our case) into the GAN framework which supports a new kind of loss function (based on likelihood rather than classification loss), and at the same time gives a meaningful measure of the quality of the outputs generated by the network. Experiments with MNIST show that the model learns to generate realistic images, and at the same time computes likelihoods that are correlated with the quality of the generated images. We show that PGAN is better able to cope with instability problems that are usually observed in the GAN training procedure. We investigate this from three aspects: the probability landscape of the discriminator, gradients of the generator, and the perfect discriminator problem.
Grid-Obstacle Representations with Connections to Staircase Guarding
Venue: arXiv cs.CG 2017
Year: 2017

In this paper, we study grid-obstacle representations of graphs where we assign grid-points to vertices and define obstacles such that an edge exists if and only if an $xy$-monotone grid path connects the two endpoints without hitting an obstacle or another vertex. It was previously argued that all planar graphs have a grid-obstacle representation in 2D, and all graphs have a grid-obstacle representation in 3D. In this paper, we show that such constructions are possible with significantly smaller grid-size than previously achieved. Then we study the variant where vertices are not blocking, and show that then grid-obstacle representations exist for bipartite graphs. The latter has applications in so-called staircase guarding of orthogonal polygons; using our grid-obstacle representations, we show that staircase guarding is \textsc{NP}-hard in 2D.
Emotion Controlled Spectrum Mobility Scheme for Efficient Syntactic Interoperability In Cognitive Radio Based Internet of Vehicles
Venue: arXiv cs.NI 2017
Year: 2017

Blind spots are one of the causes of road accidents in the hilly and flat areas. These blind spot accidents can be decreased by establishing an Internet of Vehicles (IoV) using Vehicle-2-Vehicle (V2V) and Vehicle-2-Infrastrtructure (V2I) communication systems. But the problem with these IoV is that most of them are using DSRC or single Radio Access Technology (RAT) as a wireless technology, which has been proven to be failed for efficient communication between vehicles. Recently, Cognitive Radio (CR) based IoV have to be proven best wireless communication systems for vehicular networks. However, the spectrum mobility is a challenging task to keep CR based vehicular networks interoperable and has not been addressed sufficiently in existing research. In our previous research work, the Cognitive Radio Site (CR-Site) has been proposed as in-vehicle CR-device, which can be utilized to establish efficient IoV systems. H In this paper, we have introduced the Emotions Inspired Cognitive Agent (EIC_Agent) based spectrum mobility mechanism in CR-Site and proposed a novel emotions controlled spectrum mobility scheme for efficient syntactic interoperability between vehicles. For this purpose, a probabilistic deterministic finite automaton using fear factor is proposed to perform efficient spectrum mobility using fuzzy logic. In addition, the quantitative computation of different fear intensity levels has been performed with the help of fuzzy logic. The system has been tested using active data from different GSM service providers on Mangla-Mirpur road. This is supplemented by extensive simulation experiments which validate the proposed scheme for CR based high-speed vehicular networks. The qualitative comparison with the existing-state-of the-art has proven the superiority of the proposed emotions controlled syntactic interoperable spectrum mobility scheme within cognitive radio based IoV systems.
Enhanced Emotion Enabled Cognitive Agent Based Rear End Collision Avoidance Controller for Autonomous Vehicles
Venue: arXiv cs.AI 2017
Year: 2017

Rear end collisions are deadliest in nature and cause most of traffic casualties and injuries. In the existing research, many rear end collision avoidance solutions have been proposed. However, the problem with these proposed solutions is that they are highly dependent on precise mathematical models. Whereas, the real road driving is influenced by non-linear factors such as road surface situations, driver reaction time, pedestrian flow and vehicle dynamics, hence obtaining the accurate mathematical model of the vehicle control system is challenging. This problem with precise control based rear end collision avoidance schemes has been addressed using fuzzy logic, but the excessive number of fuzzy rules straightforwardly prejudice their efficiency. Furthermore, these fuzzy logic based controllers have been proposed without using proper agent based modeling that helps in mimicking the functions of an artificial human driver executing these fuzzy rules. Keeping in view these limitations, we have proposed an Enhanced Emotion Enabled Cognitive Agent (EEEC_Agent) based controller that helps the Autonomous Vehicles (AVs) to perform rear end collision avoidance with less number of rules, designed after fear emotion, and high efficiency. To introduce a fear emotion generation mechanism in EEEC_Agent, Orton, Clore & Collins (OCC) model has been employed. The fear generation mechanism of EEEC_Agent has been verified using NetLogo simulation. Furthermore, practical validation of EEEC_Agent functions has been performed using specially built prototype AV platform. Eventually, the qualitative comparative study with existing state of the art research works reflect that proposed model outperforms recent research.
Face Parsing via Recurrent Propagation
Venue: arXiv cs.CV 2017
Year: 2017

Face parsing is an important problem in computer vision that finds numerous applications including recognition and editing. Recently, deep convolutional neural networks (CNNs) have been applied to image parsing and segmentation with the state-of-the-art performance. In this paper, we propose a face parsing algorithm that combines hierarchical representations learned by a CNN, and accurate label propagations achieved by a spatially variant recurrent neural network (RNN). The RNN-based propagation approach enables efficient inference over a global space with the guidance of semantic edges generated by a local convolutional model. Since the convolutional architecture can be shallow and the spatial RNN can have few parameters, the framework is much faster and more light-weighted than the state-of-the-art CNNs for the same task. We apply the proposed model to coarse-grained and fine-grained face parsing. For fine-grained face parsing, we develop a two-stage approach by first identifying the main regions and then segmenting the detail components, which achieves better performance in terms of accuracy and efficiency. With a single GPU, the proposed algorithm parses face images accurately at 300 frames per second, which facilitates real-time applications.
Accurate Light Field Depth Estimation with Superpixel Regularization over Partially Occluded Regions
Venue: arXiv cs.CV 2017
Year: 2017

Depth estimation is a fundamental problem for light field photography applications. Numerous methods have been proposed in recent years, which either focus on crafting cost terms for more robust matching, or on analyzing the geometry of scene structures embedded in the epipolar-plane images. Significant improvements have been made in terms of overall depth estimation error; however, current state-of-the-art methods still show limitations in handling intricate occluding structures and complex scenes with multiple occlusions. To address these challenging issues, we propose a very effective depth estimation framework which focuses on regularizing the initial label confidence map and edge strength weights. Specifically, we first detect partially occluded boundary regions (POBR) via superpixel based regularization. Series of shrinkage/reinforcement operations are then applied on the label confidence map and edge strength weights over the POBR. We show that after weight manipulations, even a low-complexity weighted least squares model can produce much better depth estimation than state-of-the-art methods in terms of average disparity error rate, occlusion boundary precision-recall rate, and the preservation of intricate visual features.
Learning to segment on tiny datasets: a new shape model
Venue: arXiv cs.CV 2017
Year: 2017

Current object segmentation algorithms are based on the hypothesis that one has access to a very large amount of data. In this paper, we aim to segment objects using only tiny datasets. To this extent, we propose a new automatic part-based object segmentation algorithm for non-deformable and semi-deformable objects in natural backgrounds. We have developed a novel shape descriptor which models the local boundaries of an object's part. This shape descriptor is used in a bag-of-words approach for object detection. Once the detection process is performed, we use the background and foreground likelihood given by our trained shape model, and the information from the image content, to define a dense CRF model. We use a mean field approximation to solve it and thus segment the object of interest. Performance evaluated on different datasets shows that our approach can sometimes achieve results near state-of-the-art techniques based on big data while requiring only a tiny training set.
A Convolutional Neural Network for Search Term Detection
Venue: arXiv cs.IR 2017
Year: 2017

Pathfinding in hospitals is challenging for patients, visitors, and even employees. Many people have experienced getting lost due to lack of clear guidance, large footprint of hospitals, and confusing array of hospital wings. In this paper, we propose Halo; An indoor navigation application based on voice-user interaction to help provide directions for users without assistance of a localization system. The main challenge is accurate detection of origin and destination search terms. A custom convolutional neural network (CNN) is proposed to detect origin and destination search terms from transcription of a submitted speech query. The CNN is trained based on a set of queries tailored specifically for hospital and clinic environments. Performance of the proposed model is studied and compared with Levenshtein distance-based word matching.
Parallelizing Over Artificial Neural Network Training Runs with Multigrid
Venue: arXiv cs.NA 2017
Year: 2017

Artificial neural networks are a popular and effective machine learning technique. Great progress has been made parallelizing the expensive training phase of an individual network, leading to highly specialized pieces of hardware, many based on GPU-type architectures, and more concurrent algorithms such as synthetic gradients. However, the training phase continues to be a bottleneck, where the training data must be processed serially over thousands of individual training runs. This work considers a multigrid reduction in time (MGRIT) algorithm that is able to parallelize over the thousands of training runs and converge to the exact same solution as traditional training would provide. MGRIT was originally developed to provide parallelism for time evolution problems that serially step through a finite number of time-steps. This work recasts the training of a neural network similarly, treating neural network training as an evolution equation that evolves the network weights from one step to the next. Thus, this work concerns distributed computing approaches for neural networks, but is distinct from other approaches which seek to parallelize only over individual training runs. The work concludes with supporting numerical results for two model problems.
Automatic segmentation of the intracranialvolume in fetal MR images
Venue: arXiv cs.CV 2017
Year: 2017

MR images of the fetus allow non-invasive analysis of the fetal brain. Quantitative analysis of fetal brain development requires automatic brain tissue segmentation that is typically preceded by segmentation of the intracranial volume (ICV). This is challenging because fetal MR images visualize the whole moving fetus and in addition partially visualize the maternal body. This paper presents an automatic method for segmentation of the ICV in fetal MR images. The method employs a multi-scale convolutional neural network in 2D slices to enable learning spatial information from larger context as well as detailed local information. The method is developed and evaluated with 30 fetal T2-weighted MRI scans (average age $33.2\pm1.2$ weeks postmenstrual age). The set contains $10$ scans acquired in axial, $10$ in coronal and $10$ in sagittal imaging planes. A reference standard was defined in all images by manual annotation of the intracranial volume in $10$ equidistantly distributed slices. The automatic analysis was performed by training and testing the network using scans acquired in the representative imaging plane as well as combining the training data from all imaging planes. On average, the automatic method achieved Dice coefficients of 0.90 for the axial images, 0.90 for the coronal images and 0.92 for the sagittal images. Combining the training sets resulted in average Dice coefficients of 0.91 for the axial images, 0.95 for the coronal images, and 0.92 for the sagittal images. The results demonstrate that the evaluated method achieved good performance in extracting ICV in fetal MR scans regardless of the imaging plane.
Deriving Law-Abiding Instances
Venue: arXiv cs.PL 2017
Year: 2017

Liquid Haskell's refinement-reflection feature augments the Haskell language with theorem proving capabilities, allowing programmers to retrofit their existing code with proofs. But many of these proofs require routine, boilerplate code that is tedious to write. Moreover, many such proofs do not scale well, as the size of proof terms can grow superlinearly with the size of the datatypes involved in the proofs. We present a technique for programming with refinement reflection which solves this problem by leveraging datatype-generic programming. Our observation is that we can take any algebraic datatype, generate an equivalent representation type, and have Liquid Haskell automatically construct (and prove) an isomorphism between the original type and the representation type. This reduces many proofs down to easy theorems over simple algebraic "building block" types, allowing programmers to write generic proofs cheaply and cheerfully.
Towards A Novel Unified Framework for Developing Formal, Network and Validated Agent-Based Simulation Models of Complex Adaptive Systems
Venue: arXiv cs.MA 2017
Year: 2017

Literature on the modeling and simulation of complex adaptive systems (cas) has primarily advanced vertically in different scientific domains with scientists developing a variety of domain-specific approaches and applications. However, while cas researchers are inher-ently interested in an interdisciplinary comparison of models, to the best of our knowledge, there is currently no single unified framework for facilitating the development, comparison, communication and validation of models across different scientific domains. In this thesis, we propose first steps towards such a unified framework using a combination of agent-based and complex network-based modeling approaches and guidelines formulated in the form of a set of four levels of usage, which allow multidisciplinary researchers to adopt a suitable framework level on the basis of available data types, their research study objectives and expected outcomes, thus allowing them to better plan and conduct their respective re-search case studies.
Cherry-Picking of Code Commits in Long-Running, Multi-release Software
Venue: arXiv cs.SE 2017
Year: 2017

This paper presents Tartarian, a tool that supports maintenance of software with long-running, multi-release branches in distributed version control systems. When new maintenance code, such as bug fixes and code improvement, is committed into a branch, it is likely that such code can be applied or reused with some other branches. To do so, a developer may manually identify a commit and cherry pick it. Tartarian can support this activity by providing commit hashtags, which the developer uses as metadata to specify their intentions when committing the code. With these tags, Tartarian uses dependency graph, that represents the dependency constraints of the branches, and Branch Identifier, which matches the commit hashtags with the dependency graph, to identify the applicable branches for the commits. Using Tartarian, developers may be able to maintain software with multiple releases more efficiently.
Learning non-parametric Markov networks with mutual information
Venue: arXiv cs.LG 2017
Year: 2017

We propose a method for learning Markov network structures for continuous data without invoking any assumptions about the distribution of the variables. The method makes use of previous work on a non-parametric estimator for mutual information which is used to create a non-parametric test for multivariate conditional independence. This independence test is then combined with an efficient constraint-based algorithm for learning the graph structure. The performance of the method is evaluated on several synthetic data sets and it is shown to learn considerably more accurate structures than competing methods when the dependencies between the variables involve non-linearities.
Towards a Skill- And Ability-Based Development Process for Self-Aware Automated Road Vehicles
Venue: arXiv cs.SY 2017
Year: 2017

The development of fully automated vehicles imposes new challenges in the development process and during the operation of such vehicles. As traditional design methods are not sufficient to account for the huge variety of scenarios which will be encountered by (fully) automated vehicles, approaches for designing safe systems must be extended in order to allow for an ISO~26262 compliant development process. During operation of vehicles implementing SAE Levels 3+ safe behavior must always be guaranteed, as the human driver is not or not immediately available as a fall-back. Thus, the vehicle must be aware of its current performance and remaining abilities at all times. In this paper we combine insights from two research projects for showing how a skill- and ability-based approach can provide a basis for the development phase and operation of self-aware automated road vehicles.
A Framework for Inferring Causality from Multi-Relational Observational Data using Conditional Independence
Venue: arXiv cs.DB 2017
Year: 2017

The study of causality or causal inference - how much a given treatment causally affects a given outcome in a population - goes way beyond correlation or association analysis of variables, and is critical in making sound data driven decisions and policies in a multitude of applications. The gold standard in causal inference is performing "controlled experiments", which often is not possible due to logistical or ethical reasons. As an alternative, inferring causality on "observational data" based on the "Neyman-Rubin potential outcome model" has been extensively used in statistics, economics, and social sciences over several decades. In this paper, we present a formal framework for sound causal analysis on observational datasets that are given as multiple relations and where the population under study is obtained by joining these base relations. We study a crucial condition for inferring causality from observational data, called the "strong ignorability assumption" (the treatment and outcome variables should be independent in the joined relation given the observed covariates), using known conditional independences that hold in the base relations. We also discuss how the structure of the conditional independences in base relations given as graphical models help infer new conditional independences in the joined relation. The proposed framework combines concepts from databases, statistics, and graphical models, and aims to initiate new research directions spanning these fields to facilitate powerful data-driven decisions in today's big data world.
Semantic Instance Segmentation with a Discriminative Loss Function
Venue: arXiv cs.CV 2017
Year: 2017

Semantic instance segmentation remains a challenging task. In this work we propose to tackle the problem with a discriminative loss function, operating at the pixel level, that encourages a convolutional network to produce a representation of the image that can easily be clustered into instances with a simple post-processing step. The loss function encourages the network to map each pixel to a point in feature space so that pixels belonging to the same instance lie close together while different instances are separated by a wide margin. Our approach of combining an off-the-shelf network with a principled loss function inspired by a metric learning objective is conceptually simple and distinct from recent efforts in instance segmentation. In contrast to previous works, our method does not rely on object proposals or recurrent mechanisms. A key contribution of our work is to demonstrate that such a simple setup without bells and whistles is effective and can perform on par with more complex methods. Moreover, we show that it does not suffer from some of the limitations of the popular detect-and-segment approaches. We achieve competitive performance on the Cityscapes and CVPPP leaf segmentation benchmarks.
Robust Computer Algebra, Theorem Proving, and Oracle AI
Venue: arXiv cs.AI 2017
Year: 2017

In the context of superintelligent AI systems, the term "oracle" has two meanings. One refers to modular systems queried for domain-specific tasks. Another usage, referring to a class of systems which may be useful for addressing the value alignment and AI control problems, is a superintelligent AI system that only answers questions. The aim of this manuscript is to survey contemporary research problems related to oracles which align with long-term research goals of AI safety. We examine existing question answering systems and argue that their high degree of architectural heterogeneity makes them poor candidates for rigorous analysis as oracles. On the other hand, we identify computer algebra systems (CASs) as being primitive examples of domain-specific oracles for mathematics and argue that efforts to integrate computer algebra systems with theorem provers, systems which have largely been developed independent of one another, provide a concrete set of problems related to the notion of provable safety that has emerged in the AI safety community. We review approaches to interfacing CASs with theorem provers, describe well-defined architectural deficiencies that have been identified with CASs, and suggest possible lines of research and practical software projects for scientists interested in AI safety.