OpenRaiser/Intern-Atlas huggingface dataset:
Learning RoI Transformer for Detecting Oriented Objects in Aerial Images
Venue: arXiv cs.CV 2018
Year: 2018

Object detection in aerial images is an active yet challenging task in computer vision because of the birdview perspective, the highly complex backgrounds, and the variant appearances of objects. Especially when detecting densely packed objects in aerial images, methods relying on horizontal proposals for common object detection often introduce mismatches between the Region of Interests (RoIs) and objects. This leads to the common misalignment between the final object classification confidence and localization accuracy. Although rotated anchors have been used to tackle this problem, the design of them always multiplies the number of anchors and dramatically increases the computational complexity. In this paper, we propose a RoI Transformer to address these problems. More precisely, to improve the quality of region proposals, we first designed a Rotated RoI (RRoI) learner to transform a Horizontal Region of Interest (HRoI) into a Rotated Region of Interest (RRoI). Based on the RRoIs, we then proposed a Rotated Position Sensitive RoI Align (RPS-RoI-Align) module to extract rotation-invariant features from them for boosting subsequent classification and regression. Our RoI Transformer is with light weight and can be easily embedded into detectors for oriented object detection. A simple implementation of the RoI Transformer has achieved state-of-the-art performances on two common and challenging aerial datasets, i.e., DOTA and HRSC2016, with a neglectable reduction to detection speed. Our RoI Transformer exceeds the deformable Position Sensitive RoI pooling when oriented bounding-box annotations are available. Extensive experiments have also validated the flexibility and effectiveness of our RoI Transformer. The results demonstrate that it can be easily integrated with other detector architectures and significantly improve the performances.
Rank Projection Trees for Multilevel Neural Network Interpretation
Venue: arXiv cs.LG 2018
Year: 2018

A variety of methods have been proposed for interpreting nodes in deep neural networks, which typically involve scoring nodes at lower layers with respect to their effects on the output of higher-layer nodes (where lower and higher layers are closer to the input and output layers, respectively). However, we may be interested in picking out a prioritized collection of subsets of the inputs across a range of scales according to their importance for an output node, and not simply a prioritized ranking across the inputs as singletons. Such a situation may arise in biological applications, for instance, where we are interested in epistatic effects between groups of genes in determining a trait of interest. Here, we outline a flexible framework which may be used to generate multiscale network interpretations, using any previously defined scoring function. We demonstrate the ability of our method to pick out biologically important genes and gene sets in the domains of cancer and psychiatric genomics.
A Deep Sequential Model for Discourse Parsing on Multi-Party Dialogues
Venue: arXiv cs.CL 2018
Year: 2018

Discourse structures are beneficial for various NLP tasks such as dialogue understanding, question answering, sentiment analysis, and so on. This paper presents a deep sequential model for parsing discourse dependency structures of multi-party dialogues. The proposed model aims to construct a discourse dependency tree by predicting dependency relations and constructing the discourse structure jointly and alternately. It makes a sequential scan of the Elementary Discourse Units (EDUs) in a dialogue. For each EDU, the model decides to which previous EDU the current one should link and what the corresponding relation type is. The predicted link and relation type are then used to build the discourse structure incrementally with a structured encoder. During link prediction and relation classification, the model utilizes not only local information that represents the concerned EDUs, but also global information that encodes the EDU sequence and the discourse structure that is already built at the current step. Experiments show that the proposed model outperforms all the state-of-the-art baselines.
Robust Optimal Energy and Reserve Management of Multiple-Microgrids via Cooperative Bidding
Venue: arXiv cs.SY 2018
Year: 2018

This work proposes a cooperative trading scheme for the robust optimal energy and reserve management in a multiple-microgrid (MMG) system comprising four microgrids (MGs). This scheme includes a robust optimization (RO) model which accounts for the uncertainties in the renewable energy source (RES) generation, the load demand and the energy market prices using a scenario based worst-case exploration technique. A coordinating entity in the MMG system called a power sharing operator manages the day-ahead energy and reserve trading between the MGs and also with the main grid, thereby enabling market clearing within the MMG system. The proposed scheme is based on the iterative sub-gradient method. The results showcase the convergence characteristics of the proposed scheme. Moreover, the tradeoff between the energy and reserve costs in each MG are also analysed. Finally, the economic benefits gained by the MMG system under the proposed RO based cooperative trading scheme are evaluated.
Accurate control of a pan-tilt system based on parameterization of rotational motion
Venue: arXiv cs.GR 2018
Year: 2018

A pan-tilt camera system has been adopted by a variety of fields since it can cover a wide range of region compared to a single fixated camera setup. Yet many studies rely on factory-assembled and calibrated platforms and assume an ideal rotation where rotation axes are perfectly aligned with the optical axis of the local camera. However, in a user-created setup where a pan-tilting mechanism is arbitrarily assembled, the kinematic configurations may be inaccurate or unknown, violating ideal rotation. These discrepancies in the model with the real physics result in erroneous servo manipulation of the pan-tilting system. In this paper, we propose an accurate control mechanism for arbitrarily-assembled pan-tilt camera systems. The proposed method formulates pan-tilt rotations as motion along great circle trajectories and calibrates its model parameters, such as positions and vectors of rotation axes, in 3D space. Then, one can accurately servo pan-tilt rotations with pose estimation from inverse kinematics of their transformation. The comparative experiment demonstrates out-performance of the proposed method, in terms of accurately localizing target points in world coordinates, after being rotated from their captured camera frames.
A Deep Learning Approach for Multi-View Engagement Estimation of Children in a Child-Robot Joint Attention task
Venue: arXiv cs.RO 2018
Year: 2018

In this work we tackle the problem of child engagement estimation while children freely interact with a robot in their room. We propose a deep-based multi-view solution that takes advantage of recent developments in human pose detection. We extract the child's pose from different RGB-D cameras placed elegantly in the room, fuse the results and feed them to a deep neural network trained for classifying engagement levels. The deep network contains a recurrent layer, in order to exploit the rich temporal information contained in the pose data. The resulting method outperforms a number of baseline classifiers, and provides a promising tool for better automatic understanding of a child's attitude, interest and attention while cooperating with a robot. The goal is to integrate this model in next generation social robots as an attention monitoring tool during various CRI tasks both for Typically Developed (TD) children and children affected by autism (ASD).
Containers Orchestration with Cost-Efficient Autoscaling in Cloud Computing Environments
Venue: arXiv cs.DC 2018
Year: 2018

Containers are standalone, self-contained units that package software and its dependencies together. They offer lightweight performance isolation, fast and flexible deployment, and fine-grained resource sharing. They have gained popularity in better application management and deployment in recent years and are being widely used by organizations to deploy their increasingly diverse workloads such as web services, big data, and IoT in either proprietary clusters or cloud data centres. This has led to the emergence of container orchestration platforms, which are designed to manage the deployment of containerized applications in large-scale clusters. The majority of these platforms are tailored to optimize the scheduling of containers on a fixed-sized private cluster but are not enabled to autoscale the size of the cluster nor to consider features specific to public cloud environments. In this work, we propose a comprehensive container resource management approach that has three different objectives. The first one is to optimize the initial placement of containers by efficiently scheduling them on existing resources. The second one is to autoscale the number of resources at runtime based on the current cluster's workload. The third one is a rescheduling mechanism to further support the efficient use of resources by consolidating applications into fewer VMs when possible. Our algorithms are implemented as a plugin-scheduler for Kubernetes platform. We evaluated our framework and the effectiveness of the proposed algorithms on an Australian national cloud infrastructure. Our experiments demonstrate that considerable cost savings can be achieved by dynamically managing the cluster size and placement of applications. We find that our proposed approaches are capable of reducing the cost by 58% when compared to the default Kubernetes scheduler.
Image Score: How to Select Useful Samples
Venue: arXiv cs.CV 2018
Year: 2018

There has long been debates on how we could interpret neural networks and understand the decisions our models make. Specifically, why deep neural networks tend to be error-prone when dealing with samples that output low softmax scores. We present an efficient approach to measure the confidence of decision-making steps by statistically investigating each unit's contribution to that decision. Instead of focusing on how the models react on datasets, we study the datasets themselves given a pre-trained model. Our approach is capable of assigning a score to each sample within a dataset that measures the frequency of occurrence of that sample's chain of activation. We demonstrate with experiments that our method could select useful samples to improve deep neural networks in a semi-supervised leaning setting.
Link Delay Estimation Using Sparse Recovery for Dynamic Network Tomography
Venue: arXiv cs.NI 2018
Year: 2018

When the scale of communication networks has been growing rapidly in the past decades, it becomes a critical challenge to extract fast and accurate estimation of key state parameters of network links, e.g., transmission delays and dropped packet rates, because such monitoring operations are usually time-consuming. Based on the sparse recovery technique reported in [Wang et al. (2015) IEEE Trans. Information Theory, 61(2):1028--1044], which can infer link delays from a limited number of measurements using compressed sensing, we particularly extend to networks with dynamic changes including link insertion and deletion. Moreover, we propose a more efficient algorithm with a better theoretical upper bound. The experimental result also demonstrates that our algorithm outperforms the previous work in running time while maintaining a similar recovery performance, which shows its capability to cope with large-scale networks.
Towards Automatic Discovery of Cybercrime Supply Chains
Venue: arXiv cs.CR 2018
Year: 2018

Cybercrime forums enable modern criminal entrepreneurs to collaborate with other criminals into increasingly efficient and sophisticated criminal endeavors. Understanding the connections between different products and services can often illuminate effective interventions. However, generating this understanding of supply chains currently requires time-consuming manual effort. In this paper, we propose a language-agnostic method to automatically extract supply chains from cybercrime forum posts and replies. Our supply chain detection algorithm can identify 36% and 58% relevant chains within major English and Russian forums, respectively, showing improvements over the baselines of 13% and 36%, respectively. Our analysis of the automatically generated supply chains demonstrates underlying connections between products and services within these forums. For example, the extracted supply chain illuminated the connection between hack-for-hire services and the selling of rare and valuable `OG' accounts, which has only recently been reported. The understanding of connections between products and services exposes potentially effective intervention points.
Kiki Kills: Identifying Dangerous Challenge Videos from Social Media
Venue: arXiv cs.CV 2018
Year: 2018

There has been upsurge in the number of people participating in challenges made popular through social media channels. One of the examples of such a challenge is the Kiki Challenge, in which people step out of their moving cars and dance to the tunes of the song, 'Kiki, Do you love me?'. Such an action makes the people taking the challenge prone to accidents and can also create nuisance for the others traveling on the road. In this work, we introduce the prevalence of such challenges in social media and show how the machine learning community can aid in preventing dangerous situations triggered by them by developing models that can distinguish between dangerous and non-dangerous challenge videos. Towards this objective, we release a new dataset namely MIDAS-KIKI dataset, consisting of manually annotated dangerous and non-dangerous Kiki challenge videos. Further, we train a deep learning model to identify dangerous and non-dangerous videos, and report our results.
Deep Cosine Metric Learning for Person Re-Identification
Venue: arXiv cs.CV 2018
Year: 2018

Metric learning aims to construct an embedding where two extracted features corresponding to the same identity are likely to be closer than features from different identities. This paper presents a method for learning such a feature space where the cosine similarity is effectively optimized through a simple re-parametrization of the conventional softmax classification regime. At test time, the final classification layer can be stripped from the network to facilitate nearest neighbor queries on unseen individuals using the cosine similarity metric. This approach presents a simple alternative to direct metric learning objectives such as siamese networks that have required sophisticated pair or triplet sampling strategies in the past. The method is evaluated on two large-scale pedestrian re-identification datasets where competitive results are achieved overall. In particular, we achieve better generalization on the test set compared to a network trained with triplet loss.
Ego-Downward and Ambient Video based Person Location Association
Venue: arXiv cs.CV 2018
Year: 2018

Using an ego-centric camera to do localization and tracking is highly needed for urban navigation and indoor assistive system when GPS is not available or not accurate enough. The traditional hand-designed feature tracking and estimation approach would fail without visible features. Recently, there are several works exploring to use context features to do localization. However, all of these suffer severe accuracy loss if given no visual context information. To provide a possible solution to this problem, this paper proposes a camera system with both ego-downward and third-static view to perform localization and tracking in a learning approach. Besides, we also proposed a novel action and motion verification model for cross-view verification and localization. We performed comparative experiments based on our collected dataset which considers the same dressing, gender, and background diversity. Results indicate that the proposed model can achieve $18.32 \%$ improvement in accuracy performance. Eventually, we tested the model on multi-people scenarios and obtained an average $67.767 \%$ accuracy.
Unsupervised Domain Adaptation using Generative Models and Self-ensembling
Venue: arXiv cs.CV 2018
Year: 2018

Transferring knowledge across different datasets is an important approach to successfully train deep models with a small-scale target dataset or when few labeled instances are available. In this paper, we aim at developing a model that can generalize across multiple domain shifts, so that this model can adapt from a single source to multiple targets. This can be achieved by randomizing the generation of the data of various styles to mitigate the domain mismatch. First, we present a new adaptation to the CycleGAN model to produce stochastic style transfer between two image batches of different domains. Second, we enhance the classifier performance by using a self-ensembling technique with a teacher and student model to train on both original and generated data. Finally, we present experimental results on three datasets Office-31, Office-Home, and Visual Domain adaptation. The results suggest that selfensembling is better than simple data augmentation with the newly generated data and a single model trained this way can have the best performance across all different transfer tasks.
Multi-task Learning of Hierarchical Vision-Language Representation
Venue: arXiv cs.CV 2018
Year: 2018

It is still challenging to build an AI system that can perform tasks that involve vision and language at human level. So far, researchers have singled out individual tasks separately, for each of which they have designed networks and trained them on its dedicated datasets. Although this approach has seen a certain degree of success, it comes with difficulties of understanding relations among different tasks and transferring the knowledge learned for a task to others. We propose a multi-task learning approach that enables to learn vision-language representation that is shared by many tasks from their diverse datasets. The representation is hierarchical, and prediction for each task is computed from the representation at its corresponding level of the hierarchy. We show through experiments that our method consistently outperforms previous single-task-learning methods on image caption retrieval, visual question answering, and visual grounding. We also analyze the learned hierarchical representation by visualizing attention maps generated in our network.
Generalization in anti-causal learning
Venue: arXiv cs.LG 2018
Year: 2018

The ability to learn and act in novel situations is still a prerogative of animate intelligence, as current machine learning methods mostly fail when moving beyond the standard i.i.d. setting. What is the reason for this discrepancy? Most machine learning tasks are anti-causal, i.e., we infer causes (labels) from effects (observations). Typically, in supervised learning we build systems that try to directly invert causal mechanisms. Instead, in this paper we argue that strong generalization capabilities crucially hinge on searching and validating meaningful hypotheses, requiring access to a causal model. In such a framework, we want to find a cause that leads to the observed effect. Anti-causal models are used to drive this search, but a causal model is required for validation. We investigate the fundamental differences between causal and anti-causal tasks, discuss implications for topics ranging from adversarial attacks to disentangling factors of variation, and provide extensive evidence from the literature to substantiate our view. We advocate for incorporating causal models in supervised learning to shift the paradigm from inference only, to search and validation.
Semi-supervised Rare Disease Detection Using Generative Adversarial Network
Venue: arXiv cs.LG 2018
Year: 2018

Rare diseases affect a relatively small number of people, which limits investment in research for treatments and cures. Developing an efficient method for rare disease detection is a crucial first step towards subsequent clinical research. In this paper, we present a semi-supervised learning framework for rare disease detection using generative adversarial networks. Our method takes advantage of the large amount of unlabeled data for disease detection and achieves the best results in terms of precision-recall score compared to baseline techniques.
Spatial-temporal Fusion Convolutional Neural Network for Simulated Driving Behavior Recognition
Venue: arXiv cs.CV 2018
Year: 2018

Abnormal driving behaviour is one of the leading cause of terrible traffic accidents endangering human life. Therefore, study on driving behaviour surveillance has become essential to traffic security and public management. In this paper, we conduct this promising research and employ a two stream CNN framework for video-based driving behaviour recognition, in which spatial stream CNN captures appearance information from still frames, whilst temporal stream CNN captures motion information with pre-computed optical flow displacement between a few adjacent video frames. We investigate different spatial-temporal fusion strategies to combine the intra frame static clues and inter frame dynamic clues for final behaviour recognition. So as to validate the effectiveness of the designed spatial-temporal deep learning based model, we create a simulated driving behaviour dataset, containing 1237 videos with 6 different driving behavior for recognition. Experiment result shows that our proposed method obtains noticeable performance improvements compared to the existing methods.
Model Based In Situ Calibration with Temperature compensation of 6 axis Force Torque Sensors
Venue: arXiv cs.RO 2018
Year: 2018

It is well known that sensors using strain gauges have a potential dependency on temperature. This creates temperature drift in the measurements of six axis force torque sensors (F/T). The temperature drift can be considerable if an experiment is long or the environmental conditions are different from when the calibration of the sensor was performed. Other \textit{in situ} methods disregard the effect of temperature on the sensor measurements. Experiments performed using the humanoid robot platform iCub show that the effect of temperature is relevant. The model based \textit{in situ} calibration of six axis force torque sensors method is extended to perform temperature compensation.