OpenRaiser/Intern-Atlas huggingface dataset:
Automatic Exposure Compensation for Multi-Exposure Image Fusion
Venue: arXiv cs.CV 2018
Year: 2018

This paper proposes a novel luminance adjustment method based on automatic exposure compensation for multi-exposure image fusion. Multi-exposure image fusion is a method to produce images without saturation regions, by using photos with different exposures. In conventional works, it has been pointed out that the quality of those multi-exposure images can be improved by adjusting the luminance of them. However, how to determine the degree of adjustment has never been discussed. This paper therefore proposes a way to automatically determines the degree on the basis of the luminance distribution of input multi-exposure images. Moreover, new weights, called "simple weights", for image fusion are also considered for the proposed luminance adjustment method. Experimental results show that the multi-exposure images adjusted by the proposed method have better quality than the input multi-exposure ones in terms of the well-exposedness. It is also confirmed that the proposed simple weights provide the highest score of statistical naturalness and discrete entropy in all fusion methods.
The Secure Two-Receiver Broadcast Channel With One-Sided Receiver Side Information
Venue: arXiv cs.IT 2018
Year: 2018

This paper studies the problem of secure communcation over the two-receiver discrete memoryless broadcast channel with one-sided receiver side information and with a passive eavesdropper. We proposed a coding scheme which is based upon the superposition-Marton framework. Secrecy techniques such as the one-time pad, Carleial-Hellman secrecy coding and Wyner serecy coding are applied to ensure individual secrecy. This scheme is shown to be capacity achieving for some cases of the degraded broadcast channel. We also notice that one-sided receiver side information provides the advantage of rate region improvement, in particular when it is available at the weaker legitimate receiver.
Truncated Horizon Policy Search: Combining Reinforcement Learning & Imitation Learning
Venue: arXiv cs.LG 2018
Year: 2018

In this paper, we propose to combine imitation and reinforcement learning via the idea of reward shaping using an oracle. We study the effectiveness of the near-optimal cost-to-go oracle on the planning horizon and demonstrate that the cost-to-go oracle shortens the learner's planning horizon as function of its accuracy: a globally optimal oracle can shorten the planning horizon to one, leading to a one-step greedy Markov Decision Process which is much easier to optimize, while an oracle that is far away from the optimality requires planning over a longer horizon to achieve near-optimal performance. Hence our new insight bridges the gap and interpolates between imitation learning and reinforcement learning. Motivated by the above mentioned insights, we propose Truncated HORizon Policy Search (THOR), a method that focuses on searching for policies that maximize the total reshaped reward over a finite planning horizon when the oracle is sub-optimal. We experimentally demonstrate that a gradient-based implementation of THOR can achieve superior performance compared to RL baselines and IL baselines even when the oracle is sub-optimal.
CocoNet: A deep neural network for mapping pixel coordinates to color values
Venue: arXiv cs.CV 2018
Year: 2018

In this paper, we propose a deep neural network approach for mapping the 2D pixel coordinates in an image to the corresponding Red-Green-Blue (RGB) color values. The neural network is termed CocoNet, i.e. coordinates-to-color network. During the training process, the neural network learns to encode the input image within its layers. More specifically, the network learns a continuous function that approximates the discrete RGB values sampled over the discrete 2D pixel locations. At test time, given a 2D pixel coordinate, the neural network will output the approximate RGB values of the corresponding pixel. By considering every 2D pixel location, the network can actually reconstruct the entire learned image. It is important to note that we have to train an individual neural network for each input image, i.e. one network encodes a single image only. To the best of our knowledge, we are the first to propose a neural approach for encoding images individually, by learning a mapping from the 2D pixel coordinate space to the RGB color space. Our neural image encoding approach has various low-level image processing applications ranging from image encoding, image compression and image denoising to image resampling and image completion. We conduct experiments that include both quantitative and qualitative results, demonstrating the utility of our approach and its superiority over standard baselines, e.g. bilateral filtering or bicubic interpolation. Our code is available at https://github.com/paubric/python-fuse-coconet.
How to Blend a Robot within a Group of Zebrafish: Achieving Social Acceptance through Real-time Calibration of a Multi-level Behavioural Model
Venue: arXiv cs.NE 2018
Year: 2018

We have previously shown how to socially integrate a fish robot into a group of zebrafish thanks to biomimetic behavioural models. The models have to be calibrated on experimental data to present correct behavioural features. This calibration is essential to enhance the social integration of the robot into the group. When calibrated, the behavioural model of fish behaviour is implemented to drive a robot with closed-loop control of social interactions into a group of zebrafish. This approach can be useful to form mixed-groups, and study animal individual and collective behaviour by using biomimetic autonomous robots capable of responding to the animals in long-standing experiments. Here, we show a methodology for continuous real-time calibration and refinement of multi-level behavioural model. The real-time calibration, by an evolutionary algorithm, is based on simulation of the model to correspond to the observed fish behaviour in real-time. The calibrated model is updated on the robot and tested during the experiments. This method allows to cope with changes of dynamics in fish behaviour. Moreover, each fish presents individual behavioural differences. Thus, each trial is done with naive fish groups that display behavioural variability. This real-time calibration methodology can optimise the robot behaviours during the experiments. Our implementation of this methodology runs on three different computers that perform individual tracking, data-analysis, multi-objective evolutionary algorithms, simulation of the fish robot and adaptation of the robot behavioural models, all in real-time.
You Say 'What', I Hear 'Where' and 'Why': (Mis-)Interpreting SQL to Derive Fine-Grained Provenance
Venue: arXiv cs.DB 2018
Year: 2018

SQL declaratively specifies what the desired output of a query is. This work shows that a non-standard interpretation of the SQL semantics can, instead, disclose where a piece of the output originated in the input and why that piece found its way into the result. We derive such data provenance for very rich SQL dialects (including recursion, windowed aggregates, and user-defined functions) at the fine-grained level of individual table cells. The approach is non-invasive and implemented as a compositional source-level SQL rewrite: an input SQL query is transformed into its own interpreter that wields data dependencies instead of regular values. We deliberately design this transformation to preserve the shape of both data and query, which allows provenance derivation to scale to complex queries without overwhelming the underlying database system.
Neural Network Aided Decoding for Physical-Layer Network Coding Random Access
Venue: arXiv cs.IT 2018
Year: 2018

Hinging on ideas from physical-layer network coding, some promising proposals of coded random access systems seek to improve system performance (while preserving low complexity) by means of packet repetitions and decoding of linear combinations of colliding packets, whenever the decoding of individual packets fails. The resulting linear combinations are then temporarily stored in the hope of gathering enough linearly independent combinations so as to eventually recover all individual packets through the resolution of a linear system at the end of the contention frame. However, it is unclear which among the numerous linear combinations---whose number grows exponentially with the degree of collision---will have low probability of decoding error. Since no analytical framework exists to determine which combinations are easiest to decode, this makes the case for a machine learning algorithm to assist the receiver in deciding which linear combinations to target. For this purpose, we train neural networks that approximate the error probability for every possible linear combination based on the estimated channel gains and demonstrate the effectiveness of our approach by numerical simulations.
Routing Metrics Depending on Previous Edges: The Mn Taxonomy and its Corresponding Solutions
Venue: arXiv cs.NI 2018
Year: 2018

The routing algorithms used by current operators aim at coping with the demanded QoS requirements while optimizing the use of their network resources. These algorithms rely on the optimal substructure property (OSP), which states that an optimal path contains other optimal paths within it. However, we show that QoS metrics such as queuing delay and buffer consumption do not satisfy this property, which implies that the used algorithms lose their optimality and/or completeness. This negatively impacts the operator economy by causing a waste of network resources and/or violating Service Level Agreements (SLAs). In this paper, we propose a new so-called Mn taxonomy defining new metric classes. An Mn metric corresponds to a metric which requires the knowledge of the n previously traversed edges to compute its value at a given edge. Based on this taxonomy, we present three solutions for solving routing problems with the newly defined classes of metrics. We show that state-of-the-art algorithms based on the OSP indeed lose their original optimality and/or completeness properties while our proposed solutions do not, at the price of an increased computation time.
Exploring Server-side Blocking of Regions
Venue: arXiv cs.NI 2018
Year: 2018

One of the Internet's greatest strengths is the degree to which it facilitates access to any of its resources from users anywhere in the world. However, users in the developing world have complained of websites blocking their countries. We explore this phenomenon using a measurement study. With a combination of automated page loads, manual checking, and traceroutes, we can say, with high confidence, that some websites do block users from some regions. We cannot say, with high confidence, why, or even based on what criteria, they do so except for in some cases where the website states a reason. We do report qualitative evidence that fears of abuse and the costs of serving requests to some regions may play a role.
Temporal Aspects of Smart Contracts for Financial Derivatives
Venue: arXiv cs.SE 2018
Year: 2018

Implementing smart contracts to automate the performance of high-value over-the-counter (OTC) financial derivatives is a formidable challenge. Due to the regulatory framework and the scale of financial risk if a contract were to go wrong, the performance of these contracts must be enforceable in law and there is an absolute requirement that the smart contract will be faithful to the intentions of the parties as expressed in the original legal documentation. Formal methods provide an attractive route for validation and assurance, and here we present early results from an investigation of the semantics of industry-standard legal documentation for OTC derivatives. We explain the need for a formal representation that combines temporal, deontic and operational aspects, and focus on the requirements for the temporal aspects as derived from the legal text. The relevance of this work extends beyond OTC derivatives and is applicable to understanding the temporal semantics of a wide range of legal documentation.
Supervised Policy Update for Deep Reinforcement Learning
Venue: arXiv cs.LG 2018
Year: 2018

We propose a new sample-efficient methodology, called Supervised Policy Update (SPU), for deep reinforcement learning. Starting with data generated by the current policy, SPU formulates and solves a constrained optimization problem in the non-parameterized proximal policy space. Using supervised regression, it then converts the optimal non-parameterized policy to a parameterized policy, from which it draws new samples. The methodology is general in that it applies to both discrete and continuous action spaces, and can handle a wide variety of proximity constraints for the non-parameterized optimization problem. We show how the Natural Policy Gradient and Trust Region Policy Optimization (NPG/TRPO) problems, and the Proximal Policy Optimization (PPO) problem can be addressed by this methodology. The SPU implementation is much simpler than TRPO. In terms of sample efficiency, our extensive experiments show SPU outperforms TRPO in Mujoco simulated robotic tasks and outperforms PPO in Atari video game tasks.
The Role of Caching in Future Communication Systems and Networks
Venue: arXiv cs.NI 2018
Year: 2018

This paper has the following ambitious goal: to convince the reader that content caching is an exciting research topic for the future communication systems and networks. Caching has been studied for more than 40 years, and has recently received increased attention from industry and academia. Novel caching techniques promise to push the network performance to unprecedented limits, but also pose significant technical challenges. This tutorial provides a brief overview of existing caching solutions, discusses seminal papers that open new directions in caching, and presents the contributions of this Special Issue. We analyze the challenges that caching needs to address today, considering also an industry perspective, and identify bottleneck issues that must be resolved to unleash the full potential of this promising technique.
Sapphire: Querying RDF Data Made Simple
Venue: arXiv cs.DB 2018
Year: 2018

RDF data in the linked open data (LOD) cloud is very valuable for many different applications. In order to unlock the full value of this data, users should be able to issue complex queries on the RDF datasets in the LOD cloud. SPARQL can express such complex queries, but constructing SPARQL queries can be a challenge to users since it requires knowing the structure and vocabulary of the datasets being queried. In this paper, we introduce Sapphire, a tool that helps users write syntactically and semantically correct SPARQL queries without prior knowledge of the queried datasets. Sapphire interactively helps the user while typing the query by providing auto-complete suggestions based on the queried data. After a query is issued, Sapphire provides suggestions on ways to change the query to better match the needs of the user. We evaluated Sapphire based on performance experiments and a user study and showed it to be superior to competing approaches.
Superpixel-enhanced Pairwise Conditional Random Field for Semantic Segmentation
Venue: arXiv cs.CV 2018
Year: 2018

Superpixel-based Higher-order Conditional Random Fields (CRFs) are effective in enforcing long-range consistency in pixel-wise labeling problems, such as semantic segmentation. However, their major short coming is considerably longer time to learn higher-order potentials and extra hyperparameters and/or weights compared with pairwise models. This paper proposes a superpixel-enhanced pairwise CRF framework that consists of the conventional pairwise as well as our proposed superpixel-enhanced pairwise (SP-Pairwise) potentials. SP-Pairwise potentials incorporate the superpixel-based higher-order cues by conditioning on a segment filtered image and share the same set of parameters as the conventional pairwise potentials. Therefore, the proposed superpixel-enhanced pairwise CRF has a lower time complexity in parameter learning and at the same time it outperforms higher-order CRF in terms of inference accuracy. Moreover, the new scheme takes advantage of the pre-trained pairwise models by reusing their parameters and/or weights, which provides a significant accuracy boost on the basis of CRF-RNN even without training. Experiments on MSRC-21 and PASCAL VOC 2012 dataset confirm the effectiveness of our method.
Microservices as an Evolutionary Architecture of Component-Based Development: A Think-aloud Study
Venue: arXiv cs.SE 2018
Year: 2018

Microservices become a fast growing and popular architectural style based on service-oriented development. One of the major advantages using component-based approaches is to support reuse. In this paper, we present a study of microservices and how these systems are related to the traditional abstract models of component-based systems. This research focuses on the core properties of microservices including their scalability, availability and resilience, consistency, coupling and cohesion, and data storage capability, while highlighting their limitations and challenges in relation to components. To support our study, we investigated the existing literature and provided potential directions and interesting points in this growing field of research. As a result, using microservices as components is promising and would be a good mechanism for building applications that were used to be built with component-based approaches.
Autonomous Vehicles that Interact with Pedestrians: A Survey of Theory and Practice
Venue: arXiv cs.RO 2018
Year: 2018

One of the major challenges that autonomous cars are facing today is driving in urban environments. To make it a reality, autonomous vehicles require the ability to communicate with other road users and understand their intentions. Such interactions are essential between the vehicles and pedestrians as the most vulnerable road users. Understanding pedestrian behavior, however, is not intuitive and depends on various factors such as demographics of the pedestrians, traffic dynamics, environmental conditions, etc. In this paper, we identify these factors by surveying pedestrian behavior studies, both the classical works on pedestrian-driver interaction and the modern ones that involve autonomous vehicles. To this end, we will discuss various methods of studying pedestrian behavior, and analyze how the factors identified in the literature are interrelated. We will also review the practical applications aimed at solving the interaction problem including design approaches for autonomous vehicles that communicate with pedestrians and visual perception and reasoning algorithms tailored to understanding pedestrian intention. Based on our findings, we will discuss the open problems and propose future research directions.
Efficient Sequential and Parallel Algorithms for Estimating Higher Order Spectra
Venue: arXiv cs.DC 2018
Year: 2018

Polyspectral estimation is a problem of great importance in the analysis of nonlinear time series that has applications in biomedical signal processing, communications, geophysics, image, radar, sonar and speech processing, etc. Higher order spectra (HOS) have been used in unsupervised and supervised clustering in big data scenarios, in testing for Gaussianity, to suppress Gaussian noise, to characterize nonlinearities in time series data, and so on . Any algorithm for computing the $k$th order spectra of a time series of length $n$ needs $Ω(n^{k-1})$ time since the output size will be $Ω(n^{k-1})$ as well. Given that we live in an era of big data, $n$ could be very large. In this case, sequential algorithms might take unacceptable amounts of time. Thus it is essential to develop parallel algorithms. There is also room for improving existing sequential algorithms. In addition, parallel algorithms in the literature are nongeneric. In this paper we offer generic sequential algorithms for computing higher order spectra that are asymptotically faster than any published algorithm for HOS. Further, we offer memory efficient algorithms. We also present optimal parallel implementations of these algorithms on parallel computing models such as the PRAM and the mesh. We provide experimental results on our sequential and parallel algorithms. Our parallel implementation achieves very good speedups.
Enabling Pedestrian Safety using Computer Vision Techniques: A Case Study of the 2018 Uber Inc. Self-driving Car Crash
Venue: arXiv cs.CV 2018
Year: 2018

Human lives are important. The decision to allow self-driving vehicles operate on our roads carries great weight. This has been a hot topic of debate between policy-makers, technologists and public safety institutions. The recent Uber Inc. self-driving car crash, resulting in the death of a pedestrian, has strengthened the argument that autonomous vehicle technology is still not ready for deployment on public roads. In this work, we analyze the Uber car crash and shed light on the question, "Could the Uber Car Crash have been avoided?". We apply state-of-the-art Computer Vision models to this highly practical scenario. More generally, our experimental results are an evaluation of various image enhancement and object recognition techniques for enabling pedestrian safety in low-lighting conditions using the Uber crash as a case study.
Modeling Cognitive Processes in Social Tagging to Improve Tag Recommendations
Venue: arXiv cs.IR 2018
Year: 2018

With the emergence of Web 2.0, tag recommenders have become important tools, which aim to support users in finding descriptive tags for their bookmarked resources. Although current algorithms provide good results in terms of tag prediction accuracy, they are often designed in a data-driven way and thus, lack a thorough understanding of the cognitive processes that play a role when people assign tags to resources. This thesis aims at modeling these cognitive dynamics in social tagging in order to improve tag recommendations and to better understand the underlying processes. As a first attempt in this direction, we have implemented an interplay between individual micro-level (e.g., categorizing resources or temporal dynamics) and collective macro-level (e.g., imitating other users' tags) processes in the form of a novel tag recommender algorithm. The preliminary results for datasets gathered from BibSonomy, CiteULike and Delicious show that our proposed approach can outperform current state-of-the-art algorithms, such as Collaborative Filtering, FolkRank or Pairwise Interaction Tensor Factorization. We conclude that recommender systems can be improved by incorporating related principles of human cognition.
Theoretical Bounds on MAP Estimation in Distributed Sensing Networks
Venue: arXiv cs.IT 2018
Year: 2018

The typical approach for recovery of spatially correlated signals is regularized least squares with a coupled regularization term. In the Bayesian framework, this algorithm is seen as a maximum-a-posterior estimator whose postulated prior is proportional to the regularization term. In this paper, we study distributed sensing networks in which a set of spatially correlated signals are measured individually at separate terminals, but recovered jointly via a generic maximum-a-posterior estimator. Using the replica method, it is shown that the setting exhibits the decoupling property. For the case with jointly sparse signals, we invoke Bayesian inference and propose the "multi-dimensional soft thresholding" algorithm which is posed as a linear programming. Our investigations depict that the proposed algorithm outperforms the conventional $\ell_{2,1}$-norm regularized least squares scheme while enjoying a feasible computational complexity.