OpenRaiser/Intern-Atlas huggingface dataset:
Graph Learning for Cognitive Digital Twins in Manufacturing Systems
Venue: arXiv cs.LG 2021
Year: 2021

Future manufacturing requires complex systems that connect simulation platforms and virtualization with physical data from industrial processes. Digital twins incorporate a physical twin, a digital twin, and the connection between the two. Benefits of using digital twins, especially in manufacturing, are abundant as they can increase efficiency across an entire manufacturing life-cycle. The digital twin concept has become increasingly sophisticated and capable over time, enabled by rises in many technologies. In this paper, we detail the cognitive digital twin as the next stage of advancement of a digital twin that will help realize the vision of Industry 4.0. Cognitive digital twins will allow enterprises to creatively, effectively, and efficiently exploit implicit knowledge drawn from the experience of existing manufacturing systems. They also enable more autonomous decisions and control, while improving the performance across the enterprise (at scale). This paper presents graph learning as one potential pathway towards enabling cognitive functionalities in manufacturing digital twins. A novel approach to realize cognitive digital twins in the product design stage of manufacturing that utilizes graph learning is presented.
An Open-Publishing Response to the COVID-19 Infodemic
Venue: arXiv cs.DL 2021
Year: 2021

The COVID-19 pandemic catalyzed the rapid dissemination of papers and preprints investigating the disease and its associated virus, SARS-CoV-2. The multifaceted nature of COVID-19 demands a multidisciplinary approach, but the urgency of the crisis combined with the need for social distancing measures present unique challenges to collaborative science. We applied a massive online open publishing approach to this problem using Manubot. Through GitHub, collaborators summarized and critiqued COVID-19 literature, creating a review manuscript. Manubot automatically compiled citation information for referenced preprints, journal publications, websites, and clinical trials. Continuous integration workflows retrieved up-to-date data from online sources nightly, regenerating some of the manuscript's figures and statistics. Manubot rendered the manuscript into PDF, HTML, LaTeX, and DOCX outputs, immediately updating the version available online upon the integration of new content. Through this effort, we organized over 50 scientists from a range of backgrounds who evaluated over 1,500 sources and developed seven literature reviews. While many efforts from the computational community have focused on mining COVID-19 literature, our project illustrates the power of open publishing to organize both technical and non-technical scientists to aggregate and disseminate information in response to an evolving crisis.
Grounding Natural Language Instructions: Can Large Language Models Capture Spatial Information?
Venue: arXiv cs.CL 2021
Year: 2021

Models designed for intelligent process automation are required to be capable of grounding user interface elements. This task of interface element grounding is centred on linking instructions in natural language to their target referents. Even though BERT and similar pre-trained language models have excelled in several NLP tasks, their use has not been widely explored for the UI grounding domain. This work concentrates on testing and probing the grounding abilities of three different transformer-based models: BERT, RoBERTa and LayoutLM. Our primary focus is on these models' spatial reasoning skills, given their importance in this domain. We observe that LayoutLM has a promising advantage for applications in this domain, even though it was created for a different original purpose (representing scanned documents): the learned spatial features appear to be transferable to the UI grounding setting, especially as they demonstrate the ability to discriminate between target directions in natural language instructions.
Robust-by-Design Plans for Multi-Robot Pursuit-Evasion
Venue: arXiv cs.RO 2021
Year: 2021

This paper studies a multi-robot visibility-based pursuit-evasion problem in which a group of pursuer robots are tasked with detecting an evader within a two dimensional polygonal environment. The primary contribution is a novel formulation of the pursuit-evasion problem that modifies the pursuers' objective by requiring that the evader still be detected, even in spite of the failure of any single pursuer robot. This novel constraint, whereby two pursuers are required to detect an evader, has the benefit of providing redundancy to the search, should any member of the team become unresponsive, suffer temporary sensor disruption/failure, or otherwise become incapacitated. Existing methods, even those that are designed to respond to failures, rely on the pursuers to replan and update their search pattern to handle such occurrences. In contrast, the proposed formulation produces plans that are inherently tolerant of some level of disturbance. Building upon this new formulation, we introduce an augmented data structure for encoding the problem state and a novel sampling technique to ensure that the generated plans are robust to failures of any single pursuer robot. An implementation and simulation results illustrating the effectiveness of this approach are described.
Auto White-Balance Correction for Mixed-Illuminant Scenes
Venue: arXiv cs.CV 2021
Year: 2021

Auto white balance (AWB) is applied by camera hardware at capture time to remove the color cast caused by the scene illumination. The vast majority of white-balance algorithms assume a single light source illuminates the scene; however, real scenes often have mixed lighting conditions. This paper presents an effective AWB method to deal with such mixed-illuminant scenes. A unique departure from conventional AWB, our method does not require illuminant estimation, as is the case in traditional camera AWB modules. Instead, our method proposes to render the captured scene with a small set of predefined white-balance settings. Given this set of rendered images, our method learns to estimate weighting maps that are used to blend the rendered images to generate the final corrected image. Through extensive experiments, we show this proposed method produces promising results compared to other alternatives for single- and mixed-illuminant scene color correction. Our source code and trained models are available at https://github.com/mahmoudnafifi/mixedillWB.
Toward Efficient Federated Learning in Multi-Channeled Mobile Edge Network with Layerd Gradient Compression
Venue: arXiv cs.LG 2021
Year: 2021

A fundamental issue for federated learning (FL) is how to achieve optimal model performance under highly dynamic communication environments. This issue can be alleviated by the fact that modern edge devices usually can connect to the edge FL server via multiple communication channels (e.g., 4G, LTE and 5G). However, having an edge device send copies of local models to the FL server along multiple channels is redundant, time-consuming, and would waste resources (e.g., bandwidth, battery life and monetary cost). In this paper, motivated by the layered coding techniques in video streaming, we propose a novel FL framework called layered gradient compression (LGC). Specifically, in LGC, local gradients from a device is coded into several layers and each layer is sent to the FL server along a different channel. The FL server aggregates the received layers of local gradients from devices to update the global model, and sends the result back to the devices. We prove the convergence of LGC, and formally define the problem of resource-efficient federated learning with LGC. We then propose a learning based algorithm for each device to dynamically adjust its local computation (i.e., the number of local stochastic descent) and communication decisions (i.e.,the compression level of different layers and the layer to channel mapping) in each iteration. Results from extensive experiments show that using our algorithm, LGC significantly reduces the training time, improves the resource utilization, while achieving a similar accuracy, compared with well-known FL mechanisms.
A Tighter Upper Bound of the Expansion Factor for Universal Coding of Integers and Its Code Constructions
Venue: arXiv cs.IT 2021
Year: 2021

In entropy coding, universal coding of integers~(UCI) is a binary universal prefix code, such that the ratio of the expected codeword length to $\max\{1, H(P)\}$ is less than or equal to a constant expansion factor $K_{\mathcal{C}}$ for any probability distribution $P$, where $H(P)$ is the Shannon entropy of $P$. $K_{\mathcal{C}}^{*}$ is the infimum of the set of expansion factors. The optimal UCI is defined as a class of UCI possessing the smallest $K_{\mathcal{C}}^{*}$. Based on prior research, the range of $K_{\mathcal{C}}^{*}$ for the optimal UCI is $2\leq K_{\mathcal{C}}^{*}\leq 2.75$. Currently, the code constructions achieve $K_{\mathcal{C}}=2.75$ for UCI and $K_{\mathcal{C}}=3.5$ for asymptotically optimal UCI. In this paper, we propose a class of UCI, termed $ι$ code, to achieve $K_{\mathcal{C}}=2.5$. This further narrows the range of $K_{\mathcal{C}}^{*}$ to $2\leq K_{\mathcal{C}}^{*}\leq 2.5$. Next, a family of asymptotically optimal UCIs is presented, where their expansion factor infinitely approaches $2.5$. Finally, a more precise range of $K_{\mathcal{C}}^{*}$ for the classic UCIs is discussed.
AI Accelerator Survey and Trends
Venue: arXiv cs.AR 2021
Year: 2021

Over the past several years, new machine learning accelerators were being announced and released every month for a variety of applications from speech recognition, video object detection, assisted driving, and many data center applications. This paper updates the survey of AI accelerators and processors from past two years. This paper collects and summarizes the current commercial accelerators that have been publicly announced with peak performance and power consumption numbers. The performance and power values are plotted on a scatter graph, and a number of dimensions and observations from the trends on this plot are again discussed and analyzed. This year, we also compile a list of benchmarking performance results and compute the computational efficiency with respect to peak performance.
EVL: a typed functional language for event processing
Venue: arXiv cs.LO 2021
Year: 2021

We define EVL, a minimal higher-order functional language to deal with generic events. The notion of generic event extends the well-known notion of event traditionally used in a variety of areas, such as database management, concurrency, reactive systems and cybersecurity. Generic events were introduced in the context of a metamodel to specify obligations in access control systems. Event specifications are represented as records and we use polymorphic record types to type events in EVL. We show how the higher-order capabilities of EVL can be used in the context of Complex Event Processing (CEP), to define higher-order parameterised functions that deal with the usual CEP techniques.
Ensemble Learning using Error Correcting Output Codes: New Classification Error Bounds
Venue: arXiv cs.LG 2021
Year: 2021

New bounds on classification error rates for the error-correcting output code (ECOC) approach in machine learning are presented. These bounds have exponential decay complexity with respect to codeword length and theoretically validate the effectiveness of the ECOC approach. Bounds are derived for two different models: the first under the assumption that all base classifiers are independent and the second under the assumption that all base classifiers are mutually correlated up to first-order. Moreover, we perform ECOC classification on six datasets and compare their error rates with our bounds to experimentally validate our work and show the effect of correlation on classification accuracy.
ReaSCAN: Compositional Reasoning in Language Grounding
Venue: arXiv cs.CL 2021
Year: 2021

The ability to compositionally map language to referents, relations, and actions is an essential component of language understanding. The recent gSCAN dataset (Ruis et al. 2020, NeurIPS) is an inspiring attempt to assess the capacity of models to learn this kind of grounding in scenarios involving navigational instructions. However, we show that gSCAN's highly constrained design means that it does not require compositional interpretation and that many details of its instructions and scenarios are not required for task success. To address these limitations, we propose ReaSCAN, a benchmark dataset that builds off gSCAN but requires compositional language interpretation and reasoning about entities and relations. We assess two models on ReaSCAN: a multi-modal baseline and a state-of-the-art graph convolutional neural model. These experiments show that ReaSCAN is substantially harder than gSCAN for both neural architectures. This suggests that ReaSCAN can serve as a valuable benchmark for advancing our understanding of models' compositional generalization and reasoning capabilities.
Dual Behavior Regularized Reinforcement Learning
Venue: arXiv cs.LG 2021
Year: 2021

Reinforcement learning has been shown to perform a range of complex tasks through interaction with an environment or collected leveraging experience. However, many of these approaches presume optimal or near optimal experiences or the presence of a consistent environment. In this work we propose dual, advantage-based behavior policy based on counterfactual regret minimization. We demonstrate the flexibility of this approach and how it can be adapted to online contexts where the environment is available to collect experiences and a variety of other contexts. We demonstrate this new algorithm can outperform several strong baseline models in different contexts based on a range of continuous environments. Additional ablations provide insights into how our dual behavior regularized reinforcement learning approach is designed compared with other plausible modifications and demonstrates its ability to generalize.
"Don't Downvote A\$\$\$\$\$\$s!!": An Exploration of Reddit's Advice Communities
Venue: arXiv cs.HC 2021
Year: 2021

Advice forums are a crowdsourced way to reinforce cultural norms and moral behavior. Sites like Reddit contain massive amounts of natural language human interaction, with rules and norms unique to each individual subreddit community. To explore this data, we created a dataset with top 1000 posts from each of two such forums, r/AmItheAsshole and r/relationships, and extracted natural language features including sentiment, similarity, word frequency, and demographics using both algorithmic and manual methods. Further, we developed a method to extract demographic information from the subreddits, examined how the post authors' self-disclosures reflect the unique communities in which their posts are shared, and discussed how the authors' language use choices might be related to broader social patterns. We observed some differences between the subreddits in terms of word frequency, demographics disclosure, and gendered language. In general, both subreddits had more female posters than male, and posters tended to use more words about their opposite gender than the same. Gender-diverse posters were uncommon. Implications for future research include a more careful, inclusive focus on identity and disclosure and how that interacts with advice-seeking behavior in online communities.
Knowledge-Enhanced Evidence Retrieval for Counterargument Generation
Venue: arXiv cs.CL 2021
Year: 2021

Finding counterevidence to statements is key to many tasks, including counterargument generation. We build a system that, given a statement, retrieves counterevidence from diverse sources on the Web. At the core of this system is a natural language inference (NLI) model that determines whether a candidate sentence is valid counterevidence or not. Most NLI models to date, however, lack proper reasoning abilities necessary to find counterevidence that involves complex inference. Thus, we present a knowledge-enhanced NLI model that aims to handle causality- and example-based inference by incorporating knowledge graphs. Our NLI model outperforms baselines for NLI tasks, especially for instances that require the targeted inference. In addition, this NLI model further improves the counterevidence retrieval system, notably finding complex counterevidence better.
Adversarial Training with Contrastive Learning in NLP
Venue: arXiv cs.CL 2021
Year: 2021

For years, adversarial training has been extensively studied in natural language processing (NLP) settings. The main goal is to make models robust so that similar inputs derive in semantically similar outcomes, which is not a trivial problem since there is no objective measure of semantic similarity in language. Previous works use an external pre-trained NLP model to tackle this challenge, introducing an extra training stage with huge memory consumption during training. However, the recent popular approach of contrastive learning in language processing hints a convenient way of obtaining such similarity restrictions. The main advantage of the contrastive learning approach is that it aims for similar data points to be mapped close to each other and further from different ones in the representation space. In this work, we propose adversarial training with contrastive learning (ATCL) to adversarially train a language processing task using the benefits of contrastive learning. The core idea is to make linear perturbations in the embedding space of the input via fast gradient methods (FGM) and train the model to keep the original and perturbed representations close via contrastive learning. In NLP experiments, we applied ATCL to language modeling and neural machine translation tasks. The results show not only an improvement in the quantitative (perplexity and BLEU) scores when compared to the baselines, but ATCL also achieves good qualitative results in the semantic level for both tasks without using a pre-trained model.
Towards robustness under occlusion for face recognition
Venue: arXiv cs.CV 2021
Year: 2021

In this paper, we evaluate the effects of occlusions in the performance of a face recognition pipeline that uses a ResNet backbone. The classifier was trained on a subset of the CelebA-HQ dataset containing 5,478 images from 307 classes, to achieve top-1 error rate of 17.91%. We designed 8 different occlusion masks which were applied to the input images. This caused a significant drop in the classifier performance: its error rate for each mask became at least two times worse than before. In order to increase robustness under occlusions, we followed two approaches. The first is image inpainting using the pre-trained pluralistic image completion network. The second is Cutmix, a regularization strategy consisting of mixing training images and their labels using rectangular patches, making the classifier more robust against input corruptions. Both strategies revealed effective and interesting results were observed. In particular, the Cutmix approach makes the network more robust without requiring additional steps at the application time, though its training time is considerably longer. Our datasets containing the different occlusion masks as well as their inpainted counterparts are made publicly available to promote research on the field.
Architecture and Its Vulnerabilities in Smart-Lighting Systems
Venue: arXiv cs.CR 2021
Year: 2021

Industry 4.0 embodies one of the significant technological changes of this decade. Cyber-physical systems and the Internet Of Things are two central technologies in this change that embed or connect with sensors and actuators and interact with the physical environment. However, such systems-of-systems undergo additional restrictions in an endeavor to maintain reliability and security when building and interconnecting components to a heterogeneous, multi-domain \textit{Smart-*} systems architecture. This paper presents an application-specific, layer-based approach to an offline security analysis inspired by design science that merges preceding expertise from relevant domains. With the example of a Smart-lighting system, we create a dedicated unified taxonomy for the use case and analyze its distributed Smart-* architecture by multiple layer-based models. We derive potential attacks from the system specifications in an iterative and incremental process and discuss resulting threats and vulnerabilities. Finally, we suggest immediate countermeasures for the latter potential multiple-domain security concerns.
Identifying Ventricular Arrhythmias and Their Predictors by Applying Machine Learning Methods to Electronic Health Records in Patients With Hypertrophic Cardiomyopathy(HCM-VAr-Risk Model)
Venue: arXiv cs.LG 2021
Year: 2021

Clinical risk stratification for sudden cardiac death (SCD) in hypertrophic cardiomyopathy (HC) employs rules derived from American College of Cardiology Foundation/American Heart Association (ACCF/AHA) guidelines or the HCM Risk-SCD model (C-index of 0.69), which utilize a few clinical variables. We assessed whether data-driven machine learning methods that consider a wider range of variables can effectively identify HC patients with ventricular arrhythmias (VAr) that lead to SCD. We scanned the electronic health records of 711 HC patients for sustained ventricular tachycardia or ventricular fibrillation. Patients with ventricular tachycardia or ventricular fibrillation (n = 61) were tagged as VAr cases and the remaining (n = 650) as non-VAr. The 2-sample t test and information gain criterion were used to identify the most informative clinical variables that distinguish VAr from non-VAr; patient records were reduced to include only these variables. Data imbalance stemming from low number of VAr cases was addressed by applying a combination of over- and under-sampling strategies.We trained and tested multiple classifiers under this sampling approach, showing effective classification. We evaluated 93 clinical variables, of which 22 proved predictive of VAr. The ensemble of logistic regression and naive Bayes classifiers, trained based on these 22 variables and corrected for data imbalance, was most effective in separating VAr from non-VAr cases (sensitivity = 0.73, specificity = 0.76, C-index = 0.83). Our method (HCM-VAr-Risk Model) identified 12 new predictors of VAr, in addition to 10 established SCD predictors. In conclusion, this is the first application of machine learning for identifying HC patients with VAr, using clinical attributes.
Multiscale Manifold Warping
Venue: arXiv cs.LG 2021
Year: 2021

Many real-world applications require aligning two temporal sequences, including bioinformatics, handwriting recognition, activity recognition, and human-robot coordination. Dynamic Time Warping (DTW) is a popular alignment method, but can fail on high-dimensional real-world data where the dimensions of aligned sequences are often unequal. In this paper, we show that exploiting the multiscale manifold latent structure of real-world data can yield improved alignment. We introduce a novel framework called Warping on Wavelets (WOW) that integrates DTW with a a multi-scale manifold learning framework called Diffusion Wavelets. We present a theoretical analysis of the WOW family of algorithms and show that it outperforms previous state of the art methods, such as canonical time warping (CTW) and manifold warping, on several real-world datasets.