OpenRaiser/Intern-Atlas huggingface dataset:
Challenges and Opportunities on Using Games to Support IoT Systems Teaching
Venue: arXiv cs.CY 2021
Year: 2021

Context: New systems have emerged within the Industry 4.0 paradigm. These systems incorporate characteristics such as autonomy in decision making and acting in the context of IoT systems, continuous connectivity between devices and applications in cyber-physical systems, omnipresence properties in ubiquitous systems, among others. Thus, the engineering of these systems has changed, drastically affecting the manner of their construction process. In this context, to identify simple and playful alternatives to teach how to build them is a difficult task. Objective: To present how to teach IoT systems using games, to reveal challenges and opportunities obtained through a literature review. Method: A Structured Literature Review (StLR), supported by the Snowballing technique, was executed to find empirical studies related to teaching, games and IoT systems. Results: 12 papers were found about teaching IoT systems using games. As challenges and opportunities, many issues were identified related to IoT systems programming, modularity, hardware constraints, among others. Conclusion: In this work, research challenges and opportunities were found in the context of IoT systems teaching. Due to specific features of these systems, teaching their construction is a difficult activity to carry out.
The Parallel Coordinates Plot Revisited: Visual Extensions from Hive Plots, Heterogeneous Correlations, and an Exploration of Covid-19 Data in the United States
Venue: arXiv cs.GR 2021
Year: 2021

This paper extends an existing visualization, the Parallel Coordinates Plot (PCP), specifically its polar coordinate representation, the $\textit{Polar Parallel Coordinates Plot (P2CP)}$. With the additional incorporation of techniques borrowed from Hive Plot network visualizations, we demonstrate improved capabilities to explore multidimensional data in flatland, with a particular emphasis on the unique ability to represent 3-dimensional data. To demonstrate these techniques on P2CPs, we consider toy data, the Iris dataset, and socioeconomic data for counties in the United States. We conclude with an exploration of Covid-19 data from counties in the contiguous United States.
Adaptive Reliability Analysis for Multi-fidelity Models using a Collective Learning Strategy
Venue: arXiv cs.LG 2021
Year: 2021

In many fields of science and engineering, models with different fidelities are available. Physical experiments or detailed simulations that accurately capture the behavior of the system are regarded as high-fidelity models with low model uncertainty, however, they are expensive to run. On the other hand, simplified physical experiments or numerical models are seen as low-fidelity models that are cheaper to evaluate. Although low-fidelity models are often not suitable for direct use in reliability analysis due to their low accuracy, they can offer information about the trend of the high-fidelity model thus providing the opportunity to explore the design space at a low cost. This study presents a new approach called adaptive multi-fidelity Gaussian process for reliability analysis (AMGPRA). Contrary to selecting training points and information sources in two separate stages as done in state-of-the-art mfEGRA method, the proposed approach finds the optimal training point and information source simultaneously using the novel collective learning function (CLF). CLF is able to assess the global impact of a candidate training point from an information source and it accommodates any learning function that satisfies a certain profile. In this context, CLF provides a new direction for quantifying the impact of new training points and can be easily extended with new learning functions to adapt to different reliability problems. The performance of the proposed method is demonstrated by three mathematical examples and one engineering problem concerning the wind reliability of transmission towers. It is shown that the proposed method achieves similar or higher accuracy with reduced computational costs compared to state-of-the-art single and multi-fidelity methods. A key application of AMGPRA is high-fidelity fragility modeling using complex and costly physics-based computational models.
Blindness to Modality Helps Entailment Graph Mining
Venue: arXiv cs.CL 2021
Year: 2021

Understanding linguistic modality is widely seen as important for downstream tasks such as Question Answering and Knowledge Graph Population. Entailment Graph learning might also be expected to benefit from attention to modality. We build Entailment Graphs using a news corpus filtered with a modality parser, and show that stripping modal modifiers from predicates in fact increases performance. This suggests that for some tasks, the pragmatics of modal modification of predicates allows them to contribute as evidence of entailment.
BERTweetFR : Domain Adaptation of Pre-Trained Language Models for French Tweets
Venue: arXiv cs.CL 2021
Year: 2021

We introduce BERTweetFR, the first large-scale pre-trained language model for French tweets. Our model is initialized using the general-domain French language model CamemBERT which follows the base architecture of RoBERTa. Experiments show that BERTweetFR outperforms all previous general-domain French language models on two downstream Twitter NLP tasks of offensiveness identification and named entity recognition. The dataset used in the offensiveness detection task is first created and annotated by our team, filling in the gap of such analytic datasets in French. We make our model publicly available in the transformers library with the aim of promoting future research in analytic tasks for French tweets.
Skeleton-Graph: Long-Term 3D Motion Prediction From 2D Observations Using Deep Spatio-Temporal Graph CNNs
Venue: arXiv cs.CV 2021
Year: 2021

Several applications such as autonomous driving, augmented reality and virtual reality require a precise prediction of the 3D human pose. Recently, a new problem was introduced in the field to predict the 3D human poses from observed 2D poses. We propose Skeleton-Graph, a deep spatio-temporal graph CNN model that predicts the future 3D skeleton poses in a single pass from the 2D ones. Unlike prior works, Skeleton-Graph focuses on modeling the interaction between the skeleton joints by exploiting their spatial configuration. This is being achieved by formulating the problem as a graph structure while learning a suitable graph adjacency kernel. By the design, Skeleton-Graph predicts the future 3D poses without divergence in the long-term, unlike prior works. We also introduce a new metric that measures the divergence of predictions in the long term. Our results show an FDE improvement of at least 27% and an ADE of 4% on both the GTA-IM and PROX datasets respectively in comparison with prior works. Also, we are 88% and 93% less divergence on the long-term motion prediction in comparison with prior works on both GTA-IM and PROX datasets. Code is available at https://github.com/abduallahmohamed/Skeleton-Graph.git
Exploring Coevolutionary Dynamics of Competitive Arms-Races Between Infinitely Diverse Heterogenous Adaptive Automated Trader-Agents
Venue: arXiv cs.CE 2021
Year: 2021

We report on a series of experiments in which we study the coevolutionary "arms-race" dynamics among groups of agents that engage in adaptive automated trading in an accurate model of contemporary financial markets. At any one time, every trader in the market is trying to make as much profit as possible given the current distribution of different other trading strategies that it finds itself pitched against in the market; but the distribution of trading strategies and their observable behaviors is constantly changing, and changes in any one trader are driven to some extent by the changes in all the others. Prior studies of coevolutionary dynamics in markets have concentrated on systems where traders can choose one of a small number of fixed pure strategies, and can change their choice occasionally, thereby giving a market with a discrete phase-space, made up of a finite set of possible system states. Here we present first results from two independent sets of experiments, where we use minimal-intelligence trading-agents but in which the space of possible strategies is continuous and hence infinite. Our work reveals that by taking only a small step in the direction of increased realism we move immediately into high-dimensional phase-spaces, which then present difficulties in visualising and understanding the coevolutionary dynamics unfolding within the system. We conclude that further research is required to establish better analytic tools for monitoring activity and progress in co-adapting markets. We have released relevant Python code as open-source on GitHub, to enable others to continue this work.
Achieving Counterfactual Fairness for Causal Bandit
Venue: arXiv cs.LG 2021
Year: 2021

In online recommendation, customers arrive in a sequential and stochastic manner from an underlying distribution and the online decision model recommends a chosen item for each arriving individual based on some strategy. We study how to recommend an item at each step to maximize the expected reward while achieving user-side fairness for customers, i.e., customers who share similar profiles will receive a similar reward regardless of their sensitive attributes and items being recommended. By incorporating causal inference into bandits and adopting soft intervention to model the arm selection strategy, we first propose the d-separation based UCB algorithm (D-UCB) to explore the utilization of the d-separation set in reducing the amount of exploration needed to achieve low cumulative regret. Based on that, we then propose the fair causal bandit (F-UCB) for achieving the counterfactual individual fairness. Both theoretical analysis and empirical evaluation demonstrate effectiveness of our algorithms.
A Hierarchical Network-Oriented Analysis of User Participation in Misinformation Spread on WhatsApp
Venue: arXiv cs.SI 2021
Year: 2021

WhatsApp emerged as a major communication platform in many countries in the recent years. Despite offering only one-to-one and small group conversations, WhatsApp has been shown to enable the formation of a rich underlying network, crossing the boundaries of existing groups, and with structural properties that favor information dissemination at large. Indeed, WhatsApp has reportedly been used as a forum of misinformation campaigns with significant social, political and economic consequences in several countries. In this article, we aim at complementing recent studies on misinformation spread on WhatsApp, mostly focused on content properties and propagation dynamics, by looking into the network that connects users sharing the same piece of content. Specifically, we present a hierarchical network-oriented characterization of the users engaged in misinformation spread by focusing on three perspectives: individuals, WhatsApp groups and user communities, i.e., groupings of users who, intentionally or not, share the same content disproportionately often. By analyzing sharing and network topological properties, our study offers valuable insights into how WhatsApp users leverage the underlying network connecting different groups to gain large reach in the spread of misinformation on the platform.
DialogueBERT: A Self-Supervised Learning based Dialogue Pre-training Encoder
Venue: arXiv cs.CL 2021
Year: 2021

With the rapid development of artificial intelligence, conversational bots have became prevalent in mainstream E-commerce platforms, which can provide convenient customer service timely. To satisfy the user, the conversational bots need to understand the user's intention, detect the user's emotion, and extract the key entities from the conversational utterances. However, understanding dialogues is regarded as a very challenging task. Different from common language understanding, utterances in dialogues appear alternately from different roles and are usually organized as hierarchical structures. To facilitate the understanding of dialogues, in this paper, we propose a novel contextual dialogue encoder (i.e. DialogueBERT) based on the popular pre-trained language model BERT. Five self-supervised learning pre-training tasks are devised for learning the particularity of dialouge utterances. Four different input embeddings are integrated to catch the relationship between utterances, including turn embedding, role embedding, token embedding and position embedding. DialogueBERT was pre-trained with 70 million dialogues in real scenario, and then fine-tuned in three different downstream dialogue understanding tasks. Experimental results show that DialogueBERT achieves exciting results with 88.63% accuracy for intent recognition, 94.25% accuracy for emotion recognition and 97.04% F1 score for named entity recognition, which outperforms several strong baselines by a large margin.
Unsupervised Contextualized Document Representation
Venue: arXiv cs.CL 2021
Year: 2021

Several NLP tasks need the effective representation of text documents. Arora et. al., 2017 demonstrate that simple weighted averaging of word vectors frequently outperforms neural models. SCDV (Mekala et. al., 2017) further extends this from sentences to documents by employing soft and sparse clustering over pre-computed word vectors. However, both techniques ignore the polysemy and contextual character of words. In this paper, we address this issue by proposing SCDV+BERT(ctxd), a simple and effective unsupervised representation that combines contextualized BERT (Devlin et al., 2019) based word embedding for word sense disambiguation with SCDV soft clustering approach. We show that our embeddings outperform original SCDV, pre-train BERT, and several other baselines on many classification datasets. We also demonstrate our embeddings effectiveness on other tasks, such as concept matching and sentence similarity. In addition, we show that SCDV+BERT(ctxd) outperforms fine-tune BERT and different embedding approaches in scenarios with limited data and only few shots examples.
Backdoor Attacks on Federated Learning with Lottery Ticket Hypothesis
Venue: arXiv cs.LG 2021
Year: 2021

Edge devices in federated learning usually have much more limited computation and communication resources compared to servers in a data center. Recently, advanced model compression methods, like the Lottery Ticket Hypothesis, have already been implemented on federated learning to reduce the model size and communication cost. However, Backdoor Attack can compromise its implementation in the federated learning scenario. The malicious edge device trains the client model with poisoned private data and uploads parameters to the center, embedding a backdoor to the global shared model after unwitting aggregative optimization. During the inference phase, the model with backdoors classifies samples with a certain trigger as one target category, while shows a slight decrease in inference accuracy to clean samples. In this work, we empirically demonstrate that Lottery Ticket models are equally vulnerable to backdoor attacks as the original dense models, and backdoor attacks can influence the structure of extracted tickets. Based on tickets' similarities between each other, we provide a feasible defense for federated learning against backdoor attacks on various datasets.
Towards The Automatic Coding of Medical Transcripts to Improve Patient-Centered Communication
Venue: arXiv cs.CL 2021
Year: 2021

This paper aims to provide an approach for automatic coding of physician-patient communication transcripts to improve patient-centered communication (PCC). PCC is a central part of high-quality health care. To improve PCC, dialogues between physicians and patients have been recorded and tagged with predefined codes. Trained human coders have manually coded the transcripts. Since it entails huge labor costs and poses possible human errors, automatic coding methods should be considered for efficiency and effectiveness. We adopted three machine learning algorithms (Naïve Bayes, Random Forest, and Support Vector Machine) to categorize lines in transcripts into corresponding codes. The result showed that there is evidence to distinguish the codes, and this is considered to be sufficient for training of human annotators.
A Method For Adding Motion-Blur on Arbitrary Objects By using Auto-Segmentation and Color Compensation Techniques
Venue: arXiv cs.CV 2021
Year: 2021

When dynamic objects are captured by a camera, motion blur inevitably occurs. Such a blur is sometimes considered as just a noise, however, it sometimes gives an important effect to add dynamism in the scene for photographs or videos. Unlike the similar effects, such as defocus blur, which is now easily controlled even by smartphones, motion blur is still uncontrollable and makes undesired effects on photographs. In this paper, an unified framework to add motion blur on per-object basis is proposed. In the method, multiple frames are captured without motion blur and they are accumulated to create motion blur on target objects. To capture images without motion blur, shutter speed must be short, however, it makes captured images dark, and thus, a sensor gain should be increased to compensate it. Since a sensor gain causes a severe noise on image, we propose a color compensation algorithm based on non-linear filtering technique for solution. Another contribution is that our technique can be used to make HDR images for fast moving objects by using multi-exposure images. In the experiments, effectiveness of the method is confirmed by ablation study using several data sets.
Extremism & Whataboutism: A Case Study on Bangalore Riots
Venue: arXiv cs.SI 2021
Year: 2021

A common diversionary tactic used to deflect attention from contested issues is whataboutery which, when used by majoritarian groups to justify their behaviour against marginalised communities, can quickly devolve into extremism. We explore the manifestations of extreme speech in the Indian context, through a case study of violent protests and policing in the city of Bangalore, provoked by a derogatory Facebook post. Analyses of the dominant narratives on Twitter surrounding the incident reveal that, most of them employ whataboutism to deflect attention from the triggering post and serve as breeding grounds for religion-based extreme speech. We conclude by discussing how our study proposes an alternative lens of viewing extremism in the Global South.
K-AID: Enhancing Pre-trained Language Models with Domain Knowledge for Question Answering
Venue: arXiv cs.AI 2021
Year: 2021

Knowledge enhanced pre-trained language models (K-PLMs) are shown to be effective for many public tasks in the literature but few of them have been successfully applied in practice. To address this problem, we propose K-AID, a systematic approach that includes a low-cost knowledge acquisition process for acquiring domain knowledge, an effective knowledge infusion module for improving model performance, and a knowledge distillation component for reducing the model size and deploying K-PLMs on resource-restricted devices (e.g., CPU) for real-world application. Importantly, instead of capturing entity knowledge like the majority of existing K-PLMs, our approach captures relational knowledge, which contributes to better-improving sentence-level text classification and text matching tasks that play a key role in question answering (QA). We conducted a set of experiments on five text classification tasks and three text matching tasks from three domains, namely E-commerce, Government, and Film&TV, and performed online A/B tests in E-commerce. Experimental results show that our approach is able to achieve substantial improvement on sentence-level question answering tasks and bring beneficial business value in industrial settings.
On Conflict-Free Replicated Data Types and Equivocation in Byzantine Setups
Venue: arXiv cs.DC 2021
Year: 2021

We explore the property of equivocation tolerance for Conflict-Free Replicated Data Types (CRDTs). We show that a subclass of CRDTs is equivocation-tolerant and can thereby cope with any number of Byzantine faults: Without equivocation detection, prevention or remediation, they still fulfill strong eventual consistency (SEC). We also conjecture that there is only one operation-based CRDT design supporting non-commutative operations that fulfills SEC in Byzantine environments with any number of faults.
Fully probabilistic design for knowledge fusion between Bayesian filters under uniform disturbances
Venue: arXiv cs.LG 2021
Year: 2021

This paper considers the problem of Bayesian transfer learning-based knowledge fusion between linear state-space processes driven by uniform state and observation noise processes. The target task conditions on probabilistic state predictor(s) supplied by the source filtering task(s) to improve its own state estimate. A joint model of the target and source(s) is not required and is not elicited. The resulting decision-making problem for choosing the optimal conditional target filtering distribution under incomplete modelling is solved via fully probabilistic design (FPD), i.e. via appropriate minimization of Kullback-Leibler divergence (KLD). The resulting FPD-optimal target learner is robust, in the sense that it can reject poor-quality source knowledge. In addition, the fact that this Bayesian transfer learning (BTL) scheme does not depend on a model of interaction between the source and target tasks ensures robustness to the misspecification of such a model. The latter is a problem that affects conventional transfer learning methods. The properties of the proposed BTL scheme are demonstrated via extensive simulations, and in comparison with two contemporary alternatives.
Privacy-preserving Credit Scoring via Functional Encryption
Venue: arXiv cs.CR 2021
Year: 2021

The majority of financial organizations managing confidential data are aware of security threats and leverage widely accepted solutions (e.g., storage encryption, transport-level encryption, intrusion detection systems) to prevent or detect attacks. Yet these hardening measures do little to face even worse threats posed on data-in-use. Solutions such as Homomorphic Encryption (HE) and hardware-assisted Trusted Execution Environment (TEE) are nowadays among the preferred approaches for mitigating this type of threat. However, given the high-performance overhead of HE, financial institutions -- whose processing rate requirements are stringent -- are more oriented towards TEE-based solutions. The X-Margin Inc. company, for example, offers secure financial computations by combining the Intel SGX TEE technology and HE-based Zero-Knowledge Proofs, which shield customers' data-in-use even against malicious insiders, i.e., users having privileged access to the system. Despite such a solution offers strong security guarantees, it is constrained by having to trust Intel and by the SGX hardware extension availability. In this paper, we evaluate a new frontier for X-Margin, i.e., performing privacy-preserving credit risk scoring via an emerging cryptographic scheme: Functional Encryption (FE), which allows a user to only learn a function of the encrypted data. We describe how the X-Margin application can benefit from this innovative approach and -- most importantly -- evaluate its performance impact.