OpenRaiser/Intern-Atlas huggingface dataset:
Misrepresenting Scientific Consensus on COVID-19: The Amplification of Dissenting Scientists on Twitter
Venue: arXiv cs.SI 2021
Year: 2021

The COVID-19 pandemic has resulted in a slew of misinformation, often described as an "infodemic". Whereas previous research has focused on the propagation of unreliable sources as a main vehicle of misinformation, the present study focuses on exploring the role of scientists whose views oppose the scientific consensus. Using Nobelists in Physiology and Medicine as a proxy for scientific consensus, we analyze two separate datasets: 15.8K tweets by 13.1K unique users on COVID-19 vaccines specifically, and 208K tweets by 151K unique users on COVID-19 broadly which mention the Nobelist names. Our analyses reveal that dissenting scientists are amplified by a factor of 426 relative to true scientific consensus in the context of COVID-19 vaccines, and by a factor of 43 in the context of COVID-19 generally. Although more popular accounts tend to mention consensus-abiding scientists more, our results suggest that this false consensus is driven by higher engagement with dissent-mentioning tweets. Furthermore, false consensus mostly occurs due to traffic spikes following highly popularized statements of dissenting scientists. We find that dissenting voices are mainly discussed in French, English-speaking, Turkish, Brazilian, Argentine, Indian, and Japanese misinformation clusters. This research suggests that social media platforms should prioritize the exposure of consensus-abiding scientists as a vehicle of reversing false consensus and addressing misinformation stemming from seemingly credible sources.
A photosensor employing data-driven binning for ultrafast image recognition
Venue: arXiv cs.CV 2021
Year: 2021

Pixel binning is a technique, widely used in optical image acquisition and spectroscopy, in which adjacent detector elements of an image sensor are combined into larger pixels. This reduces the amount of data to be processed as well as the impact of noise, but comes at the cost of a loss of information. Here, we push the concept of binning to its limit by combining a large fraction of the sensor elements into a single superpixel that extends over the whole face of the chip. For a given pattern recognition task, its optimal shape is determined from training data using a machine learning algorithm. We demonstrate the classification of optically projected images from the MNIST dataset on a nanosecond timescale, with enhanced sensitivity and without loss of classification accuracy. Our concept is not limited to imaging alone but can also be applied in optical spectroscopy or other sensing applications.
You Overtrust Your Printer
Venue: arXiv cs.CR 2021
Year: 2021

Printers are common devices whose networked use is vastly unsecured, perhaps due to an enrooted assumption that their services are somewhat negligible and, as such, unworthy of protection. This article develops structured arguments and conducts technical experiments in support of a qualitative risk assessment exercise that ultimately undermines that assumption. Three attacks that can be interpreted as post-exploitation activity are found and discussed, forming what we term the Printjack family of attacks to printers. Some printers may suffer vulnerabilities that would transform them into exploitable zombies. Moreover, a large number of printers, at least on an EU basis, are found to honour unauthenticated printing requests, thus raising the risk level of an attack that sees the crooks exhaust the printing facilities of an institution. There is also a remarkable risk of data breach following an attack consisting in the malicious interception of data while in transit towards printers. Therefore, the newborn IoT era demands printers to be as secure as other devices such as laptops should be, also to facilitate compliance with the General Data Protection Regulation (EU Regulation 2016/679) and reduce the odds of its administrative fines.
Fueling the Next Quantum Leap in Cellular Networks: Embracing AI in 5G Evolution towards 6G
Venue: arXiv cs.NI 2021
Year: 2021

Cellular networks, such as 5G systems, are becoming increasingly complex for supporting various deployment scenarios and applications. Embracing artificial intelligence (AI) in 5G evolution is critical to managing the complexity and fueling the next quantum leap in 6G cellular networks. In this article, we share our experience and best practices in applying AI in cellular networks. We first present a primer on the state of the art of AI in cellular networks, including basic concepts and recent key advances. Then we discuss 3GPP standardization aspects and share various design rationales influencing standardization. We also present case studies with real network data to showcase how AI can improve network performance and enable network automation.
Winds of Change: Impact of COVID-19 on Vaccine-related Opinions of Twitter users
Venue: arXiv cs.SI 2021
Year: 2021

Administering COVID-19 vaccines at a societal scale has been deemed as the most appropriate way to defend against the COVID-19 pandemic. This global vaccination drive naturally fueled a possibility of Pro-Vaxxers and Anti-Vaxxers strongly expressing their supports and concerns regarding the vaccines on social media platforms. Understanding this online discourse is crucial for policy makers. This understanding is likely to impact the success of vaccination drives and might even impact the final outcome of our fight against the pandemic. The goal of this work is to improve this understanding using the lens of Twitter-discourse data. We first develop a classifier that categorizes users according to their vaccine-related stance with high precision (97%). Using this method we detect and investigate specific user-groups who posted about vaccines in pre-COVID and COVID times. Specifically, we identify distinct topics that these users talk about, and investigate how vaccine-related discourse has changed between pre-COVID times and COVID times. Finally, for the first time, we investigate the change of vaccine-related stances in Twitter users and shed light on potential reasons for such changes in stance. Our dataset and classifier are available at https://github.com/sohampoddar26/covid-vax-stance.
Randomized FIFO Mechanisms
Venue: arXiv cs.GT 2021
Year: 2021

We study the matching of jobs to workers in a queue, e.g. a ridesharing platform dispatching drivers to pick up riders at an airport. Under FIFO dispatching, the heterogeneity in trip earnings incentivizes drivers to cherry-pick, increasing riders' waiting time for a match and resulting in a loss of efficiency and reliability. We first present the direct FIFO mechanism, which offers lower-earning trips to drivers further down the queue. The option to skip the rest of the line incentivizes drivers to accept all dispatches, but the mechanism would be considered unfair since drivers closer to the head of the queue may have lower priority for trips to certain destinations. To avoid the use of unfair dispatch rules, we introduce a family of randomized FIFO mechanisms, which send declined trips gradually down the queue in a randomized manner. We prove that a randomized FIFO mechanism achieves the first best throughput and the second best revenue in equilibrium. Extensive counterfactual simulations using data from the City of Chicago demonstrate substantial improvements of revenue and throughput, highlighting the effectiveness of using waiting times to align incentives and reduce the variability in driver earnings.
Accretionary Learning with Deep Neural Networks
Venue: arXiv cs.LG 2021
Year: 2021

One of the fundamental limitations of Deep Neural Networks (DNN) is its inability to acquire and accumulate new cognitive capabilities. When some new data appears, such as new object classes that are not in the prescribed set of objects being recognized, a conventional DNN would not be able to recognize them due to the fundamental formulation that it takes. The current solution is typically to re-design and re-learn the entire network, perhaps with a new configuration, from a newly expanded dataset to accommodate new knowledge. This process is quite different from that of a human learner. In this paper, we propose a new learning method named Accretionary Learning (AL) to emulate human learning, in that the set of objects to be recognized may not be pre-specified. The corresponding learning structure is modularized, which can dynamically expand to register and use new knowledge. During accretionary learning, the learning process does not require the system to be totally re-designed and re-trained as the set of objects grows in size. The proposed DNN structure does not forget previous knowledge when learning to recognize new data classes. We show that the new structure and the design methodology lead to a system that can grow to cope with increased cognitive complexity while providing stable and superior overall performance.
Geometry-Aware Multi-Task Learning for Binaural Audio Generation from Video
Venue: arXiv cs.CV 2021
Year: 2021

Binaural audio provides human listeners with an immersive spatial sound experience, but most existing videos lack binaural audio recordings. We propose an audio spatialization method that draws on visual information in videos to convert their monaural (single-channel) audio to binaural audio. Whereas existing approaches leverage visual features extracted directly from video frames, our approach explicitly disentangles the geometric cues present in the visual stream to guide the learning process. In particular, we develop a multi-task framework that learns geometry-aware features for binaural audio generation by accounting for the underlying room impulse response, the visual stream's coherence with the sound source(s) positions, and the consistency in geometry of the sounding objects over time. Furthermore, we introduce a new large video dataset with realistic binaural audio simulated for real-world scanned environments. On two datasets, we demonstrate the efficacy of our method, which achieves state-of-the-art results.
Explainable Software Defect Prediction: Are We There Yet?
Venue: arXiv cs.SE 2021
Year: 2021

Explaining the prediction results of software defect prediction models is a challenging while practical task, which can provide useful information for developers to understand and fix the predicted bugs. To address this issue, recently, Jiarpakdee et al. proposed to use {two state-of-the-art} model-agnostic techniques (i.e., LIME and BreakDown) to explain the prediction results of bug prediction models. Their experiments show these tools can generate promising results and the generated explanations can assist developers understand the prediction results. However, the fact that LIME and BreakDown were only examined on a single software defect prediction model setting calls into question about their consistency and reliability across software defect prediction models with various settings. In this paper, we set out to investigate the consistency and reliability of model-agnostic technique based explanation generation approaches (i.e., LIME and BreakDown) on software defect prediction models with different settings , e.g., different data sampling techniques, different machine learning classifiers, and different prediction scenarios. Specifically, we use both LIME and BreakDown to generate explanations for the same instance under software defect prediction models with different settings and then check the consistency of the generated explanations for the instance. We reused the same defect data from Jiarpakdee et al. in our experiments. The results show that both LIME and BreakDown generate inconsistent explanations under different software defect prediction settings for the same test instances, which makes them unreliable for explanation generation. Overall, with this study, we call for more research in explainable software defect prediction towards achieving consistent and reliable explanation generation.
Are Proactive Interventions for Reddit Communities Feasible?
Venue: arXiv cs.SI 2021
Year: 2021

Reddit has found its communities playing a prominent role in originating and propagating problematic socio-political discourse. Reddit administrators have generally struggled to prevent or contain such discourse for several reasons including: (1) the inability for a handful of human administrators to track and react to millions of posts and comments per day and (2) fear of backlash as a consequence of administrative decisions to ban or quarantine hateful communities. Consequently, administrative actions (community bans and quarantines) are often taken only when problematic discourse within a community spills over into the real world with serious consequences. In this paper, we investigate the feasibility of deploying tools to proactively identify problematic communities on Reddit. Proactive identification strategies show promise for three reasons: (1) they have potential to reduce the manual efforts required to track communities for problematic content, (2) they give administrators a scientific rationale to back their decisions and interventions, and (3) they facilitate early and more nuanced interventions (than banning or quarantining) to mitigate problematic discourse.
Improving Semantic Image Segmentation via Label Fusion in Semantically Textured Meshes
Venue: arXiv cs.CV 2021
Year: 2021

Models for semantic segmentation require a large amount of hand-labeled training data which is costly and time-consuming to produce. For this purpose, we present a label fusion framework that is capable of improving semantic pixel labels of video sequences in an unsupervised manner. We make use of a 3D mesh representation of the environment and fuse the predictions of different frames into a consistent representation using semantic mesh textures. Rendering the semantic mesh using the original intrinsic and extrinsic camera parameters yields a set of improved semantic segmentation images. Due to our optimized CUDA implementation, we are able to exploit the entire $c$-dimensional probability distribution of annotations over $c$ classes in an uncertainty-aware manner. We evaluate our method on the Scannet dataset where we improve annotations produced by the state-of-the-art segmentation network ESANet from $52.05 \%$ to $58.25 \%$ pixel accuracy. We publish the source code of our framework online to foster future research in this area (\url{https://github.com/fferflo/semantic-meshes}). To the best of our knowledge, this is the first publicly available label fusion framework for semantic image segmentation based on meshes with semantic textures.
Unsupervised Time Series Outlier Detection with Diversity-Driven Convolutional Ensembles -- Extended Version
Venue: arXiv cs.LG 2021
Year: 2021

With the sweeping digitalization of societal, medical, industrial, and scientific processes, sensing technologies are being deployed that produce increasing volumes of time series data, thus fueling a plethora of new or improved applications. In this setting, outlier detection is frequently important, and while solutions based on neural networks exist, they leave room for improvement in terms of both accuracy and efficiency. With the objective of achieving such improvements, we propose a diversity-driven, convolutional ensemble. To improve accuracy, the ensemble employs multiple basic outlier detection models built on convolutional sequence-to-sequence autoencoders that can capture temporal dependencies in time series. Further, a novel diversity-driven training method maintains diversity among the basic models, with the aim of improving the ensemble's accuracy. To improve efficiency, the approach enables a high degree of parallelism during training. In addition, it is able to transfer some model parameters from one basic model to another, which reduces training time. We report on extensive experiments using real-world multivariate time series that offer insight into the design choices underlying the new approach and offer evidence that it is capable of improved accuracy and efficiency. This is an extended version of "Unsupervised Time Series Outlier Detection with Diversity-Driven Convolutional Ensembles", to appear in PVLDB 2022.
Unified Modeling of Unconventional Modular and Reconfigurable Manipulation System
Venue: arXiv cs.RO 2021
Year: 2021

Customization of manipulator configurations using modularity and reconfigurability aspects is receiving much attention. Modules presented so far in literature deals with the conventional and standard configurations. This paper presents the 3D printable, light-weight and unconventional modules: MOIRs' Mark-2, to develop any custom `n'-Degrees-of-Freedom (DoF) serial manipulator even with the non-parallel and non-perpendicular jointed configuration. These unconventional designs of modular configurations seek an easy adaptable solution for both modular assembly and software interfaces for automatic modeling and control. A strategy of assembling the modules, automatic and unified modeling of the modular and reconfigurable manipulators with unconventional parameters is proposed in this paper using the proposed 4 modular units. A reconfigurable software architecture is presented for the automatic generation of kinematic and dynamic models and configuration files, through which, a designer can design, validate using visualization, plan and execute the motion of the developed configuration as required. The framework developed is based upon an open source platform called as Robot Operating System (ROS), which acts as a digital twin for the modular configurations. For the experimental demonstration, a 3D printed modular library is developed and an unconventional configuration is assembled, using the proposed modules followed by automatic modeling and control, for a single cell of the vertical farm setup.
Blockchain for IoT: A Critical Analysis Concerning Performance and Scalability
Venue: arXiv cs.CR 2021
Year: 2021

The world has been experiencing a mind-blowing expansion of blockchain technology since it was first introduced as an emerging means of cryptocurrency called bitcoin. Currently, it has been regarded as a pervasive frame of reference across almost all research domains, ranging from virtual cash to agriculture or even supply-chain to the Internet of Things. The ability to have a self-administering register with legitimate immutability makes blockchain appealing for the Internet of Things (IoT). As billions of IoT devices are now online in distributed fashion, the huge challenges and questions require to addressed in pursuit of urgently needed solutions. The present paper has been motivated by the aim of facilitating such efforts. The contribution of this work is to figure out those trade-offs the IoT ecosystem usually encounters because of the wrong choice of blockchain technology. Unlike a survey or review, the critical findings of this paper target sorting out specific security challenges of blockchain-IoT Infrastructure. The contribution includes how to direct developers and researchers in this domain to pick out the unblemished combinations of Blockchain enabled IoT applications. In addition, the paper promises to bring a deep insight on Ethereum, Hyperledger blockchain and IOTA technology to show their limitations and prospects in terms of performance and scalability.
Investigating Cross-Linguistic Gender Bias in Hindi-English Across Domains
Venue: arXiv cs.CL 2021
Year: 2021

Measuring, evaluating and reducing Gender Bias has come to the forefront with newer and improved language embeddings being released every few months. But could this bias vary from domain to domain? We see a lot of work to study these biases in various embedding models but limited work has been done to debias Indic languages. We aim to measure and study this bias in Hindi language, which is a higher-order language (gendered) with reference to English, a lower-order language. To achieve this, we study the variations across domains to quantify if domain embeddings allow us some insight into Gender bias for this pair of Hindi-English model. We will generate embeddings in four different corpora and compare results by implementing different metrics like with pre-trained State of the Art Indic-English translation model, which has performed better at many NLP tasks than existing models.
Chaos and Logistic Map based Key Generation Technique for AES-driven IoT Security
Venue: arXiv cs.CR 2021
Year: 2021

Several efforts have been seen claiming the lightweight block ciphers as a necessarily suitable substitute in securing the Internet of Things. Currently, it has been able to envisage as a pervasive frame of reference almost all across the privacy preserving of smart and sensor-oriented appliances. Different approaches are likely to be inefficient, bringing desired degree of security considering the easiness and surely the process of simplicity but security. Strengthening the well-known symmetric key and block dependent algorithm using either chaos motivated logistic map or elliptic curve has shown a far-reaching potential to be a discretion in secure real-time communication. The popular feature of logistic maps, such as the un-foreseeability and randomness often expected to be used in dynamic key-propagation in sync with chaos and scheduling technique towards data integrity. As a bit alternation in keys, able to come up with oversize deviation, also would have consequence to leverage data confidentiality. Henceforth it may have proximity to time consumption, which may lead to a challenge to make sure instant data exchange between participating node entities. In consideration of delay latency required to both secure encryption and decryption, the proposed approach suggests a modification on the key-origination matrix along with S-box. It has plausibly been taken us to this point that the time required proportionate to the plain-text sent while the plain-text disproportionate to the probability happening a letter on the message made. In line with that the effort so far sought how apparent chaos escalates the desired key-initiation before message transmission.
Self-Supervised Class Incremental Learning
Venue: arXiv cs.LG 2021
Year: 2021

Existing Class Incremental Learning (CIL) methods are based on a supervised classification framework sensitive to data labels. When updating them based on the new class data, they suffer from catastrophic forgetting: the model cannot discern old class data clearly from the new. In this paper, we explore the performance of Self-Supervised representation learning in Class Incremental Learning (SSCIL) for the first time, which discards data labels and the model's classifiers. To comprehensively discuss the difference in performance between supervised and self-supervised methods in CIL, we set up three different class incremental schemes: Random Class Scheme, Semantic Class Scheme, and Cluster Scheme, to simulate various class incremental learning scenarios. Besides, we propose Linear Evaluation Protocol (LEP) and Generalization Evaluation Protocol (GEP) to metric the model's representation classification ability and generalization in CIL. Our experiments (on ImageNet-100 and ImageNet) show that SSCIL has better anti-forgetting ability and robustness than supervised strategies in CIL. To understand what alleviates the catastrophic forgetting in SSCIL, we study the major components of SSCIL and conclude that (1) the composition of different data augmentation improves the quality of the model's representation and the \textit{Grayscale} operation reduces the system noise of data augmentation in SSCIL. (2) the projector, like a buffer, reduces unnecessary parameter updates of the model in SSCIL and increases the robustness of the model. Although the performance of SSCIL is significantly higher than supervised methods in CIL, there is still an apparent gap with joint learning. Our exploration gives a baseline of self-supervised class incremental learning on large-scale datasets and contributes some forward strategies for mitigating the catastrophic forgetting in CIL.