OpenRaiser/Intern-Atlas huggingface dataset:
Car Detection using Unmanned Aerial Vehicles: Comparison between Faster R-CNN and YOLOv3
Venue: arXiv cs.RO 2018
Year: 2018

Unmanned Aerial Vehicles are increasingly being used in surveillance and traffic monitoring thanks to their high mobility and ability to cover areas at different altitudes and locations. One of the major challenges is to use aerial images to accurately detect cars and count them in real-time for traffic monitoring purposes. Several deep learning techniques were recently proposed based on convolution neural network (CNN) for real-time classification and recognition in computer vision. However, their performance depends on the scenarios where they are used. In this paper, we investigate the performance of two state-of-the-art CNN algorithms, namely Faster R-CNN and YOLOv3, in the context of car detection from aerial images. We trained and tested these two models on a large car dataset taken from UAVs. We demonstrated in this paper that YOLOv3 outperforms Faster R-CNN in sensitivity and processing time, although they are comparable in the precision metric.
The role of grammar in transition-probabilities of subsequent words in English text
Venue: arXiv cs.CL 2018
Year: 2018

Sentence formation is a highly structured, history-dependent, and sample-space reducing (SSR) process. While the first word in a sentence can be chosen from the entire vocabulary, typically, the freedom of choosing subsequent words gets more and more constrained by grammar and context, as the sentence progresses. This sample-space reducing property offers a natural explanation of Zipf's law in word frequencies, however, it fails to capture the structure of the word-to-word transition probability matrices of English text. Here we adopt the view that grammatical constraints (such as subject--predicate--object) locally re-order the word order in sentences that are sampled with a SSR word generation process. We demonstrate that superimposing grammatical structure -- as a local word re-ordering (permutation) process -- on a sample-space reducing process is sufficient to explain both, word frequencies and word-to-word transition probabilities. We compare the quality of the grammatically ordered SSR model in reproducing several test statistics of real texts with other text generation models, such as the Bernoulli model, the Simon model, and the Monkey typewriting model.
Learning from past scans: Tomographic reconstruction to detect new structures
Venue: arXiv cs.CV 2018
Year: 2018

The need for tomographic reconstruction from sparse measurements arises when the measurement process is potentially harmful, needs to be rapid, or is uneconomical. In such cases, prior information from previous longitudinal scans of the same or similar objects helps to reconstruct the current object whilst requiring significantly fewer `updating' measurements. However, a significant limitation of all prior-based methods is the possible dominance of the prior over the reconstruction of new localised information that has evolved within the test object. In this paper, we improve the state of the art by (1) detecting potential regions where new changes may have occurred, and (2) effectively reconstructing both the old and new structures by computing regional weights that moderate the local influence of the priors. We have tested the efficacy of our method on synthetic as well as real volume data. The results demonstrate that using weighted priors significantly improves the overall quality of the reconstructed data whilst minimising their impact on regions that contain new information.
Sequential algorithms and the computational content of classical proofs
Venue: arXiv cs.LO 2018
Year: 2018

We develop a correspondence between the theory of sequential algorithms and classical reasoning, via Kreisel's no-counterexample interpretation. Our framework views realizers of the no-counterexample interpretation as dynamic processes which interact with an oracle, and allows these processes to be modelled at any given level of abstraction. We discuss general constructions on algorithms which represent specific patterns which often appear in classical reasoning, and in particular, we develop a computational interpretation of the rule of dependent choice which is phrased purely on the level of algorithms, giving us a clearer insight into the computational meaning of proofs in classical analysis.
Hierarchical LSTMs with Adaptive Attention for Visual Captioning
Venue: arXiv cs.CV 2018
Year: 2018

Recent progress has been made in using attention based encoder-decoder framework for image and video captioning. Most existing decoders apply the attention mechanism to every generated word including both visual words (e.g., "gun" and "shooting") and non-visual words (e.g. "the", "a"). However, these non-visual words can be easily predicted using natural language model without considering visual signals or attention. Imposing attention mechanism on non-visual words could mislead and decrease the overall performance of visual captioning. Furthermore, the hierarchy of LSTMs enables more complex representation of visual data, capturing information at different scales. To address these issues, we propose a hierarchical LSTM with adaptive attention (hLSTMat) approach for image and video captioning. Specifically, the proposed framework utilizes the spatial or temporal attention for selecting specific regions or frames to predict the related words, while the adaptive attention is for deciding whether to depend on the visual information or the language context information. Also, a hierarchical LSTMs is designed to simultaneously consider both low-level visual information and high-level language context information to support the caption generation. We initially design our hLSTMat for video captioning task. Then, we further refine it and apply it to image captioning task. To demonstrate the effectiveness of our proposed framework, we test our method on both video and image captioning tasks. Experimental results show that our approach achieves the state-of-the-art performance for most of the evaluation metrics on both tasks. The effect of important components is also well exploited in the ablation study.
Autoencoder Based Residual Deep Networks for Robust Regression Prediction and Spatiotemporal Estimation
Venue: arXiv cs.LG 2018
Year: 2018

To have a superior generalization, a deep learning neural network often involves a large size of training sample. With increase of hidden layers in order to increase learning ability, neural network has potential degradation in accuracy. Both could seriously limit applicability of deep learning in some domains particularly involving predictions of continuous variables with a small size of samples. Inspired by residual convolutional neural network in computer vision and recent findings of crucial shortcuts in the brains in neuroscience, we propose an autoencoder-based residual deep network for robust prediction. In a nested way, we leverage shortcut connections to implement residual mapping with a balanced structure for efficient propagation of error signals. The novel method is demonstrated by multiple datasets, imputation of high spatiotemporal resolution non-randomness missing values of aerosol optical depth, and spatiotemporal estimation of fine particulate matter <2.5 μm, achieving the cutting edge of accuracy and efficiency. Our approach is also a general-purpose regression learner to be applicable in diverse domains.
The Crossover-Distance for ISI-Correcting Decoding of Convolutional Codes in Diffusion-Based Molecular Communications
Venue: arXiv cs.IT 2018
Year: 2018

In diffusion based molecular communication, the intersymbol interference (ISI) is an important reason for system performance degradation, which is caused by the random movement, out-of-order arrival and indistinguishability of the moleclues. In this paper, a new metric called crossover distance is introduced to measure the distance between the received bit sequence and the probably tranmitted bit sequences. A new decoding scheme of conventional codes is proposed based on crossover distance, which can enhance the communication reliability significantly. The theoretic analysis indicates that the proposed decoding algorithm provides an approximately maximal likelihood estimation of the information bits. The numerical results show that compared with uncoded systems and some existing channel codes, the proposed convolutional codes offer good performance with same throughputs.
End-to-end neural relation extraction using deep biaffine attention
Venue: arXiv cs.CL 2018
Year: 2018

We propose a neural network model for joint extraction of named entities and relations between them, without any hand-crafted features. The key contribution of our model is to extend a BiLSTM-CRF-based entity recognition model with a deep biaffine attention layer to model second-order interactions between latent features for relation classification, specifically attending to the role of an entity in a directional relationship. On the benchmark "relation and entity recognition" dataset CoNLL04, experimental results show that our model outperforms previous models, producing new state-of-the-art performances.
3D Convolution on RGB-D Point Clouds for Accurate Model-free Object Pose Estimation
Venue: arXiv cs.RO 2018
Year: 2018

The conventional pose estimation of a 3D object usually requires the knowledge of the 3D model of the object. Even with the recent development in convolutional neural networks (CNNs), a 3D model is often necessary in the final estimation. In this paper, we propose a two-stage pipeline that takes in raw colored point cloud data and estimates an object's translation and rotation by running 3D convolutions on voxels. The pipeline is simple yet highly accurate: translation error is reduced to the voxel resolution (around 1 cm) and rotation error is around 5 degrees. The pipeline is also put to actual robotic grasping tests where it achieves above 90% success rate for test objects. Another innovation is that a motion capture system is used to automatically label the point cloud samples which makes it possible to rapidly collect a large amount of highly accurate real data for training the neural networks.
On Infusing Reachability-Based Safety Assurance within Probabilistic Planning Frameworks for Human-Robot Vehicle Interactions
Venue: arXiv cs.RO 2018
Year: 2018

Action anticipation, intent prediction, and proactive behavior are all desirable characteristics for autonomous driving policies in interactive scenarios. Paramount, however, is ensuring safety on the road --- a key challenge in doing so is accounting for uncertainty in human driver actions without unduly impacting planner performance. This paper introduces a minimally-interventional safety controller operating within an autonomous vehicle control stack with the role of ensuring collision-free interaction with an externally controlled (e.g., human-driven) counterpart. We leverage reachability analysis to construct a real-time (100Hz) controller that serves the dual role of (1) tracking an input trajectory from a higher-level planning algorithm using model predictive control, and (2) assuring safety through maintaining the availability of a collision-free escape maneuver as a persistent constraint regardless of whatever future actions the other car takes. A full-scale steer-by-wire platform is used to conduct traffic weaving experiments wherein the two cars, initially side-by-side, must swap lanes in a limited amount of time and distance, emulating cars merging onto/off of a highway. We demonstrate that, with our control stack, the autonomous vehicle is able to avoid collision even when the other car defies the planner's expectations and takes dangerous actions, either carelessly or with the intent to collide, and otherwise deviates minimally from the planned trajectory to the extent required to maintain safety.
Relay-Assisted and QoS Aware Scheduling to Overcome Blockage in mmWave Backhaul Networks
Venue: arXiv cs.NI 2018
Year: 2018

In the scenario where small cells are densely deployed, the millimeter wave (mmWave) wireless backhaul network has been widely used. However, mmWave is easily blocked by obstacles, and how to forward the data of the blocked flows is still a significant challenge. To ensure backhauling capacity, the quality of service (QoS) requirements of flows should be satisfied. In this paper, we investigate the problem of optimal scheduling to maximize the number of flows satisfying their QoS requirements with relays exploited to overcome blockage. To achieve a practical solution, we propose a relay-assisted and QoS aware scheduling scheme for the backhaul networks, called RAQS. It consists of a relay selection algorithm and a transmission scheduling algorithm. The relay selection algorithm selects non-repeating relays with high link rates for the blocked flows, which helps to achieve the QoS requirements of flows as soon as possible. Then, according to the results of relay selection, the transmission scheduling algorithm exploits concurrent transmissions to satisfy the QoS requirements of flows as much as possible. Extensive simulations show RAQS can effectively overcome the blockage problem, and increase the number of completed flows and network throughput compared with other schemes. In particular, the impact of relay selection parameter is also investigated to further guide the relay selection.
Rendu basé image avec contraintes sur les gradients
Venue: arXiv cs.CV 2018
Year: 2018

Multi-view image-based rendering consists in generating a novel view of a scene from a set of source views. In general, this works by first doing a coarse 3D reconstruction of the scene, and then using this reconstruction to establish correspondences between source and target views, followed by blending the warped views to get the final image. Unfortunately, discontinuities in the blending weights, due to scene geometry or camera placement, result in artifacts in the target view. In this paper, we show how to avoid these artifacts by imposing additional constraints on the image gradients of the novel view. We propose a variational framework in which an energy functional is derived and optimized by iteratively solving a linear system. We demonstrate this method on several structured and unstructured multi-view datasets, and show that it numerically outperforms state-of-the-art methods, and eliminates artifacts that result from visibility discontinuities
Severity Level of Permissions in Role-Based Access Control
Venue: arXiv cs.CR 2018
Year: 2018

The analysis of hidden channels of information leakage with respect to role-based access control includes monitoring of excessive permissions among users. It is not always possible to completely eliminate redundancy. The problem of ranking permissions arises in order to identify the most significant, for which redundancy is most not desirable. A numerical characteristic that reflects the value or importance of permissions is called the "severity level". A number of heuristic assumptions have been formulated that make it possible to establish the dependence of the severity level of permissions on the structure of the role hierarchy. A methodology for solving the problem is proposed, using analytic hierarchy process and taking into account these assumptions. The main idea is that the decision tree of the process will be the role graph.
Partially Non-Recurrent Controllers for Memory-Augmented Neural Networks
Venue: arXiv cs.NE 2018
Year: 2018

Memory-Augmented Neural Networks (MANNs) are a class of neural networks equipped with an external memory, and are reported to be effective for tasks requiring a large long-term memory and its selective use. The core module of a MANN is called a controller, which is usually implemented as a recurrent neural network (RNN) (e.g., LSTM) to enable the use of contextual information in controlling the other modules. However, such an RNN-based controller often allows a MANN to directly solve the given task by using the (small) internal memory of the controller, and prevents the MANN from making the best use of the external memory, thereby resulting in a suboptimally trained model. To address this problem, we present a novel type of RNN-based controller that is partially non-recurrent and avoids the direct use of its internal memory for solving the task, while keeping the ability of using contextual information in controlling the other modules. Our empirical experiments using Neural Turing Machines and Differentiable Neural Computers on the Toy and bAbI tasks demonstrate that the proposed controllers give substantially better results than standard RNN-based controllers.
Group based Centrality for Immunization of Complex Networks
Venue: arXiv cs.SI 2018
Year: 2018

Network immunization is an extensively recognized issue in several domains like virtual network security, public health and social media, to deal with the problem of node inoculation so as to minimize the transmission through the links existed in these networks. We aim to identify top ranked nodes to immunize networks, leading to control the outbreak of epidemics or misinformation. We consider group based centrality and define a heuristic objective criteria to establish the target of key nodes finding in network which if immunized result in essential network vulnerability. We propose a group based game theoretic payoff division approach, by employing Shapley value to assign the surplus acquired by participating nodes in different groups through the positional power and functional influence over other nodes. We tag these key nodes as Shapley Value based Information Delimiters (SVID). Experiments on empirical data sets and model networks establish the efficacy of our proposed approach and acknowledge performance of node inoculation to delimit contagion outbreak.
Spectral methods for testing cluster structure of graphs
Venue: arXiv cs.DS 2018
Year: 2018

In the framework of graph property testing, we study the problem of determining if a graph admits a cluster structure. We say that a graph is $(k, ϕ)$-clusterable if it can be partitioned into at most $k$ parts such that each part has conductance at least $ϕ$. We present an algorithm that accepts all graphs that are $(2, ϕ)$-clusterable with probability at least $\frac{2}3$ and rejects all graphs that are $ε$-far from $(2, ϕ^*)$-clusterable for $ϕ^* \le μϕ^2 ε^2$ with probability at least $\frac{2}3$ where $μ> 0$ is a parameter that affects the query complexity. This improves upon the work of Czumaj, Peng, and Sohler by removing a $\log n$ factor from the denominator of the bound on $ϕ^*$ for the case of $k=2$. Our work was concurrent with the work of Chiplunkar et al.\@ who achieved the same improvement for all values of $k$. Our approach for the case $k=2$ relies on the geometric structure of the eigenvectors of the graph Laplacian and results in an algorithm with query complexity $O(n^{1/2+O(1)μ} \cdot \text{poly}(1/ε, 1/ϕ,\log n))$.
Eff Directly in OCaml
Venue: arXiv cs.PL 2018
Year: 2018

The language Eff is an OCaml-like language serving as a prototype implementation of the theory of algebraic effects, intended for experimentation with algebraic effects on a large scale. We present the embedding of Eff into OCaml, using the library of delimited continuations or the multicore OCaml branch. We demonstrate the correctness of the embedding denotationally, relying on the tagless-final-style interpreter-based denotational semantics, including the novel, direct denotational semantics of multi-prompt delimited control. The embedding is systematic, lightweight, performant and supports even higher-order, 'dynamic' effects with their polymorphism. OCaml thus may be regarded as another implementation of Eff, broadening the scope and appeal of that language.
Open Source Software Opportunities and Risks
Venue: arXiv cs.OH 2018
Year: 2018

Open Source Software (OSS) history is traced to initial efforts in 1971 at Massachusetts Institute of Technology (MIT) Artificial Intelligence (AI) Lab, the initial goals of OSS around Free vs. Freedom, and its evolution and impact on commercial and custom applications. Through OSS history, much of the research and has been around contributors (suppliers) to OSS projects, the commercialization, and overall success of OSS as a development process. In conjunction with OSS growth, intellectual property issues and licensing issues still remain. The consumers of OSS, application architects, in developing commercial or internal applications based upon OSS should consider license risk as they compose their applications using Component Based Software Development (CBSD) approaches, either through source code, binary, or standard protocols such as HTTP.
Batch Size Influence on Performance of Graphic and Tensor Processing Units during Training and Inference Phases
Venue: arXiv cs.LG 2018
Year: 2018

The impact of the maximally possible batch size (for the better runtime) on performance of graphic processing units (GPU) and tensor processing units (TPU) during training and inference phases is investigated. The numerous runs of the selected deep neural network (DNN) were performed on the standard MNIST and Fashion-MNIST datasets. The significant speedup was obtained even for extremely low-scale usage of Google TPUv2 units (8 cores only) in comparison to the quite powerful GPU NVIDIA Tesla K80 card with the speedup up to 10x for training stage (without taking into account the overheads) and speedup up to 2x for prediction stage (with and without taking into account overheads). The precise speedup values depend on the utilization level of TPUv2 units and increase with the increase of the data volume under processing, but for the datasets used in this work (MNIST and Fashion-MNIST with images of sizes 28x28) the speedup was observed for batch sizes >512 images for training phase and >40 000 images for prediction phase. It should be noted that these results were obtained without detriment to the prediction accuracy and loss that were equal for both GPU and TPU runs up to the 3rd significant digit for MNIST dataset, and up to the 2nd significant digit for Fashion-MNIST dataset.
A Neural Network Based Explainable Recommender System
Venue: arXiv cs.IR 2018
Year: 2018

Recommendation system could help the companies to persuade users to visit or consume at a particular place, which was based on many traditional methods such as the set of collaborative filtering algorithms. Most research discusses the model design or feature engineering methods to minimize the root mean square error (RMSE) of rating prediction, but lacks exploring the ways to generate the reasons for recommendations. This paper proposed an integrated neural network based model which integrates rating scores prediction and explainable words generation. Based on the experimental results, this model presented lower RMSE compared with traditional methods, and generate the explanation of recommendation to convince customers to visit the recommended place.