OpenRaiser/Intern-Atlas huggingface dataset:
Dense and sparse vertex connectivity in networks
Venue: arXiv cs.SI 2020
Year: 2020

The different approaches developed to analyze the structure of complex networks have generated a large number of studies. In the field of social networks at least, studies mainly address the detection and analysis of communities. In this paper, we challenge these approaches and focus on nodes that have meaningful local interactions able to identify the internal organization of communities or the way communities are assembled. We propose an algorithm, ItRich, to identify this type of nodes, based on the decomposition of a graph into successive, less and less dense, layers. Our method is tested on synthetic and real data sets and meshes well with other methods such as community detection or k-core decomposition.
Delta Descriptors: Change-Based Place Representation for Robust Visual Localization
Venue: arXiv cs.CV 2020
Year: 2020

Visual place recognition is challenging because there are so many factors that can cause the appearance of a place to change, from day-night cycles to seasonal change to atmospheric conditions. In recent years a large range of approaches have been developed to address this challenge including deep-learnt image descriptors, domain translation, and sequential filtering, all with shortcomings including generality and velocity-sensitivity. In this paper we propose a novel descriptor derived from tracking changes in any learned global descriptor over time, dubbed Delta Descriptors. Delta Descriptors mitigate the offsets induced in the original descriptor matching space in an unsupervised manner by considering temporal differences across places observed along a route. Like all other approaches, Delta Descriptors have a shortcoming - volatility on a frame to frame basis - which can be overcome by combining them with sequential filtering methods. Using two benchmark datasets, we first demonstrate the high performance of Delta Descriptors in isolation, before showing new state-of-the-art performance when combined with sequence-based matching. We also present results demonstrating the approach working with four different underlying descriptor types, and two other beneficial properties of Delta Descriptors in comparison to existing techniques: their increased inherent robustness to variations in camera motion and a reduced rate of performance degradation as dimensional reduction is applied. Source code is made available at https://github.com/oravus/DeltaDescriptors.
Unique Faces Recognition in Videos
Venue: arXiv cs.CV 2020
Year: 2020

This paper tackles face recognition in videos employing metric learning methods and similarity ranking models. The paper compares the use of the Siamese network with contrastive loss and Triplet Network with triplet loss implementing the following architectures: Google/Inception architecture, 3D Convolutional Network (C3D), and a 2-D Long short-term memory (LSTM) Recurrent Neural Network. We make use of still images and sequences from videos for training the networks and compare the performances implementing the above architectures. The dataset used was the YouTube Face Database designed for investigating the problem of face recognition in videos. The contribution of this paper is two-fold: to begin, the experiments have established 3-D Convolutional networks and 2-D LSTMs with the contrastive loss on image sequences do not outperform Google/Inception architecture with contrastive loss in top $n$ rank face retrievals with still images. However, the 3-D Convolution networks and 2-D LSTM with triplet Loss outperform the Google/Inception with triplet loss in top $n$ rank face retrievals on the dataset; second, a Support Vector Machine (SVM) was used in conjunction with the CNNs' learned feature representations for facial identification. The results show that feature representation learned with triplet loss is significantly better for n-shot facial identification compared to contrastive loss. The most useful feature representations for facial identification are from the 2-D LSTM with triplet loss. The experiments show that learning spatio-temporal features from video sequences is beneficial for facial recognition in videos.
Deep Drone Acrobatics
Venue: arXiv cs.RO 2020
Year: 2020

Performing acrobatic maneuvers with quadrotors is extremely challenging. Acrobatic flight requires high thrust and extreme angular accelerations that push the platform to its physical limits. Professional drone pilots often measure their level of mastery by flying such maneuvers in competitions. In this paper, we propose to learn a sensorimotor policy that enables an autonomous quadrotor to fly extreme acrobatic maneuvers with only onboard sensing and computation. We train the policy entirely in simulation by leveraging demonstrations from an optimal controller that has access to privileged information. We use appropriate abstractions of the visual input to enable transfer to a real quadrotor. We show that the resulting policy can be directly deployed in the physical world without any fine-tuning on real data. Our methodology has several favorable properties: it does not require a human expert to provide demonstrations, it cannot harm the physical system during training, and it can be used to learn maneuvers that are challenging even for the best human pilots. Our approach enables a physical quadrotor to fly maneuvers such as the Power Loop, the Barrel Roll, and the Matty Flip, during which it incurs accelerations of up to 3g.
A Data Streaming Process Framework for Autonomous Driving By Edge
Venue: arXiv cs.DC 2020
Year: 2020

In recent years, with the rapid development of sensing technology and the Internet of Things (IoT), sensors play increasingly important roles in traffic control, medical monitoring, industrial production and etc. They generated high volume of data in a streaming way that often need to be processed in real time. Therefore, streaming data computing technology plays an indispensable role in the real-time processing of sensor data in high throughput but low latency. In view of the above problems, the proposed framework is implemented on top of Spark Streaming, which builds up a gray model based traffic flow monitor, a traffic prediction orientated prediction layer and a fuzzy control based Batch Interval dynamic adjustment layer for Spark Streaming. It could forecast the variation of sensors data arrive rate, make streaming Batch Interval adjustment in advance and implement real-time streaming process by edge. Therefore, it can realize the monitor and prediction of the data flow changes of the autonomous driving vehicle sensor data in geographical coverage of edge computing node area, meanwhile minimize the end-to-end latency but satisfy the application throughput requirements. The experiments show that it can predict short-term traffic with no more than 4% relative error in a whole day. By making batch consuming rate close to data generating rate, it can maintain system stability well even when arrival data rate changes rapidly. The Batch Interval can be converged to a suitable value in two minutes when data arrival rate is doubled. Compared with vanilla version Spark Streaming, where there has serious task accumulation and introduces large delay, it can reduce 35% latency by squeezing Batch Interval when data arrival rate is low; it also can significantly improve system throughput by only at most 25% Batch Interval increase when data arrival rate is high.
Objective Caml for Multicore Architectures
Venue: arXiv cs.PL 2020
Year: 2020

Objective Caml is a famous dialect of the ML family languages. It is well-known for its performance as a compiled programming language, notably thanks to its incremental generational automatic memory collection. However, for historical reasons, the latter was built for monocore processors. One consequence is the runtime library assumes there is effectively no more than one thread running at a time, which allows many optimisations for monocore architectures: very few thread mutexes are sufficient to prevent more than a single thread to run at a time. This makes memory allocation and collection quite easier. The way it was built makes it not possible to take advantage of now widespread multicore CPU architectures. This paper presents our feedback on removing Objective Caml's garbage collector and designing a "Stop-The-World Stop&Copy" garbage collector to permit threads to take advantage of multicore architectures.
Heterogeneous Graph Attention Networks for Early Detection of Rumors on Twitter
Venue: arXiv cs.SI 2020
Year: 2020

With the rapid development of mobile Internet technology and the widespread use of mobile devices, it becomes much easier for people to express their opinions on social media. The openness and convenience of social media platforms provide a free expression for people but also cause new social problems. The widespread of false rumors on social media can bring about the panic of the public and damage personal reputation, which makes rumor automatic detection technology become particularly necessary. The majority of existing methods for rumor detection focus on mining effective features from text contents, user profiles, and patterns of propagation. Nevertheless, these methods do not take full advantage of global semantic relations of the text contents, which characterize the semantic commonality of rumors as a key factor for detecting rumors. In this paper, we construct a tweet-word-user heterogeneous graph based on the text contents and the source tweet propagations of rumors. A meta-path based heterogeneous graph attention network framework is proposed to capture the global semantic relations of text contents, together with the global structure information of source tweet propagations for rumor detection. Experiments on real-world Twitter data demonstrate the superiority of the proposed approach, which also has a comparable ability to detect rumors at a very early stage.
WasteNet: Waste Classification at the Edge for Smart Bins
Venue: arXiv cs.CV 2020
Year: 2020

Smart Bins have become popular in smart cities and campuses around the world. These bins have a compaction mechanism that increases the bins' capacity as well as automated real-time collection notifications. In this paper, we propose WasteNet, a waste classification model based on convolutional neural networks that can be deployed on a low power device at the edge of the network, such as a Jetson Nano. The problem of segregating waste is a big challenge for many countries around the world. Automated waste classification at the edge allows for fast intelligent decisions in smart bins without needing access to the cloud. Waste is classified into six categories: paper, cardboard, glass, metal, plastic and other. Our model achieves a 97\% prediction accuracy on the test dataset. This level of classification accuracy will help to alleviate some common smart bin problems, such as recycling contamination, where different types of waste become mixed with recycling waste causing the bin to be contaminated. It also makes the bins more user friendly as citizens do not have to worry about disposing their rubbish in the correct bin as the smart bin will be able to make the decision for them.
Computational Design and Evaluation Methods for Empowering Non-Experts in Digital Fabrication
Venue: arXiv cs.GR 2020
Year: 2020

Despite the increasing availability of personal fabrication hardware and services, the true potential of digital fabrication remains unrealized due to lack of computational techniques that can support 3D shape design by non-experts. This work develops computational methods that address two key aspects of content creation:(1) Function-driven design synthesis, (2) Design assessment. For design synthesis, a generative shape modeling algorithm that facilitates automatic geometry synthesis and user-driven modification for non-experts is introduced. A critical observation that arises from this study is that the most geometrical specifications are dictated by functional requirements. To support design by high-level functional prescriptions, a physics based shape optimization method for compliant coupling behavior design has been developed. In line with this idea, producing complex 3D surfaces from flat 2D sheets by exploiting the concept of buckling beams has also been explored. Effective design assessment, the second key aspect, becomes critical for problems in which computational solutions do not exist. For these problems, this work proposes crowdsourcing as a way to empower non-experts in esoteric design domains that traditionally require expertise and specialized knowledge.
Recent Advances in 3D Object and Hand Pose Estimation
Venue: arXiv cs.CV 2020
Year: 2020

3D object and hand pose estimation have huge potentials for Augmented Reality, to enable tangible interfaces, natural interfaces, and blurring the boundaries between the real and virtual worlds. In this chapter, we present the recent developments for 3D object and hand pose estimation using cameras, and discuss their abilities and limitations and the possible future development of the field.
Trust-UBA: A Corpus for the Study of the Manifestation of Trust in Speech
Venue: arXiv cs.HC 2020
Year: 2020

This paper describes a novel protocol for collecting speech data from subjects induced to have different degrees of trust in the skills of a conversational agent. The protocol consists of an interactive session where the subject is asked to respond to a series of factual questions with the help of a virtual assistant. In order to induce subjects to either trust or distrust the agent's skills, they are first informed that it was previously rated by other users as being either good or bad; subsequently, the agent answers the subjects' questions consistently to its alleged abilities. All interactions are speech-based, with subjects and agents communicating verbally, which allows the recording of speech produced under different trust conditions. We collected a speech corpus in Argentine Spanish using this protocol, which we are currently using to study the feasibility of predicting the degree of trust from speech. We find clear evidence that the protocol effectively succeeded in influencing subjects into the desired mental state of either trusting or distrusting the agent's skills, and present preliminary results of a perceptual study of the degree of trust performed by expert listeners. The collected speech dataset will be made publicly available once ready.
What Matters In On-Policy Reinforcement Learning? A Large-Scale Empirical Study
Venue: arXiv cs.LG 2020
Year: 2020

In recent years, on-policy reinforcement learning (RL) has been successfully applied to many different continuous control tasks. While RL algorithms are often conceptually simple, their state-of-the-art implementations take numerous low- and high-level design decisions that strongly affect the performance of the resulting agents. Those choices are usually not extensively discussed in the literature, leading to discrepancy between published descriptions of algorithms and their implementations. This makes it hard to attribute progress in RL and slows down overall progress [Engstrom'20]. As a step towards filling that gap, we implement >50 such ``choices'' in a unified on-policy RL framework, allowing us to investigate their impact in a large-scale empirical study. We train over 250'000 agents in five continuous control environments of different complexity and provide insights and practical recommendations for on-policy training of RL agents.
Leveraging Bitcoin Testnet for Bidirectional Botnet Command and Control Systems
Venue: arXiv cs.CR 2020
Year: 2020

Over the past twenty years, the number of devices connected to the Internet grew exponentially. Botnets benefited from this rise to increase their size and the magnitude of their attacks. However, they still have a weak point in their Command & Control (C&C) system, which is often based on centralized services or require a complex infrastructure to keep operating without being taken down by authorities. The recent spread of blockchain technologies may give botnets a powerful tool to make them very hard to disrupt. Recent research showed how it is possible to embed C&C messages in Bitcoin transactions, making them nearly impossible to block. Nevertheless, transactions have a cost and allow very limited amounts of data to be transmitted. Because of that, only messages from the botmaster to the bots are sent via Bitcoin, while bots are assumed to communicate through external channels. Furthermore, for the same reason, Bitcoin-based messages are sent in clear. In this paper we show how, using Bitcoin Testnet, it is possible to overcome these limitations and implement a cost-free, bidirectional, and encrypted C&C channel between the botmaster and the bots. We propose a communication protocol and analyze its viability in real life. Our results show that this approach would enable a botmaster to build a robust and hard-to-disrupt C&C system at virtually no cost, thus representing a realistic threat for which countermeasures should be devised.
Optimizing Smart Grid Aggregators and Measuring Degree of Privacy in a Distributed Trust Based Anonymous Aggregation System
Venue: arXiv cs.CR 2020
Year: 2020

A smart grid is an advanced method for supplying electricity to the consumers alleviating the limitations of the existing system. It causes frequent meter reading transmission from the end-user to the supplier. This frequent data transmission poses privacy risks. Several works have been proposed to solve this problem but cannot ensure privacy at the optimal level. This work is based on a distributed trust-based data aggregation system leveraging a secret sharing mechanism. In this work, we show that {\em three aggregators} are enough for ensuring consumer's privacy in a distributed trust-based system. We leverage the idea of anonymity in our research and show that neither an active attacker nor a passive attacker can breach consumer's privacy. We show proof of our concept mathematically and in a cryptographic game based mechanism. We name our new proposed system \emph{"Distributed Trust Based Anonymous System (DTBAS)"}.
Kalman Filter Based Multiple Person Head Tracking
Venue: arXiv cs.CV 2020
Year: 2020

For multi-target tracking, target representation plays a crucial rule in performance. State-of-the-art approaches rely on the deep learning-based visual representation that gives an optimal performance at the cost of high computational complexity. In this paper, we come up with a simple yet effective target representation for human tracking. Our inspiration comes from the fact that the human body goes through severe deformation and inter/intra occlusion over the passage of time. So, instead of tracking the whole body part, a relative rigid organ tracking is selected for tracking the human over an extended period of time. Hence, we followed the tracking-by-detection paradigm and generated the target hypothesis of only the spatial locations of heads in every frame. After the localization of head location, a Kalman filter with a constant velocity motion model is instantiated for each target that follows the temporal evolution of the targets in the scene. For associating the targets in the consecutive frames, combinatorial optimization is used that associates the corresponding targets in a greedy fashion. Qualitative results are evaluated on four challenging video surveillance dataset and promising results has been achieved.
Emora STDM: A Versatile Framework for Innovative Dialogue System Development
Venue: arXiv cs.CL 2020
Year: 2020

This demo paper presents Emora STDM (State Transition Dialogue Manager), a dialogue system development framework that provides novel workflows for rapid prototyping of chat-based dialogue managers as well as collaborative development of complex interactions. Our framework caters to a wide range of expertise levels by supporting interoperability between two popular approaches, state machine and information state, to dialogue management. Our Natural Language Expression package allows seamless integration of pattern matching, custom NLP modules, and database querying, that makes the workflows much more efficient. As a user study, we adopt this framework to an interdisciplinary undergraduate course where students with both technical and non-technical backgrounds are able to develop creative dialogue managers in a short period of time.
Exploration by Maximizing Rényi Entropy for Reward-Free RL Framework
Venue: arXiv cs.LG 2020
Year: 2020

Exploration is essential for reinforcement learning (RL). To face the challenges of exploration, we consider a reward-free RL framework that completely separates exploration from exploitation and brings new challenges for exploration algorithms. In the exploration phase, the agent learns an exploratory policy by interacting with a reward-free environment and collects a dataset of transitions by executing the policy. In the planning phase, the agent computes a good policy for any reward function based on the dataset without further interacting with the environment. This framework is suitable for the meta RL setting where there are many reward functions of interest. In the exploration phase, we propose to maximize the Renyi entropy over the state-action space and justify this objective theoretically. The success of using Renyi entropy as the objective results from its encouragement to explore the hard-to-reach state-actions. We further deduce a policy gradient formulation for this objective and design a practical exploration algorithm that can deal with complex environments. In the planning phase, we solve for good policies given arbitrary reward functions using a batch RL algorithm. Empirically, we show that our exploration algorithm is effective and sample efficient, and results in superior policies for arbitrary reward functions in the planning phase.
CLEval: Character-Level Evaluation for Text Detection and Recognition Tasks
Venue: arXiv cs.CV 2020
Year: 2020

Despite the recent success of text detection and recognition methods, existing evaluation metrics fail to provide a fair and reliable comparison among those methods. In addition, there exists no end-to-end evaluation metric that takes characteristics of OCR tasks into account. Previous end-to-end metric contains cascaded errors from the binary scoring process applied in both detection and recognition tasks. Ignoring partially correct results raises a gap between quantitative and qualitative analysis, and prevents fine-grained assessment. Based on the fact that character is a key element of text, we hereby propose a Character-Level Evaluation metric (CLEval). In CLEval, the \textit{instance matching} process handles split and merge detection cases, and the \textit{scoring process} conducts character-level evaluation. By aggregating character-level scores, the CLEval metric provides a fine-grained evaluation of end-to-end results composed of the detection and recognition as well as individual evaluations for each module from the end-performance perspective. We believe that our metrics can play a key role in developing and analyzing state-of-the-art text detection and recognition methods. The evaluation code is publicly available at https://github.com/clovaai/CLEval.