OpenRaiser/Intern-Atlas huggingface dataset:
An Argumentation-based Approach for Identifying and Dealing with Incompatibilities among Procedural Goals
Venue: arXiv cs.AI 2020
Year: 2020

During the first step of practical reasoning, i.e. deliberation, an intelligent agent generates a set of pursuable goals and then selects which of them he commits to achieve. An intelligent agent may in general generate multiple pursuable goals, which may be incompatible among them. In this paper, we focus on the definition, identification and resolution of these incompatibilities. The suggested approach considers the three forms of incompatibility introduced by Castelfranchi and Paglieri, namely the terminal incompatibility, the instrumental or resources incompatibility and the superfluity. We characterise computationally these forms of incompatibility by means of arguments that represent the plans that allow an agent to achieve his goals. Thus, the incompatibility among goals is defined based on the conflicts among their plans, which are represented by means of attacks in an argumentation framework. We also work on the problem of goals selection; we propose to use abstract argumentation theory to deal with this problem, i.e. by applying argumentation semantics. We use a modified version of the "cleaner world" scenario in order to illustrate the performance of our proposal.
SONYC-UST-V2: An Urban Sound Tagging Dataset with Spatiotemporal Context
Venue: arXiv cs.SD 2020
Year: 2020

We present SONYC-UST-V2, a dataset for urban sound tagging with spatiotemporal information. This dataset is aimed for the development and evaluation of machine listening systems for real-world urban noise monitoring. While datasets of urban recordings are available, this dataset provides the opportunity to investigate how spatiotemporal metadata can aid in the prediction of urban sound tags. SONYC-UST-V2 consists of 18510 audio recordings from the "Sounds of New York City" (SONYC) acoustic sensor network, including the timestamp of audio acquisition and location of the sensor. The dataset contains annotations by volunteers from the Zooniverse citizen science platform, as well as a two-stage verification with our team. In this article, we describe our data collection procedure and propose evaluation metrics for multilabel classification of urban sound tags. We report the results of a simple baseline model that exploits spatiotemporal information.
Graph Neural Network based Service Function Chaining for Automatic Network Control
Venue: arXiv cs.NI 2020
Year: 2020

Software-defined networking (SDN) and the network function virtualization (NFV) led to great developments in software based control technology by decreasing expenditures. Service function chaining (SFC) is an important technology to find efficient paths in network servers to process all of the requested virtualized network functions (VNF). However, SFC is challenging since it has to maintain high Quality of Service (QoS) even for complicated situations. Although some works have been conducted for such tasks with high-level intelligent models like deep neural networks (DNNs), those approaches are not efficient in utilizing the topology information of networks and cannot be applied to networks with dynamically changing topology since their models assume that the topology is fixed. In this paper, we propose a new neural network architecture for SFC, which is based on graph neural network (GNN) considering the graph-structured properties of network topology. The proposed SFC model consists of an encoder and a decoder, where the encoder finds the representation of the network topology, and then the decoder estimates probabilities of neighborhood nodes and their probabilities to process a VNF. In the experiments, our proposed architecture outperformed previous performances of DNN based baseline model. Moreover, the GNN based model can be applied to a new network topology without re-designing and re-training.
The Robotic Vision Scene Understanding Challenge
Venue: arXiv cs.RO 2020
Year: 2020

Being able to explore an environment and understand the location and type of all objects therein is important for indoor robotic platforms that must interact closely with humans. However, it is difficult to evaluate progress in this area due to a lack of standardized testing which is limited due to the need for active robot agency and perfect object ground-truth. To help provide a standard for testing scene understanding systems, we present a new robot vision scene understanding challenge using simulation to enable repeatable experiments with active robot agency. We provide two challenging task types, three difficulty levels, five simulated environments and a new evaluation measure for evaluating 3D cuboid object maps. Our aim is to drive state-of-the-art research in scene understanding through enabling evaluation and comparison of active robotic vision systems.
Image Conditioned Keyframe-Based Video Summarization Using Object Detection
Venue: arXiv cs.CV 2020
Year: 2020

Video summarization plays an important role in selecting keyframe for understanding a video. Traditionally, it aims to find the most representative and diverse contents (or frames) in a video for short summaries. Recently, query-conditioned video summarization has been introduced, which considers user queries to learn more user-oriented summaries and its preference. However, there are obstacles in text queries for user subjectivity and finding similarity between the user query and input frames. In this work, (i) Image is introduced as a query for user preference (ii) a mathematical model is proposed to minimize redundancy based on the loss function & summary variance and (iii) the similarity score between the query image and input video to obtain the summarized video. Furthermore, the Object-based Query Image (OQI) dataset has been introduced, which contains the query images. The proposed method has been validated using UT Egocentric (UTE) dataset. The proposed model successfully resolved the issues of (i) user preference, (ii) recognize important frames and selecting that keyframe in daily life videos, with different illumination conditions. The proposed method achieved 57.06% average F1-Score for UTE dataset and outperforms the existing state-of-theart by 11.01%. The process time is 7.81 times faster than actual time of video Experiments on a recently proposed UTE dataset show the efficiency of the proposed method
Capacity-Approaching Autoencoders for Communications
Venue: arXiv cs.IT 2020
Year: 2020

The autoencoder concept has fostered the reinterpretation and the design of modern communication systems. It consists of an encoder, a channel, and a decoder block which modify their internal neural structure in an end-to-end learning fashion. However, the current approach to train an autoencoder relies on the use of the cross-entropy loss function. This approach can be prone to overfitting issues and often fails to learn an optimal system and signal representation (code). In addition, less is known about the autoencoder ability to design channel capacity-approaching codes, i.e., codes that maximize the input-output information under a certain power constraint. The task being even more formidable for an unknown channel for which the capacity is unknown and therefore it has to be learnt. In this paper, we address the challenge of designing capacity-approaching codes by incorporating the presence of the communication channel into a novel loss function for the autoencoder training. In particular, we exploit the mutual information between the transmitted and received signals as a regularization term in the cross-entropy loss function, with the aim of controlling the amount of information stored. By jointly maximizing the mutual information and minimizing the cross-entropy, we propose a methodology that a) computes an estimate of the channel capacity and b) constructs an optimal coded signal approaching it. Several simulation results offer evidence of the potentiality of the proposed method.
DMR-based Technique for Fault Tolerant AES S-box Architecture
Venue: arXiv cs.AR 2020
Year: 2020

This paper presents a high-throughput fault-resilient hardware implementation of AES S-box, called HFS-box. If a transient natural or even malicious fault in each pipeline stage is detected, the corresponding error signal becomes high and as a result, the control unit holds the output of our proposed DMR voter till the fault effect disappears. The proposed low-cost HFS-box provides a high capability of fault-tolerant against transient faults with any duration by putting low area overhead, i.e. 137%, and low throughput degradation, i.e. 11.3%, on the original implementation.
A Toolkit to Generate Social Navigation Datasets
Venue: arXiv cs.RO 2020
Year: 2020

Social navigation datasets are necessary to assess social navigation algorithms and train machine learning algorithms. Most of the currently available datasets target pedestrians' movements as a pattern to be replicated by robots. It can be argued that one of the main reasons for this to happen is that compiling datasets where real robots are manually controlled, as they would be expected to behave when moving, is a very resource-intensive task. Another aspect that is often missing in datasets is symbolic information that could be relevant, such as human activities, relationships or interactions. Unfortunately, the available datasets targeting robots and supporting symbolic information are restricted to static scenes. This paper argues that simulation can be used to gather social navigation data in an effective and cost-efficient way and presents a toolkit for this purpose. A use case studying the application of graph neural networks to create learned control policies using supervised learning is presented as an example of how it can be used.
Meta Learning for Few-Shot One-class Classification
Venue: arXiv cs.CV 2020
Year: 2020

We propose a method that can perform one-class classification given only a small number of examples from the target class and none from the others. We formulate the learning of meaningful features for one-class classification as a meta-learning problem in which the meta-training stage repeatedly simulates one-class classification, using the classification loss of the chosen algorithm to learn a feature representation. To learn these representations, we require only multiclass data from similar tasks. We show how the Support Vector Data Description method can be used with our method, and also propose a simpler variant based on Prototypical Networks that obtains comparable performance, indicating that learning feature representations directly from data may be more important than which one-class algorithm we choose. We validate our approach by adapting few-shot classification datasets to the few-shot one-class classification scenario, obtaining similar results to the state-of-the-art of traditional one-class classification, and that improves upon that of one-class classification baselines employed in the few-shot setting. Our code is available at https://github.com/gdahia/meta_occ
IndoNLU: Benchmark and Resources for Evaluating Indonesian Natural Language Understanding
Venue: arXiv cs.CL 2020
Year: 2020

Although Indonesian is known to be the fourth most frequently used language over the internet, the research progress on this language in the natural language processing (NLP) is slow-moving due to a lack of available resources. In response, we introduce the first-ever vast resource for the training, evaluating, and benchmarking on Indonesian natural language understanding (IndoNLU) tasks. IndoNLU includes twelve tasks, ranging from single sentence classification to pair-sentences sequence labeling with different levels of complexity. The datasets for the tasks lie in different domains and styles to ensure task diversity. We also provide a set of Indonesian pre-trained models (IndoBERT) trained from a large and clean Indonesian dataset Indo4B collected from publicly available sources such as social media texts, blogs, news, and websites. We release baseline models for all twelve tasks, as well as the framework for benchmark evaluation, and thus it enables everyone to benchmark their system performances.
Automatic cinematography for 360 video
Venue: arXiv cs.CV 2020
Year: 2020

We describe our method for automatic generation of a visually interesting camera path (automatic cinematography)from a 360 video. Based on the information from the scene objects, multiple shot hypotheses for different shot types are constructed and the best one is rendered.
Machine Learning and Data Science approach towards trend and predictors analysis of CDC Mortality Data for the USA
Venue: arXiv cs.LG 2020
Year: 2020

The research on mortality is an active area of research for any country where the conclusions are driven from the provided data and conditions. The domain knowledge is an essential but not a mandatory skill (though some knowledge is still required) in order to derive conclusions based on data intuition using machine learning and data science practices. The purpose of conducting this project was to derive conclusions based on the statistics from the provided dataset and predict label(s) of the dataset using supervised or unsupervised learning algorithms. The study concluded (based on a sample) life expectancy regardless of gender, and their central tendencies; Marital status of the people also affected how frequent deaths were for each of them. The study also helped in finding out that due to more categorical and numerical data, anomaly detection or under-sampling could be a viable solution since there are possibilities of more class labels than the other(s). The study shows that machine learning predictions aren't as viable for the data as it might be apparent.
Robust Neural Machine Translation: Modeling Orthographic and Interpunctual Variation
Venue: arXiv cs.CL 2020
Year: 2020

Neural machine translation systems typically are trained on curated corpora and break when faced with non-standard orthography or punctuation. Resilience to spelling mistakes and typos, however, is crucial as machine translation systems are used to translate texts of informal origins, such as chat conversations, social media posts and web pages. We propose a simple generative noise model to generate adversarial examples of ten different types. We use these to augment machine translation systems' training data and show that, when tested on noisy data, systems trained using adversarial examples perform almost as well as when translating clean data, while baseline systems' performance drops by 2-3 BLEU points. To measure the robustness and noise invariance of machine translation systems' outputs, we use the average translation edit rate between the translation of the original sentence and its noised variants. Using this measure, we show that systems trained on adversarial examples on average yield 50% consistency improvements when compared to baselines trained on clean data.
A deep-learning model for evaluating and predicting the impact of lockdown policies on COVID-19 cases
Venue: arXiv cs.SI 2020
Year: 2020

To reduce the impact of COVID-19 pandemic most countries have implemented several counter-measures to control the virus spread including school and border closing, shutting down public transport and workplace and restrictions on gathering. In this research work, we propose a deep-learning prediction model for evaluating and predicting the impact of various lockdown policies on daily COVID-19 cases. This is achieved by first clustering countries having similar lockdown policies, then training a prediction model based on the daily cases of the countries in each cluster along with the data describing their lockdown policies. Once the model is trained, it can used to evaluate several scenarios associated to lockdown policies and investigate their impact on the predicted COVID cases. Our evaluation experiments, conducted on Qatar as a use case, shows that the proposed approach achieved competitive prediction accuracy. Additionally, our findings highlighted that lifting restrictions particularly on schools and border opening would result in significant increase in the number of cases during the study period.
Physically Embedded Planning Problems: New Challenges for Reinforcement Learning
Venue: arXiv cs.AI 2020
Year: 2020

Recent work in deep reinforcement learning (RL) has produced algorithms capable of mastering challenging games such as Go, chess, or shogi. In these works the RL agent directly observes the natural state of the game and controls that state directly with its actions. However, when humans play such games, they do not just reason about the moves but also interact with their physical environment. They understand the state of the game by looking at the physical board in front of them and modify it by manipulating pieces using touch and fine-grained motor control. Mastering complicated physical systems with abstract goals is a central challenge for artificial intelligence, but it remains out of reach for existing RL algorithms. To encourage progress towards this goal we introduce a set of physically embedded planning problems and make them publicly available. We embed challenging symbolic tasks (Sokoban, tic-tac-toe, and Go) in a physics engine to produce a set of tasks that require perception, reasoning, and motor control over long time horizons. Although existing RL algorithms can tackle the symbolic versions of these tasks, we find that they struggle to master even the simplest of their physically embedded counterparts. As a first step towards characterizing the space of solution to these tasks, we introduce a strong baseline that uses a pre-trained expert game player to provide hints in the abstract space to an RL agent's policy while training it on the full sensorimotor control task. The resulting agent solves many of the tasks, underlining the need for methods that bridge the gap between abstract planning and embodied control. See illustrating video at https://youtu.be/RwHiHlym_1k.
Graviton: interchain swaps and wrapped tokens liquidity incentivisation solution
Venue: arXiv cs.CR 2020
Year: 2020

This paper discusses the issues with liquidity that inhibit adoption of so-called wrapped tokens, i.e. digital assets issued in one blockchain ecosystem (origin) with representation in other blockchain networks (destination), and an incentive model and a governance mechanism for solving these issues are suggested. The proposed liquidity model called Graviton can be implemented both within the framework of a single destination chain, or as a blockchain-agnostic solution combining various blockchain platforms together and providing liquidity to wrapped tokens in each of them. This model does not depend on how cross-chain transfer gateways are implemented, and can work with both centralized gates and bridges, or decentralized trustless gateways, as well as gateways based on oracle networks and threshold signatures.
Construction of Hyperbolic Signal Sets from the Uniformization of Hyperelliptic Curves
Venue: arXiv cs.IT 2020
Year: 2020

In this paper, we present a new approach to the problem of designing hyperbolic signal sets matched to groups by use of Whittaker's proposal in the uniformization of hyperelliptic curves via Fuchsian differential equations (FDEs). This systematic process consists of the steps: 1) Obtaining the genus, g, by embedding a discrete memoryless channel (DMC) on a Riemann surface; 2) Select a set of symmetric points in the Poincaré disk to establish the hyperelliptic curve; 3) The Fuchsian group uniformizing region comes by the use of the FDE; 4) Quotients of the FDE linearly independent solutions, give rise to the generators of the associated Fuchsian group. Equivalently, this implies the determination of the decision region (Voronoi region) of a digital signal. Hence, the following results are achieved: 1) from the solutions of the FDE, the Fuchsian group generators are established. Since the vertices of the fundamental polygon are at the boundary of unit disk, its area (largest possible) implies the least symbol error probability as a performance measure of a digital communication system; 2) a relation between the parameters of the tessellation {p,q} and the degree of the hyperelliptic curve is established. Knowing g, related to the hyperelliptic curve degree, and p, number of sides of the fundamental polygon derived from Whittaker's uniformizing procedure, the value of q is obtained from the Euler characteristic leading to one of the {4g,4g} or {4g+2, 2g+1} or {12g-6,3} tessellation. These tessellations are important due to their rich geometric and algebraic structures, both required in classical and quantum coding theory applications.
Computer-Aided Generation of N-shift RWS
Venue: arXiv cs.DC 2020
Year: 2020

Generating schedules for shift workers is essential for many employers, whether the employer is a small or a large industrial complex, research laboratory, or other businesses involving shift works. Previous methods for creating rotational workforce schedules included interactions between the schedule maker and the algorithm, including defining the length of sequences of consecutive days of working shifts. In this method, an algorithm takes into account inputs (or constraints) from the schedule maker and then presents the possible solutions (incl. that all shifts must be filled, working hours per week, minimal resting time, etc.) in a first phase. The schedule maker can then select which solutions are most feasible to proceed with in the second phase, where the final schedules are then constructed and exported.
Coordinated Container Migration and Base Station Handover in Mobile Edge Computing
Venue: arXiv cs.NI 2020
Year: 2020

Offloading computationally intensive tasks from mobile users (MUs) to a virtualized environment such as containers on a nearby edge server, can significantly reduce processing time and hence end-to-end (E2E) delay. However, when users are mobile, such containers need to be migrated to other edge servers located closer to the MUs to keep the E2E delay low. Meanwhile, the mobility of MUs necessitates handover among base stations in order to keep the wireless connections between MUs and base stations uninterrupted. In this paper, we address the joint problem of container migration and base-station handover by proposing a coordinated migration-handover mechanism, with the objective of achieving low E2E delay and minimizing service interruption. The mechanism determines the optimal destinations and time for migration and handover in a coordinated manner, along with a delta checkpoint technique that we propose. We implement a testbed edge computing system with our proposed coordinated migration-handover mechanism, and evaluate the performance using real-world applications implemented with Docker container (an industry-standard). The results demonstrate that our mechanism achieves 30%-40% lower service downtime and 13%-22% lower E2E delay as compared to other mechanisms. Our work is instrumental in offering smooth user experience in mobile edge computing.