OpenRaiser/Intern-Atlas huggingface dataset:
FlowNorm: A Learning-based Method for Increasing Convergence Range of Direct Alignment
Venue: arXiv cs.RO 2019
Year: 2019

Many approaches have been proposed to estimate camera poses by directly minimizing photometric error. However, due to the non-convex property of direct alignment, proper initialization is still required for these methods. Many robust norms (e.g. Huber norm) have been proposed to deal with the outlier terms caused by incorrect initializations. These robust norms are solely defined on the magnitude of each error term. In this paper, we propose a novel robust norm, named FlowNorm, that exploits the information from both the local error term and the global image registration information. While the local information is defined on patch alignments, the global information is estimated using a learning-based network. Using both the local and global information, we achieve an unprecedented convergence range in which images can be aligned given large view angle changes or small overlaps. We further demonstrate the usability of the proposed robust norm by integrating it into the direct methods DSO and BA-Net, and generate more robust and accurate results in real-time.
Intelligence in Strategic Games
Venue: arXiv cs.GT 2019
Year: 2019

The article considers strategies of coalitions that are based on intelligence information about moves of some of the other agents. The main technical result is a sound and complete logical system that describes the interplay between coalition power modality with intelligence and distributed knowledge modality in games with imperfect information.
A Generalized and Robust Method Towards Practical Gaze Estimation on Smart Phone
Venue: arXiv cs.CV 2019
Year: 2019

Gaze estimation for ordinary smart phone, e.g. estimating where the user is looking at on the phone screen, can be applied in various applications. However, the widely used appearance-based CNN methods still have two issues for practical adoption. First, due to the limited dataset, gaze estimation is very likely to suffer from over-fitting, leading to poor accuracy at run time. Second, the current methods are usually not robust, i.e. their prediction results having notable jitters even when the user is performing gaze fixation, which degrades user experience greatly. For the first issue, we propose a new tolerant and talented (TAT) training scheme, which is an iterative random knowledge distillation framework enhanced with cosine similarity pruning and aligned orthogonal initialization. The knowledge distillation is a tolerant teaching process providing diverse and informative supervision. The enhanced pruning and initialization is a talented learning process prompting the network to escape from the local minima and re-born from a better start. For the second issue, we define a new metric to measure the robustness of gaze estimator, and propose an adversarial training based Disturbance with Ordinal loss (DwO) method to improve it. The experimental results show that our TAT method achieves state-of-the-art performance on GazeCapture dataset, and that our DwO method improves the robustness while keeping comparable accuracy.
Conditional Invertible Flow for Point Cloud Generation
Venue: arXiv cs.LG 2019
Year: 2019

This paper focuses on a novel generative approach for 3D point clouds that makes use of invertible flow-based models. The main idea of the method is to treat a point cloud as a probability density in 3D space that is modeled using a cloud-specific neural network. To capture the similarity between point clouds we rely on parameter sharing among networks, with each cloud having only a small embedding vector that defines it. We use invertible flows networks to generate the individual point clouds, and to regularize the embedding vectors. We evaluate the generative capabilities of the model both in qualitative and quantitative manner.
Conservation AI: Live Stream Analysis for the Detection of Endangered Species Using Convolutional Neural Networks and Drone Technology
Venue: arXiv cs.CV 2019
Year: 2019

Many different species are adversely affected by poaching. In response to this escalating crisis, efforts to stop poaching using hidden cameras, drones and DNA tracking have been implemented with varying degrees of success. Limited resources, costs and logistical limitations are often the cause of most unsuccessful poaching interventions. The study presented in this paper outlines a flexible and interoperable framework for the automatic detection of animals and poaching activity to facilitate early intervention practices. Using a robust deep learning pipeline, a convolutional neural network is trained and implemented to detect rhinos and cars (considered an important tool in poaching for fast access and artefact transportation in natural habitats) in the study, that are found within live video streamed from drones Transfer learning with the Faster RCNN Resnet 101 is performed to train a custom model with 350 images of rhinos and 350 images of cars. Inference is performed using a frame sampling technique to address the required trade-off control precision and processing speed and maintain synchronisation with the live feed. Inference models are hosted on a web platform using flask web serving, OpenCV and TensorFlow 1.13. Video streams are transmitted from a DJI Mavic Pro 2 drone using the Real-Time Messaging Protocol (RMTP). The best trained Faster RCNN model achieved a mAP of 0.83 @IOU 0.50 and 0.69 @IOU 0.75 respectively. In comparison an SSD-mobilenetmodel trained under the same experimental conditions achieved a mAP of 0.55 @IOU .50 and 0.27 @IOU 0.75.The results demonstrate that using a FRCNN and off-the-shelf drones is a promising and scalable option for a range of conservation projects.
Using learning analytics to provide personalized recommendations for finding peers
Venue: arXiv cs.HC 2019
Year: 2019

This work aims to propose a method to support students in finding appropriate peers in collaborative and blended learning settings. The main goal of this research is to bridge the gap between pedagogical theory and data driven practice to provide personalized and adaptive guidance to students who engage in computer supported learning activities. The research hypothesis is that we can use Learning Analytics to model students' cognitive state and to assess whether the student is in the Zone of Proximal Development. Based on this assessment, we can plan how to provide scaffolding based on the principles of Contingent Tutoring and how to form study groups based on the principles of the Zone of Proximal Development.
A Study of Annotation and Alignment Accuracy for Performance Comparison in Complex Orchestral Music
Venue: arXiv cs.MM 2019
Year: 2019

Quantitative analysis of commonalities and differences between recorded music performances is an increasingly common task in computational musicology. A typical scenario involves manual annotation of different recordings of the same piece along the time dimension, for comparative analysis of, e.g., the musical tempo, or for mapping other performance-related information between performances. This can be done by manually annotating one reference performance, and then automatically synchronizing other performances, using audio-to-audio alignment algorithms. In this paper we address several questions related to those tasks. First, we analyze different annotations of the same musical piece, quantifying timing deviations between the respective human annotators. A statistical evaluation of the marker time stamps will provide (a) an estimate of the expected timing precision of human annotations and (b) a ground truth for subsequent automatic alignment experiments. We then carry out a systematic evaluation of different audio features for audio-to-audio alignment, quantifying the degree of alignment accuracy that can be achieved, and relate this to the results from the annotation study.
An experimental platform for gathering user behavioural data via browser APIs
Venue: arXiv cs.CR 2019
Year: 2019

Websites are capable of learning a wide range of information about the platform on which a browser is executing. One major source of such information is the set of standardised Application Programming Interfaces (APIs) provided within the browser, which can be accessed by JavaScript downloaded by a website; this information can then either be used by the JavaScript or sent back to the originating site. As has been widely discussed, much of this information can threaten user privacy. The main purpose of this paper is to document a publicly available platform designed to enable further investigation of one class of such threats, namely those based on analysing user behavioural data. The platform has two main components: a Chrome extension that gathers user keystroke and mouse data via browser APIs, and server software that collects and stores this data for subsequent experimentation.
Fine-grained evaluation of German-English Machine Translation based on a Test Suite
Venue: arXiv cs.CL 2019
Year: 2019

We present an analysis of 16 state-of-the-art MT systems on German-English based on a linguistically-motivated test suite. The test suite has been devised manually by a team of language professionals in order to cover a broad variety of linguistic phenomena that MT often fails to translate properly. It contains 5,000 test sentences covering 106 linguistic phenomena in 14 categories, with an increased focus on verb tenses, aspects and moods. The MT outputs are evaluated in a semi-automatic way through regular expressions that focus only on the part of the sentence that is relevant to each phenomenon. Through our analysis, we are able to compare systems based on their performance on these categories. Additionally, we reveal strengths and weaknesses of particular systems and we identify grammatical phenomena where the overall performance of MT is relatively low.
LOST: A flexible framework for semi-automatic image annotation
Venue: arXiv cs.CV 2019
Year: 2019

State-of-the-art computer vision approaches rely on huge amounts of annotated data. The collection of such data is a time consuming process since it is mainly performed by humans. The literature shows that semi-automatic annotation approaches can significantly speed up the annotation process by the automatic generation of annotation proposals to support the annotator. In this paper we present a framework that allows for a quick and flexible design of semi-automatic annotation pipelines. We show that a good design of the process will speed up the collection of annotations. Our contribution is a new approach to image annotation that allows for the combination of different annotation tools and machine learning algorithms in one process. We further present potential applications of our approach. The source code of our framework called LOST (Label Objects and Save Time) is available at: https://github.com/l3p-cv/lost.
Explainable AI for Intelligence Augmentation in Multi-Domain Operations
Venue: arXiv cs.AI 2019
Year: 2019

Central to the concept of multi-domain operations (MDO) is the utilization of an intelligence, surveillance, and reconnaissance (ISR) network consisting of overlapping systems of remote and autonomous sensors, and human intelligence, distributed among multiple partners. Realising this concept requires advancement in both artificial intelligence (AI) for improved distributed data analytics and intelligence augmentation (IA) for improved human-machine cognition. The contribution of this paper is threefold: (1) we map the coalition situational understanding (CSU) concept to MDO ISR requirements, paying particular attention to the need for assured and explainable AI to allow robust human-machine decision-making where assets are distributed among multiple partners; (2) we present illustrative vignettes for AI and IA in MDO ISR, including human-machine teaming, dense urban terrain analysis, and enhanced asset interoperability; (3) we appraise the state-of-the-art in explainable AI in relation to the vignettes with a focus on human-machine collaboration to achieve more rapid and agile coalition decision-making. The union of these three elements is intended to show the potential value of a CSU approach in the context of MDO ISR, grounded in three distinct use cases, highlighting how the need for explainability in the multi-partner coalition setting is key.
Abstract Transducers
Venue: arXiv cs.FL 2019
Year: 2019

Several abstract machines that operate on symbolic input alphabets have been proposed in the last decade, for example, symbolic automata or lattice automata. Applications of these types of automata include software security analysis and natural language processing. While these models provide means to describe words over infinite input alphabets, there is no considerable work on symbolic output (as present in transducers) alphabets, or even abstraction (widening) thereof. Furthermore, established approaches for transforming, for example, minimizing or reducing, finite-state machines that produce output on states or transitions are not applicable. A notion of equivalence of this type of machines is needed to make statements about whether or not transformations maintain the semantics. We present abstract transducers as a new form of finite-state transducers. Both their input alphabet and the output alphabet is composed of abstract words, where one abstract word represents a set of concrete words. The mapping between these representations is described by abstract word domains. By using words instead of single letters, abstract transducers provide the possibility of lookaheads to decide on state transitions to conduct. Since both the input symbol and the output symbol on each transition is an abstract entity, abstraction techniques can be applied naturally. We apply abstract transducers as the foundation for sharing task artifacts for reuse in context of program analysis and verification, and describe task artifacts as abstract words. A task artifact is any entity that contributes to an analysis task and its solution, for example, candidate invariants or source code to weave.
Meet the Family of Statistical Disclosure Attacks
Venue: arXiv cs.CR 2019
Year: 2019

Disclosure attacks aim at revealing communication patterns in anonymous communication systems, such as conversation partners or frequency. In this paper, we propose a framework to compare between the members of the statistical disclosure attack family. We compare different variants of the Statistical Disclosure Attack (SDA) in the literature, together with two new methods; as well as show their relation with the Least Squares Disclosure Attack (LSDA). We empirically explore the performance of the attacks with respect to the different parameters of the system. Our experiments show that i) our proposals considerably improve the state-of-the-art SDA and ii) confirm that LSDA outperforms the SDA family when the adversary has enough observations of the system.
Learning from My Partner's Actions: Roles in Decentralized Robot Teams
Venue: arXiv cs.RO 2019
Year: 2019

When teams of robots collaborate to complete a task, communication is often necessary. Like humans, robot teammates should implicitly communicate through their actions: but interpreting our partner's actions is typically difficult, since a given action may have many different underlying reasons. Here we propose an alternate approach: instead of not being able to infer whether an action is due to exploration, exploitation, or communication, we define separate roles for each agent. Because each role defines a distinct reason for acting (e.g., only exploit, only communicate), teammates now correctly interpret the meaning behind their partner's actions. Our results suggest that leveraging and alternating roles leads to performance comparable to teams that explicitly exchange messages. You can find more images and videos of our experimental setups at http://ai.stanford.edu/blog/learning-from-partners/.
Conditional Driving from Natural Language Instructions
Venue: arXiv cs.RO 2019
Year: 2019

Widespread adoption of self-driving cars will depend not only on their safety but largely on their ability to interact with human users. Just like human drivers, self-driving cars will be expected to understand and safely follow natural-language directions that suddenly alter the pre-planned route according to user's preference or in presence of ambiguities, particularly in locations with poor or outdated map coverage. To this end, we propose a language-grounded driving agent implementing a hierarchical policy using recurrent layers and gated attention. The hierarchical approach enables us to reason both in terms of high-level language instructions describing long time horizons and low-level, complex, continuous state/action spaces required for real-time control of a self-driving car. We train our policy with conditional imitation learning from realistic language data collected from human drivers and navigators. Through quantitative and interactive experiments within the CARLA framework, we show that our model can successfully interpret language instructions and follow them safely, even when generalizing to previously unseen environments. Code and video are available at https://sites.google.com/view/language-grounded-driving.
Node-Weighted Network Design in Planar and Minor-Closed Families of Graphs
Venue: arXiv cs.DS 2019
Year: 2019

We consider node-weighted survivable network design (SNDP) in planar graphs and minor-closed families of graphs. The input consists of a node-weighted undirected graph $G=(V,E)$ and integer connectivity requirements $r(uv)$ for each unordered pair of nodes $uv$. The goal is to find a minimum weighted subgraph $H$ of $G$ such that $H$ contains $r(uv)$ disjoint paths between $u$ and $v$ for each node pair $uv$. Three versions of the problem are edge-connectivity SNDP (EC-SNDP), element-connectivity SNDP (Elem-SNDP) and vertex-connectivity SNDP (VC-SNDP) depending on whether the paths are required to be edge, element or vertex disjoint respectively. Our main result is an $O(k)$-approximation algorithm for EC-SNDP and Elem-SNDP when the input graph is planar or more generally if it belongs to a proper minor-closed family of graphs; here $k=\max_{uv} r(uv)$ is the maximum connectivity requirement. This improves upon the $O(k \log n)$-approximation known for node-weighted EC-SNDP and Elem-SNDP in general graphs [Nutov, TALG'12]. We also obtain an $O(1)$ approximation for node-weighted VC-SNDP when the connectivity requirements are in $\{0,1,2\}$; for higher connectivity our result for Elem-SNDP can be used in a black-box fashion to obtain a logarithmic factor improvement over currently known general graph results. Our results are inspired by, and generalize, the work of [Demaine, Hajiaghayi and Klein, TALG'14] who obtained constant factor approximations for node-weighted Steiner tree and Steiner forest problems in planar graphs and proper minor-closed families of graphs via a primal-dual algorithm.
A Combined Deep Learning-Gradient Boosting Machine Framework for Fluid Intelligence Prediction
Venue: arXiv cs.CV 2019
Year: 2019

The ABCD Neurocognitive Prediction Challenge is a community driven competition asking competitors to develop algorithms to predict fluid intelligence score from T1-w MRIs. In this work, we propose a deep learning combined with gradient boosting machine framework to solve this task. We train a convolutional neural network to compress the high dimensional MRI data and learn meaningful image features by predicting the 123 continuous-valued derived data provided with each MRI. These extracted features are then used to train a gradient boosting machine that predicts the residualized fluid intelligence score. Our approach achieved mean square error (MSE) scores of 18.4374, 68.7868, and 96.1806 for the training, validation, and test set respectively.
Using Cloud and Fog Computing for Large Scale IoT-based Urban Sound Classification
Venue: arXiv cs.NI 2019
Year: 2019

The Internet of Things (IoT) has become the forefront of bridging different technologies together. It brings rise to online computational services that make mundane tasks convenient. However, the volume of devices connecting to the network started to increase. In turn, services that thrived on centralized storage are being strained and overloaded. As applications and software advances, processing and computational power become a concern to technology companies. With data risks and large numbers of connected devices, cloud computing has become outdated. Devices are forced to commit unnecessary expenses to stay relevant in the market due to the increase in software complexity. This need for change resulted in the introduction of edge computing. Edge computing distributes the computational strain between the server and the devices. This contribution allows the cloud to accommodate more users and devices are no longer in need to make significant changes to their design every so often. Many real-time applications have evolved to require high amounts of processing power to execute. For example, sound classification comes with massive computational needs due to its affiliation with neural networks and deep learning. This paper aims to create a feasible and deployable real-time sound classification system. There were three configurations tested in this paper. The results of our experiments show that cloud computing and edge computing alone cannot cater to a technological market that is exponentially growing in size and complexity. However, the same results show promise in finding optimal configurations in terms of a combination of end device power consumption, application runtime and server latency to systems instead of focusing on a single model. Overall, it is better to take into consideration the strengths and weaknesses of each computing architecture.
Developing Self-Adaptive Microservice Systems: Challenges and Directions
Venue: arXiv cs.SE 2019
Year: 2019

A self-adaptive system can dynamically monitor and adapt its behavior to preserve or enhance its quality attributes under uncertain operating conditions. This article identifies key challenges for the development of microservice applications as self-adaptive systems, using a cloud-based intelligent video surveillance application as a motivating example. It also suggests potential new directions for addressing most of the identified challenges by leveraging existing microservice practices and technologies.
On the asymptotic analysis of the high-order statistics of the channel capacity over generalized fading channels
Venue: arXiv cs.IT 2019
Year: 2019

In this article, we provide further asymptotic analysis to the higher-order statistics (HOS) of the channel capacity over generalized fading channels, especially by proposing simple and closed-form expressions each of which can be easily computed as a tight-bound revealing the existence of constant gap between the actual and asymptotic HOS of the channel capacity in the limit of both high- and low-signal to noise ratios (SNRs). As such, we show that these closed-form asymptotic expressions are insightful enough to comprehend the diversity gains. The mathematical formalism we followed in this article is illustrated with some selected numerical examples that validate the correctness of our newly derived asymptotic results.